Advances in electrical machinery with high efficiencies could significantly reduce the cost of industrial and residential energy systems, thereby reducing fossil fuel needs and emissions. Electrical machine design is a comprehensive process based on several factors, including economic factors, material limitations, specifications and special application-dependent factors. At the same time, machine design is a multi-physics task comprising of electric design, magnetic design, insulation design, thermal design and mechanical design. However, the out-of-date conventional machine design can neither reflect the advances in the past 30 years, nor exploit the trade-offs between design factors from the multi-physics nature of the electrical machine. This work focus on the development a fast and efficient method for the design and optimization of Surface Mount Permanent Magnet (SMPM) machines and induction machines, as influenced by the energy source, mechanical loads, thermal effects, and the up-to-date developments in materials and manufacturing capabilities. A new analytical design method is developed for the electromagnetic design of SMPM machines. Both distributed and concentrated winding types of SMPM machines are considered and compared. Based on the proposed electromagnetic analytical design method and a generic thermo-mechanical machine design model [1], an innovative and computationally efficient electromagnetic-thermo-mechanical integrated design method is developed for SMPM machines. Particle Swarm Optimization (PSO) is applied in a novel way based on this integrated design method for the multi-objective design optimization of SMPM machines. With the proposed method, the thermal and mechanical design is no longer treated separately and heuristically as in the traditional design, but has been systemically integrated with the electromagnetic design; the effect of power source, cooling capability, thermal limits, and up-to-date material capabilities are also reflected in the design and optimization. Superior designs compared to traditional designs can be achieved with PSO based multi-objective optimization. The proposed integrated design approach also has the merit of good computational efficiency and provides a significant time reduction of the design cycle compared to finite element analysis. A novel electromagnetic analytical design method of induction machines has been developed, which needs only six prime design variables but is able to design induction machines in fine details. The advantage over the traditional and other existing design method is that this proposed method does not have the heuristic selection of the design variables and does not need manual design iterations. The computing time is almost negligible and the design cycle is significantly reduced compared to the tradition machine design.

The construction equipment industry is developing at a fast pace, increasing the expectation on the next-generation machines. Wheel loaders and backhoe loaders are part of this evolution and all subsystems in those machines need to be developed to meet the high demands in energy eciency and productivity. One of the most important parts of the wheel loader is the loading unit. This is traditionally designed using highly experienced engineers and CAD software. To simplify the early stages of this process was an optimization tool developed to generate a design outlay. The optimization will minimize the mass of the linkage since unnecessary weight will lower the eciency. The minimum can be found by moving the joints and adjusting the shape of the device. The optimization will also include constraints to assure the correct performance of the linkage. Since there are a high number of design variables, a gradient-based optimization method was used. A finite element solver was also implemented to calculate the forces and stresses in the linkage. The linkages studied in this report are one from a typical wheel loader and one from a backhoe loader. Since these machines are extremely versatile, and used formany diferent tasks, two sets of constraints are compiled. One of the constraint sets yields a linkage suitable for machines only equipped with bucket, while the other results in an all-round linkage suitable for most tools and applications. The optimized linkages are compared to existing devices. The results show that there are some improvements possible and that the software could be used to help designers. However, the optimization problem is hard to solve due to non-smooth constraints functions and numerical instabilities. This issue could be overcome by diferent means, like using automatic diferentiation, a non-gradient based optimization method, decreasing the number of constraints or decreasing the number of design variables. / Utvecklingen av anlaggningsmaskiner sker i snabb takt och detta ökar förväntningarna på framtidens maskiner. En stor andel av alla anläggningsmaskiner är hjullastare och traktorgrävare och alla delsystem på dessa maskiner måste följa med i utvecklingen. En av de viktigaste delarna pa en hjullastare ar lastaggregatet. Det designas traditionellt med hjälp av CAD mjukvara och mycket erfarna konstruktörer. För att underlätta denna process har en optimeringsrutin utvecklats, som generarar ett designförslag. Optimeringen minskar länkagets massa genom att fytta lagringspositioner och ändra delarnas dimensioner. Detta ökar efektiviteten hos maskinen eftersom den slipper köra runt på onödig vikt. Optimeringen innehåller även villkor för att säkerställa god prestanda hos det optimerade aggregatet. Eftersom det ingår väldigt många designvariabler i optimeringen används en gradientbaserad metod. En finita element approximation används for att beräkna krafter och spänningar i länkaget. De länkage som undersöks i detta projekt är ett typsikt hjullastaraggregat och ett typiskt traktorgrävaraggregat. Eftersom dessa maskiner ar väldigt mångsidiga sammanställdes två olika uppsättningar av villkor. Den ena uppsättningen används för att optimera ett aggregat som endast ska användas med skopa, medan den andra uppsättningen används för att ta fram ett mer mångsidigt aggregat avsätt for att kunna klara av de flesta situationer och verktyg. De optimerade lastaggregaten är jämförda med produktionsaggregat och det visar sig att vissa förbättringar är möjliga. Slutsattsen är att optimeringsrutinen kan bli ett bra hjälpmedel for konstruktörer men att den behöver lite mer veriering. Villkorsfunktionen som optimeringen måste lösa är inte helt slät vilket är ett problem för en gradientbaserade metod och dessutom finns vissa numeriska instabiliteter. Dessa svårigheter kan undkommas pa olika sätt, t.ex. genom att använda automatisk derivering,byta optimeringsalgoritm, minska antalet villkor eller minska antalet variabler.

