Investigation of the surrogate model in an ANN-Meanline Hybrid model for Radial Turbine Performance Prediction
Author:
Affiliation:
1. School of Engineering, Trinity College Dublin, The University of Dublin
2. I-Form Advanced Manufacturing Research Centre, Science Foundation Ireland
3. IHI Corporation
Publisher
Gas Turbine Society of Japan
Link
https://www.jstage.jst.go.jp/article/jgpp/15/2/15_vol15no2tp02/_pdf
Reference25 articles.
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2. Wittmann, T., Lück, S., Friedrichs, J., Wiśniewski, P. and Dykas, S., 2022, “Analysis of the Condensation Phenomena Within the Radial Turbine of a Fuel Cell Turbocharger”, ASME TurboExpo 2022, Rotterdam, The Netherlands, June 13-17, GT2022-82279.
3. Chen, S., Ma, C., Zhang, H., Xu, C., Lei, F., Feng, T., Cao, G., Yang, H. and Wei, C., 2022, “Engine Performance Improvements through Turbocharger Matching and Turbine Design”, Energy Science & Engineering, Vol. 10(9), pp.3384-3396.
4. Ren, P., Stuart, C., Spence, S., Inomata, R., Kobayashi, T. and Morita, I., 2023, “Using Machine Learning for Loss Prediction in a Hybrid Meanline Modeling Method to Deliver Improved Radial Turbine Performance Prediction”, Journal of Turbomachinery, Vol. 145(7), pp.071013.
5. Zhai, J. and Boukouvala, F., 2022, “Surrogate-Based Branchand-Bound Algorithms for Simulation-Based Black-Box Optimization”, Optimization and Engineering, pp.1-29.
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