Importance of Thermal Modeling for Design Optimization Scenarios of Induction Motors
Sprache des Titels:
Energy Conversion Congress and Exposition (ECCE)
This paper is about the impact of modeling the temperature distribution of machine designs on the results for respective optimization scenarios. In the past, a lot of machine design optimizations were presented, but just a small share considers modeling of the temperature characteristics. In order to study the impact of including a temperature model, a test scenario was investigated that features the optimization of an induction machine with regard to material cost and efficiency. The analysis of the machine designs comprises the computation of electromagnetic and thermal characteristics. As there is a mutual coupling of the results of those two analyses, an iterative approach featuring sequential updates of the electromagnetic and thermal calculations based on the results obtained for the respective other computation is considered. Typical large scale optimization scenarios require the investigation of a very huge number of machine designs. Due to this circumstance and, in addition, the need for iteratively updating the thermal and electromagnetic computations when analyzing any particular design variant, analytic techniques are preferred for these investigations. This follows a lower computation time compared to complex finite element based analyses. To compare the obtained results with typically conducted analyses featuring a constant temperature, two more test scenarios are investigated with a fixed temperature of 40°C and 120°C, respectively, for the machine components regardless its size and losses. The Pareto fronts regarding the objectives material cost and efficiency for all scenarios are presented. Moreover, a detailed investigation of further characteristic data, e.g., the temperature of the stator windings and the rotor bars, is depicted. The derived results allow for a conclusion regarding the importance of incorporating a temperature model into the machine design optimization.