A Modular Reconfigurable Software Modelling Tool to Support Distributed Multidisciplinary Design and Optimisation of Complex Products G. La Rocca, M.J.L. van Tooren Faculty of Aerospace Engineering Delft University of Technology The Netherlands
[email protected]
Abstract Multidisciplinary Design and Optimisation (MDO) is recognized as the design methodology with the largest potential to help companies both pushing further the limits of their current designs and investigating novel concepts. In order to fully exploit the great potential of MDO, several issues need to be tackled first, such as the management of the design process across large distributed teams, the capability to easily reconfigure and adapt the design process by switching between various analysis tools of different levels of fidelity (both proprietary and commercial of the shelf), the need to support the generation process (enhance automation and guarantee consistency) of the multitude of discipline-tailored models required to feed the various analysis tools, etc. The development of an innovative modelling system, called the Multi Model Generator, is presented here, which specifically addresses the abovementioned issues and provides designers with a flexible, modular system to support and accelerate the preliminary design process of complex products like aircraft. Knowledge Based Engineering is being used to automate the repetitive activities that typically consume the largest part of the design process, enhancing engineers’ productivity and freeing more time for their creative work. Keywords: Knowledge Based Engineering, Object-Oriented Modelling, High Level Primitives, Multi Model Generator.
1
INTRODUCTION
According both to NASA and the Advisory Council for Aeronautics Research in Europe [1,2], the aircraft of the future, as well as the support infrastructure, will be safer, cleaner, more secure and efficient. Next future aircraft will have superior performances and set higher standards of quality. Novel aircraft configurations, featuring adaptive geometries and self-healing structures, will finally contend the so far undisputed supremacy of the actual configuration aircraft. In a couple of decades the aeronautic systems will differ from today’s systems at least as much as the actual systems differ from those of 1930. The recent progresses in the development of increasingly advanced design support tools, (CAD/CAE systems, FEA and CFD methods, just to mention some), powered by the exponentially increasing capabilities of actual computers, have surely improved aircraft performances but they have also increased development costs by a comparable amount. The new chapter of the future air transport system will not start until the number-one challenge will be met, that is provide technologically superior products and services at an affordable cost. The availability of both economical and intellectual resources is shrinking whereas the complexity of
demanded products is continuously increasing, yet aircraft manufacturers must be able to compete in a world globalized market. In order to increase engineers’ productivity and maintain competitiveness a fundamental paradigm shift is required to pass to a new knowledge based vision of business: knowledge has to be recognized not only as a fundamental business key asset, but it needs also to be managed and engineered, aiming at the highest return on investment. Knowledge Based Engineering (KBE) is a design technology that allows capturing and reusing efficiently product and process multidisciplinary knowledge, in order to reduce time and costs for engineering applications, through the automation of the repetitive design tasks [ 3 ]. Relieving designers from non-adding value activities, more time is made available to exploit creativity and engineering skills. As pointed out in this paper the most relevant feature of KBE is its capability to merge parametric CAD with rule based object-oriented design. These three characteristics have provided the means to develop an innovative aircraft modelling environment, called the Multi Model Generator (MMG). The purpose of this tool is two fold: address the lack of appropriate support tools for conceptual design and provide a valuable instrument for 1
preliminary design and multidisciplinary analysis of both traditional and novel aircraft configurations. The capabilities of the MMG to facilitate the exploration of the design space are discussed in this work, with particular emphasis on the techniques used to extract from a unique integrated aircraft model definition, a wide range of different discipline-specific views. Using combinations of specifically developed classes of objects, called High Level Primitives (HLPs), the MMG is able to support the designer instantiating large sets of different aircraft configurations and configurations’ variants. The HLPs provide designers with a powerful medium to capture not only the geometric aspects of design, but also the rules to automate many of the non-creative and time expensive operations (e.g. the generation of many specific input models to feed a range of analysis tools) that typically slow down the design evaluation process. Role and functionality of the MMG within complex distributed multidisciplinary design and optimisation environments, are discussed and examples from current and past programs are reported to demonstrate how KBE can provide a solution to some of the fundamental needs indicated by the MDO community: the development of more flexible and robust generative tools to support more automation in aircraft design, the use of high fidelity analysis tools as early as possible in the design process and the capability to handle the design process across large and distributed networks of expertises [4]. 2
THE AIRCRAFT DESIGN PROCESS: DESIGNER’S NEEDS AND SUPPORT TOOLS
