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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

2022 FORCE Development Status Update

Integrated energy systems (IESs) are essential for decarbonizing electricity and industrial sectors and fully exploiting these systems requires sophisticated planning, scheduling, and dispatching tools to maximize their socio-economic benefits. The Framework for Optimization of Resources and Economics (FORCE) is a tool suite developed at Idaho National Laboratory and is dedicated to making robust analyses of IESs easier for analysts and researchers. The report summarizes the improvements and updates to FORCE during the most recent development cycle. Particularly, we demonstrate improvements to user accessibility, stochastic time-series analysis, and vertical integration with the software tools provided in FORCE.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

NNSA/CEA Workflow Workshop Report 2021

Researchers from three NNSA labs plus CEA recently met for a half-day workshop on scientific and engineering workflows in March of 2021. Tools, projects and use cases from each institution were described. A wide range of unique capabilities and requirements were represented. The workshop highlights the fact that the CEA/NNSA workflows space mirrors the broader open science workflows community, with multiple technologies under development in a number of science domains and under a number of funding streams, with differing capabilities and focuses. Despite the number of tools, presentations and discussions have shown that these tools cover specific mission spaces at the four labs, each with distinctive capabilities that do not completely overlap with each other. We believe there is a strong interest in the short term for sharing lessons learned and collaborating on benchmarks and site evaluation of projects. This report, prepared by the NNSA/CEA Workflows Working Group, briefly summarizes the presentations in the areas of domain specific workflows, end user environments, data management, job and resource management, and infrastructure, and then identifies six broad areas for potential collaboration. A key finding is that users could benefit from greater interoperability, compatibility, and composability of the workflow technologies under development and that point-to-point collaboration opportunities should be identified to explore these aspects.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2016

Post-Flight Back Pain Following International Space Station Missions: Evaluation of Spaceflight Risk Factors

INTRODUCTION Back pain during spaceflight has often been attributed to the lengthening of the spinal column due to the absence of gravity during both short and long-duration missions. Upon landing and re-adaptation to gravity, the spinal column reverts back to its original length thereby causing some individuals to experience pain and muscular spasms, while others experience no ill effects. With International Space Station (ISS) missions, cases of back pain and injury are more common post-flight, but little is known about the potential risk factors. Thus, the purpose of this project was to perform an initial evaluation of reported post-flight back pain and injury cases to relevant spaceflight risk factors in United States astronauts that have completed an ISS mission. METHODS All US astronauts who completed an ISS mission between Expeditions (EXP) 1 and 41 (2000-2015) were included in this evaluation. Forty-five astronauts (36 males and 9 females) completed 50 ISS missions during the study time period, as 5 astronauts completed 2 ISS missions. Researchers queried medical records of the 45 astronauts for occurrences of back pain and injury. A case was defined as any reported event of back pain or injury to the cervical, thoracic, lumbar, sacral, or coccyx spine regions. Data sources for the cases included the Flight Medicine Clinic's electronic medical record; Astronaut Strength, Conditioning and Rehabilitation electronic documentation; the Private Medical Conference tool; and the Space Medicine Operations Team records. Post-flight cases were classified as an early case if reported within 45 days of landing (R + 45) or a late case if reported from R + 46 to R + 365 days after landing (R + 1y). Risk factors in the astronaut population for back pain include age, sex, prior military service, and prior history of back pain. Additionally, spaceflight specific risk factors such as type of landing vehicle and onboard exercise countermeasures were included to evaluate their contribution to post-flight cases. Prior history of back pain included back pain recorded in the medical record within 3 years prior to launch. Landing vehicle was included in the model to discern if more astronauts experienced back pain or injury following a Shuttle or Soyuz landing. Onboard exercise countermeasures were noted for those astronauts who had a mission following 2009 deployment of the Advanced Resistive Exercise Device (aRED) (EXP 19 to 41). T-test and chi-squared tests were performed to evaluate the association between each individual risk factor and post-flight case. Logistic regression was used to evaluate the combined contribution of all the risk factors on post-flight cases. Separate models were calculated for cases reported by R + 45 and R + 1y. RESULTS During the study time period, there were 13 post-flight cases reported by R + 45 and an additional 5 reported by R + 1y. Most of these cases have been reported since EXP 19 with 10 cases by R + 45 and 4 by R + 1y. Individual risk factors of age, sex, landing vehicle, and prior military service were not significantly associated with post-flight cases identified at R + 45 or R + 1y (p greater than 0.05). Having back pain or injury within 3 years prior to launch significantly increased the likelihood of becoming a case by R + 1y (p = 0.041), but not at R+45 (p=0.204). Additionally, astronauts who experienced onboard exercise countermeasures that included aRED had a significantly increased risk of becoming a case at R + 45 (p = 0.024) and R + 1y (p=0.003). Multiple logistic regression evaluating all the risk factors for cases identified no significant risk factors at either the R + 45 or R + 1y time period (p greater than 0.05). Overall model fit was poor for both the R + 45 (R(exp 2) = 0.132) and R + 1y (R(exp 2) = 0.186) cases showing that there are risk factors not represented in our model. CONCLUSIONS Regardless of cause, post-flight cases are reported more often since aRED was deployed in 2009. This may reflect improved documentation or unidentified risk factors. No spaceflight risk factor explains the data fully. Post-flight cases are probably due to multi-faceted factors that are not easily elucidated in the medical data.

