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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Shifting temporal dynamics of human mobility in the United States

In this paper we analyze the average hourly temporal dynamics of human mobility in the United States from 2019 to 2020. We discuss how large decreases in human mobility nonuniformly effect the daily temporal dynamics of aggregate human behavior. The data used are weekly activity patterns for POIs from 2019 to 2020 in the United States, provided by SafeGraph and made openly available to academic and research institutions. We use clustering methods to create metrics describing how human activity changes throughout the day/week at the county and national levels. In response to significant mobility reductions starting March 2020, daily temporal patterns of human activity changed nonuniformly. Morning activity started later, and evening activity started earlier in 2020 compared to 2019, and temporal behavioral patterns on weekdays began to look more similar to weekends. The changes in daily temporal behavior persisted throughout the year even as total mobility levels recovered. The results provide insights on the changes in human behavior in response covid-19 policies and illustrate influences on social systems, health, and transportation networks.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Dynamic signal recovery in distribution grids using compressive lossy measurements

Distribution system state estimation requires reliable aggregation of the measured data. However, the large volume of the measured data imposes a significant stress on the underlying communication infrastructure. With the challenges associated with measurement availability, current distribution systems are typically unobservable. To cope with the unobservability issue, compressive sensing theory allows us to recover system state information from a small number of measurements provided the states of the distribution system exhibit sparsity. In this paper, we evaluate the robustness of an updated Kalman filtered modified compressive sensing (KF-ModCS) technique that dynamically estimates the grid states using a small fraction of measured data. In practice, measurements used for sparsity based state estimation may also be intermittent due to communication network induced losses. Further, to understand the effect of packet losses on KF-ModCS, we provide an upper bound for the expected variances of the state estimation error for a given rate of information loss. This upper bound is further improved if the support set of the sparse signal that characterizes the state dynamics does not change over time and/or the reduced model is observable. Simulations based on two practical data sets collected from actual customers in a distribution grid validate the theoretical results.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Rapid Electrochemical Methane Functionalization Involves Pd–Pd Bonded Intermediates

High-valent Pd complexes are potent agents for the oxidative functionalization of inert C-H bonds, and it was previously shown that rapid electrocatalytic methane monofunctionalization could be achieved by electro-oxidation of Pd II to a critical dinuclear Pd III intermediate in concentrated or fuming sulfuric acid. However, the structure of this highly reactive, unisolable intermediate, as well as the structural basis for its mechanism of electrochemical formation, remained elusive. Herein, we use X-ray absorption and Raman spectroscopies to assemble a structural model of the potent methane-activating intermediate as a Pd III dimer with a Pd-Pd bond and a 5-fold O atom coordination by H x SO 4 (x-2) ligands at each Pd center. We further use EPR spectroscopy to identify a mixed-valent M-M bonded Pd 2 II,III species as a key intermediate during the Pd II -to-Pd III 2 oxidation. Combining EPR and electrochemical data, we quantify the free energy of Pd dimerization as <-4.5 kcal/mol for Pd 2 II,III and <-9.1 kcal/mol for Pd III 2 . The structural and thermochemical data suggest that the aggregate effect of metal-metal and axial metal-ligand bond formation drives the critical Pd dimerization reaction in between electrochemical oxidation steps. This work establishes a structural basis for the facile electrochemical oxidation of Pd II to a M-M bonded Pd III dimer and provides a foundation for understanding its rapid methane functionalization reactivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Implementation of FAIR principles in the IPCC: the WGI AR6 Atlas repository

The Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) has adopted the FAIR Guiding Principles. We present the Atlas chapter of Working Group I (WGI) as a test case. We describe the application of the FAIR principles in the Atlas, the challenges faced during its implementation, and those that remain for the future. We introduce the open source repository resulting from this process, including coding (e.g., annotated Jupyter notebooks), data provenance, and some aggregated datasets used in some figures in the Atlas chapter and its interactive companion (the Interactive Atlas), open to scrutiny by the scientific community and the general public. We describe the informal pilot review conducted on this repository to gather recommendations that led to significant improvements. Finally, a working example illustrates the re-use of the repository resources to produce customized regional information, extending the Interactive Atlas products and running the code interactively in a web browser using Jupyter notebooks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A Contextually Supervised Optimization-Based HVAC Load Disaggregation Methodology

This paper presents a novel contextually supervised optimization-based approach for disaggregating heating, ventilation, and air-conditioning (HVAC) loads using smart meter or Supervisory Control and Data Acquisition data. To disaggregate the load into HVAC loads, large and infrequently used loads (LIUL), and base loads, we formulate an optimization problem to minimize a set of five loss terms, consisting of the reconstruction errors of the overall load profile, the ramp rate losses, and three distinct loss functions linked with the HVAC load, base load, and LIUL, respectively. To enhance accuracy, we incorporate two forms of contextual information into the problem formulation. First, we utilize mutual information to estimate HVAC energy consumption. Second, we employ a base load dictionary to constrain HVAC load estimation errors. The obtained HVAC load profiles are fine-tuned by abnormal ramp detection followed by binary hypothesis testing. Here, the proposed method is developed and tested using sub-metered residential and commercial building data. Simulation results show that the proposed method outperforms existing methods across various data resolutions and load aggregation levels, showing excellent transferability and generalizability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

