Facilitating meta-design techniques for multi-disciplinary conceptual design

The research reported in this paper was supported by the EU FP6 funded project, SimSAC (Simulating Aircraft Stability and Control Characteristics for Use in Conceptual Design)

Acquiring symbolic design optimization problem reformulation knowledge: On computable relationships between design syntax and semantics

This thesis presents a computational method for the inductive inference of explicit and implicit semantic design knowledge from the symbolic-mathematical syntax of design formulations using an unsupervised pattern recognition and extraction approach. Existing research shows that AI / machine learning based design computation approaches either require high levels of knowledge engineering or large training databases to acquire problem reformulation knowledge. The method presented in this thesis addresses these methodological limitations. The thesis develops, tests, and evaluates ways in which the method may be employed for design problem reformulation. The method is based on the linear algebra based factorization method Singular Value Decomposition (SVD), dimensionality reduction and similarity measurement through unsupervised clustering. The method calculates linear approximations of the associative patterns of symbol cooccurrences in a design problem representation to infer induced coupling strengths between variables, constraints and system components. Unsupervised clustering of these approximations is used to identify useful reformulations. These two components of the method automate a range of reformulation tasks that have traditionally required different solution algorithms. Example reformulation tasks that it performs include selection of linked design variables, parameters and constraints, design decomposition, modularity and integrative systems analysis, heuristically aiding design “case” identification, topology modeling and layout planning. The relationship between the syntax of design representation and the encoded semantic meaning is an open design theory research question. Based on the results of the method, the thesis presents a set of theoretical postulates on computable relationships between design syntax and semantics. The postulates relate the performance of the method with empirical findings and theoretical insights provided by cognitive neuroscience and cognitive science on how the human mind engages in symbol processing and the resulting capacities inherent in symbolic representational systems to encode “meaning”. The performance of the method suggests that semantic “meaning” is a higher order, global phenomenon that lies distributed in the design representation in explicit and implicit ways. A one-to-one local mapping between a design symbol and its meaning, a largely prevalent approach adopted by many AI and learning algorithms, may not be sufficient to capture and represent this meaning. By changing the theoretical standpoint on how a “symbol” is defined in design representations, it was possible to use a simple set of mathematical ideas to perform unsupervised inductive inference of knowledge in a knowledge-lean and training-lean manner, for a knowledge domain that traditionally relies on “giving” the system complex design domain and task knowledge for performing the same set of tasks

An Intelligent Time and Performance Efficient Algorithm for Aircraft Design Optimization

Die Optimierung des Flugzeugentwurfs erfordert die Beherrschung der komplexen Zusammenhänge mehrerer Disziplinen. Trotz seiner Abhängigkeit von einer Vielzahl unabhängiger Variablen zeichnet sich dieses komplexe Entwurfsproblem durch starke indirekte Verbindungen und eine daraus resultierende geringe Anzahl lokaler Minima aus. Kürzlich entwickelte intelligente Methoden, die auf selbstlernenden Algorithmen basieren, ermutigten die Suche nach einer diesem Bereich zugeordneten neuen Methode. Tatsächlich wird der in dieser Arbeit entwickelte Hybrid-Algorithmus (Cavus) auf zwei Hauptdesignfälle im Luft- und Raumfahrtbereich angewendet: Flugzeugentwurf- und Flugbahnoptimierung. Der implementierte neue Ansatz ist in der Lage, die Anzahl der Versuchspunkte ohne große Kompromisse zu reduzieren. Die Trendanalyse zeigt, dass der Cavus-Algorithmus für die komplexen Designprobleme, mit einer proportionalen Anzahl von Prüfpunkten konservativer ist, um die erfolgreichen Muster zu finden. Aircraft Design Optimization requires mastering of the complex interrelationships of multiple disciplines. Despite its dependency on a diverse number of independent variables, this complex design problem has favourable nature as having strong indirect links and as a result a low number of local minimums. Recently developed intelligent methods that are based on self-learning algorithms encouraged finding a new method dedicated to this area. Indeed, the hybrid (Cavus) algorithm developed in this thesis is applied two main design cases in aerospace area: aircraft design optimization and trajectory optimization. The implemented new approach is capable of reducing the number of trial points without much compromise. The trend analysis shows that, for the complex design problems the Cavus algorithm is more conservative with a proportional number of trial points in finding the successful patterns

Optimisation multi-physique et multi-critère des coeurs de RNR-Na : application au concept CFV

