Wheels and tires account for approximately 25% of the overall aerodynamic drag on a vehicle. Though researchers have investigated the accurate representation of rotating tires and wheels in aerodynamic simulations, they primarily focused on the differences in the tire or wheel geometry; few studies have investigated the effects of front-tire deflectors located at the bottom of passenger car bumpers. In other that deflectors can effectively reduce drag without significantly affecting design or packaging, deflector performance should be predicted at the early stages of product development. This study accordingly clarified the simulation conditions for full-vehicle aerodynamics necessary to accurately predict the performance of front-tire deflectors by simulating two different deflector configurations under four conditions comprising different degrees of tire geometry detail and wheel rotation methods. The simulation results were subsequently compared with wind tunnel test results, indicating that the numerical simulation using the least accurate tire geometry detail could not accurately predict the performance differences according to deflector configuration. However, the differences between the drag coefficients and airflow characteristics for each deflector were predicted more accurately by improving the tire geometry detail. The prediction accuracy was further improved by using the sliding mesh method instead of the boundary condition method to model the wheel rotation. Therefore, it was concluded that the detail tire geometry and wheel rotation method are important factors for improving the accuracy of front-tire deflector performance prediction.
{"title":"Effects of Detailed Tire Geometry and Wheel Rotation on the Aerodynamic Performance of Deflectors","authors":"Akihiro Nakata, Satoshi Okamoto, Yosuke Morikawa, Takuji Nakashima","doi":"10.20485/jsaeijae.14.4_84","DOIUrl":"https://doi.org/10.20485/jsaeijae.14.4_84","url":null,"abstract":"Wheels and tires account for approximately 25% of the overall aerodynamic drag on a vehicle. Though researchers have investigated the accurate representation of rotating tires and wheels in aerodynamic simulations, they primarily focused on the differences in the tire or wheel geometry; few studies have investigated the effects of front-tire deflectors located at the bottom of passenger car bumpers. In other that deflectors can effectively reduce drag without significantly affecting design or packaging, deflector performance should be predicted at the early stages of product development. This study accordingly clarified the simulation conditions for full-vehicle aerodynamics necessary to accurately predict the performance of front-tire deflectors by simulating two different deflector configurations under four conditions comprising different degrees of tire geometry detail and wheel rotation methods. The simulation results were subsequently compared with wind tunnel test results, indicating that the numerical simulation using the least accurate tire geometry detail could not accurately predict the performance differences according to deflector configuration. However, the differences between the drag coefficients and airflow characteristics for each deflector were predicted more accurately by improving the tire geometry detail. The prediction accuracy was further improved by using the sliding mesh method instead of the boundary condition method to model the wheel rotation. Therefore, it was concluded that the detail tire geometry and wheel rotation method are important factors for improving the accuracy of front-tire deflector performance prediction.","PeriodicalId":37933,"journal":{"name":"International Journal of Automotive Engineering","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135261187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.20485/jsaeijae.14.4_92
Marko Medojevic, Hisashi Imanaga, Jacobo Antona-Makoshi, Maki Kawakoshi, Hideaki Satoh
Automated driving safety evaluation predominantly relies on scenario-based approaches. In this study, the authors adopt a functional scenario catalogue initially conceived by JAMA to evaluate automated driving safety on limited access highways. The potential of this catalogue to cover real-world crashes was investigated by comparing each scenario in the catalogue with crash patterns from two international data sources: the 2007 NHTSA’s pre-crash scenario typology for crash avoidance research report, and the 2020 IGLAD’s codebook. The results indicate the potential of the scenario catalogue to comprehensively cover both the NHTSA and the IGLAD crash scenario typologies.
