HemeLB Made Easy

HemeLB is a high-performance lattice-Boltzmann solver optimized for simulating blood flow through sparse geometries, such as those found in human vasculature. It scales to hundreds of thousands of cores on modern supercomputers.

Traditionally used for modeling cerebral blood flow and vascular remodeling in retinas, HemeLB is now expanding to simulations of full human arterial and venous trees. This work contributes to the fast-growing field of Computational Biomedicine.

This initiative has been primarily developed from the research lab of Prof. Peter V Coveney at University College London.

This tutorial page provides a concise guide to HemeLB documentation and materials.

Project Documentation

View the full document here: HemeLB Documentation

Improved Fork (Unofficial)

Alongside the official repository, an (unofficial) improved fork is available at lepotatoguy/hemelb. It adds a one-command installer for macOS and Ubuntu, clearer error messages for common mistakes, and updated documentation, and it is tested automatically on Linux and macOS.

If you are installing HemeLB for the first time, it is the easiest place to start: see here. For the upstream code and its development, use the official repository.

Project Resources Online (Official)

• Official HemeLB GitHub Repository
• Old HemeLB Website (Obsolete Version)
• CompBioMed: HemeLB Application Page
• HemeLB Carpentries Training Materials
• CompBioMed HemeLB Webinar (PDF)
• CompBioMed Webinar 10: HemeLB

Key Papers

• M.D. Mazzeo & P.V. Coveney, "HemeLB: A high performance parallel lattice-Boltzmann code for large scale fluid flow in complex geometries", Comput. Phys. Commun. (2008). DOI
• D. Groen et al., "Analysing and modelling the performance of the HemeLB lattice-Boltzmann simulation environment", J. Comput. Sci. (2013). DOI
• R.W. Nash et al., "Choice of boundary condition for lattice-Boltzmann simulation of moderate-Reynolds-number flow in complex domains", Phys. Rev. E (2014). DOI

Papers Using HemeLB

• Lo et al. (2025). A multi-component, multi-physics computational model for solving coupled cardiac electromechanics and vascular haemodynamics. Comput. Methods Appl. Mech. Eng. DOI
• Zacharoudiou et al. (2023). Development and performance of a HemeLB GPU code for human-scale blood flow simulation. Comput. Phys. Commun. DOI
• McCullough & Coveney (2023). High resolution simulation of basilar artery infarct and flow within the circle of Willis. Sci. Rep. DOI
• Zhou et al. (2022). Red blood cell dynamics in extravascular biological tissues modelled as canonical disordered porous media. Interface Focus 12. DOI
• Zhou et al. (2021). Association between erythrocyte dynamics and vessel remodelling in developmental vascular networks. J. R. Soc. Interface 18. DOI
• Enjalbert et al. (2021). Compressed vessels bias red blood cell partitioning at bifurcations. PNAS 118. DOI
• Zhou et al. (2021). Emergent cell-free layer asymmetry and biased haematocrit partition in a biomimetic vascular network. Soft Matter 17. DOI
• Bernabeu et al. (2020). Abnormal morphology biases haematocrit distribution in tumour vasculature. PNAS 117. DOI
• Zhou et al. (2020). Spatiotemporal Dynamics of Dilute Red Blood Cell Suspensions in Low-Inertia Microchannel Flow. Biophys. J. 118. DOI