skip to primary navigationskip to content

Dr Krishna Kumar

Dr Krishna Kumar

Research Associate

Computational Geomechanics

Third Floor, James Dyson Building
Department of Engineering
University of Cambridge
Trumpington Street

Cambridge , Cambridgeshire CB2 1PZ
Office Phone: 01223 748589


Krishna Kumar joined the Geotechnical Research Group at Cambridge University Engineering Department in October 2010 as a PhD student.  Krishna completed his PhD in January 2015 on  multi-scale multiphase modelling of granular flows. He was supervised by Professor Kenichi Soga. Krishna was funded by the Cambridge International Scholarship.  He is a College Research Associate at King’s College, Cambridge. Krishna is a Software Sustainability Institute Fellow.


Krishna's work involves developing massively parallel micro-/macro-scale numerical methods: Finite Element Method, Material Point Method, Lattice Boltzmann - Discrete Element coupling and Lattice Element method. You can view my PhD thesis:

Krishna also works on large-scale big data frameworks for infrastructure monitoring. The big data framework will be able to provide real-time information and alerts (in case of a potential failure) on the behaviour of various structural elements in an infrastructure project.

Krishna completed his PhD research at the University of Cambridge on multi-scale multiphase modelling of granular flows. His research was a part of a joint project with University of Montpellier II, France. Krishna is interested in understanding the mechanism of granular flow is of particular importance in predicting the run-out distances of debris flows and submarine landslides. A multi-scale approach is adopted to understand the fundamental microscopic parameters that control the complex macroscopic granular flow dynamics. A 2D/3D Material Point Method (MPM) code is extended to simulate granular flows as a continuum. A coupled GP-GPU compatible Discrete Element (DEM) – Lattice-Boltzmann Method (LBM) code with Large-scale Eddy Simulation (LES) technique is developed to study the underlying micro-mechanics of granular flows in fluid. Numerical simulations of granular flows under dry and submerged conditions were performed to understand the rheology of granular flows and the limitations continuum models have in simulating large deformation problems by investigating the mechanism of energy dissipation and flow kinematics.

Departments and Institutes

Department of Engineering:
Research Associate

Research Interests

Numerical modelling of granular flows

GPGPU computing

Big data & Machine learning for infrastructure monitoring