A Surrogate Model for a CAES Radial Inflow Turbine with Test Data-Based MLP Neural Network Algorithm

WANG Xing, ZHU Yangli, LI Wen, ZUO Zhitao, CHEN Haisheng

热科学学报 ›› 2023, Vol. 32 ›› Issue (6) : 2081-2092.

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Journal of Thermal Science

热科学学报
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热科学学报 ›› 2023, Vol. 32 ›› Issue (6) : 2081-2092. DOI: 10.1007/s11630-023-1846-3  CSTR: 32141.14.JTS-023-1846-3

A Surrogate Model for a CAES Radial Inflow Turbine with Test Data-Based MLP Neural Network Algorithm

  • WANG Xing1, ZHU Yangli2,3, LI Wen2,3, ZUO Zhitao1,2,3, CHEN Haisheng1,2,3*
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A Surrogate Model for a CAES Radial Inflow Turbine with Test Data-Based MLP Neural Network Algorithm

  • WANG Xing1, ZHU Yangli2,3, LI Wen2,3, ZUO Zhitao1,2,3, CHEN Haisheng1,2,3*
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摘要

现有研究常对向心涡轮进行全尺寸三维流动分析以找到压缩空气储能（CAES）系统效率提升方法。然而，求解时间长、计算资源消耗大成为开展该类分析的主要阻碍。因此，本研究提出了一种基于测试数据的多层感知器（MLP）神经网络代理模型来克服这一困难。该模型不是采用流场求解过程，而是通过“学习测量结果”在较短求解时间内提供可靠的涡轮气动性能和流场分布特性。研究结果表明，该模型对等熵效率、折合流量和折合功率预测的最大相对误差分别为0.03%、0.22%和0.26%。集气室、轮盖空腔和叶轮出口区的流动参数分布也与实验结果基本一致。在进气室中，当总压比降低时，在275°的圆周位置处观察到压力驻点；在轮盖空腔中，尽管存在迷宫式密封，但在导流罩出口附近发现了明显的压力变化。在转子出口处，0.0～0.4和0.6～0.8相对叶高范围内有明显的速度和压力变化。同时，在叶片高度0.0～0.4和0.6～0.8范围内，速度和压力变化明显，这是由于上通道涡流、下通道涡流和端壁二次流的影响。本研究可为CAES径向流入式水轮机的动态性能评估提供进一步的参考。

Abstract

It is usually to conduct a full-scale three-dimensional flow analysis for a radial turbine to find a way to increase the efficiency of a Compressed Air Energy Storage (CAES) system. However, long solving time and huge consumption of computing resources become a major obstacle to the analysis. Therefore, in present study, a surrogate model with test data-based multi-layer perceptron (MLP) Neural Network is proposed to overcome the difficulty. Instead of complex flow field solving process, it provides reliable turbine aerodynamic performance and flow field distribution characteristics in a short solution time by “learning the measurement results”. The validation results illustrated that the predicted maximum relative errors of isentropic efficiency, corrected mass flow rate and corrected power are only 0.03%, 0.22% and 0.26% respectively. The predicted flow distribution parameters in chamber, shroud cavity and outlet region of rotor are also basically consistent with the experimental results. In the chamber, it can be found that a pressure stagnation point is observed at circumferential angle of 270° when total pressure ratio is decreased. In the shroud cavity, obvious pressure variation is found near outlet of shroud cavity which although labyrinth seals exist. At outlet of rotor, obvious variations of velocity and pressure are found in the 0.0–0.4 and 0.6–0.8 of blade height. At the same time, obvious variations of velocity and pressure are found in the 0.0–0.4 and 0.6–0.8 of blade height and this is because the influence of upper passage vortex, lower passage vortex and end wall secondary flow. The present study can provide further reference for the dynamic performance evaluation of CAES radial inflow turbine.

关键词

CAES / surrogate model / radial inflow turbine / MLP neural network

Key words

CAES / surrogate model / radial inflow turbine / MLP neural network

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WANG Xing, ZHU Yangli, LI Wen, ZUO Zhitao, CHEN Haisheng. A Surrogate Model for a CAES Radial Inflow Turbine with Test Data-Based MLP Neural Network Algorithm[J]. 热科学学报, 2023, 32(6): 2081-2092 https://doi.org/10.1007/s11630-023-1846-3
A Surrogate Model for a CAES Radial Inflow Turbine with Test Data-Based MLP Neural Network Algorithm[J]. Journal of Thermal Science, 2023, 32(6): 2081-2092 https://doi.org/10.1007/s11630-023-1846-3

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基金

The work is supported by Strategic Priority Research Program of the Chinses Academy of Sciences (51925604); National Natural Science Foundation of China (51806211); The Science and Technology Foundation of Guizhou Province (No. [2019]1285). The authors would like to thank the above organizations for their financial support.

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Science Press, Institute of Engineering Thermophysics, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2023
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