Laughlin, M. S.↗
DOE OSTI2014

MO-F-16A-01: Implementation of MPPG TPS Verification Tests On Various Accelerators

Purpose: To demonstrate the implementation of the Medical Physics Practice Guideline (MPPG) for dose calculation and beam parameters verification of treatment planning systems (TPS). Methods: We implemented the draft TPS MPPG for three linacs: Varian Trilogy, TomoHDA and Elekta Infinity. Static and modulated test plans were created. The static fields are different than used in commissioning. Data was collected using ion chambers and diodes in a scanning water tank, Delta4 phantom and a custom phantom. MatLab and Microsoft Excel were used to create analysis tools to compare reference DICOM dose with scan data. This custom code allowed for the interpolation, registration and gamma analysis of arbitrary dose profiles. It will be provided as open source code. IMRT fields were validated with Delta4 registration and comparison tools. The time for each task was recorded. Results: The tests confirmed the strengths, and revealed some limitations, of our TPS. The agreement between calculated and measured dose was reported for all beams. For static fields, percent depth dose and profiles were analyzed with criteria in the draft MPPG. The results reveal areas of slight mismatch with the model (MLC leaf penumbra, buildup region.) For TomoTherapy, the IMRT plan 2%/2 mm gamma analysis revealed poorest agreement in the low dose regions. For one static test plan for all 10MV Trilogy photon beams, the plan generation, scan queue creation, data collection, data analysis and report took 2 hours, excluding tank setup. Conclusions: We have demonstrated the implementation feasibility of the TPS MPPG. This exercise generated an open source tool for dose comparisons between scan data and DICOM dose data. An easily reproducible and efficient infrastructure with streamlined data collection was created for repeatable robust testing of the TPS. The tests revealed minor discrepancies in our models and areas for improvement that are being investigated.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 ( https://www.nfdi4health.de/task-force-covid-19-2 ) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

