Research on Surrogate Model of Variable Geometry Turbine Performance Based on Backpropagation Neural Network
To meet the increasingly stringent performance indicators of gas turbines, the turbine inlet temperature has increased, and variable geometry turbine technology is widely applied. Therefore, this study developed a quasi-two-dimensional (quasi-2D) method for variable geometry turbine performance cons...
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MDPI AG
2025-05-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/12/5/410 |
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| author | Liping Deng Hu Wu Yuhang Liu Qi’an Xie |
| author_facet | Liping Deng Hu Wu Yuhang Liu Qi’an Xie |
| author_sort | Liping Deng |
| collection | DOAJ |
| description | To meet the increasingly stringent performance indicators of gas turbines, the turbine inlet temperature has increased, and variable geometry turbine technology is widely applied. Therefore, this study developed a quasi-two-dimensional (quasi-2D) method for variable geometry turbine performance considering cooling air mixing based on the elementary blade method and the cooling airflow mixing model. To address the high-dimensional, multi-variable data fitting problem of variable geometry turbines considering the effects of cooling air, this study adopted a BP neural network to further establish a surrogate model for variable geometry turbine performance. A sensitivity analysis of a single-stage turbine was conducted. The variable geometry cooling performance of a single-stage turbine and an E<sup>3</sup> five-stage low-pressure air turbine were calculated, and the corresponding surrogate models were established. The relative errors between the calculated mass flow rate and efficiency of the single-stage turbine and the experimental values were no more than 0.70% and 4.44%, respectively; for the five-stage air turbine, the maximum relative errors in mass flow rate and efficiency were no more than 1.67% and 1.385%, respectively. When the throat area of the single-stage turbine nozzle changed by ±30%, the maximum relative errors between the calculated mass flow rate and efficiency and their experimental values were 3.602% and 4.228%, respectively; thus, the determination coefficients of the constructed BP neural network model for the training samples were all greater than 0.999, indicating that the surrogate model has high prediction accuracy and strong generalization ability and can quickly predict variable geometry turbine cooling performance. |
| format | Article |
| id | doaj-art-d51157ff8fde44a7aed7089e71955f7e |
| institution | Kabale University |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-d51157ff8fde44a7aed7089e71955f7e2025-08-20T03:47:49ZengMDPI AGAerospace2226-43102025-05-0112541010.3390/aerospace12050410Research on Surrogate Model of Variable Geometry Turbine Performance Based on Backpropagation Neural NetworkLiping Deng0Hu Wu1Yuhang Liu2Qi’an Xie3School of Power and Energy, Northwestern Polytechnical University, Xi’an 710129, ChinaSchool of Power and Energy, Northwestern Polytechnical University, Xi’an 710129, ChinaSchool of Power and Energy, Northwestern Polytechnical University, Xi’an 710129, ChinaSchool of Power and Energy, Northwestern Polytechnical University, Xi’an 710129, ChinaTo meet the increasingly stringent performance indicators of gas turbines, the turbine inlet temperature has increased, and variable geometry turbine technology is widely applied. Therefore, this study developed a quasi-two-dimensional (quasi-2D) method for variable geometry turbine performance considering cooling air mixing based on the elementary blade method and the cooling airflow mixing model. To address the high-dimensional, multi-variable data fitting problem of variable geometry turbines considering the effects of cooling air, this study adopted a BP neural network to further establish a surrogate model for variable geometry turbine performance. A sensitivity analysis of a single-stage turbine was conducted. The variable geometry cooling performance of a single-stage turbine and an E<sup>3</sup> five-stage low-pressure air turbine were calculated, and the corresponding surrogate models were established. The relative errors between the calculated mass flow rate and efficiency of the single-stage turbine and the experimental values were no more than 0.70% and 4.44%, respectively; for the five-stage air turbine, the maximum relative errors in mass flow rate and efficiency were no more than 1.67% and 1.385%, respectively. When the throat area of the single-stage turbine nozzle changed by ±30%, the maximum relative errors between the calculated mass flow rate and efficiency and their experimental values were 3.602% and 4.228%, respectively; thus, the determination coefficients of the constructed BP neural network model for the training samples were all greater than 0.999, indicating that the surrogate model has high prediction accuracy and strong generalization ability and can quickly predict variable geometry turbine cooling performance.https://www.mdpi.com/2226-4310/12/5/410axial flow turbinequasi-2Dvariable geometryBP neural networksurrogate model |
| spellingShingle | Liping Deng Hu Wu Yuhang Liu Qi’an Xie Research on Surrogate Model of Variable Geometry Turbine Performance Based on Backpropagation Neural Network Aerospace axial flow turbine quasi-2D variable geometry BP neural network surrogate model |
| title | Research on Surrogate Model of Variable Geometry Turbine Performance Based on Backpropagation Neural Network |
| title_full | Research on Surrogate Model of Variable Geometry Turbine Performance Based on Backpropagation Neural Network |
| title_fullStr | Research on Surrogate Model of Variable Geometry Turbine Performance Based on Backpropagation Neural Network |
| title_full_unstemmed | Research on Surrogate Model of Variable Geometry Turbine Performance Based on Backpropagation Neural Network |
| title_short | Research on Surrogate Model of Variable Geometry Turbine Performance Based on Backpropagation Neural Network |
| title_sort | research on surrogate model of variable geometry turbine performance based on backpropagation neural network |
| topic | axial flow turbine quasi-2D variable geometry BP neural network surrogate model |
| url | https://www.mdpi.com/2226-4310/12/5/410 |
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