Mentor: Jeff Peterson (XCP-2), Eric Tovar (XCP-2), and Daniel Holladay (CCS-7)

Description:An equation of state (EOS) is a material-specific model that is used to describe the properties of a fluid (such as defining the relationship between pressure, density and energy of a gas). When solving the Euler equations of fluid motion, an EOS is needed to close the system. When multiple materials are present in a computational domain, an additional closure rule is needed to describe the bulk mixture pressure due to material interactions. The xRAGE Eulerian hydrodynamics code is an important tool developed at LANL used for a number of applications from inertial confinement fusion to high explosives and shock hydrodynamics. We are interested in connecting a new EOS framework, singularity-eos, with xRAGE. This project is specifically focused on running problems in xRAGE to both verify that the EOS framework is giving correct answers to problems with known solutions as well as to validate that the models are consistent with experimental results. Depending on their ability and interests, students will have a chance to learn how to: (i) run a production hydrodynamics code such as xRAGE, (ii) explore analytic and semi-analytic solutions to the Euler equations, (ii) learn about various equations of state and closure models, and/or (iv) develop Python tools to analyze simulation results. Students will work closely with members of the Eulerian Applications Project (EAP) and the Cross-Cutting Capabilities Project and can gain exposure to production multi-physics software development and GPU programming as they are interested.

Mentors: Galen Craven (T-1) and Renai Chen (T-1)

Description: In numerous molecular systems that are relevant to Department of Energy missions, the current state-of-the-art computational methods that are used to calculate chemical reaction rates either fail or give inaccurate results. Accurately determining reaction rates is often a critical step for predicting how molecules and materials change over time, and therefore for understanding their functionality. It is of great importance to understand nonequilibrium reactions since many chemical processes occur under nonequilibrium conditions. While the reaction rate problem is essentially solved for systems in thermodynamic equilibrium, in nonequilibrium cases there is currently a void of suitable theoretical approaches. In this project, we will work to solve this problem by applying fluid dynamics methods, specifically, flow field analysis, to the results of atomistic simulations in order to determine chemical reaction rates. The overall objective is to develop an innovative computational capability that will be applied to design molecules and materials with unique response properties. This research will advance the current understanding of molecular-level functionality in several energy technologies. During this research project, we will aim to (1) develop a state-of-the-art methodology to determine nonequilibrium reaction rates and (2) gain an atomistic-level understanding of the mathematical structures that govern state transitions in chemical reactions that are driven from equilibrium.

Mentors: Carl Fields (CCS-2), Philipp Edelmann (CCS-7), and Joshua Dolence (CCS-2)

Description: Simulations of core-collapse supernova (CCSN) explosions require initial conditions which include effects of rotation and magnetism. Inclusion of these properties will directly impact the explosion dynamics and multi-messenger signals produced. This project proposes to perform the first comprehensive suite of 3D simulations of rotating and magnetic CCSN progenitors. The project will leverage state-of-the-art magneto-hydrodynamic simulation frameworks to investigate the 3D convective properties of massive stellar models for various magnetic field topologies.

Mentors: Filipe Pereira (XCP-8) and Daniel Israel (XCP-4)

Description: Multi-material mixing is crucial to many areas of engineering, such as energy production, combustion, and climate. Yet, it also represents a challenge. Whereas experimental techniques have limitations in measurable quantities and characterizing all problem conditions, numerical simulations are affected by uncertainties caused by mathematical models, discretization methods, and replication of the experimental conditions. The latter aspect leads to input uncertainty and often strongly impacts comparisons between experiments and simulations. This is particularly an issue for the transition to turbulence, where small changes in initial conditions can dramatically impact the late time behavior. In this summer project, we are interested in evaluating the impact of the interface between distinct fluids on mixing computations, and testing models for the transition. Students will simulate a benchmark mixing problem with different interfaces using both direct-numerical simulation and a transition model, and apply state-of-the-art verification, validation, and uncertainty quantification (VVUQ) techniques to quantify the input uncertainty of the results. The impact of the numerical error in estimating input uncertainties will also be assessed. Opportunities exist in this project to learn about the numerical simulation of mixing problems, transition processes, and VVUQ.

Mentors: Roxanne Tutchton (T-4) and Jianxin Zhu (T-4)

Description: Mean-field theory is an important tool used in investigating strongly correlated systems. This has significant applications for modeling quantum materials and understanding the fundamental interactions of electrons in solids. For students interested in the coding and mathematical aspects of computational condensed matter, this project will involve learning about the methods used to perform cutting-edge electronic structure theory calculations using density functional theory (DFT) and mean-field theory methods. The successful candidate will help with modernizing and implementing the Gutzwiller Wavefunction Approximation (GWA) code, Cygutz, for python 3. They will also have the opportunity to learn the all-electron DFT code, WIEN2k, from expert users and perform calculations on strongly correlated materials using these advanced techniques.

Mentors: Bryan Kaiser (XCP-8) and Jesse Canfield (XCP-4)

Description: The student will run high resolution simulations of shocked metals with grooves and cavities in the surface. As the shock passes, the grooves and cavities eject mass at a much higher rate than the velocity of the free surface. The quantity of the ejecta is a nonlinear function of cavity geometry, shock loading, shock geometry, and other variables. The student will 1) utilize the simulation data to calibrate turbulence models and 2) use simulation data to enable better predictions or characterize ejecta mass from bulk properties. If successful, the calibration study will lead to publication. Students interested in turbulence modeling, baroclinic vorticity, Richtmyer-Meshkov Instability, high performance computing, equation of state modeling for metals, and/or ejecta modeling are encouraged to apply.