MO-A-16A-01: QA Procedures and Metrics: In Search of QA Usability

Radiation therapy has undergone considerable changes in the past two decades with a surge of new technology and treatment delivery methods. The complexity of radiation therapy treatments has increased and there has been increased awareness and publicity about the associated risks. In response, there has been proliferation of guidelines for medical physicists to adopt to ensure that treatments are delivered safely. Task Group recommendations are copious, and clinical physicists' hours are longer, stretched to various degrees between site planning and management, IT support, physics QA, and treatment planning responsibilities.Radiation oncology has many quality control practices in place to ensure the delivery of high-quality, safe treatments. Incident reporting systems have been developed to collect statistics about near miss events at many radiation oncology centers. However, tools are lacking to assess the impact of these various control measures. A recent effort to address this shortcoming is the work of Ford et al (2012) who recently published a methodology enumerating quality control quantification for measuring the effectiveness of safety barriers. Over 4000 near-miss incidents reported from 2 academic radiation oncology clinics were analyzed using quality control quantification, and a profile of the most effective quality control measures (metrics) was identified.There is a critical need to identify a QA metric to help the busy clinical physicists to focus their limited time and resources most effectively in order to minimize or eliminate errors in the radiation treatment delivery processes. In this symposium the usefulness of workflows and QA metrics to assure safe and high quality patient care will be explored.Two presentations will be given:Quality Metrics and Risk Management with High Risk Radiation Oncology ProceduresStrategies and metrics for quality management in the TG-100 Era Learning Objectives: Provide an overview and the need for QA usability metrics: Different cultures/practices affecting the effectiveness of methods and metrics. Show examples of quality assurance workflows, Statistical process control, that monitor the treatment planning and delivery process to identify errors. To learn to identify and prioritize risks and QA procedures in radiation oncology. Try to answer the question: Can a quality assurance program aided by quality assurance metrics help minimize errors and ensure safe treatment delivery. Should such metrics be institution specific.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

ESS-DIVE reporting format for leaf-level gas exchange data and metadata

Here we present documentation of the ESS-DIVE reporting format for leaf-level gas exchange data and metadata. This reporting format provides guidance to data contributors on how to store data to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format specifies data table variable naming and unit conventions, as well as metadata characterizing experimental conditions and protocols. For common data types that were the focus of this initial version of the reporting format, i.e., survey measurements, dark respiration, carbon dioxide and light response curves, and parameters derived from those measurements, we took a further step of defining required additional data that would maximize the potential reuse of those data types. To aid data contributors and the development of data ingest tools by data repositories we provided a translation table comparing the outputs of common gas exchange instruments. The reporting format presented here is intended to form a foundation for future development that will incorporate additional data types and variables as gas exchange systems and measurement approaches advance in the future. The reporting format documentation is maintained and updated on the ESS-DIVE Community Space GitHub. This data package is the first published version of this reporting format, and comprises a zip file of the complete content of https://github.com/ess-dive-community/essdive-leaf-gas-exchange v1.0. The zip contains the reporting format description, guidelines, variable tables and instructions in GitHub markdown language (*.md) and 2 metadata templates as spreadsheets with drop down options (*.xlsx files, also function in GoogleSheets).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Development and operational experience of the web based application to collect, manage, and release the alignment and calibration configurations for data processing at CMS

The alignment and calibration workflows at the Compact Muon Solenoid (CMS) experiment are fundamental to provide a high quality physics data and to maintain the design performance of the experiment. To facilitate the operational efforts required by the experiment, the alignment and calibration team has developed and deployed a set of web-based applications to search, navigate and prepare a consistent set of calibrations to be consumed in reconstruction of data for physics, accessible through the Condition DB Browser. The Condition DB Browser hosts also various data management tools, including a visualization tool that allows to easily inspect alignment an calibration contents, an user-defined notification agent for delivering updates on modification to the database, a logging service for the user and the automatic online-to-offline condition uploads. In this paper we report on the operational experience of this web application from 2017 data taking, with focus on new features and tools incorporated during this period.

Md, Hasbuddin↗
DOE OSTI2026

Building MCP-native hierarchical AI scientist ecosystems: a perspective on scaling multi-agent scientific discovery

