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Soumen Chakravarty

Publications and source records attributed to Soumen Chakravarty.

2 recordsLinked to original sources

Resolvent analysis to inform viscoelastic coatings for turbulent drag reduction

Viscoelastic compliant coatings offer a passive route to modify wall-bounded turbulence; however, their effectiveness for drag reduction remains unresolved. We perform resolvent analysis of turbulent boundary layers over linear viscoelastic continuum, and apply it to incompressible hydrodynamic and compressible aerodynamic zero-pressure-gradient turbulent boundary layers, using both standard and eddy viscosity resolvent formulations. Across a wide range of storage modulus E and coating thickness H, viscoelastic surfaces amplify near-wall-cycle-type modes while also attenuating the resolvent gain of very large scale motions (VLSMs) by up to 50%, which together result in a reduction of Reynolds stress. For density-matched coatings representative of aqueous incompressible flows, however, these favorable bands lie entirely within the regime where the effective coatings are linearly unstable to traveling wave flutter, rendering them practically unrealizable. Optimizing material damping does not eliminate this but provides a pathway to use weaker sub-optimal interactions. In supersonic flow, the large solid-to-fluid density ratio (O(1000)) shifts the favorable interaction to substantially higher moduli, weakening the achievable reduction in turbulence production to a few percent. However, the strongest interaction band occurs in the linearly stable regime. These results suggest that compliant wall drag reduction via coupling with high gain modes is fundamentally constrained by flow-induced structural instabilities in incompressible applications, whereas the high density ratios of supersonic flow offer a much narrower but stable window for practical coatings.

physics.flu-dyn

Deep neural networks based predictive-generative framework for designing composite materials

Designing composite materials as per the application requirements is fundamentally a challenging and time consuming task. Here we report the development of a deep neural network based computational framework capable of solving the forward (predictive) as well as inverse (generative) design problem. The predictor model is based on the popular convolution neural network architecture and trained with the help of finite element simulations. Further, the developed property predictor model is used as a feedback mechanism in the neural network based generator model. The proposed predictive-generative model can be used to obtain the micro-structure for maximization of particular elastic properties as well as for specified elastic constants. One of the major hurdle for deployment of the deep learning techniques in composite material design is the intensive computational resources required to generate the training data sets. To this end, a novel data augmentation scheme is presented. The application of data augmentation scheme results in significant saving of computational resources in the training phase. The proposed data augmentation approach is general and can be used in any setting involving the periodic micro-structures. The efficacy of the predictive-generative model is demonstrated through various examples. It is envisaged that the developed model will significantly reduce the cost and time associated with the composite material designing process for advanced applications.

cond-mat.mtrl-sci