National Energy Water Treatment & Speciation (NEWTS): A Water & Critical Minerals Database and Dashboard

The poster, presented at the American Geophysical Union (AGU) 2023 Conference, highlights NETL's National Energy Water Treatment & Speciation (NEWTS) Database & Dashboard. This poster reviews the work done to aggregate disparate water quality data for energy-related wastewater streams, to process and integrate the datasets, and enable easy integration with aqueous chemistry modeling software to support water management and beneficial reuse decision-making.

Wenzlick, Madison↗
DOE OSTI2024

Analyzing Risks of Virtual Private Network Connections

The use of Splunk for analyzing VPN logs is an effective approach for identifying vulnerabilities in network endpoints. Splunk, a powerful platform for searching, monitoring, and analyzing machine-generated data, enables organizations to aggregate VPN logs in real-time, providing insights into network activity, user behavior, and potential security risks. By indexing VPN traffic and authentication logs, security teams can track abnormal patterns such as multiple failed login attempts, unusual IP addresses, or unexpected changes in bandwidth usage, all of which could indicate potential vulnerabilities or breaches. With Splunk’s advanced search and reporting capabilities, users can create custom dashboards and alerts to detect suspicious activities. Automated searches can flag endpoints exhibiting unusual behavior, while correlation analysis can identify links between compromised devices and broader network vulnerabilities. In particular, Splunk's machine learning capabilities can be leveraged to predict and prevent threats by identifying trends that might otherwise be missed in traditional log analysis. This proactive approach to monitoring VPN logs allows for the early detection of security weaknesses, enabling rapid response and minimizing potential damage to network integrity. By enhancing endpoint visibility, Splunk plays a crucial role in securing remote connections and safeguarding sensitive information. Additionally, Splunk’s automation and alerting features allow teams to create custom workflows that notify them of vulnerable or misconfigured endpoints identified through Shodan. This synergy between Splunk’s log analysis and Shodan’s device intelligence enhances an organization’s ability to proactively identify and mitigate security risks, improving the overall resilience of their VPN infrastructure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Solar and Battery Storage Permitting and Siting Requirements - Solar Prize Round 7 (CRADA Final Report)

The purpose of this project is to identify local zoning ordinances, state permitting requirements, federal regulatory bottlenecks throughout the development process, and the potential to use Large Language Models (LLM) and aggregate permitting and interconnection data for commercial and industrial (C&I), community, and utility-scale solar photovoltaic (PV) and battery storage projects.

14 SOLAR ENERGY↗
DOE OSTI2025

eMosaic: Electrification Mosaic Platform for Grid Informed Smart Charging Management (Final Scientific/Technical Report)

ABB (Prime Contractor), in collaboration with its partners at the Utah State University (USU), Idaho National Laboratory, Rocky Mountain Power (RMP), and Electric Power Engineers (EPE), have performed research, development, and wide scale demonstration of a scalable and resilient Electrification Mosaic (eMosaic) platform for Smart Charge Management (SCM) for Electric Vehicle Infrastructure. Work was completed under DE EE0009194, titled “eMosaic Electrification Mosaic Platform for Grid Informed Smart Charging Management”, funded by the US Department of Energy. The project members developed algorithms that provide localized and bulk grid services and that reduce and stabilize costs all the way down the supply chain to the PEV owner through SCM. This platform aggregates telemetry from multiple data sources as pieces of the larger picture including personal, private fleet or transportation EVs, fast chargers and other supply equipment, weather service information, and geographically distributed charging sites such as public lots, garage and retail, and private or shared usage depots. ABB and the project team designed, tested, and improved a charging management system at local/edge and cloud levels. The ultimate objective of the project was to convincingly demonstrate that the developed secure eMosaic plat-form can be readily and favorably adopted by diverse utilities and site owners at scale. This was achieved through a demonstration plan with field deployment at several physical sites across 4 states and additional scalable simulation from high fidelity charging models.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Data-Driven and Multiscale Modeling of DNA-Templated Dye Aggregates

Dye aggregates are of interest for excitonic applications, including biomedical imaging, organic photovoltaics, and quantum information systems. Dyes with large transition dipole moments (μ) are necessary to optimize coupling within dye aggregates. Extinction coefficients (ε) can be used to determine the μ of dyes, and so dyes with a large ε (>150,000 M –1 cm –1 ) should be engineered or identified. However, dye properties leading to a large ε are not fully understood, and low-throughput methods of dye screening, such as experimental measurements or density functional theory (DFT) calculations, can be time-consuming. In order to screen large datasets of molecules for desirable properties (i.e., large ε and μ), a computational workflow was established using machine learning (ML), DFT, time-dependent (TD-) DFT, and molecular dynamics (MD). ML models were developed through training and validation on a dataset of 8802 dyes using structural features. A Classifier was developed with an accuracy of 97% and a Regressor was constructed with an R2 of above 0.9, comparing between experiment and ML prediction. Using the Regressor, the ε values of over 18,000 dyes were predicted. The top 100 dyes were further screened using DFT and TD-DFT to identify 15 dyes with a μ relative to a reference dye, pentamethine indocyanine dye Cy5. Two benchmark MD simulations were performed on Cy5 and Cy5.5 dimers, and it was found that MD could accurately capture experimental results. The results of this study exhibit that our computational workflow for identifying dyes with a large μ for excitonic applications is effective and can be used as a tool to develop new dyes for excitonic applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2018

