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A Data-Driven Approach to Determine the Single Droplet Post-Impingement Pattern on a Dry Wall Using Statistical Machine Learning Classification Methods
Technical Paper
2021-01-0552
ISSN: 0148-7191, e-ISSN: 2688-3627
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SAE WCX Digital Summit
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English
Abstract
The study of spray-wall interaction is of great importance to understand the dynamics during fuel-surface impingement process in modern internal combustion engines. The identification of droplet post-impingement pattern (contact, transition, non-contact) and droplet characteristics can quantitatively provide an estimation of energy transfer for spray-wall interaction, thus further influencing air-fuel mixing and emissions under combusting conditions. Theoretical criteria of single droplet post-impingement pattern on a dry wall have been experimentally and numerically studied by many researchers to quantify the hydrodynamic droplet behaviors. However, apart from model fidelity, another issue is the scalability. A theoretical criterion developed from one case might not be well suited to another scenario.
In this paper, a data-driven approach for single droplet-dry wall post-impingement pattern utilizing arithmetical machine learning classification methods is proposed and demonstrated. The droplet-wall post-impingement patterns are formulated as a classification problem. An experimental data library of for single droplet impinging on a dry wall (442 datasets from MTU inhouse experiments and 229 datasets from existing literature) is established for training and validating the classifications models. Typical parameters such as viscosity and density of the liquid droplet, temperature, Weber number, etc. that describe liquid properties and wall characteristics are discriminated against one another. Six well-known classification methods are applied to the database, and their performance is evaluated and compared. The performance of each classification method for individual post-impingement region is compared and characterized by four statistical measures (accuracy, precision, recall and F1 score) to obtain the best classifier. A high accuracy of classification methods reveals the potential of data-driven approach in determining different post impingement regions of the single droplet-wall interaction.
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Zhai, J. and Lee, S., "A Data-Driven Approach to Determine the Single Droplet Post-Impingement Pattern on a Dry Wall Using Statistical Machine Learning Classification Methods," SAE Technical Paper 2021-01-0552, 2021, https://doi.org/10.4271/2021-01-0552.Data Sets - Support Documents
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References
- Zhao , L. , Moiz , A.A. , Lee , S.-Y. , Naber , J. et al. Investigation of Multi-Hole Impinging Jet High Pressure Spray Characteristics under Gasoline Engine-Like Conditions SAE Technical Paper 2016-01-0847 2016 https://doi.org/10.4271/2016-01-0847
- Zhu , X. , Ahuja , N. , Zhai , J. , and Lee , S.-Y. Investigation of the Effects of Heat Transfer and Thermophysical Properties on Dynamics of Droplet-Wall Interaction SAE Technical Paper 2019-01-0296 2019 https://doi.org/10.4271/2019-01-0296
- Drake , M.C. , Fansler , T.D. , Solomon , A.S. , and Szekely , G. Jr. Piston Fuel Films as a Source of Smoke and Hydrocarbon Emissions From a Wall-Controlled Spark-Ignited Direct-Injection Engine SAE Transactions 762 783 2003 https://doi.org/10.4271/2003-01-0547
- Lindgren , R. , and Denbratt , I. Influence of Wall Properties on the Characteristics of a Gasoline Spray After Wall Impingement SAE Transactions 1202 1216 2004 https://doi.org/10.4271/2004-01-1951
- Stanton , D.W. , and Rutland , C.J. Modeling Fuel Film Formation and Wall Interaction in Diesel Engines SAE Transactions 808 824 1996 https://doi.org/10.4271/960628
- Xie , W. , Hu , Z. , Zhao , W. , Zhai , J. et al. Experimental and Numerical Studies on Spray Characteristics of an Internal Oscillating Nozzle Atomization and Sprays 29 1 2019
- Zhai , J. , Hu , Z. , Xie , W. , Chen , H. et al. Experimental Study on Spray Characteristics of The Internal Impinging Nozzle ILASS-Asia. 2017 98 98 2017
- Zhao , L. , Ahuja , N. , Zhu , X. , Zhao , Z. et al. Splashing Criterion and Topological Features of a Single Droplet Impinging on the Flat Plate SAE Technical Paper 2018-01-0289 2018 https://doi.org/10.4271/2018-01-0289
- Zhao , L. , Ahuja , N. , Zhu , X. , Zhao , Z. et al. Characterization of Impingement Dynamics of Single Droplet Impacting on a Flat Surface SAE Technical Paper 2019-01-0064 2019 https://doi.org/10.4271/2019-01-0064
