Blar i NTNU Open på forfatter "Rasheed, Adil"
-
A nonintrusive hybrid neural-physics modeling of incomplete dynamical systems: Lorenz equations
Pawar, Suraj; San, Omer; Rasheed, Adil; Navon, Ionel M. (Peer reviewed; Journal article, 2021)This work presents a hybrid modeling approach to data-driven learning and representation of unknown physical processes and closure parameterizations. These hybrid models are suitable for situations where the mechanistic ... -
Nonlinear proper orthogonal decomposition for convection-dominated flows
Ahmed, Shady E; San, Omer; Rasheed, Adil; Trian, Iliescu (Peer reviewed; Journal article, 2021)Autoencoder techniques find increasingly common use in reduced order modeling as a means to create a latent space. This reduced order representation offers a modular data-driven modeling approach for nonlinear dynamical ... -
A novel hybrid analysis and modeling approach applied to aluminum electrolysis process
Lundby, Erlend Torje Berg; Rasheed, Adil; Gravdahl, Jan Tommy; Halvorsen, Ivar Johan (Peer reviewed; Journal article, 2021)Aluminum electrolysis cells are characterized by harsh environments where several measurements have to be done manually. Due to the operational costs related to manual sampling, the sampling rates of these measurements are ... -
A nudged hybrid analysis and modeling approach for realtime wake-vortex transport and decay prediction
Ahmed, Shady E; Pawar, Suraj; San, Omer; Rasheed, Adil; Tabib, Mandar (Journal article, 2020)We put forth a long short-term memory (LSTM) nudging framework for the enhancement of reduced order models (ROMs) of fluid flows utilizing noisy measurements for air traffic improvements. Toward emerging applications of ... -
Numerical Analysis of NREL 5MW Wind Turbine: A Study Towards a Better Understanding of Wake Characteristic and Torque Generation Mechanism
Siddiqui, Muhammad Salman; Rasheed, Adil; Tabib, Mandar; Kvamsdal, Trond (Journal article; Peer reviewed, 2016)With the increased feasibility of harvesting offshore wind energy, scale of wind turbines is growing rapidly and there is a trend towards clustering together higher number of turbines in order to harvest maximum yield and ... -
Numerical benchmarking of fluid–structure interaction: An isogeometric finite element approach
Nordanger, Knut; Rasheed, Adil; Okstad, Knut Morten; Kvarving, Arne Morten; Holdahl, Runar; Kvamsdal, Trond (Journal article; Peer reviewed, 2016)In this paper we describe and evaluate an isogeometric finite element program, IFEM-FSI, for doing coupled fluid–structure interaction simulations. We investigate the role played by employing higher polynomial orders and ... -
Numerical investigation of modeling frameworks and geometric approximations on NREL 5 MW wind turbine
Siddiqui, Muhammad Salman; Rasheed, Adil; Tabib, Mandar; Kvamsdal, Trond (Journal article; Peer reviewed, 2018)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 ... -
Numerical Modeling Framework for Wind Turbine Analysis & Atmospheric Boundary Layer Interaction
Siddiqui, Muhammad Salman; Rasheed, Adil; Tabib, Mandar; Kvamsdal, Trond (Chapter, 2017)Prevailing atmospheric conditions can have a significant impact on the performance of large mega-watt wind turbines. A purely experimental evaluation of this impact is currently not possible and hence numerical techniques ... -
On closures for reduced order models— A spectrum of first-principle to machine-learned avenues
Ahmed, Shady E; Pawar, Suraj; San, Omer; Rasheed, Adil; Trian, Iliescu; Noack, Bernd R. (Peer reviewed; Journal article, 2021)For over a century, reduced order models (ROMs) have been a fundamental discipline of theoretical fluid mechanics. Early examples include Galerkin models inspired by the Orr–Sommerfeld stability equation and numerous vortex ... -
On Course Towards Model-Free Guidance: A Self-Learning Approach To Dynamic Collision Avoidance for Autonomous Surface Vehicles
