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THE DEVELOPMENT OF A DECISION SUPPORT SYSTEM FOR PROPELLER DESIGN Yoram Reich Dept. Solid Mech., Mat. and Structures, Tel Aviv University, Ramat Aviv 69978, Israel Volker Bertram Institut fur Schibau, Lammersieth 90, 22305 Hamburg, Germany Jurgen Friesch Hamburg Ship Model Basin, Bramfelder Str 164, 22305 Hamburg, Germany

ABSTRACT

The development of any design support system (DSS) is complex. It depends on the task being supported and the organization that is going to use it including its present design practices. This paper describes the initial steps in developing a DSS for marine propeller design based on the process carried out at HSVA (Hamburg Ship Model Basin). We follow a structured approach for this development. We outline the approach in detail and describe the execution of its rst steps. We emphasize the need to carefully structure the design task into simpler subtasks and map them initially to computational tasks and only subsequently to implementated modules. The initial mapping has been missing from previous reports on DSSs for propeller design. We identify some of these computational tasks, present a high level view of the DSS' architecture, and discuss our approach to implement it.

INTRODUCTION

This paper describes an ongoing work to develop a decision support system (DSS) for screw propeller design. The motivation was to investigate the possibility of improving the propeller design process by incorporating expertise accumulated over many years of experience in a DSS. We adapt an approach to building DSS previously used for bridge design (Reich [1]). The new method consists of four steps, Fig.1, where each step gradually introduces more structure into the problem until appropriate computational support methods are identi ed for subsequent implementation:

Reich 2 1. Task analysis of the propeller design process. The process is structured and the inputs and outputs for each task are identi ed. Preliminary task analysis reveals that propeller design is reasonably structured thus amenable to task subdivision. At the level of conceptual design, propellers have rather xed con guration which can be described by few parameters. In the detail design phase, the problem becomes more complex due to both complex physics and geometries involved. Subsequently, sophisticated analysis tools are employed; each of these tools requires a product description consisting of many (order 103 to 104 ) input data. 2. Identi cation of the types of knowledge and decision processes involved in each task. Part of this step delineates the parameters used, whether and how designers access information of previous designs, which analysis tools are used, which decisions are made in con ict situations, how are analysis results interpreted, etc. In addition to the knowledge used in propeller design, implicit knowledge could be extracted from existing records of propeller performance and made explicit. 3. Identi cation of the most suitable arti cial intelligence (e.g., case-based reasoning, rule-based reasoning, machine learning, or others) and traditional problem-solving methods and tools are identi ed for supporting parts of propeller design tasks. The reasons underlying the selection of particular methods are brie y discussed. 4. Implementation and integration into a DSS that employs heuristic knowledge in various forms, dierent numerical (analysis) tools, and a graphical CAD system. As a rst step of a development strategy, the initial system will be limited to conventional single-screw propellers. Based on the experience gained for this system, extensions to ducted, contra-rotating, and other special cases are planned. This paper focuses mainly on the rst two steps. An architecture of the decision support system is oered based on the three steps. When using the approach to develop a DSS, each step may provide feedback and revise the previous step. Also, if the results of one step are insucient to carry out the next step, than this step needs to be broken into ner-grain components.

