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aerospace Article

Design and Performance of Modular 3-D Printed Solid-Propellant Rocket Airframes Rachel N. Hernandez 1 , Harpreet Singh 1 , Sherri L. Messimer 2 and Albert E. Patterson 2, * 1 2

*

Department of Mechanical and Aerospace Engineering, University of Alabama in Huntsville, OKT N274, 301 Sparkman Drive, Huntsville, AL 35899, USA; [email protected] (R.N.H.); [email protected] (H.S.) Department of Industrial & Systems Engineering and Engineering Management, University of Alabama in Huntsville, OKT N143, 301 Sparkman Drive, Huntsville, AL 35899, USA; [email protected] Correspondence: [email protected] or [email protected]

Academic Editor: Konstantinos Kontis Received: 23 February 2017; Accepted: 20 March 2017; Published: 23 March 2017

Abstract: Solid-propellant rockets are used for many applications, including military technology, scientific research, entertainment, and aerospace education. This study explores a novel method for design modularization of the rocket airframes, utilizing additive manufacturing (AM) technology. The new method replaces the use of standard part subsystems with complex multi-function parts to improve customization, design flexibility, performance, and reliability. To test the effectiveness of the process, two experiments were performed on several unique designs: (1) ANSYS CFX® simulation to measure the drag coefficients, the pressure fields, and the streamlines during representative flights and (2) fabrication and launch of the developed designs to test their flight performance and consistency. Altitude and 3-axis stability was measured during the eight flights via an onboard instrument package. Data from both experiments demonstrated that the designs were effective, but varied widely in their performance; the sources of the performance differences and errors were documented and analyzed. The modularization process reduced the number of parts dramatically, while retaining good performance and reliability. The specific benefits and caveats of using extrusion-based 3-D printing to produce airframe components are also demonstrated. Keywords: modular design; aircraft design; solid-propellant rocket; design of experiments; additive manufacturing; fused deposition modeling

1. Introduction and Background The present study develops and tests a modular design method for small solid-propellant (SP) rockets utilizing the benefits of additive manufacturing (AM) technologies during design and fabrication. The design process thus developed will take advantage of the many benefits offered by AM to produce excellent rocket airframe designs while modularizing the system as much as possible. For the purposes of this study, a SP rocket will be defined as being an aircraft that uses a fast-burning solid propellant for motion, aerodynamic fins as control surfaces, and a strictly atmospheric range domain. The aircraft can fly either vertically or horizontally, may or may not have active control, and will always carry a small payload. Examples of such aircraft are legion; sounding rockets for carrying small scientific instruments [1,2], military hardware (particularly air-to-air missiles, ballistic missiles and missile defense [3–5], atmospheric booster stages on some orbital vehicles [6,7], model rockets for education, research, and entertainment [8,9], and controlled avalanche triggering devices [10]. Modularization is a product design technique in which the system is divided into subsystems or modules, which can be designed and manufactured independently and, ideally, used in several Aerospace 2017, 4, 17; doi:10.3390/aerospace4020017

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different systems. systems. Examples different Examples of of modular modular products products abound, abound, from from furniture furniture to to construction construction equipment, equipment, production tooling, tooling, office office cubicles, cubicles, and and even even homes. homes. The the modularization modularization of of production The basic basic reason reason for for the products is to overcome design and production issues, control the product lifecycle more effectively, products is to overcome design and production issues, control the product lifecycle more effectively, produce more moreflexible flexibleproduct productlines, lines,and andtotogain gainmore more output from invested time resources [11– produce output from invested time andand resources [11–13]. 13]. To best accomplish these goals, several specific objectives must be developed for the product in To[14,15]. best accomplish these goals, several design In informal terms, these are: specific objectives must be developed for the product in design [14,15]. In informal terms, these are: 1. Design tasks should be done by teams of specialists to facilitate efficient development; 1. Design tasks should be done by teams of specialists to facilitate efficient development; 2. Customize and improve the production and assembly process for the subsystems; 2. Customize and improve the production and assembly process for the subsystems; 3. Standardize every possible aspect of the system or subsystem; 3. Standardize every possible aspect of the system or subsystem; 4. Consider testing, testing, maintenance, maintenance, and and repair repair requirements requirements when when designing 4. Consider designing the the architecture; architecture; 5. Design products such that individual parts and subsystems can be easily 5. Design products such that individual parts and subsystems can be easily upgraded; upgraded; 6. Design products products to to be be easily easily reconfigurable reconfigurable for for different different missions; 6. Design missions; 7. Design Design products products such such that disassembly and recycling at end-of-life is simplified; 8. Design Design products products that that have a specific core mission but that are easily customized by the user. user. Traditionally,these these objectives objectives are are met met by by the the design design and Traditionally, and production production of of independent independent subsystems, subsystems, each containing various parts of the system (Figure 1). each containing various parts of the system (Figure 1).

Part 3

Part 5

Part 4 Part 5

Part 6

Part 7

Part 8

Part 6 Part 7

Part 9

Subassembly 4

Part 2

Part 4

Subassembly 3

Part 1

Part 3

Subassembly 2

Part 2

Subassembly 1

Part 1

Part 10

Part 8 Part 9 Part 10

Final Product Figure Part-Subsystem-System configuration. Figure 1. 1. Part-Subsystem-System configuration.

The main main issue The issue with with this this approach approach is is the the problems problems with with integration integration and and communication communication between between the various various subsystems subsystems once once they they are are assembled. the assembled. Each Each subsystem subsystem would would traditionally traditionally be be designed designed by by a team of specialists, each team using a slightly different architecture and control technique [15]. a team of specialists, each team using a slightly different architecture and control technique [15]. Many Many systems engineering (SE) product asEngine the SEused Engine used the US systems engineering (SE) product lifecyclelifecycle engines,engines, such as such the SE by the USbyNational National Aeronautics Space Administration (NASA) and the Systems-of-Systems approach Aeronautics and Spaceand Administration (NASA) [16] and the[16] Systems-of-Systems approach used by the used by the US Missile Defense Agency (MDA) [17], develop sophisticated and complex techniques US Missile Defense Agency (MDA) [17], develop sophisticated and complex techniques for dealing for dealing with these problems. integration problems. with these integration A more moreeffective, effective,if if fully proven, technique for modularization is the integration-ofA yetyet notnot fully proven, technique for modularization is the integration-of-functions functions method, in which advanced manufacturing techniques such as AM can usedto to produce produce method, in which advanced manufacturing techniques such as AM can bebeused multi-function parts using subsystems subsystems to gain the the multi-function parts [18,19]. [18,19]. This This reduces reduces or or eliminates eliminates the the need need for for using to gain benefits of modularization. This technique also removes most or all the inherent integration problems benefits of modularization. This technique also removes most or all the inherent integration problems encountered with subsystems; it well known known that that most most systems systems failures failures happen happen at at interfaces, interfaces, so so encountered with subsystems; it is is well eliminating interfaces interfaces raises raises the the reliability reliability and and quality quality of of the the entire entire system system [20,21]. [20,21]. eliminating Objections could be raised to this, as the technique removes standardization from the the Objections could be raised to this, as the technique removes standardization from modularization process and requires all the system components to be specialized. This objection is modularization process and requires all the system components to be specialized. This objection eliminated byby one [22,23]. Since is eliminated oneofofthe thebasic basiccharacteristics characteristicsofofAM AMtechnologies, technologies, mass mass customization customization [22,23]. Since AM does not require any special tooling to produce parts and the parts are infinitely customizable AM does not require any special tooling to produce parts and the parts are infinitely customizable within the design software [24], a supply chain of readily available on-the-shelf parts is not necessary. Figure 2 shows graphically how the modularization concept is modified by the use of AM.

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within the design software [24], a supply chain of readily available on-the-shelf parts is not necessary. Figure shows Aerospace22017, 4, 17graphically how the modularization concept is modified by the use of AM. 3 of 24 Function 3 Function 2 Function 1

Part A

Function 10 Function 5 Function 4

Function 7 Function 6

Part B

Part C

Function 9 Function 8

Part D

Final Product Figure 2. 2. Functions-Part-Product configuration. Figure Functions-Part-Product configuration.

While it is sometimes practical to print final products as single parts, this suppresses the other While it is sometimes practical to print final products as single parts, this suppresses the other benefits of modularization, particularly those offered by Goals 4–8 (see above); systems fabricated in benefits of modularization, particularly those offered by Goals 4–8 (see above); systems fabricated in this way usually will be throw-away products that are not meant to be repaired or upgraded. this way usually will be throw-away products that are not meant to be repaired or upgraded. Therefore, Therefore, it is assumed for most products that would be modularized that the subsystem level is the it is assumed for most products that would be modularized that the subsystem level is the highest highest level at which it is practical to combine functions into single parts. This has introduced the level at which it is practical to combine functions into single parts. This has introduced the concept of concept of feature cataloging [25], contrasted with the old method of part or subsystem cataloging. feature cataloging [25], contrasted with the old method of part or subsystem cataloging. 2. Materials and Methods Methods I: I: Modular Modular Additive Additive Manufacturing Manufacturing (AM)-Based (AM)-Based Design Design Methodology Methodology 2. Materials and This section topic of of product modularity andand develops the This section expands expandson onthe thepreviously previouslydiscussed discussed topic product modularity develops basicbasic model for a for modular AM-based design design methodology. There is not yet is a clear andauniversally the model a modular AM-based methodology. There not yet clear and accepted general definition of “product modularization” [26]; therefore, in the context present universally accepted general definition of “product modularization” [26]; therefore, in of thethe context of study and previously discussed research, the authors offer the following definition for a traditional system: the present study and previously discussed research, the authors offer the following definition for a Product modularization is the process of decomposing a complex system into standardized and traditional system: moreProduct easily manageable subsystems with five major characteristics: modularization is the process of decomposing a complex system into standardized and more easily manageable with fivepercentage major characteristics:  The subsystems are subsystems composed of a high of standard off-the-shelf components;    

