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This summary report provides an overview of all LDRD projects at Argonne that concluded in Fiscal Year 2021. Many projects are funded for multiple years, the initial fiscal year for each project is indicated by the first four digits of the LDRD project number.
This report is the final documentation for the one-year LDRD project 226360: Simulated X-ray Diffraction and Machine Learning for Optimizing Dynamic Experiment Analysis. As Sandia has successfully developed in-house X-ray diffraction tools for study of atomic structure in experiments, it has become increasingly important to develop computational analysis methods to support these experiments. When dynamically compressed lattices and orientations are not known a priori, the identification requires a cumbersome and sometimes intractable search of possible final states. These final states can include phase transition, deformation and mixed/evolving states. Our work consists of three parts: (1) development of an XRD simulation tool and use of traditional data science methods to match XRD patterns to experiments; (2) development of ML-based models capable of decomposing and identifying the lattice and orientation components of multicomponent experimental diffraction patterns; and (3) conducting experiments which showcase these new analysis tools in the study of phase transition mechanisms. Our target material has been cadmium sulfide, which exhibits complex orientation-dependent phase transformation mechanisms. In our current one-year LDRD, we have begun the analysis of high-quality c-axis CdS diffraction data from DCS and Thor experiments, which had until recently eluded orientation identification.
High-enthalpy hypersonic flight represents an application space of significant concern within the current national-security landscape. The hypersonic environment is characterized by high-speed compressible fluid mechanics and complex reacting flow physics, which may present both thermal and chemical nonequilibrium effects. We report on the results of a three-year LDRD effort, funded by the Engineering Sciences Research Foundation (ESRF) investment area, which has been focused on the development and deployment of new high-speed thermochemical diagnostics capabilities for measurements in the high-enthalpy hypersonic environment posed by Sandia's free-piston shock tunnel. The project has additionally sponsored model development efforts, which have added thermal nonequilibrium modeling capabilities to Sandia codes for subsequent design of many of our shock-tunnel experiments. We have cultivated high-speed, chemically specific, laser-diagnostic approaches that are uniquely co-located with Sandia's high-enthalpy hypersonic test facilities. These tools include picosecond and nanosecond coherent anti-Stokes Raman scattering at 100-kHz rates for time-resolved thermometry, including thermal nonequilibrium conditions, and 100-kHz planar laser-induced fluorescence of nitric oxide for chemically specific imaging and velocimetry. Key results from this LDRD project have been documented in a number of journal submissions and conference proceedings, which are cited here. The body of this report is, therefore, concise and summarizes the key results of the project. The reader is directed toward these reference materials and appendices for more detailed discussions of the project results and findings.
This summary report provides an overview of all LDRD projects at Argonne that concluded in Fiscal Year 2022. Many projects are funded for multiple years, the initial fiscal year for each project is indicated by the first four digits of the LDRD project number.
This summary report provides an overview of all LDRD projects at Argonne National Laboratory that concluded in Fiscal Year 2023. Many projects are funded for multiple years, the initial fiscal year for each project is indicated by the first four digits of the LDRD project number.
As Lawrence Livermore National Laboratory’s most significant resource for supporting internally directed research and development, the LDRD Program provides investments in cutting-edge science and technology that allow the Laboratory to attract and retain the world’s most talented scientists and engineers and enables them to expand the frontiers of knowledge and anticipate emerging national security challenges. In this annual report, we summarize how Lawrence Livermore National Laboratory (LLNL) uses LDRD investments to advance our knowledge in strategic science and technology domains, develop our world-class workforce, and foster innovation in key programmatic areas.
Laboratory Directed Research & Development (LDRD) projects at Ames National Laboratory perform compelling, collaborative, and mission-oriented research that accelerates progress in our strategic directions. The aim of these projects is to strengthen Ames National Laboratory’s emerging scientific leaders and core capabilities, to further enhance our scientific and technological vitality by stimulating R&D innovation in support of the Laboratory’s foundational strengths and strategic initiatives, and to build capabilities to respond to rapidly emerging R&D opportunities of clear potential benefit to DOE’s mission. LDRD funding strengthens our core capabilities either by exploiting targets of opportunity in expanding current capabilities, or by exploring and implementing new capabilities that provide scientific advantages and position Ames National Laboratory to be a leader in existing and emerging scientific fields.
