This content is not included in your SAE MOBILUS subscription, or you are not logged in.

A Machine Learning-Genetic Algorithm (ML-GA) Approach for Rapid Optimization Using High-Performance Computing

Journal Article
2018-01-0190
ISSN: 1946-391X, e-ISSN: 1946-3928
Published April 03, 2018 by SAE International in United States
A Machine Learning-Genetic Algorithm (ML-GA) Approach for Rapid Optimization Using High-Performance Computing
Sector:
Citation: Moiz, A., Pal, P., Probst, D., Pei, Y. et al., "A Machine Learning-Genetic Algorithm (ML-GA) Approach for Rapid Optimization Using High-Performance Computing," SAE Int. J. Commer. Veh. 11(5):291-306, 2018, https://doi.org/10.4271/2018-01-0190.
Language: English

References

  1. Probst, D.M., Senecal, P.K., Qian, P.Z., Xu, M.X., et al. , “Optimization and Uncertainty Analysis of a Diesel Engine Operating Point Using CFD,” in ASME 2016 Internal Combustion Engine Division Fall Technical Conference, American Society of Mechanical Engineers, 2016, doi:10.1115/ICEF2016-9345, V001T06A009-V001T06A009.
  2. Zhang, Q., Ogren, R.M., and Kong, S.-C. , “A Comparative Study of Biodiesel Engine Performance Optimization Using Enhanced Hybrid PSO-GA and Basic GA,” Applied Energy 165:676-684, 2016, doi:10.1016/j.apenergy.2015.12.044.
  3. Wickman, D.D., Senecal, P.K., and Reitz, R.D. , “Diesel Engine Combustion Chamber Geometry Optimization Using Genetic Algorithms and Multi-Dimensional Spray and Combustion Modeling,” SAE Technical Paper 2001-01-0547 , 2001, doi:10.4271/2001-01-0547.
  4. Hanson, R., Curran, S., Wagner, R., Kokjohn, S. et al. , “Piston Bowl Optimization for RCCI Combustion in a Light-Duty Multi-Cylinder Engine,” SAE Int. J. Engines 5:286-299, 2012, doi:10.4271/2012-01-0380.
  5. Bertram, A.M., Zhang, Q., and Kong, S.-C. , “A Novel Particle Swarm and Genetic Algorithm Hybrid Method for Diesel Engine Performance Optimization,” International Journal of Engine Research 17(7):732-747, 2016, doi:10.1177/1468087415611031.
  6. Shi, Y. and Reitz, R.D. , “Optimization of a Heavy-Duty Compression-Ignition Engine Fueled with Diesel and Gasoline-like Fuels,” Fuel 89(11):3416-3430, 2010, doi:10.1016/j.fuel.2010.02.023.
  7. Wu, Z., Rutland, C.J., and Han, Z. , “Numerical Optimization of Natural Gas and Diesel Dual-Fuel Combustion for a Heavy-Duty Engine Operated at a Medium Load,” International Journal of Engine Research, 1468087417729255, 2017, doi:10.1177/1468087417729255.
  8. Brahma, I., Rutland, C.J., Foster, D.E., and He, Y. , “A New Approach to System Level Soot Modeling,” SAE Technical Paper 2005-01-1122 , 2005, doi:10.4271/2005-01-1122.
  9. He, Y. and Rutland, C.J. , “Modeling of a Turbocharged DI Diesel Engine Using Artificial Neural Networks,” SAE Technical Paper 2002-01-2772 , 2002, doi:10.4271/2002-01-2772.
  10. He, Y. and Rutland, C.J. , “Neural Cylinder Model and its Transient Results,” SAE Technical Paper 2003-01-3232 , 2003, doi:10.4271/2003-01-3232.
  11. Rezaei, J., Shahbakhti, M., Bahri, B., and Aziz, A.A. , “Performance Prediction of HCCI Engines with Oxygenated Fuels Using Artificial Neural Networks,” Applied Energy 138:460-473, 2015, doi:10.1016/j.apenergy.2014.10.088.
  12. Haykin, S. , Neural Networks: A Comprehensive Foundation (Prentice Hall PTR, 1994).
  13. Chen, T. and Guestrin, C. , “Xgboost: A Scalable Tree Boosting System,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, ACM, 785-794, doi:10.1145/2939672.2939785.
