CFD-Based Investigation of Fuel Tank Sloshing: Crash Safety and Noise Concerns

A computational study using the Volume of Fluid (VOF) method in SimericsMP+ was conducted to investigate fuel sloshing in automotive fuel tanks under both crash and sudden stop conditions. The SEALs method was employed to rapidly generate the fuel tank mesh, enabling efficient simulation setup. At the outset, a benchmark sloshing case was simulated and compared against experimental data, showing excellent agreement to validate the simulation method. This simulation method was then applied to the fuel tank sloshing scenarios mimicking crash and sudden stop conditions. The study initially focused on a crash scenario where fuel waves impact valves, pumps, and other internal structures. Capturing these localized impact forces is critical for evaluating the risk of component failure and potential leakage. A baffle-equipped tank was simulated and compared with sensor data. Results show that the computed shock forces on valves and baffles closely matched the measurements, demonstrating the high accuracy of the CFD method in predicting crash safety performance and confirming the effectiveness of baffles in reducing fuel wave impacts. The validated framework was then applied to four new unbaffled tank designs to assess NVH performance during low-speed sudden stop maneuvers. Pressure fluctuations on tank walls, which are directly linked to cabin noise, were analyzed and compared against reference pressure measurements from physical tests to ensure compliance with NVH standards. Simulations revealed significant pressure peaks in certain designs, indicating sub-optimal acoustic performance and highlighting how the absence of internal columns or baffles amplifies wave propagation and surface loading. The computational strategy presented in this study provides a powerful tool for evaluating both crash safety and NVH behavior early in the design process. By delivering accurate predictions before physical prototypes are built, it helps guide fuel tank design development, reduces reliance on costly testing, and minimizes the risk of late-stage design failures.