Dissertations / Theses on the topic 'Multi-objective Design'

Industrial robots are the most widely manufactured and utilized type of robots in industries. Improving the design process of industrial robots would lead to further developments in robotics industries. Consequently, other dependant industries would be benefited. Therefore, there is an effort to make the design process more and more efficient and reliable. The design of industrial robots requires studies in various fields. Engineering softwares are the tools which facilitate and accelerate the robot design processes such as dynamic simulation, structural analysis, optimization, control and so forth. Therefore, designing a framework to automate the robot design process such that different tools interact automatically would be beneficial. In this thesis, the goal is to investigate the feasibility of integrating tools from different domains such as geometry modeling, dynamic simulation, finite element analysis and optimization in order to obtain an industrial robot design and optimization framework. Meanwhile, Meta modeling is used to replace the time consuming design steps. In the optimization step, various optimization algorithms are compared based on their performance and the best suited algorithm is selected. As a result, it is shown that the objectives are achievable in a sense that finite element analysis can be efficiently integrated with the other tools and the results can be optimized during the design process. A holistic framework which can be used for design of robots with several degrees of freedom is introduced at the end.

A reconfigurable manufacturing system (RMS) is designed for rapid adjustment of manufacturing capacity and functionality in response to market changes. An RMS consists of a number of reconfigurable machine tools (RMTs) which can process different jobs by quickly changing processing modules. The potential benefits of an RMS may not be achieved if an RMS is not properly designed. Most of the related studies focus on a few individual technical issues, in particular on modularity or configurability of individual RMTs. Other important concerns such as cost and processing accuracy have not been adequately addressed. As a result, many highly reconfigurable manufacturing systems turn out to be unprofitable. For the above reason, this study focuses on optimization of RMT design, including the design of modules and module warehouse, with consideration of three factors: configurability, cost and accuracy. (Abstract shortened by UMI.)

In electromagnetic design, optimisation often involves evaluating the finite element method (FEM) – repetitive evaluation of the objective function may require hours or days of computation, making the use of standard direct search methods (e.g. genetic algorithm and particle swarm) impractical. Surrogate modelling techniques are helpful tools in these scenarios. Indeed, their applications can be found in many aspects of engineering design in which a computationally expensive model is involved. Kriging, one of the most widely used surrogate modelling techniques, has become an increasingly active research subject in recent decades. This thesis focuses on four interesting research topics in surrogate-based optimisation: infill sampling efficiency, robust optimisation, and the memory problem encountered in large datasets and multi-objective optimisation. This thesis briefly provides relevant background information and introduces a number of independent novel approaches for each topic, with the aim of increasing efficiency of optimisation process and ability to handle larger datasets.

The development of the wire-mesh wheel of the Apollo Lunar Roving Vehicle was realized through a time consuming trial and error design process, primarily driven by manufacturability and physical testing. Recent wheel development, motivated by renewed interest in lunar surface exploration, utilizes more sophisticated numerical simulation tools. However, many researchers still employ trial and error or parametric approaches to designing the wheels. This thesis proposes a systematic approach to the design optimization of compliant lunar wheels. The problem is decomposed into system and component level analyses. The system level analysis investigates the effect of elastic wheel behaviour on rover and mission performance metrics. This is realized by optimizing concept independent wheel design variables using multi-disciplinary models coupled with optimization algorithms. Wheel concepts are explored by prototyping and physical testing, as well as numerical modelling. The mobility performance metrics of cellular, segmented and iRings wheels are compared to a baseline rubber wheel. In the component level analysis, a multi-objective optimization algorithm is coupled with numerical simulations of wheel-ground interaction to find optimal cellular wheel designs. The effectiveness of the methodology to optimize cellular wheel concepts is verified, and the limitations of the approach examined. Finally, a discussion to extend the proposed methodology to alternative wheel concepts is provided.
Le développement de la roue treillis métallique de l'Apollo Lunar Roving Vehicle a été réalisé par un processus d'essais et d'erreurs. Les récents développements de roues flexibles, motivé par un regain d'intérêt pour l'exploration lunaire, ont maintenant à leur disposition des outils de simulation numérique plus sophistiqués. Cependant, la majorité des chercheurs emploient toujours des méthodes expérimentales ou paramétriques pour développer leurs roues. Cette thèse propose une nouvelle approche systématique pour l'optimisation de concepts de roues lunaires flexibles. Le problème est décomposé en deux analyses se rapportant au niveau du système et celui des composantes. L'analyse au niveau du système étudie l'effet du comportement de la roue élastique sur des mesures de performance lors d'une mission du rover. Ceci est réalisé en optimisant les paramètres décrivant une roue flexible à l'aide de modèles multidisciplinaires. Différents concepts de roues sont explorés à l'aide de prototypes et d'essais physiques, ainsi que de modélisations numériques. La performance de chacun des concepts de roues flexibles cellulaires, iRings et segmentés sont comparées à un pneu standard. L'analyse au niveau des composantes effectue une optimisation multi-objective afin de déterminer, par le biais de simulations numériques, le concept optimal de roues flexibles cellulaires. L'efficacité de la méthodologie pour optimiser la roue cellulaire est ensuite vérifiée et les limites de cette approche sont examinées en détail. Finalement, une discussion sur l'application de la méthodologie proposée à des concepts de roues arbitraires est abordée.

Thesis: Ph. D. in Architecture: Building Technology, Massachusetts Institute of Technology, Department of Architecture, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 201-219).
During the design process in architecture, building performance and human experience are increasingly understood through computation. Within this context, this dissertation considers how data science and interactive optimization techniques can be combined to make simulation a more effective component of a natural early design process. It focuses on conceptual design, since technical principles should be considered when global decisions are made concerning the massing, structural system, and other design aspects that affect performance. In this early stage, designers might simulate structure, energy, daylighting, thermal comfort, acoustics, cost, and other quantifiable objectives. While parametric simulations offer the possibility of using a design space exploration framework to make decisions, their resulting feedback must be synthesized together, along with non-quantifiable design goals.
Previous research has developed optimization strategies to handle such multi-objective scenarios, but opportunities remain to further adapt optimization for the creative task of early building design, including increasing its interactivity, flexibility, accessibility, and ability to both support divergent brainstorming and enable focused performance improvement. In response, this dissertation proposes new approaches to parametric design space formulation, interactive optimization, and diversity-based design. These methods span in utility from early ideation, through global design exploration, to local exploration and optimization. The first presented technique uses data science methods to interrogate, transform, and, for specific cases, generate design variables for exploration. The second strategy involves interactive stepping through a design space using estimated gradient information, which offers designers more freedom compared to automated solvers during local exploration.
The third method addresses computational measurement of diversity within parametric design and demonstrates how such measurements can be integrated into creative design processes. These contributions are demonstrated on an integrated early design example and preliminarily validated using a design study that provides feedback on the habits and preferences of architects and engineers while engaging with data-driven tools. This study reveals that performance-enabled environments tend to improve simulated design objectives, while designers prefer more flexibility than traditional automated optimization approaches when given the choice. Together, these findings can stimulate further development in the integration of interactive approaches to multi-objective early building design. Key words: design space exploration, conceptual design, design tradeoffs, interactive design tools, structural design, sustainable design, multi-objective optimization, data science, surrogate modeling
by Nathan C. Brown.
Ph. D. in Architecture: Building Technology
Ph.D.inArchitecture:BuildingTechnology Massachusetts Institute of Technology, Department of Architecture

Researchers have applied genetic algorithms (GAs) and other heuristic optimization methods to perform truss optimization in recent years. Although a substantial amount of research has been performed on the optimization of truss member sizes, nodal coordinates, and member connections, research that seeks to simultaneously optimize the topology, geometry, and member sizes of trusses is still uncommon. In addition, most of the previous research is focused on the problem domains that are limited to a structured domain, which is defined by a fixed number of nodes, members, load locations, and load magnitudes. The objective of this research is to develop a computational method that can design efficient roof truss systems. This method provides an engineer with a set of near-optimal trusses for a specific unstructured problem domain. The unstructured domain only prescribes the magnitude of loading and the support locations. No other structural information concerning the number or locations of nodes and the connectivity of members is defined. An implicit redundant representation (IRR) GA (Raich 1999) is used in this research to evolve a diverse set of near-optimal truss designs within the specified domain that have varying topology, geometry, and sizes. IRR GA allows a Pareto-optimal set to be identified within a single trial. These truss designs reflect the tradeoffs that occur between the multiple objectives optimized. Finally, the obtained Pareto-optimal curve will be used to provide design engineers with a range of highly fit conceptual designs from which they can select their final design. The quality of the designs obtained by the proposed multi-objective IRR GA method will be evaluated by comparing the trusses evolved with trusses that were optimized using local perturbation methods and by trusses designed by engineers using a trial and error approach. The results presented show that the method developed is very effective in simultaneously optimizing the topology, geometry, and size of trusses for multiple objectives.

