采样点 - - 基于应力放松实验数据的粘弹性材料的微分麦克斯韦尔模型的独立识别
1Department of Technology Fundamentals, Faculty of Production Engineering, University of Life Sciences in Lublin, 20-612 Lublin, Poland.
Materials (Basel, Switzerland)
|April 13, 2024
概括
本研究使用噪音放松模量数据确定了对粘弹性材料的最佳分数麦克斯韦模型参数. 随机选择的采样点确保准确的模型恢复,独立于特定的测量时间.
科学领域:
- 类风病学 类风病学 类风病学
- 分数微积分的计算.
- 材料科学 材料科学 材料科学
背景情况:
- 分数顺序的学模型越来越多地用于描述材料粘性弹性.
- 分数麦克斯韦模型是一个关键的非整数顺序的风学模型.
- 从杂的实验数据中识别模型参数是一项挑战.
研究的目的:
- 确定物质放松模块识别的最佳分数麦克斯韦模型参数.
- 开发一种对噪声稳固且独立于采样点选择的方法.
- 验证模型的准确性和收性质.
主要方法:
- 放松模块的加权最小平方近似值.
- 抽样时间点的随机化,用于压力放松实验.
- 随机收分析和数值模拟.
主要成果:
- 最佳的分数麦克斯韦模型参数即使在未知材料特性的情况下也可以恢复.
- 识别的参数是对所需的最佳模型的强烈一致的估计.
- 已经证明了指数趋同率,显示了对噪声和抽样随机化的稳定性.
结论:
- 为最佳模型识别提供了一个简单的方案.
- 抽样中的随机化保证了对独立于抽样点的参数的趋同.
- 已识别的分数麦克斯韦模型对测量干扰具有强度.
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