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

Accelerating the Generation of Static Coupling Injection Maps Using a Data-Driven Emulator

Journal Article
2021-01-0550
ISSN: 2641-9637, e-ISSN: 2641-9645
Published April 06, 2021 by SAE International in United States
Accelerating the Generation of Static Coupling Injection Maps Using a Data-Driven Emulator
Sector:
Citation: Mondal, S., Torelli, R., Lusch, B., Milan, P. et al., "Accelerating the Generation of Static Coupling Injection Maps Using a Data-Driven Emulator," SAE Int. J. Adv. & Curr. Prac. in Mobility 3(3):1408-1424, 2021, https://doi.org/10.4271/2021-01-0550.
Language: English

References

  1. Weight of Shipment by Transportation Mode Bureau of Transportation Statistics 2015
  2. Sims , R. , Schaeffer , R. , Creutzig , F. , Cruz-Núñez , X. et al. Transport Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change
  3. Battistoni , M. , Magnotti , G.M. , Genzale , C.L. , Arienti , M. et al. Experimental and Computational Investigation of Subcritical Near-Nozzle Spray Structure and Primary Atomization in the Engine Combustion Network Spray D SAE International Journal of Fuels and Lubricants 11 4 337 352 2018 https://doi.org/10.4271/2018-01-0277
  4. Quan , S. , Senecal , P.K. , Pomraning , E. , Xue , Q. et al. A One-Way Coupled Volume of Fluid and Eulerian-Lagrangian Method for Simulating Sprays ASME 2016 Internal Combustion Engine Fall Technical Conference, ICEF 2016 2016 https://doi.org/10.1115/ICEF20169390
  5. Saha , K. , Quan , S. , Battistoni , M. , Som , S. et al. Coupled Eulerian Internal Nozzle Flow and Lagrangian Spray Simulations for GDI Systems SAE Technical Paper 2017-01-0834 2017 https://doi.org/10.4271/2017-01-0834
  6. Traver , M. , Pei , Y. , Tzanetakis , T. , Torelli , R. et al. Investigation and Simulation of Gasoline in a Diesel Fuel Injector for Gasoline Compression Ignition Applications Tschöke , H. , Marohn , R. 11. Tagung Einspritzung und Kraftstoffe 2018. Proceedings, Springer Vieweg Wiesbaden 2019
  7. Nocivelli , L. , Sforzo , B.A. , Tekawade , A. , Yan , J. et al. Analysis of the Spray Numerical Injection Modeling for Gasoline Applications SAE Technical Paper 2020-01-0330 2020 https://doi.org/10.4271/2020-01-0330
  8. Nocivelli , L. , Yan , J. , Saha , K. , Magnotti , G.M. et al. Effect of Ambient Pressure on the Behavior of Single-Component Fuels in a Gasoline Multi-Hole Injector ASME 2019 Internal Combustion Engine Division Fall Technical Conference, ICEF 2019 2019
  9. Pratama , R.H. , Huang , W. , and Moon , S. Unveiling Needle Lift Dependence on Near-Nozzle Spray Dynamics of Diesel Injector Fuel 285 119088 2021 https://doi.org/10.1016/j.fuel.2020.119088
  10. Manin , J. , Pickett , L.M. , and Yasutomi , K. Stereoscopic High-Speed Microscopy to Understand Transient Internal Flow Processes in High-Pressure Nozzles Experimental Thermal and Fluid Science 114 110027 2020 https://doi.org/10.1016/j.expthermflusci.2019.110027
  11. Westlye , F.R. , Battistoni , M. , Skeen , S.A. , Manin , J. et al. Penetration and Combustion Characterization of Cavitating and Non-Cavitating Fuel Injectors UNDER Diesel Engine Conditions SAE Technical Paper 2016-01-0860 2016 https://doi.org/10.4271/2016-01-0860
  12. Battistoni , M. , Som , S. , and Powell , C.F. Highly Resolved Eulerian Simulations of Fuel Spray Transients in Single and Multi-Hole Injectors: Nozzle Flow and Near-Exit Dynamics Fuel 251 709 729 2019 https://doi.org/https://doi.org/10.1016/j.fuel.2019.04.076
  13. Torelli , R. , Matusik , K.E. , Nelli , K.C. , Kastengren , A.L. et al. Evaluation of Shot-to-Shot In-Nozzle Flow Variations in a Heavy-Duty Diesel Injector Using Real Nozzle Geometry SAE International Journal of Fuels and Lubricants 11 4 379 295 2018 https://doi.org/10.4271/2018-01-0303
  14. Koukouvinis , P. , Gavaises , M. , Li , J. , and Wang , L. Large Eddy Simulation of Diesel Injector Including Cavitation Effects and Correlation to Erosion Damage Fuel 175 26 39 2016 https://doi.org/https://doi.org/10.1016/j.fuel.2016.02.037
  15. Musculus , M.P.B. and Kattke , K. Entrainment Waves in Diesel Jets SAE International Journal of Engines 2 1 1170 1193 2009 https://doi.org/10.4271/2009-01-1355
  16. Higgins , I. , Matthey , L. , Pal , A. , Burgess , C. et al. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework International Conference on Learning Representations 2 5 6 2017
  17. Milan , P.J. , Torelli , R. , Lusch , B. , and Magnotti , G.M. Data-Driven Model Reduction of Multiphase Flow in a Single-Hole Automotive Injector Atomization and Sprays 30 6 401 429 2020 https://doi.org/10.1615/AtomizSpr.2020034830
