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. / <p>QC 20160923</p>

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.

No description available.

The primary objective of this study is to develop new computational methods for robust design optimization (RDO) and reliability-based design optimization (RBDO) of high-dimensional, complex engineering systems. Four major research directions, all anchored in polynomial dimensional decomposition (PDD), have been defined to meet the objective. They involve: (1) development of new sensitivity analysis methods for RDO and RBDO; (2) development of novel optimization methods for solving RDO problems; (3) development of novel optimization methods for solving RBDO problems; and (4) development of a novel scheme and formulation to solve stochastic design optimization problems with both distributional and structural design parameters. The major achievements are as follows. Firstly, three new computational methods were developed for calculating design sensitivities of statistical moments and reliability of high-dimensional complex systems subject to random inputs. The first method represents a novel integration of PDD of a multivariate stochastic response function and score functions, leading to analytical expressions of design sensitivities of the first two moments. The second and third methods, relevant to probability distribution or reliability analysis, exploit two distinct combinations built on PDD: the PDD-SPA method, entailing the saddlepoint approximation (SPA) and score functions; and the PDD-MCS method, utilizing the embedded Monte Carlo simulation (MCS) of the PDD approximation and score functions. For all three methods developed, both the statistical moments or failure probabilities and their design sensitivities are both determined concurrently from a single stochastic analysis or simulation. Secondly, four new methods were developed for RDO of complex engineering systems. The methods involve PDD of a high-dimensional stochastic response for statistical moment analysis, a novel integration of PDD and score functions for calculating the second-moment sensitivities with respect to the design variables, and standard gradient-based optimization algorithms. The methods, depending on how statistical moment and sensitivity analyses are dovetailed with an optimization algorithm, encompass direct, single-step, sequential, and multi-point single-step design processes. Thirdly, two new methods were developed for RBDO of complex engineering systems. The methods involve an adaptive-sparse polynomial dimensional decomposition (AS-PDD) of a high-dimensional stochastic response for reliability analysis, a novel integration of AS-PDD and score functions for calculating the sensitivities of the failure probability with respect to design variables, and standard gradient-based optimization algorithms, resulting in a multi-point, single-step design process. The two methods, depending on how the failure probability and its design sensitivities are evaluated, exploit two distinct combinations built on AS-PDD: the AS-PDD-SPA method, entailing SPA and score functions; and the AS-PDD-MCS method, utilizing the embedded MCS of the AS-PDD approximation and score functions. In addition, a new method, named as the augmented PDD method, was developed for RDO and RBDO subject to mixed design variables, comprising both distributional and structural design variables. The method comprises a new augmented PDD of a high-dimensional stochastic response for statistical moment and reliability analyses; an integration of the augmented PDD, score functions, and finite-difference approximation for calculating the sensitivities of the first two moments and the failure probability with respect to distributional and structural design variables; and standard gradient-based optimization algorithms, leading to a multi-point, single-step design process. The innovative formulations of statistical moment and reliability analysis, design sensitivity analysis, and optimization algorithms have achieved not only highly accurate but also computationally efficient design solutions. Therefore, these new methods are capable of performing industrial-scale design optimization with numerous design variables.

A robustness and reliability based multidisciplinary analysis and optimization framework for aircraft design is presented. Robust design optimization and Reliability Based Design Optimization are merged into a uni ed formulation which streamlines the setup of optimization problems and aims at preventing foreseeable implementation issues in uncertainty based design. Surrogate models are evaluated to circumvent the intensive computations resulting from using direct evaluation in nondeterministic optimization. Three types of models are implemented in the framework: quadratic interpolation, regression Kriging and artificial neural networks. Regression Kriging presents the best compromise between performance and accuracy in deterministic wing design problems. The performance of the simultaneous implementation of robustness and reliability is evaluated using simple analytic problems and more complex wing design problems, revealing that performance benefits can still be achieved while satisfying probabilistic constraints rather than the simpler (and not as computationally intensive) robust constraints. The latter are proven to to be unable to follow a reliability constraint as uncertainty in the input variables increases. The computational effort of the reliability analysis is further reduced through the implementation of a coordinate change in the respective optimization sub-problem. The computational tool developed is a standalone application and it presents a user-friendly graphical user interface. The multidisciplinary analysis and design optimization tool includes modules for aerodynamics, structural, aeroelastic and cost analysis, that can be used either individually or coupled.