- Knowledge - Problem-Solving Used Methods 66 6 66 6

Tasks Analysis

Computational Tools

Figure 1: Steps in developing a decision support system for propeller design

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GENERAL CHARACTERISTICS OF PROPELLER DESIGN

Propeller design is an iterative process which usually strives to optimize the eciency of a propeller subject to some constraints which may con ict with one another. The severeness of constraints depends on the ship type. For example, submarine propellers have strict constraints concerning cavitation-induced noise. Subsequently the eciencies of these propellers are lower than for cargo ships, but the primary optimization goal is still eciency. Traditionally, propeller design was based on design charts. These charts were created by tting theoretical models to data derived from actual model or full size tests and therefore their number was limited. By and large, propeller design was performed manually. In contrast, contemporary propeller design relies heavily on computer tools. Some of the traditional propeller diagrams, like for the Wageningen B-series of propellers, have been transformed into polynomial expressions allowing easy interpolation and optimization within the traditional propeller geometries. This is still a popular starting point for modern propeller design. Then, a succession of ever more sophisticated analysis programs is employed to modify and ne-tune the propeller geometry. Both hydrodynamic analysis programs (boundary element codes) and structural analysis programs ( nite element codes) require detailed input of the geometry and yield a multitude of data (pressures, stresses, etc). These programs require expertise in their data preparation, option selection, and result interpretation. The propeller designer must perform repeatedly a series of tasks, usually analyses and redesigns, to arrive at a nal design that satis es all the design requirements. Time and cost constraints limit the number of iterative cycles performed in practice. How rapidly the iterative process converges (and thus to some extent how good the nal design is) depends on the experience of the individual designer. An experienced propeller designer uses heuristics serving as `short-cuts' to a good, if not optimum design. Propeller design know-how resides typically with only few, sometimes even only one designer in each company who may or may not document his know-how and pass it on to his successor. The design is then performed by this specialist after an initial meeting with customers and colleagues from the ship model testing who supply the required input for the design. In this regard, propeller design diers signi cantly from ship design which requires much more interaction between experts of various disciplines. Another dierence between ship design and propeller design is that propeller design is quite structured and in principle well understood, while ship design is a largely creative and ill-structured process which requires frequent revisions as customers re-negotiate contracts adding further requirements. Propeller design is thus in principle easier than ship design, relatively well understood and structured, thus amenable to being modelled in software for its use in a decision support system. There are various incentives to develop a working decision

Reich 4 support system for marine propeller design: { Preservation of know-how to avoid major problems when an experienced propeller designer leaves the company on short notice. { Increased productivity as the propeller designer is relieved from standard tasks performed by the system. { Improved response time. { Improved reliability since once computer tools are built, they always perform the same. A decision support system must be developed carefully to ensure that the underlying assumptions (e.g., propeller charts) and their accuracy is maintained within the support system (Subrahmanian et al. [2]). This will allow designers to make correct thoughtful choices and thus use the system eectively.

PREVIOUS WORK ON PROPELLER DESIGN KNOWLEDGEBASED SYSTEMS

Haimov et al. [3], Petrov et al. [4] described the development of a propeller design knowledge-based system (KBS) at the Bulgarian Ship Hydrodynamic Centre (BSHC). In principle, their goal was similar to ours, but we dier in the approach to the design of the decision support system. The starting point of that research was to see how much an existing CAD system for propeller design can bene t from adding a KBS. Petrov et al. [4] conducted a task analysis of propeller design as performed by BSHC experts. They arrived at a detailed six-stage process that includes the preliminary and detailed design, analysis, manufacturing, test, and documentation. Haimov et al. [3] further elaborated this task analysis by specifying the attributes that describe a propeller and the activities related to it. Both papers continued to describe a prototype architecture for a propeller design KBS integrated with a database and analyses tools.

The prototype system was named PrKBS (Propeller Knowledge-based System). It included a KBS part implemented in CLIPS, a database (ORACLE), and conventional software (implemented in FORTRAN 77). The KBS part performed control functions in the integrated system, in which { if the information was insucient for relevant design decisions { dierent modules of the existing CAD system were called for: retrieval, modi cation, insertion to/from the database management system, start of computational procedures, etc. What is missing in these papers is the link between the task analysis of the domain and the nal system architecture. According to our approach, this link consists of: a detailed mapping between the design tasks and some abstract problem-solving methods from AI and traditional computer science; the subsequent mapping between these abstract methods and computer tools; and the detailed implementation of the methods with the tools including their integration. Rather than discussing these critical issues, the papers expanded on the lower level details of the working of

