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All sources NASA NTRS DOE OSTI

DOE OSTI2014

ORBiT - The Oak Ridge Biosurveillance Toolkit

We describe ORBiT, the Oak Ridge Biosurveillance Toolkit, the objective of which is to provide 1) forecasting and early warning, and 2) an extensible data analytics platform for biosurveillance by enabling the use of traditional and non-traditional datasets, consisting of heterogeneous data types and modalities. ORBiT includes a data collection interface, analytic components, higher-order statistical tools, and a visual front-end with visual analytics. ORBiT is implemented as a distributed analytic platform consisting of a software stack atop of Hadoop and uses Titan, a distributed graph database as a backend for data storage.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Cyber-Physical System Implementation for Manufacturing With Analytics in the Cloud Layer

Effective and efficient modern manufacturing operations require the acceptance and incorporation of the fourth industrial revolution, also known as Industry 4.0. Traditional shop floors are evolving their production into smart factories. To continue this trend, a specific architecture for the cyber-physical system is required, as well as a systematic approach to automate the application of algorithms and transform the acquired data into useful information. This work makes use of an approach that distinguishes three layers that are part of the existing Industry 4.0 paradigm: edge, fog, and cloud. Each of the layers performs computational operations, transforming the data produced in the smart factory into useful information. Trained or untrained methods for data analytics can be incorporated into the architecture. A case study is presented in which a real-time statistical control process algorithm based on control charts was implemented. The algorithm automatically detects changes in the material being processed in a computerized numerical control (CNC) machine. The algorithm implemented in the proposed architecture yielded short response times. The performance was effective since it automatically adapted to the machining of aluminum and then detected when the material was switched to steel. The data were backed up in a database that would allow traceability to the line of g-code that performed the machining.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Autonomous Energy Grids

Current frameworks to monitor, control, and optimize large-scale energy systems are becoming increasingly inadequate because of significantly high penetration levels of variable generation and distributed energy resources being integrated into electric power systems; the deluge of data from pervasive metering of energy grids; and a variety of new market mechanisms, including multilevel ancillary services. This paper outlines the concept of autonomous energy grids (AEGs). These systems are supported by a scalable, reconfigurable, and self-organizing information and control infrastructure, are extremely secure and resilient (self-healing), and can self-optimize in real time to ensure economic and reliable performance while systematically integrating energy in all forms. AEGs rely on cellular building blocks that can self-optimize when isolated from a larger grid and participate in optimal operation when interconnected to a larger grid. This paper describes the key concepts and research necessary in the broad domains of optimization theory, control theory, big data analytics, and complex system theory and modeling to realize the AEG vision.

autonomous energy grids↗

DOE OSTI2017

Autonomous Energy Grids: Preprint

With much higher levels of distributed energy resources - variable generation, energy storage, and controllable loads just to mention a few - being deployed into power systems, the data deluge from pervasive metering of energy grids, and the shaping of multi-level ancillary-service markets, current frameworks to monitoring, controlling, and optimizing large-scale energy systems are becoming increasingly inadequate. This position paper outlines the concept of 'Autonomous Energy Grids' (AEGs) - systems that are supported by a scalable, reconfigurable, and self-organizing information and control infrastructure, can be extremely secure and resilient (self-healing), and self-optimize themselves in real-time for economic and reliable performance while systematically integrating energy in all forms. AEGs rely on scalable, self-configuring cellular building blocks that ensure that each 'cell' can self-optimize when isolated from a larger grid as well as partaking in the optimal operation of a larger grid when interconnected. To realize this vision, this paper describes the concepts and key research directions in the broad domains of optimization theory, control theory, big-data analytics, and complex system modeling that will be necessary to realize the AEG vision.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Plutonium isotope ratio measurements by total evaporation-thermal ionization mass spectrometry (TE-TIMS): an evaluation of uncertainties using traceable standards from the New Brunswick Laboratory

