Specialized Reduced Models of Dynamic Flows in 2-Stroke Engines
The complexity of scavenging by ports and its impact on engine efficiency create the need to understand and to model it as realistically as possible. However, there are few empirical scavenging models and these are highly specialized. In a design optimization process, they appear very restricted and their field of use is limited. This paper presents a comparison of two methods to establish and reduce a model of the scavenging process in 2-stroke diesel engines. To solve the lack of scavenging models, a CFD model has been developed and is used as the referent case. However, its large size requires a reduction. Two techniques have been tested depending on their fields of application: The NTF method and neural networks. They both appear highly appropriate drastically reducing the model’s size (over 90% reduction) with a low relative error rate (under 10%). Furthermore, each method produces a reduced model which can be used in distinct specialized fields of application: the distribution of a quantity (mass fraction for example) in the cylinder at each time step (pseudo-dynamic model) or the qualification of scavenging at the end of the process (pseudo-static model).
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 Maekawa M. "Text of Course". JSME G36. 23, 1957;
 Hopkinson B. "The Charging of Two-Cycle Internal Combustion Engines". Journal of the American Society for Naval Engineers. 26, 3, 974–85, 1914;
 Mattarelli E. "Virtual design of a novel two-stroke high-speed direct-injection Diesel engine". International Journal of Engine Research. 10, 3, 175–93, 2009;
 Trescher D. "Development of an Efficient 3–D CFD Software to Simulate and Visualize the Scavenging of a Two-Stroke Engine". Archive of Computational Methods in Engineering. 15, 1, 67–111, 2008;
 Lamas Galdo M.I., Rodríguez Vidal C.G. "Computational Fluid Dynamics Analysis of the Scavenging Process in the MAN B&W 7S50MC Two-Stroke Marine Diesel Engine". Journal of Ship Research. 56, 3, 154–61, 2012;
 Hong G., Mack A.N.F., Menolotto F., Jamieson C.S., Main S.G. "Numerical Visualization of Air Short-Circuiting in a Small two-stroke SI engine". SAE Technical paper 2004-32-0009. 2004;
 Prasad B.V.V.S.U., Sharma C.S., Anand T.N.C., Ravikrishna R.V. "High swirl-inducing piston bowls in small diesel engines for emission reduction". Applied Energy. 88, 2355–67, 2011;
 Cagin S., Fischer X., Bourabaa N., Delacourt E., Morin C., Coutellier D. "A Methodology for a New Qualified Numerical Model of a 2-Stroke Diesel Engine Design". The International Conference On Advances in Civil, Structural and Mechanical Engineering - CSME 2014. Hong-Kong; 2014.
 Zhang Q.X. "Modelling The Scavenging Process in a Two-Stroke I.C. Engine". Thesis (Coursework Master thesis). 1995.
 Kato S., Nakagawa H., Kawahara Y., Adachi T., Nakashima M. "Numerical analysis of the scavenging flow in a two-stroke-cycle gasoline engine". JSME international journal. Series 2, Fluids engineering, heat transfer, power, combustion, thermophysical properties. 34, 3, 385–90, 1991;
 Pitta S.R., Kuderu R. "A computational fluid dynamics analysis on stratified scavenging system of medium capacity two-stroke internal combustion engines". Thermal Science. 12, 1, 33–42, 2008;
 Noor M.M., Kadirgama K., Devarajan R., Al. E. "CFD Simulation and Validation of the In-Cylinder Within a Motored Two Stroke Si Engine". 2nd International Conference on Science & Technology, Penang. 2008.
 Ingvorsen K.M., Meyer K.E., Schnipper T., Walther J.H., Mayer S. "Swirling Flow in Model of Large Two Stroke Diesel Engine". 16th International Symposium on Applications of Laser Techniques to Fluid Mechanics, Lisbon. 2012.
 Blair G.P. "Design and Simulation of two-stroke engines". Engineers, Society of Automotive; 1996.
 Benson R.S., Whitehouse N.D. "Internal Combustion Engines". Pergamon Press; 1983.
 Sher E. "A new practical model for the scavenging process in a two-stroke cycle engine". SAE Technical paper 850085. 1985;
 Heywood J.B. "Internal Combustion Engines Fundamentals". McGraw-Hill. Duffy A, Moms JM, editors. 1988.
 Cagin S., Bourabaa N., Delacourt E., et al. "Scavenging Process Analysis in a 2-Stroke Engine by CFD Approach for a Parametric 0D Model Development". 7th International Exergy, Energy and Environment Symposium. Valenciennes; 2015.
 Cichocki A., Zdunek R., Choi S., Plemmons R., Amari S.-I. "Non-Negative Tensor Factorization using Alpha and Beta Divergences". IEEE International Conference on Acoustics, Speech and Signal Processing. Honolulu, HI; 2007.
 Shashua A., Hazan T. "Non-negative tensor factorization with applications to statistics and computer vision". ICML ’05 Proceedings of the 22nd International Conference on Machine learning. New-York; p. 792–9, 2005.
 Lee D.D., Seung H.S. "Learning the parts of objects by nonnegative matrix factorization". Nature. 401, 788–91, 1999;
 Riedmiller M., Braun H. "A direct adaptive method for faster backpropagation learning: the RPROP algorithm". In: Ruspini EH, editor. Proceedings of the IEEE International Conference on Neural Networks. New-York: IEEE Press; p. 586–91, 1993.
 Igel C., Hüsken M. "Empirical evaluation of the improved Rprop learning algorithms". Neurocomputing. 50, C, 105–23, 2003;
 Riedmiller M. "Advanced supervised learning in multi-layer perceptrons—from backpropagation to adaptive learning algorithms". Computer Standards Interfaces. 16, 5, 265–78, 1994;