چكيده به لاتين
Current system of vehicle air conditioners due to existing compressor in refrigeration cycle, gets directly it's required power from vehicle's engine that this matter causes sharp drop of engine's useful power, increased fuel consumption,... ejector refrigeration system with using undervalued thermal energy, the possibility of using waste heating, it's low cost installation and maintenance and the possibility of combination whit others refrigeration cycles for optimization, don't play much share in market for itself. So in this thesis, first investigate ejector refrigeration cycle in vehicle's temperature conditions with using radiator's waste heating. With regards to low coefficient of performance, this cycle combined with compression cycle along with ejector. Achieved results from comparing compression refrigeration cycle with compression cycle with ejector show 37 to 62% performance coefficient improvement with regards to different operator's temperatures and comparing with hybrid cycle show 154 to 193% performance coefficient improvement that accompanied with compressor's work reduction, also performance coefficient charts and the suction ratio according to operator and generator's temperature changes, it is determined that it has inversely ratio with generator's temperature changes and direct ratio with operator's temperature changes. Then neural network trained with levenberg -Marquardt leading network with hybrid cycle analysis data and the fact that finding optimization conditions of cycle function was essential affair and cause it's performance coefficient increasing, this cycle optimized with nerve network and smart algorithm. That in the best temperature conditions, performance coefficient gets to 30.
