Shear thickening fluids (STF) enable passive dampers with strongly nonlinear, load-dependent energy dissipation, offering an alternative to magnetorheological fluid-filled active dampers. The design of STF-based dampers requires a reliable prediction of the damping characteristic from the rheology and device geometry. This work presents a multi-level modelling framework that links rheological measurements, analytical modelling, computational fluid dynamics, and experimental validation to enable accurate design of STF dampers using our previous analytical and optimisation-based approaches. Complex STF suspensions were synthesised in-house, and their viscosity functions were determined using rotational rheometry over a wide shear-rate range. The fluids were tested in an in-house damper test rig, providing force–velocity characteristics under realistic operating conditions. An analytical model based on generalised Poiseuille flow was derived to predict the nonlinear damping behaviour for arbitrary rheological laws. In parallel, three-dimensional CFD simulations were performed in OpenFOAM using standard simpleFOAM and pimpleFOAM solvers with user-defined non-Newtonian viscosity models to resolve the internal flow and shear-rate distribution within the damper. The predicted damping curves from the analytical and CFD models show good agreement with experimental measurements, confirming that the device response can be traced back to local shear-thickening mechanisms. The simulations provide additional insight into spatially inhomogeneous shear fields that cannot be captured by simplified models. The study highlights the capabilities and limitations of conventional CFD tools for strongly non-Newtonian applications and provides practical guidelines for designing shear-thickening-fluid-filled dampers. The presented workflow establishes a consistent path from material characterisation to device-level prediction, supporting the development of cost-efficient, quasi-adaptive damping technologies. This work has been supported by the Hungarian National Research, Development and Innovation Centre under contract No. PD 146259.