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Recommendations for Improving ASC Data Management

The Advanced Simulation & Computing (ASC) Program started in 1995 and has an essential mission that requires data stewardship for massive amounts of high performance computing data. This report provides general user requirements as determined through interviews of key users, describes the design attributes for high performance computing storage systems, and identifies improvements that would enable users to better manage their data.

97 MATHEMATICS AND COMPUTING↗

Assessment of Data-Management Infrastructure Needs for Production Use of Advanced Machine Learning and Artificial Intelligence: Tri-Lab Level II Milestone (8554)

A robust data-management infrastructure is a key enabler for National Security Enterprise (NSE) capabilities in artificial intelligence and machine learning. This document describes efforts from a team of researchers at Sandia National Laboratories, Los Alamos National Laboratory, and Livermore National Laboratory to complete ASC Level II milestone #8854 “Assessment of Data-Management Infrastructure Needs for Production use of Advanced Machine learning and Artificial Intelligence.”

97 MATHEMATICS AND COMPUTING↗

Data Services for Visualization and Analysis - ASC Level II Milestone (7186)

A new in transit Data Service is presented and compared to the traditional file-based workflow and the newly refactored in situ Catalyst workflow. Each workflow is enabled by the IOSS mesh interface equipped with data management layers for Exodus and CGNS (file-based), Catalyst (in situ), and FAODEL (in transit). FAODEL is a distributed object store that can transmit data across MPI allocations. Catalyst is a Para View-based visualization capability developed as part of the CSSE Data Services effort. The workflows considered here take SPARC data into Catalyst for visualization post-processing. Although still in unoptimized form, we show that the in transit approach is a viable alternative to file-based and in situ workflows and offers several advantages to both simulation and post-processing developers. Since IOSS is a mature interface with wide adoption across Sandia and externally, each workflow can be reconfigured to use different simulations that generate mesh data and post-processing tools that consume it.

97 MATHEMATICS AND COMPUTING↗

ASC Data Science Infrastructure Project June 2023 [Slides]

Data Science Infrastructure (DSI) Project offers a standardized but flexible way of storing data and associated metadata to support AI/ML and data intensive scientific workflows: Establish requirements via use case interviews; Develop DSI framework to support flexible, secure, and accessible data stores tailored to researcher needs; Augment existing institutional frameworks for code management, performance testing and file indexing via automated DSI interfaces; Deploy DSI framework to support key use cases in collaboration with domain scientists.

97 MATHEMATICS AND COMPUTING↗

Feasibility study ASCS remote sensing/compliance determination system

A short-term technical study was performed by the MSC Earth Observations Division to determine the feasibility of the proposed Agricultural Stabilization and Conservation Service Automatic Remote Sensing/Compliance Determination System. For the study, the term automatic was interpreted as applying to an automated remote-sensing system that includes data acquisition, processing, and management.

Duggan, I. E.↗

Spiner vs EOSPAC6: capabilities, performance, and accuracy considerations

The Cross-Cutting Capabilities Project (XCAP) has significant interest in collecting and implementing a common set of libraries to be used across the various production hydro-codes at LANL. A common set of libraries will help facilitate comparisons between codes. In addition to minimizing variables for comparisons and physics validation, efforts toward optimization and porting of packages for future architectures will be more efficient. As part of this push Singularity is being considered as an inclusive materials interface library. One crucial part of this library is the importing, inverting, and interpolation of equation of state (EOS) data. Currently the Lagrangian Applications Project (LAP) and Safety Applications Project (SAP) are using EOSPAC6 for delivery EOS data. The Eulerian Applications Project (EAP) has historically used TEOS and more recently an implementation of Singularly via a package called Spiner for EOS interpolation. Singularity currently has the option of using several analytical EOS models, directly employing SESAME via Spiner, or directly using SESAME via EOSPAC6 (not optimized yet). Assuming Singularity moves forward as a common platform for the implementation of materials models, a decision will need to be made on which EOS interpolation package XCAP should move forward with, given resources and people are finite. We will attempt to address pros and cons of Spiner and EOSPAC6 and the trade-offs that should be considered during the decision making process. This report is intended as an ASC-PEM-EOS perspective on what is needed in an EOS interpolation package. Performance and Accuracy sections will mostly address data from comparison studies in LA-UR-22-22699.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

