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

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

Optimization performance of irreversible refrigerators base on evolutionary algorithm

In early works done by authors, performance analysis of refrigeration systems such as power input, refrigeration load and coefficient of performance (COP) was investigated. In this article a new function called “Coefficient of Performance Exergy” or COPE has been introduced. Two objective functions of coefficient of performance exergy and exergy destruction are optimized simultaneously using the multi-objective optimization algorithm NSGAII. COPE has been maximized and exergy destruction has been minimized in order to get the best performance. Decision making has been done by means of two methods of LINAMP and TOPSIS. Finally an error analysis done for optimized values shows that LINAMP method is preferable against TOPSIS method