Research Papers

Motor Cooling Modeling: An Inverse Method for the Identification of Convection Coefficients

[+] Author and Article Information
Tanguy Davin

Université de Lille Nord de France,
Université de Valenciennes
et du Hainaut-Cambrésis,
le Mont Houy,
Valenciennes Cedex 9 59313, France;
Direction de la Recherche,
1 avenue du golf,
Guyancourt 78280, France
e-mail: tanguy.davin@gmail.com

Julien Pellé, Souad Harmand

Université de Lille Nord de France,
Université de Valenciennes
et du Hainaut-Cambrésis,
le Mont Houy,
Valenciennes Cedex 9 59313, France

Robert Yu

Direction de la Recherche,
1 avenue du golf,
Guyancourt 78280, France

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF THERMAL SCIENCE AND ENGINEERING APPLICATIONS. Manuscript received October 5, 2016; final manuscript received February 20, 2017; published online April 25, 2017. Assoc. Editor: Hongbin Ma.

J. Thermal Sci. Eng. Appl 9(4), 041009 (Apr 25, 2017) (13 pages) Paper No: TSEA-16-1284; doi: 10.1115/1.4036303 History: Received October 05, 2016; Revised February 20, 2017

The present study focuses on oil cooling for electric motors. A 40 kW test machine in which oil was introduced at each side of the machine to directly cool the stator coil end-windings was previously implemented. The lumped system analysis is used to model the thermal behavior of this test electric machine. An inverse method is applied to interpret the data obtained by the experimental setup. The inverse method leads to interior convection coefficients that help describe the heat transfer mechanisms.

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Fig. 1

Schematics of the test machine (left) and characteristic principal dissipation powers (right): from the winding to the oil (O), water (W) and external air (Aext), total values for both sides

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Fig. 2

Mesh grid represented in the axial and radial sections

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Fig. 3

Internal oil/air mixture convection on different surfaces of the machine (left); grid 3 used for identification (right)

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Fig. 4

Applied algorithm of the inverse method (minimizing the criterion function)

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Fig. 5

Sketch of the thermocouples in the test machine (left) and photograph of a thermocouple stuck on a flange (right)

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Fig. 6

Evolution of the identified coefficients (left) and the temperature differences (right) during the algorithm progress

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Fig. 9

Comparison with the literature [2] of the convection coefficient on the internal surfaces of the side-chamber

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Fig. 7

Influence of the convection surface grid for M8 tests at a 50 °C oil

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Fig. 8

Influence of the assumptions on the internal air measurements, tests with air only, Twinding¯ = 110 °C

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Fig. 11

Coefficients identified on end-windings for tests with oil at 75 °C

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Fig. 10

Characteristic identified coefficients for oil tests (here for M8 nozzle injection, 50 °C oil, 102 L/h)

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Fig. 15

Influence of the oil temperature on the convection coefficient on the flange for the dripping (top) and multijet (bottom) injectors

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Fig. 12

Comparison of the coefficient identified on end-windings for all datasets

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Fig. 13

Coefficients identified on the flange+carter zone for tests with a 75 °C oil

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Fig. 14

Coefficients identified on the stack side for tests with oil at 75 °C



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