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Article

Optimizing Gas Turbine Performance Using the Surrogate Management Framework and High-Fidelity Flow Modeling

1
Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA
2
School of Engineering, Brown University, Providence, RI 02912, USA
3
U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA
*
Author to whom correspondence should be addressed.
Energies 2020, 13(17), 4283; https://doi.org/10.3390/en13174283
Received: 20 July 2020 / Revised: 12 August 2020 / Accepted: 13 August 2020 / Published: 19 August 2020
This work couples high-fidelity moving-domain finite element compressible flow modeling with a Surrogate Management Framework (SMF) for optimization to effectively design a variable speed gas turbine stage. The superior accuracy of high-fidelity modeling, however, comes with relatively high computational costs, which are further amplified in the iterative design process that relies on parametric sweeps. An innovative approach is developed to reduce the number of iterations needed for optimal design, leading to a significant reduction in the computational cost without sacrificing the high fidelity of the analysis. The proposed design optimization approach is applied to a novel incidence-tolerant turbomachinery blade technology that articulates the stator- and rotor-blade positions of an annular single-stage high pressure turbine to achieve peak performance. This work also extends our understanding of rotor–stator interactions by simulating complex internal flows occurring during multi-speed turbine operation. Potential variable-speed gas turbine stage designs and the proposed optimization approach are presented to provide valuable insight into this new turbomachinery technology that can positively impact future propulsion systems. View Full-Text
Keywords: compressible flow; stabilized and multiscale FEM; surrogate management framework; design optimization; gas turbine compressible flow; stabilized and multiscale FEM; surrogate management framework; design optimization; gas turbine
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MDPI and ACS Style

Kozak, N.; Rajanna, M.R.; Wu, M.C.H.; Murugan, M.; Bravo, L.; Ghoshal, A.; Hsu, M.-C.; Bazilevs, Y. Optimizing Gas Turbine Performance Using the Surrogate Management Framework and High-Fidelity Flow Modeling. Energies 2020, 13, 4283. https://doi.org/10.3390/en13174283

AMA Style

Kozak N, Rajanna MR, Wu MCH, Murugan M, Bravo L, Ghoshal A, Hsu M-C, Bazilevs Y. Optimizing Gas Turbine Performance Using the Surrogate Management Framework and High-Fidelity Flow Modeling. Energies. 2020; 13(17):4283. https://doi.org/10.3390/en13174283

Chicago/Turabian Style

Kozak, Nikita, Manoj R. Rajanna, Michael C.H. Wu, Muthuvel Murugan, Luis Bravo, Anindya Ghoshal, Ming-Chen Hsu, and Yuri Bazilevs. 2020. "Optimizing Gas Turbine Performance Using the Surrogate Management Framework and High-Fidelity Flow Modeling" Energies 13, no. 17: 4283. https://doi.org/10.3390/en13174283

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