DETERMINATION OF DRAG AND LIFT RELATED COEFFICIENTS OF AN AUV USING COMPUTATIONAL AND EXPERIMENTAL FLUID DYNAMICS METHODS

Determination of hydrodynamic coefficients is a vital part of predicting the dynamic behavior of an Autonomous Underwater Vehicle (AUV). The aim of the present study was to determine the drag and lift related hydrodynamic coefficients of a research AUV, using Computational and Experimental Fluid Dynamics methods. Experimental tests were carried out at AUV speed of 1.5 m s-1 for two general cases: I. AUV without control surfaces (Hull) at various angles of attack in order to calculate Hull related hydrodynamic coefficients and II. AUV with control surfaces at zero angle of attack but in different stern angles to calculate hydrodynamic coefficients related to control surfaces. All the experiments carried out in a towing tank were also simulated by a commercial computational fluid dynamics (CFD) code. The hydrodynamic coefficients obtained from the numerical simulations were in close agreement with those obtained from the experiments.

Quantifying Non-Stationarity with Information Theory

We introduce an index based on information theory to quantify the stationarity of a stochastic process. The index compares on the one hand the information contained in the increment at the time scale τ of the process at time t with, on the other hand, the extra information in the variable at time t that is not present at time t−τ. By varying the scale τ, the index can explore a full range of scales. We thus obtain a multi-scale quantity that is not restricted to the first two moments of the density distribution, nor to the covariance, but that probes the complete dependences in the process. This index indeed provides a measure of the regularity of the process at a given scale. Not only is this index able to indicate whether a realization of the process is stationary, but its evolution across scales also indicates how rough and non-stationary it is. We show how the index behaves for various synthetic processes proposed to model fluid turbulence, as well as on experimental fluid turbulence measurements.

And…Action! Setting the Scene for Accurate Visual CFD Comparisons Using Ray Tracing

Increased graphical capabilities of contemporary computer hardware make ray tracing possible for a much wider range of applications. In science, and numerical fluid mechanics in particular, visual inspections still play a key role in both understanding flows, predicted by computational fluid dynamics, exhibiting features observable in real-life, such as interfaces or smoke, and when comparing such flows against experimental observations. Usually, little attention is paid to the visualisation itself, unless when the render is used solely for its eye-catching appearance. In this work, we argue that the use of ray tracing software can help make comparisons between computational and experimental fluid dynamics more robust and meaningful, and that, in some cases, it is even a necessity. Several visualisation problems which can be overcome through application of this methodology are discussed, and the use of ray tracing is exemplified for several common test cases in the maritime field. Using these examples the benefits of ray tracing are shown, and it is concluded that ray tracing can improve the reliability of scientific visual comparisons.

Enhanced data assimilation of 4D LPT with physics informed neural networks

According to recent trend of explosive growth of computation power and accumulated data, demand for the deep learning application in various research fields is increasing. As following this trend, remarkable achievements are presented in the experimental fluid mechanics field. One of the most outstanding research is Physics Informed Neural Networks (PINN) Raissi et al. (2020). Physical knowledge, which has been accumulated by humans, is imposed on the neural networks. PINN was used the automatic differentiation for implementing the governing equations as a physical constraint. By utilizing this concept, physical constraints make neural networks finding physical meaning of phenomena instead of simply fitting to the label data.