Cyber-Physical Systems (CPS) are widely deployed in critical applications which are subject to strict timing constraints. To ensure correct timing behavior, much of the effort has been dedicated to the development of validation and verification methods for CPS (e.g., system models and their timing and schedulability analysis). As CPS is becoming increasingly complex, there is an urgent need for efficient optimization techniques that can aid the design of large-scale systems. Specifically, techniques that can find good design options in a reasonable amount of time while meeting all the timing and other critical requirements are becoming vital. However, the current mindset is to use existing schedulability analysis and optimization techniques for the design optimization of time-critical CPS. This has resulted in two issues in today's CPS design: 1) Existing timing and schedulability analysis are very difficult and inefficient to be integrated into well-established optimization frameworks such as mathematical programming; 2) New system models and timing analysis are being developed in a way that is increasingly unfriendly to optimization. Due to these difficulties, existing practice for optimization mostly relies on meta or ad-hoc heuristics, which suffers either from sub-optimality or limited applicability. In this dissertation, we seek to address these issues and explore two new directions for developing optimization algorithms for time-critical CPS. The first is to develop {em optimization-oriented timing analysis}, that are efficient to formulate in mathematical programming framework. The second is a domain-specific optimization framework. The framework leverages domain-specific knowledge to provide methods that abstract timing analysis into a simple mathematical form. This allows to efficiently handle the complexity of timing analysis in optimization algorithms. The results on a number of case studies show that the proposed approaches have the potential to significantly improve upon scalability (several orders of magnitude faster) and solution quality, while being applicable to various system models, timing analysis techniques, and design optimization problems in time-critical CPS. / Doctor of Philosophy / Cyber-Physical Systems (CPS) tightly intertwine computing units and physical plants to accomplish complex tasks such as control and monitoring. They are often deployed in critical applications subject to strict timing constraints. For example, many control applications and tasks are required to finished within bounded latencies. To guarantee such timing correctness, much of the effort has been dedicated to studying methods for delay and latency estimation. These techniques are known as schedulability analysis/timing analysis. As CPS becomes increasingly complex, there is an urgent need for efficient optimization techniques that can aid the design of large-scale and correct CPS. Specifically, techniques that can find good design options in reasonable amount of time while meeting all the timing and other critical requirements are becoming vital. However, most of the existing schedulability analysis are either non-linear, non-convex, non-continuous or without closed form. This gives significant challenge for integrating these analysis into optimization. In this dissertation, we explore two new paradigm-shifting approaches for developing optimization algorithms for the design of CPS. Experimental evaluations on both synthetic and industrial case studies show that the new approaches significantly improve upon existing optimization techniques in terms of scalability and quality of solution.

Total knee replacement is the gold standard treatment for restoring mobility and relieving pain associated with osteoarthritis when other medical therapy has failed. Revision surgery is necessary when the replaced knee fails, which is often a result of implant damage (such as wear) or poor kinematics. Design optimization is a method for finding the best shape for a component using an optimization approach considering one or multiple performance metrics. The shape of a parametric candidate design can be manipulated by an optimization algorithm, which seeks to minimize an objective function subject to performance constraints and design space limitations. During multiobjective design optimization, multiple performance measures are minimized simultaneously, the relative importance of each determined using a weighted sum. This approach can also be used to derive a Pareto curve or frontier which graphically describes the relationships (or trade-offs) between the performance measures. It was hypothesized that a trade-off exists between wear and kinematics performance in total knee replacements. The objective of this research was to test this hypothesis by using multiobjective design optimization to describe this relationship with a Pareto curve. It was first necessary to develop and validate numerical frameworks for wear and kinematics simulations, using models constructed using a parametric modeller. The Pareto curve was then generated using a combination of single objective and multiobjective design optimizations considering these two performance measures. Single objective optimization for wear yielded a theoretical design with superior wear resistance when compared to a typical commercially available knee design. Single objective optimization for kinematics yielded a theoretical design capable of higher flexion, as well as more natural laxity characteristics. After performing multiobjective design optimization, the resulting Pareto curve showed that there is, in fact, a trade-off between wear and kinematics performance. When considering optimum designs, in order to improve the wear performance it was necessary to sacrifice kinematics performance, and vice-versa. This previously suspected but never verified nor quantified relationship can be used to improve total knee replacement designs, as well as help healthcare providers select the best implants for their patients. / Thesis (Ph.D, Mechanical and Materials Engineering) -- Queen's University, 2010-04-14 13:43:42.639

