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Opportunities in Jet-Impingement Cooling for Gas-Turbine Engines

Author

Listed:
  • Sandip Dutta

    (Mechanical Engineering Department, Clemson University, Clemson, SC 29634, USA)

  • Prashant Singh

    (Mechanical Engineering Department, Mississippi State University, Box 9552, Starkville, MS 39762, USA)

Abstract

Impingement heat transfer is considered one of the most effective cooling technologies that yield high localized convective heat transfer coefficients. This paper studies different configurable parameters involved in jet impingement cooling such as, exit orifice shape, crossflow regulation, target surface modification, spent air reuse, impingement channel modification, jet pulsation, and other techniques to understand which of them are critical and how these heat-transfer-enhancement concepts work. The aim of this paper is to excite the thermal sciences community of this efficient cooling technique and instill some thoughts for future innovations. New orifice shapes are becoming feasible due to innovative 3D printing technologies. However, the orifice shape variations show that it is hard to beat a sharp-edged round orifice in heat transfer coefficient, but it comes with a higher pressure drop across the orifice. Any attempt to streamline the hole shape indicated a drop in the Nusselt number, thus giving the designer some control over thermal budgeting of a component. Reduction in crossflow has been attempted with channel modifications. The use of high-porosity conductive foam in the impingement space has shown marked improvement in heat transfer performance. A list of possible research topics based on this discussion is provided in the conclusion.

Suggested Citation

  • Sandip Dutta & Prashant Singh, 2021. "Opportunities in Jet-Impingement Cooling for Gas-Turbine Engines," Energies, MDPI, vol. 14(20), pages 1-29, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6587-:d:655102
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    References listed on IDEAS

    as
    1. Sandip Dutta & Reid Smith, 2020. "Nonlinear Optimization of Turbine Conjugate Heat Transfer with Iterative Machine Learning and Training Sample Replacement," Energies, MDPI, vol. 13(17), pages 1-23, September.
    2. R. Jisung Park & Joshua Goodman & Michael Hurwitz & Jonathan Smith, 2020. "Heat and Learning," American Economic Journal: Economic Policy, American Economic Association, vol. 12(2), pages 306-339, May.
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    Cited by:

    1. Sandip Dutta & Inderjot Kaur & Prashant Singh, 2022. "Review of Film Cooling in Gas Turbines with an Emphasis on Additive Manufacturing-Based Design Evolutions," Energies, MDPI, vol. 15(19), pages 1-35, September.
    2. Wen Wang & Yan Yan & Yeqi Zhou & Jiahuan Cui, 2022. "Review of Advanced Effusive Cooling for Gas Turbine Blades," Energies, MDPI, vol. 15(22), pages 1-28, November.

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