Thermal energy storage (TES) modules are specifically designed to respond to transient thermal loading. Their dynamic response depends on the overall structure of the module, including module geometry and dimensions, the internal spatial distribution of phase change material (PCM) and conductive heat-spreading elements, and the thermophysical properties of the different materials composing the module. However, due to the complexity of analyzing a system’s dynamic thermal response to transient input signals, optimal design of a TES module for a particular application is challenging. Conventional design approaches are limited by (1) the computational cost associated with high fidelity simulation of heat transfer in nonlinear systems undergoing a phase transition and (2) the lack of model integration with robust optimization tools. To overcome these challenges, I derive reduced-order dynamic models of two different metal-PCM composite TES modules and validate them against a high fidelity CFD model. Through simulation and validation of both turbulent and laminar flow cases, I demonstrate the accuracy of the reduced-order models in predicting, both spatially and temporally, the evolution of the dynamic model states and other system variables of interest, such as PCM melt fraction. The validated models are used to conduct univariate and bivariate parametric studies to understand the effects of various design parameters on different performance metrics. Finally, a case study is presented in which the models are used to conduct detailed design optimization for the two HX geometries.

Les travaux présentés dans cette thèse s’inscrivent dans le cadre de l’électrification des sous-systèmes embarqués notamment pour des véhicules électriques/hybrides. Dans ce domaine, un des objectifs permanents est la réduction des coûts et des délais lors de la conception de chaînes d’actionnement mécatroniques. Pour y parvenir, il est nécessaire de doter le concepteur de méthodologies et d’outils adaptés lui permettant de fiabiliser sa démarche de conception et de lever le maximum de risques avant de réaliser les premiers prototypes. Ces systèmes mécatroniques embarqués mobilisent des briques technologiques essentielles dont fait partie le convertisseur d’électronique de puissance. Les performances de ce système reposent sur la capacité des méthodologies de conception à considérer les contraintes pluridisciplinaires liées à son environnement, l’adéquation des technologies, des topologies et des lois de commandes. Ces travaux de thèse montrent comment nous pouvons répondre à ces exigences et besoins à travers le développement d’une méthodologie de conception multi-physique et multi-niveau de convertisseurs multicellulaires (entrelacés) prédisposés par essence à une reconfiguration aisée. Cette méthodologie, basée sur une optimisation sous contraintes multi-physiques, permet des choix systématiques d’architecture optimale et des technologies de composants à partir d’une base de données constructeurs. Elle intègre l’aspect fiabilité dans la conception dès la phase de pré-dimensionnement au même niveau que les autres contraintes (électriques, rendement, thermiques, encombrement, compatibilité électromagnétique). Afin de bien profiter des avantages de ce type de convertisseurs entrelacés, cette intégration de la fiabilité dans la conception «fiabilisation par conception» est parachevée par l’élaboration d’une architecture de commande tolérante aux défauts «fiabilisation par la commande» permettant, une fois le convertisseur conçu, d’augmenter sa disponibilité par reconfiguration matérielle ou logicielle (loi de commande). / This PhD thesis work is in the context of electric/hybrid vehicle embedded subsystems electrification. In mechatronic design field, the permanent objectives are costs and delays reducing. To achieve this, there is need of design methodologies and appropriate tools to perform a reliable design approach and leave maximum of risks before making the first prototypes. Embedded mechatronic systems mobilize technological brick keys which include the power electronic converter. Their performances are based on the capacity of the design methodologies to consider the environment multi-disciplinary constraints, the adequacy of the technologies, topologies and control laws. This thesis work shows how we can meet these requirements and needs through the development of multi-physics and multi-level design methodology for multi-level converters (interleaved) predisposed to an easy reconfiguration. This methodology, based on optimization under multi-physics constraints allows systematic choice of optimal architecture and component technologies from manufacturer database. It integrates the reliability aspect in the design since the pre-sizing process in the same level as the other constraints (electric, efficiency, thermal, volume, electromagnetic compatibility). In order take advantages of such interleaved converters, the integration of reliability in the design "reliability by design" is completed by the development of fault tolerant control architecture "reliability by control" which increase the availability by reconfiguring hardware or software (control law) of the designed converter.

Safety-critical applications have to function correctly even in presence of faults. This thesis deals with techniques for tolerating effects of transient and intermittent faults. Reexecution, software replication, and rollback recovery with checkpointing are used to provide the required level of fault tolerance. These techniques are considered in the context of distributed real-time systems with non-preemptive static cyclic scheduling. Safety-critical applications have strict time and cost constrains, which means that not only faults have to be tolerated but also the constraints should be satisfied. Hence, efficient system design approaches with consideration of fault tolerance are required. The thesis proposes several design optimization strategies and scheduling techniques that take fault tolerance into account. The design optimization tasks addressed include, among others, process mapping, fault tolerance policy assignment, and checkpoint distribution. Dedicated scheduling techniques and mapping optimization strategies are also proposed to handle customized transparency requirements associated with processes and messages. By providing fault containment, transparency can, potentially, improve testability and debugability of fault-tolerant applications. The efficiency of the proposed scheduling techniques and design optimization strategies is evaluated with extensive experiments conducted on a number of synthetic applications and a real-life example. The experimental results show that considering fault tolerance during system-level design optimization is essential when designing cost-effective fault-tolerant embedded systems.

