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January 24, 2026International Journal of Turbo and Jet Engines0 citations

Hybrid thermodynamic and CFD modeling of a UAV-class micro gas turbine engine: performance validation and optimization

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SCSwati ChauhanGWG. Jims John Wessley

Key Points

  • The aim is to develop a simulation framework to predict and validate UAV-class micro gas turbine engine performance.
  • Hybrid framework integrating 1D thermodynamic analysis, system-level validation, and 2D CFD simulations.
  • Parametric studies on pressure ratios from 1.3 to 1.5 and Mach numbers between 0.1 and 0.6.
  • Utilization of Gas Turbine Simulation Program (GSP), Flownex, and ANSYS Fluent.
  • CFD predicted nozzle exit velocities exceeding 750 m/s with minimal flow separation.
  • Temperature predictions agreed within 5% across simulation tools.
  • Notable deviations in pressure and mass-flow observed in turbulence-dominated areas.

Abstract

Abstract The presented study proposes a hybrid multi-resolution simulation framework for predicting and validating the performance of UAV-class micro gas turbine engines in the 4–6 kN thrust range. The approach integrates 1D thermodynamic cycle analysis using the Gas Turbine Simulation Program (GSP), intermediate system-level validation through Flownex, and high-fidelity 2D CFD simulations in ANSYS Fluent for component-level analysis. Parametric studies were conducted for pressure ratios between 1.3 and 1.5 and Mach numbers from 0.1 to 0.6, at a design thrust of 4.56 kN and turbine inlet temperature of 1053 K. CFD results predicted nozzle exit velocities exceeding 750 m/s with minimal flow separation. Temperature predictions across tools showed strong agreement within 5 %, while pressure and mass-flow deviations were observed in turbulence-dominated regions. The framework demonstrates a balanced trade-off between computational efficiency and fidelity, offering a reliable methodology for early-stage design and validation of compact UAV propulsion systems.

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Cite This Study

Chauhan et al. (2026) studied this question.

synapsesocial.com/papers/6974610cbb9d90c67120aecbhttps://doi.org/10.1515/tjj-2025-0076
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