This thesis focuses on practical and quantitative methods for measuring effectiveness in naval ship design. An Overall Measure of Effectiveness (OMOE) model or function is an essential prerequisite for optimization and design trade-off. This effectiveness can be limited to individual ship missions or extend to missions within a task group or larger context. A method is presented that uses the Analytic Hierarchy Process combined with Multi-Attribute Value Theory to build an Overall Measure of Effectiveness and Overall Measure of Risk function to properly rank and approximately measure the relative mission effectiveness and risk of design alternatives, using trained expert opinion to replace complex analysis tools. A validation of this method is achieved through experimentation comparing ships ranked by the method with direct ranking of the ships through war gaming scenarios. The second part of this thesis presents a mathematical ship synthesis model to be used in early concept development stages of the ship design process. Tools to simplify and introduce greater accuracy are described and developed. Response Surface Models and Design of Experiments simplify and speed up the process. Finite element codes such as MAESTRO improve the accuracy of the ship synthesis models which in turn lower costs later in the design process. A case study of an Advanced Logistics Delivery Ship (ALDV) is performed to asses the use of RSM and DOE methods to minimize computation time when using high-fidelity codes early in the naval ship design process.
Master of Science

As a major energy consumer and CO2 emitter, buildings have an undeniably important role to play in cutting carbon emissions and combating climate change. Over the recent decades, green buildings have gained increasing attention and popularity from various stakeholders in the construction industry. Green building design practice builds upon the conventional building design practice but adds the concerns of environmental impacts and occupants’ well-being in the design philosophy. Many researchers advocate utilizing optimization for green building design due to its capability in obtaining improved design solutions and providing building designers a better understanding of the design space. A comprehensive and in-depth review on previous relevant optimization models has revealed the following two limitations which might undermine their application in practice. Firstly, the focus of optimization in most of these models was on the reduction of cost and energy consumption while occupants’ comfort level in terms of indoor environmental quality was seldom considered. Secondly, for those models which have set comfort level of indoor environmental quality as a design objective, only thermal comfort was taken into account and thus they failed to address other essential factors governing indoor environmental quality such as visual comfort and indoor air quality. Aiming at addressing the limitations of previous related studies, this research has developed an improved optimization model for green building design with a more comprehensive set of design objectives, namely minimization of cost, minimization of energy consumption, and maximization of occupants’ comfort level in terms of indoor environmental quality. The importance of the three design objectives and the necessity for including them in the model were verified through a series of semi-structured interviews with respondents from different stakeholder groups in relation to green building design and construction. The three design objectives are evaluated in the developed model in terms of (i) cost according to life cycle cost; (ii) energy consumption analyzed by a widely-adopted building energy performance simulation program – EnergyPlus; and (iii) comfort level of indoor environmental quality by adopting an empirical-based multivariate-logistic regression model identified from literatures. Non-dominated Sorting Genetic Algorithm II, a powerful multi-objective optimization technique, was selected as the optimization engine in the developed model. The developed model was then implemented into to a prototype tool in the MATLAB environment which can be utilized by building designers to determine the appropriate design solutions. Through a hypothetical office building design problem, the applicability of the model was demonstrated. Finally, the developed model was validated through demonstration and face-to-face discussion with experts.
published_or_final_version
Civil Engineering
Master
Master of Philosophy

Thesis: S.M. in Building Technology, Massachusetts Institute of Technology, Department of Architecture, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 107-113).
Using computational tools, fast and accurate predictions of building performance are increasingly possible. In parallel, the expectations of a high-performance building have been rising in contemporary architecture, as designers must synthesize many inputs to arrive at a design that fulfills a wide range of requirements. Despite the clear need for assistance in prioritizing and managing different design objectives, advances in performance analysis have not commonly translated into guidance in early stage design, as the limits of the traditional design process and a separation of disciplines have relegated performance feedback to later phases. In order to facilitate better design on a holistic level, researchers in related areas have developed multiobjective optimization (MOO), which is a methodology intended for navigating complex design spaces while managing and prioritizing multiple objectives. However, after reviewing existing design optimization research and considering current usage of optimization in AEC practice, a number of clear research questions arise: How can conceptual, architectural design problems be formulated and solved using MOO in a way that generates diverse, high-performing solutions? What is the best way for the designers of buildings and structures to interact with MOO problems? Finally, how does the use of MOO in the conceptual phase affect design possibilities and outcomes? This thesis addresses these key research questions, along with a number of secondary questions, through a combination of design case studies, tool development, user experience testing, and historical analysis. First, it presents a conceptual framework for implementing MOO within architectural parametric design tools in flexible, interactive way. Next, it shows the outcomes of a conceptual design exercise in which participants are given increasing access to performance feedback. Finally, through the application of MOO to three long span roof case studies, it demonstrates how MOO can lead to diverse, high-performing results that are difficult to generate through other means, before introducing a new way in which multi-objective techniques can be used to analyze historical structures. Together, these contributions encourage more widespread and effective use of multi-objective optimization in conceptual design, leading to better performing buildings and structures without overly constraining creative, innovative designers. Key words: multi-objective optimization, design space exploration, conceptual design, design tradeoffs, interactive design tools, structural design, embodied and operational energy.
by Nathan C. Brown.
S.M. in Building Technology

Engineering systems are designed to perform particular functions to a given specification. Optimisation is the process of finding the design which best achieves these goals or objectives. Until now, the aerodynamic design of missile systems has been performed using single-objective optimisation and semi-empirical models. An optimisation framework has been developed and tailored for the multi-objective optimisation of projectiles. The framework is demonstrated on three different configurations of projectile: body-tails, body-canards-tails and body-canards-tails with a rotating nose, with up to 7 input parameters and 6 objective functions. Incorporated in the framework are sampling schemes, Kriging surrogate modelling, Fourier decomposition, genetic algorithms and adaptive sampling. The MISL3 semi-empirical and Cart3D CFD aero-prediction codes are used to generate an aerodynamic performance dataset. Three aerodynamic optimisation problems are solved for objective functions based on lateral acceleration, time-to-target, range, static stability and roll moment coefficient. Quantitative validation is carried out on the Kriging surrogate model, determining that the mean RMS error is less than 3%. Adaptive sampling is applied to refine the surrogate model. The results are presented in the form of Pareto fronts showing the trade-off in two and three-dimensional objective space. This gives the designer key information on the trade-off and performance of a range of projectile designs. With the exception of range, all of the objective functions were found to be competing. A set of optimal designs were identified, with lateral accelerations of up to 40g. The framework was demonstrated to be robust when dealing with unexpected conditions such as failed simulations. Implementing such a framework in the design of missile systems provides the designer with a wide range of design options and has the potential to provide shorter time-to-market and cost savings for the industry.

In this thesis the problem of conceptual engineering design and the possible use of adaptive search techniques and other machine based methods therein are explored. For the multi-objective optimisation (MOO) within conceptual design problem, genetic algorithms (GA) adapted to MOO are used and various techniques explored: weighted sums, lexicographic order, Pareto method with and without ranking, VEGA-like approaches etc. Large number of runs are performed for findingZ Dth e optimal configuration and setting of the GA parameters. A novel method, weighted Pareto method is introduced and applied to a real-world optimisation problem. Decision support methods within conceptual engineering design framework are discussed and a new preference method developed. The preference method for translating vague qualitative categories (such as "more important 91 , 4m.9u ch less important' 'etc. ) into quantitative values (numbers) is based on fuzzy preferences and graph theory methods. Several applications of preferences are presented and discussed: * in weighted sum based optimisation methods; s in weighted Pareto method; * for ordering and manipulating constraints and scenarios; e for a co-evolutionary, distributive GA-based MOO method; The issue of complexity and sensitivity is addressed as well as potential generalisations of presented preference methods. Interactive dynamical constraints in the form of design scenarios are introduced. These are based on a propositional logic and a fairly rich mathematical language. They can be added, deleted and modified on-line during the design session without need for recompiling the code. The use of machine-based agents in conceptual design process is investigated. They are classified into several different categories (e. g. interface agents, search agents, information agents). Several different categories of agents performing various specialised task are developed (mostly dealing with preferences, but also some filtering ones). They are integrated with the conceptual engineering design system to form a closed loop system that includes both computer and designer. All thesed ifferent aspectso f conceptuale ngineeringd esigna re applied within Plymouth Engineering Design Centre / British Aerospace conceptual airframe design project.

