Toward Aerodynamic Shape Optimization for the Design of Natural-Laminar-Flow Wings

AMS Seminar Series

Speaker: Michael Piotrowski, Computational Aerosciences Branch, NASA Advanced Supercomputing Division
August 26, 2021

Presentation

Seminar Slide Deck (PDF-19.8MB)

Abstract

The design of aircraft configurations using tools capable of incorporating and exploiting natural laminar flow, which requires the ability to predict laminar-turbulent transition efficiently, could play a key role in reducing the environmental impact of aviation. To accelerate this design, methods for the efficient prediction of boundary-layer transition in a RANS-based flow solver and discrete-adjoint gradient-based optimization algorithm are presented. A local-correlation based transition model is coupled with the Spalart-Allmaras turbulence model and integrated in a Newton-Krylov-Schur flow solver. A source term time stepping restriction is introduced to prevent unstable solution updates for the fully coupled, fully implicit solver. A smooth-variant of the transition model is developed with approximations to discontinuous and stiff source-term functions. The new, smooth model is demonstrated to produce significantly improved numerical behaviour and provides smooth gradients for the gradient-based optimization algorithm. Compressibility corrections are introduced to extend the transition model empirical correlations to transonic flow regimes typical of commercial transport aircraft, with the resulting model validated using two- and three-dimensional transonic transition test cases. Finally, the model is integrated in the discrete-adjoint gradient-based optimization algorithm and applied to two-dimensional drag-minimization studies. The results demonstrate the capability of the algorithm to explore the natural-laminar-flow configuration design space.

Biography

Michael Piotrowski is a NASA Pathways Student Intern in the Computational Aerosciences Branch of the NASA Advanced Supercomputing Division at NASA Ames. He joined the LAVA group in 2018, where he works on turbulence and transition modeling, code development, and application to AATT and TTT projects. He is also a PhD Candidate at the University of Toronto Institute for Aerospace Studies under the supervision of David W. Zingg, where the focus of his studies is on the development of efficient methods for boundary-layer transition prediction in a gradient-based optimization algorithm to explore the design of transonic, natural-laminar-flow wings.

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