During the conceptual design phase of complex products like aircraft, the designer needs to have space for his ideas. In this early stage of the design process his experience and design skills meet customer requirements to spark a viable concept. Nowadays CAD systems are by far the most common software tools used to support design. Current generation CAD systems are mainly feature-based, which means that they have a standard set of parameterized primitives (points, lines, solid volumes, holes, chamfers etc.) that can be adjusted and combined together to represent a design. The very generic nature of CAD primitives is the main reason why such systems are used during the conceptual design phase; nevertheless, the same generic nature is the inherent cause of the limited support they can provide to the designer. As a matter of fact, CAD primitives have very inadequate knowledge recording and learning capabilities [5]. In the end CAD systems can only output models, which are human driven records of the geometric results of a fully human centered design process. The time required to a designer to give a shape to an aircraft concept through assembly of CAD primitives is far too large, and many of the advanced capabilities offered by new generation CAD systems, e.g. for rapid rivets or cutter paths locating are of very limited support to this conceptual stage. With respect to the high level of design abstraction needed at this point, feature-based modelling is neither efficient, nor effective. For a CAD program an aircraft wing will always be 2
a set of surfaces and solids, never, for instance, a lift generating object compiled of different wing sections with leading and trailing edge devices and an internal generic structural concept. What the designer needs is an efficient way to virtually manipulate ideas and trade-off different possible solutions. This calls for the possibility to analyse many concepts and configurations, already in early stages of the design process. Hence the designer should not be hampered by the manipulation of an infinite set of lines and points, but provided with a limited set of entities, embedding a clear functionality (e.g. generate lift, provide control and thrust, carry loads, store payload etc.) and featuring knowledge capability (i.e. the capability to autonomously act and react to the occurrence of certain events). These entities should be easy for the designer to adjust and link together to create instances of his design concepts. In the conceptual design phase of a complex product, in order to stay within acceptable time limits and keep the process manageable by a limited number of experts, the amount of configurations to be considered is typically kept very low. Also the use of high fidelity analysis tools in this early phase of the design process is very limited, because their high associated costs, in terms of time and required computation resources. As a consequence, the aircraft life cycle cost is determined to a very large extent (70-80%) just by using low fidelity tools. Complete aircraft configurations generated by conceptual design tools are largely based on semi empirical models and statistical methods rather than first-principle. In this case the reliability of the obtained results is often arguable and, as soon as new and nonconventional aircraft concepts need to be evaluated, such methods result inadequate, because of the lack of any previous reference and statistical data. Besides, over simplification and approximation of the design problem might simply postpone or hide some problems: important design issues might be left out of consideration and eventually reveal as showstoppers during later stages of the product development, increasing delivery time due to re-work or redesign. The capability to introduce high fidelity analysis tools since the early phases of the design process becomes a crucial need to lower the risk associated with the development of innovative complex products. Advanced analysis tools, such as CFD or FEM, however, require the generation of complex and very specific models, which conceptual design tools are typically not able to deliver, and calls for the use of dedicated design environments. The lengthy pre-processing procedures associated with setting up the various analysis occupy an excessive part of the whole design cycle time and represent a tremendous bottleneck in the manually iterative approach typical of traditional design methodology. Solutions are needed to guarantee the consistency of all the models generated for the various disciplinary analysis tools yet provide the high level of customization required. How KBE technology can address these needs is discussed in next section.
3
KBE TECHNOLOGY DESIGN
TO
SUPPORT
AIRCRAFT
Knowledge based engineering (KBE) with its inherent capability to combine the design flexibility of a CAD system with the versatility and control power offered by the programming approach, represents a promising solution for several of the designers’ needs. Though KBE has being in service since more than 20 years, the recent development of affordable KBE tools plus the increased industry’s need to efficiently retain and exploit corporate knowledge are creating a new momentum and setting the conditions for an authentic KBE renaissance. Figure 1 shows the design paradigm according to the implementation of knowledge based engineering principles. The product (or generative) model represents the central repository of the design knowledge and plays a pivotal role in the design process. It represents the formalization of the design team relevant knowledge (engineering rules and reasoning mechanisms) by means of a scripting language; typically a high level programming language, such as Object-Oriented Common Lisp or C++ enriched with sets of commands and methods to drive an integrated parametric CAD kernel (e.g. PARASOLID ).