The accuracy and precision of isotope amount ratio measurements using thermal ionization mass spectrometry (TIMS) instrumentation are described, and the measurement of Pu materials is emphasized. The mass fractionation observed for Am, Ga, Pu, and U for isotope amount ratio measurements using the total evaporation (TE) technique is compared with theoretical estimates to demonstrate the advantage of the TE methodology and to investigate systematic biases in the major isotope amount ratios of U and Pu certified reference material (CRM) standards from the U.S. provider of CRMs. The quality of the Pu isotopic data generated by TIMS instruments in an analytical laboratory is demonstrated by the application of the double ratio technique to estimate the 241 Pu half-life. Analytical data on traceable Pu CRMs from the New Brunswick Laboratory (NBL), generated as part of routine measurements supporting various programs, are used for this half-life estimation. Although the 241 Pu abundances in CRMs of 136, 137, 138, and 126-A are approximately 200–2000× smaller than those in the 241 Pu material used in the previous Institute for Reference Materials and Measurements (IRMM) evaluation of the 241 Pu half-life, the half-life value estimated in this work shows excellent agreement with the currently accepted value from the IRMM. This agreement also demonstrates the pedigree of the Pu isotopic standards from the NBL and the quality of the isotope amount ratio measurements using TIMS instrumentation. For both the major and minor Pu isotope amount ratios, this report describes the relative importance of the factors affecting the uncertainty of TIMS measurements, which are considered the gold standard in isotope ratio measurements (LA-UR-24-29199).

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2023

Improving Progressive Retrieval for HPC Scientific Data using Deep Neural Network

As the disparity between compute and I/O on high-performance computing systems has continued to widen, it has become increasingly difficult to perform post-hoc data analytics on full-resolution scientific simulation data due to the high I/O cost. Error-bounded data decomposition and progressive data retrieval framework has recently been developed to address such a challenge by performing data decomposition before storage and reading only part of the decomposed data when necessary. However, the performance of the progressive retrieval framework has been suffering from the over-pessimistic error control theory, such that the achieved maximum error of recomposed data is significantly lower than the required error. Therefore, more data than required is fetched for recomposition, incurring additional I/O overhead. In order to tackle this issue, we propose a DNN-based progressive retrieval framework that can better identify the minimum amount of data to be retrieved. Our contributions are as follows: 1) We provide an in-depth investigation of the recently developed progressive retrieval framework; 2) We propose two designs of prediction models (named D-MGARD and E-MGARD) to estimate the amount of retrieved data size based on error bounds. 3) We evaluate our proposed solutions using scientific datasets generated by real-world simulations from two domains. Evaluation results demonstrate the effectiveness of our solution in accurately predicting the amount of retrieval data size, as well as the advantages of our solution over the traditional approach to reducing the I/O overhead. Based on our evaluation, our solution is shown to read significantly less data (5% - 40% with D-MGARD, 20% - 80% with E-MGARD).

Wang, Jinzhen↗

DOE OSTI2015

Corrective Action Decision Document for Corrective Action Unit 568. Area 3 Plutonium Dispersion Sites, Nevada National Security Site, Nevada Revision 0

The purpose of this Corrective Action Decision Document is to identify and provide the rationale for the recommendation of corrective action alternatives (CAAs) for the 14 CASs within CAU 568. Corrective action investigation (CAI) activities were performed from April 2014 through May 2015, as set forth in the Corrective Action Investigation Plan for Corrective Action Unit 568: Area 3 Plutonium Dispersion Sites, Nevada National Security Site, Nevada; and in accordance with the Soils Activity Quality Assurance Plan, which establishes requirements, technical planning, and general quality practices. The purpose of the CAI was to fulfill data needs as defined during the DQO process. The CAU 568 dataset of investigation results was evaluated based on a data quality assessment. This assessment demonstrated that the dataset is complete and acceptable for use in fulfilling the DQO data needs. Based on the evaluation of analytical data from the CAI, review of future and current operations at the 14 CASs, and the detailed and comparative analysis of the potential CAAs, the following corrective actions are recommended for CAU 568: • No further action is the preferred corrective action for CASs 03-23-17, 03-23-22, 03-23-26. • Closure in place is the preferred corrective action for CAS 03-23-19; 03-45-01; the SE DCBs at CASs 03-23-20, 03-23-23, 03-23-31, 03-23-32, 03-23-33, and 03-23-34; and the Pascal-BHCA at CAS 03-23-31. • Clean closure is the preferred corrective action for CASs 03-08-04, 03-23-30, and 03-26-04; and the four well head covers at CASs 03-23-20, 03-23-23, 03-23-31, and 03-23-33.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

