Yiwei Wang
University of California-Riverside, CA·Mathematics & Statistics
Shampoo, blood, battery electrolytes and the liquid crystals in display screens are all complex fluids. Each contains tiny internal structures, such as rod-like molecules that line up, soft particles that stretch, or charged ions that push and pull on each other.
- Fields
Mathematics & Statistics
Physics
Engineering
- Topics
complex fluids
energetic variational approach
EnVarA
- Grants
- 1 known grant, through Aug 2027
What they work on
- Develop high-order variational Lagrangian schemes, which track moving parcels of material, for generalized diffusion models, built directly from the energy and dissipation description of the fluid.
- Build variational operator-splitting schemes for reaction-diffusion models that handle chemical reactions on a fixed Eulerian grid and diffusion with Lagrangian methods, while keeping the model's energy structure.
- Design entropy-stable numerical schemes for reactive flows with changing temperature, so that simulations stay thermodynamically consistent and obey the second law of thermodynamics.
- Carry out numerical analysis and extensive computational studies of each new scheme to establish its accuracy, stability and efficiency.
More about the research
Heat, chemical reactions and mechanical forces interact across many sizes and time scales in these fluids, so they behave in ways that water or air do not. Computer simulations are essential for studying them. However, a simulation is only trustworthy if it obeys the same physical laws as the real fluid, including how energy is stored and lost.
Why it matters
Fluids such as battery electrolytes, blood and liquid crystals are hard to simulate reliably because heat, chemistry and mechanics interact inside them. The methods developed here are built to respect energy and thermodynamic laws by design. They could make computer studies of these materials more trustworthy and efficient, which may eventually inform work on batteries, biological fluids and display devices.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Adrian Sandu
Virginia Polytechnic Institute and State University, VA·Computer Science
Develop high-fidelity multiphysics models of jet engine combustion and flow together with scalable reduced-order models, both with quantified uncertainties, as the computational core of a predictive digital twin.
Qingguo Hong
Missouri University of Science and Technology, MO·Mathematics & Statistics
Develop discretizations of multiple-network poroelastic (MPET) models that remain robust across parameter ranges, strongly conserve mass, and keep the stress tensor exactly symmetric.
Ignacio Tomas
Texas Tech University, TX·Mathematics & Statistics
Develop semi-implicit time-stepping schemes for the Euler-Maxwell and Euler-Poisson fluid models of charge transport that keep density positive and energy stable, even when fast plasma oscillations are present.
The school
University of California-Riverside: 21 labs on PI Pages, $232M a year on research, 328 research doctorates a year.
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