Blar i NTNU Open på forfatter "Rasheed, Adil"
-
Data-driven recovery of hidden hysics in reduced order modeling of fluid flows
Pawar, Suraj; Ahmed, Shady E; San, Omer; Rasheed, Adil (Peer reviewed; Journal article, 2020)In this article, we introduce a modular hybrid analysis and modeling (HAM) approach to account for hidden physics in reduced order modeling (ROM) of parameterized systems relevant to fluid dynamics. The hybrid ROM framework ... -
Data-driven recovery of hidden physics in reduced order modeling of fluid flows
Pawar, Suraj; Ahmed, Shady E; San, Omer; Rasheed, Adil (Peer reviewed; Journal article, 2020)In this article, we introduce a modular hybrid analysis and modeling (HAM) approach to account for hidden physics in reduced order modeling (ROM) of parameterized systems relevant to fluid dynamics. The hybrid ROM framework ... -
Deep learning assisted physics-based modeling of aluminum extraction process
Robinson, Haakon; Lundby, Erlend Torje Berg; Rasheed, Adil; Gravdahl, Jan Tommy (Peer reviewed; Journal article, 2023)Modeling complex physical processes such as the extraction of aluminum is mainly done using pure physics-based models derived from first principles. However, the accuracy of these models can often suffer due to a partial ... -
Deep neural network enabled corrective source term approach to hybrid analysis and modeling
Blakseth, Sindre Stenen; Rasheed, Adil; Kvamsdal, Trond; San, Omer (Peer reviewed; Journal article, 2022)In this work, we introduce, justify and demonstrate the Corrective Source Term Approach (CoSTA)—a novel approach to Hybrid Analysis and Modeling (HAM). The objective of HAM is to combine physics-based modeling (PBM) and ... -
Deep Reinforcement Learning Controller for 3D Path-following and Collision Avoidance by Autonomous Underwater Vehicles
Havenstrøm, Simen Theie; Rasheed, Adil; San, Omer (Peer reviewed; Journal article, 2020)In this article, we explore the feasibility of applying proximal policy optimization, a state-of-the-art deep reinforcement learning algorithm for continuous control tasks, on the dual-objective problem of controlling an ... -
Demonstrating the impact of bidirectional coupling on the performance of an ocean-met model
Rasheed, Adil; Tabib, Mandar; Süld, Jakob Kristoffer; Kristiansen, Jørn; Kvamsdal, Trond (Journal article; Peer reviewed, 2017)The mass, momentum and energy fluxes between the atmosphere and ocean surface depend on the state of the ocean surface. The fluxes in turn can significantly alter the nature of the marine boundary layer and the state of ... -
Digital Twin of a Building Powered by Artificial Intelligence and Demonstrated in Virtual Reality
Elfarri, Elias Mohammed (Master thesis, 2022)En digital tvilling er definert som en virtuell representasjon av et fysisk objekt, oppnådd gjennom data og simulatorer for sanntidsprediksjon, optimalisering, overvåking, kontroll og forbedret beslutningstaking. Dessverre ... -
Digital Twin: Values, Challenges and Enablers from a modelling perspective
Rasheed, Adil; San, Omer; Kvamsdal, Trond (Journal article; Peer reviewed, 2020)Digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making. Recent advances ... -
Digital Twins in intensive aquaculture — Challenges, opportunities and future prospects
Føre, Martin; Alver, Morten Omholt; Alfredsen, Jo Arve; Rasheed, Adil; Hukkelås, Thor; Bjelland, Hans Vanhauwaert; Su, Biao; Ohrem, Sveinung Johan; Kelasidi, Eleni; Norton, Tomas; Papandroulakis, Nikos (Peer reviewed; Journal article, 2024)Digital Twin technology has emerged to become a key enabling technology in the ongoing transition into Industry 4.0. A Digital Twin is in essence a digital representation of an asset that provides better insight into its ... -
Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions
Stadtmann, Florian; Rasheed, Adil; Kvamsdal, Trond; Johannessen, Kjetil Andre; San, Omer; Kölle, Konstanze; Tande, John Olav Giæver; Barstad, Idar; Benhamou, Alexis; Brathaug, Thomas; Christiansen, Tore; Firle, Anouk-Letizia; Fjeldly, Alexander; Frøyd, Lars; Gleim, Alexander; Høiberget, Alexander; Meissner, Catherine; Nygård, Guttorm; Olsen, Jørgen; Paulshus, Håvard; Rasmussen, Tore; Rishoff, Elling; Scibilia, Francesco; Skogås, John Olav (Peer reviewed; Journal article, 2023) -
