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首页> 外文期刊>International Journal of Heat and Mass Transfer >Low cost surrogate model based evolutionary optimization solvers for inverse heat conduction problem
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Low cost surrogate model based evolutionary optimization solvers for inverse heat conduction problem

机译：基于低成本代理模型的逆导热问题进化优化求解器

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摘要

Using temperature measurements from inside a solid to determine boundary conditions is a common inverse heat conduction problem. These problems are ill-posed and a robust mathematical solution is not available. Stochastical search algorithms like genetic algorithm (GA) and particle swarm optimization (PSO) have been found to be effective in dealing with these problems. However, they require large population size and do not use the gradient information and, therefore, their computational costs are higher than their gradient based alternatives. This is especially true when using a computationally expensive method like finite element analysis as the direct solver. A computationally cheaper substitute is using surrogate models. They construct an approximation to the direct problem using a set of available data and the underlying physics of the problem. This idea has been employed in this research. The result is a method that has the stability and effectiveness of evolutionary algorithms with a much lower computational cost.
机译：使用固体内部的温度测量值确定边界条件是常见的逆导热问题。这些问题不适当地解决，因此无法使用可靠的数学解决方案。已经发现诸如遗传算法（GA）和粒子群优化（PSO）的随机搜索算法可以有效地解决这些问题。但是，它们需要庞大的人口规模，并且不使用梯度信息，因此，其计算成本高于基于梯度的替代方法。当使用像有限元分析这样的计算量大的方法作为直接求解器时，尤其如此。在计算上更便宜的替代方法是使用代理模型。他们使用一组可用数据和问题的基本物理原理来构造直接问题的近似值。这个想法已经在这项研究中被采用。结果是一种方法，该方法具有进化算法的稳定性和有效性，而计算成本却低得多。

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