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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Data-driven occupant-behavior analytics for residential buildings

Many advances have been made in building technology to help save energy, but influencing the behavior of the occupants is still necessary to achieve low-energy use targets. One of the most practical ways to influence and change occupant behaviors is through incentives. Developing incentives for energy-saving and quantifying the impact of occupant behaviors are both active areas of research. Here, we propose a data analytics framework for detecting changes in occupant behaviors, which will help build an analytics feedback loop from behavior impact to incentive design. The framework has two major parts. The first forecasts energy consumption for each occupant, while the second determines a probability distribution for changes in energy consumption. The parts are interchangeable with other existing machine learning and statistical methods. A specific instantiation of the framework, using kernel ridge-regression for forecasting and k-means to find an empirical behavior distribution, is described in detail. An HVAC use-case with 5 different incentivized behaviors is used as an example to show that the framework can detect behavior changes induced by incentives. Furthermore, we show that some simpler behavior-change detection methods do not work, further justifying the use of advanced analytics.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

High-precision shock equation of state measurements for metallic fluid carbon between 15 and 20 Mbar

Diamond is an efficient ablator material to convert the energy of high-power giant lasers into ablation pressure with applications for High-Energy-Density (HED) science, planetary science, and Inertial Confinement Fusion (ICF) research at the National Ignition Facility (NIF). Unfortunately, current theoretical equation of state models cannot reproduce all the observed experimental data in the multi-megabar regime particularly relevant for HED and ICF research. New experimental data on the behavior of carbon at extreme pressures and temperatures are, therefore, essential to improve our predictive capability to design and analyze dynamic compression experiments for HED or ICF research and build improved equation of state models in the future. In this paper, we report high-precision laser-driven shock compression measurements on diamond single crystals at the Omega Laser Facility. Using ultrafast Doppler optical Velocimetry Interferometer System for Any Reflector (VISAR) to track the leading shock front and a quartz plate as an in situ reference, we obtain relative pressure-density shock equation-of-state measurements between 15 and 20 Mbar with an impedance-matching procedure. We also report shock-and-release measurements in a spherical geometry at the NIF. The new data provide tight constraints on the compressibility of warm dense carbon along the Hugoniot of full density diamond, allowing us to discriminate between existing theoretical equation-of-state models. We find that both LLNL LEOS 9061 and LANL Sesame 7835 models capture well the shock compressibility in the explored range. LANL Sesame 7835 also reproduces well the observed shock-and-release behavior of diamond near 10–20 Mbar.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Differential Privacy in Grid Kitchen: Implementation & Software Documentation

Sharing of power grid feeder models faces significant challenges due to the potential risk of exposing sensitive operational information. Traditional anonymization techniques have shown notable limitations in other sensitive domains, as evidenced by documented re-identification attacks that combine supposedly anonymized datasets with auxiliary information, raising concerns that similar vulnerabilities could affect power grid data. Consequently, there is a pressing need for a more rigorous privacy protection strategy that not only delivers formal mathematical guarantees but also preserves the analytical value of the shared models. To address this challenge, we have enhanced the Grid Kitchen framework by implementing differential privacy mechanisms within the distribution model dehydration pipeline. This implementation carefully calibrates and applies noise to sensitive attributes in feeder models according to configurable privacy levels—low, moderate, and high—each offering different balances between data utility and privacy protection. Our approach uses established noise functions (Gaussian for continuous data and Discrete Laplace for integer values) with parameters carefully calibrated so that the impact of individual data points is effectively masked in the final output. The integration leverages our Noise Catalog, which we developed to categorize feeder model properties by component type, data type, and sensitivity. This catalog guides the application of appropriate noise functions and privacy parameters ($\varepsilon$ and $\delta$) to each attribute, ensuring consistent privacy protection across the model while maintaining its structural integrity and analytical usefulness. This implementation also includes evaluation tools that allow model owners to assess the impact of privacy-preserving transformations before sharing data with external parties. This report provides documentation for the differential privacy capabilities added to the Grid Kitchen project. It includes a primer on differential privacy concepts and their importance in modern data sharing, details the architecture of our implementation, explains the privacy modes and parameter configurations, and offers practical guidance on using the code for applying differential privacy to grid feeder models. Through examples and code snippets, we demonstrate the effective application of these privacy-enhancing technologies, enabling utility operators and researchers to confidently share grid data while protecting sensitive information.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Added Value for Integrated Marine Energy Data Systems (February 2021)

