Interaction between grid and design space refinement for bluff body-facilitated mixing

The interaction between grid refinement and features in design space is investigated for time dependent Navier-Stokes flows over a bluff body. A mixing measure and total pressure loss are calculated for a range of geometries and several grid refinements. In particular, 52 geometries are analyzed and a response surface surrogate-based outlier analysis revealed that as the grid is refined, three of these configurations stand out with off-trend mixing indices. Both grid refinement and multiple surrogate modeling exercises reveal that very high mixing indices are found in a very small island in design space. This important design region manifests itself only when the grid resolution is adequate. The high value of the mixing index is due to interaction between viscous flows and abrupt geometric variations of the bluff body . based on selected number of original CFD solutions 1-11 . In addition to offering a low-cost alternative for evaluating designs, surrogate models also offer advantages associated with the fact that they require a large number of designs to be evaluated together. Besides obvious advantages in terms of parallel computation, this can also reveal cases with significantly different behavior than others. Histogram and outlier analysis can identify and examine designs exhibiting unusual departure from the overall trend. The outliers can occur due to incomplete convergence or high errors due to inappropriate computational set-up such as grid distributions or boundary conditions. In that case, outlier analysis helps find and possibly correct these problems. However, outlier may also represent designs where the physical behavior changes significantly. In this study, the mixing problem associated with bluff body flows is investigated. Mixing is a process with many practical applications, including propulsion and power generation, homogenization of multiple materials and/or species, and various heat exchange and geophysical processes. In man-made devices, �

[1]  A. Keane,et al.  Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling , 2003 .

[2]  Simon J. Cox,et al.  Two Dimensional Airfoil Optimisation Using CFD in a Grid Computing Environment , 2003, Euro-Par.

[3]  Wesfreid,et al.  Downstream evolution of the Bénard-von Kármán instability. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[4]  Raphael T. Haftka,et al.  Response Surface Techniques for Diffuser Shape Optimization , 2000 .

[5]  Wei Shyy,et al.  Shape Optimization of Supersonic Turbines Using Response Surface and Neural Network Methods , 2001 .

[6]  Wei Shyy,et al.  Shape optimization of supersonic turbines using global approximation methods , 2002 .

[7]  J. Tukey,et al.  The Fitting of Power Series, Meaning Polynomials, Illustrated on Band-Spectroscopic Data , 1974 .

[8]  Wei Shyy,et al.  Computational-fluid-dynamics-based design optimization for single-element rocket injector , 2004 .

[9]  Wei Shyy,et al.  Preliminary Design Optimization for a Supersonic Turbine for Rocket Propulsion , 2000 .

[10]  P. J. Huber.  Robust Estimation of a Location Parameter , 1964 .

[11]  Wei Shyy,et al.  Neural Network and Response Surface Methodology for Rocket Engine Component Optimization , 2000 .

[12]  William H. Press,et al.  Numerical Recipes: FORTRAN , 1988 .

[13]  P. Wang,et al.  Numerical simulation of the wake flow behind trapezoidal bluff bodies , 2002 .

[14]  A. Gosman,et al.  Solution of the implicitly discretised reacting flow equations by operator-splitting , 1986 .

[15]  Wei Shyy,et al.  Assessment of Response Surface-Based Optimization Techniques for Unsteady Flow Around Bluff Bodies , 2002 .