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Flow characteristic optimization of a multi-stage orifice plate using surrogate-based modeling and Bayesian optimization

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Abstract

The efficient design of a throttle element is still a challenging open problem in fluid flow engineering, considering the flow characteristics, turbulence, and cavitation phenomenon. An optimization framework based on the Optimized Latin Hypercube Sampling method and Bayesian optimization is proposed, where the Optimized Latin Hypercube Sampling method is used to build a preliminary surrogate model within limited sampling points, then the best candidate location is guessed by the Bayesian optimization. First, two numerical cases are discussed to verify the correctness of the proposed optimization framework in mathematics, both cases find the best candidate in less than 200 iterations. Second, a two-stage throttle plate is used to verify whether the optimization framework is suitable for an engineering problem. The computational fluid dynamics is less than 5% error compared to the experimental data. In the two-variable case, the optimal candidate is better than the candidate of the exhaustive method with fewer simulations. Some common features of the geometric structure and flow characteristics are discussed in the five-variable case. Finally, the complex engineering design of the multi-stage orifice plate is optimized, and the best candidate is generated after 130 iterations, showcasing close flow velocity performance comparable to the two-stage case, a unique “X”-shaped flow path is observed in this candidate. The proposed optimization framework efficiently realizes high-performance orifice plates with less computational resources.

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References

  • Bates SJ, Sienz J, Langley DS (2003) Formulation of the audze-eglais uniform latin hypercube design of experiments. Adv Eng Softw 34(8):493–506

    Article  Google Scholar 

  • Corbera S, Olazagoitia JL, Lozano JA (2016) Multi-objective global optimization of a butterfly valve using genetic algorithms. ISA Trans. 63:401–412

    Article  Google Scholar 

  • Costin WJ, Allen CB (2013) Numerical study of radial basis function interpolation for data transfer across discontinuous mesh interfaces. Int J Numer Methods Fluids 72(10):1076–1095

    Article  MathSciNet  MATH  Google Scholar 

  • Fatahi Nafchi R, Yaghoobi P, Reaisi Vanani H et al (2021) Eco-hydrologic stability zonation of dams and power plants using the combined models of smce and cequalw2. Appl Water Sci 11(7):109

    Article  Google Scholar 

  • Frazier PI (2018) A tutorial on bayesian optimization. Mach Learn. https://doi.org/10.48550/arXiv.1807.02811

    Article  Google Scholar 

  • Gan R, Li B, Tang T et al (2022) Noise optimization of multi-stage orifice plates based on rbf neural network response surface and adaptive nsga-ii. Ann Nucl Energy 178(109):372

    Google Scholar 

  • Lei L, Gang Y, Jing G (2022) Physics-guided neural network for underwater glider flight modeling. Appl Ocean Res 121(103):082

    Google Scholar 

  • Lei L, Gang Y, Jing G et al (2022) Gliding hydrodynamic modeling and identification of underwater glider based on differential evolution algorithm. Ocean Eng 244(110):250

    Google Scholar 

  • Lei L, Tang T, Gang Y et al (2022) Hierarchical neural network-based hydrological perception model for underwater glider. Ocean Eng 260(112):101

    Google Scholar 

  • Liefvendahl M, Stocki R (2006) A study on algorithms for optimization of Latin hypercubes. J Stat Plan Inference 136(9):3231–3247

    Article  MathSciNet  MATH  Google Scholar 

  • Nafchi RF, Samadi-Boroujeni H, Vanani HR et al (2021) Laboratory investigation on erosion threshold shear stress of cohesive sediment in Karkheh dam. Environ Earth Sci 80:1–15

    Article  Google Scholar 

  • Osman H, Elsayed K, Sedrak M (2019) Shape optimization of an orifice meter using the Adjoint method and surrogate based optimization. Flow Meas Instrum 70(101):652

    Google Scholar 

  • Pałys L, Mrzygłód MW (2021) Using metamodeling and fluid-structure interaction analysis in multi-objective optimization of a butterfly valve. Comput Assist Methods Eng Sci 28(1):17–38

    Google Scholar 

  • Seeger M (2004) Gaussian processes for machine learning. Int J Neural Syst 14(02):69–106

    Article  Google Scholar 

  • Shayannejad M, Ghobadi M, Ostad-Ali-Askari K (2022) Modeling of surface flow and infiltration during surface irrigation advance based on numerical solution of saint-venant equations using preissmann’s scheme. Pure Appl Geophys 179(3):1103–1113

    Article  Google Scholar 

  • Soleimani S, Eckels S (2022) Multi-objective optimization of 3d micro-fins using NSGA-II. Int J Heat Mass Trans 197(123):315

    Google Scholar 

  • Tang T, Gao L, Li B et al (2019) Cavitation optimization of a throttle orifice plate based on three-dimensional genetic algorithm and topology optimization. Struct Multidiscip Optim 60(3):1227–1244

    Article  Google Scholar 

  • Tang T, Yang G, Zhang D et al (2020) A hydrodynamic prediction model of throttle orifice plate using space filling and adaptive sampling method. Struct Multidiscip Optim 62(3):1563–1578

    Article  Google Scholar 

  • Viana FA, Venter G, Balabanov V (2010) An algorithm for fast optimal Latin hypercube design of experiments. Int J Num Methods Eng 82(2):135–156

    Article  MathSciNet  MATH  Google Scholar 

  • Wang C, Yang M, Wang Y et al (2022) Design and optimization of cylindrical hull with non-uniform arch ribs for underwater gliders based on approximate model and experiments. Ocean Eng 259(111):831

    Google Scholar 

  • Zhang Y, Lai J, He C et al (2022) Cavitation optimization of single-orifice plate using cfd method and neighborhood cultivation genetic algorithm. Nuclear Eng Technol 54(5):1835–1844

    Article  Google Scholar 

Download references

Funding

This work was supported by the Open Research Fund Program of Hubei Provincial Key Laboratory of Chemical Equipment Intensification and Intrinsic Safety under Grant [no. 2020KA02]; the Science Foundation of Wuhan Institute of Technology under Grant [no. K2021014].

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Authors and Affiliations

  1. School of Mechanical & Electrical Engineering, Wuhan Institute of Technology, Wuhan, 430205, People’s Republic of China

    Tengfei Tang, Li Xiao, Yili Peng & Hongjian Zhou

  2. Wing Robot Limited, Kowloon, Hong Kong, People’s Republic of China

    Lei Lei

  3. Hubei Provincial Key Laboratory of Chemical Equipment Intensification and Intrinsic Safety, Wuhan, 430205, People’s Republic of China

    Tengfei Tang, Li Xiao, Yili Peng & Hongjian Zhou

  4. Department of Mechanical Science & Engineering, Huazhong University of Science and Technology, Wuhan, 430074, People’s Republic of China

    Lei Lei

Authors
  1. Tengfei Tang
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  2. Lei Lei
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  3. Li Xiao
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  4. Yili Peng
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  5. Hongjian Zhou
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Correspondence to Lei Lei.

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The Python code of the proposed algorithm is unavailable due to the confidential issue. The source data are available upon request to the corresponding author.

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Responsible Editor: Hongyi Xu.

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Tang, T., Lei, L., Xiao, L. et al. Flow characteristic optimization of a multi-stage orifice plate using surrogate-based modeling and Bayesian optimization. Struct Multidisc Optim 66, 188 (2023). https://doi.org/10.1007/s00158-023-03647-0

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  • DOI: https://doi.org/10.1007/s00158-023-03647-0

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