- Habchi , C. , Foucart , H. , and Baritaud , T. Influence of the Wall Temperature on the Mixture Preparation in DI Gasoline Engines Oil & Gas Science and Technology. 54 2 211 222 1999
- Ohnesorge , W.v. The Formation of Drops by Nozzles and the Breakup of Liquid Jets UT Faculty/Researcher Works 2019
- Zhai , J. , Ahuja , N. , Zhao , L. , Zhu , X. et al. An Analytical Energy-Budget Model for Diesel Droplet Impingement on an Inclined Solid Wall SAE Technical Paper 2020-01-1158 2020 https://doi.org/10.4271/2020-01-1158
- Zhai , J. , Lee , S.-Y. , Ahuja , N. , Zhao , L. et al. An Energy Model of Droplet Impingement on an Inclined Wall Under Isothermal and Non-Isothermal Environments International Journal of Heat and Mass Transfer. 156 119892 2020
- Stow , C.D. , and Hadfield , M.G. An Experimental investigation of Fluid Flow Resulting from the Impact of a Water Drop with an Unyielding Dry Surface Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences 373 1755 419 441 1981
- Bai , C. , and Gosman , A. Development of Methodology for Spray Impingement Simulation SAE Transactions 550 568 1995 https://doi.org/10.4271/950283
- Mundo , C. , Sommerfeld , M. , and Tropea , C. Droplet-Wall Collisions: Experimental Studies of the Deformation and Breakup Process International Journal of Multiphase Flow 21 2 151 173 1995
- Yarin , A. , and Weiss , D. Impact of Drops on Solid Surfaces: Self-Similar Capillary Waves, and Splashing as a New Type of Kinematic Discontinuity Journal of Fluid Mechanics 283 141 173 1995
- Cossali , G. , Coghe , A. , and Marengo , M. The Impact of a Single Drop on a Wetted Solid Surface Experiments in Fluids 22 6 463 472 1997
- Fukumoto , M. , Nishioka , E. , and Nishiyama , T. New Criterion for Splashing in Flattening of Thermal Sprayed Particles onto Flat Substrate Surface Surface and Coatings Technology. 161 2-3 103 110 2002
- Vander Wal , R.L. , Berger , G.M. , and Mozes , S.D. The Splash/Non-Splash Boundary Upon a Dry Surface and Thin Fluid Film Experiments in Fluids 40 1 53 59 2006
- Bird , J.C. , Tsai , S.S. , and Stone , H.A. Inclined to Splash: Triggering and Inhibiting a Splash with Tangential Velocity New Journal of Physics 11 6 063017 2009
- Palacios , J. , Hernández , J. , Gómez , P. , Zanzi , C. et al. Experimental Study of Splashing Patterns and the Splashing/Deposition Threshold in Drop Impacts onto Dry Smooth Solid Surfaces Experimental Thermal and Fluid Science 44 571 582 2013
- Pan , K.-L. , Tseng , K.-C. , and Wang , C.-H. Breakup of a Droplet at High Velocity Impacting a Solid Surface Experiments in Fluids 48 1 143 156 2010
- Ma , T. , Feng , L. , Wang , H. , Liu , H. et al. A Numerical Study of Spray/Wall Impingement Based on Droplet Impact Phenomenon International Journal of Heat and Mass Transfer 112 401 412 2017
- Abu-Mostafa , Y.S. , Magdon-Ismail , M. , and Lin , H.-T. Learning from Data 4 New York, NY AMLBook 2012
- Al-Saud , M. , Eltamaly , A.M. , Mohamed , M.A. , and Kavousi-Fard , A. An Intelligent Data-Driven Model to Secure Intravehicle Communications Based on Machine Learning IEEE Transactions on Industrial Electronics 67 6 5112 5119 2019
- Borjali , A. , Monson , K. , and Raeymaekers , B. Predicting the Polyethylene Wear rate in Pin-on-Disc Experiments in the Context of Prosthetic Hip Implants: Deriving a Data-Driven Model Using Machine Learning Methods Tribology International 133 101 110 2019
- Reichstein , M. , Camps-Valls , G. , Stevens , B. , Jung , M. et al. Deep Learning and Process Understanding for Data-Driven Earth System Science Nature 566 7743 195 204 2019
- Ebrahimifakhar , A. , Kabirikopaei , A. , and Yuill , D. Data-Driven Fault Detection and Diagnosis for Packaged Rooftop Units Using Statistical Machine Learning Classification Methods Energy and Buildings 225 110318 2020
- Kaiser , E. , Noack , B.R. , Cordier , L. , Spohn , A. , et al. 2013
- Nair , A.G. and Taira , K. 2017
- Koza , J.R. , and Koza , J.R. Genetic Programming: On the Programming of Computers by Means of Natural Selection 1 MIT Press 1992
- Duriez , T. , Parezanovic , V. , Laurentie , J.-C. , Fourment , C. , et al. Closed-Loop Control of Experimental Shear Flows Using Machine Learning 7th AIAA Flow Control Conference 2014
- Gautier , N. , Duriez , T. , Aider , J.-L. , Noack , B. , et al. 2014
- Ding , C. , and Lam , K.P. Data-Driven Model for Cross Ventilation Potential in High-Density Cities Based on Coupled CFD Simulation and Machine Learning Building and Environment. 165 106394 2019
- Fukami , K. , Fukagata , K. , and Taira , K. Machine Learning Based Spatio-Temporal Super Resolution Reconstruction of Turbulent Flows 11566 2020
