The US Navy is shifting to power projection from the sea which stresses the capabilities of its current fleet and exposes a need for a new surface connector. The design of complex systems in the presence of changing requirements, rapidly evolving technologies, and operational uncertainty continues to be a challenge. Furthermore, the design of future naval platforms must take into account the interoperability of a variety of heterogeneous systems and their role in a larger system-of-systems context. To date, methodologies to address these complex interactions and optimize the system at the macro-level have lacked a clear direction and structure and have largely been conducted in an ad-hoc fashion. Traditional optimization has centered around individual vehicles with little regard for the impact on the overall system. A key enabler in designing a future connector is the ability to rapidly analyze technologies and perform trade studies using a system-of-systems level approach. The objective of this work is a process that can quantitatively assess the impacts of new capabilities and vessels at the systems-of-systems level. This new methodology must be able to investigate diverse, disruptive technologies acting on multiple elements within the system-of-systems architecture. Illustrated through a test case for a Medium Exploratory Connector (MEC), the method must be capable of capturing the complex interactions between elements and the architecture and must be able to assess the impacts of new systems). Following a review of current methods, six gaps were identified, including the need to break the problem into subproblems in order to incorporate a heterogeneous, interacting fleet, dynamic loading, and dynamic routing. For the robust selection of design requirements, analysis must be performed across multiple scenarios, which requires the method to include parametric scenario definition. The identified gaps are investigated and methods recommended to address these gaps to enable overall operational analysis across scenarios. Scenarios are fully defined by a scheduled set of demands, distances between locations, and physical characteristics that can be treated as input variables. Introducing matrix manipulation into discrete event simulations enables the abstraction of sub-processes at an object level and reduces the effort required to integrate new assets. Incorporating these linear algebra principles enables resource management for individual elements and abstraction of decision processes. Although the run time is slightly greater than traditional if-then formulations, the gain in data handling abilities enables the abstraction of loading and routing algorithms. The loading and routing problems are abstracted and solution options are developed and compared. Realistic loading of vessels and other assets is needed to capture the cargo delivery capability of the modeled mission. The dynamic loading algorithm is based on the traditional knapsack formulation where a linear program is formulated using the lift and area of the connector as constraints. The schedule of demands from the scenarios represents additional constraints and the reward equation. Cargo available is distributed between cargo sources thus an assignment problem formulation is added to the linear program, requiring the cargo selected to load on a single connector to be available from a single load point. Dynamic routing allows a reconfigurable supply chain to maintain a robust and flexible operation in response to changing customer demands and operating environment. Algorithms based on vehicle routing and computer packet routing are compared across five operational scenarios, testing the algorithms ability to route connectors without introducing additional wait time. Predicting the wait times of interfaces based on connectors en route and incorporating reconsideration of interface to use upon arrival performed consistently, especially when stochastic load times are introduced, is expandable to a large scale application. This algorithm selects the quickest load-unload location pairing based on the connectors routed to those locations and the interfaces selected for those connectors. A future connector could have the ability to unload at multiple locations if a single load exceeds the demand at an unload location. The capability for multiple unload locations is considered a special case in the calculation of the unload location in the routing. To determine the unload location to visit, a traveling salesman formulation is added to the dynamic loading algorithm. Using the cost to travel and unload at locations balanced against the additional cargo that could be delivered, the order and locations to visit are selected. Predicting the workload at load and unload locations to route vessels with reconsideration to handle disturbances can include multiple unload locations and creates a robust and flexible routing algorithm. The incorporation of matrix manipulation, dynamic loading, and dynamic routing enables the robust investigation of the design requirements for a new connector. The robust process will use shortfall, capturing the delay and lack of cargo delivered, and fuel usage as measures of performance. The design parameters for the MEC, including the number available and vessel characteristics such as speed and size were analyzed across four ways of testing the noise space. The four testing methods are: a single scenario, a selected number of scenarios, full coverage of the noise space, and feasible noise space. The feasible noise space is defined using uncertainty around scenarios of interest. The number available, maximum lift, maximum area, and SES speed were consistently design drivers. There was a trade-off in the number available and size along with speed. When looking at the feasible space, the relationship between size and number available was strong enough to reverse the number available, to desiring fewer and larger ships. The secondary design impacts come from factors that directly impacted the time per trip, such as the time between repairs and time to repair. As the noise sampling moved from four scenario to full coverage to feasible space, the option to use interfaces were replaced with the time to load at these locations and the time to unload at the beach gained importance. The change in impact can be attributed to the reduction in the number of needed trips with the feasible space. The four scenarios had higher average demand than the feasible space sampling, leading to loading options being more important. The selection of the noise sampling had an impact of the design requirements selected for the MEC, indicating the importance of developing a method to investigate the future Naval assets across multiple scenarios at a system-of-systems level.

