# David Trebotich

David Trebotich
Staff Scientist
Phone: +1 510 486 5984
Fax: +1 510 486 6900
MS 50A-3123
Berkeley, CA 94720 US

David Trebotich is a staff scientist in the Applied Numerical Algorithms Group at Lawrence Berkeley National Laboratory. His research involves end-to-end development of high resolution algorithms for complex flows in multiscale systems using adaptive, finite volume methods and implementation into hpc science application codes (e.g., Chombo-Crunch). Current applications of interest revolve around solution to flow and transport equations in complex media--geological (subsurface), engineered (battery electrodes, paper drying felt). Prior to the Berkeley lab, David was staff scientist at the Lawrence Livermore National Laboratory in the Center for Applied Scientific Computing from 2001 to 2009, and before that a post-doc at UC Berkeley after completing his Ph.D. there in 1998.

»Videos of recent visualizations.

#### Current Research

• Exascale Computing Project subsurface application development (ECP)
• High resolution simulation of pore scale reactive transport processes associated with carbon sequestration (EFRC-NCGC)
• Dewetting/rewetting in paper manufacturing (AMO HPC4MFG with LLNL and Agenda 2020)
• Battery manufacturing processes (AMO)
• Robust embedded boundary methods for science applications
• Adaptive multiscale, multimodel methods

## Reports

### Brian Van Straalen, David Trebotich, Terry Ligocki, Daniel T. Graves, Phillip Colella, Michael Barad,"An Adaptive Cartesian Grid Embedded Boundary Method for the Incompressible Navier Stokes Equations in Complex Geometry",LBNL Report Number: LBNL-1003767,2012,LBNL LBNL Report Numb,

We present a second-order accurate projection method to solve the
incompressible Navier-Stokes equations on irregular domains in two
and three dimensions.  We use a finite-volume discretization
obtained from intersecting the irregular domain boundary with a
Cartesian grid.  We address the small-cell stability problem
associated with such methods by hybridizing a conservative
discretization of the advective terms with a stable, nonconservative
discretization at irregular control volumes, and redistributing the
difference to nearby cells.  Our projection is based upon a
finite-volume discretization of Poisson's equation.  We use a
second-order, $L^\infty$-stable algorithm to advance in time.  Block
structured local refinement is applied in space.  The resulting
method is second-order accurate in $L^1$ for smooth problems.  We
demonstrate the method on benchmark problems for flow past a
cylinder in 2D and a sphere in 3D as well as flows in 3D geometries
obtained from image data.