Data-Driven Computational Fluid Dynamics Model for Predicting Drag Forces on Truck Platoons

Details

• Creators:
  Meidani, Hadi ; Kazemi, Amir
• Corporate Creators:
  Illinois Center for Transportation
• Corporate Contributors:
  Center for Connected and Automated Transportation (CCAT) ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Trucks
  • Drag forces
  • Fuel consumption
  • Neural networks
  • Data models Truck Platooning
  • Data-Driven Models
  • Deep Neural Networks
• Publication/ Report Number:
  ICT-21-036
• DOI:
  https://doi.org/10.36501/0197-9191/21-036
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report
• Corporate Publisher:
  University of Michigan. Center for Connected and Automated Transportation ; Illinois Center for Transportation
• Abstract:
  Fuel-consumption reduction in the truck industry is significantly beneficial to both energy economy and the environment. Although estimation of drag forces is required to quantify fuel consumption of trucks, computational fluid dynamics (CFD) to meet this need is expensive. Data-driven surrogate models are developed to mitigate this concern and are promising for capturing the dynamics of large systems such as truck platoons. In this work, we aim to develop a surrogate-based fluid dynamics model that can be used to optimize the configuration of trucks in a robust way, considering various uncertainties such as random truck geometries, variable truck speed, random wind direction, and wind magnitude. Once trained, such a surrogate-based model can be readily employed for platoon-routing problems or the study of pavement performance.
• Format:
  PDF
• Funding:
  ICT-21-036;UILU-ENG-2021-2036
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/66882/dot_66882_DS1.pdf
• File Type:
  [PDF - 4.72 MB]
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:4dd7f5fe3beca3fae51e022494d45c723ede70a62a9bdfa25b43b2563cb65ae21a082a9b74443e21f75677dac4e1265a9ef47265703e20abc5fdf4368ab94e83