Study of the turbulence in the air-side and water-side boundary layers in experimental laboratory wind induced surface waves

This study detailed the structure of turbulence in the air-side and water-side boundary layers in wind-induced surface waves. Inside the air boundary layer, the kurtosis is always greater than 3 (the value for normal distribution) for both horizontal and vertical velocity fluctuations. The skewness for the horizontal velocity is negative, but the skewness for the vertical velocity is always positive. On the water side, the kurtosis is always greater than 3, and the skewness is slightly negative for the horizontal velocity and slightly positive for the vertical velocity. The statistics of the angle between the instantaneous vertical fluctuation and the instantaneous horizontal velocity in the air is similar to those obtained over solid walls. Measurements in water show a large variance, and the peak is biased towards negative angles. In the quadrant analysis, the contribution of quadrants Q2 and Q4 is dominant on both the air side and the water side. The non-dimensional relative contributions and the concentration match fairly well near the interface. Sweeps in the air side (belonging to quadrant Q4) act directly on the interface and exert pressure fluctuations, which, in addition to the tangential stress and form drag, lead to the growth of the waves. The water drops detached from the crest and accelerated by the wind can play a major role in transferring momentum and in enhancing the turbulence level in the water side.On the air side, the Reynolds stress tensor's principal axes are not collinear with the strain rate tensor, and show an angle α σ≈=-20°to-25°. On the water side, the angle is α σ≈=-40°to-45°. The ratio between the maximum and the minimum principal stresses is σ a/σ b=3to4 on the air side, and σ a/σ b=1.5to3 on the water side. In this respect, the air-side flow behaves like a classical boundary layer on a solid wall, while the water-side flow resembles a wake. The frequency of bursting on the water side increases significantly along the flow, which can be attributed to micro-breaking effects - expected to be more frequent at larger fetches. © 2012 Elsevier B.V.

Internal representations of temporal statistics and feedback calibrate motor-sensory interval timing.

Humans have been shown to adapt to the temporal statistics of timing tasks so as to optimize the accuracy of their responses, in agreement with the predictions of Bayesian integration. This suggests that they build an internal representation of both the experimentally imposed distribution of time intervals (the prior) and of the error (the loss function). The responses of a Bayesian ideal observer depend crucially on these internal representations, which have only been previously studied for simple distributions. To study the nature of these representations we asked subjects to reproduce time intervals drawn from underlying temporal distributions of varying complexity, from uniform to highly skewed or bimodal while also varying the error mapping that determined the performance feedback. Interval reproduction times were affected by both the distribution and feedback, in good agreement with a performance-optimizing Bayesian observer and actor model. Bayesian model comparison highlighted that subjects were integrating the provided feedback and represented the experimental distribution with a smoothed approximation. A nonparametric reconstruction of the subjective priors from the data shows that they are generally in agreement with the true distributions up to third-order moments, but with systematically heavier tails. In particular, higher-order statistical features (kurtosis, multimodality) seem much harder to acquire. Our findings suggest that humans have only minor constraints on learning lower-order statistical properties of unimodal (including peaked and skewed) distributions of time intervals under the guidance of corrective feedback, and that their behavior is well explained by Bayesian decision theory.