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使用ANN进行气内和性能优化的实验研究
Shekhar T Shinde1,2, Kishor R Borole2,3, Kedarnath Chaudhary4
1Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune 411043, India.
MethodsX
|July 4, 2025
概括
-铁合金为内燃机内提供了优越的耐用性,显示了压力降低和性能提高. 人工神经网络的优化进一步提高了材料选择的预测性维护和效率.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算流体动力学的流体动力学.
背景情况:
- 内燃机中的气内是关键组件,需要高结构完整性和优化性能.
- 材料的选择和理解热力学应力反应是提高发动机效率和耐用性的关键.
研究的目的:
- 调查气里面的结构完整性和性能优化.
- 在发动机条件下评估铁,铁合金和合金.
- 使用人工神经网络 (ANN) 来优化性能参数.
主要方法:
- 有限元分析 (FEA) 用于评估热力学应力,磨损和散热.
- 对材料特性进行实验性评估.
- 用于预测不同热量和负载条件下的材料行为.
主要成果:
- -铁合金表现出最低的组合应力,表明优越的热应力抵抗力.
- 铁表现出过度的热诱导应力,而合金由于热膨胀而发生变形.
- 基于ANN的优化成功地将应力值降低了高达20%.
结论:
- -铁合金是最适合用于气里面的材料,因为它具有抗应力.
- ANN优化是一个可行的工具,用于材料选择,预测性维护和改善气内性能.
- 结合计算和实验方法,可以优化发动机组件设计,以提高强度和性能.
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