Databases Coupling for Morphed-Mesh Simulations and Application on Fan Optimal Design

Aerodynamic databases collected either by experimental or numerical approaches are relatively “local” in a large-scale design space, surrounding the reference configurations or operating conditions. However, the exploration of the design space requires knowledge of the “dark” space where few data is available. Therefore, the coupling of “remote” databases is necessary. Databases had been generated by performing CFD (Computational Fluid Dynamics) simulations with meshes morphed from different geometrical configurations. Then an ordinary least square method was used to obtain derivatives out of databases. Direct co-Kriging method was used to interpolate those derivative-integrated databases. Derivability studies were carried out on two main sub-models: regression model and correlation model. Appropriate models were proposed respectively. Referring to 2 geometries and 2 operating conditions, 4 second order integrated databases had been generated for an automotive engine cooling fan. Progressively database coupling shows the advantage of the proposed approach. Optimizations has been done to improve the fan performances at different operating conditions.

[1]  Markus P. Rumpfkeil,et al.  Optimizations Under Uncertainty Using Gradients, Hessians, and Surrogate Models , 2013 .

[2]  Kyung K. Choi,et al.  Metamodeling Method Using Dynamic Kriging for Design Optimization , 2011 .

[3]  Eberhard Nicke,et al.  A Database of Optimal Airfoils for Axial Compressor Throughflow Design , 2017 .

[4]  Dimitri J. Mavriplis,et al.  Derivative-Enhanced Variable Fidelity Surrogate Modeling for Aerodynamic Functions , 2013 .

[5]  Pierre Sagaut,et al.  Comparison of Gradient-Based and Gradient-Enhanced Response-Surface-Based Optimizers , 2010 .

[6]  Stefan Görtz,et al.  Alternative Cokriging Method for Variable-Fidelity Surrogate Modeling , 2012 .

[7]  G. Gary Wang,et al.  Review of Metamodeling Techniques in Support of Engineering Design Optimization , 2007, DAC 2006.

[8]  Karen Willcox,et al.  Provably Convergent Multifidelity Optimization Algorithm Not Requiring High-Fidelity Derivatives , 2012 .

[9]  Eric Walter,et al.  An informational approach to the global optimization of expensive-to-evaluate functions , 2006, J. Glob. Optim..

[10]  G. Matheron Principles of geostatistics , 1963 .

[11]  S. Dabo‐Niang,et al.  Statistical modeling of spatial big data: An approach from a functional data analysis perspective , 2018 .

[12]  P. K. Senecal,et al.  Optimization and uncertainty analysis of a diesel engine operating point using CFD , 2016 .

[13]  Paul G. Constantine,et al.  Active Subspaces - Emerging Ideas for Dimension Reduction in Parameter Studies , 2015, SIAM spotlights.

[14]  Søren Nymand Lophaven,et al.  Aspects of the Matlab toolbox DACE , 2002 .

[15]  Zhonghua Han,et al.  High anisotropy space exploration with co-Kriging method , 2019 .

[16]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[17]  Leifur Þ. Leifsson,et al.  Multiobjective Aerodynamic Optimization by Variable-Fidelity Models and Response Surface Surrogates , 2016 .

[18]  Stéphane Aubert,et al.  Fluid Structure Interaction Problems in Turbomachinery Using RBF Interpolation and Greedy Algorithm , 2014 .

[19]  Manuel Henner,et al.  Meta-model based optimization of a large diameter semi-radial conical hub engine cooling fan , 2015 .

[20]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[21]  Donald R. Jones,et al.  A Taxonomy of Global Optimization Methods Based on Response Surfaces , 2001, J. Glob. Optim..

[22]  Manuel Henner,et al.  Space Infill Study of Kriging Meta-Model for Multi-Objective Optimization of an Engine Cooling Fan , 2014 .

[23]  A. Forrester,et al.  Design and analysis of 'noisy' computer experiments , 2006 .

[24]  D G Krige,et al.  A statistical approach to some mine valuation and allied problems on the Witwatersrand , 2015 .

[25]  C. Allen,et al.  Unified fluid–structure interpolation and mesh motion using radial basis functions , 2008 .