Skip to main content

ÍS
EN

Forsíða

Forsíða

Seyedreza Hassanianmoaref - Doctoral Graduate Student

Seyedreza Hassanianmoaref

–

School of Engineering and Natural Sciences

Seyedreza Hassanianmoaref

Doctoral Graduate Student

and Lecturer

Location
Gróska / Gr-318
Email
seh [at] hi.is

Unit
Computer Science

Published works

2024

Wind Velocity and Forced Heat Transfer Model for Photovoltaic ModuleFluids
Turbulent Flow Prediction-Simulation: Strained Flow with Initial Isotropic Condition Using a GRU Model Trained by an Experimental Lagrangian Framework, with Emphasis on Hyperparameter OptimizationFluids
Optimizing Wind Energy Production: Leveraging Deep Learning Models Informed with On-Site Data and Assessing Scalability through HPCActa Polytechnica Hungarica

2023

Leading-Edge Erosion and Floating Particles: Stagnation Point Simulation in Particle-Laden Turbulent Flow via Lagrangian Particle TrackingMachines
Iceland Wind Farm Assessment Case Study and Development: An Empirical Data from Wind and Wind TurbineCleaner Energy Systems
A wake loss model asymmetry induced by the circulation of a vertical axis wind turbineInternational Conference on Future Energy Solutions (FES2023), IEEE
Vertical Axis Wind Turbine Powers Telecom Towers: Green and Clean ConfigurationThe 6th International Conference on Electrical Engineering and Green Energy (CEEGE 2023), IEEE
Buckling Assessment in the Dynamics Mechanisms, Stewart Platform Case Study: In the Context of Loads and Joints, Deflection Positions GradientComputation
Turbulent flow prediction: Lagrangian Particle Tracking-Deep Learning (LPT-DL) based modelsBulletin of the American Physical Society
An experiment generates a specified mean strained rate turbulent flow: Dynamics of particlesPhysics of Fluids
A Novel Implicit Model Determines the Photovoltaic Panel Temperature and Environmental EffectsAPS March Meeting 2023
A proposed hybrid two-stage DL-HPC method for wind speed forecasting: using the first average forecast output for long-term forecastingIACM COMPUTATIONAL FLUIDS CONFERENCE (CFC2023)
Lattice Boltzmann method speedup via computing accelerating compiler and parallel computing: flow over cylinder with high Reynolds34th International Conference on Parallel Computational Fluid Dynamics (ParCFD2023)
Artificial Intelligence and High-Performance Computing in the Context of Particle-Laden Turbulent Flow and Wind EnergyBulletin of the American Physical Society
Deciphering the dynamics of distorted turbulent flows: Lagrangian particle tracking and chaos prediction through transformer-based deep learning modelsPhysics of Fluids

2022

Numerical Investigation on the Acceleration Vibration Response of Linear ActuatorOALib
Deep Learning Forecasts a Strained Turbulent Flow Velocity Field in Temporal Lagrangian Framework: Comparison of LSTM and GRUFluids
Implicit Equation for Photovoltaic Module Temperature and Efficiency via Heat Transfer Computational ModelThermo
The Capability of Recurrent Neural Networks to Predict Turbulence Flow via Spatiotemporal FeaturesIEEE 10th Jubilee International Conference on Computational Cybernetics and Cyber-Medical Systems (ICCC 2022)
A Review in Context to Wind Effect on NOCT Model for Photovoltaic PanelPeer Review Journal of Solar & Photoenergy Systems
Mechanical Elements Analysis of Stewart Platform: Computational ApproachInternational Journal of Science and Research (IJSR)
Lagrangian Particle Tracking Data of a Straining Turbulent Flow Assessed Using Machine Learning and Parallel Computing33rd Parallel Computational Fluid Dynamics (ParCFD) 2022
Parallel computing accelerates sequential deep networks model in turbulent flow forecastingConference: The International Conference for High Performance Computing, Networking, Storage, and Analysis, SC22
Deep Learning Forecasts a Strained Turbulent Flow Velocity Field in Temporal Lagrangian Framework: Comparison of LSTM and GRUFluids

2021

Practice and Experience in using Parallel and Scalable Machine Learning with Heterogenous Modular Supercomputing Architectures2021 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2021 - In conjunction with IEEE IPDPS 2021
Practice and Experience in using Parallel and Scalable Machine Learning with Heterogenous Modular Supercomputing Architectures2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
A Practical Approach for Estimating the Optimum Tilt Angle of a Photovoltaic Panel for a Long Period—Experimental Recorded DataSolar

Keywords for Specialisation:

Computational Fluid Dynamics

Turbulence flow

Renewable energy

High-Performance Computing

Machine learning