An Investigation of the Application of Phase Change Materials in Practical Thermal Management Systems

This work investigates the application of alternative cooling techniques to thermal management. In the first section, this work presents models and extensive simulation studies on an alternative cooling strategy based upon phase change materials (PCMs) for the thermal management system of a LED headlight assembly. These studies have shown that properly chosen PCMs, when suspended in metal foam matrices, increased the thermal conductivity of the PCM. The increased thermal conductivity can enhance the cooling characteristics of a practical thermal management system for a LED headlight system. To further enhance the advantages of using PCMs, nanoparticle enhanced fluids (nanofluids) are desirable as an additional source of cooling. The use of nanofluids motivates the development of a new diagnostic tool for multiphase flows and a minimization algorithm for analyzing the data. For this purpose, the second section of this work develops a new technique that is based on wavelength-multiplexed laser extinction (WMLE) to measure particle sizes in multiphase flows. In the final section of this work, the simulated algorithm (SA) is investigated for analyzing the data collected in this work. Specifically, the parallelization of the SA technique is investigated to reduce the high computational cost associated with the SA algorithm

Simulated Annealing

The book contains 15 chapters presenting recent contributions of top researchers working with Simulated Annealing (SA). Although it represents a small sample of the research activity on SA, the book will certainly serve as a valuable tool for researchers interested in getting involved in this multidisciplinary field. In fact, one of the salient features is that the book is highly multidisciplinary in terms of application areas since it assembles experts from the fields of Biology, Telecommunications, Geology, Electronics and Medicine

Metaheuristics applied to the optimization of continuous functions

Optimization is a field of mathematics which studies and develops mathematical methods with the aim of optimizing a wide range of problems. In physics these methods are central. Essentially all the dynamical equations in physics can be expressed as a series of optimization problems in terms of action integrals. Optimization can better be explained as finding the optima, also known as extremes, of a mathematical object. Such object may be a continuous function, as the case of this thesis. The approaches for solving optimization problems are generally divided into two categories, deterministic optimization and stochastic optimization. The main difference is that the deterministic approach applies calculus and the stochastic approach applies a search technique. For solving complex optimization problems, the stochastic approach has long proven to be most efficient. This thesis focuses on improving the two stochastic search methods: Simulated Annealing and the Genetic Algorithm. This is performed by implementing two newly developed methods. The first method is the Tangent-based Evaluation method, which is better suited to detect abnormalities in continuous functions than the common one-point evaluation method. The other method is the Analytic Swap method for generation of solutions. Solution generation is an important part of any stochastic algorithm. Usually the new solutions generated by a random function, but the Analytic Swap method combines randomness with analytics to generate better solutions

Laser Absorption Chemical Species Tomography

This thesis outlines two advancements in the field of limited data absorption tomography. First, a novel reconstruction algorithm integrating the use of level set methods is presented that incorporates the additional a priori knowledge of a distinct interface between the species of interest and co-flow. The added a priori further reduces the ill-posedness of the system to produce a final concentration distribution that explains the laser absorption measurements and is qualitatively consistent with advection/diffusion transport. The algorithm is demonstrated by solving a simulated laser tomography experiment on a turbulent methane plume, and is compared with the current state-of-the-art reconstruction algorithm. Given the limited number of attenuated measurements, accurate reconstructions are also highly dependent on the locations sampled by the measurement array. This thesis displays how the mathematical properties of the coefficient matrix, A, formed by the locations of the lasers, are related to the information content of the attenuation data using a Tikhonov reconstruction framework. This formulation, in turn, becomes a basis for a beam arrangement design algorithm that minimizes the reliance on additional assumed information about the concentration distribution. Using genetic algorithms, beam arrangements can be optimized for a given application by incorporating physical constraints of the beam locations. The algorithm is demonstrated by optimizing unconstrained and constrained arrangements of light sources and detectors. Simulated experiments are performed to validate the optimality of the arrangements

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Tomographic imaging of combustion zones using tunable diode laser absorption spectroscopy (TDLAS)

