Projects

Hassanian Lab
UNIVERSITY OF ICELAND

Projects

Research across fluid dynamics, energy systems and intelligent computing.

Current research & programmes

DP4PL

Data-driven and physics-informed learning techniques for particle-laden turbulent flow. Dr. Hassanian serves as Principal Investigator.

European computing & innovation

EuroCC 3 — Work Package Deputy
EDIH 2.0 — Technical Committee
AIFA-ICE — Technical Steering Committee

2026 · BSc PROJECT

Fabrication and Experimental Flow Measurement in a Wind-Tunnel Test Section

Student: Askur Freyr Andrason · BSc student

This 2026 BSc project, undertaken by Askur Freyr Andrason, combines fabrication and experimental testing of a wind-tunnel test section with a cylindrical test element. A Pitot tube will provide the reference velocity for hot-wire calibration, and hot-wire measurements will characterise the downstream velocity field and fluctuations. The work includes setup fabrication, calibration and measurements at selected downstream positions.

Wind-tunnel fabrication and experimental flow measurementConceptual test-section schematic based on the supplied layout. A cylindrical test element is followed by three downstream measurement stations. A Pitot tube provides a calibration reference and a hot-wire probe is shown at one illustrative station. Geometry is labeled with variables only. Not to scale.Wind-tunnel fabrication & experimental flow measurement2026 BSc project · Hassanian Lab · Conceptual schematic — not to scaleCylindricaltest elementFlow, UPitot-tube referenceHot-wire probeLx₀HWDhczbx₁x₂x₃xyzGeometry: L, W, H = test-section dimensions · D, hc = element diameter and height · zb = base elevationPositions: x₀ = element location · x₁, x₂, x₃ = downstream distances from the element centrelineProbe locations are illustrative. No numerical dimensions, measured data, or experimental results are shown.
Conceptual schematic of the wind-tunnel test section and measurement arrangement. Dimensions are expressed as variables; probe locations are illustrative and the drawing is not to scale.

2026 · BSc PROJECT

Experimental Characterisation and AI-Based Health Monitoring of Centrifugal Pumps

Student: Ás Teitur Andrason · BSc student

This BSc project uses a centrifugal-pump test rig to collect experimental data across different operating conditions and rotational speeds. It will investigate changes in pump performance and efficiency and develop a dataset describing healthy operation. The data will support the training and evaluation of AI models for detecting early signs of faults, with the aim of enabling condition-based maintenance in industrial pumping systems.

Centrifugal-pump rig: experimental data and AI health monitoringHigh-level conceptual overview of a centrifugal-pump test rig, variable speed control, measurement and data acquisition, and a planned workflow from experimental data to AI-based health monitoring and maintenance support. The hydraulic arrangement is illustrative. Detailed specifications and wiring are omitted.Centrifugal-pump rig & AI health monitoring2026 BSc project · Ás Teitur Andrason · Hassanian LabEXPERIMENTAL TEST RIGVary operating conditions and rotational speedFluid supplyCentrifugal pumpMotorSpeed controlAdjustable rotationPerformance and efficiency across operating pointsMeasurement & acquisitionPressureTemperatureVibrationFlowSignal conditioning → data recordingExperiment control and analysisPLANNED DATA-TO-MAINTENANCE WORKFLOWExperimental datasetAI health monitoringMaintenance supportCharacterise healthy operationat different operating pointsLearn normal pump behaviourand identify deviationsEarly fault indicationfor industrial pumping systemsConceptual overview only · Hydraulic layout is illustrative · Specifications, wiring and detailed rig geometry are omitted
Conceptual overview of the centrifugal-pump project and planned AI health-monitoring workflow. The hydraulic layout is illustrative; hardware specifications, wiring and detailed rig geometry are omitted.

Project archive

Existing project descriptions, dates, roles and collaborations are retained below.

Ongoing Projects

  • DP4PL: Data-driven and physics-informed learning techniques: for particle-laden turbulent flow
    Role: PI
    International collaboration:
    University of Iceland
    Technical University of Denmark (DTU)
    University of Stuttgart
    Otto-von-Guericke University of Magdeburg
    Julich Supercomputing Centre, Germany
  • Singular Spectrum Decomposition for wind speed turbulent forecasting using deep learning
    Role: Lead and researcher
    International collaboration:
    Prof. Jon Atli Benediktsson; University of Iceland
    Prof. Olafur Petur Palsson; University of Iceland
    Prof. Morris Riedel; University of Iceland, Julich Supercomputing Centre, Germany
    Prof. Asdis Helgadottir; University of Iceland
    Prof. Gabriele Cavallaro; University of Iceland, Julich Supercomputing Centre, Germany
  • Wind turbine power forecasting under leading edge erosion
    Role: Lead and researcher
  • Wind speed from remote sensing and reanalysis data for wind speed forecasting and planetary boundary layer
    Role: Lead and researcher

