Fluid-structure interaction visualization

University of Greenwich — School of Engineering

Biomechanics,Multiphysics& Rheology

BMR Lab

Exploring the intersection of fluid mechanics, structural dynamics, and biological systems through advanced computational modelling and experimental research.

Research Metrics

350+

Total Citations

25

Publications

9

h-index

0+

Citations

0

Publications

0

h-index

0

i10-index

Follow the flow

About the Lab

Where Physics Meets
Physiological Reality

The BMR Lab, led by Dr Osama Maklad at the University of Greenwich, investigates complex phenomena at the intersection of biomechanics, multiphysics simulation, and rheology.

Our research spans from studying the fluid-structure interaction in human eye biomechanics to rheological characterisation of viscoelastic and complex fluids, turbulence characteristics of pulsatile flows, and food hydrocolloids texture analysis.

Meet the Team

Research Expertise

Fluid-Structure Interaction
Computational Fluid Dynamics
Direct Numerical Simulation
Ocular Biomechanics
Non-Newtonian Fluids
Viscoelasticity & Soft Matter
Data-Driven Fluid Dynamics
Turbulent Pulsatile Flows
Soft Matter
Conjugate Heat Transfer
Electronics Cooling
Microfluidics
Aeroacoustics
Aerodynamics
High Performance Computing

Research Verticals

Three Pillars of Discovery

Biomechanics

Ocular & Tissue Engineering

Biomechanics

Fluid-structure interaction studies of the human eye, corneal material characterisation using air-puff tonometry, and poly-ε-lysine hydrogels for corneal tissue engineering.

FSICorneal BiomechanicsAir-Puff TestTissue Engineering
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Rheology

Complex & Viscoelastic Fluids

Rheology

Rheological characterisation of non-Newtonian fluids, self-assembling dipeptide surfactants, yield stress fluids, LAOS analysis of protein-pectin crosslinked networks.

Non-NewtonianViscoelasticLAOSDipeptide Assembly
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Multiphysics

CFD & Turbulence Simulation

Multiphysics

Direct numerical simulations of turbulent pulsatile flows, data-driven fluid dynamics, reduced order modelling, and machine learning approaches for complex flow systems.

DNSCFDPulsatile FlowMachine Learning
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Latest Work

Recent Publications

Food Science2026

Editorial for the Special Issue “Food Hydrocolloids and Hydrogels: Rheology and Texture Analysis”

O Maklad, A Miller

Gels 12 (9), 804, 2026

Biomechanics2026

Reduced order modelling of air-puff test for corneal material characterisation

OM Maklad, M Hao

Journal of Microscopy, 2026

Multiphysics2026

Thermo-hydraulic performance of microchannel heat sinks with square pin-fin geometries: a numerical investigation

A Morsi, MA Razzaq, Y Khalid, M Naser, M Elkholy, I Elhagali, O Maklad

The 32nd THERMINIC International Workshop on Thermal Investigations of ICs and Systems, 2026

Food Science2026

High Moisture Extrusion of Fava Bean Protein Blend: Evaluating Potato Protein as Gluten Replacement for Texture Enhancement

V Baeghbali, A Garlapati, SR Euston, O Maklad, P Acharya

Food Hydrocolloids, 2026

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