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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Domain Specific Language Support for Exascale

Domain-Specific Languages (DSLs) offer an attractive path to Exascale software since they provide expressive power through appropriate abstractions and enable domain-specific optimizations. But the advantages of a DSL compete with the difficulties of implementing a DSL, even for a narrowly defined domain. The DTEC project addresses how a variety of DSLs can be easily implemented to leverage existing compiler analysis and transformation capabilities within the ROSE open source compiler as part of a research program focusing on Exascale challenges. The OSU contributions to the DTEC project are in the area of code generation from high-level DSL descriptions, as well as verification of the automatically-generated code.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

DeFault: DEep‐Learning‐Based FAULT Delineation Using the IBDP Passive Seismic Data at the Decatur CO2 Storage Site

Abstract The carbon capture, utilization, and storage (CCUS) framework is an essential component in reducing greenhouse gas emissions, with its success hinging on the comprehensive knowledge of subsurface geology and geomechanics. Passive seismic event relocation and fault detection offer vital insights into subsurface structures and the ability to monitor fluid migration pathways. Accurate identification and localization of seismic events, however, face significant challenges, including the necessity for high‐quality seismic data and advanced computational methods. To address these challenges, we introduce a novel deep learning method, , specifically designed for passive seismic source relocation and fault delineating for passive seismic monitoring projects. By leveraging data domain‐adaptation, allows us to train a neural network with labeled synthetic data and apply it directly to field data. Using , the passive seismic sources are automatically clustered based on their recording time and spatial locations, and subsequently, faults and fractures are delineated accordingly. We demonstrate the efficacy of on a field case study involving injection related microseismic data from Decatur, Illinois area. Our approach accurately and efficiently relocated passive seismic events, identified faults and could aid in potential damage induced by seismicity. Our results highlight the potential of as a valuable tool for passive seismic monitoring, emphasizing its role in ensuring CCUS project safety. This research bolsters the understanding of subsurface characterization in CCUS, illustrating machine learning’s capacity to refine these methods. Ultimately, our work has significant implications for CCUS technology deployment, an essential strategy in combating climate change. Plain Language Summary In our quest to tackle climate change, we use a strategy known as carbon capture, utilization, and storage (CCUS) to keep greenhouse gases out of the atmosphere. This strategy relies heavily on our ability to understand what's happening deep under the earth's surface. To make sure we store super critical safely, we need to accurately map out the geological structure, especially faults, but this is tough without high‐quality data and complex computer programs. We've developed a new tool called “DeFault,” which uses advanced machine learning to improve how we find and map these underground features. “DeFault” is smart enough to learn from numerically simulated data and then apply what it’s learned to real‐world situations. It groups together seismic activity—tiny tremors and shifts in the earth—based on when and where they happen, which helps us spot where there might be cracks or faults. We tested “DeFault” in Illinois, where CO 2 is injected underground, and it successfully pinpointed where these tremors occurred and mapped out the faults, helping to prevent accidents accurately in the future. Our study shows that “DeFault” will be a powerful ally in making CCUS safer and more effective, especially for the Illinois Basin Decatur Project. Key Points Faults and fractures introduced by carbon storage can be monitored by passive seismicity DeFault algorithm enables an automatic process for accurate and efficient passive seismic event locating and clustering

58 GEOSCIENCES↗
DOE OSTI2023

MILK : a Python scripting interface to MAUD for automation of Rietveld analysis

Modern diffraction experiments ( e.g. in situ parametric studies) present scientists with many diffraction patterns to analyze. Interactive analyses via graphical user interfaces tend to slow down obtaining quantitative results such as lattice parameters and phase fractions. Furthermore, Rietveld refinement strategies ( i.e. the parameter turn-on-off sequences) tend to be instrument specific or even specific to a given dataset, such that selection of strategies can become a bottleneck for efficient data analysis. Managing multi-histogram datasets such as from multi-bank neutron diffractometers or caked 2D synchrotron data presents additional challenges due to the large number of histogram-specific parameters. To overcome these challenges in the Rietveld software Material Analysis Using Diffraction ( MAUD ), the MAUD Interface Language Kit ( MILK ) is developed along with an updated text batch interface for MAUD . The open-source software MILK is computer-platform independent and is packaged as a Python library that interfaces with MAUD . Using MILK , model selection ( e.g. various texture or peak-broadening models), Rietveld parameter manipulation and distributed parallel batch computing can be performed through a high-level Python interface. A high-level interface enables analysis workflows to be easily programmed, shared and applied to large datasets, and external tools to be integrated with MAUD . Through modification to the MAUD batch interface, plot and data exports have been improved. The resulting hierarchical folders from Rietveld refinements with MILK are compatible with Cinema: Debye–Scherrer , a tool for visualizing and inspecting the results of multi-parameter analyses of large quantities of diffraction data. In this manuscript, the combined Python scripting and visualization capability of MILK is demonstrated with a quantitative texture and phase analysis of data collected at the HIPPO neutron diffractometer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Titan on the Red (advanced AI / ML system) [Slides]

