Qingguo Hong
Missouri University of Science and Technology, MO·Mathematics & Statistics
Many materials are porous and soaked with fluid: soil and rock, bone, the brain and other living tissues. Poromechanics describes how such materials deform as fluid flows through them.
- Fields
Mathematics & Statistics
Computer Science
Engineering
- Topics
poromechanics
poroelasticity
Biot's model
- Grants
- 1 known grant, through Aug 2027
What they work on
- Develop discretizations of multiple-network poroelastic (MPET) models that remain robust across parameter ranges, strongly conserve mass, and keep the stress tensor exactly symmetric.
- Design efficient iterative solvers, including preconditioners, multigrid methods and fixed-stress splitting schemes, for the large linear systems produced by these poromechanics discretizations.
- Create physics-oriented deep learning methods for poromechanics problems in which parameters are anisotropic, discontinuous or randomly distributed over domains with complex geometry.
More about the research
The classic model is Biot's model. A newer generalization, multiple-network poroelastic theory (MPET), tracks several interacting fluid networks at once and is increasingly used in medicine and biomechanics. Solving MPET on a computer is hard. Physical parameters can vary over huge ranges, and numerical methods may break basic physical laws such as conservation of mass.
Why it matters
Reliable, efficient simulation of fluid-filled porous materials could support work in geosciences, medicine and biophysics. One example is biomechanical models of tissues described by multiple-network poroelastic theory. Methods that conserve mass and stay accurate across parameter ranges could make such simulations more trustworthy for scientists and engineers.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Lin Mu
University of Georgia, GA·Mathematics & Statistics
Design a finite element scheme for incompressible flow that achieves pressure robustness and exact mass conservation at once, using a divergence-preserving discretization rather than the trade-off made by traditional solvers.
Mingchao Cai
Morgan State University, MD·Mathematics & Statistics
Develop monolithic parameter-robust preconditioners, which speed up solvers for the whole coupled system, for quasi-static and fully dynamic thermo-poroelastic models, with mathematical proofs of stability and convergence.
Georg Stadler
New York University, NY·Mathematics & Statistics
Develop lifted Newton methods that move severely nonlinear PDE systems into a higher-dimensional space and apply nonlinear transformations before linearization, a form of nonlinear preconditioning.
The school
Missouri University of Science and Technology: 7 labs on PI Pages, $70M a year on research, 96 research doctorates a year.
Reach out
A start for your email. Fill in the brackets.