The thermal design of buildings as a multi-criterion optimisation process since there is always a pay-off (balance) to be made between capital expenditure and the operating cost of the building. This thesis investigates an approach to solving 'whole building' optimisation problems. In particular simultaneous optimisation of the plant size for a fixed arrangement of air conditioning equipment, and the control schedule for its operation to condition the space within a discrete selection of building envelopes. The optimisation is achieved by examining a combination of the cost of operating the plant, the capital cost of the plant and building construction, and maximum percentage people dissatisfied during the occupation of the building. More that one criterion is examined at a time by using multi-criteria optimisation methods. Therefore rather than a single optimum, a payoff between the solutions is sort. The benefit of this is that it provides a more detailed information about the characteristics of the problem and more design solutions available to the end user. The optimisation is achieved using a modified genetic algorithm using Pareto ranking selection to provide the multi-criterion fitness selection. Specific methods for handling the high number of constraints within the problem are examined. A specific operator is designed and demonstrated to deal with the discontinuous effects of the three separate seasons, which are used for the plant selection and for the three separate control schedules. Conclusions are made with respect to the specific application of the multi-criterion optimisation to, building services systems, their control, and the viability of 'whole building design' optimisation.

In this article, a design methodology for complex composite aircraft structures is presented. The developed approach combines a multi-objective optimization method and a parameterized simulation model using a design concept database. Due to the combination of discrete and continuous design variables describing the structures, evolutionary algorithms are used within the presented optimization approach. The approach requires an evaluation of the design alternatives that is performed by parameterized simulation models. The variability of these models is achieved using a design concept database that contains different layouts for each implemented structural part. Due to the complexity of the generated aircraft structures, the finite element method is applied for the calculation of the structural behaviour. The applicability of the developed design approach will be demonstrated by optimizing two composite aircraft fuselage examples. The obtained results show that the developed methodology is useful and reliable for designing complex aircraft structures.

The overall objective of this research was to realise the practical application of Hierarchical Asynchronous Parallel Evolutionary Algorithms for Multi-objective and Multidisciplinary Design Optimisation (MDO) of UAV Systems using high fidelity analysis tools. The research looked at the assumed aerodynamics and structures of two production UAV wings and attempted to optimise these wings in isolation to the rest of the vehicle. The project was sponsored by the Asian Office of the Air Force Office of Scientific Research under contract number AOARD-044078. The two vehicles wings which were optimised were based upon assumptions made on the Northrop Grumman Global Hawk (GH), a High Altitude Long Endurance (HALE) vehicle, and the General Atomics Altair (Altair), Medium Altitude Long Endurance (MALE) vehicle. The optimisations for both vehicles were performed at cruise altitude with MTOW minus 5% fuel and a 2.5g load case. The GH was assumed to use NASA LRN 1015 aerofoil at the root, crank and tip locations with five spars and ten ribs. The Altair was assumed to use the NACA4415 aerofoil at all three locations with two internal spars and ten ribs. Both models used a parabolic variation of spar, rib and wing skin thickness as a function of span, and in the case of the wing skin thickness, also chord. The work was carried out by integrating the current University of Sydney designed Evolutionary Optimiser (HAPMOEA) with Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA) tools. The variable values computed by HAPMOEA were subjected to structural and aerodynamic analysis. The aerodynamic analysis computed the pressure loads using a Boeing developed Morino class panel method code named PANAIR. These aerodynamic results were coupled to a FEA code, MSC.Nastran® and the strain and displacement of the wings computed. The fitness of each wing was computed from the outputs of each program. In total, 48 design variables were defined to describe both the structural and aerodynamic properties of the wings subject to several constraints. These variables allowed for the alteration of the three aerofoil sections describing the root, crank and tip sections. They also described the internal structure of the wings allowing for variable flexibility within the wing box structure. These design variables were manipulated by the optimiser such that two fitness functions were minimised. The fitness functions were the overall mass of the simulated wing box structure and the inverse of the lift to drag ratio. Furthermore, six penalty functions were added to further penalise genetically inferior wings and force the optimiser to not pass on their genetic material. The results indicate that given the initial assumptions made on all the aerodynamic and structural properties of the HALE and MALE wings, a reduction in mass and drag is possible through the use of the HAPMOEA code. The code was terminated after 300 evaluations of each hierarchical level due to plateau effects. These evolutionary optimisation results could be further refined through a gradient based optimiser if required. Even though a reduced number of evaluations were performed, weight and drag reductions of between 10 and 20 percent were easy to achieve and indicate that the wings of both vehicles can be optimised.

Master of Engineering (Research)
The overall objective of this research was to realise the practical application of Hierarchical Asynchronous Parallel Evolutionary Algorithms for Multi-objective and Multidisciplinary Design Optimisation (MDO) of UAV Systems using high fidelity analysis tools. The research looked at the assumed aerodynamics and structures of two production UAV wings and attempted to optimise these wings in isolation to the rest of the vehicle. The project was sponsored by the Asian Office of the Air Force Office of Scientific Research under contract number AOARD-044078. The two vehicles wings which were optimised were based upon assumptions made on the Northrop Grumman Global Hawk (GH), a High Altitude Long Endurance (HALE) vehicle, and the General Atomics Altair (Altair), Medium Altitude Long Endurance (MALE) vehicle. The optimisations for both vehicles were performed at cruise altitude with MTOW minus 5% fuel and a 2.5g load case. The GH was assumed to use NASA LRN 1015 aerofoil at the root, crank and tip locations with five spars and ten ribs. The Altair was assumed to use the NACA4415 aerofoil at all three locations with two internal spars and ten ribs. Both models used a parabolic variation of spar, rib and wing skin thickness as a function of span, and in the case of the wing skin thickness, also chord. The work was carried out by integrating the current University of Sydney designed Evolutionary Optimiser (HAPMOEA) with Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA) tools. The variable values computed by HAPMOEA were subjected to structural and aerodynamic analysis. The aerodynamic analysis computed the pressure loads using a Boeing developed Morino class panel method code named PANAIR. These aerodynamic results were coupled to a FEA code, MSC.Nastran® and the strain and displacement of the wings computed. The fitness of each wing was computed from the outputs of each program. In total, 48 design variables were defined to describe both the structural and aerodynamic properties of the wings subject to several constraints. These variables allowed for the alteration of the three aerofoil sections describing the root, crank and tip sections. They also described the internal structure of the wings allowing for variable flexibility within the wing box structure. These design variables were manipulated by the optimiser such that two fitness functions were minimised. The fitness functions were the overall mass of the simulated wing box structure and the inverse of the lift to drag ratio. Furthermore, six penalty functions were added to further penalise genetically inferior wings and force the optimiser to not pass on their genetic material. The results indicate that given the initial assumptions made on all the aerodynamic and structural properties of the HALE and MALE wings, a reduction in mass and drag is possible through the use of the HAPMOEA code. The code was terminated after 300 evaluations of each hierarchical level due to plateau effects. These evolutionary optimisation results could be further refined through a gradient based optimiser if required. Even though a reduced number of evaluations were performed, weight and drag reductions of between 10 and 20 percent were easy to achieve and indicate that the wings of both vehicles can be optimised.