Born out of a need to make LANL Weapons Program archival material available to scientists and engineers. Material is the end result of decades of consolidation of mini-libraries and mini archives at LANL. Latest consolidation brought together LANL’s digital archives and physical archives. This houses over 75 years of nuclear weapons research, designs, procedures, videos, photos, and other reports. For the Titan on the Red machine learning project, the system must be able to automatically extract metadata from digitized documents, perform natural-language search, enforce security classification and NTK protocols, be expandable to ingest various data stores (Online Vault, PDMLink, shared drives, SharePoint, etc.), and utilize commercial, public domain, and LANL ontologies.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

FLASH: FPGA-Accelerated Smart Switches with GCN Case Study

Some communication switches, e.g., the Mellanox SHArP and those in the IBM BlueGene clusters, are augmented to process packets at the application level with fixed-function collectives. This approach, however, lacks flexibility, which limits their applicability in diverse and dynamic workloads. Recently, a new type of programmable packet processor, which uses high-level languages, e.g., P4, has emerged as possible candidates. P4-based switches, however, fall short in certain applications, including machine learning, where capabilities not currently supported by P4 are needed. These include more complex calculation, such as sparse computation and fused multiply-accumulate, data-intensive floating point operations, data reuse, and significant memory. The problem addressed here is that such a switch augmentation needs to support: a large amount of state, significant flexible compute capability, and ease of programming, all while maintaining full functionality, including ensuring high throughput, and demonstrating utility. In this work, we propose a programmable look-aside-type accelerator that can be embedded into, or attached to, existing communication switch pipelines and that is capable of processing packets at line-rate. The proposed in-switch accelerator is based on mixing an ISA (subset of RISC-V instructions) with dataflow graphs (found in CGRAs). To augment performance, vector instructions are also supported. To facilitate usability, we have developed a complete toolchain to compile user-provided C/C++ codes to appropriate back-end instructions for configuring the accelerator. While this approach is flexible enough to support various workloads, in this paper, we consider Graph Convolutional Networks (GCNs) as a case study. Experimental results show that this approach considerably improves the performance of distributed GCN applications.

Haghi, Pouya↗
DOE OSTI2019

Development of eXternal Nuclear Reaction Analysis (XNRA) Detection Technique for Quantifying Light Isotope Concentrations

The National Nuclear Security Administration’s Tritium Sustainment Program is responsible for the design, development, demonstration, testing, analysis, and characterization of tritium-producing burnable absorber rods (TPBARs) and their components used to produce tritium for the nation’s strategic stockpile. The FY19 call for proposals included the specific basic science research topic, “Demonstration and evaluation of advanced characterization methods, particularly for quantifying the concentration of light isotopes ( 1 H, 3 H, 3 He, and 4 He, 6 Li and 7 Li) in metal or ceramic matrices”. Last year the same language appeared in the call for proposals, and a project IWO-389859 was awarded to the Ion Beam Lab (IBL) at Sandia-NM which was successful using Elastic Recoil Detection, but in the future could have resulted in tritium contamination that jeopardized other equally important NNSA projects. An alternative approach using deuterium nuclear reaction analysis was proposed and funded in FY2019 which was also successful and eliminated any possibility of contaminating the Ion Beam Laboratory with tritium, and will be described in this report.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

Programming with BIG data in R: Scaling analytics from one to thousands of nodes

Here, we present a tutorial overview showing how one can achieve scalable performance with R. We do so by utilizing several package extensions, including those from the pbdR project. These packages consist of high performance, high-level interfaces to and extensions of MPI, PBLAS, ScaLAPACK, I/O libraries, profiling libraries, and more. While these libraries shine brightest on large distributed platforms, they also work rather well on small clusters and often, surprisingly, even on a laptop with only two cores. Our tutorial begins with recommendations on how to get more performance out of your R code before considering parallel implementations. Because R is a high-level language, a function can have a deep hierarchy of operations. For big data, this can easily lead to inefficiency. Profiling is an important tool to understand the performance of an R code for both serial and parallel improvements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Performance and Architecture Lab Modeling Tool

