Axial-Flow Compressor Model Based on a Cascade Stacking Technique and Neural Networks
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This paper introduces a cascade-stacking technique for the development of a gas turbine multi-stage axial-flow compressor model. A large database of stationary and rotating cascade performance is first obtained by quasi three-dimensional CFD simulations and used to train neural networks for the prediction of cascade performance under generalized conditions. Then the model directly calculates the operating point of a compressor having known geometry characteristics, including variable inlet guide/stator vane effects, as a function of mass flow rate and rotational speed. The model can also be used as a valuable preliminary design tool, obtaining geometry characteristics by imposing flow patterns.
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1961 ◽
Vol 27
(183)
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pp. 1801-1808
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2018 ◽
Vol 78
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pp. 271-279
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2020 ◽
Vol 96
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pp. 105554
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