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

Autor:	Debashis Panda, Manoj Kumar, Ranjit K. Sahoo, Saumendra Sarangi, Ashok Kumar Satapathy
Rok vydání:	2020
Předmět:	Adaptive neuro fuzzy inference system Multidisciplinary Artificial neural network business.industry Computer Science::Neural and Evolutionary Computation 010102 general mathematics Particle swarm optimization Cooling capacity 01 natural sciences Backpropagation symbols.namesake symbols Artificial intelligence 0101 mathematics Pulse tube refrigerator Carnot cycle business Gas compressor Mathematics
Zdroj:	Arabian Journal for Science and Engineering. 45:9579-9597
ISSN:	2191-4281 2193-567X
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::66e9bfef65efbfed0d1f5b7113c11519 https://doi.org/10.1007/s13369-020-04685-2 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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