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Atomization and Sprays

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ISSN Print: 1044-5110

ISSN Online: 1936-2684

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DATA-DRIVEN MODEL REDUCTION OF MULTIPHASE FLOW IN A SINGLE-HOLE AUTOMOTIVE INJECTOR

Volume 30, Issue 6, 2020, pp. 401-429
DOI: 10.1615/AtomizSpr.2020034830
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ABSTRACT

Fuel injector design has a substantial influence on the performance and emissions of direct injection engines. To date, large eddy simulations coupled with a single-fluid mixture modeling approach have shown great success in evaluating the complex interplay among injector design, fuel properties, and operating conditions on the injector performance. However, this simulation approach is too computationally expensive to be used by industry routinely for injector design due to the fine temporal and spatial resolution required to resolve wall-bounded flow within the injector. The work presented in this paper highlights a potential pathway to addressing this issue. To study the influence of injector design, fuel properties, and operating conditions on injector performance, large eddy simulations were performed to model the turbulent multiphase flow development through a side-oriented single-hole diesel injector. Using Latin hypercube sampling, the design space spanning a range of fuel properties, operating conditions, and needle lifts were explored. Two techniques for dimensionality reduction, namely proper orthogonal decomposition and autoencoders, were compared to evaluate their accuracy in representing the flow in a reduced dimensional space. Based on the findings from this work, recommendations are provided in using machine learning approaches within the context of emulation to construct reduced-order models for internal flow development relevant to automotive applications.

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CITED BY
  1. Chang Yu-Hung, Wang Xingjian, Zhang Liwei, Li Yixing, Mak Simon, Wu Chien-Fu J., Yang Vigor, Reduced-Order Modeling for Complex Flow Emulation by Common Kernel-Smoothed Proper Orthogonal Decomposition, AIAA Journal, 59, 9, 2021. Crossref

  2. Mondal Sudeepta, Torelli Roberto, Lusch Bethany, Milan Petro Junior, Magnotti Gina M., Accelerating the Generation of Static Coupling Injection Maps Using a Data-Driven Emulator, SAE Technical Paper Series, 1, 2021. Crossref

  3. Milan Petro Junior, Mondal Sudeepta, Torelli Roberto, Lusch Bethany, Maulik Romit, Magnotti Gina M., Data-Driven Modeling of Large-Eddy Simulations for Fuel Injector Design, AIAA Scitech 2021 Forum, 2021. Crossref

  4. Zapata Usandivaras José Felix, Urbano Annafederica, Bauerheim Michael, Cuenot Bénédicte, Data Driven Models for the Design of Rocket Injector Elements, Aerospace, 9, 10, 2022. Crossref

  5. Mondal Sudeepta, Magnotti Gina M., Lusch Bethany, Maulik Romit, Torelli Roberto, Machine Learning-Enabled Prediction of Transient Injection Map in Automotive Injectors With Uncertainty Quantification, Journal of Engineering for Gas Turbines and Power, 145, 4, 2023. Crossref

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