Multi-disciplinary ship synthesis models and multi-objective optimization techniques are increasingly being used in ship design. Multi-disciplinary models allow designers to break away from the traditional design spiral approach and focus on searching the design space for the best overall design instead of the best discipline-specific design. Complex design problems such as these often have high levels of uncertainty associated with them, and since most optimization algorithms tend to push solutions to constraint boundaries, the calculated "best" solution might be infeasible if there are minor uncertainties related to the model or problem definition. Consequently, there is a need to address uncertainty in optimization problems to produce effective and reliable results. This thesis focuses on adding a third objective, uncertainty, to the effectiveness and cost objectives already present in a multi-disciplinary ship synthesis model. Uncertainty is quantified using a "confidence of success" (CoS) calculation based on the mean value method. CoS is the probability that a design will satisfy all constraints and meet performance objectives. This work proves that the CoS concept can be applied to synthesis models to estimate uncertainty early in the design process. Multiple sources of uncertainty are realistically quantified and represented in the model in order to investigate their relative importance to the overall uncertainty. This work also presents methods to encourage a uniform distribution of points across the Pareto front. With a well defined front, designs can be selected and refined using a gradient based optimization algorithm to optimize a single objective while holding the others fixed.
Master of Science

Hoy en día, los controladores proporcionales integrales y proporcionales integrales derivativos son los algoritmos de control más utilizado en la industria. Por otra parte, los controladores fraccionarios han recibido atención recientemente, por parte de la comunidad científica y desde el punto de vista industrial. Debido a esto, en esta tesis algunos de los escenarios implican la sintonización de estos controladores mediante el procedimiento de diseño mediante la optimización multi-objetivo. Este procedimiento se centra en proporcionar un equilibrio razonable entre los objetivos en conflicto y brinda al diseñador la posibilidad de apreciar la comparación de los objetivos de diseño. Esta tesis se divide en tres partes. La primera parte, presenta los fundamentos del sistema de control y discusión de los diferentes compromisos: entre los modos de operación servo / regulación y del rendimiento / robustez. Por otro lado, se ha proporcionado un marco conceptual acerca de la optimización multi-objetivo. La segunda parte, introduce la solución de Nash como una técnica de selección multi-criterio, para seleccionar un punto del frente de Pareto, que represente el mejor compromiso entre los objetivos de diseño. Esta solución es una selección semi-automática escogida en la aproximación del frente de Pareto y ofrece un buen compromiso entre los objetivos de diseño. Luego, se presenta el Multi-stage approach para el proceso de optimización multi-objetivo. Este enfoque implica dos algoritmos: un algoritmo determinista y algoritmo evolutivo. En el cual ambos algoritmos se complementen entre sí a pesar de sus desventajas y mejoran los resultados de la optimización en términos de convergencia y precisión. Además, se introduce el objetivo basado en la fiabilidad, en la descripción del problema multi-objetivo, este se utiliza para medir la degradación del rendimiento. Vale la pena mencionar que, debido a la existencia de incertidumbres en el diseño y fabricación, teniendo este objetivo de diseño le dará otra perspectiva al diseñador en el mundo real. Con el fin de validar el método, dos casos de estudios se ha considerado, el problema de control de la caldera (The Boiler Control Benchmark) para la sintonización de controladores y como segundo caso, una pila Peltier nolineal. Por último, la tercera parte de esta tesis, presentan las contribuciones a la sintonización de controladores. En primer lugar, se propone un conjunto de reglas de sintonía basado en la solución de Nash para un controlador proporcional-integral, en donde la robustez / rendimiento han sido considerados. Por otra parte, como un segundo caso se presenta las reglas de sintonía para un controlador proporcional-integral-derivativo, donde se han considerado el compromiso de robustez/rendimiento y los modos de operación servo / regulación. Además, se proponen reglas de sintonía para el controlador proporcional-integral-derivativo-fraccional-orden implementado el Multi-stage approach para la optimización multi-objetivo.
Nowadays, the proportional integral and proportional integral derivatives are the most used control algorithm in the industry. Moreover, the fractional controllers have received attention recently for both, the research community and from the industrial point of view. Owing to this, in this thesis some of the scenarios involve the tuning of these controllers by using the Multiobjective Optimization Design procedure. This procedure focuses on providing reasonable trade-off among the conflictive objectives and brings the designer the possibility to appreciate the comparison of the design objectives. This thesis is divided in three parts. The first part, presented the fundamentals of the control system showing and discussing the different trade-offs between performance/robustness and servo/regulation operation modes. On the other hand a background on multi-objective optimization has been provided. The second part, introduces the Nash solution as a multi-criteria decision making technique, to select a point from the Pareto front that represent the best compromise among the design objective. This solution provides a semi-automatic selection from the Pareto front approximation and offers a good trade-off between the goal objectives. Hereafter, a Multi-stage approach for the multi-objective optimization process is presented. This approach involves two algorithms: a deterministic and evolutionary algorithm. In which both algorithms complement each other in despite of their drawbacks and improve the results of the overall optimization in terms of convergence and accuracy. Further, the introduction of reliability based objective into the multi-objective problem is carried out, to measure the performance degradation. It is worthwhile to mention that, due to the existence of uncertainties in real-world designing and manufacturing having this design objective will give another perspective to the designer. In order to validate the approach, two different case studies has been considered, the Boiler control problem for controller tuning and as second case, a non-linear Peltier Cell. Finally, the third part of this thesis, the contributions on controller tuning have been presented. First, a set of tuning rules based on the NS for a proportional-integral (PI) controller have been devised, where the robustness/performance trade-off have been considered. Moreover, as a second case it is presented a tuning for proportional-integral-derivative controller where the trade-off of the performance/robustness and servo/regulation operation mode has been considered. Moreover, the fractional-order-proportional-integral-derivative controller is tuned by using the Multi-stage approach for the MOO process.

In recent years, due to growing environmental awareness regarding global warming, green cars, such as hybrid electric vehicles, have gained a lot of importance. With the decreasing cost of rare earth magnets, brushless permanent magnet motors, such as the Interior Permanent Magnet Motor, have found usage as part of the traction drive system in these types of vehicles. As a design issue, building a motor with a performance curve that suits both city and highway driving has been treated in this thesis as a multi-objective problem; matching specific points of the torque-speed curve to the desired performance output. Conventionally, this has been treated as separate problems or as a combination of several individual problems, but doing so gives little information about the trade-offs involved. As a means of identifying the compromising solutions, we have developed a stochastic optimizer for tackling electromagnetic device optimization and have also demonstrated a new innovative way of studying how different design parameters affect performance.

Automated systems for large-span roof truss optimization provide engineers with the flexibility to consider multiple alternatives during conceptual design. This investigation extends previous work on multi-objective roof truss optimization to include the design preferences of a human user. The incorporation of user preferences into the optimization process required creation of a mechanism to identify and model preferences as well as discovery of an appropriate location within the algorithm for preference application. The first stage of this investigation developed a characteristic feature vector to describe the physical appearance of an individual truss. The feature vector translates visual elements of a truss into quantifiable properties transparent to the computer algorithm. The nine elements in the feature vector were selected from an assortment of geometrical and behavioral factors and describe truss simplicity, general shape, and chord shape. Using individual feature vectors, a truss population may be divided into groups of similar design. Partitioning the population simplifies the feedback process by allowing users to identify groups that best suit their design preferences. Several unsupervised clustering mechanisms were evaluated for their ability to generate truss classifications that matched human judgment and minimized intra-group deviation. A one-dimensional Kohonen self-organizing map was selected. The characteristic feature vectors of truss designs within user-selected groups provided a basis for determining whether or not a user would like a new design. After analyzing user inputs, prediction algorithm trials sought to reproduce these inputs and apply them to the prediction of acceptable designs. This investigation developed a hybrid method combining rough set reduct techniques and a back-propagation neural network. This hybrid prediction mechanism was embedded into the operations of an Implicit Redundant Representation Genetic Algorithm. Locations within the ranking and selection processes of this algorithm formed the basis of a study to investigate the effect of user preference on truss optimization. Final results for this investigation prove that incorporating a user's aesthetic design preferences into the optimization project generates more design alternatives for the user to examine; that these alternatives are more in line with a user's conceptual perception of the project; and that these alternatives remain structurally optimal.