The Ristra Project: FY20/21 Milestone Report

The ASC Advanced Technology Development and Mitigation (ATDM) sub-program was established in 2014 to develop new simulation tools operating on exascale-class computers to serve NNSA (see Appendix B). Over the course of ATDM, LANL management have set a strategy for exascale-class application codes that follows two supportive and mutually risk-mitigating paths: evolution for established production integrated design codes (IDCs) – with a strong pedigree within the user community – based upon existing programming paradigms(MPI+X); and a new start ATDM project, Ristra, a high-risk/high-reward push for a next-generation multi-physics, multi-scale simulation toolkit based on emerging advanced programming systems(with an initial focus on data-flow task-based models exemplified by Legion). The role of Ristra as the high-risk/high-reward path for LANL’s codes was fully consistent with the goals of ATDM as described in Appendix B, in particular its emphasis on evolving ASC capabilities through novel computing programming models and computing technologies.

97 MATHEMATICS AND COMPUTING↗

NASA Glenn Research Center Support of the ASRG Project

A high efficiency radioisotope power system is being developed for long-duration NASA space science missions. The U.S. Department of Energy (DOE) managed a flight contract with Lockheed Martin Space Systems Company (LMSSC) to build Advanced Stirling Radioisotope Generators (ASRGs), with support from NASA Glenn Research Center (GRC). Sunpower Inc. held two parallel contracts to produce Advanced Stirling Convertors (ASCs), one with DOELockheed Martin to produce ASC-F flight units, and one with GRC for the production of ASC-E3 engineering unit pathfinders that are built to the flight design. In support of those contracts, GRC provided testing, materials expertise, government furnished equipment, inspections, and related data products to DOELockheed Martin and Sunpower. The technical support includes material evaluations, component tests, convertor characterization, and technology transfer. Material evaluations and component tests have been performed on various ASC components in order to assess potential life-limiting mechanisms and provide data for reliability models. Convertor level tests have been used to characterize performance under operating conditions that are representative of various mission conditions. Technology transfers enhanced contractor capabilities for specialized production processes and tests. Despite termination of flight ASRG contract, NASA continues to develop the high efficiency ASC conversion technology under the ASC-E3 contract. This paper describes key government furnished services performed for ASRG and future tests used to provide data for ongoing reliability assessments.

Technology Transfer↗

NASA Glenn Research Center Support of the Advanced Stirling Radioisotope Generator Project

A high-efficiency radioisotope power system was being developed for long-duration NASA space science missions. The U.S. Department of Energy (DOE) managed a flight contract with Lockheed Martin Space Systems Company to build Advanced Stirling Radioisotope Generators (ASRGs), with support from NASA Glenn Research Center. DOE initiated termination of that contract in late 2013, primarily due to budget constraints. Sunpower, Inc., held two parallel contracts to produce Advanced Stirling Convertors (ASCs), one with Lockheed Martin to produce ASC-F flight units, and one with Glenn for the production of ASC-E3 engineering unit "pathfinders" that are built to the flight design. In support of those contracts, Glenn provided testing, materials expertise, Government-furnished equipment, inspection capabilities, and related data products to Lockheed Martin and Sunpower. The technical support included material evaluations, component tests, convertor characterization, and technology transfer. Material evaluations and component tests were performed on various ASC components in order to assess potential life-limiting mechanisms and provide data for reliability models. Convertor level tests were conducted to characterize performance under operating conditions that are representative of various mission conditions. Despite termination of the ASRG flight development contract, NASA continues to recognize the importance of high-efficiency ASC power conversion for Radioisotope Power Systems (RPS) and continues investment in the technology, including the continuation of the ASC-E3 contract. This paper describes key Government support for the ASRG project and future tests to be used to provide data for ongoing reliability assessments.

