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Ride and handling are important areas for safety and improved vehicle control during driving. To meet the demands on ride and handling a number of measures can be taken. This master thesis work has focused on the early design phase. At the early phases of design, the level of details is low and the design freedom is big. By introducing a tool to support the early vehicle body design, the potential of finding more efficient structures increases. In this study, topology optimization of a vehicle front structure has been performed using OptiStruct by Altair Engineering. The objective has been to find the optimal topology of beams and rods to achieve high stiffness of the front structure for improved ride and handling. Based on topology optimization a proposal for a beam layout in the front structure area has been identified. A vital part of the project has been to describe how to use topology optimization as a tool in the design process. During the project different approaches has been studied to come from a large design space to a low weight architecture based on a beam-like structure. The different approaches will be described and our experience and recommendations will be presented. Also the general result of a topology-optimized architecture for vehicle body stiffness will be presented.

The main objective of the topology optimization is to fulfill the objective function with the minimum amount of material. This reduces the overall cost of the structure and at the same time reduces the assembly, manufacturing and maintenance costs because of the reduced number of parts in the final structure. The concept of reliability analysis can be incorporated into the deterministic topology optimization method; this incorporated scheme is referred to as Reliability-based Topology Optimization (RBTO). In RBTO, the statistical nature of constraints and design problems are defined in the objective function and probabilistic constraint. The probabilistic constraint can specify the required reliability level of the system. In practical applications, however, finding global optimum in the presence of uncertainty is a difficult and computationally intensive task, since for every possible design a full stochastic analysis has to be performed for estimating various statistical parameters. Efficient methodologies are therefore required for the solution of the stochastic part and the optimization part of the design process. This research will explore a reliability-based synthesis method which estimates all the statistical parameters and finds the optimum while being less computationally intensive. The efficiency of the proposed method is achieved with the combination of topology optimization and stochastic approximation which utilizes a sampling technique such as Latin Hypercube Sampling (LHS) and surrogate modeling techniques such as Local Regression and Classification using Artificial Neural Networks (ANN). Local regression is comparatively less computationally intensive and produces good results in case of low probability of failures whereas Classification is particularly useful in cases where the reliability of failure has to be estimated with disjoint failure domains. Because classification using ANN is comparatively more computationally demanding than Local regression, classification is only used when local regression fails to give the desired level of goodness of fit. Nevertheless, classification is an indispensible tool in estimating the probability of failure when the failure domain is discontinuous. Representative examples will be demonstrated where the method is used to design customized meso-scale truss structures and a macro-scale hydrogen storage tank. The final deliverable from this research will be a less computationally intensive and robust RBTO procedure that can be used for design of truss structures with variable design parameters and force and boundary conditions.

The principal goal of this thesis is to develop a systematic method for the design of composite heat sinks (CHSs) that serve as passive and transient cooling devices for microelectronics. This is accomplished by posing the CHS design problem as a topology optimization problem wherein a phase-change material and a high-conductivity material are to be optimally distributed. Two different types of formulations are proposed. The first one aims to maximize the time of operation before a tolerable temperature is reached at the interface between a heat source and the CHS. The second one aims to minimize the maximum temperature across the heating interface for a given time of operation. The two materials are interpolated in topology optimization using the usual mixture law with penalty. The phase-change is modeled using the apparent heat capacity method in which the specific heat is taken as a nonlinear function of the temperature so that the latent heat absorption is accounted for at the melting point. The ensuing new transient topology optimization problem involving an interpolated material property that depends on the state variable is solved using continuous optimization algorithm. The validity of the phase-change modeling is verified with a one dimensional model as well as experimentation. Analytical sensitivity analysis is derived and verified with the finite difference derivatives. Several examples are solved to illustrate the intricacies of the problem and the effectiveness and the limitations of the proposed design method. Prototypes of an intuitively conceived CHS and optimized one are made. An experimental setup is devised to test the two prototypes. Based on the insight gained from the experiments, an improved conduction model is studied to also incorporate convective heat transfer also into the model.