Dans cette Thèse, la conception robuste avec un modèle incertain d'un système électromécanique avec vibro-impact est fait. Le système électromécanique est constitué d'un chariot, dont le mouvement est excité par un moteur à courant continu et un marteau embarqué dans ce chariot. Le marteau est relié au chariot par un ressort non linéaire et par un amortisseur linéaire, de façon qu'un mouvement relatif existe entre eux. Une barrière flexible linéaire, placé à l'extérieur du chariot limite les mouvements de marteau. En raison du mouvement relatif entre le marteau et la barrière, impacts peuvent se produire entre ces deux éléments. Le modèle du système développé prend en compte l'influence du courant continu moteur dans le comportement dynamique du système. Certains paramètres du système sont incertains, tels comme les coefficients de rigidité et d'amortissement de la barrière flexible. L'objectif de la Thèse est de réaliser une optimisation de ce système électromécanique par rapport aux paramètres de conception afin de maximiser l'impact puissance sous la contrainte que la puissance électrique consommée par le moteur à courant continu est inférieure à une valeur maximale. Pour choisir les paramètres de conception dans le problème d'optimisation, une analyse de sensibilité a été réalisée afin de définir les paramètres du système les plus sensibles. L'optimisation est formulée dans le cadre de la conception robuste en raison de la présence d'incertitudes dans le modèle. Les lois de probabilités liées aux variables aléatoires du problème sont construites en utilisant le Principe du Maximum l'Entropie et les statistiques de la réponse stochastique du système sont calculées en utilisant la méthode de Monte Carlo. L'ensemble d'équations non linéaires sont présentés, et un solveur temporel adapté est développé. Le problème d'optimisation non linéaire stochastique est résolu pour différents niveaux d'incertitudes, et aussi pour le cas déterministe. Les résultats sont différents, ce qui montre l'importance de la modélisation stochastique / In this Thesis, the robust design with an uncertain model of a vibro-impact electromechanical system is done. The electromechanical system is composed of a cart, whose motion is excited by a DC motor (motor with continuous current), and an embarked hammer into this cart. The hammer is connected to the cart by a nonlinear spring component and by a linear damper, so that a relative motion exists between them. A linear flexible barrier, placed outside of the cart, constrains the hammer movements. Due to the relative movement between the hammer and the barrier, impacts can occur between these two elements. The developed model of the system takes into account the influence of the DC motor in the dynamic behavior of the system. Some system parameters are uncertain, such as the stiffness and the damping coefficients of the flexible barrier. The objective of the Thesis is to perform an optimization of this electromechanical system with respect to design parameters in order to maximize the impact power under the constraint that the electric power consumed by the DC motor is lower than a maximum value. To chose the design parameters in the optimization problem, an sensitivity analysis was performed in order to define the most sensitive system parameters. The optimization is formulated in the framework of robust design due to the presence of uncertainties in the model. The probability distributions of random variables are constructed using the Maximum Entropy Principle and statistics of the stochastic response of the system are computed using the Monte Carlo method. The set of nonlinear equations are presented, and an adapted time domain solver is developed. The stochastic nonlinear constrained design optimization problem is solved for different levels of uncertainties, and also for the deterministic case. The results are different and this show the importance of the stochastic modeling

Dans cette Thèse, la conception robuste avec un modèle incertain d'un système électromécanique avec vibro-impact est fait. Le système électromécanique est constitué d'un chariot, dont le mouvement est excité par un moteur à courant continu et un marteau embarqué dans ce chariot. Le marteau est relié au chariot par un ressort non linéaire et par un amortisseur linéaire, de façon qu'un mouvement relatif existe entre eux. Une barrière flexible linéaire, placé à l'extérieur du chariot limite les mouvements de marteau. En raison du mouvement relatif entre le marteau et la barrière, impacts peuvent se produire entre ces deux éléments. Le modèle du système développé prend en compte l'influence du courant continu moteur dans le comportement dynamique du système. Certains paramètres du système sont incertains, tels comme les coefficients de rigidité et d'amortissement de la barrière flexible. L'objectif de la Thèse est de réaliser une optimisation de ce système électromécanique par rapport aux paramètres de conception afin de maximiser l'impact puissance sous la contrainte que la puissance électrique consommée par le moteur à courant continu est inférieure à une valeur maximale. Pour choisir les paramètres de conception dans le problème d'optimisation, une analyse de sensibilité a été réalisée afin de définir les paramètres du système les plus sensibles. L'optimisation est formulée dans le cadre de la conception robuste en raison de la présence d'incertitudes dans le modèle. Les lois de probabilités liées aux variables aléatoires du problème sont construites en utilisant le Principe du Maximum l'Entropie et les statistiques de la réponse stochastique du système sont calculées en utilisant la méthode de Monte Carlo. L'ensemble d'équations non linéaires sont présentés, et un solveur temporel adapté est développé. Le problème d'optimisation non linéaire stochastique est résolu pour différents niveaux d'incertitudes, et aussi pour le cas déterministe. Les résultats sont différents, ce qui montre l'importance de la modélisation stochastique / In this Thesis, the robust design with an uncertain model of a vibro-impact electromechanical system is done. The electromechanical system is composed of a cart, whose motion is excited by a DC motor (motor with continuous current), and an embarked hammer into this cart. The hammer is connected to the cart by a nonlinear spring component and by a linear damper, so that a relative motion exists between them. A linear flexible barrier, placed outside of the cart, constrains the hammer movements. Due to the relative movement between the hammer and the barrier, impacts can occur between these two elements. The developed model of the system takes into account the influence of the DC motor in the dynamic behavior of the system. Some system parameters are uncertain, such as the stiffness and the damping coefficients of the flexible barrier. The objective of the Thesis is to perform an optimization of this electromechanical system with respect to design parameters in order to maximize the impact power under the constraint that the electric power consumed by the DC motor is lower than a maximum value. To chose the design parameters in the optimization problem, an sensitivity analysis was performed in order to define the most sensitive system parameters. The optimization is formulated in the framework of robust design due to the presence of uncertainties in the model. The probability distributions of random variables are constructed using the Maximum Entropy Principle and statistics of the stochastic response of the system are computed using the Monte Carlo method. The set of nonlinear equations are presented, and an adapted time domain solver is developed. The stochastic nonlinear constrained design optimization problem is solved for different levels of uncertainties, and also for the deterministic case. The results are different and this show the importance of the stochastic modeling