Figure 2 : The product model. what-if design, without repetitive involvement in activities associated with the generation of the data and information actually needed to evaluate the various what-ifs. The integration of rule-based, object-oriented design with parametric CAD, as provided by KBE, offers the possibility for flexible modelling of both product and process. Rules combined in objects allow the effective manipulation of engineering knowledge. In order to support the design of aircraft, this feature has been implemented with the High Level Primitives: objects containing product and engineering knowledge that can be used and re-used in different aircraft configuration. 4
Figure 1: The Knowledge Based Engineering supported design paradigm. This integrated environment, where Artificial Intelligence meets CAD, represents the most relevant feature of KBE systems. The functionality of the product model (or generative model) can be described by the simplified representation of Figure 2: a set of input values is assigned to the parameters used by the engineering rules and reasoning mechanisms encapsulated in the product model, the KBE system brings these rules to bear in a systematic way and the engineered design is automatically generated as output. As sketched in Figure 1, the designer is fully in charge of the creative part of the design process: just by editing the input values for the product model, he can exploit the generative capabilities of the product model to automatically produce a potentially infinite set of design configurations and variants, eventually ready to be verified with (external) analysis tools. The designer can focus on
THE OBJECT ORIENTED AIRCRAFT MODEL
The Object Oriented (OO) modelling paradigm nicely suits the typical engineering view on product definition and design process; models built from objects allow a good mimic of the real world [6]. Indeed the concept of objects is richly used in the field of knowledge cognitive psychology, and the subspecialization concerned with knowledge representation. It has been demonstrated that people tend to represent knowledge in term of hierarchies, where the lower parts in the hierarchy are specializations of more general classes (sitting at higher hierarchical level) and may have characteristics that override those inherited from the general classes. Research in the area of concept representation, demonstrated that people represent memory of objects in terms of a prototypical schema, which incorporates all the key representative characteristics of the objects in the form of a generalized abstract schema. This prototypical schema becomes then the root of a hierarchy that possesses specializations. The more an item resembles ‘something’, the more it is categorized in that ‘something’ abstraction, hence it is included within an implicitly defined range of typicality. This natural process of memory and knowledge representation provides us with an efficient and economical way of arranging information and gives rise to the concept of 3
cognitive economy, i.e. storing of information with the least possible effort [6]. An example of how this knowledge representation schema can apply to model the aircraft concept is shown in Figure 3. At the top of the hierarchy there is the prototypical schema of the aircraft: this aircraft abstraction features all the typical characteristics of a small tourism aircraft plus those of a commercial jetliner plus those of a fighter aircraft. An actual commercial airliner will be just a specialization of the prototypical aircraft abstraction. In turn there will be further specializations of the commercial airliner such as a high-wing turboprop, a T-tail fuselage mounted engines, a freight configuration etc. Additionally each one of these specializations inherits some characteristics of the category abstraction it belongs and overrides others. For example, the freight airliner overrides some of the commercial airliner abstraction characteristics, such as the
Figure 3 : Object-Oriented modeling of the aircraft. The prototypical schema and the concept of inheritance. presence of windows in the fuselage, but inherits some characteristics, such as the wings and the tail. Given a set of top level requirements, a good designer would like to investigate several aircraft concepts and see which one has the best potential to fulfil them, e.g. a traditional airliner and a blended wing body aircraft configuration might a couple of the aircraft concepts that are worth to be investigated. These apparently very different design solutions are actually linked by several elements of similarity: they all feature some components, with the common purpose to fulfil certain functions, such as generate lift, supply thrust, provide control and stability, accommodate payload etc. Even if these components have different shapes and are recombined in different topological configurations, it is still possible to discern the recurrent presence of wing-like elements, fuselage sections, engines and connection parts (see Fig. 4). These recurrent elements of similarity are what is addressed in the next section as the High Level Primitives (HLPs): generic entities with a similar functionality, shape and behaviour. 4
5 DEFINITION OF THE HIGH LEVEL PRIMITIVES The HLPs can be interpreted as special bricks, kind of rubber LEGO blocks, which can be individually morphed, combined and assembled with a typical build-up approach in order to generate a potentially infinite amount of different aircraft configurations. In Figure 4, it is shown that, just using four of these HLPs, namely the wing-trunk, fuselage-trunk, engine parts and connection element primitives, it is possible to generate many aircraft configurations and many variants of each configuration, including non-conventional aircraft such as delta wings, joined wings, canards, three lifting surfaces configurations, blended wing body aircraft, etc. The Object-Oriented methodology, through the concepts of classification, inheritance, encapsulation and polymorphism provide the tools to capture and model this concept of primitives. KBE fully supports the object-oriented formulation, hence it can be used to develop a computerized modelling environment where these primitives can be defined and (re)used multiple times in different combinations, to help the designer building a virtual representation of the aircraft concept he has in mind and move forward with its assessment. The HLPs are actually defined as classes; the methods defined inside the classes represent the gene print (the commonality elements) of the HLPs: they encapsulate the knowledge to perform certain tasks and the rules and constraints that determine the behaviour of the class instantiation objects in different situations. The classes’ attributes (i.e. the HLP-parameters) allow the morphologic variation of the primitive (i.e. represent the individuality elements): when different values are assigned as input a different shape is featured by the instantiated object, which makes it unique among all the other class instantiation objects. It means that the flexibility of the HLP is directly related to the choice (and number) of parameters used to define the class: the parameters represent the degrees of freedom of the HLP and determine the range of specific instantiations that can be generated, i.e. they determine the range of typicality of the prototypical schema discussed
Figure 4 : The High Level Primitives. Example of the wing-trunk multiple instantiation in different aircraft configurations.
