Abstract
We consider parameter identification of a hydride decomposition model by scanning the parameter space in parallel. Such problem is resource demanding, but suits best for Desktop Grid computing. Considering task retrieval as a game, we show that the search process can be improved to produce solutions faster in comparison with random search, with no or minor additional cost.
Highlights
Inverse problems of parameter identification using experimental data are important in material science and physical chemistry
In [5] we described the solution of parameter identification of an aluminum hydride decomposition model by scanning the parameter space
Our aim was to find kinetic parameters for decomposition of the hydride of aluminum, and to study the opportunities of employing Desktop Grids to solve this kind of problems and to find ways of improving the search process, so that high-dimensional problems would be solved quicker and at lower costs
Summary
Inverse problems of parameter identification using experimental data are important in material science and physical chemistry. The model’s ability to reproduce observations is the main criteria of its quality. A model usually contains multiple parameters which have physical sense. Nonlinear interaction of different processes makes independent determination of parameters by superposition techniques hard or even impossible. The common approach of choosing a single limiting reaction can only be used when this reaction is significantly slower than all others, which is seldom the case of close-to-equilibrium processes. A good model should fit a single experimental curve, but a series of them, even better if parameter sets are similar. Constructed on basic principles (e.g., conservation laws) and descriptions of elementary reactions, a model usually contains more than one parameter
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