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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Science-Driven Data Management for Multi-Tiered Storage (Final Report)

Scientific discovery at the exascale will not be possible without significant new research in the management, storage and retrieval over the long lifespan of the extreme amounts of data that will be produced. Our thesis is that adding application level knowledge about data to guide the actions of the storage system provides substantial benefits to the organization, storage, and access to extreme scale data, resulting in improved productivity for computational science. In this project we will demonstrate novel techniques to facilitate efficient mapping of data objects, even partitioning individual variables, from the user space onto multiple storage tiers, and enable application-guided data reductions and transformations to address capacity and bandwidth bottlenecks. Our goal is to address the associated Input/ Output (I/O) and storage challenges in the context of current and emerging storage landscapes, and expedite insights into mission critical scientific processes.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Area V: Waste Management and Subsurface Science (FY23 Q2)

The objective of Topic Area V is to evaluate the feasibility and safety of an intermediate-depth borehole for disposal of IAEC nuclear waste. The IAEC nuclear waste is currently stored in above ground and/or in near-surface facilities that provide short-term radiological worker safety but do not provide long-term, full security and safeguards protection for the nuclear material. Disposal of the IAEC nuclear waste several hundred meters below the surface in a sealed borehole would provide long-term security and safeguards due to its depth underground, as well as long-term radiological safety, due to its isolation in a stable geological formation.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Machine learning-driven predictive resource management in complex science workflows

Here, the collaborative efforts of large communities in science experiments, often comprising thousands of global members, reflect a monumental commitment to exploration and discovery. Recently, advanced and complex data processing has gained increasing importance in science experiments. Data processing workflows typically consist of multiple intricate steps, and the precise specification of resource requirements is crucial for each step to allocate optimal resources for effective processing. Estimating resource requirements in advance is challenging due to a wide range of analysis scenarios, varying skill levels among community members, and the continuously increasing spectrum of computing options. One practical approach to mitigate these challenges involves initially processing a subset of each step to measure precise resource utilization from actual processing profiles before completing the entire step. While this two-staged approach enables processing on optimal resources for most of the workflow, it has drawbacks such as initial inaccuracies leading to potential failures and suboptimal resource usage, along with overhead from waiting for initial processing completion, which is critical for fast-turnaround analyses. In this context, our study introduces a novel pipeline of machine learning models within a comprehensive workflow management system, the Production and Distributed Analysis (PanDA) system. These models employ advanced machine learning techniques to predict key resource requirements, overcoming challenges posed by limited upfront knowledge of characteristics at each step. Accurate forecasts of resource requirements enable informed and proactive decision-making in workflow management, enhancing the efficiency of handling diverse, complex workflows across heterogeneous resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

NNSA-IAEC Area: Workshop on Nuclear Waste Management and Subsurface Science (8-10 November 2022)

The 7 th workshop of the NNSA-IAEC collaboration was conducted on November 8-10 2022. The workshop was held at the geologic survey of Israel, Jerusalem. The IAEC hosted the workshop. The main topic of the workshop evolved around the results of the characterization borehole drilled in the Yamin plain during 2022 and associated experimental work and modeling results. The new data will be integrated for the applicability analysis of intermediate depth borehole (IDB) disposal for spent fuel in the Yamin plain. The first day of the workshop (November 8 th ) was dedicated to a geological field trip in the Judea desert depicting a similar cross section to that found in the Yamin plain. During the 9 th and 10 th of November the characterization BH results were presented, results of new work on the Miocene Hazeva fm. were presented, and this year’s outcome of the different collaboration groups were given.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

NA-22 Quarterly Report: April-June, 2020

At LLNL we contribute to two projects in NNSA-IAEC Science and Technology Working Group Area V: Basic Science of Waste Management & Subsurface Science. The motivation behind Topic Area V is to evaluate the scientific and safety case for an intermediate borehole to house Israel’s nuclear waste. WM1 (SNL lead) is Thermomechanical Damage (initially called Damage Accumulation and Wellbore Stability). This work focuses on evaluating the damage to the host rock induced by excavation and heat released from a possible radioactive waste repository/borehole. WM2 (LLNL lead) is Radionuclide Facilitated Transport in Carbonate Rock (initially called Colloidal Transport of Radionuclides). This work focuses on better understanding the transport of radionuclides and colloids from a nuclear waste repository/borehole in a vadose zone environment through experimentation and modeling. In the second quarter (this report) our work has been delayed in part due to COVID-19. LLNL went into shelter in place (SIP), minimum safe operations mid-March. Operations at LLNL have been slowly ramping up with 50% of the workforce back onsite in limited capacity and telecommuting widely used. The good news is that our labs opened up the last week in June and experiments are expected to start up again for WM2 Radionuclide Facilitated Transport in July. LLNL’s contribution to WM1 Thermochemical Damage modeling efforts also started back up in late June. We expect to be able to catch up on much of our work by the end of next quarter. The three national laboratories communication on a bi-monthly basis to make sure Area V projects remain on track and we have a monthly conference call with our Israeli counterparts to make sure we are communicating priorities and coordinating project details.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2020

