Abstract
Efficient grain transportation via barge along major waterways like the Mississippi and Brazilian Amazon is vital for global food security and economic stability. This paper presents a Computational Fluid Dynamics (CFD)-based digital twin methodology tailored for crop logistics in barge transportation. Integrating sensor data with predictive analytics, the digital twin continuously monitors temperature, humidity, and airflow dynamics to optimize grain storage conditions during transit. Case studies from commercial grain logistics show how this approach enhances efficiency, reduces costs, and ensures grain quality, demonstrating the transformative potential of IoT and digital twin technologies in global food supply chains.
