Blar i NTNU Open på forfatter "Rasheed, Adil"
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Frame invariant neural network closures for Kraichnan turbulence
Pawar, Suraj; San, Omer; Rasheed, Adil; Vedula, Prakash (Peer reviewed; Journal article, 2022)Numerical simulations of geophysical and atmospheric flows have to rely on parameterizations of subgrid scale processes due to their limited spatial resolution. Despite substantial progress in developing parameterization ... -
From Beginner to Expert: Deep Reinforcement Learning Controller for 3D Path Following and Collision Avoidance by Autonomous Underwater Vehicles
Havenstrøm, Simen Theie (Master thesis, 2020)Tradisjonelle kybernetiske metoder har mange verktøy og teknikker som kan anvendes for en rekke klasser dynamiske systemer. En forutsetning for å kunne anvende mange av de tradisjonelle metodene, er en pålitelig matematisk ... -
GAN Based Super-Resolution of Near-Surface 3D Atmospheric Wind Flow with Physics Informed Loss Function
Wold, Jacob Wulff (Master thesis, 2023)I møte med økende behov for grønn energi er det forventet med stor økning i vindkraftutbygging de neste årene. Billig høyoppløslig vinddata kan hjelpe vindutviklere å optimere produksjon og redusere risiko ved vindutbygging. For ... -
GANs enabled super-resolution reconstruction of wind field
Tran, Duy Tan; Robinson, Haakon; Rasheed, Adil; San, Omer; Tabib, Mandar; Kvamsdal, Trond (Peer reviewed; Journal article, 2020)Atmospheric flows are governed by a broad variety of spatio-temporal scales, thus making real-time numerical modeling of such turbulent flows in complex terrain at high resolution computationally unmanageable. In this ... -
GANs enabled super-resolution reconstruction of wind field
Tran, Duy Tan; Robinson, Haakon; Rasheed, Adil; San, Omer; Kvamsdal, Trond (Peer reviewed; Journal article, 2020)Atmospheric flows are governed by a broad variety of spatio-temporal scales, thus making real-time numerical modeling of such turbulent flows in complex terrain at high resolution computationally unmanageable. In this ... -
Generalized workflow with uncertainty quantification for detecting abnormalities in lung sounds
Elsetrønning, Andrine (Master thesis, 2021)Mål: Hovedmålet med denne masteroppgaven er å finne en generaliserbar prosedyre for å detektere unormale lungelyder. Den foreslåtte prosedyren bør være generell nok til å kunne bli benyttet til andre lydbaserte ... -
Geometric Change Detection in Digital Twins using 3D Machine Learning
Sundby, Tiril; Graham, Julia Maria; Rasheed, Adil; Tabib, Mandar; San, Omer (Peer reviewed; Journal article, 2021)Digital twins are meant to bridge the gap between real-world physical systems and virtual representations. Both stand-alone and descriptive digital twins incorporate 3D geometric models, which are the physical representations ... -
Geometric change detection in the context of Digital Twin, leveraging Dynamic Mode Decomposition, Object Detection and innovations in 3D technology
Graham, Julia Maria (Master thesis, 2021)I takt med den økende digitaliseringen har Digital Tvilling-teknologi vist seg å bli en betydningsfull teknologi for industri. Ved simulering, prediksjon og optimering av fysiske produksjonssystemer og prosesser, frigjør ... -
High Fidelity Computational Fluid Dynamics Assessment of Wind Tunnel Turbine Test
Siddiqui, Muhammad Salman; Kvamsdal, Trond; Rasheed, Adil (Journal article; Peer reviewed, 2019)We present, what to our best knowledge, is the most accurate numerical investigation of the wind tunnel tests carried out over a model wind turbine (known as NTNU Blind Test) at the Norwegian University of Sciences and ... -
High Fidelity Simulation and Reduced Order Modeling of Wind Turbines
Siddiqui, Muhammad Salman (Doctoral theses at NTNU;2018:172, Doctoral thesis, 2018) -
High-Resolution Cfd Modelling and Prediction of Terrain-Induced Wind Shear and Turbulence for Aviation Safety.
