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Machine-Learning Methods for Complex Flows

Author

Listed:
  • Ricardo Vinuesa

    (FLOW, Engineering Mechanics, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden)

  • Soledad Le Clainche

    (School of Aerospace Engineering, Universidad Politécnica de Madrid, 28040 Madrid, Spain)

Abstract

We are delighted to introduce this Special Issue focused on novel machine-learning (ML) methods aimed at predicting, modeling, and controlling a variety of complex fluid flow scenarios [...]

Suggested Citation

  • Ricardo Vinuesa & Soledad Le Clainche, 2022. "Machine-Learning Methods for Complex Flows," Energies, MDPI, vol. 15(4), pages 1-5, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1513-:d:752376
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    References listed on IDEAS

    as
    1. Andrés Omar Tiseira Izaguirre & Roberto Navarro García & Lukas Benjamin Inhestern & Natalia Hervás Gómez, 2020. "Design and Numerical Analysis of Flow Characteristics in a Scaled Volute and Vaned Nozzle of Radial Turbocharger Turbines," Energies, MDPI, vol. 13(11), pages 1-19, June.
    2. Binghua Li & Jesús Garicano-Mena & Yao Zheng & Eusebio Valero, 2020. "Dynamic Mode Decomposition Analysis of Spatially Agglomerated Flow Databases," Energies, MDPI, vol. 13(9), pages 1-23, April.
    3. Mikhail Tokarev & Egor Palkin & Rustam Mullyadzhanov, 2020. "Deep Reinforcement Learning Control of Cylinder Flow Using Rotary Oscillations at Low Reynolds Number," Energies, MDPI, vol. 13(22), pages 1-11, November.
    4. Soledad Le Clainche, 2019. "Prediction of the Optimal Vortex in Synthetic Jets," Energies, MDPI, vol. 12(9), pages 1-26, April.
    5. Ricardo Vinuesa & Hossein Azizpour & Iolanda Leite & Madeline Balaam & Virginia Dignum & Sami Domisch & Anna Felländer & Simone Daniela Langhans & Max Tegmark & Francesco Fuso Nerini, 2020. "The role of artificial intelligence in achieving the Sustainable Development Goals," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    6. Fahimeh Hadavimoghaddam & Mehdi Ostadhassan & Ehsan Heidaryan & Mohammad Ali Sadri & Inna Chapanova & Evgeny Popov & Alexey Cheremisin & Saeed Rafieepour, 2021. "Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations," Energies, MDPI, vol. 14(4), pages 1-16, February.
    7. Shanti Bhushan & Greg W. Burgreen & Wesley Brewer & Ian D. Dettwiller, 2021. "Development and Validation of a Machine Learned Turbulence Model," Energies, MDPI, vol. 14(5), pages 1-34, March.
    8. Jose J. Aguilar-Fuertes & Francisco Noguero-Rodríguez & José C. Jaen Ruiz & Luis M. García-RAffi & Sergio Hoyas, 2021. "Tracking Turbulent Coherent Structures by Means of Neural Networks," Energies, MDPI, vol. 14(4), pages 1-15, February.
    9. Manuel Viqueira-Moreira & Esteban Ferrer, 2020. "Insights into the Aeroacoustic Noise Generation for Vertical Axis Turbines in Close Proximity," Energies, MDPI, vol. 13(16), pages 1-18, August.
    10. Juan Ángel Martín & Pedro Paredes, 2021. "Transition Prediction in Incompressible Boundary Layer with Finite-Amplitude Streaks," Energies, MDPI, vol. 14(8), pages 1-14, April.
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