  18. Richards , P.S. and Pomraning , E. 2020
  19. Yasutomi , K. , Hwang , J. , Pickett , L.M. , Sforzo , B. et al. Transient Internal Nozzle Flow in Transparent Multi-Hole Diesel Injector SAE Technical Paper 2020-01-0830 2020 https://doi.org/10.4271/2020-01-0830
  20. Battistoni , M. , Duke , D. , Swantek , A. , Tilocco , F. et al. Effects of Noncondensable Gas on Cavitating Nozzles Atomization and Sprays 25 453 483 2015 https://doi.org/10.1615/AtomizSpr.2015011076
  21. McKay , M.D. , Beckman , R.J. , and Conover , W.J. A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code Technometrics 21 2 239 245 1979 https://doi.org/10.2307/1268522
  22. Pomraning , E. and Rutland , C.J. Dynamic One-Equation Nonviscosity Large-Eddy Simulation Model AIAA Journal 40 4 689 701 2020 https://doi.org/10.2514/2.1701
  23. Reitz , R. Modeling Atomization Processes in High-Pressure Vaporizing Sprays Atomisation Spray Technology 3 309 337 1988
  24. Beale , J.C. and Reitz , R.D. Modeling Spray Atomization with the Kelvin-Helmholtz/Rayleigh-Taylor Hybrid Model 9 6 623 650 1999 https://doi.org/10.1615/AtomizSpr.v9.i6.40
  25. 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 https://doi.org/10.1080/00102209508907782
  26. Amsden , A.A. KIVA-II: A Computer Program for Chemically Reactive Flows with Sprays 1989
  27. Nunno , A.C. , K , P. , Som , S.
  28. Westbrook , C.K. , Pitz , W.J. , Herbinet , O. , Curran , H.J. et al. A Comprehensive Detailed Chemical Kinetic Reaction Mechanism for Combustion of n-Alkane Hydrocarbons from n-Octane to n-Hexadecane Combustion and Flame 156 1 181 199 2009 https://doi.org/https://doi.org/10.1016/j.combustflame.2008.07.014
  29. Hinton , G.E. and Salakhutdinov , R.R. Reducing the Dimensionality of Data with Neural Networks Science 313 5786 504 2006 https://doi.org/10.1126/science.1127647
  30. Bishop , C.M. Pattern Recognition and Machine Learning New York Springer 2006
  31. Abadi , M. , Agarwal , A. , Barham , P. , Brevdo , E. , et al. TensorFlow : Large-Scale Machine Learning on Heterogeneous Distributed Systems OSDI’16: Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation 2016
  32. Liu , W. , Wang , Z. , Liu , X. , Zeng , N. et al. A Survey of Deep Neural Network Architectures and their Applications Neurocomputing 234 11 26 2017 https://doi.org/https://doi.org/10.1016/j.neucom.2016.12.038
  33. Kingma , D.P. and Welling , M. Auto-Encoding Variational Bayes International Conference on Learning Representations 2014
  34. Doersch , C. 2016
  35. Tolstikhin , I. , Bousquet , O. , Gelly , S. , and Schölkopf , B. Wasserstein Auto-Encoders International Conference on Learning Representations 2018
  36. Rasmussen , C.E. and Williams , C.K.I. Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) The MIT Press 2005
  37. Guo , H. , Torelli , R. , Bautista Rodriguez , A. , Tekawade , A. et al. Internal Nozzle Flow Simulations of the ECN Spray C Injector under Realistic Operating Conditions SAE International Journal of Advances and Current Practices in Mobility 2229 2240 2020 https://doi.org/10.4271/2020-01-1154
  38. Magnotti , G.M. , Battistoni , M. , Saha , K. , and Som , S. Influence of Turbulence and Thermophysical Fluid Properties on Cavitation Erosion Predictions in Channel Flow Geometries SAE International Journal of Advances and Current Practices in Mobility 2 4 2229 2240 2020 https://doi.org/10.4271/2019-01-0290
  39. Shi , J. and Arafin , M.S. 2010
  40. Magnotti , G.M. and Som , S. Assessing Fuel Property Effects on Cavitation and Erosion Propensity Using a Computational Fuel Screening Tool ASME 2019 Internal Combustion Engine Division Fall Technical Conference, ICEF 2019 2019 https://doi.org/10.1115/ICEF2019-7269
  41. Akaike , H. A New Look at the Statistical Model Identification IEEE Transactions on Automatic Control 19 6 716 723 1974 https://doi.org/10.1109/TAC.1974.1100705
  42. Caruana , R. , Lawrence , S. , and Giles , C. Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping Advances in Neural Information Processing Systems 13 402 408 2000
  43. Kingma , D. and Ba , J. Adam: A Method for Stochastic Optimization International Conference on Learning Representations 2014
  44. Magnotti , G.M. and Genzale , C.L. A Novel Spray Model Validation Methodology Using Liquid-Phase Extinction Measurements 25 5 397 424 2015 https://doi.org/10.1615/AtomizSpr.2014010377
  45. ECN Modeling Standards https://ecn.sandia.gov/diesel-spray-combustion/computational-method/modeling-standards/

Cited By