Performance Visualization for TAU Instrumented Scientific Workflows

In exascale scientific computing, it is essential to efficiently monitor, evaluate and improve performance. Visualization and especially visual analytics are useful and inevitable techniques in the exascale computing era to enable such a human-centered experience. In this ongoing work, we present a visual analytics framework for performance evaluation of scientific workflows. Ultimately, we aim to solve two current challenges: the capability to deal with workflows, and the scalability toward exascale scenario. On the way to achieve these goals, in this work, we first incorporate TAU (Tuning and Analysis Utilities) instrumentation tool and improve it to accommodate workflow measurements. Then we establish a web-based visualization framework, whose back end handles data storage, query and aggregation, while front end presents the visualization and takes user interaction. In order to support the scalability, a few level-of-detail mechanisms are developed. Finally, a chemistry workflow use case is adopted to verify our methods

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Solar and Battery Storage Permitting and Siting Requirements - Solar Prize Round 7 (CRADA Final Report)

The purpose of this research project was to aggregate zoning and permitting data for utility-scale solar PV and battery energy storage systems. The ultimate goal of this collaboration is to lower solar and battery energy storage system soft costs by streamlining regulatory due diligence and reducing the burden of conducting feasibility assessments for solar and storage systems. The below sections describe the specific research completed by NLR (the contractor) in furtherance of the agreement with Vanox (the participant).

14 SOLAR ENERGY↗
DOE OSTI2016

Achieving Actionable Results from Available Inputs: Metamodels Take Building Energy Simulations One Step Further

Modeling commercial building energy usage can be a difficult and time-consuming task. The increasing prevalence of optimization algorithms provides one path for reducing the time and difficulty. Many use cases remain, however, where information regarding whole-building energy usage is valuable, but the time and expertise required to run and post-process a large number of building energy simulations is intractable. A relatively underutilized option to accurately estimate building energy consumption in real time is to pre-compute large datasets of potential building energy models, and use the set of results to quickly and efficiently provide highly accurate data. This process is called metamodeling. In this paper, two case studies are presented demonstrating the successful applications of metamodeling using the open-source OpenStudio Analysis Framework. The first case study involves the U.S. Department of Energy's Asset Score Tool, specifically the Preview Asset Score Tool, which is designed to give nontechnical users a near-instantaneous estimated range of expected results based on building system-level inputs. The second case study involves estimating the potential demand response capabilities of retail buildings in Colorado. The metamodel developed in this second application not only allows for estimation of a single building's expected performance, but also can be combined with public data to estimate the aggregate DR potential across various geographic (county and state) scales. In both case studies, the unique advantages of pre-computation allow building energy models to take the place of topdown actuarial evaluations. This paper ends by exploring the benefits of using metamodels and then examines the cost-effectiveness of this approach.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Empirical prediction of saline water atomization pressure loss and spray phase change using local flow pressure analysis

A pressure analysis technique was developed to obtain spray spatial evaporation profile in a thermal desalination process. The technique can replace temperature-based evaporation measurement methods that are challenged by liquid-phase interference. It also provides an alternative to optical methods with high cost and complexity. Fundamental analysis was developed to determine local humidity ratios from changes in local static and dynamic pressures. The analysis was applied to data obtained from an external mixing air-assist atomizer. An empirical model was developed for the pressure loss in atomization process and was used in the analysis of humidity ratio. The results were used to develop empirical correlations for local humidity ratio in spray mediums using inlet conditions. Utilizing inlet conditions to predict evaporation profiles is an important contribution as it eliminates the need to take local spray measurements. Obtaining local data is often difficult and expensive; Our method and correlation circumvent the need for that. Finally, the correlations presented in this paper apply to saline water sprays with total dissolved salt of 0-10 wt%. The salinity-specific correlations are accurate to within ±10% of experimental data. The individual models were aggregated into a unified correlation for salinities within 0-10 wt%. The unified correlation is accurate to within ±18% of experimental data.

42 ENGINEERING↗
DOE OSTI2018

BuildingSync

BuildingSync® is a common schema for energy audit data that can be utilized by different software and databases involved in the energy audit process. It allows data to be more easily aggregated, compared and exchanged between different databases and software tools. This streamlines the energy audit process, improving the value of the data and facilitating achievement of greater energy efficiency.

Long, Nicholas↗