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This dissertation investigates the support of conceptual engineering design through the decomposition of multi-dimensional search spaces into regions of high performance. Such decomposition helps the designer identify optimal design directions by the elimination of infeasible or undesirable regions within the search space. Moreover, high levels of interaction between the designer and the model increases overall domain knowledge and significantly reduces uncertainty relating to the design task at hand. The aim of the research is to develop the archetypal Cluster Oriented Genetic Algorithm (COGA) which achieves search space decomposition by using variable mutation (vmCOGA) to promote diverse search and an Adaptive Filter (AF) to extract solutions of high performance [Parmee 1996a, 1996b]. Since COGAs are primarily used to decompose design domains of unknown nature within a real-time environment, the elimination of apriori knowledge, speed and robustness are paramount. Furthermore COGA should promote the in-depth exploration of the entire search space, sampling all optima and the surrounding areas. Finally any proposed system should allow for trouble free integration within a Graphical User Interface environment. The replacement of the variable mutation strategy with a number of algorithms which increase search space sampling are investigated. Utility is then increased by incorporating a control mechanism that maintains optimal performance by adapting each algorithm throughout search by means of a feedback measure based upon population convergence. Robustness is greatly improved by modifying the Adaptive Filter through the introduction of a process that ensures more accurate modelling of the evolving population. The performance of each prospective algorithm is assessed upon a suite of two-dimensional test functions using a set of novel performance metrics. A six dimensional test function is also developed where the areas of high performance are explicitly known, thus allowing for evaluation under conditions of increased dimensionality. Further complexity is introduced by two real world models described by both continuous and discrete parameters. These relate to the design of conceptual airframes and cooling hole geometries within a gas turbine. Results are promising and indicate significant improvement over the vmCOGA in terms of all desired criteria. This further supports the utilisation of COGA as a decision support tool during the conceptual phase of design.

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In the last ten years or so, many interactive aircraft design software packages have been released into the market. One drawback of these packages is that they assume prior knowledge in the field of aircraft design. Also, their main purpose being the preliminary aircraft design in a commercial environment, and are not intended for instructional use. Aircraft Design is an iterative process, and the students in the formative years of training must realise that one year of study is not enough to embrace all the necessary underlying concepts in this field. Most universities present the aircraft design as a classical Problem-Based Learning scenario, where students work in groups, with the group size varying between 5 and 8 students., each with a designated role, to carry out a specific task. The students work through the classical process of preliminary design based largely on textbook methods. Therefore, the need for a preliminary design tool (software) that helps the students to understand, analyse, and evaluate their aircraft design process exists. The developed software does everything that is needed in the preliminary design environment. Students are interactively guided through the design process, in a manner that facilitates lifelong learning. Comprehensive output is provided to highlight the “what if scenarios”. The software consists of many modules such as input (user interface), weight estimation, flight performance, cost estimation, take-off analysis, parametric studies, optimisation, and dynamic stability. Due to the large number of input design variables, a full interactive Graphical-User-Interface (GUI) is developed to enable students to evaluate their designs quickly. Object-Oriented-Programming (OOP) is used to create the GUI environment. The stability and control derivatives computed in this work are largely based on analytical techniques. However, a facility is provided in the software to create the data input file required to run a software package produced by USAF, called DATCOM, that enables computation of the dynamic stability and control derivatives that can be ultimately used in flight simulation work. Amongst all the variables used in aircraft design, aircraft weight is the most significant. A new weight estimation module has been developed to increase the accuracy of estimation to better than 5%. Its output results agree very favourably with the published data of current commercial aircraft such as Airbus and Boeing. Also, a new formula is proposed to estimate the engine weight based on its thrust in the absence of the data available with high degree of accuracy. In order to evaluate the effectiveness of the design under consideration, a comprehensive methodology has been developed that can predict the aircraft price as a function of aircraft weight. The Direct Operating Cost (DOC) is also calculated using methods proposed by ATA, NASA, and AEA. Finally, a walk-through of two case studies are presented, one for large transport aircraft and other for small business jet, to show how typical undergraduate students will proceed with the design and to demonstrate the effectiveness of the developed software.