Reich 5 CLIPS and its interaction with the database and other CAD software. Thus, it is unclear how portable, extensible, or even functional was the prototype system. Our task analysis reveals some dierences in the design processes performed by BSHC and the HSVA experts. Certainly, there are dierences in the heuristics employed and the models and actual test data available to these experts. Moreover, our approach tries to carry out the development process systematically, while justifying the methods and tools we select. More recently Dai et al. [5], Dai and Hambric [6] incorporated propeller-design knowledge of the David Taylor Model Basin (DTMB) in a combined arti cial intelligence/numerical optimization shell. The system called PADS (propeller automated design system) employed KBS technology for representing heuristic preliminary design knowledge and genetic algorithms (GA) for the detailed optimization. Similar to the previous research, the authors oered little justi cation for the architecture of PADS. They employed KBS technology because it could supposedly emulate an expert propeller designer: a generally unsubstantiated and unnecessary argument; and advocated for the use of genetic algorithms as \extremely desirable if the design problem has multiple local optima and the topology is unfamiliar to the designer". While propeller design features multiple local optima, namely for each selected number of blades, these are few and can be determined by some outer loop exploring each alternative. The topology is rather well-known to the designer. Therefore our experience indicates that genetic algorithms may not oer an advantage to the task of eciently nding a (nearly) optimal design. The DTMB system was predominantly based on naval experience, while our system is guided by knowledge from HSVA's extensive experience about modern cargo ships. Other systems like PropExpert from HydroComp indicate by their name the use of a KBS or an expert system approach; nevertheless, they are rather user-friendly systems for less sophisticated selection of propellers from a database or form series. Such a selection suces for smaller boats, but not { unless for a rst estimate { for modern ships with their inherent high requirements on cavitation, noise, and vibration characteristics.

OUTLINE OF THE PROPELLER DESIGN PROCEDURE AT HSVA

Propeller design is a compromise which depends on the requirements of the customer. Usually, the propeller is optimized for eciency subject to more or less restrictive constraints concerning cavitation, geometry, strength etc. A true optimization is virtually impossible for modern propellers as the description of the nal geometry involves typically some hundred osets and the evaluation of the eciency based on numerical hydrodynamics codes requires considerable time. Additional constraints are inherently involved in the design process, but often not explicitly formulated. These additional constraints re ect the personal \design phi-

Reich 6 losophy" of a designer or company and may lead to considerably dierent \optimal" propellers for the same customer requirements. An example for such a \design philosophy" could be the constraint that no cavitation should occur on the pressure side of the propeller. The following procedure and ultimately our decision support system will re ect the design philosophy of HSVA. While often the word \optimization" is used, the nal design is rather \satis cing", i.e., a good solution satisfying the given constraints. The overall procedure will, however, be similar to any other state-of-the-art propeller design process. The prime mover (main engine) in uences the propeller design primarily through the propeller RPM and delivered power. Modern turbo-charged diesels, almost exclusively used for cargo ships today, are imposing a rather narrow bandwidth for the operating point (RPM-power combination) of the propeller. We limit ourselves therefore to such cases where the RPM, the ship's speed, and an estimated delivered power PD are speci ed by the customer. This covers more than 90% of the cases in HSVA practice. The procedure follows a few main steps which involve model tests, analytical tools of successive sophistication and power, and some experience in deciding trade-os in con ict situations. In the following description we also brie y list preliminary opportunities for the incorporation of AI methods to assist in the design process.

Step 1: Preparation of model experiments

Known at this stage: RPM of the full-scale propeller - ns ship speed - Vs estimate of delivered power for the ship - PD ship hull form (lines plan) classi cation society often: number of blades - Z often: diameter of propeller - D

Generally, the customer speci es within small margins what power PD has to be delivered at what speed Vs and what is the RPM of the (selected) main engine. While in theory such a combination may be impossible to realize, in practice, the shipyard engineers (i.e., the customer) have sucient experience to estimate a realistic power for a ship owner speci ed speed and rather determined RPM. The shipyard or another department in the model basin will specify a rst proposal for the ship lines. Often, the customer will also already determine the number of blades for the propeller. The number of blades is anyhow selected from a narrow range (either 4, 5, or 6 for cargo ships). A few simple rules of experience guide this selection, e.g. \if the engine has an even number of cylinders, the propeller should have an odd number of blades".

