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Response Surface Modeling of Diesel Spray Parameterized by Geometries Inside of Nozzle
Technical Paper
2011-01-0390
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
A response surface model of a diesel spray, parameterized by the internal geometries of a nozzle, is established in order to design the nozzle geometries optimally for spray mixing. The explanatory variables are the number of holes, the hole diameter, the inclined angle, the hole length, the hole inlet radius, K-factor and the sac diameter. The model is defined as a full second-order polynomial model including all the first-order interactions of the variables, and a total of 40 sets of numerical simulations based on D-optimal design are carried out to calculate the partial regression coefficients. Partial regression coefficients that deteriorate the estimate accuracy are eliminated by a validation process, so that the estimate accuracy is improved to be ±3% and ±15% for the spray penetration and the spread, respectively. Then, the model is applied to an optimization of the internal geometries for the spray penetration and the spray spread through a multi-objective genetic algorism. Through the optimization, it is found that both the penetration and the spread can be improved at the same time by reducing the pressure loss attributed to the flow separation at the hole inlet and by discharging the fuel before the turbulence produced at the hole inlet is damped. Thus, the optimization of the nozzle internal geometry could have the equivalent effects of increasing the fuel pressure in improving the penetration and the spread.
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Kurimoto, N., Suzuki, M., Yoshino, M., and Nishijima, Y., "Response Surface Modeling of Diesel Spray Parameterized by Geometries Inside of Nozzle," SAE Technical Paper 2011-01-0390, 2011, https://doi.org/10.4271/2011-01-0390.Also In
References
- Schmidt, D. P. Corradini, M. L. The internal flow of diesel fuel injector nozzles: a review International Journal of Engine Research 2 1 2001
- Liu, Z. Im, K. Wang, Y. Fezzaa, K. Lai, X. Wang, J. “Near-Nozzle Structure of Diesel Sprays Affected by Internal Geometry of Injector Nozzle: Visualized by Single-Shot X-Ray Imaging,” SAE Technical Paper 2010-01-0877 2010 10.4271/2010-01-0877
- Shimizu Arai Hiroyasu Breakup Length and Injection Angle in High Speed Jet Transactions of the Japan Society of Mechanical Engineers “B” 51 461 1985
- Bae, C. Yu, J. Kang, J. Kong, J. Lee, K. “Effect of Nozzle Geometry on the Common-Rail Diesel Spray,” SAE Technical Paper 2002-01-1625 2002 10.4271/2002-01-1625
- Chen, V. C. P. Tsui, K.-L. Barton, R. R. Meckesheimer, M. A review on design; modeling and applications of computer experiments IIE Transcations 38 4 2006
- Steffen, C. J. Jr. Response Surface Modeling of Combined Cycle Propulsion Components Using Computational Fluid Dynamics, AIAA-2002-0542
- Simpson, T. W. Mauery, T. M. Korte, J. J. Mistree, F. Comparison of Response Surface and Kriging Models for Multidisciplinary Design Optimization, AIAA-98-4755
- Jeong, S. Minemura, Y. Obayashi, S. Optimization of Combustion Chamber for Diesel Engine Using Kriging Model Journal of Fluid Science and Technology 1 2 2006
- Masuda, R. Fuyuto, T. Nagaoka, M. Von Berg, E. Tatschl, R. “Validation of Diesel Fuel Spray and Mixture Formation from Nozzle Internal Flow Calculation,” SAE Technical Paper 2005-01-2098 2005 10.4271/2005-01-2098
- Makinen, J. Piche, R. Ellman, A. Fluid transmission line modeling using a variational method Journal of Dynamic Systems Measurement and Control - Transactions of The ASME 122 1 2000
- Drew, D. A. Mathematical modeling of two-phase flow annual review of fluid mechanics 15 1983
- Alajbegovic, A. Three-dimensional cavitation calculations in nozzles Proceedings of 2nd Annual Meeting Institute for Multi fluid Science and Technology 1999
- AVL List GmbH Manual of AVL FIRE® Version 2008 2008
- Taylor, B. N. Kuyatt, C. E. Guidelines for Evaluating and Expressing the Uncertainty of NIST Measurement Results NIST Technical Note 1297 1994
- Moore, A. W. Lee, M. S. Efficient Algorithms for Minimizing Cross Validation Error Proceedings of the Eleventh International Conference on Machine Learning 1994
- Fonseca, C. M. Fleming, P. J. An overview of evolutionary algorithms in multiobjective optimization Evolutionary Computation 3 1 1995
- Sobol, I. M. On the distribution of points in a cube and the approximate evaluation of integrals USSR Computational Mathematics and Mathematical Physics 7 4 1967