PECOS develops and contributes to several open source software projects to support our modeling and uncertainty quantification efforts.
QUESO: A C++ library for the Quantification of Uncertainty for Estimation, Simulation and Optimization. QUESO is a collection of algorithms and other functionalities aimed for the solution of statistical inverse problems, for the solution of statistical forward problems, for the validation of a model and for the prediction of quantities of interest from such model along with the quantification of their uncertainties.
libMesh: A C++ finite elements library that provides a framework for the numerical simulation of partial differential equations using arbitrary unstructured discretizations on serial and parallel platforms. A major goal of the library is to provide support for adaptive mesh refinement (AMR) computations in parallel while allowing a research scientist to focus on the physics they are modeling.
GRINS: General Reacting Incompressible Navier-Stokes (GRINS) was initiated
to house common modeling work centered around using the incompressible and variable-density (low-Mach) Navier-Stokes equations utilizing the libMesh finite element library, including both MPI and MPI+threads parallelism, as provided by libMesh. GRINS has now become a tool for rapidly developing formulations and algorithms for the solution of complex multiphysics applications.
Antioch: A New Templated Implementation Of Chemistry for Hydrodynamics (Antioch) was initiated to centralize work by some of the Antioch authors within the realm of hypersonic aerodynamics, based on the libMesh finite element library. In particular, although there exist C++ chemistry libraries, such as Cantera, we had needs for both thread-safety and high performance. Thus, Antioch was born.
MASA: (Manufactured Analytical Solution Abstraction) is a library written in C++ (with C, python and Fortran90 interfaces) which provides a suite of manufactured solutions for the software verification of partial differential equation solvers in multiple dimensions.