Burman, Jörgen; Gebart, B. Rikard Influence from numerical noise in the objective function for flow design optimisation. (English) Zbl 1012.76023 Int. J. Numer. Methods Heat Fluid Flow 11, No. 1, 6-19 (2001). From the summary: The overall pressure drop in an axisymmetric contraction is minimised using two different grid sizes. We parametrize the transition region with only two design variables to creaste surface plots of objective function in design space, which is based on 121 calculations for each grid. The coarse grid has significant numerical noise in the objective function while the finer grid has less numerical noise. The optimisation is performed with two methods, a response surface model, and a gradient method (the method of feasible directions) to study the influence of numerical noise. Cited in 3 Documents MSC: 76D55 Flow control and optimization for incompressible viscous fluids 76M20 Finite difference methods applied to problems in fluid mechanics Keywords:flow design optimisation; axisymmetric contraction; objective function; coarse grid; fine grid; numerical noise; response surface model; gradient method; method of feasible directions PDFBibTeX XMLCite \textit{J. Burman} and \textit{B. R. Gebart}, Int. J. Numer. Methods Heat Fluid Flow 11, No. 1, 6--19 (2001; Zbl 1012.76023) Full Text: DOI References: [1] AEA Technology (1999),CFX 4.3 Flow Solver Guide User Guide, Computational Fluid Dynamics Services, Building 8.19, Harwell Laboratory, Harwell. [2] DOI: 10.1108/09615539910266620 · Zbl 0963.76556 · doi:10.1108/09615539910266620 [3] DOI: 10.1115/1.2817281 · doi:10.1115/1.2817281 [4] Engineous Software, Inc. (1999),iSIGHT Designer’s Guide 5.0, North Carolina Research, Triangle Park, NC. [5] Giles, M.B. (1997), ”Aerospace design: a complex task”,VKI Lecture Series, Inverse Design and Optimisation Methods, von Karman Institute for Fluid Dynamics, Rhode-Saint-Genèse. [6] Giunta, A.A. Dudley, J.M. Narducci, R. Grossman, B. Haftka, R.T. Mason, W.H. and Watson, L.T. (1994), ”Noisy aerodynamic response and smooth approximation in HSCT design”, AIAA Paper 94-4376,5th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Panama City, FL. [7] DOI: 10.1007/s001620050060 · Zbl 0912.76067 · doi:10.1007/s001620050060 [8] Narducci, R. Grossman, B. and Haftka, R.T. (1994), ”Sensitivity algorithms for an inverse design problem involving a shock wave”, AIAA Paper 94-0096,32nd AIAA Aerospace Sciences Meeting and Exhibit, Reno, NV. · doi:10.2514/6.1994-96 [9] Vanderplaats, G.N. (1999),Numerical Optimization Techniques for Engineering Design: With Applications, Vanderplaats Research & Development, Inc. · Zbl 0613.90062 [10] DOI: 10.1002/(SICI)1097-0363(19961130)23:10<991::AID-FLD422>3.0.CO;2-7 · Zbl 0888.76047 · doi:10.1002/(SICI)1097-0363(19961130)23:10<991::AID-FLD422>3.0.CO;2-7 This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.