D. Baroli

E. Gallorini

F. Piscaglia

G. Provinciali

K. Charpot

L. Valsecchi

M. Multerer

M. Zanichelli

P. Motta

English

Archivio istituzionale della ricerca - Politecnico di Milano

M2P - A NEW ECCOMAS THEMATIC CONFERENCE IACM SPECIALINTEREST CONFERENCE:Kernel-based surrogate for fluid dynamics simulation of wind assistantpropulsionD.Baroli1, M.Zanichelli2, M. Multerer1, E. Gallorini 3, F. Piscaglia3, L.Valsecchi2, P.Motta 21 Universita della Svizzera ItalianaEuler Institute, Via La Santa 1, 6962 Lugano, Switzerland2 ToolsPole OU, Tallinn, Estonia3 Politecnico di Milano, DAER, Via La Masa 34, 20156 Milano ItalyABSTRACTIn the present work, we investigate high dimensional parametrized fluid dynamics problem raising fromoptimization in real-time of wings of a Wind Assistant Propulsion for TransOceanic Cargo.To seek an optimal pareto of wind propulsion, we predict the momentum and forces acting on sailingwings by means of turbolent computational fluid dynamics. The computational fluid dynamics modelschoosed is RANS and it is performed on HPC (CINECA) with OpenFOAM. A dataset of input-outputis computed by parametrizing this fluid model, varying different angles of attack of the wings. To obtaina surrogate model, we adopt a Kernel-based Gaussian process. To reduce the complexity of surrogatemodel, accommodate the interpretability of data-driven methods, we adopt a suisse knife of low-rankapproximations (”pivoted-cholesky” [2]) and compression method ” multiresolution clustering knownsas ”samplets” [1]”.Regarding the trade-off of exploration of input design space versus accuracy of surrogate model andvariation with respect to the Pareto front of optimizer, we perform an active learning procedure based onBayesian optimization coupled with constrained optimization.The results are validated on airfoils profile simulated with Xfoils, where the input parameters are definedby control points of airfoils, and on fluid dynamics of industrial configuration of Wind Assistant Propul-sion TransOceanic Cargo.The project received funding from FF4EuroHPC-H2020 951745 simOcean.REFERENCES[1] Harbrecht, Helmut and Multerer, Michael D., Samplets: Construction and Scat-tered Data Compression. Accepted J. of Computational Physics, Available at SSRN:http://dx.doi.org/10.2139/ssrn.4053305[2] H Harbrecht, M Peters, R Schneider, On the low-rank approximation by the pivoted Choleskydecomposition, Applied numerical mathematics, 2012

Kernel-based surrogate for fluid dynamics simulation of wind assistant propulsion

https://re.public.polimi.it/bitstream/11311/1259463/1/BAROD01-23.pdf

Kernel-based surrogate for fluid dynamics simulation of wind assistant propulsion

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Archivio istituzionale della ricerca - Politecnico di Milano