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Application of Genetic Algorithms to Accelerated Determination of Material Parameters Needed in Creep-Fatigue Life Evaluation

机译：遗传算法在加速测定蠕变疲劳寿命评估中所需材料参数的应用

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摘要

The present paper investigates the applicability of Genetic Algorithms (GA) to accelerated determination of the material parameters required in order to evaluate creep-fatigue life and remaining life of Mod.9Cr-1Mo steel. The GA program, which was written in visual C++, was developed in order to estimate the values of 23 material parameters required to describe the partitioned inelastic strain range versus life equations and the creep-fatigue damage growth model based on the strain range partitioning concept. Three types of test, constant-strain amplitude creep-fatigue tests, constant-amplitude creep-fatigue crack growth tests and variable-strain waveform creep-fatigue tests, were needed in order to determine the above-described parameters experimentally. The GA analysis was able to determine the parameters without using the creep-fatigue crack growth test data. The effects of the number of data used in the GA analysis for the estimation accuracy of the material parameters and the creep-fatigue life were evaluated. The obtained results suggest that the number of tests required for GA determination of material parameters is half of the number of creep-fatigue tests (175) that must be conducted in order to determine the material parameters experimentally.
机译：本文调查了遗传算法（GA）的适用性，加速了评估MOD.9CR-1MO钢的蠕变疲劳寿命和剩余寿命所需的材料参数的测定。正在制定以Visual C ++写入的GA程序，以估计描述分区非弹性应变范围与寿命方程和基于应变范围分区概念的蠕变 - 疲劳损伤生长模型所需的23种材料参数的值。需要三种类型的试验，恒定 - 应变幅度蠕变疲劳试验，恒定蠕变疲劳裂纹生长试验和可变菌株波形蠕变疲劳试验，以便在实验上确定上述参数。 GA分析能够在不使用蠕变 - 疲劳裂纹生长试验数据的情况下确定参数。评估了GA分析中使用的数据数量的效果，用于估计材料参数和蠕变疲劳寿命的估计精度。所得结果表明，GA测定材料参数所需的测试数量是必须进行的蠕变疲劳试验（175）的一半，以便在实验上确定材料参数。

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