Analytical application performance models are critical for diagnosing performance-limiting resources, optimizing systems, and designing machines. Creating models, however, is difficult. Furthermore, models are frequently expressed in forms that are hard to distribute and validate. The Performance and Architecture Lab Modeling tool, or Palm, is a modeling tool designed to make application modeling easier. Palm provides a source code modeling annotation language. Not only does the modeling language divide the modeling task into sub problems, it formally links an application's source code with its model. This link is important because a model's purpose is to capture application behavior. Furthermore, this link makes it possible to define rules for generating models according to source code organization. Palm generates hierarchical models according to well-defined rules. Given an application, a set of annotations, and a representative execution environment, Palm will generate the same model. A generated model is a an executable program whose constituent parts directly correspond to the modeled application. Palm generates models by combining top-down (human-provided) semantic insight with bottom-up static and dynamic analysis. A model's hierarchy is defined by static and dynamic source code structure. Because Palm coordinates models and source code, Palm's models are 'first-class' and reproducible. Palm automates common modeling tasks. For instance, Palm incorporates measurements to focus attention, represent constant behavior, and validate models. Palm's workflow is as follows. The workflow's input is source code annotated with Palm modeling annotations. The most important annotation models an instance of a block of code. Given annotated source code, the Palm Compiler produces executables and the Palm Monitor collects a representative performance profile. The Palm Generator synthesizes a model based on the static and dynamic mapping of annotations to program behavior. The model -- an executable program -- is a hierarchical composition of annotation functions, synthesized functions, statistics for runtime values, and performance measurements.

Tallent, Nathan↗
DOE OSTI2025

ChatHPC: Building the Foundations for a Productive and Trustworthy AI-Assisted HPC Ecosystem

ChatHPC democratizes large language models for the high-performance computing (HPC) community by providing the infrastructure, ecosystem, and knowledge needed to apply modern generative AI technologies to rapidly create specific capabilities for critical HPC components while using relatively modest computational resources. Our divide-and-conquer approach focuses on creating a collection of reliable, highly specialized, and optimized AI assistants for HPC based on the cost-effective and fast Code Llama fine-tuning processes and expert supervision. We target major components of the HPC software stack, including programming models, runtimes, I/O, tooling, and math libraries. Thanks to AI, ChatHPC provides a more productive HPC ecosystem by boosting important tasks related to portability, parallelization, optimization, scalability, and instrumentation, among others. With relatively small datasets (on the order of KB), the AI assistants, which are created in a few minutes by using one node with two NVIDIA H100 GPUs and the ChatHPC library, can create new capabilities with Meta’s 7-billion parameter Code Llama base model to produce high-quality software with a level of trustworthiness of up to 90% higher than the 1.8-trillion parameter OpenAI ChatGPT-4o model for critical programming tasks in the HPC software stack.

Young, Aaron [ORNL] (ORCID:0000000254484667)↗
DOE OSTI2015

NSR&D Program Fiscal Year (FY) 2015 Call for Proposals Mitigation of Seismic Risk at Nuclear Facilities using Seismic Isolation

Seismic isolation (SI) has the potential to drastically reduce seismic response of structures, systems, or components (SSCs) and therefore the risk associated with large seismic events (large seismic event could be defined as the design basis earthquake (DBE) and/or the beyond design basis earthquake (BDBE) depending on the site location). This would correspond to a potential increase in nuclear safety by minimizing the structural response and thus minimizing the risk of material release during large seismic events that have uncertainty associated with their magnitude and frequency. The national consensus standard America Society of Civil Engineers (ASCE) Standard 4, Seismic Analysis of Safety Related Nuclear Structures recently incorporated language and commentary for seismically isolating a large light water reactor or similar large nuclear structure. Some potential benefits of SI are: 1) substantially decoupling the SSC from the earthquake hazard thus decreasing risk of material release during large earthquakes, 2) cost savings for the facility and/or equipment, and 3) applicability to both nuclear (current and next generation) and high hazard non-nuclear facilities. Issue: To date no one has evaluated how the benefit of seismic risk reduction reduces cost to construct a nuclear facility. Objective: Use seismic probabilistic risk assessment (SPRA) to evaluate the reduction in seismic risk and estimate potential cost savings of seismic isolation of a generic nuclear facility. This project would leverage ongoing Idaho National Laboratory (INL) activities that are developing advanced (SPRA) methods using Nonlinear Soil-Structure Interaction (NLSSI) analysis. Technical Approach: The proposed study is intended to obtain an estimate on the reduction in seismic risk and construction cost that might be achieved by seismically isolating a nuclear facility. The nuclear facility is a representative pressurized water reactor building nuclear power plant (NPP) structure. Figure 1: Project activities The study will consider a representative NPP reinforced concrete reactor building and representative plant safety system. This study will leverage existing research and development (R&D) activities at INL. Figure 1 shows the proposed study steps with the steps in blue representing activities already funded at INL and the steps in purple the activities that would be funded under this proposal. The following results will be documented: 1) Comparison of seismic risk for the non-seismically isolated (non-SI) and seismically isolated (SI) NPP, and 2) an estimate of construction cost savings when implementing SI at the site of the generic NPP.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