Large language models (LLMs) are evolving from chatbots with limited tool-using capabilities to agentic AI systems that can perform deep research, assist in proposing hypotheses, help design experiments, automate data analysis, and draft scientific reports. However, there are currently two bottlenecks limiting LLMs' real-world impact on the broader scientific research community beyond academic demonstrations: lack of interoperability (repetitive manual tool-integration is required across scenarios) and the need for scalable coordination (unstructured communication and memory become brittle as the number of agents grows). In this Perspective, we argue that the next phase of agentic scientific discovery requires the development of an ecosystem of protocol-native agents and tools organized through hierarchies inspired by human society, beyond the current paradigm of a single monolithic “AI scientist”. We use Model Context Protocol (MCP) as a concrete example of an emerging interoperability layer for scientific tool and context exchange, and we propose three complementary pathways to increase the scaling capabilities of an MCP-native scientific ecosystem by addressing the composability issues: (1) MCP servers for high-value scientific tools maintained by domain experts, (2) automated transformation of existing code repositories into MCP services, and (3) autonomous invention and evolution of new agents and workflows. Finally, we provide a practical roadmap for scaling AI-driven scientific discovery by expanding tool supply and coordination in MCP-native scientific ecosystems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

PARCS Analysis of the APR1400 Initial Core

The Design Control Document (DCD) for the APR1400 describes the core loading for the initial core and presents the results of various nuclear design calculations in Chapter 4. In the current work, the initial core cycle depletion was simulated using the Nuclear Regulatory Commission (NRC) suite of nuclear analysis tools and the results of these calculations were compared to reference results provided in the APR1400 DCD. The regulatory purpose of this work was to confirm the results reported in the DCD through independent analysis. The NRC code suite consists of SCALE/POLARIS, which is used for lattice physics analysis, GENPMAXS, which is a tool that converts the lattice physics output to a library file used by the core simulator, and PARCS/PATHS, which is a nodal core simulator. Calculations were performed that simulate the depletion of the initial core through one cycle. The authors compared the calculation results in terms of the power distributions at various points in the cycle to reference results described in the DCD. In addition to these comparisons, calculations were performed to determine point kinetics parameters that are used in downstream transient calculations using TRACE.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

COVID-19 Data Curation Effort: An Initial Analysis of the Data

During the COVID-19 pandemic of 2020, major case reporting outlets quickly coalesced around two or three primary vendors. Johns Hopkins University and The New York Times were among the more prominent, and all were of great value to the nation, particularly during the uncertain early stages of the pandemic. They primarily focused on three major attributes: number of new cases, deaths, and recovery. Recognizing that many states were reporting very detailed data sets (e.g., hospital beds) at a county level or finer, the ORNL Pandemic Modeling team embarked on a major data curation effort from March to June 2020 for the purpose of capturing this wealth of detailed data. The challenge of curating this data was daunting. The number of attributes reported by the states grew on almost on a weekly basis. States were routinely shifting their web tool strategies away from easily parsable HTML-based formatting to new Tableau and ArcGIS content. This growth in the sheer number of attributes, combined with the unpredictable shifts in data format, meant an aggressive and agile combination of automated scripting and manual scraping was required to capture new daily streams. Further, the team had to scale up staff and widen its approach for capture and storage. As a result, the team collected more than 11 million data points. Following the close of this data collection effort on June 30 th , 2020, the team embarked on a major effort to appraise what had been collected, including an inventory list, spatial completeness, temporal completeness, scale and geographic characteristics, and a determination. A report on this matter was submitted on September 15 th , 2020, titled “DOE COVID-19 Data Curation Effort: Overview of Data Collection Coverage”. Over 2000 unique attributes had been netted over a wide range of spatial scales, including state, county, zip codes, health regions, and census blocks. Over 11 million individual data points were collected across these attributes, and spatial coverage (in total) included all 50 states and multiple territories. What became apparent in the process is that in the absence of any data standards, many states reported a wide variety of unique attributes that were not always compatible with attributes reported in other states. As time continued, states began adding new attributes and offering finer grain detail in some older attributes. This meant that not all data streams existed for the entire time period; in fact, the number tended to increase dramatically towards the end. Often, states would begin an attribute series and then stop altogether. These highly variable and uncertain conditions illuminated the need for harmonization approaches that would reconcile and conflate changing attribute names and detail over time. For example, grouping racial data reported as either Black or African American, depending on the state, into a single harmonized attribute. These choices would make a within-state analysis possible during the time period and lead to potential between-state analytics later on. This was almost entirely a manual decision process, requiring some subjective decision-making at times, to prevent a fragmented, short-lived collection of time series fragments that would offer few insights into trends, patterns, and correlates. This report imports harmonized data for state and county into the World Spatio-Temporal Analytics and Mapping Project (WSTAMP). WSTAMP is a major space-time analysis and visualization tool developed at ORNL for the National Geospatial-Intelligence Agency specifically for this kind of exploratory analysis. WSTAMP offers a rich analytical and graphical environment consisting of a wide range of analytics. These include time series plots, statistical summaries, data mining techniques, trend and pattern detection, and hypothesis generation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Effect of Preheated Microstructure Vis-à-vis Process Parameters and Characterization of Orange Peel in Incremental Forming of AA1050 Sheets