A primer on artificial intelligence in plant digital phenomics: embarking on the data to insights journey

Artificial intelligence (AI) has emerged as a fundamental component of global agricultural research that is poised to impact on many aspects of plant science. In digital phenomics, AI is capable of learning intricate structure and patterns in large datasets. We provide a perspective and primer on AI applications to phenome research. We propose a novel human-centric explainable AI (X-AI) system architecture consisting of data architecture, technology infrastructure, and AI architecture design. We clarify the difference between post hoc models and 'interpretable by design' models. We include guidance for effectively using an interpretable by design model in phenomic analysis. We also provide directions to sources of tools and resources for making data analytics increasingly accessible. In conclusion, this primer is accompanied by an interactive online tutorial.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

Exploring DataVortex Systems for Irregular Applications

Emerging applications for data analytics and knowledge discovery typically have irregular or unpredictable communication patterns that do not scale well on parallel systems designed for traditional bulk-synchronous HPC applications. New network architectures that focus on minimizing (short) message latencies, rather than maximizing (large) transfer bandwidths, are emerging as possible alternatives to better support those applications with irregular communication patterns. We explore a system based upon one such novel network architecture, the Data Vortex interconnection network, and examine how this system performs by running benchmark code written for the Data Vortex network, as well as a reference MPI-over- Infiniband implementation, on the same cluster.

Data Vortex, High-Performance Computing, Networkin↗

DOE OSTI2015

ORBiT: Oak Ridge Bio-surveillance Toolkit for Public Health Dynamics

With novel emerging infectious diseases being reported across different parts of the world, there is a need to build effective bio-surveillance systems that can track, monitor and report such events in a timely manner. Apart from monitoring for emerging disease outbreaks, it is also important to identify susceptible geographic regions and populations where these diseases may have a significant impact. The digitization of health related information through electronic health records (EHR) and electronic healthcare claim reimbursements (eHCR) and the continued growth of self-reported health information through social media provides both tremendous opportunities and challenges in developing novel public health surveillance tools. In this paper, we present an overview of Oak Ridge Bio-surveillance Toolkit (ORBiT), which we have developed specifically to address data analytic challenges in the realm of public health surveillance. In particular, ORBiT provides an extensible environment to pull together diverse, large-scale datasets and analyze them to identify spatial and temporal patterns for various bio-surveillance related tasks. We demonstrate the utility of ORBiT in automatically extracting a small number of spatial and temporal patterns during the 2009-2010 pandemic H1N1 flu season using eHCR data. These patterns provide quantitative insights into the dynamics of how the pandemic flu spread across different parts of the country. We discovered that the eHCR data exhibits multi-scale patterns from which we could identify a small number of states in the United States (US) that act as bridge regions contributing to one or more specific influenza spread patterns. Similar to previous studies, the patterns show that the south-eastern regions of the US were widely affected by the H1N1 flu pandemic. Several of these south-eastern states act as bridge regions, which connect the north-east and central US in terms of flu occurrences. These quantitative insights show how the eHCR data combined with novel analytical techniques can provide important information to decision makers when an epidemic spreads throughout the country. Taken together ORBiT provides a scalable and extensible platform for public health surveillance.

big data analytics↗

DOE OSTI2017

Virtual Observation System for Earth System Model: An Application to ACME Land Model Simulations

Investigating and evaluating physical-chemical-biological processes within an Earth system model (EMS) can be very challenging due to the complexity of both model design and software implementation. A virtual observation system (VOS) is presented to enable interactive observation of these processes during system simulation. Based on advance computing technologies, such as compiler-based software analysis, automatic code instrumentation, and high-performance data transport, the VOS provides run-time observation capability, in-situ data analytics for Earth system model simulation, model behavior adjustment opportunities through simulation steering. A VOS for a terrestrial land model simulation within the Accelerated Climate Modeling for Energy model is also presented to demonstrate the implementation details and system innovations.