An Integrated Framework for Risk Assessment of Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants: Methodology Advancement and Application

This report documents activities performed by Idaho National Laboratory (INL) during fiscal year (FY) 2024 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, Digital Instrumentation and Control (DI&C) Risk Assessment project. The goal of the RISA Pathway is to optimize safety margins and minimize uncertainties to achieve economic efficiencies while maintaining high levels of safety. This is accomplished by providing scientific basis to better represent safety margins and factors that contribute to cost and safety, and by developing new technologies that reduce operating costs. The research efforts for FY 2024 encompass methodology refinement and exploration. The efforts include: (1) The implementation of a natural language processing tool to expedite key aspects of the reliability analysis methods developed by INL; (2) advances to support intersystem CCF analysis by providing guidance for and identification of coupling mechanisms that may contribute to CCF; (3) the investigation of how generative artificial intelligence tools can aid in hazard analysis and diversity and defense in depth (i.e., D3) assessments; (4) Industry collaboration, allowing the demonstration of and INL's risk assessment tools to support risk assessment of DI&C systems at early and late stages of development; (4) a roadmap for the development of a software for each of INL's risk assessment tools; (5) The development of a theory and methodology manual for a risk quantification methodology; (6) the development of a reliability analysis for machine learning (ML)-integrated control systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Provider Perspectives: Identification and Follow-up of Infants who Are Deaf or Hard of Hearing

Objective Without timely screening, diagnosis, and intervention, hearing loss can cause significant delays in a child's speech, language, social, and emotional development. In 2019, Texas had nearly twice the average rate of loss to follow-up (LFU) or loss to documentation (LTD; i.e., missing documentation of services received) among infants who did not pass their newborn hearing screening compared to the United States overall (51.1 vs. 27.5%). We aimed to identify factors contributing to LFU/LTD among infants who do not pass their newborn hearing screening in Texas. Study Design Data were collected through semistructured qualitative interviews with 56 providers along the hearing care continuum, including hospital newborn hearing screening program staff, audiologists, primary care physicians, and early intervention (EI) program staff located in three rural and urban public health regions in Texas. Following recording and transcription of the interviews, we used qualitative data analysis software to analyze themes using a conventional content analysis approach. Results Frequently cited barriers included problems with family access to care, difficulty contacting patients, problems with communication between providers and referrals, lack of knowledge among providers and parents, and problems using the online reporting system. Providers in rural areas more often mentioned problems with family access to care and contacting families compared to providers in urban areas. Conclusion These findings provide insight into strategies that public health professionals and health care providers can use to work together to help further increase the number of children identified early who may benefit from EI services. Key Points

Obstetrics & Gynecology↗
DOE OSTI2023

A Task Based Approach for Co-Scheduling Ensemble Workloads on Heterogeneous Nodes

Scientific workflows consist of multiple, connected applications, with data and results flowing from one to another in a pipeline. Traditionally, such workflows are executed in sequential order, storing intermediate data in storage disks. Co-scheduling application workflows concurrently on the same compute nodes would greatly reduce the cost of moving data to/from storage and allow real-time analysis of intermediate results. Nevertheless, most parallel programming runtimes do not allow seamless integration of various applications in a scientific workflow, in part due to the complexity of managing data and resources. The situation is even more complicated for heterogeneous systems. In this work we extend the Minos Computing Library (MCL) runtime to accelerate pipe-lined and parallel workloads where multiple applications are running in the same system. MCL’s asynchronous task library and runtime dynamically manages resources to allow co-scheduling of multiple processes sharing heterogeneous resources. In addition, we design a custom ex- tension of the Open Compute Language (OpenCL) to enable multiple processes to share device memory. We enable MCL to coordinate these shared buffers to allow for easy, fast data sharing between applications. Using malleable micro-benchmarks and two application workflows that combine scientific simulation and AI-based analysis, we show that our method outperforms traditional approaches.