The subsystems are easily serviceable and recyclable; The subsystems are composed of a high percentage of standard off-the-shelf components; The subsystem itself or any of its components can be replaced or upgraded by the user at will; The subsystems easilymission, serviceable The system has are a core butand therecyclable; specific configuration of subsystems allows it to  The subsystem itself or any of its components can be replaced or upgraded by the user at will; accomplish more than one mission;  The system has a core mission, but the specific configuration of subsystems it totoaccomplish  The modular system offers time or cost savings or another significantallows benefit the user, more than with one mission; compared a non-modular system for the same task.  The modular system offers time or cost savings or another significant benefit to the user, compared As described previously, AM technologies allow the integration of functions within a design. with a non-modular system for the same task. While traditional modularization mainly involved the collection of similar or complementary parts into As groups [27], the integration functions involves theintegration manufacturing of morewithin complex parts described previously, AMoftechnologies allow the of functions a design. instead of subsystems. This would seem counterintuitive to the casual observer, but the introduction While traditional modularization mainly involved the collection of similar or complementary parts of AM technologies and infinite tool-less involves customization into the equation this ideainstead to be into groups [27], the integration of functions the manufacturing of moreallows complex parts effective. Lack of customization within modularizations is sometimes a hard price to pay for of subsystems. This would seem counterintuitive to the casual observer, but the introduction of AM specialized systems, as the use of one-off parts is highly discouraged [27]. technologies and infinite tool-less customization into the equation allows this idea to be effective. Lack All AM parts are, modularizations by definition, one-off parts; complex andtosimple, parts are of customization within is sometimes a hard price pay for standardized specialized systems, as equally difficult to produce. However, it is important to maintain system flexibility as much as the use of one-off parts is highly discouraged [27]. possible; AMparts allows infiniteone-off flexibility during design fabrication, but none field. All AM are,almost by definition, parts; complex andand simple, standardized partsin arethe equally From this knowledge, it is clear that a modularity-based design process for additively manufactured difficult to produce. However, it is important to maintain system flexibility as much as possible; products a marriage of both modularity principles and traditional design AM allowsrequires almost infinite flexibility during design and fabrication, but none innon-modular the field. From this principles. Each of thethat authors’ modularizationdesign principles discussed above are affected by this: knowledge, it is clear a modularity-based process for additively manufactured products 



Standardization of components: An AM-based methodology eliminates standardization almost completely; all the components used are theoretically customized. In a non-AM system, this could give the optimal design for a particular application but would be prohibitively expensive. AM offers the optimization benefits without the high cost, but does not eliminate the standardization problem.

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requires a marriage of both modularity principles and traditional non-modular design principles. Each of the authors’ modularization principles discussed above are affected by this: 







Standardization of components: An AM-based methodology eliminates standardization almost completely; all the components used are theoretically customized. In a non-AM system, this could give the optimal design for a particular application but would be prohibitively expensive. AM offers the optimization benefits without the high cost, but does not eliminate the standardization problem. Serviceability Aerospace 2017, 4, 17 and recyclability of the system components: Since subsystems are replaced 4 of 24 by single complex parts, serviceability of the subsystems is nearly impossible. However, this may  notServiceability and recyclability of the system components: enough Since subsystems by and be a large problem if the complex parts are inexpensive such that are theyreplaced are easily single complex parts, serviceability of the subsystems is nearly impossible. However, this may cheaply replaced. The savings in manufacturing and assembly cost from using complex parts not beofasubsystems large problem if the complex parts inexpensive enough suchcomponents. that they are easily and instead will likely offset the are non-serviceability of the Recyclability cheaply replaced. The savings in manufacturing and assembly cost from using complex parts will most likely dramatically improve with the use of AM, as the parts will likely be made from a instead of subsystems will likely offset the non-serviceability of the components. Recyclability single material. will most likely dramatically improve with the use of AM, as the parts will likely be made from User customizability: a single material. Producing a modular system that the customer can modify at will is much more difficult using AM, as fewer aparts means less that on-the-go customizability. the user  User customizability: Producing modular system the customer can modifyHowever, at will is much could have much more input intoparts the design at the beginning, reducing However, the desirethe and need more difficult using AM, as fewer means less on-the-go customizability. user to customize. could have much more input into the design at the beginning, reducing the desire and need to customize. Core mission: While most integrated AM-made products will likely be less flexible for the user,  the Core mission: While most integrated products will possible likely be less flexible foruser the user, increased user input during designAM-made will provide the best benefit to the and the the cost increased input during design will afford providea the bestrange possible benefit to the user and theneed lower likelyuser means that the user could wider of products, reducing the lower cost likely means that the user could afford a wider range of products, reducing the need for a do-all product. for a do-all product.

In light of this knowledge, definitionofofAM-based AM-based design modularization In light of this knowledge,the theauthors authorspropose propose aa definition design modularization (Figure 3 shows thethe definition graphically, traditionalmethod): method): (Figure 3 shows definition graphically,compared comparedto to the the traditional Characteristic

Traditional Modularization Method

AM-Based Modularization Method

Individual parts grouped into subsystems

Part functions grouped into customized complex parts

All subsystems are easily serviceable

Few to none of the parts are standardized and are manufactured on-demand; no parts kept on hand, because even standard parts can be made on demand

Serviceability

All or most parts are standardized and off-theshelf; spares of custom parts kept on hand

Multi-functional parts are usually not serviceable but are easily fully replaced ondemand

Recyclability

Subsystems must be disassembled before recycling

Customized parts easily recyclable with no disassembly required

Customization During Design

Customization of subsystems is limited to available standard part configurations

Nearly infinite customization

Customization During Use

Customization of subsystems is limited to available standard part configurations

None - design of system components is fixed; however, they are easily replaced with new custom system components

Mission flexibility is easy but is determined by available standard part configurations

Mission change is very difficult but the customizability of the design and original fabrication provided less need for mission flexibility; if flexibility is needed, a new system designed for the new mission can be made on-demand

Configuration

Standardization

M ission Flexibility

Figure 3. Traditional vs. additive manufacturing (AM)-based modularization method.

Figure 3. Traditional vs. additive manufacturing (AM)-based modularization method.

AM-based product modularization is the process of decomposing a complex system into several complex multi-function components that can be optimized for a particular mission. This design methodology has five major characteristics:  

Few to none of the parts are standardized; No parts are kept in stock and all parts are made on-demand and optimized for the system and

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AM-based product modularization is the process of decomposing a complex system into several complex multi-function components that can be optimized for a particular mission. This design methodology has five major characteristics: Few to none of the parts are standardized; kept in stock and all parts are made on-demand and optimized for the system5 and of 24 mission at hand; 2017, 4, 17 5 of 24  Aerospace The partsare arerarely rarelytotonever never serviceable, easily replaced on demand  The multi-functional multi-functional parts serviceable, butbut areare easily replaced on demand and and easily recycled; easily recycled; The multi-functionalparts parts arenot rarely to never serviceable, but are easily replaced on demand   The easily customizable during  The multi-functional multi-functional parts are are not customizable customizable in in use, use, but but they they are are easily customizable during and easily recycled; design and design and fabrication; fabrication;parts are not customizable in use, but they are easily customizable during The multi-functional   The system has only and power power of of The system only one one core core mission mission per per configuration, configuration, but but the the lower lower cost cost and design and has fabrication; customizability during design and fabrication allows users to obtain more than one system to design fabrication allows usersbut to obtain morecost than one system  customizability The system hasduring only one coreand mission per configuration, the lower and power of to serve their needs. serve their needs.during design and fabrication allows users to obtain more than one system to customizability serve their needs. 3. Materials II: Rocket Rocket Design Design 3. Materials and and Methods Methods II: 

 No2017, parts are Aerospace 4, 17

3. Materials Methods II: Rocket Design In order order to toand demonstrate the AM-based modular design design process, process, aa case case study study was was developed; the In demonstrate the AM-based modular developed; the topic of the study was the modularization of small solid-propellant rocket airframes. An excellent topic ofInthe study was the modularization small solid-propellant rocket airframes. An excellent order to demonstrate the AM-based of modular design process, a case study was developed; the model toofreference reference iswas the US US Navy AIM-9X Sidewinder Sidewinder missile [28], [28], aa rocket rough airframes. sketch of of which which is shown model the AIM-9X missile rough sketch is shown topicto the studyis the Navy modularization of small solid-propellant An excellent in model Figureto 4.reference The is its with the aid aid of aa[28], jet so all control are is the US Navyto Sidewinder missile a rough whichsurfaces is shownare in Figure 4. The missile missile is steered steered toAIM-9X its target target with the of jet vane, vane, so sketch all the the of control surfaces fixed [29], a configuration that lends itself well to the use of AM to fabricate the airframe. in Figure The missile isthat steered to itself its target theuse aidof ofAM a jet to vane, so all the surfaces are fixed [29], a4.configuration lends wellwith to the fabricate the control airframe. fixed [29], a configuration that lends itself well to the use of AM to fabricate the airframe.

Figure 4. 4. Rough Rough artistic artistic representation representation of Figure of AIM-9X AIM-9X Sidewinder Sidewinder missile missile airframe. airframe. Figure 4. Rough artistic representation of AIM-9X Sidewinder missile airframe.