above. This approach based on the definition and combination of commonality and individuality elements to generate individual/specific models that anyway share common knowledge and characteristics is addressed as the family-thinking concept. In order to design a wing for example, the designer can use multiple instantiations of the wing-trunk and connection HLPs. The number of wing-trunks necessary to assemble a given wing configuration (e.g. featuring a number of kinks) can be indicated directly by the designer; the number of required connection elements to link the various trunks (e.g. when variations in the dihedral angles occur between contiguous wing-trunks) will dynamically adapt. The designer can vary the parameters values individually for the various wing-trunks (e.g. span, chord length, sweep and twist angles, number, location and type of airfoils etc.) and affect either locally or globally the shape and topology of the whole wing model. The definition of the HLPs is not just limited to the parametric description of aerodynamic surfaces, but includes also some advanced generative capabilities to model internal structure configurations. Through a specific set of parameters, the designer has the possibility to modify the position of the internal structural elements (ribs, spars etc.) as well as to change the configuration topology (i.e. vary the number of spars, ribs etc.). Since the definition of the internal structure is associated with the definition of the outer surface, when the latter is modified, e.g. by a different wing design, the shape of the internal structure components automatically adapts. Similarly it is possible to define parametrically position and connectivity of the aircraft systems with respect to the structural elements (i.e. to which structural element each non structural items is attached). When the structure configuration changes (e.g. positioning and/or number of spars and ribs change) the positioning and connectivity of the aircraft systems automatically follow and adapt. This capability to strongly affect the topology of a model configuration sets a big difference between classic
parametric CAD systems and KBE application implementing the HLPs. The HLPs modelling approach allows the designer to make discrete changes in the product definition, while in case of simple parametric CAD models it is often very difficult to introduce changes that are going beyond simple modification (perturbation) of components’ shape and position. Even expert CAD users will have hard times to recreate the proper connections between the components of a complex system when one of these components has to be dropped out during topological variations. Typically, showstopper errors will be generated by the impossibility for the CAD system to execute procedural sequences of actions, because one of the referenced features or components in the working sequence has disappeared. Though the expert CAD user has the knowledge to handle interactively those problems, the basic CAD tool does not offer him any learning capability to solve those problems autonomously. On the other hand KBE defined HLPs can be provided with adequate reasoning mechanisms to largely prevent and/or successfully manage those failures. Furthermore the ruled-based approach used to define the primitives offers also the possibility to handle some known native CAD kernel limitations, which are normally largely out of the control of the engineer and need to be solved by improvement in the CAD parametric kernel [7]. Typically, when an experienced CAD operator realizes that a given operation (e.g. a critical intersection operation between two bodies particularly oriented respect to each other) often fails due to some limitations or bugs in the geometric kernel, he can take advantage of his knowledge of the CAD tool to find an alternative procedure to successfully get to the needed results; he can use a so called workaround. This very approach can be directly taught to the HLPs (i.e. programmed as internal method), such that workaround procedures can be automatically triggered, either proactively, hence avoiding the critical situation to occur, or via a diagnosis and prognosis mechanism, after a failure occurrence has been detected. What has been discussed above actually highlights another
Figure 5 : Generation of various aircraft configurations and configurations variants with the high level primitive build up approach 5
Figure 6: UML class diagram illustrating the modular definition of the wing-trunk and connection HLPs, and the links with a number of capability modules. fundamental characteristic of KBE, i.e. the capability to capture and manage not only product knowledge, but also process knowledge: not only the what, but the how as well. This peculiar capability can be further exploited to capture within the HLPs definition other similarity aspects than just the geometrical ones, discussed so far. As a matter of fact, different aircraft models, once properly modelled in their appearance, still require several working activities to adapt and pre-process them in suitable formats to support their assessment. E.g. specific models will have to be generated for CFD analysis in order to evaluate the aerodynamic characteristics, or for FEM analysis to validate the quality of the structural design. Indeed, the assessment process of very different aircraft configurations calls for very similar analysis methods and relative procedures. A very large deal of these pre-processing procedures consists of sequences of lengthy, tedious and repetitive activities that enormously hamper and slow down the manually iterative design process. On the other hand, many of them are suitable to be captured and formalized into explicit rules, such that the various HLPs will be able to reapply them systematically and autonomously. In this way the designer can be relieved from the burden of time consuming routine activities and the conditions for extensive engineering automation are created. 6
Without entering in the fine details of the HLPs software implementation, it is worth to further clarify that an HLP is actually an aggregation of classes (Figure 6). Since a class can be used to encapsulate a process, which eventually operates on data and information generated by another class, the capability of a given HLP can be enhanced just adding some extra operative classes to the aggregation. This approach provides a relative ease in adding new functionality to the HLPs, without the need to dismantle and rebuild their structure. Even if the HLPs are presented to the user as the very basic bricks to build up an aircraft configuration, they are actually built up from a set of modules, each adding a certain capability to the HLP aggregation, e.g. the capability to create a specific structure configuration, to define a specific aerodynamic surface, to define a fuel tank, to position internal systems, to retrieve and calculate data and information concerning weights distribution etc. Different HLPs might share some of these capability modules, such that an efficient re-use of code, ease of maintenance and growth possibility of the whole system are supported. Example of some of the capability classes associated with the wing-trunk HLP are illustrated in Figure 6. It is important to stress that the HLPs cannot in any way substitute the designer in his decision making activity. The
