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Development of the Defrost Performance Evaluation Technology in Automotive Using Design Optimization Analysis Method
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
In this study, we developed the defrost performance evaluation technology using the multi-objective optimization method based on the CFD. The defrosting is one of the key factors to ensure the drivers’ safety using the forced flow having proper temperature from HVAC during drive. There are many factors affecting the defrost performance, but the configurations of guide-vane and discharge angles in the center DEF(defrosting) duct section which are main design factors of the defrost performance in automotive, so these were set to the design parameters for this study. For the shape-optimization study, the discharge mass flow rate from the HVAC which is transferred to the windshield and the discharge areas in the center defrost duct were set to the response parameters. And then, the standard deviation value of mass flow rate on the selected discharge areas checking the uniformity of discharge flow was set to the objective function to find the optimal design. The results on the windshield from optimization analysis were quantified from some kind of standards to evaluate the defrost performance, in particular, the important parts on it to secure the drivers’ safety as specified FMVSS103, to which the weighted value has been assigned. From this process, it is possible to quantify the defrost performance with various automotive models, and to find the optimized design. In case of using these methods, it is possible to reduce the calculation time, and to effectively analyze the results by controlling the design parameters systematically. These methods also make it possible to check the performance rapidly, and to propose the optimal design through the analytical verifications at the initial design stage.
CitationSeo, H., Seo, J., and Choi, B., "Development of the Defrost Performance Evaluation Technology in Automotive Using Design Optimization Analysis Method," SAE Technical Paper 2020-01-0155, 2020, https://doi.org/10.4271/2020-01-0155.
Data Sets - Support Documents
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- NHTSA , “Laboratory Test Procedure for FMVSS 103 Windshield Defrosting and Defogging System,” U.S Department of Transportation National Highway Traffic Safety Administration, 1996.
- Park, C.H. , “The Developmental Trend of Automotive Air Conditioning System,” Magazine of the SAREK 29(10):14-19, 2000 (in Korean).
- Kim, H.G. , “The Comprehension of Automotive Air Conditioning System,” Magazine of the SAREK 29(10):20-27, 2000 (in Korean).
- Abdulnour, B.S. , “Hot-Wire Velocity Measurements of Defroster and Windshield Flow,” SAE Technical Paper 970109, 1997, https://doi.org/10.4271/970109.
- Kim, I.Y. and Weck, O. , “Adaptive Weighted Sum Method for bi-Objective Optimization: Pareto Front Generation,” Structural and Multidisciplinary Optimization 29(2):149-158, 2005, https://doi.org/10.1007/s00158-004-0465-1.
- Jarrett, A. and Kim, I.Y. , “Design Optimization of Electric Vehicle Battery Cooling Plates for Thermal Performance,” Journal of Power Sources 196(23):10359-10368, 2011, https://doi.org/10.1016/j.jpowsour.2011.06.090.
- Lee, J.S., Ha, M.Y., and Min, J.K. , “Study on the Aero-Thermal Topology Optimization of Single-and Multi-Fin Shapes under Two-Dimensional External Flow Conditions,” Journal of Computational Fluids Engineering 23(4):74-83, 2018, doi:10.6112/kscfe.2018.23.4.074 (in Korean).
- Kim, S.M., Park, J.Y., Ahn, K.Y., and Baek, J.H. , “Numerical Investigation and Validation of the Optimization of a Centrifugal Compressor Using a Response Surface Method,” Journal of Power and Energy 224(2):251-259, 2009, doi:https://doi.org/10.1243/09576509JPE842.
- Ibaraki, S., Braembussche, R., Verstraete, T., Alslihi, Z., Sugimoto, K., and Tomita, I. , “Aerodynamic Design Optimization of a Centrifugal Compressor Impeller Based on and Artifical Neural Network and Genetic Algorithm,” in Proceedings of 11th International Conference on Turbochargers and Turbocharging, 65-77, 2014, doi:https://doi.org/10.1533/978081000342.65.
- Wang, X.F., Xi, G., and Wang, Z.H. , “Aerodynamic Optimization Design of Centrifugal Compressor’s Impeller with Kriging Model,” Journal of Power and Energy 220(6):589-597, 2006, doi:https://doi.org/10.1243/09576509JPE201.
- Atashkari, K., Nariman-Zadeh, N., Pilechi, A., Jamali, A., and Yao, X. , “Thermodynamic Pareto Optimization of Turbojet Engines Using Multi-Objective Genetic Algorithms,” International Journal of Thermal Sciences 44:1061-1071, 2005, doi:10.1016/j.ijthermalsci.2005.03.016.
- Locci, C., Matas, E., and Oberhumer, K. , “Acoustic Optimization of a Muffler through the Sherpa Algorithm,” SAE Technical Paper 2019-01-0844, 2019, https://doi.org/10.4271/2019-01-0844.
- Kumar, V., Tare, K., and Kapoor, S. , “Deployment of CFD for Optimization of the Air Flow Distribution over the Windscreen and Prediction of Defrost Performance,” SAE Technical Paper 2010-01-1059, 2010, https://doi.org/10.4271/2010-01-1059.
- Jahani, K. and Beigmoradi, S. , “Utilizing CFD Approach for Preeminent Assessment of Defroster Air Flow Distribution and Predicting Windscreen Deicing Behavior,” SAE Technical Paper 2014-01-0688, 2014, https://doi.org/10.4271/2014-01-0688.