Research into the improvement of the Aircraft ConceptualDesign process by the application of MultidisciplinaryOptimization (MDO) is presented. Aircraft conceptual designanalysis codes were incorporated into a variety of optimizationmethods including Orthogonal Steepest Descent (full-factorialstepping search), Monte Carlo, a mutation-based EvolutionaryAlgorithm, and three variants of the Genetic Algorithm withnumerous options. These were compared in the optimization offour notional aircraft concepts, namely an advanced multiroleexport fighter, a commercial airliner, a flying-wing UAV, and ageneral aviation twin of novel asymmetric configuration. Tobetter stress the methods, the commercial airliner design wasdeliberately modified for certain case runs to reflect a verypoor initial choice of design parameters including wingloading, sweep, and aspect ratio. MDO methods were evaluated in terms of their ability to findthe optimal aircraft, as well as total execution time,convergence history, tendencies to get caught in a localoptimum, sensitivity to the actual problem posed, and overallease of programming and operation. In all, more than a millionparametric variations of these aircraft designs were definedand analyzed in the course of this research. Following this assessment of the optimization methods, theywere used to study the issue of how the computer optimizationroutine modifies the aircraft geometric inputs to the analysismodules as the design is parametrically changed. Since thiswill ultimately drive the final result obtained, this subjectdeserves serious attention. To investigate this subject,procedures for automated redesign which are suitable foraircraft conceptual design MDO were postulated, programmed, andevaluated as to their impact on optimization results for thesample aircraft and on the realism of the computer-defined"optimum" aircraft. (These are sometimes called vehicle scalinglaws, but should not be confused with aircraft sizing, alsocalled scaling in some circles.) This study produced several key results with application toboth Aircraft Conceptual Design and MultidisciplinaryOptimization, namely:     MDO techniques truly can improve the weight and cost ofan aircraft design concept in the conceptual design phase.This is accomplished by a relatively small "tweaking" of thekey design variables, and with no additional downstreamcosts.In effect, we get a better airplane for free.     For a smaller number of variables (<6-8), adeterministic searching method (here represented by thefull-factorial Orthogonal Steepest Descent) provides aslightly better final result with about the same number ofcase evaluations     For more variables, evolutionary/genetic methods getclose to the best final result with far-fewer caseevaluations. The eight variables studied herein probablyrepresent the practical upper limit on deterministicsearching methods with today’s computer speeds.     Of the evolutionary methods studied herein, the BreederPool approach (which was devised during this research andappears to be new) seems to provide convergence in the fewestnumber ofcase evaluations, and yields results very close tothe deterministic best result. However, all of the methodsstudied produced similar results and any of them is asuitable candidate for use.     Hybrid methods, with a stochastic initial optimizationfollowed by a deterministic final "fine tuning", proved lessdesirable than anticipated.     Not a single case was observed, in over a hundred caseruns totaling over a million parametric design evaluations,of a method returning a local rather than global optimum.Even the modified commercial airliner, with poorly selectedinitial design variables far away from the global solution,was easily "fixed" by all the MDO methods studied.     The postulated set of automated redesign procedures andgeometric constraints provide a more-realistic final result,preventing attainment of an unrealistic "better" finalresult. Especially useful is a new approach defined herein,Net Design Volume, which can prevent unrealisticallyhigh design densities with relatively little setup andcomputational overhead. Further work in this area issuggested, especially in the unexplored area of automatedredesign procedures for discrete variables.

<p>Research into the improvement of the Aircraft ConceptualDesign process by the application of MultidisciplinaryOptimization (MDO) is presented. Aircraft conceptual designanalysis codes were incorporated into a variety of optimizationmethods including Orthogonal Steepest Descent (full-factorialstepping search), Monte Carlo, a mutation-based EvolutionaryAlgorithm, and three variants of the Genetic Algorithm withnumerous options. These were compared in the optimization offour notional aircraft concepts, namely an advanced multiroleexport fighter, a commercial airliner, a flying-wing UAV, and ageneral aviation twin of novel asymmetric configuration. Tobetter stress the methods, the commercial airliner design wasdeliberately modified for certain case runs to reflect a verypoor initial choice of design parameters including wingloading, sweep, and aspect ratio.</p><p>MDO methods were evaluated in terms of their ability to findthe optimal aircraft, as well as total execution time,convergence history, tendencies to get caught in a localoptimum, sensitivity to the actual problem posed, and overallease of programming and operation. In all, more than a millionparametric variations of these aircraft designs were definedand analyzed in the course of this research.</p><p>Following this assessment of the optimization methods, theywere used to study the issue of how the computer optimizationroutine modifies the aircraft geometric inputs to the analysismodules as the design is parametrically changed. Since thiswill ultimately drive the final result obtained, this subjectdeserves serious attention. To investigate this subject,procedures for automated redesign which are suitable foraircraft conceptual design MDO were postulated, programmed, andevaluated as to their impact on optimization results for thesample aircraft and on the realism of the computer-defined"optimum" aircraft. (These are sometimes called vehicle scalinglaws, but should not be confused with aircraft sizing, alsocalled scaling in some circles.)</p><p>This study produced several key results with application toboth Aircraft Conceptual Design and MultidisciplinaryOptimization, namely:</p><p>    MDO techniques truly can improve the weight and cost ofan aircraft design concept in the conceptual design phase.This is accomplished by a relatively small "tweaking" of thekey design variables, and with no additional downstreamcosts.<i>In effect, we get a better airplane for free.</i></p><p>    For a smaller number of variables (<6-8), adeterministic searching method (here represented by thefull-factorial Orthogonal Steepest Descent) provides aslightly better final result with about the same number ofcase evaluations</p><p>    For more variables, evolutionary/genetic methods getclose to the best final result with far-fewer caseevaluations. The eight variables studied herein probablyrepresent the practical upper limit on deterministicsearching methods with today’s computer speeds.</p><p>    Of the evolutionary methods studied herein, the BreederPool approach (which was devised during this research andappears to be new) seems to provide convergence in the fewestnumber ofcase evaluations, and yields results very close tothe deterministic best result. However, all of the methodsstudied produced similar results and any of them is asuitable candidate for use.</p><p>    Hybrid methods, with a stochastic initial optimizationfollowed by a deterministic final "fine tuning", proved lessdesirable than anticipated.</p><p>    Not a single case was observed, in over a hundred caseruns totaling over a million parametric design evaluations,of a method returning a local rather than global optimum.Even the modified commercial airliner, with poorly selectedinitial design variables far away from the global solution,was easily "fixed" by all the MDO methods studied.</p><p>    The postulated set of automated redesign procedures andgeometric constraints provide a more-realistic final result,preventing attainment of an unrealistic "better" finalresult. Especially useful is a new approach defined herein,<i>Net Design Volume</i>, which can prevent unrealisticallyhigh design densities with relatively little setup andcomputational overhead. Further work in this area issuggested, especially in the unexplored area of automatedredesign procedures for discrete variables.</p>