HLPs do not have any knowledge to judge the quality and the pertinence of the data received as input by the designer, apart from eventually checking their consistency with an expected data format. The HLPs are means used to create a robust modelling environment, which has the capability to deliver a valid output, whatever is the set of input received. It is designer’s responsibility to judge the quality of the final design through his knowledge and with support of analysis tools. Though the HLPs do not have any direct influence in steering the design toward certain direction, they definitely put the designer in a more favourable condition to explore the design space. 6
A MULTI MODEL GENERATOR TO SUPPORT AIRCRAFT MULTIDISCIPLINARY ANALYSIS Flexibility, modularity and process automation capabilities have been the driving requirements for the development of the aircraft modelling environment, addressed here as the Multi Model Generator (MMG). Flexibility, modularity and process automation capabilities, as matter of fact, were the driving requirements for the HLPs, which actually represent the main constitutive components of the MMG. In the specific case, the ICAD system [8] has been used to develop such KBE application, even if the theoretical approach to support the aircraft object-oriented modelling discussed before could possibly be implemented in another of the various KBE systems available on the market. In synthesis the MMG is based on the definition of an aircraft super class that, on the base of a large set of input parameters, can be instantiated into a specific aircraft design, which is again the result of automatic multiple instantiations of other component-classes, i.e. the High Level primitives. Methods and rules are programmed in the body of this generic aircraft product model, such that for different input values, different number and type of HLPs are selected, instantiated and assembled to shape the desired aircraft configuration. As discussed before, the various HLPs have the knowledge to generate their shape and perform a set of tasks. When multiple HLPs are instantiated together to generate a complete aircraft model, the latter inherits as a whole the characteristics and the capability of the single HLPs/components. E.g. if the given wing-trunk primitive has the capability to derive from its outer surface a set of specific information required e.g. to feed a given aerodynamic analysis tool (thanks to one of the capability modules associated with this HLP, like PointsGenerator of Figure 6), then the whole wing (built up as an assembly of several wing-trunk primitives) will be able to generate the data and information required for the abovementioned analysis from its complete surface (see more in Section 8). As illustrated by the design cube in Figure 7, a thorough exploration of the design space implies the possibility to move along three directions indicated by the disciplines, phases and scale axes. Typically many discipline experts are involved in the design process, equipped with a range of analysis tools. Each expert needs to operate on a specific set of data and information, which represents the specific
Figure 7 : The design cube - multidisciplinary, multiphases, multiscale. view he has on the aircraft product. E.g. the aerodynamicist’s view on the aircraft mainly consists of the outer aerodynamic surfaces, without any direct interest in the internal structure configuration or in the location of the various systems. The MMG represents our proposed solution to the need of advanced smart modelling tools, able to select and extract the various disciplinary features of a product and deliver them as sets of separate but fully relational sub-models. The term ‘sub-model’ is used here to address the various disciplinary views which are derived from the integrated aircraft (product) model generated by the MMG. These sub-models are the bases for the generation of the various disciplinary models, e.g. FE models, CFD models, weight&balance models, etc., which will be actually generated in the preprocessing environment of the various analysis tools. In order to put the experts in condition to exploit their analysis tools (commercial of the shelf or in house developed) in the most effective and efficient way, the tailoring of the sub-models for the various analysis tools becomes as much important as the sub-models consistency. E.g. if the wing planform design is changed in the aircraft model, updated sub-models for stress analysis, aerodynamic analysis and tooling design need to be promptly generated. These sub-models will be used to cascade the wing modification down to the aerodynamic model used for CFD analysis, as well as to the FE model for stress analysis and the mould model employed for tooling design. Such submodels should be directly ‘edible’ to the various analysis tools, hence ready to be submitted for analysis, without the need of any manual preprocessing. 7
STRUCTURE AND FUNCTIONALITY OF THE MULTI MODEL GENERATOR Structure and functionality of the MMG can be discussed referring at the two main functional blocks represented in Figure 8: 1. The product model, which is the main body of the MMG and contains the rules-base with the definition of the various HLPs and the utility to bring them together to build-up different aircraft configurations. 2. The reports writer, which is the set of utilities in charge to extract from the whole aircraft product model the data and information needed to build the specific sub-models 7
(i.e. the reports), when demanded for a given discipline analysis .
modelling environment and the analysis disciplines. It is here that the processing capabilities coded inside the HLPs are invoked and data and information are generated and collected to create input models for specific analysis tools. The way the MMG has been developed guarantees several advantages: •
The MMG can be operated interactively, with the user editing via keyboard the input file values and then evaluating the changes in aircraft model directly in the MMG graphical browser.
•
The MMG can be operated remotely. A complete input file can be edited by a remote user (N.B. this operation can be also automatically performed by an optimization tool, when the MMG is operative within an interconnected analysis and optimization environment) and then submitted to the MMG for a batch run. The reports/sub models can be retrieved via the web, right after the MMG has signalled his ready status.