Figure 55 shows shows an exploded view view of the the Sidewinder missile; clearly, there are are many many ways ways that that Figure missile;clearly, clearly,there there Figure 5 showsananexploded exploded view of of the Sidewinder Sidewinder missile; are many ways that this type of airframe could be optimized into subsystems or complex parts. Level 1 shows the major this type ofof airframe or complex complexparts. parts.Level Level1 shows 1 shows major this type airframecould couldbe beoptimized optimized into into subsystems subsystems or thethe major airframe pieces, pieces, while whileLevel Level22shows showspotential potential subassemblies that could be created. Level 3 shows airframe subassemblies that could be created. Level 3 shows airframe pieces, while Level 2 shows potential subassemblies that could be created. Level 3 showsthe the final stage ofofthe design modularization. Three parts remain; thenose nose cone (guidance), final stage of the AM-based design modularization. Three parts remain; thethe nose cone (guidance), the the final stage theAM-based AM-based design modularization. Three parts remain; cone (guidance), thethe upper section (payload/warhead), and the lower section (propulsion). Many other configurations upper section (payload/warhead), and the lower section (propulsion). Many other configurations upper section (payload/warhead), and the lower section (propulsion). Many other configurationsare areare possible; what isisshown isisaavery simple example todemonstrate demonstrate theconcept. concept. possible; what is shown herehere is a very simple example to demonstrate thethe concept. possible; what shown here very simple example to

Figure 5. 5. Cont. Figure Cont.

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Figure5.5.Example Examplemodularization modularization of Figure of AIM-9 AIM-9Sidewinder Sidewindermissile. missile. Figure 5. Example modularization of AIM-9 Sidewinder missile.

In order to examine this design approach further and provide a detailed example of rocket In order ordertotoexamine examinethis this design approach further provide a detailed example of design, rocket design approach and and provide a detailed example of of rocket design, a series of new rocket designs must befurther generated. A configuration similar to that the AIMdesign, a series of new rocket designs must be generated. A configuration similar to that of the AIMa series of new designs must besection generated. configuration to that of chosen; the AIM-9 missile, 9 missile, withrocket four wings at the bow of theAaircraft and foursimilar at the stern, was the front 9 missile, with four wings the bow of theand aircraft and four at the stern, was chosen; front with the bowatsection ofsection the aircraft four at the stern, was front the and rear andfour rearwings wingsat were off-set from each other at 45-degree angles. The length ofchosen; the newthe airframes was and rear wings were off-set from each other at 45-degree angles. The length of the new airframes was wings were as off-set from other at 45-degree of to thebenew airframes was specified specified 40 cm. Theeach design variable betweenangles. the twoThe waslength decided the shape of the wings, as specified as 40design cm. 6; The design variable between thedecided two toelliptical, be the shape of the as shown 40 cm. in The variable the two todecided bedelta, the shape of the wings, as wings, shown in Figure the shapesbetween chosen were thewas delta, the was clipped and rectangular. In as shown in Figure 6; the shapes chosen were the delta, the clipped delta, elliptical, and rectangular. In Figure 6; the shapes chosen were the delta, the clipped delta, elliptical, and rectangular. In order to order to best assess the effect of the fin shapes on the airframe performance, they were allocated order to best thethe effect of the finthe shapes on the airframe performance, they were allocated best assess the effect of fin shapes on airframe performance, they were allocated according to a according toassess a 2-level full-factorial design (Table 1). according to a 2-level full-factorial design (Table 1). decision, as many different combinations are choice of design fins to analyze was purely a design 2-level The full-factorial (Table 1). The choice of the finsfour possible beyond chosen. The this study was toas explore design combinations modularizationare to analyze waspurpose purely aofdesign decision, manythe different possible beyond the four chosen. purpose of this was explore theofdesign modularization method, so several designs wereThe generated purely to study explore theto effectiveness the method. Future studiesso should be designs done to more effects of theeffectiveness various fin combinations, as very method, several were generated purely to the effectiveness of the the method. method. Future method, so several designs wererigorously generatedanalyze purely the to explore explore the of Future little literature exists on the topic and most previous work was done long ago and without the aid of studies should be done to more rigorously analyze the effects of the various fin combinations, as studies should be done to more rigorously analyze the effects of the various fin combinations, as very very sophisticated design and analysis tools. little literature exists on the topic and most previous work was done long ago and without the aid little literature exists on the topic and most previous work was done long ago and without the aid of of sophisticated and analysis analysis tools. tools. sophisticated design design and

Figure 6. Selected fin shapes: (a) delta; (b) clipped delta; (c) elliptical; and (d) rectangular.

Figure 6. Selected delta; (b) (c) elliptical; Selected fin fin shapes: (a) (a) delta; (b) clipped clipped delta; delta; elliptical; and (d) rectangular. Table 1. Experimental design.

Design Lower Shape Upper Table 1.Fin Experimental design. Fin Shape Table 1. A

Clipped delta

Rectangular

AC

Clipped delta Elliptical Clipped delta Elliptical Clipped delta Elliptical Elliptical Elliptical Clippeddelta delta Clipped

Rectangular Delta Rectangular Rectangular Delta Rectangular DeltaDelta DeltaDelta

Design Lower Fin Shape Upper Fin Shape Elliptical Rectangular DesignB Lower Fin Shape Upper Fin Shape A B BD C C D D

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While this thisdesign designexperiment experimentisisquite quite simple, it demonstrates concept approach in a While simple, it demonstrates the the concept and and approach in a clear clear and effective way, paving the way for more complex studies in the future. Figure 7 shows the and effective way, paving the way for more complex studies in the future. Figure 7 shows the set of final set of final designs, createdto according to the experimental design. Similar to missile the AIM-9 case designs, created according the experimental design. Similar to the AIM-9 casemissile previously previously discussed, the airframes were designed to be made in three pieces: the booster section, the discussed, the airframes were designed to be made in three pieces: the booster section, the payload payload and section, and the nose cone, each printed as apiece. singleAs piece. Asrockets these rockets be launched, section, the nose cone, each printed as a single these will be will launched, it was it was necessary to include a set of launch rail lugs, clearly seen in Figure 7. necessary to include a set of launch rail lugs, clearly seen in Figure 7.

Figure Selected rocket Figure 7. 7. Selected rocket designs. designs.

The rockets which is is biodegradable, in in case any of rockets were weremanufactured manufacturedfrom frompolylactic polylacticacid acid(PLA), (PLA), which biodegradable, case any of the specimens or parts were lost launches; during launches; it is slightly heavier than standard ABS the specimens or parts were lost during it is slightly heavier than standard ABS (acrylonitrile (acrylonitrile butadiene plastic but more environmentally rockets were butadiene styrene) plasticstyrene) but more environmentally responsible. Theresponsible. rockets wereThe printed with the printed with the fused deposition modeling using walls 3%empty infill, for a total fused deposition modeling method, using 0.8method, mm walls and0.8 3%mm infill, for and a total weight of empty weight 80 g for aThe 40 cm airframe. Thecavity internal engine cavity in thewas booster section around 80 g forofaaround 40 cm airframe. internal engine in the booster section designed to was designed to accommodate Estes C11-3 model rocketlaunches. engines during launches. accommodate Estes C11-3 model rocket engines during 4. Materials and and Methods Methods III: 4. Materials III: Fluid Fluid Dynamics Dynamics Analysis Analysis Now on the Now that that the the designs designs have have been been established, established, performance performance analysis analysis on the rocket rocket designs designs can can begin. In order order to to guide guide expectations expectations from from the the launch launch experiments experiments and and aid aid in in interpreting interpreting the the results, results, begin. In some preliminary performance performance knowledge knowledgeisisvital. vital.To Toestimate estimatethese theseperformance performancecharacteristics, characteristics, some preliminary a adigital digitalwind windtunnel tunnelmodel modelwas wasconstructed constructedusing using the the ANSYS ANSYS CFX CFX®® computational computational fluid fluid dynamics dynamics (CFD) (CFD) package package (ANSYS (ANSYS Inc., Inc., Cecil Cecil Township, Township,PA, PA,USA). USA). First, the drag coefficients for each of the designs First, the drag coefficients for each of the designs were were calculated calculated in in order order to to provide provide input input for for aa preliminary drag coefficient was found at preliminary analytical analyticalperformance performanceanalysis. analysis.For Foreach eachofofthe thedesigns, designs,the the drag coefficient was found various speeds, fromfrom 10 to 10 100tom/s, in Figure The fluid tested wastested air at one at various speeds, 100and m/s,plotted and plotted in8.Figure 8. The fluid wasatmosphere air at one of pressure, of 28pressure, degrees Celsius, andCelsius, with a and medium factor of 0.05. cross-sectional atmosphere 28 degrees with aturbulence medium turbulence factorThe of 0.05. The crossareas for each design were identical; the differences in drag were due to thedue various geometry sectional areasrocket for each rocket design were identical; the differences in drag were to the various combinations of fins. geometry combinations of fins. The basic CFD model was built and analyzed in five major steps: 1.

2.

The geometry of each rocket was created using commercial 3-D modeling software (Siemens SolidEdge® ST9® , Siemens AG, Munich, Germany) and imported into the ANSYS geometry environment. Within this environment, a fluid domain was modeled around the rocket; the domain was square in cross-section and provided approximately 0.5 m of fluid distance from each surface of the rocket. Upon geometry and fluid domain definition, the model was meshed using the ANSYS mesh environment. The mesh was set to fine for the entire domain, generating around 1.8 million elements per rocket, 380,000 of which were in the domain very near the interface between the rocket and fluid.