As the feature size of the transistor shrinks into nanometer scale, it becomes a grand challenge for semiconductor manufacturers to achieve good manufacturability of integrated circuits cost-effectively. In this dissertation, we aim at layout optimization algorithms from both manufacturing and design perspectives to address problems in this grand challenge. Our work covers three topics in this research area: a redundant via enhanced maze routing algorithm for yield improvement, a shuttle mask floorplanner, and optimization of post-CMP topography variation. Existing methods for redundant via insertion are all post-layout optimizations that insert redundant vias after detailed routing. In the first part of this dissertation, we propose the first routing algorithm that conducts redundant via insertion during detailed routing. Our routing problem is formulated as a maze routing with redundant via constraints and transformed into a multiple constraint shortest path problem, and then solved by Lagrangian relaxation technique. Experimental results show that our algorithm can find routing solutions with remarkably higher rate of redundant via insertion than conventional maze routing. Shuttle mask is an economical method to share the soaring mask cost by placing different chips on the same mask. Shuttle mask floorplanning is a key step to pack these chips according to certain objectives and constraints related to mask manufacturing and cost. In the second part of this dissertation, we develop a simulated annealing based floorplanner that can optimize these objectives and meet the constraints simultaneously. Chemical-mechanical polishing (CMP) is a crucial manufacturing step to planarize wafer surface. Minimum post-CMP topography variation is preferred to control the defocus in lithography process. In the third of this dissertation, we present several studies on optimization of the variation. First, we enhance the shuttle mask floorplanner to minimize the post-CMP topography variation. Then we study the following singleblock positioning problem: given a shuttle mask floorplan, how to determine a movable block's optimal position with respect to post-CMP topography variation. We propose a fast incremental algorithm achieving 6x to 9x speedup. Finally, we formulate a novel CMP dummy fill problem that targets at minimizing the height variance, which is key to reduce the image distortion by defocus. Experimental results show that with the new formulation, we can significantly reduce the height variance without sacrificing the height spread much.

