基于实验数据同化的湍流模型常数标定：含滤网蒸汽阀门通流特性数值预测

房培勋; 何创新; 徐嗣华; 王鹏; 刘应征

doi:10.7638/kqdlxxb-2020.0183

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基于实验数据同化的湍流模型常数标定：含滤网蒸汽阀门通流特性数值预测

研究论文 | 更新时间：2026-05-28

- 基于实验数据同化的湍流模型常数标定：含滤网蒸汽阀门通流特性数值预测
- Calibration of turbulence model constants using measurement data assimilation: prediction of steam valve flow characteristics with filter
- 空气动力学学报 2021年39卷第2期页码：12-22
- 作者机构：
  
  1. 上海交通大学中英国际低碳学院,上海,201306
  2. 上海交通大学机械与动力工程学院叶轮机械研究所,上海,200240
  3. 上海汽轮机有限公司,上海,200240
- 作者简介：
  
  房培勋（1996-），男，安徽滁州人，硕士研究生，研究方向：实验和数值模拟的数据融合. E-mail：fpx007@sjtu.edu.com
  刘应征*（1972-），博士，教授，研究方向：实验数据动态驱动的数值模拟. E-mail：yzliu@sjtu.edu.com
- 基金信息：
- DOI：10.7638/kqdlxxb-2020.0183
  中图分类号： TK325
- 纸质出版：2021
- 稿件说明：
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房培勋, 何创新, 徐嗣华, 等. 基于实验数据同化的湍流模型常数标定: 含滤网蒸汽阀门通流特性数值预测[J]. 空气动力学学报, 2021, 39(2): 12−22.

FANG Peixun, HE Chuangxin, XU Sihua, et al. Calibration of turbulence model constants using measurement data assimilation: prediction of steam valve flow characteristics with filter[J]. 2021, 39(2): 12-22.
房培勋, 何创新, 徐嗣华, 等. 基于实验数据同化的湍流模型常数标定: 含滤网蒸汽阀门通流特性数值预测[J]. 空气动力学学报, 2021, 39(2): 12−22. DOI： 10.7638/kqdlxxb-2020.0183

FANG Peixun, HE Chuangxin, XU Sihua, et al. Calibration of turbulence model constants using measurement data assimilation: prediction of steam valve flow characteristics with filter[J]. 2021, 39(2): 12-22. DOI： 10.7638/kqdlxxb-2020.0183.

摘要

为准确预测含滤网蒸汽调节阀通流特性，采用集合卡尔曼滤波算法，通过拉丁超立方抽样生成湍流模型常数的样本库并获得对应流场计算结果，融合部分开度下的阀门蒸汽流动压比-流量实验数据，对

k-ω

SST湍流模型的常数进行了重新标定。针对独立实验测量所得的流量数据，对比分析计算结果表明：重新标定的模型常数可以有效提高通流特性预测模型的精度，适用于相近开度、不同压比的蒸汽流动工况计算，而对开度相差较大的情形，流量预测误差则较高；不同开度下的阀内流态和涡黏度在模型常数调整前后存在明显差异。本文通过修正模型常数以优化不同工况下涡黏度的分布，从而显著降低阀门通流特性预测误差，这一研究方法对于相关工程设计有很好的借鉴意义。

Abstract

To accurately predict flow characteristics of steam control valve with filter

the model constant vectors in

k-ω

SST turbulence model are recalibrated using ensemble Kalman filter against valve’s flow rate experiment data at several valve openings. In the procedure

Latin hypercubic sampling method is used to perturbate the model constants and generate samples of predicted valve flow rates. Then the samples are assimilated with independent experiment data to update model constant vectors iteratively until convergence is achieved so as to complete the recalibration. In terms of pressure ratio and flow rate measured in independent

experiments

the comparison and analysis shows that the recalibrated model constants can be well applied to simulation of steam flow at approximate valve opening but different pressure ratios; this greatly reduces the deviation of valve inlet flow rate between model predictions and experiment measurements

while the configuration with significantly different valve openings is still exposed to notable errors in predicted flow rate. The predicted flow patterns and eddy viscosity determined from simulations with and without the recalibrated model constants vary significantly at different valve openings. Given the fact that optimizing valve flow characteristic prediction via model constant recalibration is made with the optimized eddy viscosity estimation

such methodology provides good reference for related engineering design.

关键词

Keywords

references

QIAN J Y, WEI L, ZHANG M, et al. Flow rate analysis of compressible superheated steam through pressure reducing valves[J]. Energy, 2017, 135: 650-658. doi: 10.1016/j.energy.2017.06.170.

HAJŠMAN M, KOVANDOVÁ D, MATAS R. Some aspects of numerical simulation of control valves for steam turbines[J]. EPJ Web of Conferences, 2012, 25: 01052.DOI: 10.1051/epjconf/20122501052.

HALIMI B, KIM S H, SUH K Y. Engineering of combined valve flow for power conversion system[J]. Energy Conversion and Management, 2013, 65: 448-455.DOI: 10.1016/j.enconman.2012.09.012.

ZHANG W W, ZHU L Y, LIU Y L, et al. Progresses in the application of machine learning in turbulence modeling[J]. Acta Aerodynamica Sinica, 2019, 37(3): 444-454. (in Chinese)DOI: 10.7638/kqdlxxb-2019.0036.

马建文. 数据同化算法研发与实验[M]. 北京: 科学出版社, 2016.