L’objectif ultime de ce travail de thèse est d’évaluer la possibilité de valider et d’optimiser un processus de fabrication en utilisant la simulation numérique en tenant compte des incertitudes irréductibles sur le procédé, les matériaux et la géométrie du produit fabriqué. La prise en compte des incertitudes nécessite de quantifier les effets des variations des paramètres du modèle sur les sorties de celui-ci, en propageant ces variations via la simulation numérique pour évaluer leurs effets sur les sorties. Dans ce travail nous avons proposé une procédure pour déterminer le seuil de sensibilité du modèle numérique afin de construire des plans d’expériences numériques cohérents avec ce seuil. Nous avons également montré que, compte tenu des incertitudes sur les matériaux et la géométrie du produit, il est possible d’optimiser certains paramètres du procédé pour contrôler les effets des incertitudes sur les variations dimensionnelles et morphologiques du produit. Pour cela, nous avons proposé une procédure d’optimisation basée sur un algorithme NSGA-II et une méta-modélisation du procédé. L’application à l’emboutissage d’une tôle en U, retour élastique inclus, montre qu’il s’agit d’un problème de conception robuste pour lequel nous obtenons l’ensemble des compromis entre l’écart à la moyenne et l’écart type d’une fonction « performance » du procédé correctement choisie. Finalement l’analyse de ces résultats nous permet de quantifier le lien entre la notion de robustesse d’une solution optimisée du procédé et les critères de mesure de la qualité du produit / The ultimate objective of this thesis is to evaluate the possibility to validate and optimize a manufacturing process using numerical simulation and taking into account the irreducible uncertainties in the process, materials and geometry of manufactured product. Taking into account the uncertainties requires quantifying the effects of variations of model parameters on the outputs, by propagating these variations via computer simulation to assess their effects on the outputs. In this work, we have proposed a procedure to determine the sensitivity threshold of the numerical model to build numerical Design of Experiments consistent with this threshold. We have also shown that, given the uncertainties in the materials and the geometry of the product, it is possible to optimize certain process parameters to control the effects of uncertainties on the dimensional and morphological variations of the product. For this, we have proposed an optimization procedure based on NSGA-II algorithm and a meta-modeling of the process. The application for deep drawing of a U-shaped sheet metal part, springback included shows that it is a robust design problem for which we get all the compromise between the deviation from the mean and standard deviation of a "performance" depending on the process correctly chosen. Finally, the analysis of these results allows us to quantify the relationship between the notion of robustness of an optimized solution of the process and criteria for measuring the quality of the product