京都大学 / 新制・課程博士 / 博士(工学) / 甲第24575号 / 工博第5081号 / 新制||工||1973(附属図書館) / 京都大学大学院工学研究科建築学専攻 / (主査)教授 大崎 純, 教授 池田 芳樹, 准教授 藤田 皓平 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Constant-force mechanisms are mechanical devices that provide a near-constant output force over a prescribed deflection range. This thesis develops various optimization-based methods for designing robust constant-force mechanisms. The configuration of the mechanisms that are the focus of this research comprises a cam and a compliant spring fixed at one end while making contact with the cam at the other end. This configuration has proven to be an innovative solution in several applications because of its simplicity in manufacturing and operation. In this work, several methods are introduced to design these mechanisms, and reduce the sensitivity of these mechanisms to manufacturing uncertainties and frictional effects. The mechanism's sensitivity to these factors is critical in small scale applications where manufacturing variations can be large relative to overall dimensions, and frictional forces can be large relative to the output force. The methods in this work are demonstrated on a small scale electrical contact on the order of millimeters in size. The method identifies a design whose output force is 98.20% constant over its operational deflection range. When this design is analyzed using a Monte Carlo simulation the standard deviation in constant force performance is 0.76%. When compared to a benchmark design from earlier research, this represents a 34% increase in constant-force performance, and a reduction from 1.68% in the standard deviation of performance. When this new optimal design is evaluated to reduce frictional effects a design is identifed that shows a 36% reduction in frictional energy loss while giving up, however, 18.63% in constant force.

Automotive companies are exposed to tough competition and therefore strive to design better products in a cheaper and faster manner. This challenge requires continuous improvements of methods and tools, and simulation models are therefore used to evaluate every possible aspect of the product. Optimization has become increasingly popular, but its full potential is not yet utilized. The increased demand for accurate simulation results has led to detailed simulation models that often are computationally expensive to evaluate. Metamodel-based design optimization (MBDO) is an attractive approach to relieve the computational burden during optimization studies. Metamodels are approximations of the detailed simulation models that take little time to evaluate and they are therefore especially attractive when many evaluations are needed, as e.g. in multidisciplinary design optimization (MDO). In this thesis, state-of-the-art methods for metamodel-based design optimization are covered and different multidisciplinary design optimization methods are presented. An efficient MDO process for large-scale automotive structural applications is developed where aspects related to its implementation is considered. The process is described and demonstrated in a simple application example. It is found that the process is efficient, flexible, and suitable for common structural MDO applications within the automotive industry. Furthermore, it fits easily into an existing organization and product development process and improved designs can be obtained even when using metamodels with limited accuracy. It is therefore concluded that by incorporating the described metamodel-based MDO process into the product development, there is a potential for designing better products in a shorter time.

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The device optimization is a very important element in semiconductor technology advancement. Its objective is to find a design point for a semiconductor device so that the optimized design goal meets all specified constraints. As in other engineering fields, a nonlinear optimizer is often used for design optimization. One major drawback of using a nonlinear optimizer is that it can only partially explore the design space and return a local optimal solution. This dissertation provides an adaptive optimization design methodology to allow the designer to explore the design space and obtain a globally optimal solution. One key element of our method is to quickly compute the set of all feasible solutions, also called the acceptability region. We described a polytope-based representation for the acceptability region and an adaptive linearization technique for device performance model approximation. These efficiency enhancements have enabled significant speed-up in estimating acceptability regions and allow acceptability regions to be estimated for a larger class of device design tasks. Our linearization technique also provides an efficient mechanism to guarantee the global accuracy of the computed acceptability region. To visualize the acceptability region, we study the orthogonal projection of high-dimensional convex polytopes and propose an output sensitive algorithm for projecting polytopes into two dimensions.

