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Reduction of fuel consumption of a small-scale gas turbine engine with fine bubble fuel

Author

Listed:
  • Nakatake, Yasuhito
  • Yamashita, Hirofumi
  • Tanaka, Hiroshi
  • Goto, Hidechika
  • Suzuki, Takashi

Abstract

Thermal efficiency of the small-scale gas turbines (SSGTs) is much less than that of large-scale gas turbine. Therefore, improvement of thermal efficiency of SSGTs is very important. In this paper, fine bubble fuel (FBF) which is fuel mixed with air fine bubbles (AFBs) was introduced to SSGT to improve its thermal efficiency. Control fuel (CF) and FBF were tested on a SSGT. Specific fuel consumption (SFC) of the engine can be reduced about 2.6%–5.7% at a maximum and about 1.7%–2.7% as an average by using FBF compared to CF. To validate the effect AFBs on the reduction of fuel consumption (FC), droplet size distribution of CF and FBF injected by a nozzle was measured. It was found that atomization of droplets can be promoted by introducing AFBs to fuel, and D32 can be reduced about 4.4%. However, from the results of a simulation of combustion in the combustion chamber, considerable effect of atomization was not observed. Therefore, the effect of atomization of fuel on the reduction of FC is relatively small, and there are other reasons, such as promotion of diffusive combustion due to air which exists in fuel as AFBs.

Suggested Citation

  • Nakatake, Yasuhito & Yamashita, Hirofumi & Tanaka, Hiroshi & Goto, Hidechika & Suzuki, Takashi, 2020. "Reduction of fuel consumption of a small-scale gas turbine engine with fine bubble fuel," Energy, Elsevier, vol. 194(C).
    • Handle: RePEc:eee:energy:v:194:y:2020:i:c:s0360544219325174
    DOI: 10.1016/j.energy.2019.116822
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    References listed on IDEAS

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    1. Nakatake, Yasuhito & Kisu, Shintaro & Shigyo, Kenta & Eguchi, Toshihiko & Watanabe, Takashi, 2013. "Effect of nano air-bubbles mixed into gas oil on common-rail diesel engine," Energy, Elsevier, vol. 59(C), pages 233-239.
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    Cited by:

    1. Abubaker, Ahmad M. & Darwish Ahmad, Adnan & Salaimeh, Ahmad A. & Akafuah, Nelson K. & Saito, Kozo, 2022. "A novel solar combined cycle integration: An exergy-based optimization using artificial neural network," Renewable Energy, Elsevier, vol. 181(C), pages 914-932.

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