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The meshed model was then imported into ANSYS CFD Pre® , in which the analysis characteristics are set. The center of mass and center of pressure for the rocket were found automatically by the software. The airspeed was set, as well as the ANSYS CFX® properties within this domain. Convergence tolerance was specified to be 1 × 10−5 with no limit on the allowed number of model iterations. 4. The model was then analyzed in ANSYS CFX® to find the drag coefficients on the rocket bodies. The drag was measured by tracking the force on the rocket body, in the direction of fluid flow, until steady state was reached. The average number of iterations to convergence of 1 × 10−5 was about 650, requiring around 25 min to run for each case. Aerospace 2017, 4,the 17 model was analyzed in ANSYS CFD Post® . 8 of 24 5. Finally, 3.

Figure 8. Drag Coefficients from ANSYS CFX® Analysis; cross-section of aircraft shown. Figure 8. Drag Coefficients from ANSYS CFX® Analysis; cross-section of aircraft shown. ® , two other pieces of information were gathered. In order to be able Within theCFD ANSYS CFD Post The basic model was built and analyzed in five major steps: to make some predictions about the stability and performance of the airframes before launch, a contour 1. The geometry of each rocket was created using commercial 3-D modeling software (Siemens map of the pressure gradient after model convergence was collected, as well as the streamlines of the SolidEdge® ST9®, Siemens AG, Munich, Germany) and imported into the ANSYS geometry air flowing over the airframes. The results are shown in Figures 9 and 10. In the cases shown, the environment. Within this environment, a fluid domain was modeled around the rocket; the traveling speed was 50 m/s. It was assumed that the angle of attack was constant during the CFD domain was square in cross-section and provided approximately 0.5 m of fluid distance from analysis, with no major deviation from perfectly vertical flight. each surface of the rocket. There are noticeable differences between the pressure gradients in Figure 9, particularly between 2. Upon geometry and fluid domain definition, the model was meshed using the ANSYS mesh the upper fin shapes. The formation of the pressure wave ahead of the fins indicates that the rectangular environment. The mesh was set to fine for the entire domain, generating around 1.8 million fins will produce more drag, which was also found in Figure 8 above. The results do not indicate any elements per rocket, 380,000 of which were in the domain very near the interface between the severe imbalances that will cause major instability during the flight. However, the streamline plots rocket and fluid. (Figure 10) tell a different story. The flow pattern over Design A indicates that there may be significant 3. The meshed model was then imported into ANSYS CFD Pre®, in which the analysis disturbance in the flow during launch. It is anticipated that Design A will be the most unstable of the characteristics are set. The center of mass and center of pressure for the rocket were found four designs during flight. The others appear to be stable, with Design D providing the most straight automatically by the software. The airspeed was set, as well as the ANSYS CFX® properties and stable flow pattern. within this domain. Convergence tolerance was specified to be 1 × 10−5 with no limit on the allowed number of model iterations. 4. The model was then analyzed in ANSYS CFX® to find the drag coefficients on the rocket bodies. The drag was measured by tracking the force on the rocket body, in the direction of fluid flow, until steady state was reached. The average number of iterations to convergence of 1 × 10−5 was about 650, requiring around 25 min to run for each case. 5. Finally, the model was analyzed in ANSYS CFD Post®.

Within the ANSYS CFD Post®, two other pieces of information were gathered. In order to be able to make some predictions about the stability and performance of the airframes before launch, a contour map of the pressure gradient after model convergence was collected, as well as the streamlines of the air flowing over the airframes. The results are shown in Figures 9 and 10. In the

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Figure 9. 3-D pressure contour map from ANSYS CFX® ((v = = 50 m/s) . 50 m/s). Figure 9. 3-D pressure contour map from ANSYS CFX® ( = 50 m/s) .

Figure 50 m/s). Figure 10. 10. Streamline Streamline map map from from ANSYS ANSYS CFX CFX®® ((v = 50 m/s). Figure 10. Streamline map from ANSYS CFX® ( = 50 m/s).

There are noticeable differences thesection, pressure gradients inliterature Figure 9, particularly To better understand the resultsbetween from this brief extra review wasbetween done to There are noticeable differences between the pressurea gradients in Figure 9, particularly between the upper fin shapes. The formation of the pressure wave ahead of the fins indicates that the find similar results from the published literature. A similar CFD model to the one presented in the the upper fin shapes. The formation of the pressure wave ahead of the fins indicates that the rectangular fins will produce more drag, which was also found in Figure 8 above. The results do not present study was by Grover Aerospace [30], found whichin used rockets withThe onlyresults four control rectangular fins willcompleted produce more drag, which was also Figure 8 above. do not indicate any asevere imbalances thatThe will cause major instability the flight. the surfaces and much higher velocity. drag coefficients found are during comparable to thoseHowever, found in this indicate any severe imbalances that will cause major instability during the flight. However, the streamline plots (Figure 10) tell a different story. The flow pattern over Design A indicates that there chapter, after taking onto consideration the differences in velocity; they are, on average, around 0.1 streamline plots (Figure 10) tell a different story. The flow pattern over Design A indicates that there may bethan significant disturbance inbut thethis flow during launch. It by is anticipated that in Design A will be the lower the ones found here, is easily explained the differences velocity and a few may be significant disturbance in the flow during launch. It is anticipated that Design A will be the most unstable of In the four designs during flight. appearNiskanen to be stable, with Design D control surfaces. a second interesting study thatThe wasothers performed, [31] found that drag most unstable of the four designs during flight. The others appear to be stable, with Design D providing the most straight and stable flow pattern. coefficient varied from 0.30 to 0.40 in simulation and from 0.25 to 0.40 in wind tunnel tests, both with providing the most straight and stable flow pattern.

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subsonic flight speed. The rocket design studied had three control surfaces and flew at a significantly faster speed than the rockets used in the present study. Accounting for these characteristics, the results were comparable to those found in this paper and in [30]. 5. Materials and Methods IV: Analytical Calculations As the drag coefficients were calculated in the previous section, an analytical dynamic analysis can be performed on the rockets in order to predict the flight altitude (apogee). The flight analysis is subjected to the following assumptions: 1. 2. 3.

The air is static (no significant wind); The properties of the air remain constant throughout the flight; At the end of a time delay following burnout, the rocket motor will stop free-flight motion by ejecting the payload, in this case an instrument package and recovery system.

The engine characteristics from the manufacturer are shown in Table 2. The manufacturer stated that the delay time between burnout and payload ejection is subjected to a tolerance of 10%. The exact velocity profile of the rocket during flight is not known, so minimum and maximum apogees will be calculated based on the minimum and maximum drag coefficients calculated in the previous section. It will be assumed that all of the original propellant will be burned off during the powered launch time, and therefore the mass will change with time. Launch site characteristics are shown in Table 3. Table 2. Estes C11-3 model rocket engine performance specifications [32]. Maximum Lift Weight (g)

Maximum Thrust (N)

Thrust Duration (s)

Impulse (N·s)

Initial Weight (g)

Propellant Weight (g)

Time Delay (s)

170

22.1

0.8

10.00

32.2

11.00

3

Table 3. Launch site characteristics. Launch Site Altitude (m) 1

Gravity Coefficient g (m/s2 ) 2

Air Temp (◦ C) 1

9.79651

32.5

274 1

2

Air Pressure (KPa) 1

Relative Humidity (%) 1

101.83

88

Air Density (kg/m3 ) 3 1.144

3

Site measurement; Based on latitude and elevation; Based on pressure, humidity, and temperature.

The flight calculations [33–35] for a dynamic flight path provide the total flight altitude that should be expected from launch. The flight is divided into four regions (Figure 11): (1) pre-flight, when the motor is providing thrust to accelerate the rocket, but no motion has occurred yet; (2) powered flight, when the motor is providing thrust to the airframe; (3) free flight, where the rocket continues to coast until the payload ejection time; and (4) recovery, where the recovery system has been deployed Aerospace 4, 17falls until it reaches the ground. 11 of 24 and the2017, rocket Thrust Duration

Powered Flight

Recovery

Payload Ejection

Free Flight

Burnout

Start of Motion

PreFlight

Time Delay

Figure 11. Rocket flight regions. Figure 11. Rocket flight regions.

Figure 12 shows the free body diagram for a rocket launch. The thrust force propels the vehicle forward, while weight and drag forces oppose it. In this study, the airframes weighed approximately 80 g, the motor weighed 32 g at lift off and 21 g at burnout, the recovery system (a plastic streamer) weighed approximately 10 g, and the instruments weighted approximately 10 g, for a total of about 132 g (1.2945 N) at launch and 121 g (1.1866 N) at burnout. The thrust-to-weight ratio therefore is

Payload Ejection

Burnout

Start of Motion Aerospace 2017, 4, 17

Figure 11. Rocket flight regions.

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Figure 12 shows the free body diagram for a rocket launch. The thrust force propels the vehicle forward, while weight and drag forces oppose it. In In this this study, study, the airframes weighed approximately 80 g, the motor weighed 32 g at lift off and 21 g at burnout, the recovery system (a plastic streamer) weighed approximately 10 g, and the instruments weighted approximately 10 g, for a total of about (1.2945 N) at at launch launch and and 121 121 gg (1.1866 (1.1866 N) N) at at burnout. burnout. The thrust-to-weight ratio therefore is 132 g (1.2945 17.0722, where 13.2563 is the minimum allowable for motor Table 2). The coefficient 17.0722, where 13.2563 is the minimum allowable for thethe motor (see(see Table 2). The dragdrag coefficient was was found in Section 4; a summary the extrema is shown in Table found in Section 4; a summary of theofextrema is shown in Table 4. 4.

