Modelagem e otimização de experimentos para o tratamento térmico de recozimento: um estudo com o algoritmo simulated annealing

Esta dissertação apresenta um estudo da modelagem de experimentos aplicados a um processo industrial de tratamento térmico. A motivação deste trabalho surgiu diante das dificuldades associadas aos processos de recozimento industrial de aços do tipo baixa liga, na tentativa de encontrar temperaturas nas quais as durezas superficiais dos aços atingissem valores suficientemente baixos, adequados para etapas posteriores de fabricação, em especial a usinagem. Inicialmente forem realizados diversos experimentos com diferentes aços, onde a dureza superficial é obtida em função da temperatura de recozimento e dos teores de carbono e silício das amostras utilizadas. Em seguida propôs-se um modelo quadrático para modelar a dureza superficial como função dessas três variáveis. A estimação de parâmetros do modelo proposto foi realizada com o emprego do algoritmo Simulated Annealing, uma meta-heurística para otimização global que procura imitar o processo de recozimento de um material sólido. Finalmente, usando-se o modelo proposto, foi resolvido o chamado problema inverso, o qual consiste na estimação da temperatura de recozimento em função dos teores de carbono e silício e da dureza desejada.

Aplicação da técnica simulated annealing na investigação da ciclagem de nitrogênio na inteface água-sedimento

Neste trabalho é apresentado a aplicação de um método de otimização a fim de estimar parâmetros que normalmente estão presentes na modelagem matemática da dinâmica de espécies químicas na interface água-sedimento. O Problema Direto aqui consistiu na simulação das concentrações das espécies orgânicas e inorgânicas (amônia e nitrato) de nitrogênio, num ambiente idealizado, o qual foi fracionado em quatro camadas: uma camada de água (1 metro) e três camadas de sedimento (0-1 cm, 1-2 cm e 2-10 cm). O Problema Direto foi resolvido pelo Método de Runge Kutta, tendo sido gerada uma simulação de 50 dias. Na estimativa dos coeficientes de difusão e porosidade foi aplicado o Método Simulated Annealing (SA). A eficiência da estratégia aqui adotada foi avaliada através do confronto entre dados experimentais sintéticos e as concentrações calçadas pela solução do Problema Direto, adotando-se os parâmetros estimados pela SA. O melhor ajuste entre dados experimentais e valores calculados se deu quando o parâmetro estimado foi a porosidade. Com relação à minimização da função objetivo, a estimativa desse parâmetro também foi a que exigiu menor esforço computacional. Após a introdução de um ruído randômico às concentrações das espécies nitrogenadas, a técnica SA não foi capaz de obter uma estimativa satisfatória para o coeficiente de difusão, com exceção da camada 0-1 cm sedimentar. Para outras camadas, erros da ordem de 10 % foram encontrados (para amônia na coluna dágua, pro exemplo). Os resultados mostraram que a metodologia aqui adotada pode ser bastante promissora enquanto ferramenta de gestão de corpos dágua, especialmente daqueles submetidos a um regime de baixa energia, como lagos e lagoas costeiras.

Simulated Annealing: Rigorous finite-time guarantees for optimization on continuous domains

Simulated annealing is a popular method for approaching the solution of a global optimization problem. Existing results on its performance apply to discrete combinatorial optimization where the optimization variables can assume only a finite set of possible values. We introduce a new general formulation of simulated annealing which allows one to guarantee finite-time performance in the optimization of functions of continuous variables. The results hold universally for any optimization problem on a bounded domain and establish a connection between simulated annealing and up-to-date theory of convergence of Markov chain Monte Carlo methods on continuous domains. This work is inspired by the concept of finite-time learning with known accuracy and confidence developed in statistical learning theory.

Neutronic optimization in high conversion Th-233U fuel assembly with simulated annealing

This paper reports on fuel design optimization of a PWR operating in a self sustainable Th-233U fuel cycle. Monte Carlo simulated annealing method was used in order to identify the fuel assembly configuration with the most attractive breeding performance. In previous studies, it was shown that breeding may be achieved by employing heterogeneous Seed-Blanket fuel geometry. The arrangement of seed and blanket pins within the assemblies may be determined by varying the designed parameters based on basic reactor physics phenomena which affect breeding. However, the amount of free parameters may still prove to be prohibitively large in order to systematically explore the design space for optimal solution. Therefore, the Monte Carlo annealing algorithm for neutronic optimization is applied in order to identify the most favorable design. The objective of simulated annealing optimization is to find a set of design parameters, which maximizes some given performance function (such as relative period of net breeding) under specified constraints (such as fuel cycle length). The first objective of the study was to demonstrate that the simulated annealing optimization algorithm will lead to the same fuel pins arrangement as was obtained in the previous studies which used only basic physics phenomena as guidance for optimization. In the second part of this work, the simulated annealing method was used to optimize fuel pins arrangement in much larger fuel assembly, where the basic physics intuition does not yield clearly optimal configuration. The simulated annealing method was found to be very efficient in selecting the optimal design in both cases. In the future, this method will be used for optimization of fuel assembly design with larger number of free parameters in order to determine the most favorable trade-off between the breeding performance and core average power density.

Design of triangular lattice photonic crystals using genetic algorithms

Genetic Algorithms (GAs) were used to design triangular lattice photonic crystals with large absolute band-gap. Considering fabricating issues, the algorithms represented the unit cell with large pixels and took the largest absolute band-gap under the fifth band as the objective function. By integrating Fourier transform data storage mechanism, the algorithms ran efficiently and effectively and optimized a triangular lattice photonic crystal with scatters in the shape of 'dielectric-air rod'. It had a large absolute band gap with relative width (ratio of gap width to midgap) 23.8%.

How Good are Genetic Algorithms at Finding Large Cliques: An Experimental Study

This paper investigates the power of genetic algorithms at solving the MAX-CLIQUE problem. We measure the performance of a standard genetic algorithm on an elementary set of problem instances consisting of embedded cliques in random graphs. We indicate the need for improvement, and introduce a new genetic algorithm, the multi-phase annealed GA, which exhibits superior performance on the same problem set. As we scale up the problem size and test on \hard" benchmark instances, we notice a degraded performance in the algorithm caused by premature convergence to local minima. To alleviate this problem, a sequence of modi cations are implemented ranging from changes in input representation to systematic local search. The most recent version, called union GA, incorporates the features of union cross-over, greedy replacement, and diversity enhancement. It shows a marked speed-up in the number of iterations required to find a given solution, as well as some improvement in the clique size found. We discuss issues related to the SIMD implementation of the genetic algorithms on a Thinking Machines CM-5, which was necessitated by the intrinsically high time complexity (O(n3)) of the serial algorithm for computing one iteration. Our preliminary conclusions are: (1) a genetic algorithm needs to be heavily customized to work "well" for the clique problem; (2) a GA is computationally very expensive, and its use is only recommended if it is known to find larger cliques than other algorithms; (3) although our customization e ort is bringing forth continued improvements, there is no clear evidence, at this time, that a GA will have better success in circumventing local minima.

Parallel genetic algorithms for the solution of inverse heat conduction problems

A parallel genetic algorithm (PGA) is proposed for the solution of two-dimensional inverse heat conduction problems involving unknown thermophysical material properties. Experimental results show that the proposed PGA is a feasible and effective optimization tool for inverse heat conduction problems

Fuzzy Coding of Genetic Algorithms

A well-cited paper suggesting fuzzy coding as an alternative to the conventional binary, grey and floating-point representations used in genetic algorithms.