58 GEOSCIENCES↗

DOE OSTI2019

Suite of Comprehensive Rapid Analysis Tools for Estimation (SOCRATES) - 19076

Long-term monitoring and remedy implementation require ongoing assessment and analytics, much of which relies on intensive and time-consuming data integration and engineering, data manipulation, and 'big-data' analytics. Many of these analyses, such as groundwater flow direction, contaminant rate attenuation, and non-parametric statistics, are spatial-temporal in nature and involve coalescing disparate data sets from a variety of sources with measurement frequencies from milliseconds to years, as well as total data volumes often exceeding the petabyte scale. Web-based analytical tools are advantageous for ongoing real-time or near-real-time ingestion of data from various sources for general data access and visualization, and also allow for complex, data rich calculations by leveraging expansive networks and virtually unlimited compute power. A web-based framework offers a natural architecture for such endeavors, as access to compute nodes through auto-scaling and dynamic frameworks enables speedy data manipulation and unlimited access to any data set that is made available via an http request. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Physics-coupled data-driven design of high-temperature alloys

We present a materials design loop, which streamlines physics-coupled machine learning (ML) surrogate models to discover new alloy chemistries with improved properties. The efficacy is demonstrated by discovering a high-temperature alumina-forming austenitic (AFA) stainless steel with enhanced creep, followed by experimental validation. The ML models have been trained using a well-curated, highly consistent experimental dataset augmented with synthetic microstructural features from a computational thermodynamic approach. We have populated a large number of hypothetical AFA alloys to explore the high-dimensional composition space and have predicted their creep properties by providing the same synthetic input features obtained from the trained ML models. Uncertainties from the ML training were taken as thresholds for truncating predicted results to identify alloys with improved or deteriorated creep. Individual elemental compositions have been determined via probability density distribution analysis from the group of alloys at the top and bottom of the predicted creep values for further virtual and experimental validations. In conclusion, we anticipate that this workflow can be applied to screen desired conditions, such as chemistry and processing parameters, in high-dimensional space through physics-guided data analytics.

Alloy design↗

DOE OSTI2018

On Concavity and Utilization Analytics of Wide-Area Network Transport Protocols

Wide-area data transfers in high-performance computing infrastructures and big data applications are increasingly carried over dynamically provisioned dedicated network connections, which provide high capacities with no competing traffic. Throughput of TCP and UDT over wide-area connections depends, often in a complex non-linear way, on the congestion control mechanism, buffer sizes, and the number of parallel streams. In addition, our extensive TCP and UDT throughput measurements and time traces over a suite of physical and emulated 10 Gbps connections with 0-366 ms round-trip times (RTTs) show significant statistical and temporal variations. Consequently, parameter selection and optimization for transport methods require data analytics to complement analytical throughput models. We present analytics based on the concavity-convexity of throughput profiles which provide insights into peak throughput and superior/inferior trend compared to linear interpolations based on RTT. In particular, we propose the utilization-concavity coefficient, a scalar metric that incorporates both utilization and concavity profile to characterize the overall performance of a transport protocol. These measurements-based anaytics enable us to select a transport protocol and its parameters for a given host and connection configuration to achieve high throughput with statistical guarantees.

Rao, Nageswara↗

DOE OSTI2017

From Dr. Steven Ashby, Director of PNNL

Powered by the creativity and imagination of more than 4,000 exceptional scientists, engineers and support professionals, at PNNL we advance the frontiers of science and address some of the most challenging problems in energy, the environment and national security. As DOE’s premier chemistry, environmental sciences and data analytics laboratory, we provide national leadership in four areas: deepening our understanding of climate science; inventing the future power grid; preventing nuclear proliferation; and speeding environmental remediation. Other areas where we make important contributions include energy storage, microbial biology and cyber security. PNNL also is home to EMSL (the Environmental Molecular Sciences Laboratory), one of DOE’s scientific user facilities. We apply these science strengths to address both national and international problems in complex adaptive systems that are too difficult for one institution to tackle alone. Take earth systems, for instance. The earth is a complex adaptive system because it involves everything from climate and microbial communities in the soil to emissions from cars and coal-powered industrial plants. All of these factors and others ultimately influence not only our environment and overall quality of life, but cause the earth to adapt in ways that must be further addressed. PNNL researchers are playing a vital role in finding solutions across every area of this complex adaptive system.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