Drag

Weight

Thrust

Figure 12. Rocket free body diagram. Figure 12. Rocket free body diagram. Table 4. Minimum and Maximum Drag Coefficients. Table 4. Minimum and Maximum Drag Coefficients.

Design Design A A B B C C D D

/ )) Minimum Minimum Cd ((v = = 10 m/s 0.5012 0.5012 0.5127 0.5127 0.4965 0.4965 0.4908 0.4908

) ) Maximum Maximum(Cd=(v = 100/m/s 0.5836 0.5836 0.5914 0.5914 0.5673 0.5673 0.5627 0.5627

As shown in Figure 11, the flight time of the rocket until maximum altitude is the sum of the As shown in Figure 11, the flight time of the rocket until maximum altitude is the sum of the powered flight time and the free flight time. Equation (1) demonstrates the relationship. powered flight time and the free flight time. Equation (1) demonstrates the relationship. = + (1) Alttotal = Alt pow + Alt f ree (1) The altitude reached at the end of powered flight is given by Equation (2), where the is the rocket mass (kg), reached is theatdrag factor is theby motor thrust (N), g is the the mgravity The altitude the end of (dimensionless), powered flight is given Equation (2), where is the 2), and is the burnout velocity (m/s). coefficient (m/s rocket mass (kg), D is the drag factor (dimensionless), T is the motor thrust (N), g is the gravity coefficient (m/s2 ), and v is the burnout velocity (m/s).− g − =− ln 2  − g  m T − mg − Dv2 ln Alt pow = − 2D T − mg

(2) (2)

The altitude reached at the end of free flight is shown in Equation (3), where the m is the rocket mass (kg), D is the drag factor (dimensionless), g is the gravity coefficient (m/s2 ), and v is the burnout velocity (m/s).   m mg + Dv2 Alt f ree = ln (3) 2D mg

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Burnout velocity is calculated using Equations (4) and (5), where the m is the rocket mass (kg), D is the drag factor (dimensionless), T is the motor thrust (N), t is the motor burn time (s), and g is the gravity coefficient (m/s2 ). r T − mg 1 − e− x (4) v= D 1 + e− x where:

p

( T − mg) D (5) m The burn time (distinct from the thrust time) is given by Equation (6). This is a function of the motor thrust T (N) and impulse I (N·s). I t= (6) T The manufacturer of the engine gives an impulse efficiency rating of 0.90. Considering this, Equation (6) becomes Equation (7). I t = 0.9 (7) T Finally, the drag factor is given by Equation (8). Area A is the cross-sectional area of the airframe (m2 ), ρ is the air density (kg/m3 ), and Cd is the drag coefficient (see Table 4). x = 2t

D=

1 ρC A 2 d

(8)

Given the information in Tables 2–4, the drag factors for the four designs were calculated and are shown in Table 5. The values of air density and cross-sectional area are constant and the drag coefficient was varied. Table 6 contains the results for this section. Table 5. Minimum and maximum drag factors. Design

Air Density (kg/m3 )

A B C D

Area A (m2 ) 10−3

1.2 × 1.2 × 10−3 1.2 × 10−3 1.2 × 10−3

1.144 1.144 1.144 1.144

Min Cd 0.5012 0.5127 0.4965 0.4908

Max Cd 0.5836 0.5914 0.5673 0.5627

Min D

Max D

10−4

4.00583 × 10−4 4.05937 × 10−4 3.89395 × 10−4 3.86237 × 10−4

3.44024 × 3.51917 × 10−4 3.40798 × 10−4 3.36885 × 10−4

Table 6. Expected launch performance range. Minimum Cd

Maximum Cd

Design

Max v (m/s)

Powered Alt (m)

Free Alt (m)

Total Alt (m)

Max v (m/s)

Powered Alt (m)

Free Alt (m)

Total Alt (m)

A B C D

65.54 65.50 65.55 65.57

13.51 13.50 13.51 13.51

157.84 156.72 158.30 158.87

144.64 143.74 145.02 145.47

65.28 65.26 65.33 65.35

11.51 11.62 11.74 11.70

123.28 123.51 126.13 126.18

134.78 135.13 137.87 137.88

6. Materials and Methods V: Experimental Launches To test the effectiveness of the designs, to collect experimental data to compare to the analytical solution, a series of launches were performed on the various rocket designs. The technical specifications were discussed in depth in the previous sections. Table 7 shows the results from the launches. All the designs performed well except Design A, which was predicted by the streamline analysis in Section 4. Both launches of Design A (two separate aircraft) failed to reach any significant altitude and both were observed to tumble severely during the launches, crashing before the payload ejection in both cases. The instrument used was an AltimeterThree from Jolly Logic [36], one of the best compact instrument packages available for rockets, drones, and other small aircraft.

were discussed in depth in the previous sections. Table 7 shows the results from the launches. All the designs performed well except Design A, which was predicted by the streamline analysis in Section 4. Both launches of Design A (two separate aircraft) failed to reach any significant altitude and both were observed to tumble severely during the launches, crashing before the payload ejection in both cases. The instrument used was an AltimeterThree from Jolly Logic [36], one of the best compact Aerospace 2017, 4, 17 13 of 25 instrument packages available for rockets, drones, and other small aircraft. Table7.7.Launch Launchdata. data. Table Design

Design

A B C D

A B C D

Launch

Launch

1 2 1 2 1 2 1 2

1 2 1 2 1 2 1 2

Powered Powered Flight (s) Flight (s) 0.35 0.35 0.30 0.30 0.35 0.35 0.35 0.35 0.30 0.30 0.35 0.35 0.40 0.40 0.35 0.35

Free Powered Free Flight Total Burnout v Free (s) Powered Total v Flight Flight Alt (m) Free Flight Alt (m) Altitude (m) Burnout (m/s) Flight (s) Flight Alt (m) Alt (m) Altitude (m) (m/s) 0.40 8.23 6.71 14.94 54.66 0.40 8.23 6.71 14.94 54.66 1.85 6.40 10.67 17.07 46.72 1.85 6.40 10.67 17.07 46.72 4.20 4.57 131.98 136.55 59.07 4.20 4.57 131.98 136.55 59.07 5.05 4.27 135.03 139.29 55.23 5.05 4.27 135.03 139.29 55.23 4.70 6.10 112.78 118.87 48.57 4.70 6.10 112.78 118.87 48.57 5.10 5.18 138.68 143.87 5.10 5.18 138.68 143.87 54.0654.06 4.30 7.62 117.35 124.97 62.88 4.30 7.62 117.35 124.97 62.88 4.75 6.10 126.80 132.89 4.75 6.10 126.80 132.89 56.8456.84

7. Results 7. Results Figure 13 shows the altitude results for the experiment. The experiment was run twice in order Figure 13 shows the altitude results for the experiment. The experiment was run twice in order to to collect consistency data, as well as performance data. collect consistency data, as well as performance data.

Figure Figure13. 13. Launch Launch altitude altitudedata. data. (Detailed (Detailed dataset datasetcan canbe beaccessed accessedin inthe thesupplementary supplementarymaterial.) material.)

In addition to collecting the altitude data, the instrument package was able to capture the 3-D acceleration of the rocket as well. Both longitudinal and latitudinal acceleration were captured (Figure 14). The longitudinal acceleration was the normal, expected launch acceleration. Latitudinal acceleration gives a good view of the stability of the aircraft during flight. The results are shown in Figure 15; it is very clear from these plots that the different designs had noticeably different stabilities. The coefficient of gravity (g) is 9.79651 m/s2 .

In addition to collecting the altitude data, the instrument package was able to capture the 3-D acceleration of of the the rocket rocket as as well. well. Both Both longitudinal longitudinal and and latitudinal latitudinal acceleration acceleration were were captured captured acceleration (Figure14). 14).The Thelongitudinal longitudinalacceleration accelerationwas wasthe thenormal, normal,expected expectedlaunch launchacceleration. acceleration.Latitudinal Latitudinal (Figure accelerationgives givesaagood goodview viewof ofthe thestability stabilityof ofthe theaircraft aircraftduring duringflight. flight.The Theresults resultsare areshown shownin in acceleration Figure15; 15;ititisisvery veryclear clearfrom fromthese theseplots plotsthat thatthe thedifferent differentdesigns designshad hadnoticeably noticeablydifferent differentstabilities. stabilities. Figure Aerospace 2017, 4, 17of gravity (g) is 9.79651 m/s22. 14 of 25 The coefficient The coefficient of gravity (g) is 9.79651 m/s .