Technology Transfer↗

Advanced Stirling Convertor Control Unit Testing at NASA Glenn Research Center in the Radioisotope Power Systems System Integration Laboratory

Future NASA missions could include long-duration flyby, orbital, lander, or rover applications where generating power from sunlight may be limited. Radioisotope power systems (RPSs) provide a dependable power source for missions where inadequate sunlight or operational requirements make other power systems impractical. Over the past 16 years, NASA Glenn Research Center has been supporting the development of RPSs. The advanced Stirling radioisotope generator (ASRG) utilized a pair of advanced Stirling convertors (ASCs). Although flight development of the ASRG has been canceled, much of the technology and hardware continued development and testing to guide future activities. Specifically, a controller for the convertor(s) is an integral part of a Stirling-based RPS. For the ASRG design, the controller maintains stable operation of the convertors, regulates the alternating current produced by the linear alternator of the convertor, provides a specified direct-current output voltage for the spacecraft, and synchronizes the piston motion of the two convertors to minimize vibration as well as manage and maintain operation with a stable piston amplitude and hot-end temperature. It not only provides power to the spacecraft but also must regulate convertor operation to avoid damage to internal components and maintain safe thermal conditions after fueling. Lockheed Martin Coherent Technologies, Inc., has designed, developed, and tested an ASC control unit engineering development unit (ACU EDU) to support this effort. GRC used the ACU EDU as part of its nonnuclear representation of a RPS that also consists of a Dual advanced Stirling convertor simulator (DASCS), and associated support equipment to perform a test in the Radioisotope Power Systems System Integration Laboratory (RSIL). The RSIL was designed and built with flexibility to evaluate hardware utilizing RPS technology. The RSIL provides insight into the electrical interactions between as many as three radioisotope power generators, associated control strategies, and typical electric system loads. The first phase of testing included a DASCS that was developed by Johns Hopkins University Applied Physics Laboratory and simulates the operation and electrical behavior of a pair of ASCs in real time via a combination of hardware and software. Testing included the following spacecraft electrical energy storage configurations: capacitor, battery, and supercapacitor. Testing of the DASCS and ACU in each energy storage configuration included simulation of a typical mission profile and transient voltage and current data during load turnon and turnoff. Testing for these devices also included the initiation of several system faults such as short circuits, electrical bus overvoltage, undervoltage, and a "dead bus" recovery to restore normal power operations. The goal of this testing was to verify operation of the ACU(s) when connected to a spacecraft electrical bus. The results of these tests are presented here.

General↗

Advanced Stirling Convertor Control Unit Testing at NASA Glenn Research Center in the Radioisotope Power Systems System Integration Laboratory

