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Implementation of artificial neural-networks to model the performance parameters of Stirling engine

机译：人工神经网络对斯特林发动机性能参数建模的实现

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摘要

The Stirling engine is defined as a simple form of external-combustion engine which employs a compressible working fluid. From a theoretical point of view the Stirling engine can be very effective at Carnot efficiency to convert heat into mechanical work. It is an environmental friendly heat engine which could reduce CO2 emission through combustion process. Performance of Stirling engine changes via changing several variables and the aforementioned variables should be optimized to accomplish maximum performance of Stirling engine. Among all the variables representing the performance of Stirling engine, output power and shaft torque are most common for illustrating the performance of Stirling engines. In this research predicting two aforementioned variables with high accuracy and flexibility is investigated. To gain this goal, a predictive and easy-to-use tool is developed based on the concept of the artificial neural network (ANN) to predict output power and shaft torque of the Stirling engine. Furthermore, precise data samples from previous researches are employed to construct this easy-to-use model. Based on the outputs obtained from an easy-to-use model developed in this research, ANN model could help experts in designing of Stirling engine with low degree of uncertainty.
机译：斯特林发动机被定义为采用可压缩工作流体的内燃机的一种简单形式。从理论上讲，斯特林发动机在将卡诺效率转换为机械功方面非常有效。这是一种环保的热机，可以通过燃烧过程减少二氧化碳的排放。通过改变几个变量来改变斯特林发动机的性能，并且应当优化上述变量以实现斯特林发动机的最大性能。在代表斯特林发动机性能的所有变量中，输出功率和轴扭矩最常见于说明斯特林发动机的性能。在这项研究中，对预测具有高精度和灵活性的上述两个变量进行了研究。为了实现此目标，基于人工神经网络（ANN）的概念开发了一种预测性且易于使用的工具，以预测斯特林发动机的输出功率和轴转矩。此外，采用以前研究的精确数据样本来构建此易于使用的模型。基于从本研究开发的易于使用的模型获得的输出，ANN模型可以帮助专家设计具有低不确定性的斯特林发动机。

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