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Adil Rasheed

Senior Research Scientist

Adil Rasheed

Senior Research Scientist

Adil Rasheed
Phone: 902 91 771
Department: Mathematics and Cybernetics
Office: Trondheim

Publications and responsibilities

Publication
https://www.sintef.no/en/publications/publication/?pubid=CRIStin+1653615

Reduced-basis methods (RB methods or RBMs) form one of the most promising techniques to deliver numerical solutions of parametrized PDEs in real-time with reasonable accuracy. For incompressible flow problems, RBMs based on LBB stable velocity–pressure spaces do not generally inherit the stability o...

Authors Fonn Eivind Brummelen Harald van Kvamsdal Trond Rasheed Adil
Year 2019
Type Journal publication
Publication
https://www.sintef.no/en/publications/publication/?pubid=CRIStin+1632279

The key to the better design of an industrial scale wind turbine is to understand the influence of blade geometry and its dynamics on the complicated flow-structures. An industrial-scale wind turbine can be numerically represented using various approaches (from simpler 2D steady flow to complex 3D w...

Authors Siddiqui Muhammad Salman Rasheed Adil Tabib Mandar Kvamsdal Trond
Year 2019
Type Journal publication
Publication
https://www.sintef.no/en/publications/publication/?pubid=CRIStin+1653614

In this article, we demonstrate the use of artificial neural networks as optimal maps which are utilized for convolution and deconvolution of coarse-grained fields to account for sub-grid scale turbulence effects. We demonstrate that an effective eddy-viscosity is predicted by our purely data-driven...

Authors Maulik Romit San Omer Rasheed Adil Vedula Prakash
Year 2018
Type Journal publication
Publication
https://www.sintef.no/en/publications/publication/?pubid=CRIStin+1634695

In this investigation, a data-driven turbulence closure framework is introduced and deployed for the subgrid modelling of Kraichnan turbulence. The novelty of the proposed method lies in the fact that snapshots from high-fidelity numerical data are used to inform artificial neural networks for predi...

Authors Maulik Romit San Omer Rasheed Adil Vedula Prakash
Year 2018
Type Journal publication
Publication
https://www.sintef.no/en/publications/publication/?pubid=CRIStin+1634693

Numerical solution of the incompressible Navier-Stokes equations poses a significant computational challenge due to the solenoidal velocity field constraint. In most computational modeling frameworks, this constraint requires the solution of a Poisson equation at every step of the underlying time in...

Authors Rahman Sk. Mashfiqur Rasheed Adil San Omer
Year 2018
Type Conference lecture and academic presentation
Publication
https://www.sintef.no/en/publications/publication/?pubid=CRIStin+1634692

In this study, we demonstrate the use of artificial neural networks as optimal maps which are utilized for the convolution and deconvolution of coarse-grained fields to account for sub-grid scale turbulence effects. We demonstrate that an effective eddy-viscosity is characterized by our purely data-...

Authors Maulik Romit San Omer Rasheed Adil Vedula Prakash
Year 2018
Type Conference lecture and academic presentation
Publication
https://www.sintef.no/en/publications/publication/?pubid=CRIStin+1632289

This work involves the use of combined forces of data-driven machine learning models and high fidelity density functional theory for the identification of new potential thermoelectric materials. The traditional method of thermoelectric material discovery from an almost limitless search space of chem...

Year 2018
Type Journal publication
Publication
https://www.sintef.no/en/publications/publication/?pubid=CRIStin+1625565

Numerical solution of the incompressible Navier–Stokes equations poses a significant computational challenge due to the solenoidal velocity field constraint. In most computational modeling frameworks, this divergence-free constraint requires the solution of a Poisson equation at every step of the un...

Authors Rahman Sk. Mashfiqur San Omer Rasheed Adil
Year 2018
Type Journal publication
Publication
https://www.sintef.no/en/publications/publication/?pubid=CRIStin+1625551

We put forth a robust reduced-order modeling approach for near real-time prediction of mesoscale flows. In our hybrid-modeling framework, we combine physics-based projection Methods with neural network closures to account for truncated modes. We introduce a weighting parameter between the Galerkin p...

Authors Rahman Sk. Mashfiqur San Omer Rasheed Adil
Year 2018
Type Journal publication