Experience of Direct Numerical Simulation of Turbulence on Supercomputers

Belyaev, Kirill V.; Garbaruk, A. V.; Shur, M. L.; Strelets, M. Kh.; Spalart, Philippe R.

doi:10.1007/978-3-319-55669-7_6

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“…The computational cost of the NS equations is approximately proportional to O(Re 3 ), where Re denotes the Reynolds number [90]. Direct numerical simulation relies on some of the most powerful computers in the world to produce solutions over simple geometries such as spheres and flat plates [91]. The standard CFD approximations shown in figure 2.2 are well known and can be found in [92].…”

Section: Introductionmentioning

confidence: 99%

Non-intrusive reduced order modelling for aerodynamic applications

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During the design and optimisation of aerodynamic components, the simulations to be performed involve a large number of parameters related to the geometry and flow conditions. In this scenario, the simulation of all possible configurations is not af-fordable. To overcome this problem, the present work proposes a novel multi-output neural network (NN) for the prediction of aerodynamic coefficients of aerofoils and wings using compressible flow data. Contrary to existing NNs that are designed to predict aerodynamic quantities of interest, the proposed network considers as output the pressure or stresses at a number of selected points on the aerodynamic surface. The proposed approach is compared against the more traditional networks where the aero-dynamic coefficients are directly the outputs of the network. Furthermore, a detailed comparison of the proposed NN against the popular proper orthogonal decomposi-tion (POD) method is presented. The numerical results, involving high dimensional problems with flow and geometric parameters, show the benefits of the proposed ap-proach.The proposed NN is used to accelerate the evaluation of the objective function in an inverse aerodynamic shape design problem. The optimisation algorithm uses the gradient-free modified cuckoo search method. Applications in two and three dimen-sions are shown, demonstrating the potential of the proposed framework in the con-text of both optimisation and inverse design problems. The performance of the pro-posed optimisation framework is also compared against existing frameworks where the more traditional NNs are employed.

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Section: Introductionmentioning

confidence: 99%

Non-intrusive reduced order modelling for aerodynamic applications

Balla¹

View full text Add to dashboard Cite

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Experience of Direct Numerical Simulation of Turbulence on Supercomputers

Cited by 1 publication

References 5 publications

Non-intrusive reduced order modelling for aerodynamic applications

Non-intrusive reduced order modelling for aerodynamic applications

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