This thesis presents the work of two significant projects. In the first project, a suite of benchmark problems for grid energy management are presented which demonstrate several issues characteristic to the dynamic optimization of these systems. These benchmark problems include load following, cogeneration, tri-generation, and energy storage, and each one assumes perfect foresight of the entire time horizon. The Gekko Python package for dynamic optimization is introduced and two different solution methods are discussed and applied to solving these benchmarks. The simultaneous solve mode out-performs the sequential solve mode in each benchmark problem across a wide range of time horizons with increasing resolution, demonstrating the ability of the simultaneous mode to handle many degrees of freedom across a range of problems of increasing difficulty. In the second project, combined optimization of propulsion system design, flight trajectory planning and battery mass optimization is applied to solar-regenerative high-altitude long-endurance (SR-HALE) aircraft through a sequential iterative approach. This combined optimization approach yields an increase of 20.2% in the end-of-day energy available on the winter solstice at 35°N latitude, resulting in an increase in flight time of 2.36 hours. The optimized flight path is obtained by using nonlinear model predictive control to solve flight and energy system dynamics over a 24 hour period with a 15 second time resolution. The optimization objective is to maximize the total energy in the system while flying a station-keeping mission, staying within a 3 km radius and above 60,000 ft. The propulsion system design optimization minimizes the total energy required to fly the optimal path. It uses a combination of blade element momentum theory, blade composite structures, empirical motor and motor controller mass data, as well as a first order motor performance model. The battery optimization seeks to optimally size the battery for a circular orbit. Fixed point iteration between these optimization frameworks yields a flight path and propulsion system that slightly decreases solar capture, but significantly decreases power expended. Fully coupling the trajectory and design optimizations with this level of accuracy is infeasible with current computing resources. These efforts show the benefits of combining design and trajectory optimization to enable the feasibility of SR-HALE flight.

Using MIL-HDBK-419A, MATLAB and Nomographs, Shielding Effectiveness for the Magnetic Field, Electric Field, and Plane Wave were calculated over a frequency range from 10 Hz to 1 GHz. The three shielding tapes used included superalloy, aluminum, and mumetal. Calculations for Shielding Effectiveness involve the computation of Absorption Loss, Reflection Loss, and Re-Reflection Correction Factor. From the outcome of the calculations, it was suitable to conclude that all three metals fulfill the 40 dB Shielding Effectiveness requirements for SGEMP fields for frequencies greater or equal to 1 MHz. Accordingly, all three shielding tapes provide at least 40 dB of shielding to protect certain frequencies against SGEMP Magnetic Field. However, results vary for frequencies below 1 MHz.

Indiana University-Purdue University Indianapolis (IUPUI) / Nanocomposite ceramics have been widely studied in order to tailor desired properties at high temperatures. Methodologies for development of material design are still under effect. While finite element modeling (FEM) provides significant insight on material behavior, few design researchers have addressed the design paradox that accompanies this rapid design space expansion. A surrogate optimization model management framework has been proposed to make this design process tractable. In the surrogate optimization material design tool, the analysis cost is reduced by performing simulations on the surrogate model instead of high fidelity finite element model. The methodology is incorporated to and the optimal number of silicon carbide (SiC) particles, in a silicon-nitride(Si3N4) composite with maximum fracture energy [2]. Along with a deterministic optimization algorithm, model uncertainties have also been considered with the use of robust design optimization (RDO) method ensuring a design of minimum sensitivity to changes in the parameters. These methodologies applied to nanocomposites design have a significant impact on cost and design cycle time reduced.

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Coupling of material, process, and performance models is an important step towards a fully integrated material-process-performance design of structural components. In this research, alternative approaches for introducing the effects of manufacturing and material microstructure in plasticity constitutive models are studied, and a cyberinfrastructure framework is developed for coupled process-performance simulation and optimization of energy absorbing components made of magnesium alloys. The resulting mixed boundary/initial value problem is solved using nonlinear finite element analysis whereas the optimization problem is decomposed into a hierarchical multilevel system and solved using the analytical target cascading methodology. The developed framework is demonstrated on process-performance optimization of a sheetormed, energy-absorbing component using both classical and microstructure-based plasticity models. Sheetorming responses such as springback, thinning, and rupture are modeled and used as manufacturing process attributes whereas weight, mean crush force, and maximum crush force are used as performance attributes. The simulation and optimization results show that the manufacturing effects can have a considerable impact on design of energy absorbing components as well as the optimum values of process and product design variables.