Multi-objective optimization of cooling galleries inside pistons of a diesel engine

Abstract In order to solve a trade-off problem between temperature and thermal stress of pistons for cross section design of a cooling gallery, a multi-objective optimization and a co-simulation workflow are carried out to calculate an optimal design and thermal solutions inside the piston’s thermal system of a diesel engine. Six structural parameters are selected as decision variables to describe the cross section shape of the cooling gallery. An oil filling ratio is regarded as an additional variable for finding the optimal volume fracture of the cooling gallery. The objective functions include the maximum temperature and the maximum thermal stress. In order to avoid a new gallery shape over the borders of the piston’s cross section, several constraints need to be satisfied in the optimization. Multiple tools of the optimization, including the Sobol Sequence, the Support Vector Machine for regression, a variant of the non-dominated sorting genetic algorithm II, and the k-means clustering method, are integrated together to seek the optimal solutions for the design problem. The predicted results by the SVR models agree well with those obtained by the co-simulation method with a high determination coefficient greater than 0.91. A set of Pareto optimal solutions is obtained through the evolution of 100 generations on the basis of the SVR models. The results reveal that the Pareto optimal solutions are effective due to two thermal solutions out of the three representative solutions that are partitioned by k-means clustering. The design proposal selected from the three representative solutions indicates that the optimal position of the gallery cross section should be far away from the grooves and close to the bottom of the combustion chamber and the top region of the inner chamber. The cooling oil filling ratio should be increased to approximately 50% by increasing the mass flow rate of the cooling oil or the cooling oil capture capacity of the gallery in order to satisfy the design goal of reducing piston temperature. The optimization results reflects an advance of design improvement methodology in the field of piston cooling systems.

[1]  Vivek Patel,et al.  Multi-objective optimization of a rotary regenerator using tutorial training and self-learning inspired teaching-learning based optimization algorithm (TS-TLBO) , 2016 .

[2]  I. Sobol On the distribution of points in a cube and the approximate evaluation of integrals , 1967 .

[3]  Hamed Safikhani,et al.  Modeling and Pareto based multi-objective optimization of wavy fin-and-elliptical tube heat exchangers using CFD and NSGA-II algorithm , 2017 .

[4]  Marek Kowal,et al.  Intelligent Systems in Technical and Medical Diagnostics , 2014 .

[5]  Lin Cheng,et al.  Multi-objective optimization of cooling air distributions of grate cooler with different clinker particles diameters and air chambers by genetic algorithm , 2017 .

[6]  Kwang-Yong Kim,et al.  Multi-objective optimization of a double-faced type printed circuit heat exchanger , 2013 .

[7]  Antonio J. Torregrosa,et al.  A contribution to film coefficient estimation in piston cooling galleries , 2010 .

[8]  Peng Wang,et al.  The reciprocating motion characteristics of nanofluid inside the piston cooling gallery , 2015 .

[9]  Smith Eiamsa-ard,et al.  Pareto based multi-objective optimization of turbulent heat transfer flow in helically corrugated tubes , 2016 .

[10]  Kazuhiko Ogata,et al.  COOLING EFFECTS OF "COCKTAIL SHAKER" SYSTEMS ON PISTONS , 1974 .

[11]  Vesa Ojalehto,et al.  Bilevel heat exchanger network synthesis with an interactive multi-objective optimization method , 2012 .

[12]  Mohammad Hassan Shojaeefard,et al.  Modeling and combined application of the modified NSGA-II and TOPSIS to optimize a refrigerant-to-air multi-pass louvered fin-and-flat tube condenser , 2016 .

[13]  Gang Li,et al.  Experimental visualization of gas–liquid two-phase flow during reciprocating motion , 2015 .

[14]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[15]  Bennett L. Fox,et al.  Algorithm 647: Implementation and Relative Efficiency of Quasirandom Sequence Generators , 1986, TOMS.

[16]  Chin-Hsiang Cheng,et al.  Numerical Predictions of Thermal Convection in a Rectangular Enclosure with Oscillating Wall , 2005 .

[17]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[18]  Karim Mazaheri,et al.  Turbine blade cooling passages optimization using reduced conjugate heat transfer methodology , 2016 .

[19]  K. Zeng,et al.  Experimental visualization of gas-liquid-solid three-phase flow during reciprocating motion , 2017 .

[20]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[21]  Lin Cheng,et al.  Multi-objective optimization design of air distribution of grate cooler by entropy generation minimization and genetic algorithm , 2016 .

[22]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.