Applications are open until January 10 2026

2026 Co-Design Summer School Focus: Advancing scale-bridge simulations in high explosive applications using HARD at Scale

Many natural and engineered systems, from astrophysical supernovae to nuclear detonations and high-explosive experiments, involve rapid, intense energy release that strongly couples to both hydrodynamic and radiative processes. To understand these phenomena correctly, the study of high-explosive (HE) models is essential. Furthermore, real-world applications span an extraordinary range of spatial and temporal scales, from the submicron chemistry of reaction zones, to the mesoscale dynamics of heterogeneous media, to the macroscale propagation of shock and detonation waves. Many existing works lack the machinery to bridge these scales in a consistent manner. In this work, we propose to incorporate HE physics and multimaterial interface capabilities into HARD, a radiation hydrodynamics code based on the FleCSI framework, to overcome these challenges.

The new framework will accommodate multiple HE-burn models, including programmed burn and reactive burn, by employing an explicit multimaterial formulation. Material interfaces will be captured with a level-set method, while a ghost-fluid approach will enforce the correct pressure and velocity conditions across discontinuities. Leveraging the Singularity-EOS library, each constituent (explosive, product gases, air, etc.) can be described by its own equation of state, preserving numerical stability even in the presence of strong shocks. In parallel, we will augment HARD’s existing hydrodynamic solvers with the additional physics modules required for these capabilities.

Validation will be performed on canonical test problems such as shock-tube and Sedov-blast configurations, as well as on experimentally anchored benchmarks including one-dimensional detonation-tube and two-dimensional cylinder-expansion tests. The ExactPack library will be used to compare simulation results against analytical solutions, providing a thorough quantitative assessment of accuracy and robustness.

The increasing complexity of supercomputers, in both number of nodes and on-node hybridization, forces us to rethink our approach to high-performance computing (HPC). Task-based parallelism provides a promising path forward. CDSS 2026 proposes to use the FleCSI framework, with HARD, as the basis for the simulations. FleCSI is a compile-time configurable framework from LANL. It supports the development of multiphysics applications and introduces a functional programming model that can use multiple backends such as Legion, MPI, and HPX. FleCSI provides different topologies that are then extended via specialization, allowing it to target specific problems. CDSS 2026 aims to run simulations at scale on LANL’s world-class supercomputers.

LA-UR-25-29849

What is the Co-Design Summer School?

The Los Alamos National Laboratory Co-Design Summer School was created to train future scientists to work on the kinds of interdisciplinary teams that are demanded by today’s scientific challenges. Launched in 2011, the summer school recruits top candidates in a range of fields spanning domain sciences, applied mathematics, computational and computer sciences, and computer architecture. Participants work together to solve a focused problem that is designed to build the skills needed to tackle the grand challenges of the future. Foremost among the skills on which we focus is the ability of students to work across disciplines with other team members, while employing their own unique expertise. This is the heart of Co-Design. Past summer school challenges have included problems in kinetic theory (Boltzmann Transport Equation), molecular dynamics, hydrodynamics (Adaptive Mesh Refinement), quantum molecular dynamics, astrophysics (core-collapse supernovae and neutron star mergers), and tabulated equations of state. The summer school is hosted by the Applied Computer Science Group (CAI-1), led by Christoph Junghans.

What is Co-Design?

Co-Design is the social and technical equivalent of a multiple-constraint optimization problem. The rapid evolution of computing architectures and the expanding space between specializations in domain science and computer architecture means that it is virtually impossible for a single individual to cover all of the skills needed to solve current-day computational science challenges. Co-Design bridges this space through interactions between members of an interdisciplinary team. With the right amount of overlap, team members can communicate with each other effectively to solve a problem.