<p>This thesis concerns optimization of structures considering various uncertainties. The overall objective is to find methods to create solutions that are optimal both in the sense of handling the typical load case and minimising the variability of the response, i.e. robust optimal designs.</p><p>Traditionally optimized structures may show a tendency of being sensitive to small perturbations in the design or loading conditions, which of course are inevitable. To create robust designs, it is necessary to account for all conceivable variations (or at least the influencing ones) in the design process.</p><p>The thesis is divided in two parts. The first part serves as a theoretical background to the second part, the two appended articles. This first part includes the concept of robust design, basic statistics, optimization theory and meta modelling.</p><p>The first appended paper is an application of existing methods on a large industrial example problem. A sensitivity analysis is performed on a Scania truck cab subjected to impact loading in order to identify the most influencing variables on the crash responses.</p><p>The second paper presents a new method that may be used in robust optimizations, that is, optimizations that account for variations and uncertainties. The method is demonstrated on both an analytical example and a Finite Element example of an aluminium extrusion subjected to axial crushing.</p> / ROBDES

This thesis focuses on the design of field experiments with blocks to study treatment effects for a number of treatments. Small field plots are available but located in several blocks and each plot is assigned to a treatment in the experiment. Due to spatial correlation among the plots, the allocation of the treatments to plots has influence on the analysis of the treatment effects. When the spatial correlation is known, optimal allocations (designs) of the treatments to plots have been studied in the literature. However, the spatial correlation is usually unknown in practice, so we propose a robust criterion to study optimal designs of the treatments to plots. Neighbourhoods of correlation structures are introduced and a modified generalized least squares estimator is discussed. A simulated annealing algorithm is implemented to compute optimal/robust designs. Various results are obtained for different experimental settings. Some theoretical results are also proved in the thesis. / Graduate

In this thesis, it is aimed to design lithium extraction from boron clays using statistical design of experiments and robust design methodologies. There are several factors affecting extraction of lithium from clays. The most important of these factors have been limited to a number of six which have been gypsum to clay ratio, roasting temperature, roasting time, leaching solid to liquid ratio, leaching time and limestone to clay ratio. For every factor, three levels have been chosen and an experiment has been designed. After performing three replications for each of the experimental run, signal to noise ratio transformation, ANOVA, regression analysis and response surface methodology have been applied on the results of the experiments. Optimization and confirmation experiments have been made sequentially to find factor settings that maximize lithium extraction with minimal variation. The mean of the maximum extraction has been observed as 83.81% with a standard deviation of 4.89 and the 95% prediction interval for the mean extraction is (73.729, 94.730). This result is in agreement with the studies that have been made in the literature. However / this study is unique in the sense that lithium is extracted from boron clays by using limestone directly from the nature, and gypsum as a waste product of boric acid production. Since these two materials add about 20% cost to the extraction process, the results of this study become important. Moreover, in this study it has been shown that statistical design of experiments help mining industry to reduce the need for standardization.

The design exploration method for adaptive design systems is developed to facilitate the pursuit of a balance between the efficiency and accuracy in systems engineering design. The proposed method is modified from an existing multiscale material robust design method, the Inductive Design Exploration Method (IDEM). The IDEM is effective in managing uncertainty propagation in the model chain. However, it is not an appropriate method in other systems engineering design outside of original design domain due to its high computational cost. In this thesis, the IDEM is augmented with more efficient solution search methods to improve its capability for efficiently exploring robust design solutions in systems engineering design. The accuracy of the meta-model in engineering design is one uncertainty source. In current engineering design, response surface model is widely used. However, this method is shown as inaccurate in fitting nonlinear models. In this thesis, the local regression method is introduced as an alternative of meta-modeling technique to reduce the computational cost of simulation models. It is proposed as an appropriate method in systems design with nonlinear simulations models. The proposed methods are tested and verified by application to a Multifunctional Energetic Materials design and a Photonic Crystal Coupler and Waveguide design. The methods are demonstrated through the better accuracy of the local regression model in comparison to the response surface model and the better efficiency of the design exploration method for adaptive design systems in comparison to the IDEM. The proposed methods are validated theoretically and empirically through application of the validation square.

