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使用人工神经网络和火虫优化算法的活跃自由活塞斯特林发动机的性能研究
A P Masoumi1, A R Tavakolpour-Saleh1, V Bagherian2
1Department of Mechanical and Aerospace Engineering, Shiraz University of Technology, Shiraz, Iran.
这项研究使用机器学习,特别是人工神经网络 (ANN),来建模一个活跃的自由活塞斯特林引擎 (AFPSE). 该ANN模型有效地预测了发动机性能,达到23.07W的最大输出功率.
科学领域:
- 热力学是一种热力学.
- 机械工程 机械工程
- 人工智能的人工智能
背景情况:
- 模拟活跃的自由活塞斯特林发动机 (AFPSE) 涉及复杂的热方程,使得数据提取耗时.
- 机器学习为加快AFPSE特征分析提供了一个潜在的解决方案.
研究的目的:
- 使用机器学习探索一个活跃的自由活塞斯特林发动机 (AFPSE) 的特性.
- 开发一个计算效率高的模型来预测AFPSE性能参数.
主要方法:
- 使用5000个模拟样本开发了一个人工神经网络 (ANN).
- 输入参数包括源温度,直流电机电压,弹刚度和活塞质量.
- 具有两个隐藏层 (10和20个神经元) 的ANN模型使用火虫算法进行了优化.
主要成果:
- 该ANN模型有效估计了AFPSE参数,大大缩短了分析时间.
- 通过8.5V直流电机输入,实现了23.07W的最大输出功率.
- 火优化算法成功确定了最大化输出功率的最佳ANN参数.
结论:
- 机器学习,特别是ANN,显示出有效分析AFPSE的巨大潜力.
- 开发的ANN模型为AFPSE性能预测提供了传统模拟方法的更快的替代方案.
- 这种方法可以加速AFPSE系统的设计和优化.
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