{"title":"Investigating the Potential of a Scenario Catalogue for Automated Driving Safety Evaluation to Cover Real-World Crashes","authors":"Marko Medojevic, Hisashi Imanaga, Jacobo Antona-Makoshi, Maki Kawakoshi, Hideaki Satoh","doi":"10.20485/jsaeijae.14.4_92","DOIUrl":"https://doi.org/10.20485/jsaeijae.14.4_92","url":null,"abstract":"Automated driving safety evaluation predominantly relies on scenario-based approaches. In this study, the authors adopt a functional scenario catalogue initially conceived by JAMA to evaluate automated driving safety on limited access highways. The potential of this catalogue to cover real-world crashes was investigated by comparing each scenario in the catalogue with crash patterns from two international data sources: the 2007 NHTSA’s pre-crash scenario typology for crash avoidance research report, and the 2020 IGLAD’s codebook. The results indicate the potential of the scenario catalogue to comprehensively cover both the NHTSA and the IGLAD crash scenario typologies.","PeriodicalId":37933,"journal":{"name":"International Journal of Automotive Engineering","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135260750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01Epub Date: 2019-02-04DOI: 10.20485/jsaeijae.10.1_34
Jennifer Merickel, Robin High, Lynette Smith, Christopher Wichman, Emily Frankel, Kaitlin Smits, Andjela Drincic, Cyrus Desouza, Pujitha Gunaratne, Kazutoshi Ebe, Matthew Rizzo
Our goal is to address the need for driver-state detection using wearable and in-vehicle sensor measurements of driver physiology and health. To address this goal, we deployed in-vehicle systems, wearable sensors, and procedures capable of quantifying real-world driving behavior and performance in at-risk drivers with insulin-dependent type 1 diabetes mellitus (DM). We applied these methodologies over 4 weeks of continuous observation to quantify differences in real-world driver behavior profiles associated with physiologic changes in drivers with DM (N=19) and without DM (N=14). Results showed that DM driver behavior changed as a function of glycemic state, particularly hypoglycemia. DM drivers often drive during at-risk physiologic states, possibly due to unawareness of impairment, which in turn may relate to blunted physiologic responses (measurable heart rate) to hypoglycemia after repeated episodes of hypoglycemia. We found that this DM driver cohort has an elevated risk of crashes and citations, which our results suggest is linked to the DM driver's own momentary physiology. Overall, our findings demonstrate a clear link between at-risk driver physiology and real-world driving. By discovering key relationships between naturalistic driving and parameters of contemporaneous physiologic changes, like glucose control, this study directly advances the goal of driver-state detection through wearable physiologic sensors as well as efforts to develop "gold standard" metrics of driver safety and an individualized approach to driver health and wellness.
{"title":"Driving Safety and Real-Time Glucose Monitoring in Insulin-Dependent Diabetes.","authors":"Jennifer Merickel, Robin High, Lynette Smith, Christopher Wichman, Emily Frankel, Kaitlin Smits, Andjela Drincic, Cyrus Desouza, Pujitha Gunaratne, Kazutoshi Ebe, Matthew Rizzo","doi":"10.20485/jsaeijae.10.1_34","DOIUrl":"https://doi.org/10.20485/jsaeijae.10.1_34","url":null,"abstract":"Our goal is to address the need for driver-state detection using wearable and in-vehicle sensor measurements of driver physiology and health. To address this goal, we deployed in-vehicle systems, wearable sensors, and procedures capable of quantifying real-world driving behavior and performance in at-risk drivers with insulin-dependent type 1 diabetes mellitus (DM). We applied these methodologies over 4 weeks of continuous observation to quantify differences in real-world driver behavior profiles associated with physiologic changes in drivers with DM (N=19) and without DM (N=14). Results showed that DM driver behavior changed as a function of glycemic state, particularly hypoglycemia. DM drivers often drive during at-risk physiologic states, possibly due to unawareness of impairment, which in turn may relate to blunted physiologic responses (measurable heart rate) to hypoglycemia after repeated episodes of hypoglycemia. We found that this DM driver cohort has an elevated risk of crashes and citations, which our results suggest is linked to the DM driver's own momentary physiology. Overall, our findings demonstrate a clear link between at-risk driver physiology and real-world driving. By discovering key relationships between naturalistic driving and parameters of contemporaneous physiologic changes, like glucose control, this study directly advances the goal of driver-state detection through wearable physiologic sensors as well as efforts to develop \"gold standard\" metrics of driver safety and an individualized approach to driver health and wellness.","PeriodicalId":37933,"journal":{"name":"International Journal of Automotive Engineering","volume":"10 1","pages":"34-40"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.20485/jsaeijae.10.1_34","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39220124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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