Esta Tese propõe uma nova ferramenta para Otimização Robusta Multiobjetivo por Análise de Intervalo Não Probabilística (Non-probabilistic Interval Analysis for Multiobjective Robust Design Optimization ou NPIA-MORDO). A ferramenta desenvolvida visa à otimização dos parâmetros concentrados de suspensão em um modelo veicular completo, submetido a uma manobra direcional percorrendo diferentes perfis de pista, a fim de garantir maior conforto e segurança ao motorista. O modelo multicorpo possui 15 graus de liberdade (15-GDL), dentre os quais onze pertencem ao veículo e assento, e quatro, ao modelo biodinâmico do motorista. A função multiobjetivo é composta por objetivos conflitantes e as suas tolerâncias, como a raiz do valor quadrático médio (root mean square ou RMS) da aceleração lateral e da aceleração vertical do assento do motorista, desenvolvidas durante a manobra de dupla troca de faixa (Double Lane Change ou DLC). O curso da suspensão e a aderência dos pneus à pista são tratados como restrições do problema de otimização. As incertezas são quantificadas no comportamento do sistema pela análise de intervalo não probabilística, por intermédio do Método dos Níveis de Corte-α (α-Cut Levels) para o nível α zero (de maior dispersão), e realizada concomitantemente ao processo de otimização multiobjetivo. Essas incertezas são aplicáveis tanto nos parâmetros do problema quanto nas variáveis de projeto. Para fins de validação do modelo, desenvolvido em ambiente MATLAB®, a trajetória do centro de gravidade da carroceria durante a manobra é comparada com o software CARSIM®, assim como as forças laterais e verticais dos pneus. Os resultados obtidos são exibidos em diversos gráficos a partir da fronteira de Pareto entre os múltiplos objetivos do modelo avaliado Os indivíduos da fronteira de Pareto satisfazem as condições do problema, e a função multiobjetivo obtida pela agregação dos múltiplos objetivos resulta em uma diferença de 1,66% entre os indivíduos com o menor e o maior valor agregado obtido. A partir das variáveis de projeto do melhor indivíduo da fronteira, gráficos são gerados para cada grau de liberdade do modelo, ilustrando o histórico dos deslocamentos, velocidades e acelerações. Para esse caso, a aceleração RMS vertical no assento do motorista é de 1,041 m/s² e a sua tolerância é de 0,631 m/s². Já a aceleração RMS lateral no assento do motorista é de 1,908 m/s² e a sua tolerância é de 0,168 m/s². Os resultados obtidos pelo NPIA-MORDO confirmam que é possível agregar as incertezas dos parâmetros e das variáveis de projeto à medida que se realiza a otimização externa, evitando a necessidade de análises posteriores de propagação de incertezas. A análise de intervalo não probabilística empregada pela ferramenta é uma alternativa viável de medida de dispersão se comparada com o desvio padrão, por não utilizar uma função de distribuição de probabilidades prévia e por aproximar-se da realidade na indústria automotiva, onde as tolerâncias são preferencialmente utilizadas. / This thesis proposes the development of a new tool for Non-probabilistic Interval Analysis for Multi-objective Robust Design Optimization (NPIA-MORDO). The developed tool aims at optimizing the lumped parameters of suspension in a full vehicle model, subjected to a double-lane change (DLC) maneuver throughout different random road profiles, to ensure comfort and safety to the driver. The multi-body model has 15 degrees of freedom (15-DOF) where 11-DOF represents the vehicle and its seat and 4-DOF represents the driver's biodynamic model. A multi-objective function is composed by conflicted objectives and their tolerances, like the root mean square (RMS) lateral and vertical acceleration in the driver’s seat, both generated during the double-lane change maneuver. The suspension working space and the road holding capacity are used as constraints for the optimization problem. On the other hand, the uncertainties in the system are quantified using a non-probabilistic interval analysis with the α-Cut Levels Method for zero α-level (the most uncertainty one), performed concurrently in the multi-objective optimization process. These uncertainties are both applied to the system parameters and design variables to ensure the robustness in results. For purposes of validation in the model, developed in MATLAB®, the path of the car’s body center of gravity during the maneuver is compared with the commercial software CARSIM®, as well as the lateral and vertical forces from the tires. The results are showed in many graphics obtained from the Pareto front between the multiple conflicting objectives of the evaluated model. The obtained solutions from the Pareto Front satisfy the conditions of the evaluated problem, and the aggregated multi-objective function results in a difference of 1.66% for the worst to the best solution. From the design variables of the best solution choose from the Pareto front, graphics are created for each degree of freedom, showing the time histories for displacements, velocities and accelerations. In this particular case, the RMS vertical acceleration in the driver’s seat is 1.041 m/s² and its tolerance is 0.631 m/s², but the RMS lateral acceleration in the driver’s seat is 1.908 m/s² and its tolerance is 0.168 m/s². The overall results obtained from NPIA-MORDO assure that is possible take into account the uncertainties from the system parameters and design variables as the external optimization loop is performed, reducing the efforts in subsequent evaluations. The non-probabilistic interval analysis performed by the proposed tool is a feasible choice to evaluate the uncertainty if compared to the standard deviation, because there is no need of previous well-known based probability distribution and because it reaches the practical needs from the automotive industry, where the tolerances are preferable.