Future NASA missions could include establishing Lunar or Martian base camps, exploring Jupiters moons and travelling beyond where generating power from sunlight may be limited. Radioisotope Power Systems (RPS) provide a dependable power source for missions where inadequate sunlight or operational requirements make other power systems impractical. Over the past decade, NASA Glenn Research Center (GRC) has been supporting the development of RPSs. The Advanced Stirling Radioisotope Generator (ASRG) utilized a pair of Advanced Stirling Convertors (ASC). While flight development of the ASRG has been cancelled, much of the technology and hardware continued development and testing to guide future activities. Specifically, a controller for the convertor(s) is an integral part of a Stirling-based RPS. For the ASRG design, the controller maintains stable operation of the convertors, regulates the alternating current produced by the linear alternator of the convertor, provides a specified direct current output voltage for the spacecraft, synchronizes the piston motion of the two convertors in order to minimize vibration as well as manage and maintain operation with a stable piston amplitude and hot end temperature. It not only provides power to the spacecraft but also must regulate convertor operation to avoid damage to internal components and maintain safe thermal conditions after fueling. Lockheed Martin Coherent Technologies has designed, developed and tested an Engineering Development Unit (EDU) Advanced Stirling Convertor Control Unit (ACU) to support this effort. GRC used the ACU EDU as part of its non-nuclear representation of a RPS which also consists of a pair of Dual Advanced Stirling Convertor Simulator (DASCS), and associated support equipment to perform a test in the Radioisotope Power Systems System Integration Laboratory (RSIL). The RSIL was designed and built to evaluate hardware utilizing RPS technology. The RSIL provides insight into the electrical interactions between as many as 3 radioisotope power generators, associated control strategies, and typical electric system loads. The first phase of testing included a DASCS which was developed by Johns Hopkins UniversityApplied Physics Laboratory and simulates the operation and electrical behavior of a pair of ASCs in real time via a combination of hardware and software. Testing included the following spacecraft electrical energy storage configurations: capacitive, battery, and supercapacitor. Testing of the DASCS and ACU in each energy storage configuration included simulation of a typical mission profile, and transient voltage and current data during load turn-on/turn-off. Testing for these devices also included the initiation of several system faults such as short circuits, electrical bus over-voltage, under-voltage and a dead bus recovery to restore normal power operations. The goal of this testing was to verify operation of the ACU(s) when connected to a spacecraft electrical bus.

Dugala, Gina M.↗

Experimental Creep Life Assessment for the Advanced Stirling Convertor Heater Head

The United States Department of Energy is planning to develop the Advanced Stirling Radioisotope Generator (ASRG) for the National Aeronautics and Space Administration (NASA) for potential use on future space missions. The ASRG provides substantial efficiency and specific power improvements over radioisotope power systems of heritage designs. The ASRG would use General Purpose Heat Source modules as energy sources and the free-piston Advanced Stirling Convertor (ASC) to convert heat into electrical energy. Lockheed Martin Corporation of Valley Forge, Pennsylvania, is integrating the ASRG systems, and Sunpower, Inc., of Athens, Ohio, is designing and building the ASC. NASA Glenn Research Center of Cleveland, Ohio, manages the Sunpower contract and provides technology development in several areas for the ASC. One area is reliability assessment for the ASC heater head, a critical pressure vessel within which heat is converted into mechanical oscillation of a displacer piston. For high system efficiency, the ASC heater head operates at very high temperature (850 C) and therefore is fabricated from an advanced heat-resistant nickel-based superalloy Microcast MarM-247. Since use of MarM-247 in a thin-walled pressure vessel is atypical, much effort is required to assure that the system will operate reliably for its design life of 17 years. One life-limiting structural response for this application is creep; creep deformation is the accumulation of time-dependent inelastic strain under sustained loading over time. If allowed to progress, the deformation eventually results in creep rupture. Since creep material properties are not available in the open literature, a detailed creep life assessment of the ASC heater head effort is underway. This paper presents an overview of that creep life assessment approach, including the reliability-based creep criteria developed from coupon testing, and the associated heater head deterministic and probabilistic analyses. The approach also includes direct benchmark experimental creep assessment. This element provides high-fidelity creep testing of prototypical heater head test articles to investigate the relevant material issues and multiaxial stress state. Benchmark testing provides required data to evaluate the complex life assessment methodology and to validate that analysis. Results from current benchmark heater head tests and newly developed experimental methods are presented. In the concluding remarks, the test results are shown to compare favorably with the creep strain predictions and are the first experimental evidence for a robust ASC heater head creep life.