This summary report provides an overview of all LDRD projects at Argonne National Laboratory that concluded in Fiscal Year 2024. Many projects are funded for multiple years, the fiscal year for each project is indicated by the first four digits of the LDRD project number.
This summary report provides an overview of all LDRD projects at Argonne National Laboratory that concluded in Fiscal Year 2025. Many projects are funded for multiple years, the fiscal year for each project is indicated by the first four digits of the LDRD project number.
10 minute presentation on results of carbon-carbon manufacturing for EFAS tooling LDRD. Will be presented during the LDRD end of year "open house".
Experiment scripts and analysis tools created for the QUE LDRD. SAND2020-12250 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
In late 2023, Thomas Jefferson National Accelerator Facil- ity (Jefferson Lab) funded a Laboratory Directed Research and Development (LDRD) grant dedicated to investigating the impact of radiation on permanent magnet materials. This research initiative is specifically geared towards assessing materials slated for use in the CEBAF energy upgrade. The experimental approach involves strategically placing perma- nent magnet samples throughout the accelerator, exposing them to varying radiation doses. The simulation code BD- SIM is used to first validate the data and then to simulate the effects on future higher energy passes to study the degra- dation effects on the permanent magnets. In this paper we present the progress of that work.
It is important to gain new understanding of actinides in order to understand the fate of these elements in the environment and the factors that contribute to actinide-lanthanide extraction selectivity. Detailed structural, magnetic, and electronic studies of novel actinide compounds can provide useful information on bonding behavior to guide the development of novel separation and environmental remediation strategies. Development of new actinide compounds remains a relatively open area to study due to the numerous challenges faced when performing chemical synthesis with radioactive materials. In this LDRD, we sought to synthesize and characterize single crystals of novel polyoxometalates that selectively encapsulate plutonium and uranium. We were successful in preparation of [U III Pd II 12 (AsPh) 8 O 32 ] 5⁻ ; the first actinide-based polyoxopalladate.
This report is the final report for the LDRD project "Fast and Robust Linear Solvers using Hierarchical Matrices". The project was a success. We developed two novel algorithms for solving sparse linear systems. We demonstrated their effectiveness on ill-conditioned linear systems from ice sheet simulations. We showed that in many cases, we can obtain near-linear scaling. We believe this approach has strong potential for difficult linear systems and should be considered for other Sandia and DOE applications. We also report on some related research activities in dense solvers and randomized linear algebra.
This SAND report documents the findings of the LDRD project, "Modeling Complex Relationships in Large-Scale Data using Hypergraphs". The project ran from October 2017 through September 2019. The focus of the project was the development and application of hypergraph data analytics to Sandia relational data applications. In this project, we attempted to apply a hypergraph data analysis method—specifically, hypergraph eigenvector centrality—to Sandia mission problems to identify influential entities (people, location, times, etc.) in the data. Unfortunately, the application data led to graph and hypergraph representations containing disconnected components. To date, there are no well-established techniques for applying eigenvector centrality to such graphs and hypergraphs. In this report, we present several heuristics for computing eigenvector centrality for disconnected graphs. We believe this is an important start to understanding how to approach the similar problem for hypergraphs, but this project concluded before we made progress on that problem. The ideas, methods, and suggestions presented here can be used for further research into this challenging problem. We also present our ideas for generating graphs with known degree and centrality distributions. The goal in presenting this work is to identify a procedure for analyzing such graphs once the problem of addressing disconnected components has been addressed. When working with a single data set, this generator can be used to create many instances of graphs that can be used to analyze the robustness of the centrality computations for the original data set. Although the results did not match perfectly in the case of the Facebook Ego dataset used in the experiments presented here, this again represents a good start in the direction of a graph generator for such problems. We note that there are potential trade-offs between how the degree and centrality distributions are fit to the original data and suggested several possible avenues for follow-on research efforts.
The Laboratory Directed Research and Development (LDRD) Program Annual Report for Savannah River National Laboratory for FY2019 focuses on the following project areas: legacy waste cleanup, nuclear materials threat reduction; nuclear deterrent, and secure energy & manufacturing.