The Ring of Fire for in-Field Sport Aerodynamic Investigation

Spoelstra, Alexander; Terra, Wouter; Sciacchitano, Andrea

doi:10.3390/proceedings2060221

Cited by 2 publications

(2 citation statements)

References 15 publications

(13 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ring of Fire (RoF) Sciacchitano et al (2015), Terra et al (2017), Terra et al (2018), Spoelstra et al (2018a), Spoelstra et al (2018b), Spoelstra et al (2019), Spoelstra et al (2020), Spoelstra et al (2021), Hüttig et al (2023), Hüttig et al (2024), Spoelstra et al (2023) is a novel quantitative measurement technique that can provide insights into the flow topology of full-scale, transiting objects and determine their aerodynamic drag. It is a non-intrusive, large-scale, stereoscopic PIV measurement technique that can be implemented for a full three-dimensional flow field description.…”

Section: Ring Of Firementioning

confidence: 99%

Real-time and On-site Aerodynamics using Stereoscopic PIV and Deep Optical Flow Learning

Elrefaie,

Hüttig,

Gladkova

et al. 2024

Preprint

View full text Add to dashboard Cite

We introduce Recurrent All-Pairs Field Transforms for Stereoscopic ParticleImage Velocimetry (RAFT-StereoPIV). Our approach leverages deep optical flowlearning to analyze time-resolved and double-frame particle images from on-sitemeasurements, particularly from the 'Ring of Fire,' as well as from wind tunnelmeasurements for real-time aerodynamic analysis. A multi-fidelity datasetcomprising both Reynolds-Averaged Navier-Stokes (RANS) and Direct NumericalSimulation (DNS) was used to train our model. RAFT-StereoPIV outperforms allPIV state-of-the-art deep learning models on benchmark datasets, with a 68% error reduction on the validation dataset, Problem Class 2, and a 47% errorreduction on the unseen test dataset, Problem Class 1, demonstrating itsrobustness and generalizability. In comparison to the most recent works in thefield of deep learning for PIV, where the main focus was the methodologydevelopment and the application was limited to either 2D flow cases or simpleexperimental data, we extend deep learning-based PIV for industrialapplications and 3D flow field estimation. As we apply the trained network tothree-dimensional highly turbulent PIV data, we are able to obtain flowestimates that maintain spatial resolution of the input image sequence. Incontrast, the traditional methods produce the flow field of~16 × lower resolution. We believe that this study brings the field of experimentalfluid dynamics one step closer to the long-term goal of having experimentalmeasurement systems that can be used for real-time flow field estimation.

show abstract

Section: Ring Of Firementioning

confidence: 99%

Real-time and On-site Aerodynamics using Stereoscopic PIV and Deep Optical Flow Learning

Elrefaie,

Hüttig,

Gladkova

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…With the introduction of the Ring of Fire measurement concept by Terra et al (29) in 2017, the beneficial light scattering characteristics of HFSB were used to resolve the whole wake of road cyclists under on-site conditions by Spoelstra et. al (24). From the measured velocity fields, the aerodynamic drag was evaluated using the conservation of momentum applied to a control volume around the moving model (( 24), ( 25) & ( 26)).…”

Section: Introductionmentioning

confidence: 99%

On-Road Vehicle Aerodynamics with the Ring-of-Fire Concept

Hüttig,

Kühn,

Gericke

et al. 2024

Preprint

View full text Add to dashboard Cite

This paper presents the first full-scale particle image velocimetry (PIV) measurements to analyze the complete flow field of a car under real driving conditions. The Ring of Fire (RoF) measurement concept, introduced by Terra et al. (Exp Fluids 58:83, 2017 https://doi.org/10.1007/s00348-017-2331-0), is adapted to automotive demands to validate CFD simulations for further improvements of vehicle aerodynamics. The experiment consists of a tunnel setup, where neutrally buoyant helium-filled soap bubbles are used as flow tracers and are illuminated by two high-speed lasers; four high-speed cameras captured the particles motion in two separate Stereo-PIV-configurations with fields of view of 1.3 x 0.6 m2 and 2.8 x 2.2 m2. Data for a Volkswagen up!, while driving on a test track at a constant speed of 33.33 m/s was acquired and processed with standard multi-pass PIV algorithms, in order to quantify the flow field and estimate limits of the described measurement principle for on-road car aerodynamics. The resulting velocity fields are compared with CFD-simulations, showing agreement for the here considered cases within 9.65 - 6.97%, based on the root-mean-square error between the experimental and the numerical results. Furthermore, drag calculation from the obtained velocity fields based on moment conservation is performed and the percent difference to wind tunnel measurements reaches values below 3.0%.

show abstract

The Ring of Fire for in-Field Sport Aerodynamic Investigation

Cited by 2 publications

References 15 publications

Real-time and On-site Aerodynamics using Stereoscopic PIV and Deep Optical Flow Learning

Real-time and On-site Aerodynamics using Stereoscopic PIV and Deep Optical Flow Learning

On-Road Vehicle Aerodynamics with the Ring-of-Fire Concept

Contact Info

Product

Resources

About