An alpha type Stirling engine was optimized using meta-models considering uninterrupted electric power supply concurrently with natural gas combi boilers at homes during electricity interruptions. To predict and optimize the power and efficiency of the designed Stirling engine, an artificial neural network (ANN) model was trained as a meta-model. The ANN modeling method was used in solving a multi-objective Pareto optimization problem under some constraints to determine the optimum engine parameters. The design parameters were swept volume, hot and cold cylinder temperatures, gas constant, charge pressure and engine operation speed. Feed forward and Levenberg–Marquardt back propagation algorithms were evaluated to determine the best resulting network architecture that was found as 6–12–8–1. Subsequently, the fraction of variance (R_f) value was calculated close to 1 and the absolute mean error percentage (AMEP) was calculated as 6.07%. Trained ANN model was used in solving the multi-objective optimization problem. Using the optimum design parameters, the meta-model predicted the power as 73.3 W and efficiency as 32.2%. Co-simulation approach was followed to verify the optimization results, and the nominal power output and corresponding efficiency were calculated using the Schmidt theory and the calibrated 1-D model created by the GT-Suite software that yield respectively, 144.6 W and 85.8 W for the power and 35% and 35.1% for the cycle efficiency. Consequently, the use of an ANN model in solving the associated optimization problem proved itself as a fast, accurate enough and powerful method to find the optimum design parameters and predict the engine performance.

考虑到电力中断期间同时与家中的天然气组合锅炉同时提供不间断电力供应的情况，使用元模型对α型斯特林发动机进行了优化。为了预测和优化设计的斯特林发动机的功率和效率，将人工神经网络（ANN）模型训练为元数据-模型。在某些约束条件下，使用ANN建模方法解决了多目标Pareto优化问题，以确定最佳发动机参数。设计参数为扫气量，气缸的冷热温度，气体常数，充气压力和发动机运行速度。对前馈和Levenberg-Marquardt反向传播算法进行了评估，以确定结果最佳的网络体系结构，即6-12-8-1。随后，计算出方差分数（R _f）值接近1，并且计算出绝对平均误差百分比（AMEP）为6.07％。经过训练的人工神经网络模型用于解决多目标优化问题。采用优化设计参数，该元-model预测功率为73.3 W，效率为32.2％。遵循协同仿真方法来验证优化结果，并使用Schmidt理论和GT-Suite软件创建的校准的1-D模型计算出标称功率输出和相应的效率，分别产生144.6 W和85.8 W的功率。功率，循环效率分别为35％和35.1％。因此，使用ANN模型解决相关的优化问题证明了自己是一种快速，足够准确且功能强大的方法，可以找到最佳设计参数并预测发动机性能。