Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7175208 | International Journal of Refrigeration | 2018 | 26 Pages |
Abstract
In this work the sizes of the compressor, evaporator, gas cooler, cooler fan, and internal heat exchanger, plus the high pressure level and the cooler air flow rate of an R744 cycle were subject to multi-objective genetic optimization. Computationally inexpensive artificial neural networks were used as interface between different computation tools. As a result a set of Pareto-optimal R744 cycles was obtained to support the design engineer. For minimum volume and mass (153 l, 169â¯kg), total electric power demand was 9.2â¯kW. Vice-versa, minimum power demand (6.9â¯kW) yielded a large (272 l), heavy (198â¯kg), and expensive system.
Related Topics
Physical Sciences and Engineering
Engineering
Mechanical Engineering
Authors
Christian Luger, René Rieberer,