Blar i NTNU Open på forfatter "Rasheed, Adil"
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A deep learning enabler for non-intrusive reduced order modeling of fluid flows
Pawar, Suraj; Rahman, Sk. Mashfiqur; Vaddireddy, H; San, Omer; Rasheed, Adil; Vedula, Prakash (Journal article; Peer reviewed, 2019)In this paper, we introduce a modular deep neural network (DNN) framework for data-driven reduced order modeling of dynamical systems relevant to fluid flows. We propose various DNN architectures which numerically predict ... -
A full-scale 3D Vs 2.5D Vs 2D analysis of flow pattern and forces for an industrial-scale 5MW NREL reference wind-turbine
Tabib, Mandar; Rasheed, Adil; Siddiqui, Muhammad Salman; Kvamsdal, Trond (Journal article; Peer reviewed, 2017)NREL 5MW reference turbine, which is a popular, realistic and standardized industrial scale offshore turbine model, is used in this work for understanding the associated flow-complexities and for testing models. For such ... -
A proof-of-concept Digital Twin implementation for monitoring patients through the Clinical Pathway for Prostate Cancer in the Norwegian Health and Care Service
Pedersen, Olav Landmark (Master thesis, 2021)Digitale tvillinger har økt i popularitet innen mange bruksområder. Gevinsten av å bruke digital tvillinger i helsevesenet er høye, ettersom det vil kunne assistere helsearbeidere i utførelse av behandling og pleie. Det ... -
A Step Towards a Reduced Order Modelling of Flow Characterized by Wakes Using Proper Orthogonal Decomposition
Fonn, Eivind; Rasheed, Adil; Tabib, Mandar; Kvamsdal, Trond (Chapter; Peer reviewed, 2017)High fidelity simulations of flow might be quite demanding, because they involve up to O(106 – 109) degrees of freedom and several hours (or even days) of computational time, also on powerful hardware parallel architectures. ... -
A step towards reduced order modelling of flow characterized by wakes using Proper Orthogonal Decomposition
Fonn, Eivind; Tabib, Mandar; Siddiqui, Muhammad Salman; Rasheed, Adil; Kvamsdal, Trond (Journal article; Peer reviewed, 2017)High fidelity simulations of flow can be quite demanding, involving up to O(106) to O(109) degrees of freedom, and several hours or days of computational time, even on powerful parllel architectures. These techniques become ... -
A strategy controller for concave obstacle avoidance
Nakken, Daniel (Master thesis, 2021)Denne masteroppgaven utforsker potensialet av forsterkende læring (engelsk: reinforcement learning) for applikasjoner av robotisk manipulator kollisjons unngåelse. Oppgaven presenterer en Proximal Policy Optimization (PPO) ... -
Applications of Data-Driven Equation Discovery to Synthetic and Experimental Data
Sandvik, Fredrik Pettersen (Master thesis, 2021)Fysikkbasert modellering kan være svært nøyaktig og oversiktlig, men krever omfattende kunnskap om dynamikken i systemet man ønsker å modellere. Datadrevne metoder kan i stedet basere seg på observasjonsdata og ikke ... -
COLREG-Compliance for Autonomous Surface Vehicles using Deep Reinforcement Learning
Heiberg, Amalie (Master thesis, 2020)Bruken av og forskning innen autonome systemer har økt kraftig i senere år, inkludert i marin sektor. Ettersom transportsektoren samtidig gjennomgår en omfattende elektrifisering, lover autonom skipsfart ikke bare reduserte ... -
COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle using Deep Reinforcement Learning
Meyer, Eivind; Heiberg, Amalie; Rasheed, Adil; San, Omer (Peer reviewed; Journal article, 2020)Path Following and Collision Avoidance, be it for unmanned surface vessels or other autonomous vehicles, are two fundamental guidance problems in robotics. For many decades, they have been subject to academic study, leading ... -
Combining Grid-Based Uncertainty Propagation and Neural Networks With Uncertainty Estimation
Vesterkjær, Eirik Ekjord (Master thesis, 2020)Punktbaserte metoder for deterministisk videresending av usikkerhet kan benyttes i kombinasjon med usikkerhetsestimering for nevrale nettverk. Dette kan brukes for å modellere dynamiske tilstandsromprosesser samt deres ... -
Convolutional Neural Network and Generative Adversarial Networks Enabled Resolution Enhancement of Numerical Simulations
Tran, Duy Tan Huynh (Master thesis, 2020)Optimal vindmølleplassering og prognoser av vindmøllers kraftproduksjon krever nøyaktig kunnskap om vindfeltet. Generelt blir målekampanjer foretatt for å innhente informasjon om de rådende vindforholdene i et bestemt ... -
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 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: 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 ... -
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 ... -
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 ...