The Internet of Things paradigm establishes interaction and communication with a huge amount of actors. The concept is not a new-from-scratch one; actually, it combines a vast number of technologies and protocols and surely adaptations of pre-existing elements to offer new services and applications. One of the key technologies of the Internet of Things is the Radio Frequency Identification just abbreviated RFID. This technology proposes a set of solutions that allow tracking and tracing persons, animals and practically any item wirelessly. Considering the Internet of Things concept, multiple technologies need to be linked in order to provide interactions that lead to the implementation of services and applications. The challenge is that these technologies are not necessarily compatible and designed to work with other technologies. Within this context, the main objective of this thesis is to design a heterogeneous framework that will permit the interaction of diverse devices such as RFID, sensors and actuators in order to provide new applications and services. For this purpose in this work, our first contribution is the design and analysis of an integration architecture for heterogeneous devices. In the second contribution, we propose an evaluation model for RFID topologies and an optimization tool that assists in the RFID network planning process. Finally, in our last contribution, we implemented a simplified version of the framework by using embedded hardware and performance metrics are provided as well as the detailed configuration of the test platform

Aluminum Cross-Car Beam is significantly lighter than the conventional steel counterpart and presents superior energy absorption characteristics. The challenge is however, its considerably higher cost, rendering it difficult for the aluminum one to compete in the automotive market. In this work, using material distribution techniques and stochastic optimization, a Multidisciplinary Design Optimization procedure is developed to optimize an existing Cross-Car Beam model with respect to the cost. Topology, Topography, and gauge optimizations are employed in the development of the optimization disciplines. Based on a qualitative cost assessment, penalty functions are designed to penalize costly designs. Noise-Vibration-Harshness (NVH) performance is the key constraint of the optimization. To fulfill this requirement, natural frequencies are obtained using modal analysis. Undesirable designs with respect to the NVH criteria are gradually eliminated from the optimization cycles. The new design is verified by static loading scenario and evaluated in terms of the cost saving it offers.

Aluminum Cross-Car Beam is significantly lighter than the conventional steel counterpart and presents superior energy absorption characteristics. The challenge is however, its considerably higher cost, rendering it difficult for the aluminum one to compete in the automotive market. In this work, using material distribution techniques and stochastic optimization, a Multidisciplinary Design Optimization procedure is developed to optimize an existing Cross-Car Beam model with respect to the cost. Topology, Topography, and gauge optimizations are employed in the development of the optimization disciplines. Based on a qualitative cost assessment, penalty functions are designed to penalize costly designs. Noise-Vibration-Harshness (NVH) performance is the key constraint of the optimization. To fulfill this requirement, natural frequencies are obtained using modal analysis. Undesirable designs with respect to the NVH criteria are gradually eliminated from the optimization cycles. The new design is verified by static loading scenario and evaluated in terms of the cost saving it offers.

This thesis investigates topology optimization techniques for periodic continuum structures at the macroscopic level. Periodic structures are increasingly used in the design of structural systems and sub-systems of buildings, vehicles, aircrafts, etc. The duplication of identical or similar modules significantly reduces the manufacturing cost and greatly simplifies the assembly process. Optimization of periodic structures in the micro level has been extensively researched in the context of material design, while research on topology optimization for macrostructures is very limited and has great potential both economically and intellectually. In the present thesis, numerical algorithms based on the bi-directional evolutionary structural optimization method (BESO) are developed for topology optimization for various objectives and constraints. Soft-kill (replacing void elements with soft elements) formulations of topology optimization problems for solid-void solutions are developed through appropriate material interpolation schemes. Incorporating the optimality criteria and algorithms for mesh-independence and solution-convergence, the present BESO becomes a reliable gradient based technique for topology optimization. Additionally, a new combination of genetic algorithms (GAs) with BESO is developed in order to stochastically search for the global optima. These enhanced BESO algorithms are applied to various optimization problems with the periodicity requirement as an extra constraint aiming at producing periodicity in the layout. For structures under static loading, the present thesis addresses minimization of the mean compliance and explores the applications of conventional stiffness optimization for periodic structures. Furthermore, this thesis develops a volume minimization formulation where the maximum deflection is constrained. For the design of structures subject to dynamic loading, this thesis develops two different approaches (hard-kill and soft-kill) to resolving the problem of localized or artificial modes. In the hard-kill (completely removing void elements) approach, extra control measures are taken in order to eliminate the localized modes in an explicit manner. In the soft-kill approach, a modified power low material model is presented to prevent the occurrence of artificial and localized modes. Periodic stress and strain fields cannot be assumed in structures under arbitrary loadings and boundaries at the macroscopic level. Therefore being different from material design, no natural base cell can be directly extracted from macrostructures. In this thesis, the concept of an imaginary representative unit cell (RUC) is presented. For situations when the structure cannot be discretized into equally-sized elements, the concept of sensitivity density is developed in order for mesh-independent robust solutions to be produced. The RUC and sensitivity density based approach is incorporated into various topology optimization problems to obtain absolute or scaled periodicities in structure layouts. The influence of this extra constraint on the final optima is investigated based on a large number of numerical experiments. The findings shown in this thesis have established appropriate techniques for designing and optimizing periodic structures. The work has provided a solid foundation for creating a practical design tool in the form of a user-friendly computer program suitable for the conceptual design of a wide range of structures.