Meyer, Eivind (Master thesis, 2020)I denne masteroppgåva vart det demonstrert at djup forsterkande læring (engelsk: Deep Reinforcement Learning / DRL) kan nyttast for å trena eit reaktivt, autonomt fartøy utstyrt med påmonterte avstandssensorar til å navigera ... -
On interactions between wind turbines and the marine boundary layer
Siddiqui, Muhammad Salman; Rasheed, Adil; Tabib, Mandar; Fonn, Eivind; Kvamsdal, Trond (Chapter; Peer reviewed, 2017)Most mesoscale models are developed with grid resolution in the range of kilometers. Therefore, they may require spatial averaging to analyze flow behavior over the domain of interest. In doing so, certain important features ... -
On the applicability of a perceptually driven generative-adversarial framework for super-resolution of wind fields in complex terrain
Larsen, Thomas Nakken (Master thesis, 2020)Det har nylig blitt gjort store fremskritt innenfor datasyn for konstruksjon av høyoppløste bilder fra referansebilder med lav oppløsning ved å benytte høy-\\dimensjonale aktiveringer fra ``feature-extractors" for å danne ... -
On the Piecewise Affine Representation of Neural Networks
Robinson, Haakon (Master thesis, 2019)Det kan vises at nevrale nett kan uttrykkes som stykkevise affine (PWA) funksjoner. Men, forskning har fokusert på å telle de lineære regionene, fremfor å finne den eksplitte PWA formen. Denne oppgaven fremfører en algoritme ... -
On the Potential of Utilizing Laboratory-Scale Experimental Setup as Proxy For Real-Life Applications: Time Series Analysis and Prediction for Hole Cleaning
Haugstvedt, Emil Johannesen (Master thesis, 2023)Hullrensing er en viktig del av oljeboring. Hullresning innebærer å fjerne borekaks fra brønnen ved hjelp av en borevæske. Ytelsen til hullresingsprosessen kan måles ved sirkulasjonstrykket (ECD), som påvirkes av forskjellige ... -
Path Following and Collision Avoidance for Quadcopters using Deep Reinforcement Learning
Sundøen, Ludvig Løken (Master thesis, 2022)Klassiske kontrollmetoder avhenger av nøyaktige modeller. Slike modeller eksisterer ikke alltid for komplekse systemer, og kontroll er begrenset til enkle oppgaver på lavt nivå. Modellfrie Reinforcement Learning (RL) ... -
Path-following and Collision Avoidance using World Models
Brudeli, Kristian (Master thesis, 2023)Denne masteroppgaven presenterer en applikasjon av den modellbaserte forsterkningslæringsagen- ten DreamerV2 for banefølging og kollisjonsunngåelse med autonome overflatsfartøy. Fartøyet lærer gjennom interaksjon med ... -
Physics guided machine learning using simplified theories
Pawar, Suraj; San, Omer; Aksoylu, Burak; Rasheed, Adil; Kvamsdal, Trond (Peer reviewed; Journal article, 2021)Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences. In this Letter, we introduce a modular ... -
Physics Guided Machine Learning: Injecting neural networks with simplified theories
Laache, Torkel (Master thesis, 2021)Eksponentiell vekst i datakraft og tilgjengelighet av store datasett har popularisert og forbedret maskinlæring betydelig de siste årene. Nevrale nettverk er sterke verktøy som kan oppdage mønstre i komplekse datasett og ... -
Physics guided neural networks for modelling of non-linear dynamics
Robinson, Haakon; Pawar, Suraj; Rasheed, Adil; San, Omer (Peer reviewed; Journal article, 2022)The success of the current wave of artificial intelligence can be partly attributed to deep neural networks, which have proven to be very effective in learning complex patterns from large datasets with minimal human ... -
PoroTwin: A Digital Twin for a FluidFlower Rig
Keilegavlen, Eirik; Fonn, Eivind; Johannessen, Kjetil Andre; Eikehaug, Kristoffer; Both, Jakub Wiktor; Fernø, Martin; Kvamsdal, Trond; Rasheed, Adil; Nordbotten, Jan Martin (Peer reviewed; Journal article, 2022)We present a framework for integrated experiments and simulations of tracer transport in heterogeneous porous media using digital twin technology. The physical asset in our setup is a meter-scale FluidFlower rig. The digital ...