Krause, David L.↗

Advancing Asset Management in Water Infrastructure Systems

Aging water system infrastructure, including drinking water, wastewater, and stormwater, poses a growing challenge for utilities and municipalities. These water systems have well documented challenges with respect to their age, condition, and level of service. ASCE annual report cards consistently rate these infrastructure systems in the United States as underfunded, overcapacity, or past service life (ASCE 2025 Report Card). For example, Chini and Stillwell (2017) estimated that the mean water loss in drinking water systems, i.e., non-revenue water, is approximately 16% across the United States. These concerns are not just relegated to the United States, with Courtenay, British Columbia, identifying 17% of their water main pipes as in a ‘poor’ condition state, defined as a category condition 5 out of 5 (City of Courtenay, 2024). These cases illustrate the challenges utilities are facing to manage extensive networks of infrastructure to deliver a consistent and high level of service. For buried infrastructure such as water systems, studies suggest that preventative interventions can lead to lower maintenance costs and fewer service disruptions (Mazumder et al, 2018; Li et al, 2014). The demonstrated need and benefit of appropriately applied asset management is juxtaposed against the relatively sparse literature that evaluates water systems within an asset management construct. Since 2020, just 37 papers specifically reference asset management in the Journal of Water Resources Planning and Management. Of those, only a few specifically look to develop strategies for improved asset management. Therefore, we highlight four key research areas that represent opportunities for advancement of asset management research for water systems. First, advances in condition assessment and forecasting are needed to better estimate asset deterioration using diverse datasets. Second, machine learning (ML) and artificial intelligence (AI) hold promise for predictive maintenance and investment prioritization, though questions of generalizability and model transparency remain. Third, applying a value of information framework can guide utilities in making cost-effective sensor deployment and data collection decisions, to direct monitoring strategies towards data-informed asset management decisions. Finally, integrated infrastructure management is critical, requiring coordinated planning with other infrastructure systems and stakeholder engagement to reduce costs and enhance service delivery.

Chini, Christopher M.↗

Advanced Simulation and Computing: ASC FY24 Implementation Plan

The DOE National Nuclear Security Administration (NNSA) Stockpile Stewardship Program (SSP) is an integrated technical program for maintaining the safety, security, and reliability of the U.S. nuclear stockpile. The SSP incorporates nuclear test data, computational modeling and simulation, and experimental facilities to advance understanding of nuclear weapons. The suite of data analyzed comes from activities including previous nuclear tests, stockpile surveillance, experimental research, and development and engineering programs. This integrated national program requires the continued use of experimental facilities and the computational capabilities to support the SSP missions. These component parts, in addition to an appropriately scaled production capability, enable NNSA to support stockpile requirements. The ultimate goal of the SSP, and thus of the Advanced Simulation and Computing (ASC) program, is to ensure that the U.S. maintains a safe, secure, and effective strategic deterrent. The ASC program is a cornerstone of the SSP, providing simulation capabilities and computational resources to support the annual stockpile assessment and certification process, study advanced nuclear weapons design and manufacturing processes, analyze accident scenarios and weapons aging, and provide the tools to enable stockpile Life Extension Programs (LEPs) and the resolution of Significant Finding Investigations (SFIs). This work requires a balance of resources, including technical staff, hardware, simulation software, and computer science solutions. The ASC program focuses on increasing the predictive capabilities in a three-dimensional (3D) simulation environment while maintaining support to the SSP. The Program continues to improve its unique tools for understanding and solving progressively more difficult stockpile problems (sufficient resolution, dimensionality, and scientific details), and quantifying critical margins and uncertainties. Resolving each issue requires increasingly difficult analyses because the aging process has progressively moved the stockpile further from the original test base. While the focus remains on the U.S. nuclear weapons program, where possible, the Program also enables the use of high-performance computing (HPC) and simulation tools to address broader national security needs, such as foreign nuclear weapon assessments and nuclear counterterrorism. The 2022 Nuclear Posture Review (NPR) calls for NNSA to “deliver a modern, adaptive nuclear security enterprise based on an integrated strategy for risk management, production-based resilience, science and technology innovation, and workforce initiatives.” Furthermore, “NNSA will establish a Science and Technology Innovation Initiative to accelerate the integration of science and technology (S&T) throughout its activities.” Executing this strategy necessitates the continued emphasis on developing and sustaining high-quality scientific and engineering staff, as well as supporting computational and experimental capabilities. These components constitute the foundation of the nuclear weapons program. The continued success of the SSP and LEPs is predicated upon the ability to credibly certify the stockpile, without a return to underground nuclear tests (UGTs). Shortly after the nuclear test moratorium entered into force in 1992, the Accelerated Strategic Computing Initiative (ASCI) was established to provide an extensive simulation capability to underpin stockpile certification. While computing and simulation have always been essential to the success of the nuclear weapons program, the program goal of ASCI was to execute NNSA’s vision of using these tools in support of the stockpile stewardship mission. The ASCI program was essential to the successful demonstration of the SSP, providing critical nuclear weapons simulation and modeling capabilities. ASCI officially evolved into the ASC program in fiscal year (FY) 2005, but the mission remains essentially the same: provide the simulation and computational capabilities that underpin the ability to maintain a safe, secure, effective nuclear weapon stockpile, without returning to underground nuclear testing. The capabilities that the ASC program provides at the national laboratories play a vital role in the nuclear security enterprise and are necessary for fulfilling the stockpile stewardship and life extension requirements outlined for NNSA. The Program develops modern simulation tools that provide insights into stockpile aging issues, provide the computational and simulation tools that enable designers and analysts to certify the current stockpile and life-extended nuclear weapons, and inform the decision-making process when any modifications in nuclear warheads or the associated manufacturing processes are deemed necessary. Furthermore, ASC is enhancing the predictive simulation capabilities that are essential to evaluate weapons effects, design experiments, and ensure test readiness. The ASC program continues to improve its unique tools to solve stockpile problems— with a focus on sufficient resolution, dimensionality, and scientific detail—to enable Quantification of Margins and Uncertainties (QMU) and to resolve the increasingly difficult analyses needed for stockpile stewardship. The needs of the Stockpile Management and Production Modernization programs (formerly Directed Stockpile Work) also drive the requirements for simulation and computational resources. These requirements include planned LEPs, stockpile support activities, and mitigation efforts against the potential for technical surprise. All of the weapons within the current stockpile are in some stage of the life extension process. The simulation and computational capabilities are crucial for successful execution of these life extensions and for ensuring NNSA can certify these life-extended weapons without conducting a UGT.

