Abstract
Being able to predict temperature rise inside a machine is as important as predicting its performance and life. Because temperature measurements and computational thermal simulations can be time consuming, thermal paths inside the through-flow universal motor were described by means of simple lumped parameter thermal network. Once the model was built, its unknown convection coefficients were tuned with the genetic algorithm tool in MatLab. The model has been applied and successfully verified with measurements on two different types of a vacuum cleaner motor. Taking account of impeller losses as one of the model inputs makes temperature estimates more accurate regardless of machine’s operational regime.
Original language | English |
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Pages (from-to) | 405-412 |
Number of pages | 8 |
Journal | Tehnicki Vjesnik |
Volume | 24 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2017 Apr |
Externally published | Yes |
Subject classification (UKÄ)
- Applied Mechanics
Free keywords
- Genetic algorithm
- Lumped parameter network
- Open-circuit cooling
- Steady-state thermal model
- Universal motor
- Vacuum cleaner motor