LongitudinalAcceleration Acceleration Longitudinal

LatitudinalAcceleration Acceleration Latitudinal

Figure14. 14.Acceleration Accelerationdirections. directions. Figure 14. Acceleration directions. Figure

Figure15. 15. Accelerationdata. data.(Detailed (Detaileddataset datasetcan canbe beaccessed accessedin inthe thesupplementary supplementary material.) 15.Acceleration supplementarymaterial.) material.) Figure

8. Analysis and Discussion: Experimental Results 8.1. ANOVA and Model Adequacy Figures 13 and 15 show the experimental results; the altitude data for each design are shown in Figure 13 and the stability performance is shown in Figure 15. It is clear in the plots that Design A

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failed on both launches, but the other six launches performed well. The design with the best altitude performance and consistency was Design B, while the most stable design during flight was Design D. The spikes in acceleration from approximately 0 to 1 s and from 4 to 5 s are due to the powered flight time and the payload ejection. In order to explore the results more deeply and harvest additional conclusions, an analysis of variance (ANOVA) was performed on the experimental data to test the influence of the modular design on a variety of experimental responses, as shown in Table 8. The basic ANOVA assumptions were tested for each response; residuals must be plotted from a general linear model and satisfy the standard Fisher assumptions of normality, equal variances between replications, and run-order independence [37]. Any datasets whose residuals do not follow a normal distribution must be transformed using a common statistical method known as the Box-Cox transformation [38]. The raw data model was adequate for an ANOVA for five of the seven responses, while the two additional responses required a transform; the details are shown in Table 8. Model adequacy tests were performed using standard methods, with the normality tested using the Anderson-Darling Test [39] and variances tested using the well-known Levine’s Test. The independence was checked by scatter-plotting the data in run order; none of the plots showed signs of patterning or data dependence. A standard test for the independence of data is the Chi-Squared test, but that could not be applied here because it requires integer data [40]. The p-values for the model adequacy test are shown in Columns 5 and 6 of Table 9. The level of significance used was α = 0.10, with a p-value above that indicating adequacy. Table 8. Experimental ANOVA responses. Response

Description

Box-Cox Transform

1 2 3 4 5 6 7

Maximum Altitude (m) Mean Altitude (m) Maximum Longitudinal Acceleration (m/s2 ) Mean Longitudinal Acceleration (m/s2 ) Maximum Latitudinal Acceleration (m/s2 ) Mean Latitudinal Acceleration (m/s2 ) Standard Deviation of Latitudinal Acceleration (m/s2 )

None required None required None required y = x3 y = ln(x) None required None required

Table 9. Experimental ANOVA data and p-values. p-Values: Experiment Response

A: Lower Fin Design

B: Upper Fin Design

1 2 3 4 4: Box-Cox 5 5: Box-Cox 6 7

0.001 0.001 0.469 0.263 0.232 0.076 0.022 0.024 0.057

0.001 0.001 0.871 0.134 0.052 0.177 0.092 0.072 0.059

p-Values: Model Adequacy A×B

Normality Test

Variance Test

Independence Test

0.001 0.001 0.279 0.116 0.040 0.347 0.624 0.048 0.297

0.678 0.553 0.913 0.013 0.170 0.060 0.945 0.534 0.532

0.932 0.950 0.375 0.131 0.321 0.118 0.215 0.405 0.287

Scatterplot Scatterplot Scatterplot Scatterplot Scatterplot Scatterplot Scatterplot Scatterplot Scatterplot

The full ANOVA results are shown in Table 9. A 2-way ANOVA was performed, so the main effects (upper and lower fin design) as well as their interactions can be explored. As stated above, the level of significance used was α = 0.10. All data p-values (Columns 2–4 in Table 9) below 0.10 indicated that the factor of interaction had a significant influence on the response in question. The conclusions were:

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indicated that the factor of interaction had a significant influence on the response in question. The conclusions were:  

  

Maximum and and mean mean altitude altitude was was very very significantly significantly influenced influenced by by the the fin fin combinations, combinations, as as well well Maximum as their interaction during flight; as their interaction during flight; Neither of of the the sets sets of of fins fins nor nor their their interaction had an an influence Neither interaction had influence on on maximum maximum longitudinal longitudinal acceleration and only had some influence on the mean of this acceleration the acceleration and only had some influence on the mean of this acceleration after the after Box-Cox Box-Cox transform; transform; The lower lower fin fin design design has has aaweak weak influence influence on on the the latitudinal latitudinal acceleration, acceleration, which which was much more The pronounced after after the the Box-Cox Box-Cox transform; transform; pronounced Thefin findesigns designs and their interaction a but weak but significant on latitudinal the mean The and their interaction had ahad weak significant influenceinfluence on the mean latitudinal acceleration; acceleration; The The fin fin designs, designs, but but not not their their interaction, interaction, had had aa weak weak but significant influence on the standard deviation deviation of of the the latitudinal latitudinal acceleration. acceleration.

In of flight flight performance, performance,the theformal formalconclusions conclusionsfrom fromthe thegraphical graphical and statistical analysis In terms terms of and statistical analysis of of tested cases thatcombination the combination of elliptical lower with rectangular fins thethe tested cases werewere that the of elliptical lower fins withfins rectangular upper finsupper produced produced the bestconsistent and mostaltitude consistent altitude performance, while combining clipped the best and most performance, while combining clipped delta lower finsdelta withlower delta fins with delta uppers produced the most stable flight. The combination of clipped delta lower fins uppers produced the most stable flight. The combination of clipped delta lower fins and rectangular and rectangular upper fins produced a highly unstable and unreliable design. upper fins produced a highly unstable and unreliable design. 8.2. 8.2. Experimental Experimental Error Error Analysis Analysis Clearly, there were were discrepancies discrepancies between between the the theoretical theoretical and and experimental experimental data. data. Figures Clearly, there Figures 16–18 16–18 show one-on-one comparison altitude and show the the one-on-one comparison of of the the theoretical theoretical and and experimental experimental results results for for the the altitude and burnout velocity. The ranges for the theoretical results were those based on the minimum and burnout velocity. The ranges for the theoretical results were those based on the minimum and maximum maximum drag drag coefficients, coefficients, while while those those for for the the experimental experimental results results were were from from the the two two experimental experimental iterations. bebe introduced into thethe experiment via via fourfour modes: (1) iterations. The Thediscrepancies discrepancieswere werethought thoughttoto introduced into experiment modes: rocket skin fluid friction from 3-D printing lines; (2) potential payload ejection before true apogee; (3) (1) rocket skin fluid friction from 3-D printing lines; (2) potential payload ejection before true apogee; launch rail friction; and (4) angle of attack variations. Each of these possible sources of error were (3) launch rail friction; and (4) angle of attack variations. Each of these possible sources of error were examined experiment was was considered considered to to be be trivial trivial examined in in detail. detail. General General random random uncertainty uncertainty within within the the experiment in magnitude compared with the obvious error sources and was therefore not considered in detail in magnitude compared with the obvious error sources and was therefore not considered in detail in in this this analysis. analysis.

Figure 16. Theoretical vs. experimental experimental performance, powered flight.

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Theoretical vs. experimental experimental performance, performance, free flight. Figure 17. Theoretical ® model, comparing the drag coefficients for the ® Skin using an an ANSYS ANSYS CFX CFX® Skin friction friction was was tested tested using model, comparing the drag coefficients for the nose nose cone cone for for smooth smooth and and realistically realistically layered layered models. models. Payload Payload ejection ejection time time was was compared compared with with the the true apogee to determine if the ejection prematurely disrupted the flight. Launch rail friction true apogee to determine if the ejection prematurely disrupted the flight. Launch rail friction was was analyzed influenced the the powered powered flight flight altitude. altitude. analyzed to to determine determine if if it it significantly significantly influenced

Figure Figure 18. 18. Theoretical Theoretical vs. vs. experimental experimental performance, performance, burnout burnout velocity. velocity.

8.3. 8.3. Error Error Analysis: Analysis: Skin Skin Friction Friction Since the the parts parts used used in in this this experiment experiment were were 3-D 3-D printed, printed, they they have have aa slightly slightly rougher rougher surface surface Since finish finish than than parts parts made made using using injection injection molding molding or or other other common common plastic plastic fabrication fabrication techniques. techniques. Logically, this could have some effect on the drag coefficient around the nose cones of the rockets, as Logically, this could have some effect on the drag coefficient around the nose cones of the rockets, the laminar boundary around thethe surface is thinnest at the beginning of the flow [37]. However, in as the laminar boundary around surface is thinnest at the beginning of the flow [37]. However, many cases, a rougher surface at the beginning of the flow may actually decrease the drag, as it forces in many cases, a rougher surface at the beginning of the flow may actually decrease the drag, as it the flow become turbulent earlier earlier [41,42].[41,42]. While it is obvious that this caused some kind effect, forces thetoflow to become turbulent While it is obvious that this caused someofkind of whether that effect positive or negative was unknown, requiring a brief experiment to answer the effect, whether that is effect is positive or negative was unknown, requiring a brief experiment to answer question. the question. An built to to testtest thethe influence of the roughness on the An ANSYS ANSYSCFX CFX®®®simulation simulationwas was built influence of surface the surface roughness onnose the cone drag,drag, comparing the drag coefficient between thethe smooth and nose cone comparing the drag coefficient between smooth anda arealistically realisticallyrough rough surface surface −3 3.. Only (Figure 19). The The relative relative roughness roughnessfor forthe thegeometry geometrytested testedwas wasabout about1.97 1.97××10 10−−3 (Figure 19). Only the the nose nose cone cone was tested, as as background backgroundresearch research[41,42], [41,42],and andthe theflowline flowlineanalysis analysis (Figure indicated that most was tested, (Figure 10)10) indicated that most or or all of the of the drag induced by surface roughness would occur on the cone. Computational all of the of the drag induced by surface roughness would occur on the cone. Computational expense expense wasina the factor in theas decision asrequired well andseveral required several hours to toof a was a factor decision well and hours to perform theperform analysisthe to aanalysis tolerance −6 −6 − 6 tolerance of 1the × 10 on the realistic 1 × 10 on realistic cone alone.cone alone. The smooth cone model was created using a standard revolved axis protrusion, while the realistic cone was built as a series of 0.20 mm layer, with protruding ridges equal to the lateral

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The smooth cone model was created using a standard revolved axis protrusion, while the realistic Aerospace 2017, 4, 17 18 of 24 cone was built as a series of 0.20 mm layer, with protruding ridges equal to the lateral uncertainty for the printer that used to that create theused original parts.the Theoriginal flow was standard dry air atstandard one atmosphere uncertainty for was the printer was to create parts. The flow was dry air of pressure, flowing over the cone surface at a normal speed of 50 m/s. Both models were analyzed at one atmosphere of pressure, flowing over the cone surface at a normal speed of 50 m/s. Both models twice different and fresh formulations for each; the resultsfor were identical for thewere two were using analyzed twicecomputers using different computers and fresh formulations each; the results runs of each thatmodel, the setup was correct andsetup consistent. identical for model, the twoindicating runs of each indicating that the was correct and consistent.

