Suction Muffler, Optimization, Genetic Algorithm
The standards for refrigerators energy consumption are increasing faster in the recent past years, so, the situation demands compressor with a higher efficiency level. To develop such compressors the performance of the most important compressor´s components must be increased. However, the pressure for fast time to market are turning more difficulty the optimization process of a big amount of components, specially using experimental validations. One of the most important components in the compressor is the suction muffler. The main suction muffler functions are the thermal insulation of gas from the evaporator and noise attenuation. To increase the performance of muffler is mandatory to modify the length and diameter of tubes, the geometry and volume of chambers. During the muffler development is common the necessity of modifications in at least six parameters, and is easy to verify that, with this number of parameters is almost impossible to find the optimized solution only using the iterative method, so, it is necessary to use some algorithm to optimize all these parameters to find the best solution for the suction muffler. This paper presents a series of suction muffler optimization, using in house computational code for muffler simulation and the commercial code modeFRONTIER to optimize the objective functions. The optimizing algorithms selected to this task are Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Game Theory (MOGT) and Multi-Objective Genetic Algorithm II (MOGA-II). Using the same variables and objective functions for all algorithms, the performances of algorithms are evaluated to define the best strategy to optimize the muffler.