Validating the k-ω Turbulence Model for 3D Flows within the CFD Solver Eilmer

Stennett, Samuel; Chan, W. Y. K.; Gildfind, David; Jacobs, Peter A.

doi:10.4028/www.scientific.net/amm.846.67

Cited by 2 publications

(2 citation statements)

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“…Despite the degree of accuracy of the calculated skin friction coefficient, the boundary layer profiles are observed to deviate from the experimental data by some small amount. A similar result has been reported for Eilmer's structured grid explicit solver [162]. One proposed reason for this discrepancy is that the k − ω turbulence model is developing the boundary layer slower than in reality.…”

Section: Numerical Setupsupporting

confidence: 78%

“…The experimental data for this validation case was originally reported by Mabey et al [160], and was included in a critical compilation of compressible turbulent boundary data by AGARD [161]. This data has been used to validate the k − ω turbulence model in a previous version of Eilmer [88], and has been more recently used to validate the k − ω turbulence model in Eilmer's structured-grid explicit solver [162]. The flat plate was tested in the supersonic wind tunnel at R.A.E.…”

Section: Turbulent Flat Platementioning

confidence: 99%

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Adjoint-based aerodynamic design optimisation in hypersonic flow

Damm¹

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The drive for improved flexibility and reusability in satellite launch systems has brought a resurgence in the popularity of scramjet-powered access-to-space launch vehicle concepts. A challenge in scramjet-powered accelerator vehicles is achieving positive thrust margins at the high-speed end of their flight envelopes. As a consequence, scramjet-powered vehicles typically rely on highly integrated airframe-engine configurations. However, due to the tight integration, the geometries and the flow physics are complex. An automated optimisation method seems an excellent candidate approach to explore the complex design space of hypersonic vehicles. This thesis focuses on the development and application of a particular optimisation method, adjoint-based optimisation, to aid in efficient aerodynamic design in hypersonic flows. Shape optimisation of scramjet-powered vehicles requires many design parameters to capture the geometric detail, and, since the flow physics is complex, the fidelity of Reynolds-Averaged Navier-Stokes (RANS) analyses is desirable. The combination of many design parameters and an expensive objective function evaluation drives the need for gradient evaluation methods that scale well with the number of design variables. An advantage of the adjoint method is that all shape sensitivities for an objective function are evaluated at the cost of only one flow solution and one adjoint solution. As a result of its efficiency, the adjoint method has become widely used for aircraft design, evolving to the design optimisation of full configurations. Despite the wide use of adjoint methods in aircraft optimisation, there has been very little application to hypersonic vehicle design. Several high-speed adjoint solvers have been reported in the literature, however, a majority of these works have only verified the adjoint sensitivities, few have followed on to demonstrate the method for use in design optimisation. The contribution of this work is the description and application of a discrete adjoint solver in high-speed compressible flow optimisation. What is unique in this work is a demonstration that complex-step differentiation works well to linearise a second-order spatially accurate unstructured RANS solver. In particular, the approach presented in this work utilises the k − ω turbulence model in high-speed ducted flow configurations. As part of this work, to provide flow analysis with a rapid turnaround , an unstructured steadystate RANS solver driven by a Jacobian-Free Newton-Krylov method was developed. Turbulence is modelled using the two-equation k − ω turbulence model. The Newton method is globalised by using the pseudo-transient approach. A restarted GMRES method is used to solve the system of linear equations arising when solving for the Newton steps. Evaluation of the matrix-vector products required in the GMRES algorithm is accomplished by Fréchet derivatives using imaginary perturbations in the complex plane. This is necessary to achieve robust convergence of the types of turbulent hypersonic flows...

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Section: Numerical Setupsupporting

confidence: 78%

Section: Turbulent Flat Platementioning

confidence: 99%