With 62% of medals at the upcoming 2026 Winter Olympics in Milano-Cortina to be awarded in skiing disciplines and the remaining 38% in ice-based events, understanding the determinants of performance is critical. Despite extensive examination of athletes’ physiological, biomechanical, and psychological attributes, the role of tribology—particularly in understanding friction on snow and ice—has received less attention. This is a scientific perspective article outlining key tribological factors and highlighting their importance in Olympic winter sports. In skiing, optimising the ski–snow interaction requires a comprehensive understanding of how friction is influenced by snow crystal morphology, temperature, ski base material and structure, ski stiffness, skier technique, and environmental conditions. For ice-based events, friction is determined by a combination of ice surface roughness, temperature, and sport-specific preparation techniques, as well as equipment design (e.g. blade material and geometry, the running surface finish of curling stones) and athlete technique (e.g. angle of attack in speed skating, sweeping in curling). Ice preparation techniques further influence friction, with specific conditions tailored to each sport. In conclusion, advancements in Olympic winter sports have been significant. However, future breakthroughs in performance may lie in applying tribological insights to optimise the complex interactions between athletes, equipment, and the unique properties of snow and ice. This perspective article aims to guide future research by synthesising current understanding and identifying emerging challenges in winter sports tribology.

Particle Image Velocimetry (PIV) for flow visualization has advanced with the integration of deep learning algorithms. These methods enable end-to-end processing, extracting dense flow fields directly from raw particle images. However, conventional deep learning-based PIV models, which predominantly rely on convolutional architectures, are limited in their ability to utilize contextual information and capture dependencies between pixels across sequential images, impacting the prediction accuracy. We introduce Twins-PIVNet, a deep learning framework for PIV optical flow estimation that leverages a spatial attention-based vision transformer architecture. Its self-attention mechanism captures multi-scale features of particle motion, significantly improving the dense flow field estimation. Trained on synthetic PIV datasets covering a wide range of flow conditions, Twins-PIVNet has been evaluated on both synthetic and experimental datasets, demonstrating superior accuracy and performance. In comparative studies, Twins-PIVNet outperforms existing optical flow and conventional methods, achieving accuracy improvements of 51% for backstep flow, 42% for DNS-turbulence, and 33% for surface quasi-geostrophic flow. Additionally, it also exhibits strong generalization on experimental PIV data, demonstrating robustness in handling real-world PIV uncertainties. Despite its attention mechanism, Twins-PIVNet maintains faster inference and training times compared to other PIV models, offering an optimal balance between complexity, efficiency, and performance.

A bi-directional digital holographic imaging system is presented that is designed to take images automatically out in the field. The main objective is to acquire sufficient information to be able to estimate the scattering phase function for different type of snowfall. The imaging system consists of a 3D-printed frame and two orthogonal telecentric imaging arms, one in the forward direction and one in the side scattering direction for which the reference arms are directed along different paths. All images are acquired using pulsed visible light.

We experimentally demonstrate the presence of a capillary bridge in the contact between an ice particle and a smooth aluminum surface at a relative humidity of approximately 50% and temperatures below the melting point. We conduct the experiments in a freezer with a controlled temperature and consider the mechanical instability of the bridge upon separation of the ice particle from the aluminum surface at a constant speed. We observe that a liquid bridge forms, and this formation becomes more pronounced as the temperature approaches the melting point. We also show that the separation distance is proportional to the cube root of the volume of the bridge. We hypothesize that the volume of the liquid bridge can be used to provide a rough estimate of the thickness of the liquid layer on the ice particle since in the absence of other driving mechanisms, some of the liquid on the surface must have been pulled to the bridge area. We show that the estimated value lies within the range previously reported in the literature. With these assumptions, the estimated thickness of the liquid layer decreases from nearly 56 nm at T = −1.7°C to 0.2 nm at T = −12.7°C. The dependence can be approximated with a power law, proportional to (TM − T)−β, where β < 2.6 and TM is the melting temperature. We further observe that for a rough surface, the capillary bridge formation in the considered experimental conditions vanishes.

Current optical flow-based neural networks for particle image velocimetry (PIV) are largely trained on synthetic datasets emulating real-world scenarios. While synthetic datasets provide greater control and variation than what can be achieved using experimental datasets for supervised learning, it requires a deeper understanding of how or what factors dictate the learning behaviors of deep neural networks for PIV. In this study, we investigate the performance of the recurrent all-pairs field transforms-PIV (RAFTs-PIV) network, the current state-of-the-art deep learning architecture for PIV, by testing it on unseen experimentally generated datasets. The results from RAFT-PIV are compared with a conventional cross-correlation-based method, Adaptive PIV. The experimental PIV datasets were generated for a typical scenario of flow past a circular cylinder in a rectangular channel. These test datasets encompassed variations in particle diameters, particle seeding densities, and flow speeds, all falling within the parameter range used for training RAFT-PIV. We also explore how different image pre-processing techniques can impact and potentially enhance the performance of RAFT-PIV on real-world datasets. Thorough testing with real-world experimental PIV datasets reveals the resilience of the optical flow-based method's variations to PIV hyperparameters, in contrast to the conventional PIV technique. The ensemble-averaged root mean squared errors between the RAFT-PIV and Adaptive PIV estimations generally range between 0.5–2 (px) and show a slight reduction as particle densities increase or Reynolds numbers decrease. Furthermore, findings indicate that employing image pre-processing techniques to enhance input particle image quality does not improve RAFT-PIV predictions; instead, it incurs higher computational costs and impacts estimations of small-scale structures.