Made available in DSpace on 2016-12-12T20:25:13Z (GMT). No. of bitstreams: 1 Gustavo Assis da Silva.pdf: 2694304 bytes, checksum: 361de063d220eeeebb77985807d4fc22 (MD5) Previous issue date: 2016-02-22 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / This work addresses the use of the topology optimization of continuum structures under uncertainties in material properties associated to stiffness. The perturbation approach is used to perform the uncertainties quantification and the midpoint method is used for the random field discretization, where a decorrelation technique is used to reduce the computational effort. The finite element method is used for the domain discretization and the SIMP approach is used as material parameterization. Two problems are analyzed: the compliance minimization with volume constraint and the volume minimization with local stress constraints. The first problem is solved by using a optimality criteria method and the second problem by using the augmented Lagrangian method with a gradient based minimization method proposed in this work. The qp approach is used to avoid the singularity phenomenon in the problem with local stress constraints. Although this approach can be used considering uncertainty in any material property associated to stiffness ,the examples in this work show uncertainty only in Young s modulus. Different correlation lengths are considered to verify its influence in the optimum topologies. It is shown that the optimum topology, in both problems analyzed, becomes more distinct from the deterministic topology when the correlation length is reduced. / Este trabalho aborda o uso da otimização topológica de estruturas contínuas sob incertezas nas propriedades do material associadas à rigidez. O método de perturbação é utilizado para a quantificação de incertezas e o método do ponto médio é utilizado para a discretização do campo aleatório, onde uma abordagem de desacoplamento é utilizada para reduzir o custo computacional. O método dos elementos finitos é utilizado para a discretização do domínio e o modelo SIMP é utilizado na parametrização material. Dois problemas são analisados: o problema de minimização de flexibilidade com restrição de volume e o problema de minimização de volume com restrição local de tensão. O primeiro problema é solucionado utilizando-se um método de critério de ótimo e o segundo problema utilizando-se o método do Lagrangiano aumentado juntamente com um método de minimização baseado em gradiente proposto neste trabalho. Considerando-se o problema com restrição local de tensão, utilizou-se a relaxação qp para evitar o fenômeno de singularidade. Embora esta abordagem possa ser utilizada considerando-se incerteza em qualquer propriedade do material associada à rigidez, os exemplos ilustrados no trabalho apresentam incerteza apenas no módulo de elasticidade. Diferentes tamanhos de correlação são considerados de forma a verificar a sua influência na topologia ótima. Verifica-se que a topologia obtida, em ambos os problemas apresentados, torna-se mais distinta da topologia determinística com a redução do tamanho de correlação.

Energy conversion devices using solid-state magnetocaloric materials have the potential to reduce energy consumption and mitigate environmental pollutants. To overcome the limited magnetic entropy change of magnetocaloric materials, magnetic refrigeration devices typically use the active magnetic regenerator (AMR) cycle. AMR devices have demonstrated promising performance, however costs must be reduced for broad market penetration. Although the magnet cost is of greatest importance for commercialization, literature has decoupled magnet design from AMR optimization. And while multilayered regenerators can improve performance without increasing cost, a number of questions remain unanswered as a result of the prohibitive parameter space. This dissertation explores methods of improving AMR performance and decreasing cost both at the subsystem level, namely the magnetocaloric regenerator, fluid flow system and magnetic field source, and the device level by coupling the regenerator and magnet design problems in a cost optimization framework. To improve AMR performance, multilayered regenerators with second-order magnetocaloric materials are experimentally and numerically investigated, yielding insight on how individual layers behave and interact over a wide range of regenerator compositions and operating parameters. An efficient AMR modeling approach is presented where individual layers are treated as cascaded AMR elements, and simulations are in excellent agreement with experiments. Insights from the computationally efficient model are used to inform device modifications, and a no-load temperature span of 40 K is measured in close proximity to the simulated optimum; one of the highest in literature. To simultaneously decrease AMR costs, a permanent magnet optimization framework is explored that is conducive to nonlinear objectives and constraints. This is used to investigate the optimal design of permanent magnet structures with reduced rare-earth permanent magnet materials. The regenerator and magnet design problems are then coupled in a permanent magnet topology optimization to minimize the combined capital and operating costs of an AMR. The optimal magnetic field waveform and the optimal means of producing this waveform are simultaneously obtained. The lifetime ownership costs of the optimized AMR device are shown to be in the realm of existing entry-level cooling devices. The presented cost optimization framework is of interest to both scientists and engineers, and demonstrates the importance of fast AMR models in identifying system designs, regenerator compositions and operating regimes that increase AMR performance and decrease cost. / Graduate