L’optimisation de formes de structures mécaniques est de nos jours incontournable dans l’industrie mécanique (automobile, aéronautique etc.). Pour rester dans la compétition mondiale, les entreprises se doivent de concevoir des structures qui, en plus de respecter des performances mécaniques précises, doivent être moins couteuses avec des délais de plus en plus courts. Les ingénieurs doivent alors réaliser des formes pour leurs structures qui soient un meilleur compromis entre les performances mécaniques et fonctionnelles, le poids, le coût de fabrication etc. Dans ce manuscrit, nous proposons une démarche d’intégration de l’optimisation de formes de structures dans un processus de conception fonctionnelle d'un point de vue méthodologique. Elle a pour avantages de prendre en compte l’intégration des connaissances métiers nécessaires à la conception de la structure dans le processus d’optimisation. Ce processus ne peut être appliqué de manière efficace que si l’on maitrise les fondamentaux de l’optimisation, ce qui nous a conduit dans un premier temps à faire un point sur l’état de l’art en optimisation de formes en général, en présentant les algorithmes mis en jeux dans les différentes disciplines à disposition des ingénieurs. Notre proposition de processus d’optimisation a été complétée par des arbres de décision, permettant à l’ingénieur de faire des choix en fonction des problèmes d’optimisation dans les métiers de la fonderie, de la plasturgie et de l’emboutissage. Ce processus d’optimisation intégré à une démarche de conception fonctionnelle sera illustré par des exemples industriels nous permettant de valider notre proposition et de montrer l’efficacité des choix issus des arbres de décision. / Engineering design optimization of mechanical structures is nowadays essential in the mechanical industry (automotive, aeronautics etc.). To remain competitive in the globalized world, companies need to create and design structures that in addition to complying specific mechanical performance should be less expensive with short production time. Engineers must then realize forms and shapes that are a better compromise, between mechanical and functional performance, weight, manufacturing costs etc. In this manuscript, we propose an integration of optimization process in a functional process design of a methodological point of view. It has the advantage of taking into account the integration of business knowledge needed to design the structures in the optimization process. The process can’t be properly applied only if we master the fundamentals of the optimization. This led us in a first time, to talk about the engineering design optimization of mechanical structures in general, and the algorithms used in the different disciplines available to engineers. Our proposed optimization process was completed by decision trees that allowed the engineers to make choices based on their optimization problems in foundry, plastics and stamping companies. This optimization process integrated with a functional design approach will be illustrated with industrial examples that allow us to validate our proposal and demonstrate the effectiveness of the choices from decision trees.

Genetic Algorithms (GAs) are powerful tools to solve large scale design optimization problems. The research interests in GAs lie in both its theory and application. On one hand, various modifications have been made on early GAs to allow them to solve problems faster, more accurately and more reliably. On the other hand, GA is used to solve complicated design optimization problems in different applications. The study in this thesis is both theoretical and applied in nature. On the theoretical side, an improved GA�Evolution Direction Guided GA (EDG-GA) is proposed based on the analysis of Schema Theory and Building Block Hypothesis. In addition, a method is developed to study the structure of GA solution space by characterizing interactions between genes. This method is further used to determine crossover points for selective crossover. On the application side, GA is applied to generate optimal tolerance assignment plans for a series of manufacturing processes. It is shown that the optimal tolerance assignment plan achieved by GA is better than that achieved by other optimization methods such as sensitivity analysis, given comparable computation time.

Requirement verification and validation is a critical component of building and delivering space vehicles with testing as the preferred method. This Master’s Project presents the space vehicle test process from planning through test design and execution. It starts with an overview of the requirements, validation, and verification. The four different verification methods are explained including examples as to what can go wrong if the verification is done incorrectly. Since the focus of this project is on test, test verification is emphasized. The philosophy behind testing, including the “why” and the methods, is presented. The different levels of testing, the test objectives, and the typical tests are discussed in detail. Descriptions of the different types of tests are provided including configurations and test challenges. While most individuals focus on hardware only, software is an integral part of any space product. As such, software testing, including mistakes and examples, is also presented. Since testing is often not performed flawlessly the first time, sections on anomalies, including determining root cause, corrective action, and retest is included. A brief discussion of defect detection in test is presented. The project is actually presented in total in the Appendix as a Power Point document.