Performance Evaluation of a GM-Type Double Inlet Pulse Tube Refrigerator Using Artificial Intelligence Approach with Experimental Validation

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.