  14. Tikhonov, A.N., Arsenin, V.I.A.K., and John, F. , Solutions of Ill-Posed Problems (Washington, DC: Winston, 1977).
  15. Altman, N.S. , “An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression,” The American Statistician 46(3):175-185, 1992, doi:10.1080/00031305.1992.10475879.
  16. Liaw, A. and Wiener, M. , “Classification and Regression by randomForest,” R News 2(3):18-22, 2002.
  17. Hearst, M.A., Dumais, S.T., Osuna, E., Platt, J. et al. , “Support Vector Machines,” IEEE Intelligent Systems and Their Applications 13(4):18-28, 1998, doi:10.1109/5254.708428.
  18. Samadani, E., Shamekhi, A.H., Behroozi, M.H., and Chini, R. , “A Method for Pre-Calibration of DI Diesel Engine Emissions and Performance Using Neural Network and Multi-Objective Genetic Algorithm,” Iranian Journal of Chemistry and Chemical Engineering (IJCCE) 28(4):61-70, 2009.
  19. Vaughan, A. and Bohac, S.V. , “A Cycle-to-Cycle Method to Predict HCCI Combustion Phasing,” in Proceedings of the ASME Internal Combustion Engine Division 2013 Fall Technical Conference, 2013, doi:10.1115/ICEF2013-19203.
  20. Vaughan, A. and Bohac, S.V. , “An Extreme Learning Machine Approach to Predicting near Chaotic HCCI Combustion Phasing in Real-Time,” arXiv preprint arXiv:1310.3567, 2013.
  21. Validi, A., Chen, J.-Y., and Ghafourian, A. , “HCCI Intelligent Rapid Modeling by Artificial Neural Network and Genetic Algorithm,” Journal of Combustion 2012:1-11, 2012, doi:10.1155/2012/854393.
  22. Alonso, J.M., Alvarruiz, F., Desantes, J.M., Hernndez, L. et al. , “Combining Neural Networks and Genetic Algorithms to Predict and Reduce Diesel Engine Emissions,” IEEE Transactions on Evolutionary Computation 11(1):46-55, 2007, doi:10.1109/TEVC.2006.876364.
  23. Brahma, I. and Rutland, C.J. , “Optimization of Diesel Engine Operating Parameters Using Neural Networks,” SAE Technical Paper 2003-01-3228 , 2003, doi:10.4271/2003-01-3228.
  24. Costa, M., Bianchi, G.M., Forte, C., and Cazzoli, G. , “A Numerical Methodology for the Multi-Objective Optimization of the DI Diesel Engine Combustion,” Energy Procedia 45:711-720, 2014, doi:10.1016/j.egypro.2014.01.076.
  25. Coghlan, S. et al. , “Argonne Applications for the IBM Blue Gene/Q, Mira,” IBM Journal of Research and Development 57(1/2):12: 1-12: 11, 2013, doi:10.1147/JRD.2013.2238371.
  26. Ihaka, R. and Gentleman, R. , “R: A Language for Data Analysis and Graphics,” Journal of Computational and Graphical Statistics 5(3):299-314, 1996, doi:10.2307/1390807.
  27. Zhang, Y., Kumar, P., Traver, M., and Cleary, D. , “Conventional and Low Temperature Combustion Using Naphtha Fuels in a Multi-Cylinder Heavy-Duty Diesel Engine,” SAE Int. J. Engines 9:1021-1035, 2016, doi:10.4271/2016-01-0764.
  28. Pei, Y., Zhang, Y., Kumar, P., Traver, M. et al. , “CFD-Guided Heavy Duty Mixing-Controlled Combustion System Optimization with a Gasoline-Like Fuel,” SAE Int. J. Commer. Veh. 10(2):532-546, 2017, doi:10.4271/2017-01-0550.
  29. Richards, K., Senecal, P., and Pomraning, E. , Converge Theory Manual (Madison, WI: Convergent Sciences Inc, 2014). http://www.convergecfd.com.
  30. Han, Z. and Reitz, R.D. , “Turbulence Modeling of Internal Combustion Engines Using RNG κ-ε Models,” Combustion Science and Technology 106(4-6):267-295, 1995.
  31. Reitz, R.D. and Diwakar, R. , “Structure of High-Pressure Fuel Sprays,” SAE Technical Paper 870598 , 1987, doi:10.4271/870598.
  32. Reitz, R. , “Modeling Atomization Processes in High-Pressure Vaporizing Sprays,” Atomisation and Spray Technology 3(4):309-337, 1987.