This work concentrates on enabling the usage of a specific variant of tunable diode laser absorption spectroscopy (abbr. TDLAS) for tomogaphically reconstructing spatially varying temperature and concentrations of gases with as few reconstruction artifacts as possible. The specific variant of TDLAS used here is known as wavelength modulation with second harmonic detection (abbr. WMS-2f) which uses the wavelength dependent absorbance information of two different spectroscopic transitions to determine temperature and concentration values. Traditionally, WMS-2f has generally been applied to domains where temperature although unknown, was spatially largely invariant while concentration was constant and known to a reasonable approximation (_x0006_+/- 10% ). In case of unknown temperatures and concentrations with large variations in space such techniques do not hold good since TDLAS is a “line-of-sight” (LOS) technique. To alleviate this problem, computer tomographic methods were developed and used to convert LOS projection data measured using WMS-2f TDLAS into spatially resolved local measurements. These locally reconstructed measurements have been used to determine temperature and concentration of points inside the flame following a new temperature and concentration determination strategy for WMS-2f that was also developed for this work. Specifically, the vibrational transitions (in the 1.39 microns to 1.44 microns range) of water vapor (H2O) in an axi-symmetric laminar flame issuing from a standard flat flame burner (McKenna burner) was probed using telecom grade diode lasers. The temperature and concentration of water vapor inside this flame was reconstructed using axi-symmetric Abel de-convolution method. The two different sources of errors in Abel’s deconvolution - regularization errors and perturbation errors, were analyzed and strategies for their mitigation were discussed. Numerical studies also revealed the existence of a third kind of error - tomographic TDLAS artifact. For 2D tomography, studies showing the required number of views, number of rays per view, orientation of the view and the best possible algorithm were conducted. Finally, data from 1D tomography was extrapolated to 2D and reconstructions were benchmarked with the results of 1D tomography

Spectrally Resolved Absorption Tomography for Reacting, Turbulent Gas Phase Systems: Theory and Application

This work proposes tomographic absorption spectroscopy as a complementary measurement method to other non-intrusive methods that are applied in the research of reactive gas-phase flows. A coherent methodological framework based on conventional Bayesian inference is presented, that contains new methods and improvements in several key procedures. The framework relies on linear hyperspectral absorption tomography, that is favored for its higher computational efficiency compared to nonlinear tomography, and separates tomographic reconstruction and spectroscopic regression. The methods target the analysis of direct absorption spectroscopic measurements like direct tunable diode laser absorption spectroscopy. The improved key procedures include a spatial resolution measure based on a modified Maximum-a-posteriori covariance matrix. This resolution measure is applicable to sparse and dense beam arrangements alike, without inconsistencies arising from unprobed mesh nodes. The compatibility with resolution measures based on point spread functions is demonstrated in simulations. Additionally, the design question of the spatial-temporal resolution trade-off is discussed on spatio-temporal correlation maps with a constraint imposed by the effective measurement data-rate. Typical data-rates of spectrally resolved tomographic absorption spectroscopy setups often do not allow for capturing turbulent structures. In consequence, the optimum trade-off for quasi-stationary systems often is the focus on spatial resolution, neglecting temporal resolution. A regularization parameter choice method, relying on residuals of the spectroscopic regressions, is introduced. The idea is to balance noise amplification through under-regularization, and incompatibility with the spectroscopic model through excessive spatial-averaging of temperature structures due to over-regularization. This method allows to partially reclaim the informative advantage of nonlinear tomography, by inferring information on temperature structures from the nonlinear temperature dependence of the spectroscopic model. The selected prior parameters are shown to result in spatial resolutions matching spatial structures in the application cases. The same model error used to judge the compatibility with the spectroscopic model for parameter selection, leads to a temperature bias if temporally averaged data of a turbulent system is fitted by a homogeneous spectroscopic model. Ideas from methods to prevent this bias in spatial averaging are transferred to temporal averaging. The resulting temperature fluctuation model reduces the bias and additionally gives a qualitative measure of temperature fluctuations. The often neglected problem of estimating absorbance spectra from intensity traces is treated with Bayesian inference. This new Bayesian absorbance estimation method is shown to be numerically efficient if large numbers of absorbance traces are to be inferred like in tomography. Unlike fitting methods it is compatible with inhomogeneous line-of-sights without modification or computational penalties. Further, the incident intensity shape is not restricted to arbitrary model functions, but modeled with all degrees of freedom. The framework of methods is applied to practically relevant scenarios in the industrial characterization of selective catalytic reduction systems, and in the research of oxy-fuel combustion. The application cases feature different levels of complexity, with turbulent and laminar flows, stationary and instationary processes, axisymmetric and two dimensional flows, as well as homogeneous and inhomogeneous temperature distributions. Also the scalability of the methods is demonstrated by experiments with beam counts from 8 to 10440, and (pseudo) temporal resolutions of up to 5 kHz. For all application cases a specific discussion of uncertainty and spatial resolution is provided