The cost of releasing a new drug on the market has increased rapidly in the last decade. The reasons for this increase vary with the drug, but the need to make correct decisions earlier in the drug development process and to maximize the information gained throughout the process is evident. Optimal experimental design (OD) describes the procedure of maximizing relevant information in drug development and drug treatment processes. While various optimization criteria can be considered in OD, the most common is to optimize the unknown model parameters for an upcoming study. To date, OD has mainly been used to optimize the independent variables, e.g. sample times, but it can be used for any design variable in a study. This thesis addresses the OD of multiple continuous or discrete design variables for nonlinear mixed effects models. The methodology for optimizing and the optimization of different types of models with either continuous or discrete data are presented and the benefits of OD for such models are shown. A software tool for optimizing these models in parallel is developed and three OD examples are demonstrated: 1) optimization of an intravenous glucose tolerance test resulting in a reduction in the number of samples by a third, 2) optimization of drug compound screening experiments resulting in the estimation of nonlinear kinetics and 3) an individual dose-finding study for the treatment of children with ciclosporin before kidney transplantation resulting in a reduction in the number of blood samples to ~27% of the original number and an 83% reduction in the study duration. This thesis uses examples and methodology to show that studies in drug development and drug treatment can be optimized using nonlinear mixed effects OD. This provides a tool than can lower the cost and increase the overall efficiency of drug development and drug treatment.

abstract: This dissertation is to address product design optimization including reliability-based design optimization (RBDO) and robust design with epistemic uncertainty. It is divided into four major components as outlined below. Firstly, a comprehensive study of uncertainties is performed, in which sources of uncertainty are listed, categorized and the impacts are discussed. Epistemic uncertainty is of interest, which is due to lack of knowledge and can be reduced by taking more observations. In particular, the strategies to address epistemic uncertainties due to implicit constraint function are discussed. Secondly, a sequential sampling strategy to improve RBDO under implicit constraint function is developed. In modern engineering design, an RBDO task is often performed by a computer simulation program, which can be treated as a black box, as its analytical function is implicit. An efficient sampling strategy on learning the probabilistic constraint function under the design optimization framework is presented. The method is a sequential experimentation around the approximate most probable point (MPP) at each step of optimization process. It is compared with the methods of MPP-based sampling, lifted surrogate function, and non-sequential random sampling. Thirdly, a particle splitting-based reliability analysis approach is developed in design optimization. In reliability analysis, traditional simulation methods such as Monte Carlo simulation may provide accurate results, but are often accompanied with high computational cost. To increase the efficiency, particle splitting is integrated into RBDO. It is an improvement of subset simulation with multiple particles to enhance the diversity and stability of simulation samples. This method is further extended to address problems with multiple probabilistic constraints and compared with the MPP-based methods. Finally, a reliability-based robust design optimization (RBRDO) framework is provided to integrate the consideration of design reliability and design robustness simultaneously. The quality loss objective in robust design, considered together with the production cost in RBDO, are used formulate a multi-objective optimization problem. With the epistemic uncertainty from implicit performance function, the sequential sampling strategy is extended to RBRDO, and a combined metamodel is proposed to tackle both controllable variables and uncontrollable variables. The solution is a Pareto frontier, compared with a single optimal solution in RBDO. / Dissertation/Thesis / Ph.D. Industrial Engineering 2012