Mentors: Amy Lovell (T-2) and Denise Neudecker (XCP-5)

Description: Fission is important for a variety of applications from basic science to stockpile stewardship, but a consistent calculation of the whole process is currently unfeasible due, in part, to computational limits.  Instead, the calculation is divided based on the time scale of the fission process.  In the LANL-developed fission fragment decay code, CGMF, fission fragment initial conditions are parametrized and fit to available data, typically depending on which initial conditions are most sensitive to which fission observable (properties of the emitted neutrons and gamma rays).  These fits can be highly dependent on what type of optimization method is used and what data are included in the optimization; the resulting parameters can have a large impact on fission properties that are difficult to measure.  In this project, students will compare various types of optimizations (chi-square minimization, Bayesian Markov Chain Monte Carlo, Kalman filter) and their impacts on parameter mean values and uncertainties.  Additionally, students will explore the impact of the quality of the experimental data included in the fits, using LANL-developed templates of experimental uncertainties to understand the changes in fitting parameters when full uncertainties are considered for experimental data (verse only considering statistical uncertainties).  Finally, students will have the opportunity to learn about the use of emulators for computationally expensive calculations, where high performance computing still presents a bottleneck for full parametric uncertainty propagation through demanding codes.

Mentors: JeeYeon Plohr (XCP-5)

Description: Dynamic response of materials are understood and modeled via several mechanisms. Strength (or plasticity), twinning, ductile/brittle damage, and fracture are the examples. The interplay between these mechanisms manifests in complex behavior and is often material-specific. Stainless steel is one of the most commonly used metal in industry and defense applications, and its quasi-brittle behavior has not been properly described yet. We will use Lagrangian code FLAG to calibrate a strength/damage model QBrT (Quasi-Brittle Transition) for SS304L, a variant of stainless steel. We will apply it to the explosives-driven simulations and compare with the previous results.

Mentors: Chris Ticknor (T-1), Beth Lindquist (XTD-NTA), and Ryan Jadrich (XTD-NTA)

Description: As a rule, the behavior of matter strongly depends on its external conditions. An everyday example of this phenomenon is the melting of ice if it is left at room temperature. However, we often need to be able to understand and predict how matter will behave at more extreme conditions than those available on a kitchen counter. Accurate descriptions of the behavior of matter in extreme conditions is important to many areas of study, including earth science and astrophysics. For instance, the iron atoms in the inner core of the earth are arranged into a different structure than those in a cast-iron pan on earth's surface. Furthermore, the behavior of molecules is particularly intriguing (and complicated!) under extreme conditions because bizarre chemical reactions can occur. At the same time, the study of materials in extreme conditions presents challenges in terms of collecting sufficient quantities of high-quality data to inform our physics models. Therefore, we need to propagate uncertainty in the underlying data forward to the model in a process called uncertainty quantification. Possible avenues to be explored in this project include: (i) advanced optimization of models with large, complex data sets, (ii) advanced descriptions of multi-phase behavior of materials such as water, or (iii) machine learning of molecular behavior. All possible projects could lead to the assessment of the modeling quality with uncertainty quantification.

Mentors: Tariq Aslam (T-1) and Eduardo Lozano (T-1)

Description: Computational fluid dynamics are routinely used to simulate the compressible reactive flow in detonating high explosives. When multiple phases are introduced, one must decide on appropriate closure rules to determine the relative phase volumes and energy distribution between the phases at each time step. The next generation of reactive flow models is currently underway and it requires computationally efficient techniques to solve the multiphase closure problem. Working closely with the modeling team, the selected candidates will develop a fast pressure-temperature equilibration method that meets these requirements. They will also conduct code verification and model validation against high-explosive test cases. The outcome of this project will advance the modeling and algorithmic capabilities of existing hydrodynamic codes.

Mentors: Ari Le (XCP-6) and Adam Stanier (T-5)

Description: Magnetic reconnection rearranges the magnetic field line topology in a conducting plasma and allows an explosive release of energy. Examples of phenomena that depend crucially on reconnection are solar and astrophysical flares, substorms in Earth’s magnetosphere that drive the auroras, and disruptive instabilities in magnetic fusion devices. Particle-in-cell simulations enable first-principles studies of plasma heating and particle acceleration during reconnection. For this project, kinetic and hybrid (kinetic ion/fluid electron) versions of LANL’s VPIC code will be used to study how ions are energized during magnetic reconnection in large systems and how the increased pressure of the heated plasma feeds back on the reconnection process.

Mentors: Daniil Svyatsky (T-5), David Moulton (T-5), and Yu Zhang (EES-14)

Description: 

In this project, we would hope to improve the ATS (advanced terrestrial simulator) representation of the preferential flows (macropore pathways) for integrated watershed hydrology to model interaction between subsurface and overland flows in the case of large storm events. The summer student will work on adding new physical processes into the current ATS model using C/C++, GitHub version control, and HPC. This newly-improved model will be used to better understand the hydrologic processes and transport of nutrients in the coupled systems.

Expected project outcomes: Students can gain good modeling experience to simulate hydrologic processes by using a state-of-the-art hydrologic model with its numerical methods and parallel computing. This will be part of a cutting-edge research project to advance our modeling capability for integrating hydrology simulations.

We expect at least a modeling-oriented publication from this study.