Development of helium turbine loss model based on knowledge transfer with Neural Network and its application on aerodynamic design
Changxing Liu (1, 3), Zhengping Zou (1, 3), Pengcheng Xu (1 and, 2), Yifan Wang (1, 3) ((1) National Key Laboratory of Science and, Technology on Aero-Engine Aero-thermodynamics, (2) School of Energy & Power, Engineering, Beihang University, (3) Research Institute of Aero-Engine

TL;DR
This paper introduces a novel helium turbine loss model that combines knowledge transfer and neural networks, enabling accurate performance prediction and guiding design choices for helium turbines, outperforming traditional gas turbine models.
Contribution
A new helium turbine loss model using knowledge transfer and neural networks is developed, improving prediction accuracy and informing design parameter selection.
Findings
Prediction errors below 0.5% for over 90% of test samples.
Helium turbines require different design guidelines than gas turbines.
Contra-rotating helium turbines offer size, weight, and aerodynamic advantages.
Abstract
Helium turbines are widely used in the Closed Brayton Cycle for power generation and aerospace applications. The primary concerns of designing highly loaded helium turbines include choosing between conventional and contra-rotating designs and the guidelines for selecting design parameters. A loss model serving as an evaluation means is the key to addressing this issue. Due to the property disparities between helium and air, turbines utilizing either as working fluid experience distinct loss mechanisms. Consequently, directly applying gas turbine experience to the design of helium turbines leads to inherent inaccuracies. A helium turbine loss model is developed by combining knowledge transfer and the Neural Network method to accurately predict performance at design and off-design points. By utilizing the loss model, design parameter selection guidelines for helium turbines are obtained.…
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Taxonomy
TopicsTurbomachinery Performance and Optimization · Advanced Thermodynamic Systems and Engines · Refrigeration and Air Conditioning Technologies