LYNCH P. The origins of computer weather prediction and climate modeling[J]. Journal of Computational Physics, 2008, 227(7): 3431-3444.DOI: 10.1016/j.jcp.2007.02.034.

CANCHUMUNI S W A, EMERICK A A, PACHECO M A C. History matching geological facies models based on ensemble smoother and deep generative models[J]. Journal of Petroleum Science and Engineering, 2019, 177: 941-958. doi: 10.1016/j.petrol.2019.02.037.

PAN H Z, YANG L Z, LIU C Q, et al. Muti-model collaborative retrieval of suspend sediment concentration in the estuary of Pearl River based on data assimilation[J]. Journal of Sediment Research, 2019, 44(4): 47-53. (in Chinese)DOI: 10.16239/j.cnki.0468-155x.2019.04.008.

FAN X P, QU G, LIU Y F. Bridge extreme stress prediction based on new data assimilation algorithm[J]. Journal of Jilin University (Engineering and Technology Edition), 2020, 50(2): 572-580. (in Chinese)DOI: 10.13229/j.cnki.jdxbgxb20181017.

KATO H, OBAYASHI S. Data assimilation for turbulent flows[C ] //16th AIAA Non-Deterministic Approaches Conference, National Harbor, Maryland. Reston, Virginia: AIAA, 2014.doi: 10.2514/6.2014-1177 .

MARGHERI L, MELDI M, SALVETTI M V, et al. Epistemic uncertainties in RANS model free coefficients[J]. Computers Fluids, 2014, 102: 315-335. doi: 10.1016/j.compfluid.2014.06.029.

YANG M C, XIAO Z X. Parameter uncertainty quantification for a four-equation transition model using a data assimilation approach[J]. Renewable Energy, 2020, 158: 215-226. doi: 10.1016/j.renene.2020.05.139.

WANG P, LIU Y Z. Unsteady flow behavior of a steam turbine control valve in the choked condition: Field measurement, detached eddy simulation and acoustic modal analysis[J]. Applied Thermal Engineering, 2017, 117: 725-739. doi: 10.1016/j.applthermaleng.2017.02.087.

WANG P, MA H Y, LIU Y Z. Proper orthogonal decomposition and extended-proper orthogonal decomposition analysis of pressure fluctuations and vortex structures inside a steam turbine control valve[J]. Journal of Engineering for Gas Turbines and Power, 2019, 141(4).DOI: 10.1115/1.4040903.

WANG P, XU S, HE L, et al. Unsteady behavior of wall-detached flow inside a steam turbine control valve[J]. Physics of Fluids, 2019, 31(10): 105101.DOI: 10.1063/1.5124359.

ZENG L F, GAO D P, QI W Y, et al. The influence of Reynolds number on modeling experiment of control valve[J]. Proceedings of the CSEE, 2019, 39(5): 1405-1411. (in Chinese)DOI: 10.13334/j.0258-8013.pcsee.180828.

DOMNICK C B, BENRA F K, BRILLERT D, et al. Numerical investigation on the time-variant flow field and dynamic forces acting in steam turbine inlet valves[J]. Journal of Engineering for Gas Turbines and Power, 2015, 137(8): 081601.DOI: 10.1115/1.4029309.

MENTER F R. Two-equation eddy-viscosity turbulence models for engineering applications[J]. AIAA Journal, 1994, 32(8): 1598-1605.DOI: 10.2514/3.12149.

DENG Z W, HE C X, WEN X, et al. Recovering turbulent flow field from local quantity measurement: turbulence modeling using ensemble-Kalman-filter-based data assimilation[J]. Journal of Visualization, 2018, 21(6): 1043-1063.DOI: 10.1007/s12650-018-0508-0.

MONS V, CHASSAING J C, GOMEZ T, et al. Reconstruction of unsteady viscous flows using data assimilation schemes[J]. Journal of Computational Physics, 2016, 316: 255-280. doi: 10.1016/j.jcp.2016.04.022.

EVENSEN G. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics[J]. Journal of Geophysical Research: Oceans, 1994, 99(C5): 10143-10162.DOI: 10.1029/94JC00572.

ZANAZZI G, RICE T, SELL M, et al. Experimental and numerical investigation into the aerodynamics of a novel steam turbine valve and its field application[J]. Journal of Engineering for Gas Turbines and Power, 2014, 136(9): 091601.DOI: 10.1115/1.4026860.

WANG P, LIU Y Z. Influence of a circular strainer on unsteady flow behavior in steam turbine control valves[J]. Applied Thermal Engineering, 2017, 115: 463-476.DOI: 10.1016/j.applthermaleng.2016.12.073.

CHATTOPADHYAY H, KUNDU A, SAHA B K, et al. Analysis of flow structure inside a spool type pressure regulating valve[J]. Energy Conversion and Management, 2012, 53(1): 196-204.DOI: 10.1016/j.enconman.2011.08.021.

EVANS S, LARDEAU S. Validation of a turbulence methodology using the SST k-ω model for adjoint calculation[C ] //54th AIAA Aerospace Sciences Meeting, San Diego, California, USA. Reston, Virginia: AIAA, 2016. doi: 10.2514/6.2016-0585 .

ZHONG W, WANG T G. Influence of closure coefficient of turbulence model on cfd simulations of s series airfoils[J]. Acta Energiae Solaris Sinica, 2013, 34(10): 1690-1696. (in Chinese)DOI: 10.3969/j.issn.0254-0096.2013.10.00.

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