基于人工神经网络的自由活塞膨胀机-直线发电机输出性能敏感性分析

Sensitivity analysis of output performance of free piston expander-linear generator based on artificial neural network

  • 摘要: 为了优化用于余热回收的自由活塞膨胀机–直线发电机的输出性能,提出了一种基于人工神经网络预测的方法。以输出性能为指标,对进气压力、进气温度、进气持续时间、膨胀持续时间和排气持续时间这5个输入参数进行敏感性分析,并利用正交试验的极差分析结果进行对比验证。研究表明,输入因素影响程度由大到小分别为进气压力、进气温度、进气持续时间、膨胀持续时间、排气持续时间;最佳的输入因素参数配置为,进气压力0.3 MPa,进气持续时间40 ms,膨胀持续时间70 ms,排气持续时间70 ms和进气温度30 ℃,峰值输出电压可达8.96 V,峰值输出功率可达33.74 W。该方法适用于研究参数的敏感性,可为后续的参数优化与系统改进工作提供有价值的参考依据。

     

    Abstract: In order to optimize the output performance of the free-piston expander-linear generator for waste heat recovery, a method based on artificial neural network prediction is proposed. Taking output performance as an index, sensitivity analysis is carried out on five input parameters, namely intake pressure, intake temperature, intake duration, expansion duration and exhaust duration. The results of the range analysis of orthogonal experiment are compared and verified. The results show that the influence degree of input factors is intake pressure, intake temperature, intake duration, expansion duration and exhaust duration, respectively. The optimal input factor parameter configuration is intake pressure 0.3 MPa, intake duration 40 ms, expansion duration 70 ms, exhaust duration 70 ms and intake temperature 30 ℃, peak output voltage up to 8.96 V, peak output power up to 33.74 W. This method is suitable for studying the sensitivity of parameters and can provide a valuable reference for the subsequent parameter optimization and system improvement.

     

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