The emerging grid technology is receiving great attention from researchers and applications that need computational and data capabilities to enhance performance and efficiency. Multi-Objective Design Optimisation (MODO) is computationally and data challenging. The challenges become even more with the emergence of evolutionary computing (EC) techniques which produce multiple solutions in a single simulation run. Other challenges are the complexity in mathematical models and multidisciplinary involvement of experts, thus making MODO collaborative and interactive in nature. These challenges call for a problem solving environment (P SE) that can provide computational and optimisation resources to MODO experts as services. Current PSEs provide only the technical specifications of the services which is used by programmers and do not have service specifications for designers that use the system to support design optimisation as services. There is need for PSEs to have service specification document that describes how the services are provided to the end users. Additionally, providing MODO resources as services enabled designers to share resources that they do not have through service subscription. The aim of this research is to develop specifications and architecture of a grid service for MODO. The specifications provide the service use cases that are used to build MODO services. A service specification document is proposed and this enables service providers to follow a process for providing services to end users. In this research, literature was reviewed and industry survey conducted. This was followed by the design, development, case study and validation. The research studied related PSEs in literature and industry to come up with a service specification document that captures the process for grid service definition. This specification was used to develop a framework for MODO applications. An architecture based on this framework was proposed and implemented as DECGrid (Decision Engineering Centre Grid) prototype. Three real-life case studies were used to validate the prototype. The results obtained compared favourably with the results in literature. Different scenarios for using the services among distributed design experts demonstrated the computational synergy and efficiency in collaboration. The mathematical model building service and optimisation service enabled designers to collaboratively build models using the collaboration service. This helps designers without optimisation knowledge to perform optimisation. The key contributions in this research are the service specifications that support MODO, the framework developed which provides the process for definining the services and the architecture used to implement the framework. The key limitations of the research are the use of only engineering design optimisation case studies and the prototype is not tested in industry.

Engineering and Physical Sciences Research Council
This thesis describes the development of a multi-objective automated optimisation system to be used for the design optimisation of micro-scale combustion devices. The developed system described within integrates a commercial computational fluid dynamics package and a multi-objective variant of the Tabu Search optimisation algorithm for continuous problems, which is a heuristic optimisation technique that exhibits local search characteristics. Recent advances in micro-fabrication techniques have resulted in increasing interest from industry and academia to investigate the possibility of replacing the current conventional power supply “battery” with a miniaturised combustion power generation system based on micro-electro-mechanical systems (MEMS) technology. The microcombustor is one of the crucial components of such a power system. The aim is to improve the main micro-scale combustor design characteristics and to satisfy manufacturability considerations from the very beginning of the whole design process. The main combustor design requirements, challenges and design parameters that influence the device performance at a micro-scale were first defined. Within the optimisation design cycle a robust parameterisation scheme, the geometry and numerical grid representations were implemented. These were achieved by incorporating the knowledge gained from the parametric design study by understanding the design space in depth and identifying issues and their solutions during this design study such as geometry overlapping and mesh refinement. Cont/d.

This thesis encompasses the analysis of TSN systems and their multi-objective design methods. TSN nodes are networked through interpolation and activation, similar to a gain-scheduling or local model/controller network. However, to achieve accuracy and ease of commissioning without requiring a large number of nodes, an algorithm has been developed first to identify optimum transition nodes within the entire operating envelope. Then the TSN approaches a nonlinear plant globally, not just locally, without requiring linearization. If desired or necessary, global optimisation provides an enhancement in the design process for TSNs. Since optimising only one aspect (a single objective) of performance while compromising others is undesirable, multi-objective designs have been developed concurrently to deliver or improve multiple aspects of performance. Following the development of a TSN, it is applied to nonlinear system modelling, and this TSN is termed a Trajectory-Scheduling Model (TSM). A TSM possesses the same properties and design features as the TSN generic framework. A nonlinear system, a coupled liquid-tank, is used to examine this modelling technique. Results verify the feasibility and effectiveness of the methods developed and validates the TSM. Further, the TSN technique is applied to nonlinear controller design, by way of a Trajectory-Scheduling Controller (TSC) network. It is illustrated through the design of a networked, easy-to-understand and easy-to-use PID control system for the coupled liquid-tank. Results show that the methods developed offer a high-performance linear control system with nonlinear capabilities to handle practical systems operating in a broad range and to cope with conflict between setpoint following at transient and disturbance rejection at steady state. This method is then applied to the PID network design problems for two nonlinear chemical processes.