Computational design has become a predominant element of various engineering tasks. However, the ever increasing complexity of numerical models creates the need for efficient methodologies. Specifically, computational design under uncertainties remains sparsely used in engineering settings due to its computational cost. This dissertation proposes a coherent framework for various branches of computational design under uncertainties, including model update, reliability assessment and reliability-based design optimization. Through the use of machine learning techniques, computationally inexpensive approximations of the constraints, limit states, and objective functions are constructed. Specifically, a novel adaptive sampling strategy allowing for the refinement of any approximation only in relevant regions has been developed, referred to as generalized max-min. This technique presents various computational advantages such as ease of parallelization and applicability to any metamodel. Three approaches tailored for computational design under uncertainties are derived from the previous approximation technique. An algorithm for reliability assessment is proposed and its efficiency is demonstrated for different probabilistic settings including dependent variables using copulas. Additionally, the notion of fidelity map is introduced for model update settings with large number of dependent responses to be matched. Finally, a new reliability-based design optimization method with local refinement has been developed. A derivation of sampling-based probability of failure derivatives is also provided along with a discussion on numerical estimates. This derivation brings additional flexibility to the field of computational design. The knowledge acquired and techniques developed during this Ph.D. have been synthesized in an object-oriented MATLAB toolbox. The help and ergonomics of the toolbox have been designed so as to be accessible by a large audience.

The initial stages of reliability-based design optimization involve the formulation of objective functions and constraints, and building a model to estimate the reliability of the design with quantified uncertainties. However, even experienced hands often overlook important objective functions and constraints that affect the design. In addition, uncertainty reduction measures, such as tests and redesign, are often not considered in reliability calculations during the initial stages. This research considers two areas that concern the design of engineering systems: 1) the trade-off of the effect of a test and post-test redesign on reliability and cost and 2) the search for multiple candidate designs as insurance against unforeseen faults in some designs. In this research, a methodology was developed to estimate the effect of a single future test and post-test redesign on reliability and cost. The methodology uses assumed distributions of computational and experimental errors with re-design rules to simulate alternative future test and redesign outcomes to form a probabilistic estimate of the reliability and cost for a given design. Further, it was explored how modeling a future test and redesign provides a company an opportunity to balance development costs versus performance by simultaneously designing the design and the post-test redesign rules during the initial design stage.The second area of this research considers the use of dynamic local surrogates, or surrogate-based agents, to locate multiple candidate designs. Surrogate-based global optimization algorithms often require search in multiple candidate regions of design space, expending most of the computation needed to define multiple alternate designs. Thus, focusing on solely locating the best design may be wasteful. We extended adaptive sampling surrogate techniques to locate multiple optima by building local surrogates in sub-regions of the design space to identify optima. The efficiency of this method was studied, and the method was compared to other surrogate-based optimization methods that aim to locate the global optimum using two two-dimensional test functions, a six-dimensional test function, and a five-dimensional engineering example.

A set of techniques is developed that allows the incorporation of flight dynamics metrics as an additional discipline in a high-fidelity aerodynamic optimization. Specifically, techniques for including static stability constraints and handling qualities constraints in a high-fidelity aerodynamic optimization are demonstrated. These constraints are developed from stability derivative information calculated using high-fidelity computational fluid dynamics (CFD). Two techniques are explored for computing the stability derivatives from CFD. One technique uses an automatic differentiation adjoint technique (ADjoint) to efficiently and accurately compute a full set of static and dynamic stability derivatives from a single steady solution. The other technique uses a linear regression method to compute the stability derivatives from a quasi-unsteady time-spectral CFD solution, allowing for the computation of static, dynamic and transient stability derivatives. Based on the characteristics of the two methods, the time-spectral technique is selected for further development, incorporated into an optimization framework, and used to conduct stability-constrained aerodynamic optimization. This stability-constrained optimization framework is then used to conduct an optimization study of a flying wing configuration. This study shows that stability constraints have a significant impact on the optimal design of flying wings and that, while static stability constraints can often be satisfied by modifying the airfoil profiles of the wing, dynamic stability constraints can require a significant change in the planform of the aircraft in order for the constraints to be satisfied.