Figure 19. 19. Skin Skin drag drag analysis analysis models. models. Figure

The analysis indicated strongly (Table 10) that the realistic roughness on the skin actually The analysis indicated strongly (Table 10) that the realistic roughness on the skin actually provided provided an advantage by reducing the drag coefficient. This result was interesting, but not an advantage by reducing the drag coefficient. This result was interesting, but not unexpected; unexpected; such results are fairly common [41,42] for blunt-type geometries such as cones in a such results are fairly common [41,42] for blunt-type geometries such as cones in a turbulent flow. turbulent flow. The roughness likely caused the flow to trip earlier, providing thicker laminar The roughness likely caused the flow to trip earlier, providing thicker laminar boundary layer coverage boundary layer coverage on the surface in the flow. This deserves further study in future work. on the surface in the flow. This deserves further study in future work. Table 10. Drag coefficients for smooth and rough cones. Table 10. Drag coefficients Cd for smooth and rough cones.

Surface

Smooth Model

NoseSurface Cone Nose Cone

Rough Model

Smooth Model Cd 0.3994

Rough Model Cd 0.2358

0.3994

0.2358

8.4. Error Analysis: Payload Ejection 8.4. Error Analysis: Payload Ejection It was noted earlier in this paper that much of the discrepancy between the theoretical and experimental performance was due to much payload before true apogee the wastheoretical reached. Upon It was noted earlier inlikely this paper that of ejection the discrepancy between and further study performance of the raw data, this phenomenon is obvious; 11true clearly shows the payload experimental likely was due to payload ejectionTable before apogee wasthat reached. Upon ejectionstudy was indeed a significant factor is onobvious; the totalTable altitude of the shows rocketsthat andthe most likely further of the raw data, thislimiting phenomenon 11 clearly payload explainswas the different thelimiting theoretical andonexperimental altitude values, except the likely failed ejection indeed a between significant factor the total altitude of the rockets andfor most Design A. Indifferent the six successful launches, the aircraft continued for a significant of the timefailed after explains the between the theoretical and experimental altitude values, amount except for ejectionA. (see and Figure 13), indicating thatcontinued the rockets significant momentum Design In Table the six11 successful launches, the aircraft formaintained a significant amount of time after before ejection. This factor be considered strongly in futuresignificant experiments; a motor ejection (see Table 11 limiting and Figure 13),should indicating that the rockets maintained momentum with a ejection. much longer delay orfactor no ejection be utilized. before This limiting shouldshould be considered strongly in future experiments; a motor with a much longer delay or no ejection should be utilized. Table 11. Experimental apogee vs. payload ejection. Design

Launch

1 Design Launch

A

BA CB C

D

D

2 11 2 2 11 2 2 1 1 2 2 1 2

Table 11. Experimental apogee vs. payload ejection. Post-Burnout Burnout Velocity (m/s) Payload Ejection Time (s) Experimental Apogee Time (s) Post-Burnout 54.66 4.25 0.40 Burnout Velocity (m/s) Payload Ejection Time (s) Experimental Apogee Time (s) 46.72 4.50 1.85 54.66 4.25 0.40 59.07 4.50 5.20 46.72 4.50 1.85 55.23 4.10 5.05 59.07 4.50 5.20 48.57 4.20 4.70 55.23 4.10 5.05 54.06 4.80 5.10 48.57 4.20 4.70 62.88 4.05 4.30 54.06 4.80 5.10 56.84 4.05 4.75 62.88 56.84

8.5. Error Analysis: Launch Rail Friction

4.05 4.05

4.30 4.75

The friction between the rockets and the launch rail was the most likely culprit for the high error found in the powered flight time performance (Figure 16), as the error for the free flight (Figure 17) is much lower or not present. It could also explain the lower-than-expected burnout velocity (Figure

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8.5. Error Analysis: Launch Rail Friction The friction between the rockets and the launch rail was the most likely culprit for the high error Aerospace 2017, 4, 17 19 of 24 found in the powered flight time performance (Figure 16), as the error for the free flight (Figure 17) is much or influence not present. It could alsosource, explaina the lower-than-expected burnout velocity to (Figure 18). 18). Tolower test the of this friction simple analytical model was constructed calculate To test the influence of this friction source, a simple analytical model was constructed to calculate the potential friction factor. The free body diagram is shown in Figure 20. It was assumed that the the potential friction factor. The=free diagram is showninterface. in Figure 20. It was assumed that the dynamic friction coefficient 0.05body [43] for a plastic-steel dynamic friction mass coefficient µkrocket = 0.05was [43]132 for g a plastic-steel interface. The launch of each (1.295 N), and the engines used produced a thrust of The launch mass of each rocket was 132 g (1.295 N), and the thrust engines used produced a thrust 22.1 N. Using basic static moment analysis (Figure 20), the upper reaction ( 1) was found of to 22.1 N. Using basic static moment analysis (Figure 20), the upper thrust reaction (RF1) was found to be be 1.653 N and the lower ( 2) was 1.652 N, for a total of 3.305 N. Thus, the dynamic friction force 1.653 was: N and the lower (RF2) was 1.652 N, for a total of 3.305 N. Thus, the dynamic friction force was:

+ RF22) = 0.165 0.165N N = µk ((RF11+ Ff rict = )=

(9) (9)

While this is not a large force, it is certainly significant in the problem at hand; it adds 12.7% to While this is not a large force, it is certainly significant in the problem at hand; it adds 12.7% to the apparent weight of the aircraft at launch for a total weight of 1.4595 N (148.8 g); this is nearly the the apparent weight of the aircraft at launch for a total weight of 1.4595 N (148.8 g); this is nearly the weight of the recovery system and instrument package combined. The new thrust-to-weight ratio is weight of the recovery system and instrument package combined. The new thrust-to-weight ratio is 15.142, not far from the minimum allowable ratio of 13.256. This additional friction force certainly 15.142, not far from the minimum allowable ratio of 13.256. This additional friction force certainly contributed to the error found in this experiment, particularly the errors shown in Figure 16. contributed to the error found in this experiment, particularly the errors shown in Figure 16. 18 mm

Lug 1 Rocket Body

Thrust Reaction Force

5 mm 3.2 mm

247 mm

Lug 2

6.35 mm

Thrust Reaction Force

Rocket Body

Dynamic Friction Thrust Reaction Force

Dynamic Friction

Not to scale

Thrust Force

Figure 20. Launch rail friction. Figure 20. Launch rail friction.

8.6. Error Analysis: Analysis: Angle Angle of of Attack Attack Variation Variation 8.6. Error One analyses was that thethe angle of One of of the the basic basic assumptions assumptionsmade madeduring duringthe theCFD CFDand andanalytical analytical analyses was that angle attack was constant during flight. If this assumption is invalid, the wetted area of the airframe changes, of attack was constant during flight. If this assumption is invalid, the wetted area of the airframe increasing the drag area. Since area. the rockets in this study had a total of eight at two changes, increasing the drag Since used the rockets used in this study had acontrol total ofsurfaces eight control locations, rolllocations, rotation angle is also influential order to analyze theorder potential effects this surfaces atthe two the roll rotation angle in is this. also In influential in this. In to analyze the may have, a brief sensitivity analysis was performed. Design D, the most stable of the four designs, potential effects this may have, a brief sensitivity analysis was performed. Design D, the most stable was in ANSYS CFX® ininthe same CFX way®as 4 but with the angle attack of theanalyzed four designs, was analyzed ANSYS inin theChapter same way as in Chapter 4 butofwith the offset. angle ◦ ◦ ◦ Three offsets were used: 1 , 2 , and 5 . These are reasonable points of observation, as the observed of attack offset. Three offsets were used: 1°, 2°, and 5°. These are reasonable points of observation, as rocket behavior during flight (on the sixflight successful flights) did not show significant from the the observed rocket behavior during (on the six successful flights) did notdeviation show significant constant In order to test the effect of roll angle, two orientations were one with the deviationflight fromangle. the constant flight angle. In order to test the effect of roll angle, twoused; orientations were

used; one with the upper control surfaces set at 90° and the other with the lower control surfaces set the same way. Two different air velocities were used, 50 and 100 m/s. The model setup is shown in Figure 21, and the results of this study are shown in Figure 22.

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upper control surfaces set at 90◦ and the other with the lower control surfaces set the same way. Two different air velocities were used, 50 and 100 m/s. The model setup is shown in Figure 21, and the Aerospace 2017, 4, 17 20 of 24 Aerospace 2017, 4, study 17 20 of 24 results of this are shown in Figure 22.

Figure 21. Angle of attack sensitivity study configuration. Figure 21. 21. Angle Angle of of attack attack sensitivity sensitivity study study configuration. configuration. Figure

Figure Figure22. 22. Angle Angleof ofattack attacksensitivity sensitivitystudy studyresults. results. Figure 22. Angle of attack sensitivity study results.