Nuclear reactor core design is a highly multidisciplinary task where neutronics, thermal-hydraulics, fuel thermo-mechanics and fuel cycle are involved. The problem is moreover multi-objective (several performances) and highly dimensional (several tens of design parameters).As the reference deterministic calculation codes for core characterization require important computing resources, the classical design method is not well suited to investigate and optimize new innovative core concepts. To cope with these difficulties, a new methodology has been developed in this thesis. Our work is based on the development and validation of simplified neutronics and thermal-hydraulics calculation schemes allowing the full characterization of Sodium-cooled Fast Reactor core regarding both neutronics performances and behavior during thermal hydraulic dimensioning transients.The developed methodology uses surrogate models (or metamodels) able to replace the neutronics and thermal-hydraulics calculation chain. Advanced mathematical methods for the design of experiment, building and validation of metamodels allows substituting this calculation chain by regression models with high prediction capabilities.The methodology is applied on a very large design space to a challenging core called CFV (French acronym for low void effect core) with a large gain on the sodium void effect. Global sensitivity analysis leads to identify the significant design parameters on the core design and its behavior during unprotected transient which can lead to severe accidents. Multi-objective optimizations lead to alternative core configurations with significantly improved performances. Validation results demonstrate the relevance of the methodology at the predesign stage of a Sodium-cooled Fast Reactor core.La conception du coeur d’un réacteur nucléaire est fortement multidisciplinaire (neutronique, thermo-hydraulique, thermomécanique du combustible, physique du cycle, etc.). Le problème est aussi de type multi-objectif (plusieurs performances) à grand nombre de dimensions (plusieurs dizaines de paramètres de conception).Les codes de calculs déterministes utilisés traditionnellement pour la caractérisation des coeurs demandant d’importantes ressources informatiques, l’approche de conception classique rend difficile l’exploration et l’optimisation de nouveaux concepts innovants. Afin de pallier ces difficultés, une nouvelle méthodologie a été développée lors de ces travaux de thèse. Ces travaux sont basés sur la mise en oeuvre et la validation de schémas de calculs neutronique et thermo-hydraulique pour disposer d’un outil de caractérisation d’un coeur de réacteur à neutrons rapides à caloporteur sodium tant du point de vue des performances neutroniques que de son comportement en transitoires accidentels.La méthodologie mise en oeuvre s’appuie sur la construction de modèles de substitution (ou métamodèles) aptes à remplacer la chaîne de calcul neutronique et thermo-hydraulique. Des méthodes mathématiques avancées pour la planification d’expériences, la construction et la validation des métamodèles permettent de remplacer cette chaîne de calcul par des modèles de régression au pouvoir de prédiction élevé.La méthode est appliquée à un concept innovant de coeur à Faible coefficient de Vidange sur un très large domaine d’étude, et à son comportement lors de transitoires thermo-hydrauliques non protégés pouvant amener à des situations incidentelles, voire accidentelles. Des analyses globales de sensibilité permettent d’identifier les paramètres de conception influents sur la conception du coeur et son comportement en transitoire. Des optimisations multicritères conduisent à des nouvelles configurations dont les performances sont parfois significativement améliorées. La validation des résultats produits au cours de ces travaux de thèse démontre la pertinence de la méthode au stade de la préconception d’un coeur de réacteur à neutrons rapides refroidi au sodium

Using Modeling Knowledge to Guide Design Space Search

Automated search of a space of candidate designs seems an attractive way to improve the traditional engineering design process. To make this approach work, however, the automated design system must include both knowledge of the modeling limitations of the method used to evaluate candidate designs and also an effective way to use this knowledge to influence the search process. We suggest that a productive approach is to include this knowledge by implementing a set of model constraint functions which measure how much each modeling assumptions is violated, and to influence the search by using the values of these model constraint functions as constraint inputs to a standard constrained nonlinear optimization numerical method. We test this idea in the domain of conceptual design of supersonic transport aircraft, and our experiments indicate that our model constraint communication strategy can decrease the cost of design space search by one or more orders of magnitude. To appear: Artificial ..

Using Modeling Knowledge to Guide Design Space Search

Automated search of a space of candidate designs seems an attractive way to improve the traditional engineering design process. To make this approach work, however, the automated design system must include both knowledge of the modeling limitations of the method used to evaluate candidate designs and also an effective way to use this knowledge to influence the search process. We suggest that a productive approach is to include this knowledge by implementing a set of model constraint functions which measure how much each modeling assumptions is violated, and to influence the search by using the values of these model constraint functions as constraint inputs to a standard constrained nonlinear optimization numerical method. We test this idea in the domain of conceptual design of supersonic transport aircraft, and our experiments indicate that our model constraint communication strategy can decrease the cost of design space search by one or more orders of magnitude.Technical report hpcd-tr-3