Thesis (PhD (Logistics))--University of Stellenbosch, 2011.
ENGLISH ABSTRACT: The design of a water distribution system (WDS) involves finding an acceptable trade-off between cost minimisation and the maximisation of numerous system benefits, such as hydraulic reliability and surplus capacity. The primary design problem involves cost-effective specifica- tion of a pipe network layout and pipe sizes (which are typically available in a discrete set of commercial diameters) in order to satisfy expected consumer water demands within required pressure limits. The problem may be extended to consider the design of additional WDS com- ponents, such as reservoirs, tanks, pumps and valves. Practical designs must also cater for the uncertainty of demand, the requirement of surplus capacity for future growth, and the hydraulic reliability of the system under different demand and potential failure conditions. A detailed literature review of exact and approximate approaches towards single-objective (minimum cost) WDS design optimisation is provided. Essential topics which have to be included in any modern WDS design paradigm (such as demand estimation, reliability quantification, tank design and pipe layout) are discussed. A number of formative concepts in multi-objective evo- lutionary optimisation are also reviewed (including a generic problem formulation, performance evaluation measures, comparative testing strategies, and suitable classes of metaheuristics). The two central themes of this dissertation are conducting multi-objective WDS design optimi- sation using metaheuristics, and a critical examination of surrogate measures used to quantify WDS reliability. The aim in the first theme is to compare numerous modern metaheuristics, in- cluding several multi-objective evolutionary algorithms, an estimation of distribution algorithm and a recent hyperheuristic named AMALGAM (an evolutionary framework for the simulta- neous incorporation of multiple metaheuristics applied here for the first time to a real-world problem), in order to determine which approach is most capable with respect to WDS design optimisation. Several novel metaheuristics are developed, as well as a number of new variants of existing algorithms, so that a total of twenty-three algorithms were compared. Testing with respect to eight small-to-large-sized WDS benchmarks from the literature reveals that the four top-performing algorithms are mutually non-dominated with respect to the vari- ous performance metrics. These algorithms are NSGA-II, TAMALGAMJndu, TAMALGAMndu and AMALGAMSndp (the last three being novel variants of AMALGAM). However, when these four algorithms are applied to the design of a very large real-world benchmark, the AMALGAM paradigm outperforms NSGA-II convincingly, with AMALGAMSndp exhibiting the best perfor- mance overall. As part of this study, a novel multi-objective greedy algorithm is developed by combining several heuristic design methods from the literature in order to mimic the design strategy of a human engineer. This algorithm functions as a powerful local search. However, it is shown that such an algorithm cannot compete with modern metaheuristics, which employ advanced strategies in order to uncover better solutions with less computational effort. The second central theme involves the comparison of several popular WDS reliability surro- gate measures (namely the Resilience Index, Network Resilience, Flow Entropy, and a novel mixed surrogate measure) in terms of their ability to produce designs that are robust against pipe failure and water demand variation. This is the first systematic study on a number of WDS benchmarks in which regression analysis is used to compare reliability surrogate measures with probabilistic reliability typically derived via simulation, and failure reliability calculated by considering all single-pipe failure events, with both reliability types quantified by means of average demand satisfaction. Although no single measure consistently outperforms the others, it is shown that using the Resilience Index and Network Resilience yields designs that achieve a better positive correlation with both probabilistic and failure reliability, and while the Mixed Surrogate measure shows some promise, using Flow Entropy on its own as a quantifier of re- liability should be avoided. Network Resilience is identified as being a superior predictor of failure reliability, and also having the desirable property of supplying designs with fewer and less severe size discontinuities between adjacent pipes. For this reason, it is recommended as the surrogate measure of choice for practical application towards design in the WDS industry. AMALGAMSndp is also applied to the design of a real South African WDS design case study in Gauteng Province, achieving savings of millions of Rands as well as significant reliability improvements on a preliminary engineered design by a consulting engineering firm.
AFRIKAANSE OPSOMMING: Die ontwerp van waterverspreidingsnetwerke (WVNe) behels die soeke na ’n aanvaarbare afruiling tussen koste-minimering en die maksimering van ’n aantal netwerkvoordele, soos hidroliese betroubaarheid en surpluskapasiteit. Die primere ontwerpsprobleem behels ’n koste-doeltreffende spesifikasie van ’n netwerkuitleg en pypgroottes (wat tipies in ’n diskrete aantal kommersiele deursnedes beskikbaar is) wat aan gebruikersaanvraag binne sekere drukspesifikasies voldoen. Die probleem kan uitgebrei word om die ontwerp van verdere WVN-komponente, soos op- gaardamme, opgaartenks, pompe en kleppe in ag te neem. Praktiese WVN-ontwerpe moet ook voorsiening maak vir onsekerheid van aanvraag, genoegsame surpluskapsiteit vir toekom- stige netwerkuitbreidings en die hidroliese betroubaarheid van die netwerk onder verskillende aanvraag- en potensiele falingsvoorwaardes. ’n Omvattende literatuurstudie word oor eksakte en benaderde oplossingsbenaderings tot enkel- doelwit (minimum koste) WVN-ontwerpsoptimering gedoen. Sentrale temas wat by heden- daagse WVN-ontwerpsparadigmas ingesluit behoort te word (soos aanvraagvooruitskatting, die kwantifisering van betroubaarheid, tenkontwerp en netwerkuitleg), word uitgelig. ’n Aantal basiese konsepte in meerdoelige evolusionˆere optimering (soos ’n generiese probleemformulering, werkverrigtingsmaatstawwe, vergelykende toetsingstrategie¨e, en sinvolle klasse metaheuristieke vir WVN-ontwerp) word ook aangeraak. Die twee sentrale temas in hierdie proefskrif is meerdoelige WVN-ontwerpsoptimering deur mid- del van metaheuristieke, en ’n kritiese evaluering van verskeie surrogaatmaatstawwe vir die kwantifisering van netwerkbetroubaarheid. Die doel in die eerste tema is om ’n aantal moderne metaheuristieke, insluitend verskeie meerdoelige evolusionere algoritmes en die onlangse hiper- heuristiek AMALGAM (’n evolusionere raamwerk vir die gelyktydige insluiting van ’n aantal metaheuristieke wat hier vir die eerste keer op ’n praktiese probleem toegepas word), met mekaar te vergelyk om sodoende ’n ideale benadering tot WVN-ontwerpoptimering te identi- fiseer. Verskeie nuwe metaheuristieke sowel as ’n aantal nuwe variasies op bestaande algoritmes word ontwikkel, sodat drie en twintig algoritmes in totaal met mekaar vergelyk word. Toetse aan die hand van agt klein- tot mediumgrootteWVN-toetsprobleme uit die literatuur dui daarop dat die vier top algoritmes mekaar onderling ten opsigte van verskeie werkverrigtings- maatstawwe domineer. Hierdie algoritmes is NSGA-II, TAMALGAMJndu, TAMALGAMndu en AMALGAMSndp, waarvan laasgenoemde drie nuwe variasies op AMALGAM is. Wanneer hierdie vier algoritmes egter vir die ontwerp van ’n groot WVN-toetsprobleem ingespan word, oortref die AMALGAM-paradigma die NSGA-II oortui-gend, en lewer AMALGAMSndp die beste resultate. As deel van hierdie studie is ’n nuwe meerdoelige gulsige algoritme ontwerp wat verskeie heuristiese ontwerpsmetodologiee uit die literatuur kombineer om sodoende die on- twerpstrategie van ’n ingenieur na te boots. Hierdie algoritme funksioneer as ’n kragtige lokale soekprosedure, maar daar word aangetoon dat die algoritme nie met moderne metaheuristieke, wat gevorderde soekstrategie¨e inspan om beter oplossings met minder berekeningsmoeite daar te stel, kan meeding nie. Die tweede sentrale tema behels die vergelyking van ’n aantal gewilde surrogaatmaatstawwe vir die kwantifisering van WVN-betroubaarheid (naamlik die elastisiteitsindeks, netwerkelastisiteit, vloei-entropie en ’n gemengde surrogaatmaatstaf ) in terme van die mate waartoe hul gebruik kan word om WVNe te identifiseer wat robuust is ten opsigte van pypfaling en variasie in aanvraag. Hierdie proefskrif bevat die eerste sistematiese vergelyking deur middel van regressie-analise van ’n aantal surrogaatmaatstawwe vir die kwantifisering van WVN-betroubaarheid en stogastiese betroubaarheid (wat tipies via simulasie bepaal word) in terme van ’n aantal toetsprobleme in die literatuur. Alhoewel geen enkele maatstaf as die beste na vore tree nie, word daar getoon dat gebruik van die elastisiteitsindeks en netwerkelastisiteit lei na WNV-ontwerpe met ’n groter positiewe korrelasie ten opsigte van beide stogastiese betroubaarheid en falingsbetroubaarheid. Verder toon die gebruik van die gemengde surrogaatmaatstaf potensiaal, maar die gebruik van vloei-entropie op sy eie as kwantifiseerder van betroubaarheid behoort vermy te word. Netwerkelastisiteit word as ’n hoe-gehalte indikator van falingsbetroubaarheid geidentifiseer en het ook die eienskap dat dit daartoe instaat is om ontwerpe met ’n kleiner aantal diskontinuiteite sowel as van ’n minder ekstreme graad van diskontinuiteite tussen deursnedes van aangrensende pype daar te stel. Om hierdie rede word netwerkelastisiteit as die surogaatmaatstaf van voorkeur aanbeveel vir toepassings van WVN-ontwerpe in die praktyk. AMALGAM word ook ten opsigte van ’n werklike Suid-Afrikaanse WVN-ontwerp gevallestudie in Gauteng toegepas. Hierdie toepassing lei na die besparing van miljoene rande asook noe- menswaardige verbeterings in terme van netwerkbetroubaarheid in vergeleke met ’n aanvanklike ingenieursontwerp deur ’n konsultasiefirma.

The aim of this thesis is to develop a holistic approach to the optimal design of energy systems for building clusters or districts. The emerging Albano university campus, which is planned to be a vivid example of sustainable urban development, is used as a case study through collaboration with the property owners, Akademiska Hus and Svenska Bostäder. The design addresses aspects of energy performance, environmental performance, economic performance, and exergy performance of the energy system. A multi-objective optimization approach is applied to minimize objectives such as non-renewable primary energy consumptions, the greenhouse gas emissions, the life cycle cost, and the net exergy deficit. These objectives reflect both practical requirements and research interest. The optimization results are presented in the form of Pareto fronts, through which decision-makers can understand the options and limitations more clearly and ultimately make better and more informed decisions. Sensitivity analyses show that solutions could be sensitive to certain system parameters. To overcome this, a robust design optimization method is also developed and employed to find robust optimal solutions, which are less sensitive to the variation of system parameters. The influence of different preferences for objectives on the selection of optimal solutions is examined. Energy components of the selected solutions under different preference scenarios are analyzed, which illustrates the advantages and disadvantages of certain energy conversion technologies in the pursuit of various objectives. As optimal solutions depend on the system parameters, a parametric analysis is also conducted to investigate how the composition of optimal solutions varies to the changes of certain parameters. In virtue of the Rational Exergy Management Model (REMM), the planned buildings on the Albano campus are further compared to the existing buildings on KTH campus, based on energy and exergy analysis. Four proposed alternative energy supply scenarios as well as the present case are analyzed. REMM shows that the proposed scenarios have better levels of match between supply and demand of exergy and result in lower avoidable CO2 emissions, which promise cleaner energy structures.

QC 20160923

The naval ship concept design process often embraces novel concepts and technologies that carry with them an inherent risk of failure simply because their application is the first of its kind. Failure is recognized by gaps between actual and required measures of performance, exceeded budgets, and late deliveries. These risks can be defined and quantified as the product of the probability of an occurrence of failure and a measure of the consequence of that failure. Since the objective of engineering is to design and build things to meet requirements, within budget, and on schedule the first time, it is important to consider risk, along with cost and performance, in trade assessments and technology selections made during concept design. To this end, this thesis presents a simplified metric and methodology for measuring the risk of ship design concepts as part of a Multi-Objective Optimization tool for naval ship concept design. The purpose of this tool is to provide a consistent format and methodology for multi-objective decisions based on dissimilar objective attributes, specifically effectiveness, cost and risk. This approach provides a more efficient and robust method to search the design space for optimal concepts than the traditional â ad hocâ naval ship concept design process where selection and assessment are often based on experience, design lanes, rules-of-thumb and Imagineering. This thesis begins with the results of a literature and information search that investigates and describes risk, engineering systems safety, and state of the art risk analysis techniques currently in practice. Based on this background, a simplified metric and methodology is developed to calculate, quantify, and compare relative overall risk in a naval ship design optimization. To demonstrate this method, a naval ship risk register is developed for a notional ship design. This register identifies potential cost, performance, and schedule risk issues. Risk item descriptions are further defined as a function of the design parameters (DPs) considered for the notional ship. Risk Factors (RF) are calculated for each risk item based on the DP selection. Each RF is the product of a Probability of Failure Occurrence (PF) and Potential Consequence of Failure (CF). An Overall Measure of Risk (OMOR) function is developed to measure the level of overall risk for a single concept design based on DP selections. A ship design case study is performed incorporating the OMOR function and risk items into a ship synthesis model capable of calculating cost, performance, and effectiveness. This case study uses a Multi-Objective Genetic Optimization (MOGO) to identify and define a series of non-dominated cost-effectiveness frontiers for a range of risk (OMOR) values. This new method for ship design optimization provides a novel approach and consistent format for multi-objective decision-making based on three dissimilar objective attributes: effectiveness, cost, and risk.
Master of Science

