Aerodynamic drag reduction of heavy-duty vehicles is essential for lowering fuel consumption, with the front wheels contributing significantly to overall drag. Truck aerodynamics is typically investigated using Computational Fluid Dynamics (CFD) and small-scale wind tunnel testing. However, validating CFD results in the highly complex and unsteady front wheel wake region remains challenging. Conventional validation methods, such as wind tunnel measurements or wake-rake techniques, are often limited to simplified models or planar data.

This study addresses this limitation by applying the non-intrusive “Ring of Fire” measurement technique to reconstruct the three-dimensional front wheel wake of a full-scale truck. The objective was to design and implement a Ring of Fire setup capable of resolving the wheel wake region to support CFD validation, while also evaluating the conventional wake-rake method.

The Ring of Fire measurement technique is based on Particle Image Velocimetry (PIV). Helium-Filled Soap Bubbles (HFSBs) served as tracer particles, illuminated by high-power LEDs and recorded by high-speed cameras. A Shake-The-Box (STB) Lagrangian Particle Tracking (LPT) algorithm reconstructed three-dimensional particle trajectories, from which velocity fields were derived. For comparison, a wake-rake equipped with Kiel probes measured a two-dimensional total pressure field in the wheel wake.

Experiments were conducted on a test track using a full-scale European cab-over-engine tractor–trailer combination. Two configurations were tested: a baseline (Variant A) and a reduced-aero configuration (Variant B). Containing the HFSBs in an outdoor environment was a major challenge and was addressed using a foldable dome tent housing three vertically oriented LED units and a seeding rake. Four high speed cameras recorded the motion of the HFSBs within a measurement domain extending 1 meter from the truck surface and up to 1 meter in height, covering the longitudinal distance of 2.2 meter. A dedicated run procedure ensured synchronization between dome opening and data acquisition during truck passage.

A total of 100 runs were performed over two days. 8 valid runs for Variant A and 21 for Variant B were processed after excluding invalid measurements. Crosswind effects were assessed using conditional averaging and were found to be negligible within measurement uncertainty. Convergence analysis indicated that at least 16 combined runs were required to achieve a stable, time-averaged flow field with over 95% spatial data coverage.

For both configurations, the wheel wake region, characterized by significant reduced velocity magnitude, emerged downstream of the footstep and expanded further downstream. Overall, Variant A showed a larger wake region.

Comparison with wake-rake results revealed similar wake topology, but the Ring of Fire provided higher spatial resolution and full three-dimensional reconstruction. The study demonstrates that the Ring of Fire technique successfully captures the complete front wheel wake of a full-scale truck and offers substantial advantages over conventional planar wake-rake measurements.