NA-22 Quarterly Report: April-June, 2020

At LLNL we contribute to two projects in NNSA-IAEC Science and Technology Working Group Area V: Basic Science of Waste Management & Subsurface Science. The motivation behind Topic Area V is to evaluate the scientific and safety case for an intermediate borehole to house Israel’s nuclear waste. WM1 (SNL lead) is Thermomechanical Damage (initially called Damage Accumulation and Wellbore Stability). This work focuses on evaluating the damage to the host rock induced by excavation and heat released from a possible radioactive waste repository/borehole. WM2 (LLNL lead) is Radionuclide Facilitated Transport in Carbonate Rock (initially called Colloidal Transport of Radionuclides). This work focuses on better understanding the transport of radionuclides and colloids from a nuclear waste repository/borehole in a vadose zone environment through experimentation and modeling. In the second quarter (this report) our work has been delayed in part due to COVID-19. LLNL went into shelter in place (SIP), minimum safe operations mid-March. Operations at LLNL have been slowly ramping up with 50% of the workforce back onsite in limited capacity and telecommuting widely used. The good news is that our labs opened up the last week in June and experiments are expected to start up again for WM2 Radionuclide Facilitated Transport in July. LLNL’s contribution to WM1 Thermochemical Damage modeling efforts also started back up in late June. We expect to be able to catch up on much of our work by the end of next quarter. The three national laboratories communication on a bi-monthly basis to make sure Area V projects remain on track and we have a monthly conference call with our Israeli counterparts to make sure we are communicating priorities and coordinating project details.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

SIRIUS: Science-Driven Data Management for Multi-Tiered Storage

The data sets being generated by large applications on very large-scale systems are increasing in both size and complexity. At the same time, there are new ways available to store and access these data sets. The goal in this project is to develop software that applications can use to make use of new and existing storage technologies in more sophisticated ways. One challenge in scientific data management is handling ‘hot’ vs ‘cold’ data. Data that is hot is data that is needed (or will be needed soon) in order for the program to continue progressing, while cold data is either output (and so will not be need further during the life of the program) or will not be needed until significantly later in the program’s run. Hot data should be stored in a way that allows fast access. On most systems, economic factors lead to an inverse relationship between storage performance and storage capacity and so fast access storage is limited. This makes it important to correctly place hot and cold data and avoid cold data unnecessarily consuming precious resources. In this reporting period, we addressed this challenge in various ways and at various levels. Data management frameworks offer only limited control to applications in how data is stored. We have added software capabilities for seamlessly moving data between layers of the storage technology using promote and demote functions to existing software frameworks. This gives direct control to applications in deciding what priority data receives. Additionally, we integrated different storage layer management frameworks in order to allow data to be exchanged and moved between storage layers in a consistent way across the application. Further, applications are not always able to directly decide what storage level makes sense for a given piece of data without an understanding of the underlying storage technologies. Data storage frameworks are often positioned to make these sorts of decisions in service of the application. We have added machine-learning based capabilities to data staging frameworks in order to make intelligent decisions about where data should be stored given learning about patterns in previous usage of similar data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Area V: Waste Management and Subsurface Science Q3 Report

The objective of Topic Area V is to evaluate the feasibility and safety of an intermediate-depth borehole for disposal of IAEC nuclear waste. The IAEC nuclear waste is currently stored in above ground and/or in near-surface facilities that provide short-term radiological worker safety but do not provide long-term, full security and safeguards protection for the nuclear material. Disposal of the IAEC nuclear waste several hundred meters below the surface in a sealed borehole would provide long-term security and safeguards due to its depth underground, as well as long-term radiological safety, due to its isolation in a stable geological formation.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2026

Deliberate Motion Analytics Applied to CUAS Sensor Fusion

The Advanced Reactor Safeguards and Security (ARSS) program in the Department of Energy’s Office of Nuclear Energy (DOE-NE) seeks to identify new technology solutions for safeguards and security challenges associated with domestic deployment of advanced nuclear reactors. Research in the ARSS program is investigating alternative physical protection system (PPS) approaches that leverage new detection technologies. This report shows test results from a new form of artificial intelligence (AI) that is called deliberate motion analytics (DMA) when used to spatially and temporally fuse active radar and passive radio frequency (RF) detection that significantly improves detection of uncrewed aircraft systems (UASs). DMA is designed to filter out false positive alarms yet provide highly reliable intrusion detection at nuclear power plants (NPPs) and advanced small modular reactor (ASMR) perimeters. This form of AI is considered to be an enabling technology for security of the future and supports the ARSS investigation of alternative PPSs.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

Efficient Sampling Allocation Procedures for Optimal Quantile Selection

We propose a dynamic sampling allocation and selection paradigm for finding the alternative with the optimal quantile in a Bayesian framework. Myopic allocation policies (MAPs), analogous to existing methods in classic ranking and selection for selecting the alternative with the optimal mean, and computationally efficient selection policies are derived for selecting the alternative with the optimal quantile. Under certain conditions, we prove that the proposed MAPs and selection procedures are consistent, which means that the best quantile would be eventually correctly selected as the sample size goes to infinity. Numerical experiments demonstrate that the proposed schemes can significantly improve the performance.

Computer Science↗
DOE OSTI2023

Residuals-based distributionally robust optimization with covariate information

We consider data-driven approaches that integrate a machine learning prediction model within distributionally robust optimization (DRO) given limited joint observations of uncertain parameters and covariates. Our framework is flexible in the sense that it can accommodate a variety of regression setups and DRO ambiguity sets. We investigate asymptotic and finite sample properties of solutions obtained using Wasserstein, sample robust optimization, and phi-divergence-based ambiguity sets within our DRO formulations, and explore cross-validation approaches for sizing these ambiguity sets. Through numerical experiments, we validate our theoretical results, study the effectiveness of our approaches for sizing ambiguity sets, and illustrate the benefits of our DRO formulations in the limited data regime even when the prediction model is misspecified.

97 MATHEMATICS AND COMPUTING↗