Reich 7 Then the propeller of optimal eciency is determined based on the Wageningen-BSeries. This is a series for propeller forms which was extensively tested in Wageningen at the Dutch model basin MARIN. The results were documented in curves for thrust and torque coecients and eciency. The curves are available either in the form of polynomial expressions easy to program or plotted for manual evaluation. Of course, today in practice computer codes automatically interpolate and determine the optimum within this family of propeller forms. (Unfortunately, the performance of these older propellers is insucient for today's expectations and the propeller thus determined will only be used as a starting point for the actual design.) This procedure yields the average pitch-to-diameter ratio Pm =D and the diameter D. An upper limit for the diameter is speci ed from the ship geometry. Sometimes the customer already speci es the diameter, otherwise it is a result of the optimization. The expanded area ratio AE =A0 is usually part of the optimization result, but may be restricted with respect to cavitation if problems in this respect are foreseen. In this case, a limiting value for AE =A0 is derived from Burill diagrams. Then, from a database of stock propellers (propeller models on stock) the most suitable propeller is selected. This is the propeller with the same number of blades, closest in Pm =D to the optimized propeller. If several stock propellers coincide with the desired Pm=D, the propeller closest in AE =A0 among these is selected. A selection constraint comes from upper and lower limits for the diameter of the stock propeller which are based on experience for the experimental facilities of HSVA. E.g. the ship models may not exceed 11 meter in length to avoid in uence of canal restrictions, but should be larger than 4 meter to avoid problems with laminar ow eects. As the ship length is speci ed and the model scale for propeller and ship must be the same, this yields one of the constraints for upper and lower value of the diameter of the stock propeller. Usually, the search of the database is limited to the last 300 stock propellers, i.e., the most recent designs. The selected stock propeller then determines the model scale and the ship model may be produced and tested. The output of the model tests relevant for the propeller designer is: { nominal wake distribution (axial, tangential and radial velocities in the propeller plane) { thrust deduction fraction - t { eective wake fraction - w { relative rotative eciency - R { delivered power - PD The delivered power PD is of secondary importance (assuming that it is close to the customer's estimate). It indicates how much the later propeller design has to strive for a high eciency. If the predicted PD is considerably too high, then the ship form has to be changed and the tests repeated. This iterative process is of no concern for the propeller designer who continues his work, once the ship form has been updated

Reich 8 and the combination PD -n-Vs is again determined as main propeller design input. Opportunities for incorporating AI methods: { Predict propeller maximum eciency from a series or a database of new propellers. { Select most suitable stock propeller.

Step 2: Estimate eective wake distribution full-scale

Known at this stage: all of the above and... number of blades Z diameter of propeller D blade area ratio AE =A0 thrust deduction fraction t eective wake fraction w relative rotative eciency R nominal wake eld (axial, tangential, radial velocity components)

Ship-propeller interaction is dicult to capture. The in ow is taken from experiments and based on experience modi ed to account for scale eects (model/full-scale ship). The radial distribution of the axial velocity component is transformed from the nominal (without propeller action) value for the model to an eective (with propeller) value for the full-scale ship. The other velocity components are assumed to be not aected. Several methods have been coded to perform this transformation. To some extent, the selection of the \appropriate" code follows clear rational decision, e.g., one method is based on empirical data for full ships like tankers, another method for slender ships like container ships. But still the designer expert runs usually several codes, looks at the results and selects the \most plausible" based on \intuition". This appears to be a typical situation where arti cial intelligence techniques may indeed advance our eorts in automation. The remaining interaction eects like thrust deduction fraction t and relative rotative eciency R are usually taken as constant with respect to the results of ship model tests with propellers. Opportunities for incorporating AI methods: { Selecting most suitable code for scale eect transformation. { Automatic plausibility check of results.

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Step 3: Determine pro le thickness according to classi cation society Known at this stage: all of the above

Classi cation societies have simple rules to determine the minimum thickness of the foils. The rules of all major classi cation societies have been implemented in a program that adjusts automatically the (maximum) thickness of all pro les to the limit value prescribed by the classi cation society. Opportunities for incorporating AI methods: { None.

Step 4: Lifting-line and lifting-surface calculations Known at this stage: all of the above and... max. thickness at few radii

As additional input, default values are taken for pro le form (NACA series), distribution of chord length and skew. If this step is repeated at a later stage, the designer may deviate from the defaults. At this stage, the rst analytical methods are employed. The lifting-line method computes the ow for a 2-d pro le, i.e. the 3-d ow is approximated by a succession of 2-d ows. This method is numerically stable and eective. The method needs an initial starting value for the circulation distribution. This is taken as a semielliptical distribution. The computation yields then the optimal radial distribution of the circulation. These results are directly used for a 3-d lifting-surface program. The lifting-surface code was developed at MIT by Kerwin and Lee [7], and yields as output the radial distribution of pro le camber and pitch. This whole step is rather straight-forward `number-crunching' involving little human intervention. Opportunities for incorporating AI methods: { None.