  33. Schmidt, D.P. and Rutland, C. , “A New Droplet Collision Algorithm,” Journal of Computational Physics 164(1):62-80, 2000, doi:10.1006/jcph.2000.6568.
  34. Frossling, N. , Evaporation, Heat Transfer, and Velocity Distribution in Two-Dimensional and Rotationally Symmetrical Laminar Boundary-Layer Flow (Hampton, VA: National Aeronautics and Space Admin Langley Research Center, 1956).
  35. Amsden, A.A., O'rourke, P., and Butler, T. , KIVA-II: A Computer Program for Chemically Reactive Flows with Sprays (NM, USA: Los Alamos National Lab, 1989).
  36. Bae, C. and Kim, J. , “Alternative Fuels for Internal Combustion Engines,” Proceedings of the Combustion Institute 36(3):3389-3413, 2017, doi:10.1016/j.proci.2016.09.009.
  37. Liu, Y.-D., Jia, M., Xie, M.-Z., and Pang, B. , “Enhancement on a Skeletal Kinetic Model for Primary Reference Fuel Oxidation by Using a Semidecoupling Methodology,” Energy & Fuels 26(12):7069-7083, 2012, doi:10.1021/ef301242b.
  38. Heywood, J.B. , Internal Combustion Engine Fundamentals (New York: McGraw-Hill, 1988).
  39. Hiroyasu, H. and Kadota, T. , “Models for Combustion and Formation of Nitric Oxide and Soot in Direct Injection Diesel Engines,” SAE Technical Paper 760129 , 1976, doi:10.4271/760129.
  40. Nagle, J. , “Oxidation of Carbon between 1000-2000°C,” in Proceeding of the 5th Conference on Carbon, 1982, Pergamon Press, 1982.
  41. Babajimopoulos, A., Assanis, D., Flowers, D., Aceves, S. et al. , “A Fully Coupled Computational Fluid Dynamics and Multi-Zone Model with Detailed Chemical Kinetics for the Simulation of Premixed Charge Compression Ignition Engines,” International Journal of Engine Research 6(5):497-512, 2005, doi:10.1243/146808705X30503.
  42. Pal, P., Probst, P., Pei, P., Zhang, P. et al. , “Numerical Investigation of a Gasoline-like Fuel in a Heavy-Duty Compression Ignition Engine Using Global Sensitivity Analysis,” SAE Int. J. Fuels Lubr. 10:56-68, 2017, doi:10.4271/2017-01-0578.
  43. Senecal, P.K. and Reitz, R.D. , “Simultaneous Reduction of Engine Emissions and Fuel Consumption Using Genetic Algorithms and Multi-Dimensional Spray and Combustion Modeling,” SAE Technical Paper 2000-01-1890 , 2000, doi:10.4271/2000-01-1890.
  44. Polley, E.C. and Van der Laan, M.J. , “Super Learner in Prediction,” U.C. Berkeley Division of Biostatistics Working Paper Series, Working Paper 266, 2010.
  45. Polley, E., LeDell, E., Kennedy, C., Lendle, S. et al. , Package ‘SuperLearner’ (CRAN, 2017).
  46. Van der Laan, M.J., Polley, E.C., and Hubbard, A.E. , “Super Learner,” Statistical Applications in Genetics and Molecular Biology 6(1), 2007, doi:10.2202/1544-6115.1309.
  47. Sapp, S., van der Laan, M.J., and Canny, J. , “Subsemble: An Ensemble Method for Combining Subset-Specific Algorithm Fits,” Journal of Applied Statistics 41(6):1247-1259, 2014, doi:10.1080/02664763.2013.864263.
  48. Mullen, K.M. , “Continuous Global Optimization in R,” Journal of Statistical Software 60(6):1-45, 2014.
  49. Bergmeir, C.N., Molina Cabrera, D., and Benítez Sánchez, J.M. , “Memetic Algorithms with Local Search Chains in R: The Rmalschains Package,” American Statistical Association 75(4), 2016, doi:10.18637/jss.v075.i04.
  50. Bergmeir, C., Molina, D., and Benıtez, J. , “Rmalschains: Continuous Optimization Using Memetic Algorithms with Local Search Chains (MA-LS-Chains) in R,” Journal of Statistical Software, 2012.
  51. Probst, D.M. , Optimization and Model Interrogation (Convergent Science Advanced Training Slides, 2017).

Cited By