A Computational Framework for Formalizing Rollover Risk in Heavy-Duty Vehicles: Application to Concrete Truck Mixers

This study introduces a computational framework that formalizes rollover risk in heavy-duty vehicles by integrating simulation-informed physical modeling with sensor-driven decision logic. The approach combines high-fidelity fluid–structure interaction modeling (via CFD) with multibody vehicle dynamics simulations to capture the complex behavior of rotating, partially filled mixer tanks under dynamic conditions. Rollover thresholds were identified by extracting the maximum safe speeds across a range of maneuvers (e.g., steady-state turning and step steering), using tire lift-off as the critical indicator. These thresholds were then formalized into decision rules using onboard sensor data, such as lateral acceleration, steering input, and tank rotation speed, allowing a real-time rollover warning system to continuously compare current vehicle states against critical limits. By systematically extracting critical force and moment responses and translating them into limit values provided by conventional onboard sensors (lateral acceleration, roll angle, steering input), the framework bridges high-fidelity simulation and real-time monitoring. A concrete truck mixer is used as a case study to demonstrate the utility of this approach in formalizing rollover thresholds for real-world decision support. Beyond the specific vehicle type, this work contributes to the broader discourse on how computational methods can contribute to new control or assistance strategies for safety-critical systems.