97 MATHEMATICS AND COMPUTING↗

Viability of S3 Object Storage for the ASC Program at Sandia

Recent efforts at Sandia such as DataSEA are creating search engines that enable analysts to query the institution’s massive archive of simulation and experiment data. The benefit of this work is that analysts will be able to retrieve all historical information about a system component that the institution has amassed over the years and make better-informed decisions in current work. As DataSEA gains momentum, it faces multiple technical challenges relating to capacity storage. From a raw capacity perspective, data producers will rapidly overwhelm the system with massive amounts of data. From an accessibility perspective, analysts will expect to be able to retrieve any portion of the bulk data, from any system on the enterprise network. Sandia’s Institutional Computing is mitigating storage problems at the enterprise level by procuring new capacity storage systems that can be accessed from anywhere on the enterprise network. These systems use the simple storage service, or S3, API for data transfers. While S3 uses objects instead of files, users can access it from their desktops or Sandia’s high-performance computing (HPC) platforms. S3 is particularly well suited for bulk storage in DataSEA, as datasets can be decomposed into object that can be referenced and retrieved individually, as needed by an analyst. In this report we describe our experiences working with S3 storage and provide information about how developers can leverage Sandia’s current systems. We present performance results from two sets of experiments. First, we measure S3 throughput when exchanging data between four different HPC platforms and two different enterprise S3 storage systems on the Sandia Restricted Network (SRN). Second, we measure the performance of S3 when communicating with a custom-built Ceph storage system that was constructed from HPC components. Overall, while S3 storage is significantly slower than traditional HPC storage, it provides significant accessibility benefits that will be valuable for archiving and exploiting historical data. There are multiple opportunities that arise from this work, including enhancing DataSEA to leverage S3 for bulk storage and adding native S3 support to Sandia’s IOSS library.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