Step 5: Smoothing results of Step 4

Known at this stage: all of the above and... radial distribution of pro le camber (estimate) radial distribution of pitch (estimate)

The results of the 3-d panel code are generally not smooth and feature singularities at the hub and tip of the propeller. So far, the human designer deletes \stray" points (point-to-point oscillations) and speci es values at hub and tip based on experience. A Fortran program was written to incorporate some of the manual steps, but this

Reich 10 program is a typical ad hoc solution that \grew organically" and is now dicult to maintain and extend. A more exible approach is required to replace it. Opportunities for incorporating AI methods: { Incorporate knowledge and automated procedures about smoothing.

Step 6: Final hydrodynamic analysis

Known at this stage: all of the above (updated)

The propeller is analysed in all operating conditions using a lifting-surface analysis program and taking into account the complete wake distribution. The output can be broadly described as the cavitational and vibrational characteristics of the propeller. The work involves sometimes the inspection of plots by the designer. Other checks are already automated. This step involves rather complex decisions and capabilities of pattern recognition that will be dicult to automate. The designer modi es now, based on his \experience" (sometimes rather resembling a trial-and-error process), the geometry (foil length, skew, camber, pitch, pro le form and even as a last resort diameter). However, the previous steps are not repeated and this step can be treated as a self-contained module. Opportunities for incorporating AI methods: { Incorporating expert knowledge on which parameter values are acceptable and which parameters to change in case of problems.

Step 7: Check against classi cation society rules Known at this stage: all of the above (updated)

A nite-element analysis is used to calculate the strength of the propeller under the pressure loading. This analysis involves experience in element type selection and mesh generation. The von-Mises stress criterion is plotted and inspected. As the analysis is still limited to a radially averaged in ow, a safety margin is added to account for the real in ow. In most cases, there is no problem. But if the stress is too high in some region (usually the trailing edge), the geometry is adjusted and Step 6 is repeated. The possible geometry modi cations at this stage are minor and local; they have no strong in uence on the hydrodynamics and therefore one or two iterations usually suce to satisfy the strength requirements.

Reich 11 Opportunities for incorporating AI methods: { Determine deviations from constraints and recommend geometry redesign modi cations.

MAPPING TO AI PROBLEM-SOLVING METHODS/TOOLS

Our approach to building a DSS is to select the issues we address based on their cost-eectiveness. Our primary goals are to capture valuable, scarce expertise and shorten the design process. The goal to capture valuable expertise leads us to record whatever knowledge experts use in propeller design. This seems an enormous task. Therefore, we prioritize the tasks based on their diculty and required expertise. The goal to shorten the design process suggests that we automate tasks amenable to automation thus allow their execution by less expert designers or other practitioners. This can relieve valuable expert resources and reduce communication paths between practitioners. One primary example is the task of stock propeller selection for model tests. Initial analysis suggests that the task can be reduced to the use of several heuristics and the interaction with a database. The bene ts for the company are relatively large, because this step involves inter-departmental interaction and human communication which is always time-consuming. If the selection expertise could be captured, the whole process could be shifted to the project manager in the ship model (towing tank) department. We rst analyzed where computational tools can make the most impact on the design process. These opportunities to incorporate AI and other computational tools were listed at the end of each design step in the previous section. We now address them in descending order based on the possibility to implement them. The most cost eective support is the one for the stock propeller selection discussed above. The second involves the incorporation of knowledge on what is acceptable and which parameters need to be changed in the case of problems. Activities related to such reasoning constitutes roughly half the time involved in design. The third task involves providing computational support for the smoothing of the propeller surface description. This task involves rather tedious human labor as each pro le has to be plotted, inspected, edited and smoothed. It seems reasonable to extract heuristics to represent these activities. Following are supporting the selection the code for scale-eect transformation and supporting the geometric redesign under strength constraints. Initial analysis suggests that their implementation is more dicult than the previous tasks.

