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## Robust uncertainty modelling of energy variations in electric motors for efficient and reliable mechanical structural analysis

#### Murat Mayda [1]

##### 100 175

The mechanical power and rotational speed of electric motors have significantly uncertainties arising from energy variations such as the magnetic energy, the flux linkage variation of the winding, and the air gap due to the rotor position. The main aim of this work is to address the effect of the uncertain output power and rotational speed of an electric motor on mechanical structural analysis, especially on the torsional analysis, and accordingly to model the statistical characteristics of variations in torque of the motors in consideration of different powers and speeds for further efficient and reliable mechanical structural analysis under uncertainty. To perform these tasks, a case study that is the torsional loading of a shaft by an electric motor and generator, is carried out. The results show that the uncertainty of power and speed in electric motors considerably affects the probability of failure of the shaft in case of exceeding the maximum shear stress, and increasing the speed at a given power does not significantly change the COV value of the torque whereas increasing the power at a given speed can relatively change the COV value of the torque. The obtained average of the COV values (0.0023) of torque with normal distribution is fairly sufficient for indicating the variations in the torque of electric motors. Moreover, the obtained torque uncertainty can be easily and efficiently used in the mechanical structural analysis under both deterministic and stochastic cases.

Electric motors, energy variations, uncertainty, modelling, structural analysis
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Primary Language en Engineering, Mechanical Research Article Author: Murat Mayda (Primary Author)Country: Turkey
 Bibtex @research article { ijeat400164, journal = {International Journal of Energy Applications and Technologies}, issn = {}, eissn = {2548-060X}, address = {İlker ÖRS}, year = {2018}, volume = {5}, pages = {9 - 12}, doi = {10.31593/ijeat.400164}, title = {Robust uncertainty modelling of energy variations in electric motors for efficient and reliable mechanical structural analysis}, key = {cite}, author = {Mayda, Murat} } APA Mayda, M . (2018). Robust uncertainty modelling of energy variations in electric motors for efficient and reliable mechanical structural analysis. International Journal of Energy Applications and Technologies, 5 (1), 9-12. DOI: 10.31593/ijeat.400164 MLA Mayda, M . "Robust uncertainty modelling of energy variations in electric motors for efficient and reliable mechanical structural analysis". International Journal of Energy Applications and Technologies 5 (2018): 9-12 Chicago Mayda, M . "Robust uncertainty modelling of energy variations in electric motors for efficient and reliable mechanical structural analysis". International Journal of Energy Applications and Technologies 5 (2018): 9-12 RIS TY - JOUR T1 - Robust uncertainty modelling of energy variations in electric motors for efficient and reliable mechanical structural analysis AU - Murat Mayda Y1 - 2018 PY - 2018 N1 - doi: 10.31593/ijeat.400164 DO - 10.31593/ijeat.400164 T2 - International Journal of Energy Applications and Technologies JF - Journal JO - JOR SP - 9 EP - 12 VL - 5 IS - 1 SN - -2548-060X M3 - doi: 10.31593/ijeat.400164 UR - https://doi.org/10.31593/ijeat.400164 Y2 - 2018 ER - EndNote %0 International Journal of Energy Applications and Technologies Robust uncertainty modelling of energy variations in electric motors for efficient and reliable mechanical structural analysis %A Murat Mayda %T Robust uncertainty modelling of energy variations in electric motors for efficient and reliable mechanical structural analysis %D 2018 %J International Journal of Energy Applications and Technologies %P -2548-060X %V 5 %N 1 %R doi: 10.31593/ijeat.400164 %U 10.31593/ijeat.400164 ISNAD Mayda, Murat . "Robust uncertainty modelling of energy variations in electric motors for efficient and reliable mechanical structural analysis". International Journal of Energy Applications and Technologies 5 / 1 (April 2018): 9-12. https://doi.org/10.31593/ijeat.400164