Missions concerning small-body celestial objects are of growing interest due to the resources and information they can provide. Such missions require detailed information about the surface of the body for interactions, such as landing on the surface, as well as predicting the gravity field of the object. This work provides a means of optimizing the mission elements of trajectory and imaging target schedules so that the level of knowledge of the surface can be increased. The information required to increase one's knowledge of the surface is described as a set of conditions placed on the collection of images taken of each facet of the surface; these requirements constitute the concept of "coverage" and were provided by NASA. Currently, no comparable optimization capability exists. The trajectory optimization task is done using an adapted form of the Non-dominated Sorting Genetic Algorithm-2 (NSGA-2) in which the genetic mutation and recombination operators are replaced with operators inspired by a different Evolutionary Algorithm, Differential Evolution. Since small-body objects have irregular distributions of mass, this optimization accounts for this by using a higher fidelity gravity model; the expense of the calculation causing a significant increase in fitness evaluation time. The trajectory optimization uses the maximization of possible coverage (the coverage achieved is every surface element were targeted for imaging at every opportunity) and minimization of a time quantity that typifies covering less but doing so quickly as the primary optimization objectives with an additional ancillary objective which rewards the fulfillment of the individual aspects of coverage so as to better condition improvement in the first objective. The optimization of imaging schedules is handled using a less adapted version of NSGA-2 in which the base operations were only tailored slightly. This differs from the previous task in that limitation are placed on the imaging process; namely that the camera may only target a single surface element at each opportunity and is thus only able to observe the faces caught within the narrow field-of-view. This optimization trades the minimization of time objective and the ancillary objective for the minimization of the required rotational effort of the imaging spacecraft. Both works result in sets of solutions to their respective problems that capture the trade-space between the considered objectives. The last work detailed here examines the consequences of how velocity domains are phrased in space trajectory optimization problems. Multiple means of framing the optimization domain are examined and the results detail the complications encountered by the more common formulations for a set of test problems.

Conceptual design optimization (CDO) is a technique proposed for the structured evaluation of different design concepts. Design grammars provide a flexible modular modelling architecture. The model is generated by a compiler based on predefined components and rules. The rules describe the composition of the model; thus, different models can be optimized by the CDO in one run. This allows considering a mission design including the mission analysis and the system design. The combination of a CDO approach with a model based on design grammars is shown for the concept study of a near-Earth asteroid mission. The mission objective is to investigate two asteroids of different kinds. The CDO reveals that a mission concept using two identical spacecrafts flying to one target each is better than a mission concept with one spacecraft flying to two asteroids consecutively.

Multiple Cooling Multi Objective Simulated Annealing algorithm has been combined with a conceptual design code written by the author to carry out a multi objective design optimization of an Agricultural Aerial Robot. Both the single and the multi objective optimization problems are solved. The performance figures of merits for different aircraft configurations are compared. In this thesis the potential of optimization as a powerful design tool to the aerospace problems is demonstrated.

The objective of this research is to develop a flexible design grammar and genetic algorithm representation to be used in a multi-objective optimization method to design efficient steel roof trusses given space dimensions and loading requirements by the user. The goal of implementing the method as a multi-objective problem is to obtain a set of near-optimal trusses for the defined unstructured problem domain, not just a single near-optimal design. The method developed was required to support the exploration of a broad range of conceptual designs before making design decisions. Therefore, a method was developed that could define numerous design variables, support techniques to locate global or near-global optimal designs, and improve the efficiency of the computational procedures implemented. This research effort was motivated by the need to consider structural designs that may be beyond the established conventions of designers in the search for cost-efficient, structurally-sound designs. An effective design grammar that is capable of generating stable trusses is defined in this research. The design grammar supports the optimization of member size, in addition to truss geometry and topology. Multi-objective genetic algorithms were used to evolve sets of Pareto-optimal trusses that had varying topology, geometry, and member sizes. The Pareto-optimal curves provided design engineers with a range of near-optimal design alternatives that showed the tradeoffs that occur in meeting the stated objectives. Designers can select their final design from this set based on their own individual weighting of the design objectives. Trials are performed using a multiobjective genetic algorithm that works with the design grammar to evolve trusses for different span lengths. In addition to evaluate the performance of the developed optimization method further, trials were performed on a benchmark truss problem domain and the results obtained were compared with results obtained by other researchers. The results of the performance evaluation trials for the proposed method, in which the sizing, shape and topology were simultaneously performed, indicated that the method was effective in evolving a variety of truss topologies compared to previous published results, which evolved from a ground structure. The diverse topologies, however, were obtained over several trials instead of being found in a Pareto-optimal set found by a single trial. In addition, the proposed method was not able to locally optimize the member section sizes. Additional trials were performed to determine the benefit of applying local optimization to the member section sizes for a given truss topology or geometry provided by the method. The results indicate that significant weight reduction could be achieved by performing local optimization to the truss designs obtained by the proposed multi-objective optimization method.

Approved for public release; distribution is unlimited.
The aim of this work is to explore the applicability and usability of multi-objective optimization into various aspects of the design of an autonomous underwater vehicle (AUV). First, I begin with an introduction of the systems engineering design process and the background work for the multi-objective optimization process. Furthermore, I investigate and analyze the existing multi-objective optimization methods in decision making. I focus on various design aspects of an AUV such as the hull design, the weight distribution, the propulsion and, especially, the power supply technology. The objectives I used in the model are the minimization of the power needed to propel the vehicle and the maximization of both the weight of the energy section and the total range. Implementation of both the model and the optimization are carried out using Matlab, particularly the global optimization toolbox and the multi-objective genetic algorithm solver, whereas a special case of two objectives is implemented in Excel using Visual Basic and Excel solver. This research also explores the potential for a designer to use goals in the multi-objective optimization as well as approaches that let a designer choose one particular solution once all Pareto optimal solutions are found.

An approach is proposed in this work to support the preliminary design of High-Lift aircraft configurations through the use of Multi-Objective optimisation tech¬niques. For this purpose a framework is developed which collates a Free-Form De¬formation parametrisation technique, a number of Computational Fluid Dynamics suites of different fidelity levels, a rapid aero-structure coupling procedure and two multi-objective optimisation techniques, namely Multi-Objective Tabu Search and Non-dominated Sorting Genetic Algorithm-II. The proposed optimisation framework is used for the execution of several design studies. Firstly, the deployment settings and elements' shape of the 2D multi-element GARTEUR A310 test case are optimised for take-off conditions. Consider¬able performance improvements are achieved using both the optimisation algorithms, though the sensitivity of the optimum designs to changes in operating conditions is highlighted. Therefore, a new optimisation set-up is proposed which successfully identifies operational robust designs. Secondly, the framework is extended to the optimisation of 3D geometries, using a Quasi-three-dimensional approach for the evaluation of the aerodynamic performance. The application to the deployment settings optimisation of the (DLF F11) KH3Y configuration illustrates that the method can be applied to more complicated real-world design cases. In particular, the deployment settings of slat and flaps (inboard and outboard segments) are suc¬cessfully optimised for landing conditions. Finally, a rapid aero-structure coupling procedure is implemented, in order to perform static aero-elastic analysis within the optimisation process. The KH3Y optimisation study is repeated including, this time, the effects of structural deformations. Different optima deployment settings are identified compared to the rigid case, illustrating that, despite being of reduced magnitude, wing deformations influence the optimum high-lift system settings. Furthermore, an industrial development and application of multi-objective opti-misation techniques is also presented. In the proposed approach, a reduced order model based on Proper Orthogonal Decomposition methods is used in an offline-online optimisation strategy. The results of the optimisation process for the RAE2822 single-element aerofoil and for the GARTEUR A310 multi-element aerofoil illustrate the potential of the method, as well as its limitations. The technical analysis is com-pleted with a description of the Agile project management approach used to run the project. Finally, future work directions have been identified and recommended.

