Abstract
The interaction between inertial, elastic and aerodynamic forces for structures subjected to a fluid flow may cause unstable coupled vibrations that can endanger the structure itself. Predicting these interactions is a time consuming but crucial task in an aircraft design process. In order to reduce the computational time surrogate reduced order models can be used in both structural and aerodynamic models. More over it is possible to avoid launching CFD computations at every time step. A database of aerodynamic pressure distribution on the structural component can be created conveniently sampling the space of the structural model DoF. Starting from the knowledge of the pre-computed data-set a Gaussian Process can be applied to predict the pressure distribution on an unexplored point of the space of DoF. The knowledge of the standard deviation can be used to give indications on where to launch further CFD computations to enrich the database. This technique will be first applied to a database of pressures obtained using the software Xfoil®, later it will be applied to CFD simulations of type RANS launched with elsA® on one Flap track Fairing of an Airbus aircraft.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sandboge, R.: Fluid-structure interaction with OpenFSITM and MD NastranTM structural solver. Ann. Arbor 1001, 9 (2010)
Montgomery, D.C., Peck, E.A., Vining, G.G.: Introduction to Linear Regression Analysis. Wiley, Hoboken (2015)
Drela, M.: XFOIL: An Analysis and Design System for Low Reynolds Number Airfoils. Low Reynolds Number Aerodynamics. Springer, Heidelberg (1989)
Cambier, L., Veuillot, J.: Status of the elsA CFD software for flow simulation and multidisciplinary applications. AIAA Paper 664, 2008 (2008)
Sadek, R.A.: SVD based image processing applications: state of the art, contributions and research challenges. arXiv preprint arXiv:1211.7102 (2012)
Friswell, M., Mottershead, J.E.: Finite Element Model Updating in Structural Dynamics, vol. 38. Springer Netherlands (1995)
Schmid, P.J.: Dynamic mode decomposition of numerical and experimental data. J. Fluid Mech. 656, 5–28 (2010)
Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). The MIT Press, Cambridge (2005)
Rasmussen, C.E., Ghahramani, Z.: Infinite mixtures of Gaussian process experts. Adv. Neural Inf. Process. Syst. 2, 881–888 (2002)
Chen, T., Ren, J.: Bagging for Gaussian process regression. Neurocomputing 72(7), 1605–1610 (2009)
Deisenroth, M.P., Ng, J.W.: Distributed Gaussian processes. arXiv preprint arXiv:1502.02843 (2015)
Cao, Y., Fleet, D.J.: Generalized product of experts for automatic and principled fusion of gaussian process predictions. CoRR, Vol. abs/1410.7827 (2014)
MathWorks, I.: MATLAB: the language of technical computing. In: Desktop Tools and Development Environment, version 7, vol. 9. MathWorks (2005)
Vassberg, J.C., DeHaan, M.A., Rivers, S.M., Wahls, R.A.: Development of a common research model for applied CFD validation studies (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Chiplunkar, A., Bosco, E., Morlier, J. (2018). Gaussian Process for Aerodynamic Pressures Prediction in Fast Fluid Structure Interaction Simulations. In: Schumacher, A., Vietor, T., Fiebig, S., Bletzinger, KU., Maute, K. (eds) Advances in Structural and Multidisciplinary Optimization. WCSMO 2017. Springer, Cham. https://doi.org/10.1007/978-3-319-67988-4_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-67988-4_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67987-7
Online ISBN: 978-3-319-67988-4
eBook Packages: EngineeringEngineering (R0)