•
As a consequence of the point above, many nongeographically collocated users can use the MMG submitting their list of required reports/sub-models and their customized version of the input file.
•
For a given version of the product model (stored with an appropriate file revision system), each input file defines univocally one aircraft configuration/variant. It might result very efficient to store just one copy of the given input file version (and re-submit it to he MMG whenever required) rather than store a multitude of large output models/reports, generated with that given input file.
•
The MMG code has been developed in a modular approach. Each HLP is a module, which again features a surface-module and a structure-module. Again these modules share a number of capability modules (see Figure 6). In this way maintenance and further development of the application is facilitated. When a single module is improved it can be simply plugged-in to replace the old version, provided that the interface remains the same. Relying on a correct use of the interfaces system, new modules can be generated (e.g. to create new report writers) and bolted onto others, to add extra functionalities to the MMG.
8
DEMONSTRATION AND VALIDATION OF THE MMG
Figure 8 : The MMG and its main functional blocks: the product model and the report writers Prior to launch the MMG, the user has to fill an input file that contains the list of all the parameters used to define the fullblown parametric description of the aircraft. The MMG is also connected to an amount of external libraries (e.g. airfoils and fuselage cross sections libraries) and data repository (e.g. reference weight for non-structural items), whose contents are automatically retrieved when required for a specific instantiation. All the parameters employed to generate the aircraft have their value clearly exposed to the user in the input file and it has been avoided to hardcode any value inside the product model. The user can specify what specific reports he is interested in getting out of the MMG, just preselecting them from the list with all the possible reports the MMG is able to generate. E.g. when a user is interested solely in the generation of the aircraft wet surface sub model, he has to select the generation of the wet-surface report and assign all the input parameters relative to the definition of the aircraft external surface. In this case the parameters related to the internal structure definition can be actually ignored, because the MMG, during the generation of the wet-surface report, will not perform any operation concerning the generation of spars, ribs, frames etc. hence it will not need those input values. This specific feature, denoted demand-driven instantiation, has a large impact on the efficiency and computational responsiveness of the MMG: when specific information is requested to the product model, not the entire product model is evaluated, but just the branches and relations which are strictly required to match the specific user requests. As shown in Figure 8, the reports-writer block represents the actual link between the 8
A successful prototype version of a DEE was developed within the EC sponsored project MOB - Multidisciplinary Design and Optimization of Blended Wing Body Aircraft Configurations [9,10]. In that project it has been shown how KBE can impact the design process and turn MDO from a great potential to a real working concept. In Figure 10 the role of the MMG within the MOB distributed design environment is represented. The MMG, starting from a unique definition of a BWB aircraft configuration, extracts a set of different, yet coherent sub-models tailored to the various analysis tools provided by a broad group of partners from the industry and academic world: low and high fidelity models for aerodynamic analysis, 2-D planform models
Figure 10 : Role of the MMG within the MOB distributed computational design engine. including movables surfaces for aeroelastic analysis, structure models (including materials, and design variable groups information) for FEM analysis and optimization, fuel tanks and systems masses distribution for weigh and balance assessment. The MMG provided also the capability to focus on a specific detail of the aircraft, a door cut-out in this case, and provide the base to apply a multi-level analysis and optimization strategy. A system of bidirectional interfaces was set up in order to feedback into the MMG the parametric changes resulting from the analysis and optimization process. In [ 11 ] other examples of computational design environments (referred as Design and Engineering Engines) developed at Delft University of Technology are discussed. The experience gained so far has shown how this approach can be effectively used for many different cases, from the investigation of the ground effect of sport cars, to the design of fuselage panels including piezo-elements for active sound damping [5], or loads calculations on large commercial airliner [12]. Some examples of report writers are briefly described below, and references are provided to more detailed documents and reports. In particular the links between the MMG and the structure and aerodynamic disciplines and the approach employed to support multilevel analysis and optimisation and for manufacturing studies on aircraft components are discussed, as relevant examples of the methodology developed to integrate the MMG with external analysis and design services, without any attempt to duplicate or incorporate any of those capabilities directly inside one KBE application. 8.1 Link with Structural Analysis Efforts have been made to create a seamless link between the geometry modelling and the FEM analysis environment, automating the complete model and pre-processing and analysis phases. The way this process is typically performed requires combined efforts and good communication between draftsmen and FEM experts. A lot of manual, lengthy and repetitive operations are required to assemble a FE model, starting from a CAD geometry model. One of the most time consuming steps is represented by the segmentation of the model surfaces in meshable elements (i.e. surface elements
