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Published October 10, 2022 | Published
Journal Article Open

Efficient global resolvent analysis via the one-way Navier-Stokes equations

  • 1. ROR icon California Institute of Technology

Abstract

Resolvent analysis is a powerful tool for modelling and analysing transitional and turbulent flows and, in particular, for approximating coherent flow structures. Despite recent algorithmic advances, computing resolvent modes for flows with more than one inhomogeneous spatial coordinate remains computationally expensive. In this paper we show how efficient and accurate approximations of resolvent modes can be obtained using a well-posed spatial marching method for flows that contain a slowly varying direction, i.e. one in which the mean flow changes gradually. First, we derive a well-posed and convergent one-way equation describing the downstream-travelling waves supported by the linearized Navier–Stokes equations. The method is based on a projection operator that isolates downstream-travelling waves. Integrating these one-way Navier–Stokes (OWNS) equations in the slowly varying direction, which requires significantly less CPU and memory resources than a direct solution of the linearized Navier–Stokes equations, approximates the action of the resolvent operator on a forcing vector. Second, this capability is leveraged to compute approximate resolvent modes using an adjoint-based optimization framework in which the forward and adjoint OWNS equations are marched in the downstream and upstream directions, respectively. This avoids the need to solve direct and adjoint globally discretized equations, therefore bypassing the main computational bottleneck of a typical global resolvent calculation. The method is demonstrated using the examples of a simple acoustics problem, a Mach 1.5 turbulent jet and a Mach 4.5 transitional zero-pressure-gradient flat-plate boundary layer. The optimal OWNS results are validated against corresponding global calculations, and the close agreement demonstrates the near-parabolic nature of these flows.

Additional Information

© The Author(s), 2022. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. The authors thank Mr. L. Heidt for his assistance in analysing and measuring the FLOPS and memory requirements reported in § 3.6. A.T. was supported in part by a catalyst grant from the Michigan Institute for Computational Discovery and Engineering (MICDE). G.R., O.K., E.P. and T.C. were supported by the Boeing Company through a Strategic Research and Development Relationship Agreement CT-BA-GTA-1 and by ONR grants N0014-11-1-0753, N00014-16-1-2445 and N00014-21-1-2158. O.K. also acknowledges support from the Natural Sciences and Engineering Research Council of Canada via the Postgraduate Doctoral Scholarship (PGSD3-532522-2019). The authors report no conflict of interest.

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Additional details

Created:
August 22, 2023
Modified:
January 23, 2024