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

Prediction of Vehicle Cabin Occupant Thermal Comfort Using Deep Learning and Computational Fluid Dynamics

Journal Article
12-04-03-0022
ISSN: 2574-0741, e-ISSN: 2574-075X
Published August 19, 2021 by SAE International in United States
Prediction of Vehicle Cabin Occupant Thermal Comfort Using Deep Learning and Computational Fluid Dynamics
Sector:
Citation: Warey, A., Kaushik, S., Khalighi, B., Cruse, M. et al., "Prediction of Vehicle Cabin Occupant Thermal Comfort Using Deep Learning and Computational Fluid Dynamics," SAE Intl. J CAV 4(3):269-278, 2021, https://doi.org/10.4271/12-04-03-0022.
Language: English

References

  1. AAA Newsroom 2019 https://www.aaa.com/AAA/common/AAR/files/AAA-Electric-Vehicle-Range-Testing-Report.pdf
  2. Kaushik , S. , Han , T. , and Chen , K. Development of a Virtual Thermal Manikin to Predict Thermal Sensation in Automobiles SAE Technical Paper 2012-01-0315 2012 https://doi.org/10.4271/2012-01-0315
  3. Chen , K. , Kaushik , S. , Han , T. , Ghosh , D. et al. Thermal Comfort Prediction and Validation in a Realistic Vehicle Thermal Environment SAE Technical Paper 2012-01-0645 2012 https://doi.org/10.4271/2012-01-0645
  4. Wang , L. and Marek-Sadowska , M. Machine Learning in Simulation-Based Analysis Proceedings of the 2015 Symposium on International Symposium on Physical Design Monterey, CA 2015 https://doi.org/10.1145/2717764.2717786
  5. Lee , K.Y. , Suh , Y.K. , and Cho , K.W. Development of a Simulation Result Management and Prediction System Using Machine Learning Techniques International Journal of Data Mining and Bioinformatics 19 1 2017 75 96 https://doi.org/10.1504/IJDMB.2017.10009481
  6. Blakely , L. , Reno , M.J. , and Broderick , R.J. Decision Tree Ensemble Machine Learning for Rapid QSTS Simulations IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) Washington, DC 1 5 2018 http://doi.org/10.1109/ISGT.2018.8403323
  7. Arnold-Medabalimi , N. , Huang , C. , and Duraisamy , K. Data-Driven Modal Decomposition Techniques for High-Dimensional Flow Fields Pitsch , H. and Attili , A. Data Analysis for Direct Numerical Simulations of Turbulent Combustion Cham Springer 2020 https://doi.org/10.1007/978-3-030-44718-2 7
  8. Singh , A.P. , Duraisamy , K. , and Morgan , B.E. 2019 https://doi.org/10.2172/1499961
  9. Bhatnagar , S. , Afshar , Y. , Pan , S. , Duraisamy , K. et al. Prediction of Aerodynamic Flow Fields Using Convolutional Neural Networks Comput Mech 64 2019 525 545 https://doi.org/10.1007/s00466-019-01740-0
  10. Johnson , R. , Kaczynski , D. , Zeng , W. , Warey , A. et al. Prediction of Combustion Phasing Using Deep Convolutional Neural Networks SAE Technical Paper 2020-01-0292 2020 https://doi.org/10.4271/2020-01-0292
  11. Hintea , D. , Brusey , J. , and Gaura , E. A Study on Several Machine Learning Methods for Estimating Cabin Occupant Equivalent Temperature Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics (ICINCO) Colmar, France 2015
  12. Warey , A. , Kaushik , S. , Khalighi , B. , Cruse , M. et al. Data-Driven Prediction of Vehicle Cabin Thermal Comfort: Using Machine Learning and High-Fidelity Simulation Results International Journal of Heat and Mass Transfer. 148 2020 119083 https://doi.org/10.1016/uiheatmasstransfer.2019.119083
  13. Collins , D. , Rednic , R. , and Thake , C.D. Infrared Heating as an Adjunct to Achieve Vehicle Occupant Thermal Comfort Extrem Physiol Med 4 2015 A82 https://doi.org/10.1186/2046-7648-4-S1-A82
  14. Han , T. , Huang , L. , Kelly , S. , Huizenga , C. et al. Virtual Thermal Comfort Engineering SAE Technical Paper 2001-01-0588 2001 https://doi.org/10.4271/2001-01-0588
  15. Han , T. and Huang , L. A Model for Relating a Thermal Comfort Scale to EHT Comfort Index SAE Technical Paper 2004-01-0919 2004 https://doi.org/10.4271/2004-01-0919
  16. Fanger , P.O. Thermal Comfort: Analysis and Applications in Environmental Engineering Copenhagen Danish Technical Press 1970
  17. Djongyang , N. , Tchinda , R. , and Njomo , D. Thermal Comfort: A Review Paper Renewable and Sustainable Energy Reviews 14 9 2010 2626 2640 https://doi.org/10.1016/i.rser.2010.07.040
  18. Gilani , S. , Khan , M.H. , and Pao , W. Thermal Comfort Analysis of PMV Model Prediction in Air Conditioned and Naturally Ventilated Buildings Energy Procedia 75 2015 1373 1379 https://doi.org/10.1016/i.egypro.2015.07.218
  19. Chollet , F. 2015 Keras https://github.com/keras-team/keras 2020
  20. Chollet , F. Deep Learning with Python 1st Shelter Island Manning Publications Co. 2018
  21. Abadi , M. , Barham , P. , Chen , J. , Chen , Z. et al. TensorFlow: A System for Large-Scale Machine Learning Proceedings of the12th USENIX Symposium on Operating Systems Design and Implementation Savannah, GA 2016
  22. Keras Team, Keras Tuner 2019 Keras Tuner https://github.com/keras-team/keras-tuner 2020
  23. Nair , V. and Hinton , G.E. Rectified Linear Units Improve Restricted Boltzmann Machines Proceedings of the 27th International Conference on Machine Learning Haifa, Israel 2010
  24. Clevert , D. , Unterthiner , T. , and Hochreiter , S. Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) Proceedings of the 4th International Conference on Learning Representations San Juan, Puerto Rico 2016
  25. Ramachandran , P. , Zoph , B. , and Le , Q.V. 2017
  26. Ruder , S. 2017
  27. Srivastava , N. , Hinton , G. , Krizhevsky , A. , Sutskever , I. et al. Dropout: A Simple Way to Prevent Neural Networks from Overfitting J. Mach. Learn. Res. 15 2014 1929 1958
  28. Smith , L. 2017
  29. Kenstler , B. 2018 https://github.com/bckenstler/CLR 2020
  30. Wittmann , F. 2019 https://github.com/WittmannF/LRFinder 2020
  31. Borges , J. 2019 https://github.com/icborges/DeepStack 2020

Cited By