with adequate aspect ratio and skeweness, no more than four edges, each edge matching with just one edge of the neighbouring surfaces). The specific knowledge required to perform this model segmentation has been elicited from FEM experts, translated in explicit rules and implemented in the SurfaceSplitter capability modules, aggregated to the HLPs (see Figure 6). Every time a new input model for FEM analysis is required, the surfaces cutting routine can automatically take action and create a consistent set of meshable surfaces, no matter how the current aircraft configuration might have been changed in shape and topology. The pure geometry information is transferred via IGES files (Figure 9, left branch), whereas a complementary stream of XML files (called FEM tables) has been activated to bring outside the MMG environment other non-geometric information (Figure 9, right branch), which is required to automate the generation of FEM model. A smart PATRAN session file has been programmed using PCL (PATRAN Command Language) to finalize the set up process of the FEM model and finally run the structural analysis and/or optimization, using one of the supported solvers [13]. 8.2 Link with Aerodynamic Analysis
Figure 9 : Automatic segmentation of the aircraft geometry and compilation of FEM-tables for transferring non geometric information. Similarly to the structural analysis case, a seamless integration has been pursued between the MMG modelling environment and external aerodynamic analysis tools, either commercial off the shelf (COTS) and proprietary codes. See in Figure 12 an example of consistent models generated for high and low aerodynamic analysis. The easiest form of link to external analysis tools is based on the direct exchange of geometry via IGES or STEP files. Capabilities modules have been developed to automate the pre-processing of the aircraft surfaces in order to match the specific requirements of the aerodynamic customer, such as splitting the surfaces in predefined sets of patches (e.g. upper and lower surfaces etc.). This approach actually works only if the analysis tool to be linked with the MMG is able to accept standard file format 9
Figure 12 : Generation of consistent models for high and low fidelity aerodynamic analysis such STEP or IGES. For other cases a special capability module (PointsGenerator, Figure 6) has been developed to translate the aircraft surfaces into a cloud of points, whose Cartesian coordinates are automatically evaluated and written into custom formatted ASCII files. This strategy has offered the possibility to deliver high quality surface model to powerful in house developed aerodynamic tools which were not provided with own adequate modelling capability. The MMG user has the possibility to adjust the amount and distribution of these points via a specific set of control parameter in the input file, to affect also locally the density of the cloud of points. Thanks to this flexible approach the points stored in the cloud can be used directly as mesh seeds for a grid, or as corner points for a panel discretization, or just as constraints to re-spline a surface [9].
A similar multi modelling approach has been implemented for the detailed generation of aircraft components such as wing movables. The MMG, based on the input information relative to the definition of shape and position of the movables, extracts from the global aircraft model, a kind of boundary condition model, which is used by another (slave) MMG to start the generation of the actual wing movable (Figure 11). When the geometry of the aircraft generated by the master MMG is modified, a new set of input information is sent to the slave MMG such that the shape and the topology of the component’s structure can adapt. Interesting to note that in case of aircraft movables such flaps, rudders, ailerons, etc., the slave MMG is built up using the same HLPs used to define the wing in the master model. In this way there is significant reuse of knowledge and software modules, yet the level of complexity of the model is kept in control and different KBE developers can be in charge of the different MMGs. Similarly this kind of master-slave modelling approach has been applied for the generation of the tooling for the structural components of the movables, e.g. the moulds for rubber pressing the movables’ ribs [14]. In this way three levels of scale from the design cube of Figure 7 have been addressed, yet maintaining consistency of data and without ending up developing a monolithic design tool, surely difficult to manage and maintain.
8.3 Link with tools for sublevel optimization of structural details and manufacturing studies of aircraft components. Since the modelling, analysis and optimization process of a complete and detailed aircraft structure might easily become unmanageable due to the large number of possible design variables and constraints, a multi level modelling approach has been applied in the development of the MMG [10]. When analysis and optimization of items such as door frames, windows cut-outs, etc., is required, the MMG is programmed to ignore, in a first instance, the presence of those items, in order to simply the surface segmentation routine described in section 8.1. Then a scanning routine is applied to search all the surface elements which are interested by the presence of the given structural detail (e.g. a door cut out). At this point only this reduced set of surface elements is further processed and exported for carry on with the design and optimisation of the given detail (e.g. design and optimal sizing of the door frames and doubles). The cutting and trimming process applied to the surfaces disturbed by the given detail (see the door cut out example in Figure 11, bottom left) does not involve or affect the global aircraft model, but on the other hand the shape of the ‘disturbed’ elements will reflect all the geometrical changes occurring in the global model (Figure 11). In other words the associativity of the global and the local model will be maintained. 10
Figure 11 : generation of structural details (a door cut out) and components (an aircraft movable) for multilevel analysis and design. 9
SUMMARY
In order to meet the technical challenges set for the next future of air transport, solutions are needed to produce better designed products at more affordable costs. The increasing scarcity of economical and human resources and the growing complexity of products together demand new design methodologies and tools to enhance designers and engineers productivity. A better aircraft design requires generating relevant product knowledge since early phases of the design process, which is a condition that available tools and current design approach struggle to fulfil. Designers need more flexible and powerful tools that allow virtual access to their ideas, providing the base for an effective multidisciplinary design, and increase the time and the freedom to investigate multiple what-ifs about their design.