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2006.
Includes bibliographical references (leaves 56-57).
Multi-disciplinary System Design Optimization was used to design the geometry and to select the materials for the structural facade of a building. A multi-objective optimization model was developed, capable of optimizing the design of the facade on the basis of a lighting analysis of the interior, of a thermal analysis of the cooling loads corresponding to the skin configuration, and of a finite elements analysis of the supporting structure. The system also considers the need for transparency in the facade due to view requirements of the occupants, and the cost of cladding materials. A scalarization approach to MDO, via utility functions, was chosen, and the overall objective function was optimized using Genetic Algorithms.
by Riccardo Merello.
M.Eng.

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, June 2016.
Cataloged from PDF version of thesis. "June 2016."
Includes bibliographical references (pages 51-52).
This thesis explores the benefits and tradeoffs of two significant constructability considerations in structural steel floor framing design. A new constructability strategy combining both standardization and steel availability is proposed, providing a clear, quantitative methodology for constructability integration. This strategy can be easily incorporated into various projects and software implementations to be used in structural engineering design practices. Analysis of this methodology also indicates that structural weight tradeoffs remain fairly insignificant, allowing standardization down to approximately 20-30% of the initial number of unique sections with less than a 20% increase in structural weight. Thus, this thesis establishes a new multi-objective approach to steel floor framing design and promotes a better understanding of buildability integration for more efficient and economical structural design solutions. Keywords: constructability, buildability, standardization, availability, structural steel floor framing, culling, multi-objective design
by Melody Tan.
M. Eng.

Sustainable approaches have been extensively proposed in product, process and system levels. However, a lack of applicable solutions for these methods is identified in the existing research. This research considers uncertainties affecting sustainable systems and comprehensively discusses the need for the optimal design in product and system levels under uncertainty. Based on the economic, social and environmental requirements of a sustainable product, and uncertainties in engineering systems, two innovative methods are proposed. The methods, including agent-based modeling (ABM) and Big Data, quantify effects of users’ preference changes as a significant uncertainty source in a product design process. The effect of quantified uncertainties on the product sustainability is then evaluated, and solutions to reduce the effects are developed. Through a novel control engineering method, uncertainties are modeled in the design process of a product. Using two mathematical models, the cost and environmental impacts in the design process are minimized under users’ preference changes. The models search for an optimal number of iterations in the design process to achieve a sustainable solution. The methods have been extended to model and optimize the sustainable system design under uncertainties. Design of Eco-Industrial Parks (EIPs) is a practical and scientific solution to achieve sustainable industries. To improve the feasibility of flow exchanges between industries in an EIP under several uncertainties, this research provides a perspective analysis for establishing flow exchanges between industries. The sources of uncertainties in the EIPs are then comprehensively studied, and research gaps are highlighted. Finally, models to optimize flow exchanges between industries are presented and the validity of models is evaluated using real data. A major is including all sustainability pillars in the proposed approach. The research addresses users’ preferences to highlight the role of individuals in the society. Moreover, the economic and environmental objective functions have been considered for optimal decision making in the design process. This research underlines the role of uncertainty studies in the sustainable system design. Multiple classifications, perspective analysis, and optimization objectives are presented to help decision makers with the optimal design of sustainable systems under uncertainties.
February 2017

Building construction industry has significant impact on sustainability. The construction, operation and maintenance of buildings account for approximately 50% of global energy usage and anthropogenic greenhouse gas (GHG) emissions. In recent years, the embodied energy and carbon are identified increasingly important in terms of sustainability throughout building life cycle. Incorporation of sustainable development in building structural design becomes undoubtedly crucial. The effective building design requires smart and holistic tools that can process multi-objective and inter-connected domain knowledge to provide genuine sustainable buildings. With the advancement of information and communication technologies, various methods and techniques have been applied to accomplish the multiple objectives of sustainable development in building design. One of the most successful approaches is building information modelling (BIM), which requires further enhancement of interoperability. The emergence of Semantic Web technology provides more opportunity to improve the information modelling, knowledge management and system integration. The research presented in this thesis investigates how ontology and Semantic Web rules can be used in a knowledge-based holistic system, in order to integrate information about structural design and sustainability, and facilitate decision-making in design process by recommending appropriate solutions for different use cases. A research prototype namely OntoSCS incorporating OWL ontology and SWRL rules has been developed and tested in typical structural design cases. The holistic approach considers five inter-connected dimensions of sustainability, including structural feasibility, embodied energy and carbon, cost, durability and safety. In addition, the selection of structural material supplier and criteria in sustainability assessment are taken into account as well. This research concludes that the Semantic Web technology can be applied to structural design at early stage to provide multi-criteria optimised solution. The methodology and framework employed in this study can be further adapted as a generic multi-criteria and holistic decision support system for other domains in construction sector.

During approach and landing, the HL-20 follows a typical reusable launch vehicle (RLV) autoland trajectory: deep descent, followed by a parabolic flare, and final descent. The trajectory shape is determined by six independent parameters. An artificial neural network (ANN) is designed to generate the trajectory parameters for the HL-20 based on desired objectives using MATLAB®’s Neural Network Toolbox. This research examines three mission objectives: specifying flight time, specifying the final downrange position error, and specifying the average error between the desired angle of attack and actual angle of attack. The ANN successfully produces parameters that meet mission objectives and, in some cases, improve upon nominal errors. It is also demonstrated that the ANN structure and ANN training vectors have a profound impact on the success of the neural network.

Mask Projection Stereolithography (MPSLA) is a high resolution manufacturing process that builds parts layer by layer in a photopolymer. In this research, a process planning method to fabricate MPSLA parts with constraints on dimensions, surface finish and build time is formulated. As a part of this dissertation, a MPSLA system is designed and assembled. The irradiance incident on the resin surface when a given bitmap is imaged onto it is modeled as the Irradiance model . This model is used to formulate the Bitmap generation method which generates the bitmap to be imaged onto the resin in order to cure the required layer. Print-through errors occur in multi-layered builds because of radiation penetrating beyond the intended thickness of a layer, causing unwanted curing. In this research, the print through errors are modeled in terms of the process parameters used to build a multi layered part. To this effect, the Transient layer cure model is formulated, that models the curing of a layer as a transient phenomenon, in which, the rate of radiation attenuation changes continuously during exposure. In addition, the effect of diffusion of radicals and oxygen on the cure depth when discrete exposure doses, as opposed to a single continuous exposure dose, are used to cure layers is quantified. The print through model is used to formulate a process planning method to cure multi-layered parts with accurate vertical dimensions. This method is demonstrated by building a test part on the MPSLA system realized as a part of this research. A method to improve the surface finish of down facing surfaces by modulating the exposure supplied at the edges of layers cured is formulated and demonstrated on a test part. The models formulated and validated in this dissertation are used to formulate a process planning method to build MPSLA parts with constraints on dimensions, surface finish and build time. The process planning method is demonstrated by means of a case study.

Power converter has played a very important role in modern electric power systems. The control of power converters is necessary to achieve high performance. In this study, a dc-dc buck converter is studied. The parameters of a notional proportional-integral controller are to be selected. Genetic algorithms (GAs), which have been widely used to solve multi-objective optimization problems, is used in order to locate appropriate controller design. The control metrics are specified as phase margin in frequency domain and voltage error in time-domain. GAs presented the optimal tradeoffs between these two objectives. Three candidate control designs are studied in simulation and experimentally. There is some agreement between the experimental results and the simulation results, but there are also some discrepancies due to model error. Overall, the use of multi-domain, multi-objective-optimization-based approach has proven feasible.

Motivated by an electric power portfolio selection problem, a sampling method is developed for simulation-based robust design that builds on existing multi-objective statistical improvement methods. It uses a Bayesian surrogate model regressed on both design and noise variables, and makes use of methods for estimating epistemic model uncertainty in environmental uncertainty metrics. Regions of the design space are sequentially sampled in a manner that balances exploration of unknown designs and exploitation of designs thought to be Pareto optimal, while regions of the noise space are sampled to improve knowledge of the environmental uncertainty. A scalable test problem is used to compare the method with design of experiments (DoE) and crossed array methods, and the method is found to be more efficient for restrictive sample budgets. Experiments with the same test problem are used to study the sensitivity of the methods to numbers of design and noise variables. Lastly, the method is demonstrated on an electric power portfolio simulation code.