Quantifying Uncertainty in HPC Job Queue Time Predictions

High Performance Computing (HPC) has developed at an unprecedented pace in recent decades. This growth has demanded corresponding development in the area of HPC Operational Data Analytics (ODA), which encompasses a wide range of data analysis techniques, ML/AI efforts, tools, and visualizations. Published studies in ODA offer a variety of practical ways to inform HPC users, administrators, procurement managers, and other stakeholders. Uncertainty analysis, however, is rare in the related published literature. For instance, we identify only 1 out of 14 existing studies focused on job queue time prediction that investigates the uncertainty aspect of their proposed predictions. We recognize the utmost importance uncertainty quantification can have in such predictive analytics solutions, with consequences in how users interpret information they receive, and attempt to bridge this gap. With the goal of improving access to such insights, we develop a process for determining upper and lower bounds of the predicted queue times of a regression model at a specified confidence level. Our current research is focused on the uncertainty in predicting job queue times, yet our approach may be employed in predicting other metrics.

HPC↗

DOE OSTI2016

Comparison of Laboratory Analytical Methods for Uranium Analysis Superior Steel FUSRAP Site - 16606

The Superior Steel Site processed uranium metal in support of the Atomic Energy Commission's fuel-element development program in the 1950's, and was subsequently classified as a Formerly Utilized Sites Remedial Action Program (FUSRAP) Site. Previous investigations at the site identified uranium contamination both inside and outside the buildings associated with historic uranium metalworking activities. The U.S. Army Corps of Engineers (USACE) is currently engaged in the Remedial Investigation (RI) phase in accordance with the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) of 1980, as amended. As part of the RI, gamma spectroscopy analysis of environmental samples was performed on-site using a laboratory-grade, high-purity germanium (HPGe) spectrometer. The gamma spectroscopy analysis included isotopes of uranium and their progeny. As a quality assurance measure, a subset of samples already analyzed on-site were sent to a certified, commercial, off-site laboratory for analysis using a suite of analytical methods including gamma spectroscopy, alpha spectroscopy, and mass spectroscopy. As an element of the quality assurance program implemented for the RI, an assessment of the correlations between the various analytical methods employed by both the on-site and off-site laboratory was performed. This assessment revealed inconsistencies between on-site and off-site gamma spectroscopy analytical results, which raised concerns about the appropriateness and suitability of the analytical data. To resolve the data quality concerns and better understand the source(s) of analytical results variability, the Superior Steel RI team (USACE together with its contractor, Amec Foster Wheeler) developed a quality control test whose design objective was to control or practically eliminate sampling and sample preparation variability in order to focus more clearly on analytical variability. This paper discusses the results of this supplemental quality control test, showing the differences in the various analytical methods (both on-site and off-site). It demonstrates in a practical and real world setting how sample preparation and analytical variability, if not controlled and understood can impact data quality and data defensibility. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2026

The role of quantum computing in advancing scientific high-performance computing: A perspective from the ADAC institute

Quantum computing (QC) has gained significant attention over the past two decades due to its potential for speeding up classically demanding tasks. This transition from an academic focus to a thriving commercial sector is reflected in substantial global investments. While advancements in qubit counts and functionalities continue at a rapid pace, current quantum systems still lack the scalability for practical applications, facing challenges such as too high error rates and limited coherence times. Here, this perspective paper examines the relationship between QC and high-performance computing (HPC), highlighting their complementary roles in enhancing computational efficiency. It is widely acknowledged that even fully error-corrected QC will not be suited for all computational tasks. Rather, future compute infrastructures are anticipated to employ quantum acceleration within hybrid systems that integrate HPC and QC. While QC can enhance classical computing, traditional HPC remains essential for maximizing quantum acceleration. This integration is a priority for supercomputing centers and companies, sparking innovation to address the challenges of merging these technologies. The novelty of this work lies in its unique perspective, reflecting the collective insights of the Accelerated Data Analytics and Computing (ADAC) Institute, a global consortium of over 20 leading HPC centers. Recognizing the growing importance of QC, ADAC established a Quantum Computing Working Group in 2023 to foster collaboration and knowledge-sharing among its members. This paper synthesizes insights from the group’s collaborative efforts and incorporates findings from a member survey that captures shared experiences, ongoing projects, and strategic directions. By outlining the current landscape and challenges of QC integration into HPC ecosystems, this work offers HPC specialists practical and forward-looking guidance on the opportunities and implications of QC in computationally intensive endeavors.

Accelerated Data Analytics and↗