The present investigation systematically analyzes and compares the influence of microstructure and crystallographic texture of AA1050 sheet to that of the most critical process parameters, namely tool dimension, and incremental step depth, in single-point incremental forming (SPIF) process. The sheet samples were preheated at different temperatures to reform the original, i.e., distorted and misoriented grain distribution. The results from experimental and statistical analyses report higher sensitivity of global spring back, surface topography and forming limits strains to variations in microstructure and texture than tool diameter and incremental step depth. The preheating of the sheets resulted in the formation of preferential texture by grain reformation, which leads to the increase in the fraction of homogeneous orientated grains. Such grains increase the maximum forming limit strains and reduce the global spring back in SPIF. Fractography analysis of the failed components revealed that improvement in formability due to grain homogenization (developed texture) may be attributed to larger average void diameter. Empirical models were also developed, and optimized levels of parameters were identified to maximize the efficiency of the SPIF process. Further, qualitative and quantitative characterization of orange peel roughness was carried out by segregating the external formed surface into surfaces subjected to orange peel roughness and tool-induced texture.

36 MATERIALS SCIENCE↗
NASA NTRS2000

AWE: Aviation Weather Data Visualization Environment

Weather is one of the major causes of aviation accidents. General aviation (GA) flights account for 92% of all the aviation accidents, In spite of all the official and unofficial sources of weather visualization tools available to pilots, there is an urgent need for visualizing several weather related data tailored for general aviation pilots. Our system, Aviation Weather Data Visualization Environment AWE), presents graphical displays of meteorological observations, terminal area forecasts, and winds aloft forecasts onto a cartographic grid specific to the pilot's area of interest. Decisions regarding the graphical display and design are made based on careful consideration of user needs. Integral visual display of these elements of weather reports is designed for the use of GA pilots as a weather briefing and route selection tool. AWE provides linking of the weather information to the flight's path and schedule. The pilot can interact with the system to obtain aviation-specific weather for the entire area or for his specific route to explore what-if scenarios and make "go/no-go" decisions. The system, as evaluated by some pilots at NASA Ames Research Center, was found to be useful.

Spirkovska, Lilly↗
DOE OSTI2017

Prediction of Bond Dissociation Energies/Heats of Formation for Diatomic Transition Metal Compounds: CCSD(T) Works

It was recently reported that the coupled cluster singles and doubles with perturbative triples method, CCSD(T), should not be used as a benchmark tool for the prediction of dissociation energies (heats of formation) for the 1st row transition metal diatomics based on a comparison with the experimental thermodynamic values for a set of 20 diatomics. In the present work reported here, the bond dissociation energies as well as the heats of formation for those diatomics have been calculated by the Feller-Peterson-Dixon approach at the CCSD(T)/complete basis set (CBS) level of theory including scalar relativistic corrections and correlation of the outer shell of core electrons in addition to the valence electrons. Revised experimental values for the hydrides are presented that are based on new heterolytic R-H bond dissociation energies, which are needed for analysis of the mass spectrometry experiments. The agreement between the calculated bond dissociation energies and the revised experimental values of the hydrides is good. Good agreement of the calculated bond dissociation energies/heats of formation is also found for most of the chlorides, oxides, and sulfides given the experimental error bars from experiment and those of the transition metal atoms in the gas phase. Thus, reliable results can be achieved by the CCSD(T) method at the CBS limit. The use of PW91 orbitals for the CCSD(T) calculations improves the predictions for some compounds with large T 1 diagnostics at the HF-CCSD(T) level. The optimized bond distances and calculated vibrational frequencies for the diatomics also agree well with the available experimental values.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

