In this contribution, we consider the Dynamic Mode Decomposition (DMD) framework as a purely data-driven tool to investigate both standard and actuated turbulent channel databases via Direct Numerical Simulation (DNS). Both databases have comparable Reynolds number Re » 3600. The actuation consists in the imposition of a stream wise-varying sinusoidal spanwise velocity at the wall, known to lead to drag reduction. Specifically, a composite-based DMD analysis is conducted, with hybrid snapshots composed by skin friction and Reynolds stresses. A small number of dynamic modes (~3-9) are found to recover accurately the DNS Reynolds stresses near walls. Moreover, the DMD modes retrieved propagate at a range ofphase speeds consistent with those reported in the literature. We conclude that composite DMD is an attractive, purely data-driven tool to study turbulent flows. On the one hand, DMD is helpful to identify features associated with the drag, and on the other hand, it reveals the changes in flow structure when actuation is imposed.
Dynamic Mode Decomposition (DMD) techniques have risen as prominent feature identification methods in the field of fluid dynamics. Any of the multiple variables of the DMD method allows to identify meaningful features from either experimental or numerical flow data on a data-driven manner. Performing a DMD analysis requires handling matrices V ∈ R n p × N , where n p and N are indicative of the spatial and temporal resolutions. The DMD analysis of a complex flow field requires long temporal sequences of well resolved data, and thus the memory footprint may become prohibitively large. In this contribution, the effect that principled spatial agglomeration (i.e., reduction in n p via clustering) has on the results derived from the DMD analysis is investigated. We compare twelve different clustering algorithms on three testcases, encompassing different flow regimes: a synthetic flow field, a R e D = 60 flow around a cylinder cross section, and a R e τ ≈ 200 turbulent channel flow. The performance of the clustering techniques is thoroughly assessed concerning both the accuracy of the results retrieved and the computational performance. From this assessment, we identify DBSCAN/HDBSCAN as the methods to be used if only relatively high agglomeration levels are affordable. On the contrary, Mini-batch K-means arises as the method of choice whenever high agglomeration n p ˜ / n p ≪ 1 is possible.
Fixed-abrasive lapping (FAL) is a new machining technology and is adopted to manufacture hard brittle materials to obtain the high surface quality. In the same machining condition, K9 glasses are lapped by abrasives and fixed-abrasive, respectively. Two grain sizes of diamond abrasives are adopted in every lapping means. Differential chemical etch method (DCEM) is employed to measure the depth of subsurface damage (SSD) of different lapping means. Surface damages are compared by Microscope. The results show that the depth of SSD is 53 and 15.2μm after abrasives lapping (AL) by 40 and 28μm diamond abrasives. FAL with 40 and 28μm diamond abrasive leads to 4.5 and 3.4μm subsurface damage depth, respectively. FAL can get smaller surface damage and shallower depth of SSD than AL. And FAL can obtain the higher surface quality than AL.
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