Index Terms—Parallel systems, Scheduling and Task ↗
DOE OSTI2017

A framework for modeling and optimizing dynamic systems under uncertainty

Algebraic modeling languages (AMLs) have drastically simplified the implementation of algebraic optimization problems. However, there are still many classes of optimization problems that are not easily represented in most AMLs. These classes of problems are typically reformulated before implementation, which requires significant effort and time from the modeler and obscures the original problem structure or context. In this work we demonstrate how the Pyomo AML can be used to represent complex optimization problems using high-level modeling constructs. We focus on the operation of dynamic systems under uncertainty and demonstrate the combination of Pyomo extensions for dynamic optimization and stochastic programming. We use a dynamic semibatch reactor model and a large-scale bubbling fluidized bed adsorber model as test cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

BuildingSync (R) in Action: Example Implementations

Interoperability is a particular challenge in building energy auditing, in which tools and data requirements may vary widely depending upon locale or intended use of the audit result. There are, however, many reasons to represent the data in a common format. BuildingSync (R) is such an effort to develop a standardized language to flexibly represent and exchange building energy auditing data between software tools. The schema has been developed with extensive collaboration between the United States Department of Energy (DOE) and industry experts. BuildingSync facilitates a consistent history of energy audit data across the life of a building or a group of buildings, and thus enables lower costs and higher energy performance results. It also facilitates easier aggregation and analysis of audits conducted by different companies using different software. This paper presents two examples of building energy analysis efforts that are utilizing BuildingSync - (1) BayREN Integrated Commercial Retrofits (BRICR), a utility custom incentive program at the Bay Area Regional Energy Network (BayREN) that uses BuildingSync to exchange building data, and (2) New York City's ordinance on benchmarking and auditing data. These efforts utilize BuildingSync in conjunction with other DOE-sponsored tools, such as the Asset Score Reporting Platform, SEED Platform, and OpenStudio. Using the lessons learned from these efforts, the paper also makes recommendations for applicability to future projects that may benefit from BuildingSync.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Uranium Enrichment Plant Characteristics - A Training Manual for the IAEA

One of the primary objectives of the International Atomic Energy Agency (IAEA) Safeguards Training Program is to help new inspectors gain knowledge and develop skills that will be required for them to carry out their duties. The formal instructional program for inspectors within the Agency begins with the Introductory Course on Agency Safeguards (ICAS). Time and resource constraints dictate that this course is devoted specifically to safeguards-related information with the assumption that the students have a basic familiarity with nuclear technology. In some cases, a new inspector’s prior experience may have been specialized to the extent that he is unfamiliar with the basic technology of some of the types of nuclear facilities he will be inspecting. In other cases, a student may find it useful to review the English vocabulary for technical terms he may have encountered in another language. A glossary of important terms is provided in Appendix A.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Adapting to Artificial Intelligence

There is no need to be afraid of the emerging advances in artificial intelligence, says Argonne’s Marius Stan, Program Lead for Intelligent Materials Design. Although artificial intelligence may upend the job market in some ways, he says, it may also create new industries and positions as employees begin to designing and making robots. “It’s the problem and the solution,” Stan says. Meanwhile, artificial intelligence is playing a significant role in defining new research methods that bring us to resolution or discovery more quickly than ever before. Argonne is using or developing many of these technologies as AI extends to new branches of science and technology, sprouting new buds that represent various facets of human function and cognition — from learning to language processing to visual recognition.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Backend-agnostic, Quantum-classical Framework for Simulations of Chemistry in C ++

As quantum computing hardware systems continue to advance, the research and development of performant, scalable, and extensible software architectures, languages, models, and compilers is equally as important to bring this novel coprocessing capability to a diverse group of domain computational scientists. For the field of quantum chemistry, applications and frameworks exist for modeling and simulation tasks that scale on heterogeneous classical architectures, and we envision the need for similar frameworks on heterogeneous quantum-classical platforms. Furthermore, we present the XACC system-level quantum computing framework as a platform for prototyping, developing, and deploying quantum-classical software that specifically targets chemistry applications. We review the fundamental design features in XACC, with special attention to its extensibility and modularity for key quantum programming workflow interfaces and provide an overview of the interfaces most relevant to simulations of chemistry. A series of examples demonstrating some of the state-of-the-art chemistry algorithms currently implemented in XACC are presented, while also illustrating the various APIs that would enable the community to extend, modify, and devise new algorithms and applications in the realm of chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