- Colvert , B. , Liu , G. , Dong , H. , and Kanso , E. FLOWTAXIS in the Wakes of Oscillating Airfoils Theoretical & Computational Fluid Dynamics 6808 2020
- Bhattacharjee , D. , Klose , B. , Jacobs , G.B. , and Hemati , M.S. Data-Driven Selection of Actuators for Optimal Control of Airfoil Separation Theoretical and Computational Fluid Dynamics 34 4 557 575 2020
- Bai , Z. , Brunton , S.L. , Brunton , B.W. , Kutz , J.N. et al. Data-Driven Methods In Fluid Dynamics: Sparse Classification from Experimental Data Whither Turbulence and Big Data in the 21st Century Springer 2017 323 342
- Brenner , M.P. , Eldredge , J.D. , and Freund , J.B. Perspective on Machine Learning for Advancing Fluid Mechanics Physical Review Fluids 4 10 100501 2019
- Brunton , S. , Noack , B. , and Koumoutsakos , P. Machine Learning for Fluid Mechanics 2019
- Taira , K. , Brunton , S.L. , Dawson , S.T.M. , Rowley , C.W. et al. Modal Analysis of Fluid Flows: An Overview AIAA Journal 55 12 4013 4041 2017
- Taira , K. , Hemati , M.S. , Brunton , S.L. , Sun , Y. et al. Modal Analysis of Fluid Flows: Applications and Outlook AIAA Journal 58 11 1 25 2019
- Chang , C.-W. , and Nam , T.D. Classification of Machine Learning Frameworks for Data-Driven Thermal Fluid Models International Journal of Thermal Sciences 135 559 579 2019
- Yonemoto , Y. , and Kunugi , T. Analytical Consideration of Liquid Droplet Impingement on Solid Surfaces Scientific Reports 7 1 2362 2017
- Naber , J.D. , and Farrell , P.V. Hydrodynamics of Droplet Impingement on a Heated Surface SAE Transactions 1346 1361 1993 https://doi.org/10.4271/930919
- Hatakenaka , R. , Breitenbach , J. , Roisman , I.V. , Tropea , C. et al. Magic Carpet Breakup of a Drop Impacting onto a Heated Surface in a Depressurized Environment International Journal of Heat and Mass Transfer 145 118729 2019
- Chandra , S. , and Avedisian , C. On the Collision of a Droplet with a Solid Surface Proceedings of the Royal Society of London. Series A: Mathematical and Physical Sciences 432 13 41 1884
- Bhat , M. , and Sivakumar , D. Post-Spreading Behavior of Impacting Fuel Drops on Stainless Steel Surface Experimental Thermal and Fluid Science 102 74 80 2019
- Kang , B.S. , and Lee , D.H. On the Dynamic Behavior of a Liquid Droplet Impacting Upon an Inclined Heated Surface Experiments in Fluids 29 4 380 387 2000
- Šikalo , Š. , Marengo , M. , Tropea , C. , and Ganić , E. Analysis of Impact of Droplets on Horizontal Surfaces Experimental Thermal and Fluid Science 25 7 503 510 2002
- James , G. , Witten , D. , Hastie , T. , and Tibshirani , R. An Introduction to Statistical Learning 112 Springer 2013
- Chiang , L.H. , Russell , E.L. , and Braatz , R.D. Fault Detection and Diagnosis in Industrial Systems Springer Science & Business Media 2000
- Bishop , C.M. Pattern Recognition and Machine Learning Springer 2006
- Fernandez-Delgado , M. , Cernadas , E. , Barro , S. , and Amorim , D. Do We Need Hundreds of Classifiers to Solve Real World Classification Problems? Journal of Machine Learning Research. 15 3133 3181 2014
- Ho , T.K. Random Decision Forests Proceedings of 3rd International Conference on Document Analysis and Recognition 1995
- Cortes , C. , and Vapnik , V. Support-Vector Networks Machine Learning 20 3 273 297 1995
- Russell , S. and Norvig , P. Artificial Intelligence: A Modern Approach 2002
- Altman , N.S. An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression The American Statistician 46 3 175 185 1992
- Chen , T. , He , T. , Benesty , M. , and Khotilovich , V. Package ‘Xgboost’ 2019
- Freund , Y. , Schapire , R. , and Abe , N. A Short Introduction to Boosting Journal-Japanese Society for Artificial Intelligence 14 771-780 1612 1999
- Pedregosa , F. , Varoquaux , G. , Gramfort , A. , Michel , V. et al. Scikit-Learn: Machine Learning in Python The Journal of Machine Learning Research 12 2825 2830 2011
- Provost , F. and Fawcett , T. Analysis and Visualization of Classifier Performance: Comparison Under Imprecise Class and Cost Distributions Proc of the 3rd International Conference on Knowledge Discovery and Data Mining 1997
- Powers , D.M. Evaluation: From Precision, Recall and F-measure to ROC, Informedness, Markedness and Correlation 2010 16061 2020
- Lipton , Z.C. , Elkan , C. , and Narayanaswamy , B. Thresholding Classifiers to Maximize F1 Score 1402 1892 14 2014
- Witten , I.H. , and Frank , E. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations ACM Sigmod Record. 31 1 76 77 2002
- Murphy , A.H. The Finley Affair: A Signal Event in the History of Forecast Verification Weather and Forecasting. 11 1 3 20 1996