2025

  • Data-driven deep learning models in particle-laden turbulent flow
    Role: Lead and researcher
    International collaboration:
    Prof. Andrea Beck; Institute of Aerodynamics and Gas Dynamics,
    Dr.-Ing. Farshad Gharibi; Laboratory of Fluid Dynamics and Technical Flows, Otto-von-Guericke University of Magdeburg
  • Proper Orthogonal Decomposition Analysis of Turbulent Energy Spectra within a Lagrangian Particle Tracking Framework
    Role: Lead
  • Data-driven deep learning model for predicting ambient temperature: environment and solar energy
    Role: Lead and researcher

2024

  • Turbulent Flow Prediction-Simulation and Hyperparameter Optimization
    International collaboration:
    University of Iceland
    Julich Supercomputing Centre, Germany
  • Wake loss model asymmetry induced by the circulation of a vertical axis wind turbine
    Role: Lead

2023

  • Deep learning and Machine learning in turbulent flow
    Supervisor: Prof. Morris Riedel; University of Iceland, Co-advisor: Prof. Asdis Helgadottir
    International collaboration:
    Work Package of The European Center of Excellence in Exascale Computing “Research on AI- and Simulation-Based Engineering at Exascale” (CoE RAISE)
    Conseil Européen pour la Recherche Nucléaire (CERN)
    Delft University of Technology (TUDelft)
    Forschungszentrum Jülich GmbH (FZJ)
    University of Iceland (UOI)
    The Cyprus Institute (CYI)
    RWTH Aachen University (RWTH)
    Barcelona Supercomputing Center
    Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique (CERFACS)
    BULL SAS (BULL)
    Riga Technical University (RTU)
    Flanders Make (FM)
    Safran Helicopter Engines (SAFRAN)
    ParTec AG (ParTec)

2022

  • CFD, machine learning, and high-performance computing
    Supervisor: Prof. Morris Riedel; University of Iceland, Co-advisor: Prof. Asdis Helgadottir
    International collaboration:
    Work Package of The European Center of Excellence in Exascale Computing “Research on AI- and Simulation-Based Engineering at Exascale” (CoE RAISE)
    Conseil Européen pour la Recherche Nucléaire (CERN)
    Delft University of Technology (TUDelft)
    Forschungszentrum Jülich GmbH (FZJ)
    University of Iceland (UOI)
    The Cyprus Institute (CYI)
    RWTH Aachen University (RWTH)
    Barcelona Supercomputing Center
    Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique (CERFACS)
    BULL SAS (BULL)
    Riga Technical University (RTU)
    Flanders Make (FM)
    Safran Helicopter Engines (SAFRAN)
    ParTec AG (ParTec)

2021

  • Estimation and forecasting of a deep learning model in fluid dynamics
    Supervisor: Prof. Morris Riedel; University of Iceland, Co-advisor: Prof. Asdis Helgadottir
    International collaboration:
    Work Package of The European Center of Excellence in Exascale Computing “Research on AI- and Simulation-Based Engineering at Exascale” (CoE RAISE)
    Conseil Européen pour la Recherche Nucléaire (CERN)
    Delft University of Technology (TUDelft)
    Forschungszentrum Jülich GmbH (FZJ)
    University of Iceland (UOI)
    The Cyprus Institute (CYI)
    RWTH Aachen University (RWTH)
    Barcelona Supercomputing Center
    Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique (CERFACS)
    BULL SAS (BULL)
    Riga Technical University (RTU)
    Flanders Make (FM)
    Safran Helicopter Engines (SAFRAN)
    ParTec AG (ParTec)
  • Wind Energy and data-driven deep learning model

2020

  • Data-Driven Deep Learning and Fluid Dynamics
    Supervisor: Prof. Morris Riedel; University of Iceland, Co-advisor: Prof. Asdis Helgadottir
  • Solar energy and numerical method

2019

  • Leading-edge erosion and wind turbine
    Supervisor: Prof Armann Gylfason; Reykjavik University

2018

  • Strained turbulent flow, experimental laboratory research
    Supervisor: Prof Armann Gylfason; Reykjavik University

2017

  • Floating Roof Oil Storage Tank, Roof Instability and Tilting due to high-velocity flow inlet

2016

  • Pressure Vessel design under stress and high pressure

2015

  • Centrifugal pump design as a booster for an offshore pipeline

2014

  • Corrosive, erosive, fouling, and buffering fluids in the heat exchanger

2013

  • Oil Wellhead facility design under specific conditions

2012

  • Gas Injection Compressor design for a single oil well

2009-2011

  • Centrifugal pump vibration analysis
  • Centrifugal pumps (8 QTY) designed for heavy fluid and special hydrostatic test conditions

Future projects

Future projects will be added here when their scope and details are confirmed.

Discuss research collaboration

Explore Hassanian Lab · Home   /   Research   /   Projects   /   Team   /   Publications   /   News   /   About   /   Contact

Scroll to Top