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Calibration of Turbulent Model Constants Based on Experimental Data Assimilation: Numerical Prediction of Subsonic Jet Flow Characteristics

Author

Listed:
  • Xin He

    (School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, China)

  • Changjiang Yuan

    (School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, China)

  • Haoran Gao

    (Institute Office, Civil Aviation Flight University of China, Guanghan 618307, China)

  • Yaqing Chen

    (CAAC Key Laboratory of Flight Technology and Safety, Civil Aviation Flight University of China, Guanghan 618307, China)

  • Rui Zhao

    (School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, China)

Abstract

Experimental measurements and numerical simulations are two primary methods for studying turbulence. However, these methods often struggle to balance the accuracy and breadth of results. In order to accurately predict the flow characteristics of subsonic jet exhaust and provide a research foundation for the runway crossing operation after the takeoff point, this study utilizes the ensemble Kalman filter algorithm to recalibrate the SA turbulence model constants by integrating NASA’s experimental particle image velocimetry (PIV) data with a sample library generated using Latin hypercube sampling to obtain corresponding flow field calculations. The modified model constants effectively improve the prediction of jet flow characteristics, reducing the spatially averaged relative error along the horizontal axis behind the nozzle from 13.04% to 4.6%. This study focuses on enhancing the accuracy of numerical predictions for subsonic jet flows via the adjustment of turbulence model constants. The recalibrated model constants are then validated to improve the prediction of jet flows under various conditions. The findings have important implications for acquiring high-fidelity data on rear engine jet flows after takeoff, enabling precise determination of safety separation distances, and enhancing the operational efficiency of airports.

Suggested Citation

  • Xin He & Changjiang Yuan & Haoran Gao & Yaqing Chen & Rui Zhao, 2023. "Calibration of Turbulent Model Constants Based on Experimental Data Assimilation: Numerical Prediction of Subsonic Jet Flow Characteristics," Sustainability, MDPI, vol. 15(13), pages 1-18, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10219-:d:1181098
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    References listed on IDEAS

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    1. Song Yang & Zubin Ai & Chao Zhang & Shun Dong & Xun Ouyang & Rong Liu & Ping Zhang, 2022. "Study on Optimization of Tunnel Ventilation Flow Field in Long Tunnel Based on CFD Computer Simulation Technology," Sustainability, MDPI, vol. 14(18), pages 1-15, September.
    2. Yongfei Yang & Gaowei Wang & Weidong Shi & Wei Li & Leilei Ji & Hongliang Wang, 2022. "Turbulence Characteristics in the Mixing Layer of a Submerged Cavitating Jet at High Reynolds Numbers," Sustainability, MDPI, vol. 14(19), pages 1-17, September.
    3. Hongbo Mi & Chuan Wang & Xuanwen Jia & Bo Hu & Hongliang Wang & Hui Wang & Yong Zhu, 2023. "Hydraulic Characteristics of Continuous Submerged Jet Impinging on a Wall by Using Numerical Simulation and PIV Experiment," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
    4. Qinghong Zhang & Zhouhao Shi & Weidong Shi & Zhanshan Xie & Linwei Tan & Yongfei Yang, 2022. "Research on Flow Field Characteristics in Water Jet Nozzle and Surface Damage Caused by Target Impact," Sustainability, MDPI, vol. 14(15), pages 1-13, July.
    5. Luis A. Gallo & Edwin L. Chica & Elkin G. Flórez, 2022. "Numerical Optimization of the Blade Profile of a Savonius Type Rotor Using the Response Surface Methodology," Sustainability, MDPI, vol. 14(9), pages 1-18, May.
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