Asseen seeninin Figure impact of varying the of angle ofhad attack had impact a trivial on the As Figure 22,22, the the impact of varying the angle attack a trivial onimpact the measured As seen in Figure 22, the impact of varying the angle of attack had a trivial impact on the measured drag onWhile the body. all of the orientations were subjected a higher drag force on theforce body. all ofWhile the orientations were subjected to a highertodrag forcedrag thanforce the measured drag force on the body. While all of the orientations were subjected to a higher drag force than therun original (from4),Chapter 4), the largestatvariation at 50around m/s was around 3.46% (an original (fromrun Chapter the largest variation 50 m/s was 3.46% (an increase of increase 0.03 N), than the original run (from Chapter 4), the largest variation at 50 m/s was around 3.46% (an increase of 0.03it N), was 3.81% 0.14 N) at 100 m/s. While not an overwhelming factor in while waswhile 3.81%it(increase of (increase 0.14 N) atof100 m/s. While not an overwhelming factor in this study, of 0.03 N), while it was 3.81% (increase of 0.14 N) at 100 m/s. While not an overwhelming factor in study, it does add to the apparent mass; the results that would be a major itthis does add to the apparent vehicle mass;vehicle the results imply that thisimply would be this a major consideration this study, it does add to the apparent vehicle mass; the results imply that this would be a major consideration at higher orientation of not the appear rocket did not appear to have a noticeable at higher velocities. The velocities. orientationThe of the rocket did to have a noticeable impact on the consideration at higher velocities. The orientation of the rocket did not appear to have a noticeable impact on the results, as no patterns or trends appeared in the data. results, as no patterns or trends appeared in the data. impact on the results, as no patterns or trends appeared in the data. 9. 9. Analysis Analysis and and Discussion: Discussion: Manufacturing Manufacturing Implications Implicationsof ofError ErrorAnalysis Analysis 9. Analysis and Discussion: Manufacturing Implications of Error Analysis The Theerror erroranalysis analysisdiscussed discussedin inSection Section88provides providessome someguidance guidancefor forthe theproduction productionof ofthe thenext next The error analysis discussed in Section 8 provides some guidance for the production of the next generation generationof ofsmall smallmodular modularrockets rocketsin inorder orderto toavoid avoidthe thepitfalls pitfallsencountered encounteredininthe thecurrent currentstudy. study. generation of small modular rockets in order to avoid the pitfalls encountered in the current study. The skin skin friction friction study study shows shows the the influence influence of of the the surface surface roughness roughness of of the the parts, parts, which which isis 1.1. The 1. The skin friction study shows the influence of the surface roughness of the parts, which is controlled primarily by varying the layer thickness and the build speed. Designers should controlled primarily by varying the layer thickness and the build speed. Designers should controlled primarily by varying the layer thickness and the build speed. Designers should consider their choice of process settings on the skin friction, eventually finding the optimal consider their choice of process settings on the skin friction, eventually finding the optimal design settings to balance easier manufacturing with increased performance. design settings to balance easier manufacturing with increased performance. 2. Payload ejection time can be controlled mainly by the choice of engines for the flights. 3-D 2. Payload ejection time can be controlled mainly by the choice of engines for the flights. 3-D printing produces custom geometry, so accommodating a variety of engines is a simple matter. printing produces custom geometry, so accommodating a variety of engines is a simple matter.

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2.

3.

4.

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consider their choice of process settings on the skin friction, eventually finding the optimal design settings to balance easier manufacturing with increased performance. Payload ejection time can be controlled mainly by the choice of engines for the flights. 3-D printing produces custom geometry, so accommodating a variety of engines is a simple matter. The information collected in this study could aide in the proper selection of engines for the types of rocket in this study. Clearly, the launch rail friction had an impact on the results of this experiment. A better launch method should be used for these types of rockets, including tray or tube launching, in future launches. The influence of the angle of attack variations increases with velocity, so designers should make sure that the airframes are as symmetric and well balanced as possible. They should also be checked for printing errors before launching to ensure that no unbalance exists in the geometry.

10. Analysis and Discussion: Design Modularization The main purpose of this study was to develop, design, and test an airframe design based on feature modularization into complex parts, instead of part modularization into subassemblies. The rocket airframes used in this study were built in three parts; the booster, the payload, and the nose cone. In a real aircraft, such as a sounding rocket or missile, the booster section would contain all the propulsion components, the payload section would contain the warhead or instruments, and the nose cone would carry the guidance system and computer. Based on the need of the mission and the sizes of the AM equipment available, the number and scope of the complex parts can be adjusted. The rockets used in this study were deliberately manufactured out of polylactic acid (PLA) because it biodegradable and fully recyclable; if any rockets were lost, the pollution to the environment would be minimal. Rockets made from the standard ABS would be significantly lighter but are not at all environmentally-friendly or as easily recyclable. The modularization technique dramatically reduced the effort in creating the designed experiment used in this study. Two different booster and two different payload designs were made, then combined into four separate unique rocket designs that had significantly different performances. If the experimental design had not called for measurable and predictable combinations of the factors, there is virtually no limit to the unique designs that could be made. Production time was very brief as well, requiring around 10 h to design, verify, set up, print, and assemble the rockets. While most commercial model rocket producers would likely be able to produce rockets much faster, this depends on months or years of design, testing, and preparation to produce stock parts. The design modularization technique can produce a successful launch of a completely unique design 10 h after the original idea is proposed. The benefits of this technique to the manufacturing of airframes in general is obvious. When compared to the cost of commercially available alternatives to the rockets used in this study, the modular designs were very cost efficient. Not including the labor of the authors, each of the rockets required about 100 g of material (including supports and waste) and about 8 h of electricity at 30 W, a total of about $2.25 per airframe. Even if the designer chose to use expensive proprietary filament instead of open source filament (as was used in this study), the cost would remain under $10.00 per airframe. The motors for each launch cost about $6.00, but this should not be considered, as any rocket would require the same launch cost. Similar alternatives from the world of model rockets are shown in Table 12 (source: Amazon.com, February 2017). Clearly, the modular 3-D printed rockets are far more cost effective. Most of these have airframes made from cardboard or very thin plastic, unlike the strong airframes used in this study. In all cases, the airframe weight is comparable due to the ability to 3-D print the airframe as a complex hollow structure.

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Table 12. Commercial model rocket alternatives. Commercial Model Rocket Design

Cost

Estes 2452 Athena Estes Hi-Flier Estes Alpha III Estes 7000 Bull Pup Estes 2440 Magician

$12.07 $9.99 $24.99 $15.43 $19.99

While the method could obviously be used to produce cheap customized model rockets, the greatest benefit would be in the production of sounding rockets and military equipment. The model rockets provide an interesting and relatable case study for exploring this design method, but much further exploration and development of the method is needed before it would be trusted for more vital and complex applications. However, the benefits are clear and this further development is certainly warranted. 11. Conclusions The present study explored a design modularization method that combines features into complex individual parts instead of parts into subassemblies. The results strongly suggest a number of general conclusions about the design modularization method and about the feasibility of the 3-D printing of rocket airframes, including: 





 













Design modularization via combination of features into complex parts is a feasible design methodology for rockets and other airframe-based products; 3-D printing, particularly extrusion-based processes, is an excellent method for producing the complex multi-feature parts needed for optimized airframes; It is possible and feasible to produce airframes that have excellent recyclability and low environmental impact by utilizing biodegradable materials in the design; The airframe designs can be adjusted and customized nearly infinitely; The exact shape of the rocket fins and their combinations have a very significant influence on the performance and stability of the airframe; AM and modular design greatly simplify the measurement and adjustment of the fin influences on performance; While the performance of the rockets fell short of the analytical predictions, the sources of the errors were very simple to identify due to the simplified design; The print lines on 3-D printed parts seemed to provide an advantage, not a disadvantage, to the rockets as it reduced the drag coefficient of the nose cone; The payload ejection time was too short for the motors chosen, a factor that should be considered heavily in future studies; The launch rail introduced a significant amount of friction, causing the rockets to launch and burn out at slower speeds; Five of the seven experimental responses produced data whose residuals followed a normal distribution without any statistical transforms, suggesting that very little random error is contained in the experiment.

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In conclusion, this study was unique and was very successful as a detailed case study in modular design; very little research has been done in this area, and this study should provide background and motivation for further work. The performance of the modular rockets was very easily analyzed, with the sources of error easily found and explained; the modularization greatly eased analysis of the errors, as there were far fewer parts to consider. Future studies using modular design methods with AM can easily correct these sources of error, as the complex parts have great design and geometry freedom and are not limited by commercial part standards. In a standard design, it may be difficult or impossible to correct sources of error found during experimentation if the design is dependent on a series of standard parts. Using part modularization, few or no standard parts are used, so infinite adjustment and correction can be performed on designs in nearly real-time. Supplementary Materials: The following is available online at http://www.mdpi.com/2226-4310/4/2/17/s1, Table S1: Flight data for rocket design A–D. Acknowledgments: No specific research grants or external funding were used in the completion of this work. The authors thank College of Engineering and Department of Industrial & Systems Engineering and Engineering Management, University of Alabama in Huntsville for providing laboratory space and materials for the completion of the experiments. Author Contributions: Rachel N. Hernandez led the effort to complete background research and digital models for 3-D printing and assisted with experimental design and experiment execution. Harpreet Singh provided background research, aerodynamic models, and assisted with experiment execution. Sherri L. Messimer provided advice and input concerning the experimental design and interpretation of results. Albert E. Patterson lead the research team, assisted with experimental design, performed the manufacturing of the samples, supervised and assisted with the experiments, and performed the data and error analyses. Rachel N. Hernandez and Albert E. Patterson wrote the initial proposal for lab space and resources to complete the work. All four authors contributed to the writing/editing of the paper. Conflicts of Interest: The authors declare no conflict of interest.

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