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Experimental design optimization and thermophysical parameter estimation of composite materials using genetic algorithms.

机译：使用遗传算法的复合材料实验设计优化和热物理参数估计。

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摘要

Thermophysical characterization of anisotropic composite materials is extremely important. Accuracy in the estimation of the thermophysical properties can be improved if the experiments are designed carefully. However, the typically used parametric study for the design optimization is limited and the commonly used gradient-based estimation methods are unstable for ill-conditioned problems.; The objectives of this research were to develop methodologies for both Experimental Design Optimization (EDO) used for the determination of thermal properties, and Simultaneous Parameter Estimation (SPE). Because of their advantageous features, Genetic Algorithms (GAs) were investigated for use as a strategy for both EDO and SPE. The EDO and SPE approaches involved the maximization of an optimality criterion associated with the sensitivity matrix of the unknown parameters, and the minimization of the ordinary least-squares error, respectively. Ultimately, two versions of a general-purpose GA-based program were developed: one is designed for analytical models while the other includes a control volume-based finite difference scheme.; The GA-based EDO/SPE methodology was successively applied to first, test cases previously solved in the literature and then, advanced studies. All involved thermal characterization of carbon/epoxy anisotropic composites and included EDOs and SPEs with up to seven experimental key parameters and nine thermophysical parameters, respectively. Finally, the kinetic characterization of the curing of three thermosetting materials was accomplished resulting in the SPE of six kinetic parameters.; Overall, the GA method was found to perform extremely well despite the high degree of correlation and low sensitivity of many parameters in all cases studied. The significance in using GAs is not only the solution to ill-conditioned problems, but also a drastically cost savings in both experimental and time expenses as they allow for the EDO and SPE of several parameters at once.
机译：各向异性复合材料的热物理特性非常重要。如果精心设计实验，可以提高热物理性质估计的准确性。然而，通常用于设计优化的参数研究是有限的，并且对于病态问题，常用的基于梯度的估计方法是不稳定的。这项研究的目的是开发用于确定热性能的实验设计优化（EDO）和同时参数估计（SPE）的方法。由于其优势，遗传算法（GAs）被研究用作EDO和SPE的策略。 EDO和SPE方法分别涉及与未知参数的灵敏度矩阵相关的最优准则的最大化和普通最小二乘误差的最小化。最终，开发了基于通用GA的通用程序的两个版本：一个用于分析模型，而另一个包括基于控制量的有限差分方案。基于GA的EDO / SPE方​​法首先被先应用于文献中已解决的测试案例，然后用于高级研究。所有这些都涉及碳/环氧各向异性复合材料的热表征，包括EDO和SPE，分别具有多达七个实验关键参数和九个热物理参数。最后，完成了三种热固性材料固化的动力学表征，得到了六个动力学参数的固相萃取。总体而言，尽管在所有研究的情况下许多参数都具有高度相关性和低灵敏度，但GA方法仍然表现出色。使用GA的意义不仅在于解决病态问题，而且还因为可同时允许多个参数的EDO和SPE而在实验和时间方面节省大量成本。

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  • 作者

    Garcia, Sandrine.;

  • 作者单位

    Virginia Polytechnic Institute and State University.;

  • 授予单位 Virginia Polytechnic Institute and State University.;
  • 学科 Engineering Mechanical.
  • 学位 Ph.D.
  • 年度 1999
  • 页码 280 p.
  • 总页数 280
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 机械、仪表工业;
  • 关键词

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