Tabib, Mandar; Rasheed, Adil; Kvamsdal, Trond (Chapter, 2019)The paper describes the ability of a high-resolution Computational Fluid Dynamics model to predict terrain-induced turbulence and wind shear close to the ground for aviation safety. The capabilities of the model are ... -
Hybrid analysis and modeling for next generation of digital twins
Pawar, Suraj; Ahmed, Shady E; San, Omer; Rasheed, Adil (Peer reviewed; Journal article, 2021)The physics-based modeling has been the workhorse for many decades in many scientific and engineering applications ranging from wind power, weather forecasting, and aircraft design. Recently, data-driven models are ... -
Hybrid deep-learning POD-based parametric reduced order model for flow around wind-turbine blade
Tabib, Mandar; Tsiolakis, Vasileios; Pawar, Suraj; Ahmed, Shady E.; Rasheed, Adil; Kvamsdal, Trond; San, Omer (Peer reviewed; Journal article, 2022)In this study, we present a parametric, non-intrusive reduced order modeling (NIROM) framework as a potential digital-twin enabler for fluid flow around an aerofoil. A wind turbine blade has its basic foundation in the ... -
Implementation and comparison of three isogeometric Navier–Stokes solvers applied to simulation of flow past a fixed 2D NACA0012 airfoil at high Reynolds number
Nordanger, Knut; Holdahl, Runar; Kvarving, Arne Morten; Rasheed, Adil; Kvamsdal, Trond (Journal article; Peer reviewed, 2015)Implementation of three different Navier–Stokes solvers in an isogeometric finite element framework is presented in this paper. The three solvers Chorin projection method and Coupled formulation , both with the Spalart–Allmaras ... -
Improving Credit Management Practices: A Transdisciplinary Approach to Optimizing Risk and Profitability
Aaberge, Marte; Johanraj, Jananni (Master thesis, 2023)Effektiv kredittstyring krever en delikat balanse mellom å maksimere lønnsomhet og minimere risiko. Imidlertid mangler dagens praksis innen kredittstyring ofte evnen til å håndtere dynamiske endringer og samspillet mellom ... -
Improving Credit Management Practices: A Transdisciplinary Approach to Optimizing Risk and Profitability
Aaberge, Marte; Johanraj, Jananni (Master thesis, 2023)Effektiv kredittstyring krever en delikat balanse mellom å maksimere lønnsomhet og minimere risiko. Imidlertid mangler dagens praksis innen kredittstyring ofte evnen til å håndtere dynamiske endringer og samspillet mellom ... -
Increasing Validity and Uncovering Utility in Machine Learning Studies: An Illustrative Approach to Essential Concepts and Procedures in Model Development and Assessment
Austnes, Bendik (Master thesis, 2021)De siste årene har det skjedd store fremskritt innen dyp læring. I takt med stadig økende tilgang på datakraft og brukervennelige maskinlæringsmetoder, har stadig flere forskningsdisipliner tatt i bruk dyp læring. Mange ... -
Industrial scale turbine and associated wake development -comparison of RANS based Actuator Line Vs Sliding Mesh Interface Vs Multiple Reference Frame method
Tabib, Mandar; Siddiqui, Muhammad Salman; Rasheed, Adil; Kvamsdal, Trond (Journal article; Peer reviewed, 2017)This current work compares the three methodologies (Actuator Line model (ALM), Sliding Mesh Interface (SMI) and Multiple Reference Frame (MRF)) in modeling an industrial scale reference turbine at different tip speed ratios ... -
Influence of Tip Speed Ratio on Wake Flow Characteristics Utilizing Fully Resolved CFD Methodology
Siddiqui, Muhammad Salman; Rasheed, Adil; Kvamsdal, Trond; Tabib, Mandar (Journal article; Peer reviewed, 2017)Dominant flow structures in the wake region behind the turbine employed in the Blind Test campaign [1], [2] is investigated numerically. The effect on the wake configuration at variable operating conditions are studied. ... -
Interface learning of multiphysics and multiscale systems
Ahmed, Shady E; San, Omer; Kara, Kursat; Younis, Rami; Rasheed, Adil (Peer reviewed; Journal article, 2020)Complex natural or engineered systems comprise multiple characteristic scales, multiple spatiotemporal domains, and even multiple physical closure laws. To address such challenges, we introduce an interface learning paradigm ...