Effect of turbulence intensity on the performance of an offshore vertical axis wind turbine
Siddiqui, Muhammad Salman; Rasheed, Adil; Kvamsdal, Trond; Tabib, Mandar (Journal article; Peer reviewed, 2015)Offshore wind energy is one of the most competitive renewable energy resources available to us, which until now been under- exploited. Most of the problems associated with wind farm installation like land acquisition, low ... -
Enhancing elasticity models with deep learning: A novel corrective source term approach for accurate predictions
Sørbø, Sondre; Blakseth, Sindre Stenen; Rasheed, Adil; Kvamsdal, Trond; San, Omer (Peer reviewed; Journal article, 2024)With the recent wave of digitalization, specifically in the context of safety–critical applications, there has been a growing need for computationally efficient, accurate, generalizable, and trustworthy models. Physics-based ... -
An environmental disturbance observer framework for autonomous surface vessels
Menges, Daniel; Rasheed, Adil (Peer reviewed; Journal article, 2023)This paper proposes a robust disturbance observer framework for maritime autonomous surface vessels considering model and measurement uncertainties. The core contribution lies in a nonlinear disturbance observer, reconstructing ... -
An evolve-then-correct reduced order model for hidden fluid dynamics
Pawar, Suraj; Ahmed, Shady E; San, Omer; Rasheed, Adil (Journal article; Peer reviewed, 2020)n this paper, we put forth an evolve-then-correct reduced order modeling approach that combines intrusive and nonintrusive models to take hidden physical processes into account. Specifically, we split the underlying dynamics ... -
Fast divergence-conforming reduced basis methods for stationary and transient flow problems
Fonn, Eivind; van Brummelen, Harald; Kvamsdal, Trond; Rasheed, Adil (Peer reviewed; Journal article, 2020)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 [1]. For the Navier-Stokes equation, RBMs based ... -
Fast divergence-conforming reduced basis methods for steady Navier–Stokes flow
Fonn, Eivind; Brummelen, Harald van; Kvamsdal, Trond; Rasheed, Adil (Journal article; Peer reviewed, 2018)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 ... -
Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensor observation data
Vaddireddy, Harsha; Rasheed, Adil; Staples, Anne; San, Omer (Peer reviewed; Journal article, 2020)We put forth a modular approach for distilling hidden flow physics from discrete and sparse observations. To address functional expressiblity, a key limitation of the black-box machine learning methods, we have exploited ... -
Finite-Volume High-Fidelity Simulation Combined with Finite-Element-Based Reduced-Order Modeling of Incompressible Flow Problems
Fonn, Eivind; Kvamsdal, Trond; Rasheed, Adil (Journal article; Peer reviewed, 2019)We present a nonintrusive approach for combining high-fidelity simulations using Finite-Volume (FV) methods with Proper Orthogonal Decomposition (POD) and Galerkin Reduced-Order Modeling (ROM) methodology. By nonintrusive ... -
Forward sensitivity approach for estimating eddy viscosity closures in nonlinear model reduction
Ahmed, Shady E; Bhar, Kinjal; San, Omer; Rasheed, Adil (Peer reviewed; Journal article, 2020)In this paper, we propose a variational approach to estimate eddy viscosity using forward sensitivity method (FSM) for closure modeling in nonlinear reduced order models. FSM is a data assimilation technique that blends ... -
Fractal Analysis and Its Application on Time-Series Data - An Innovative Method for Condition Monitoring of Hole Cleaning Operations
Musæus, Lars Gjardar (Master thesis, 2022)Innenfor hullrensing i oljeboring bransjen, har ny og moderne utvikling av sensorer gjort det mulig å monitorere tilstanden til en boring- og renseoperasjon mens den er i drift. Dette medfører en stor mengde data som kan ...