Key
22109
Conference Year
2016
Keywords
Automated Optimization, Physical based Simulation, Household Appliance Industry, Air Conditioning, Dehumidification Unit
Abstract
The major topics of current developments in the household appliance industry are cost reduction and the optimization of energy efficiency. For that purpose mathematical simulation models are increasingly used. However, most of these models are based on measurements. Hence, a geometrical optimization of the refrigerant cycle components is practically impossible. If physical based models are used optimization is mostly done manually by varying individual geometric properties. Due to the large amount of parameters and possible combinations a determination of the optimal parameter set is not possible. Moreover, each parameter combination requires a separate setting of the overall system and the control strategy which makes it even more difficult to achieve an optimal solution. Therefore IAV developed very detailed geometrical and physical based simulation models for different types of refrigerant compressors, heat exchangers and expansion valves. These submodels were combined into one main model and validated by measurements. All of those component models as well as the system model were extended by a special optimization algorithm. That makes it possible to automatically optimize the geometrical parameters of any component in the integrated system, considering given boundary conditions. Finally, the energy efficiency of the refrigeration system can be increased in compliance with the predefined installation space. This contribution first introduces the physical based modelling of rotary piston compressors, round tube finned heat exchangers and capillary tubes. Then the combination of these component models to a system model and the automated optimization approach will be described. As a next step, the automated geometry optimization will be demonstrated using a standard merchantable household dehumidification unit. The results will be illustrated for different objective functions and boundary conditions. Finally, the achieved improvements are presented and compared with the basic unit. The contribution concludes with an outlook for further investigations.