Aerodynamic drag reduction of a passenger car through shape optimization of outside rear view mirrors

Outside rear-view mirrors (ORVMs) contribute significantly to vehicle safety but also represent a notable source of aerodynamic drag. This study investigates aerodynamic drag reduction in a passenger car through the shape optimization of ORVMs, while maintaining or enhancing rearward visibility. Three-dimensional models were developed, and Computational Fluid Dynamics (CFD) simulations were conducted using SOLIDWORKS Flow Simulation for vehicle speeds between 70 and 90 km/h. In parallel, a digital ORVM concept employing wide-angle cameras was evaluated for its impact on the field of view (FOV). The optimized mirror configuration achieved a 16% reduction in aerodynamic drag at 90 km/h compared to the baseline design. The digital ORVM increased the FOV by 376%, resulting in an 87.37% reduction in blind spot area. Based on lifetime vehicle operation assumptions, the proposed design yields an estimated fuel saving of 310 L and a reduction of 716 kg of CO 2 emissions over 200,000 km. The results demonstrate that ORVM shape optimization, combined with digital mirror systems, can effectively improve both aerodynamic efficiency and driver visibility, thereby enhancing vehicle performance and sustainability.

Authors

Institutions

Publication Details

Journal
Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering
Published
2026-02-18
DOI
https://doi.org/10.1177/09544070261421268
Primary Topic
Aerodynamics and Fluid Dynamics Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Aerodynamic drag reduction of a passenger car through shape optimization of outside rear view mirrors

Ramesh Raja Subburaj, Shwetha Shri Rajasekaran, Sunil Sankarapandian, Nooh Nafees Ahamed Riffai
Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering
Aerodynamics and Fluid Dynamics Research
article

Aerodynamic drag reduction of a passenger car through shape optimization of outside rear view mirrors

Ramesh Raja Subburaj, Shwetha Shri Rajasekaran, Sunil Sankarapandian, Nooh Nafees Ahamed Riffai
article en

Abstract

Outside rear-view mirrors (ORVMs) contribute significantly to vehicle safety but also represent a notable source of aerodynamic drag. This study investigates aerodynamic drag reduction in a passenger car through the shape optimization of ORVMs, while maintaining or enhancing rearward visibility. Three-dimensional models were developed, and Computational Fluid Dynamics (CFD) simulations were conducted using SOLIDWORKS Flow Simulation for vehicle speeds between 70 and 90 km/h. In parallel, a digital ORVM concept employing wide-angle cameras was evaluated for its impact on the field of view (FOV). The optimized mirror configuration achieved a 16% reduction in aerodynamic drag at 90 km/h compared to the baseline design. The digital ORVM increased the FOV by 376%, resulting in an 87.37% reduction in blind spot area. Based on lifetime vehicle operation assumptions, the proposed design yields an estimated fuel saving of 310 L and a reduction of 716 kg of CO 2 emissions over 200,000 km. The results demonstrate that ORVM shape optimization, combined with digital mirror systems, can effectively improve both aerodynamic efficiency and driver visibility, thereby enhancing vehicle performance and sustainability.

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering
Anna University, Chennai (IN)
Openalex Percentile: Top 52%
Aerodynamics and Fluid Dynamics Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.