The current stimuli of climate change and rising oil prices have spurred the development of hybrid electric (HEV), and battery electric vehicles (BEV): collectively termed EVs. However, the battery technology needs much development: at the time of writing, the range of a BEV is too low to be practical in many situations. A critical limitation is the sensitivity of batteries to temperature: the heat generated during operation affects their performance and reduces the lifetime. This study investigates battery cooling using cooling plates: thin rectangular fabrications inserted between battery cells. A coolant pumped through internal channels absorbs heat and transports it away from the battery. Previous studies of liquid heat exchangers have indicated that the geometry of the channels plays a significant role in the performance; however, there is a lack of rigorous numerical optimization applied to EV cooling plates. By developing a numerical optimization framework utilizing parametric geometry generation and computational fluid dynamics, this research has investigated the characteristics of optimum cooling plate geometry with respect to three objectives: average temperature, temperature uniformity, and coolant pressure drop. By applying each objective separately, improvements of up to 70% have been made compared to a reference design. The influence of boundary conditions on performance and optimum design has been assessed, and multi-objective optimization has investigated the trade-off between competing objective functions. Although care should be taken when extrapolating the results beyond the geometry and conditions in the study, some general design principles can be proposed. Objectives of average temperature and pressure drop can both be satisfied by a common design with wide cooling channels, but different characteristics are needed for temperature uniformity. Additional assessments have revealed that optimizations of temperature uniformity are especially sensitive to the boundary conditions, whereas the other objective functions are largely insensitive. The optimization process developed in this work can be applied to any potential cooling plate design and will lead to gains in the targeted performance measure. In doing so, the performance of the EV will be incrementally improved, thereby advancing the day when an EV is not only an environmental choice, but also a practical choice. / Thesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2011-09-07 16:24:14.6

2011 IEEE Congress on Evolutionary Computation (CEC). June 5-8, 2011, Ritz-Carlton, New Orleans, LA, USA

Today uncertainties of petroleum supply and concerns over global warming call for further advancement of green vehicles with higher energy efficiency and lower green house gas (GHG) emissions. Development of advanced hybrid electric powertrain technology plays an important role in the green vehicle transformation with continuously improved energy efficiency and diversified energy sources. The added complexity of the multi-discipline based, advanced hybrid powertrain systems make traditional powertrain design method obsolete, inefficient, and ineffective. This research follows the industrial leading model-based design approach for hybrid electric vehicle powertrain development and introduces the optimization based methods to address several key design challenges in hybrid electric powertrain and its control system design. Several advanced optimization methods are applied to identify the proper hybrid powertrain architecture and design its control strategies for better energy efficiency. The newly introduced optimization based methods can considerably alleviate the design challenges, avoid unnecessary design iterations, and improve the quality and efficiency of the powertrain design. The proposed method is tested through the design and development of a prototype extended range electric vehicle (EREV), UVic EcoCAR. Developments of this advanced hybrid vehicle provide a valuable platform for verifying the new design method and obtaining feedbacks to guide the fundamental research on new hybrid powertrain design methodology. / Graduate

A set of techniques is developed that allows the incorporation of flight dynamics metrics as an additional discipline in a high-fidelity aerodynamic optimization. Specifically, techniques for including static stability constraints and handling qualities constraints in a high-fidelity aerodynamic optimization are demonstrated. These constraints are developed from stability derivative information calculated using high-fidelity computational fluid dynamics (CFD). Two techniques are explored for computing the stability derivatives from CFD. One technique uses an automatic differentiation adjoint technique (ADjoint) to efficiently and accurately compute a full set of static and dynamic stability derivatives from a single steady solution. The other technique uses a linear regression method to compute the stability derivatives from a quasi-unsteady time-spectral CFD solution, allowing for the computation of static, dynamic and transient stability derivatives. Based on the characteristics of the two methods, the time-spectral technique is selected for further development, incorporated into an optimization framework, and used to conduct stability-constrained aerodynamic optimization. This stability-constrained optimization framework is then used to conduct an optimization study of a flying wing configuration. This study shows that stability constraints have a significant impact on the optimal design of flying wings and that, while static stability constraints can often be satisfied by modifying the airfoil profiles of the wing, dynamic stability constraints can require a significant change in the planform of the aircraft in order for the constraints to be satisfied.