Tasks elaboration

Upon inspecting the 7 design steps and following preliminary analysis of the expertise involved in propeller design, we can extract four general procedures that are intermixed in dierent ways in each of the design steps:

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Mapping between some numeric input to numeric output data related by some continuous non-linear mapping (e.g., estimating parameters such as propeller eciency). This includes performing complex numerical analyses. Selection between available alternatives (e.g., stock propeller, smoothing code, etc.). Local modi cations with small overall impact (e.g., smoothing, geometry redesign, etc.). Veri cation (against design code, constraints, etc.).

The rst general task is the most common. Such a mapping can be supported by the use of neural networks (NN) or instance-based learning (IBL) which are techniques developed in machine learning research. Both methods can be used to predict or approximate arbitrary functions by forming model-free mappings from examples of input-output data in contrast to traditional statistical methods such as multiple regression that assume a certain model over the data and have been shown to perform worse than the learning programs. For example, the input data can describe propeller geometry and operating conditions (i.e., Z , AE =A0, P=D, J etc.) and the output can be the performance of the propeller (i.e., KT , KQ , and R). A preliminary study on the subject by Neocleous and Schizas [8] is being replicated and extended by us. The second task is critical for selecting the best stock propeller. A selection is in uenced by previous experience, constraints, and general heuristic knowledge. Casebased reasoning (CBR) (Kolodner [9]), or some other traditional selection method can be used to perform this task. For the purpose of this task, cases in the case memory will include the description of complete propeller designs including hull lines, and other parameters used in the 7 design steps. The output of using the method will be the best stock propeller to use for a required model test. Once the DSS is implemented and used, it will keep records of all the design activities. Thus, cases could be formed from the complete record of all propeller design processes. These records will include all input parameters, methods used, their underlying reasons, the intermediate results. The third task can be performed by a variety of methods including CBR and small sets of rules that are executed to recommend modi cation activities. The fourth task could be supported by the use of programs that evaluate the design on-line and criticize it if it does not satisfy some code, constraints, or common practice rules. These programs could be implemented in a variety of ways depending on the nature of the evaluation they conduct.

Tasks implementation

The next stage in the design of the DSS is the planning of the implementation of the techniques. Since in their stand-alone method of use, each of the methods operates

Reich 13 with its own control loop, special attention should be given to the proper integration of the techniques. The integration of NN or IBL for function approximation in an overall architecture is relatively easy. The mapping will be created o-line, by training on input-output pairs (step (a) in Fig.2). The mapping will be captured by some mechanism (e.g., matrices in NN, indices and weights in IBL) and will be used as ordinary functions when needed (step (b) in Fig.2). Future feedback or experience could always be incorporated o-line again in step (a). (a) Model Building (b) Model Use

Input data

Output Data

(propeller data) (propeller eciency)

New Input Data

Model - NN or IBL - (I/O mapping)

Predicted - Model - Output

Figure 2: Using NN or IBL to build mappings The integration of CBR and Rule-based reasoning, Giarratano and Riley [10], for supporting the selection task and for serving as the overall infrastructure is more complex. Fig.3 shows the control loop of the two reasoning methods. Rule-base reasoning is the more familiar but CBR is not more complex. Building it involves coding previous experiences and organizing them with indices. Their use involves retrieving an existing case from memory that best matches a new design context by using the indices, and adapting it to satisfy the new design goals and constraints. The retrieval part which is of interest, e.g., in the selection of stock propeller, is the easiest to implement among the two processes. The adaptation process can make use of its own small KBS that is potentially implemented in rules. The appropriate integration depends upon the nature of knowledge we uncover as we proceed with the knowledge acquisition process. The last question is selecting an implementation tool or language. Candidates include C (or C++), CLIPS (with C function extensions), or some other KBS shell. CLIPS may not be the best tool for the task but it is generally available and we wish to test its applicability to serve as an integration framework for small to large numerical codes combined with heuristics. We can implement a CBR mechanism in CLIPS or as C functions that can be called from CLIPS rules. Consequently, the question at this stage is which tool eases the implementation. Or, which tool makes the implementation more \natural". We will select the most appropriate implementation language for each of the procedures we implement.

