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Fluctuating Hydrodynamics
In this figure showing the development of spinodal decomposition in a near-critical van der Waals Argon system, the liquid (red) and vapor (blue) domains spontaneously develop when the system is quenc…
Atmospheric Modeling
This 3-d cloud was simulated using a low Mach number model that accurately incorporates
water phase transitions in moist air. Iso-contours of liquid water are depicted, intercepted by a vertical plan…
High-Order Combustion Simulations
This image was featured on the cover of Combustion Theory and Modeling and was generated using a new numerical algorithm for integrating the multicomponent, reacting, compressible Navier-Stokes equati…
Fluctuating Hydrodynamics
To study the effects of thermal fluctuations in fluids at the microscale, we have developed a new low Mach number fluctuating hydrodynamics code for multicomponent mixtures. The image shows the develo…
Computational Cosmology
CCSE and C3 have collaborated to develop a new, massively parallel N-body hydro cosmology simulation code. The code is being used at LBL to study the Lyman alpha forest.
Compressible Astrophysics
This simulation of the death of a massive star used the compressible astrophysics code, CASTRO, and was featured on Nature magazine's Images of the Month. CASTRO is a massively parallel radiation-hydr…
Low Swirl Burner
NOx emissions in a simulation of the low swirl burner experiment in the LBNL Combustion Laboratory, capturing the complex cellular burning structures in this lean premixed hydrogen-air flame that lead…
The Center for Computational Sciences and Engineering (CCSE) develops and applies advanced computational methodologies to solve large-scale scientific and engineering problems arising in the Department of Energy (DOE) mission areas involving energy, environment, and industrial technology. CCSE researchers design algorithms for multiscale, multiphysics problems described by nonlinear systems of partial differential equations; develop implementations of algorithms that target current and next-generation massively parallel computational architectures; and design new efficient optimization strategies. Sample application areas include multiscale stochastic systems, astrophysics, cosmology, multiphase flow, and particle accelerators. CCSE researchers work collaboratively with application scientists to develop state-of-the-art solution methodologies in these fields.
CCSE is also the home of AMReX, a software framework for massively parallel block-structured adaptive mesh refinement (AMR) codes.
For more about CCSE, see ccse.lbl.gov
Group Lead: Andy Nonaka
