Abstract
Event-based particle image velocimetry (PIV) offers a promising approach for high-speed flow diagnostics. However, conventional processing pipelines, constrained by fixed event accumulation times and uniform inter-frame intervals, do not fully leverage the asynchronous nature of event data, compromising the fidelity of reconstructed velocity fields. This Letter presents a time-adaptive event-based PIV framework (TA-EB-PIV) whose core mechanism dynamically adjusts event accumulation time and sampling interval based on local velocity estimates. This approach overcomes the constraints of fixed temporal parameters, effectively suppresses near-wall reflection interference, and improves particle image density. Experiments on flow past a NACA0012 airfoil at a 12° angle of attack (Re ≈ 6800) demonstrate the effectiveness of the proposed method. Compared to conventional fixed-interval PIV, TA-EB-PIV reduces the RMS divergence by 26.1%, standard deviation by 26.3%, and mean absolute divergence by 31.0%; outlier vectors by 24.8%; random error δ(U)/U
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from 1.10% to 0.46%; and Reynolds shear stress uncertainty by nearly 30%; free-stream fluctuating velocity overestimation is reduced by 30.4% with near-wall noise markedly suppressed. POD analysis further reveals a more prominent spectral peak and an energy decay consistent with the −5/3 Kolmogorov scaling law. By decoupling the correlation window from fixed time steps, this method achieves self-adaptive temporal parameters, establishing a novel, to the best of our knowledge, framework for high-fidelity velocity reconstruction in complex flows.