In the present article, a methodology is suggested to enhance the cooling performance of a double inlet pulse tube refrigerator (DIPTR), which is affected by multiple operating and geometrical parameters, using artificial intelligence methods. A procedure based on artificial intelligence method is adopted to generate the optimum range of inputs to achieve the maximum obtainable performance of the DIPTR. Artificial neural network (ANN) is developed using three different activation functions at the outer layer. It is observed that purelin and tansig activation functions predict the results (cooling power and percentage Carnot respectively) more accurately in accordance with the numerical results. In addition, it is observed that the particle swarm optimization (PSO) of the weights and bias of ANN is capable of representing the non-linear mathematical relationship among inputs and outputs more accurately in case of a DIPTR. It is further observed that the hybrid scheme of the artificial-neuro-fuzzy-inference system (ANFIS) can estimate both cooling capacity and percentage Carnot apparently more precise than the backpropagation algorithm. A numerical model is developed, based on the finite volume discretization of the governing equations with ideal gas assumption to generate the data matrix, which is essential to develop the ANN and ANFIS. Finally, an experimental analysis is conducted to validate the optimum range of inputs (low and high-pressures of compressor, waiting time of rotary valve and frequency) obtained from artificial intelligence models.

在本文中，提出了一种使用人工智能方法来增强双入口脉冲管制冷机（DIPTR）的冷却性能的方法，该制冷机受多个操作和几何参数的影响。采用基于人工智能方法的程序来生成最佳输入范围，以实现DIPTR的最大可获得性能。在外层使用三种不同的激活函数开发了人工神经网络（ANN）。据观察，purelin和正切S型激活函数根据数值结果更准确地预测结果（分别为冷却功率和卡诺百分比）。此外，可以发现，在DIPTR的情况下，ANN的权重和偏差的粒子群优化（PSO）能够更准确地表示输入和输出之间的非线性数学关系。进一步观察到，人工神经模糊推理系统（ANFIS）的混合方案可以估计制冷能力和卡诺百分率，这显然比反向传播算法更为精确。基于控制方程的有限体积离散和理想气体假设，建立了一个数值模型，以生成数据矩阵，这对于开发ANN和ANFIS是必不可少的。最后，