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Rule-Based Reasoning Input:

Goal

-

Working Memory 6Rule Rule Matching ? Firing Rule Base



-

Output:

Reccomendation for Action

Database: Facts + Procedures

Case-Based Reasoning Input:

Partial Case

-

Working Memory

Case 6 Retrieval Case Base

6Case

-

Output:

Best Case after Adaptation

Adaptation Database: Adaptation Knowledge

Figure 3: Rule-based and Case-based reasoning

THE ARCHITECTURE OF THE SYSTEM

The architecture of the system, Fig.4, is geared towards providing the infrastructure to carry out the tasks listed in the previous section. The input speci cation data is given to the system by the user and is stored in the working memory which serves as the temporary memory of the present design. The process manager inspects the speci cations and invokes the speci c KBSs or other tools depending on the particular needs and based on the sequence of the 7-step design process. Each procedure makes use of its knowledge which is coded in the form suitable for the procedure and also uses data and other simple functions stored in various databases and libraries. The user has the option to override the process through interacting with the process manager. The architecture is extensible in that additional task-speci c KBSs could be integrated. This puts the integration burden on the process manager which will be further developed after a more extensive knowledge acquisition process.

SUMMARY

We described the initial design of a DSS for propeller design that is based on the design process of HSVA. As any design, it is in uenced by its context; therefore, dierent designs of a DSS might emerge from dierent propeller design processes carried out elsewhere. The main contribution of this paper is thus not the particular system but the description of the DSS design process which can be used for building

Reich 15 other DSS in marine engineering. We have illustrated brie y how the resulting DSS may be used. Initially we intend to develop the stock propeller selection procedure as a stand-alone tool. Upon its successful use and depending on particular needs in its development we will proceed with the remaining procedures. Legend:

Tool/Method Content/Description

-

Propeller Speci cations

Working Memory



User 66

-

Propeller Geometry + Estimated Performance

.. .............................

KBS

........... .......... .........

Process Manager



.......................... ......... .......... ......

Procedures CBR

Selection of Stock Propeller

KBS

Smoothing

KBS

Geometry Redesign

KBS Other tasks

.. ....................... ......... .......... ......

Databases

Database

Propeller Database

Library

Prediction of Numeric Data by NN or IBL

Figure 4: DSS Architecture

Library

Smoothing Procedures

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REFERENCES [1] Y. Reich. Design knowledge acquisition: Task analysis and a partial implementation. Knowledge Acquisition, 3(3):237{254, 1991. [2] E. Subrahmanian, S.L. Konda, S.N. Levy, Y. Reich, A.W. Westerberg, and I.A. Monarch. Equations aren't enough: Informal modeling in design. Arti cial Intelligence in Engineering Design, Analysis, and Manufacturing, 7(4):257{274, 1993. [3] A. Haimov, B. Racheb, Pl. Petrov, and R. Bonev. Knowledge base/data base interaction in the development of a design expert system. In HADMAR'91, Vol. 2, pages 68{1{68{6, 1991. [4] P. Petrov, V. Hadjimikhalev, and A. Haimov. A knowledge-based propeller CAD system. In PRADS'92, pages 1.610{1.624, 1992. [5] C. Dai, S. Hambric, L. Mulvihill, S.S. Tong, and D. Powell. A prototype marine propulsor design tool using arti cial intelligence and numerical optimization techniques. Transactions SNAME, 102(57-66), 1994. [6] C. Dai and S. Hambric. Propeller design optimization technique for the minimization of propeller induced vibration using arti cial intelligence and numerical optimization. In H. Kim and J.W. Lee, editors, Practical Design of Ships and Mobile Units, PRADS'95, pages 1.194{1.206, Seoul, Korea, 1995. The Society of Naval Architects of Korea. [7] J.E. Kerwin and C.S. Lee. Prediction of steady and unsteady marine propeller performance by numerical lifting-surface theory. Transactions SNAME, 86(218253), 1978. [8] C.C. Neocleous and C.N. Schizas. Arti cial neural networks in marine propeller design. In Proceedings of ICNN`95 - International Conference on Neural Networks, Vol.2, pages 1098{1102, New York, NY, 1995. IEEE Computer Society Press. [9] J. Kolodner. Case-Based Reasoning. Morgan Kaufmann, San Mateo, CA, 1993. [10] J. Giarratano and G. Riley. Expert Systems: Principles and Programming. PWS Publishing, Boston, MA, 1994.