FY2018 Discovery Through Situational Awareness: Anomalies, Oscillations, and Classification

This technical report is a summary of work performed under the DOE GMLC program under the task titled “Discovery through Situational Awareness”. The focus of this work was to apply big data, statistical techniques to PMU data to do the following: 1) baseline PMU data and find anomalies with a focus on phase angle pairs, 2) identify the frequencies of dominant modal oscillations between different parts of a power system, and 3) identify and classify frequency events in PMU data. This work was performed on PMU data from the Western Interconnect. Many of these algorithms will be applied to wide area phase angle pair data on the Eastern Interconnect in a tool called ESAMS (Eastern Interconnect Situational Awareness Monitoring System). ESAMS was built by EPG (Electric Power Group) and PNNL and is currently being installed on the Eastern Interconnect. The results discussed in this report were made from 14 PMUs from data ranging from Oct 2016 to May 2018. The PMU data were measured 60 times each second. Fourteen phase angle pairs were included, with reactive and active power being calculated at 14 sites. All results have been de-identified for this report. The methodologies applied in the ESAMS tool are described in Section 2. A baseline of PMU data is created using the last 120 days of data. This baseline describes typical behavior as understood from the data. The baseline is then applied to the next day’s data and anomalies from that baseline are identified. Plots are created to help describe what is anomalous during that specific moment in time. These algorithms are currently being installed on the Eastern Interconnect. FY19 work will include discussing the results and findings from the ESAMS tool. A new method of identifying the frequencies of dominant modal oscillations between different parts of a power system is described in Section 3. A list of 199 frequency events provided by Bonneville Power Administration were used to create algorithms to detect frequency events and to create classification rules that can be used to identify future frequency events. Section 4 discusses the results the performance of 7 different event detection, clustering-based methods. Accuracy percentages ranged from 93.59% to 99.16%, with most methods being very fast. GBM (Gradient Boosting Machine) performed well and quickly. These 7 different methods were also tested to determine how well each built classification rules on training data and then applied those rules to identify future frequency events. Accuracy percentages ranged from 89.09% to 99.9%, with all methods performing quickly. GLM (General Linear Model) was viewed as the top performer when considering accuracy and processing time. Because the list of events consisted of 80% of them as active power events and only 20% as faults, all methods struggled to identify events as faults. Small faults were especially difficult to identify. A separate algorithm was created to help with this. Additional work will be done in FY19 on event detection using more data and more events and refinements to the methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

GPS-Free Navigation in Buildings - NA-241_2018_final_report_SBLibby_12_31_18

This project’s purpose is to evaluate candidate navigation solutions for GPS free inspection tools that can be used in tours of large building interiors. While the IAEA is currently using a system (ANPS – autonomous navigational and positioning sensor) that provides a satisfactory solution under ideal conditions for tours up to one hour, significant improvements for longer tours under variable conditions (also including the added features of on-board processing and time synchronization) are needed. In principle, next generation COTS portable inertial measurement unit (IMU) sensors with satisfactory accuracy, SWAP (size, weight, power), low error, and bias drift can provide sufficiently accurate dead reckoning navigation in a lengthy tour of a large building in the absence of GPS.

42 ENGINEERING↗
DOE OSTI2022

Development of advanced diagnostic tools, models, and technoeconomic analyses for high-temperature heat transfer coefficient heat exchangers

This is a final technical report of a system designed to image fall particle receivers. High temperature heat is hard to store and move and using low cost materials, similar to sand, are ideal. However, particle-to-particle interactions can impact heat transfer mechanisms in these classes of materials. This report highlights our effects to utilize x-ray imaging to understand these dynamics.

14 SOLAR ENERGY↗