The lead time from the first design sketch to a reliable product assessment has to be significantly reduced, by implementing a lean design approach in the design and analysis process. Knowledge Based Engineering (KBE) can provide tools to harness and exploit engineering knowledge and design skills, and accelerate the transition of new concepts and technologies into operation. KBE allows designers to capture and reuse product and process multidisciplinary knowledge in an integrated way, in order to reduce time and cost for engineering applications via the automation of repetitive design tasks and a systematic application of design best practices. Object-oriented analysis and modelling can provide the analytical and structured approach to develop models of complex systems, which can then be translated into KBE applications. The development of High Level Primitives, described in this paper, offers a feasible solution to support designers in shaping the concepts they have in mind and assess their behaviour in a faster and more reliable way. The concept of the primitives has been exploited in the generation of a complex aircraft modelling system, the Multi Model Generator, which, during the course of various projects, has demonstrated its effectiveness in supporting multidisciplinary design and optimisation also of a non conventional aircraft configuration, such as the MOB blended wing body aircraft. Generative tools such as the MMG represent a fundamental ingredient for the development of large and complex computational environments (Design and Engineering Engines). Such distributed design systems provide a possible answer to the industry need of an open and modular design and analysis system able to take advantage of the many expertises, which are often dispersed outside the wall of the single company. The flexible and modular nature of the MMG structure is the key to enable a prompt integration of new and different analysis capabilities, providing link also with high fidelity analysis tools, yet keeping high the level of flexibility and the giving the possibility to maintain the system and continuously upgrade it. 10 ACKNOWLEDGMENTS Financial support for this research has been provided by the European Commission for the research project “MOB - A Computational Design Engine Incorporating MultiDisciplinary Design and Optimisation for Blended Wing Body Configuration” and by the Dutch Technology Foundation for the research project “Parametric Modelling and Meshless Discretisation Methods for Knowledge Based Engineering Applications”. 11 REFERENCES [1]
[2]
and Executive Summary [online reports], URL: http://www.acare4europe.com/html/background.html. [3] Cooper, S., Fan, I., and Li, G., 2001, “Achieving Competitive Advantage Through Knowledge Based Engineering -A Best Practice Guide,” White Paper for the Department of Trade and Industry, Cranfield, UK. [4] Giesing, J.P., and Barthelemy, J.M., 1998, “A Summary of Industry MDO Applications and Needs,” 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, St. Louis, MO, Sept. 2-4. [5] van Tooren, M.J.L., La Rocca, G., Krakers, L., and Beukers, A., 2003, “Design and technology in aerospace. Parametric modeling of complex structure systems including active components,” 13th International Conference on Composite Materials, S.Diego, CA. [6] Sully, P., 1993, Modelling the world with objects, Prentice-Hall, ISBN 0-13-587791-1, Englewood Cliff, New Jersey. [7] Staubach, J.B., 2003, “Multidisciplinary Design Optimisation, MDO, the Next Frontier of CAD/CAE in the Design of Aircraft Propulsion Systems,” AIAA/ICAS International Air and Space Symposium and Exposition, AIAA 2003-2803, Dayton, OH, Jul. 14-17. [8] The ICAD System, Software Package, Ver. 8.1, 2002, Knowledge Technologies International Inc., Lexington, MA. [9] Morris, A.J., et al., 2004, “MOB - A European Project on Multidisciplinary Design Optimisation,” 24th ICAS Congress, Yokohama, Japan, Aug. 29-Sep. 3. [10] La Rocca, G., Krakers, L., and van Tooren, M.J.L., 2002, “Development of an ICAD Generative Model for Blended Wing Body Aircraft Design,” 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimisation, Atlanta, GA, Sep. 4-6. [11] van Tooren, M.J.L., Nawijn, M., Berends, J.P.T.J., Schut, E.J., 2005, “Aircraft Design support Using Knowledge Engineering and Optimisation Techniques,” 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, 13th AIAA/ASME/AHS Adaptive Structures Conference, Austin, Texas, Apr. 18-21.
[12] Cerulli, C., Berendes, J.P.T.J., van Tooren, M.J.L., Hofstee, J.W., 2005, "Parametric Modeling for Structural Dynamics Investigations in Preliminary Design", International Forum on Aeroelasticity and Structural Dynamics 2005, Munich, Germany, June st 28th- July 1 . [13] Nawijn, M., and van Tooren, M.J.L., 2006, “Automated Finite Element Analysis in a Knowledge Based Engineering Environment,” 44th AIAA Aerospace Science Meeting and Exhibit, Reno, Nevada, Jan. 912. [14] van der Laan, A., and van Tooren, M.J.L., 2005, “Parametric Modeling of Movables for Structural Analysis,” Journal of Aircraft, vol.42 no.6, pp. 16051613.
NASA, 2002, “The NASA Aeronautic Blue Print – A technology vision for aviation,” [online report], URL: http://www.hq.nasa.gov/office/aero/aboutus/tf/aero_blu eprint/index.htm. ACARE (Advisory Council for Aeronautic Research in Europe), 2002, “Strategic Research Agenda,” Vol. 1+2 11