Cette thèse concerne le développement d’outils de conception nécessaires à la réalisation de matériaux composites hybrides intégrant des patchs piézoélectriques shuntés électriquement par des circuits à capacité négative. L’impact des incertitudes sur les performances de ces systèmes hybrides innovants est à ce jour inconnu ou mal maîtrisé, ce qui peut compromettre leur fiabilité et nuire à leur applicabilité industrielle. La première contribution du travail de thèse a ainsi porté sur le développement et la caractérisation d’un circuit de shunt numérique adapté à un contrôle adaptatif pour une structure équipée d’un grand nombre de patchs. Les étapes de dimensionnement et de conception électronique du dispositif sont présentées et ont conduit à un prototype qui a montré expérimentalement sa capacité à générer un shunt de type capacité négative. La deuxième contribution du travail de thèse porte sur l’analyse de la robustesse de ces dispositifs en considérant le système dans sa globalité, depuis les paramètres géométriques (dimensions) ou matériaux jusqu’aux paramètres électriques. Une analyse des paramètres les plus influents est proposée et conduit à une mise en évidence des plages d’incertitudes tolérables pour une efficacité donnée. Enfin l’association des dispositifs considérés en un réseau distribué permet d’envisager une meilleure réduction des vibrations ou ondes acoustiques par un accroissement notamment de la largeur de bande fréquentielle dans laquelle le système est efficace. Le circuit numérique développé dans la thèse permet d’envisager cette extension au caractère distribué par sa miniaturisation, son adaptabilité et son intégrabilité. La dernière contribution du travail de thèse porte donc sur des perspectives d’extension du travail développé à un système distribué pour la génération d’une inter face active intégrée à la structure. / This thesis deals with the development of design tools needed for the realization of hybridcomposite materials incorporating piezoelectric patches electrically shunted by negativecapacitance circuits. The impact of uncertainty on the performance of these innovative hybridsystems is yet unknown or poorly controlled, which can compromise their reliability and harmtheir industrial applicability. The first thesis contribution has focused on the development andcharacterization of a digital shunt circuit adapted to an adaptive control for a structureequipped with a large number of patches. The design steps and electronic device design arepresented and led to a prototype that has shown experimentally its ability to implement anegative capacitance shunt. The second contribution of the thesis is the analysis of therobustness of these devices by considering the whole system, from geometric to materialsparameters, including the electrical parameters. An analysis of the most significantparameters is proposed and has highlighted the tolerable uncertainty ranges for a givenefficiency. Finally, the combination of the developed digital devices inside a distributednetwork provides a better reduction of acoustic waves or vibrations by increasing theefficiency bandwidth. The use of the developed digital circuit in such distributed systems hasbeen made possible by its miniaturization, adaptability and integrability. The last contributionof the thesis therefore focuses on prospects in fully integrated active interfaces.

Optimisation is one of the most important and challenging part of any engineering design. In real world design problems one faces multiobjective optimisation under constraints. The optimal solution in these cases is not unique because the objectives can contradict each other. In such cases, a set of optimal solutions which forms a Pareto frontier in the objective space is considered. There are many algorithms to generate the Pareto frontier. However, only a few of them are potentially capable of providing an evenly distributed set of the solutions. Such a property is especially important in real-life design because a decision maker is usually able to analyse only a very limited quantity of solutions. This thesis consists of two main parts. At first, it develops and gives the detailed description of two different algorithms that are able to generate an evenly distributed Pareto set in a general formulation. One is a classical approach and called Directed Search Domain (DSD) and the other, the cylindrical constraint evolutionary algorithm (CCEA), is a hybrid population based method. The efficiency of the algorithms are demonstrated by a number of challenging test cases and the comparisons with the results of the other existing methods. It is shown that the proposed methods are successful in generating the Pareto solutions even when some existing methods fail. In real world design problems, deterministic approaches cannot provide a reliable solution as in the event of uncertainty, deterministic optimal solution would be infeasible in many instances. Therefore a solution less sensitive to problem perturbation is desirable. This leads to the robust solution which is the focus of the second part of the thesis. In the literature, there are some techniques tailored for robust optimisation. However, most of them are either computationally expensive or do not systematically articulate the designer preferences into a robust solution. In this thesis, by introducing a measure for robustness in multiobjective context, a tunable robust function (TRF) is presented. Including the TRF in the problem formulation, it is demonstrated that the desirable robust solution based on designer preferences can be obtained. This not only provides the robust solution but also gives a control over the robustness level. The method is efficient as it only increases the dimension of the problem by one irrespective of the dimension of the original problem.

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