Launched in 2019, the Portal and Repository for Information on Marine Renewable Energy (PRIMRE) provides centralized access, standardization, community building, and integration of United States (U.S.) databases, tools and codes, and other resources that cover a range of marine energy information. The PRIMRE universe contains a series of Knowledge Hubs that represent data and information on testing of marine energy devices (MHKDR); environmental effects (Tethys); engineering and technical papers (Tethys Engineering); descriptions of marine energy companies and technologies (Marine Energy Projects Database); codes and models (Marine Energy Software); and guidance on testing and measurements (Telesto). Content is added to PRIMRE by applying a set of Guidelines and Best Practices (PRIMRE Guidelines). An aggregate search across the PRIMRE site enables users to find data and information from all the PRIMRE Knowledge Hubs simultaneously, using a single entry-point (PRIMRE Search). In addition to providing access to a range of data and information on marine energy development, testing, and effects, PRIMRE allows for the development of valueadded products and processes that will help move the marine energy industry forward. The PRIMRE team has recently launched two key initiatives in the U.S.-Signature Projects and Lessons Learned. Outputs and outcomes from these two initiatives will be highlighted in this paper. The Signature Projects initiative is intended to bring focus to a selection of ongoing and completed marine energy projects funded by the U.S. Department of Energy's Water Power Technologies Office (WPTO), and to inform the marine energy community of what investigations have been undertaken, what tools are available, and where gaps in information persist. Each Signature Project tags papers, reports, and data from large marine energy research projects, providing easy access and attention to all the output and associated products from each project. The Lessons Learned initiative is intended to ensure that hard-won achievements are recognized and available for those who come later, that missteps and unfortunate outcomes can be prevented in future, and that efficiencies and effective shortcuts can be publicized and used as the marine energy industry moves forward. This initiative builds off the Knowledge Hubs and reaches out to members of the marine energy community, particularly technology developers and researchers, to integrate experience in the development, deployment, assessment, success, challenges while creating this new industry and field of study.

data sharing↗
DOE OSTI2024

Current State of Neutron Source Term Calculations

SOURCES-4C has some significant deficiencies that are mostly due to problems with the default data libraries that it uses. Some research groups have modified these libraries to fix most of the problems, but not all of them. Adding modern data libraries to SOURCES-4C is one of the most promising approaches to resolving these issues. Other promising options include using either NEDIS or a GEANT4 tool. Both of these codes approximately match the accuracy of the modified SOURCES-4C code. The NEDIS code is Russian. The GEANT4 tool might be computationally slow and it might also no longer be actively maintained. There are a handful of other (α,n) codes, but they are not accurate.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Metocean Reference Station Best Practices: MORS-1 Case Study

The goal of a Metocean Reference Station should be to aid both near-term development of the offshore wind energy industry as well as long-term climate and energy research. Capitalizing on existing shared-use facilities wherever possible, reference stations should provide data valued by industry users as well as research users in a cost-effective way. Cost-effectiveness is a critical component of developing a reference site, as the real value of the site's data collection efforts is the length of the time series it is able to sustain. This report seeks to lay out the best practices toward developing and maintaining metocean reference stations in the United States. The best practices described here focus on suitable platforms, sensor integration, and long-term operations of the reference station itself, as best practices of operations for individual sensors for the research community or validation enterprises focused on industrial use of metocean data are well described in the literature. This work focuses on potential stations in the United States because the market for reference data and validation facilities is less well defined in the United States, given the young age of the rapidly emerging offshore wind energy industry here.

17 WIND ENERGY↗
DOE OSTI2020

AmeriFlux US-xAE NEON Klemme Range Research Station (OAES)

This is the AmeriFlux version of the carbon flux data for the site US-xAE NEON Klemme Range Research Station (OAES). Site Description - The 1,560-acre Klemme Range Research Station is a grassland site managed for livestock grazing and pasture, located in the middle of the Rolling Red Plains Resource Area. The Rolling Red Plains extends from south of the Red River to north of the Oklahoma/Kansas border consisting of approximately 9.4 million acres, which occupies a significant portion of Western Oklahoma excluding the Oklahoma Panhandle.

Network), NEON (National Ecological Observatory↗
DOE OSTI2020

Columnar Vertical Profile (CVP) Methodology for Validating Polarimetric Radar Retrievals in Ice Using In Situ Aircraft Measurements

Abstract A novel way to process polarimetric radar data collected via plan position indicator (PPI) scans and display those data in a time–height format is introduced. The columnar vertical profile (CVP) methodology uses radar data collected via multiple elevation scans, limited to data within a set region in range and azimuth relative to the radar, to create vertical profiles of polarimetric radar data representative of that limited region in space. This technique is compared to others existing in the literature, and various applications are discussed. Polarimetric ice microphysical retrievals are performed on CVPs created within the stratiform rain region of two mesoscale convective systems sampled during two field campaigns, where CVPs follow the track of research aircraft. Aircraft in situ data are collocated to microphysical retrieval data, and the accuracy of these retrievals is tested against other retrieval techniques in the literature.

Engineering↗
DOE OSTI2020

Data Collection and Analysis Challenges and Mitigation Strategies for Quantitative Human Factors Research Studies in Nuclear Power Plant Modernization

The United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) program Plant Modernization Pathway is conducting targeted research and development (R&D) to address aging and reliability concerns with the legacy instrumentation and control and related information systems of the U.S. LWR fleet. In this effort, the application of human factors engineering (HFE) provides an important role in ensuring new digital plant technologies enable broad innovation and business improvement with continued operational safety. Evaluation is a key activity in HFE, which often occurs iteratively through the system design lifecycle. While qualitative methods are important in collecting information of how users perform tasks through observations, quantitative methods are equally important in assessing system design based on performance. In collecting and analyzing this quantitative data, there are notable challenges in the nuclear HFE domain that may threaten the validity and reliability of the inferences made in these studies. Notable challenges include small sample size and limited resources, large error variance and small effect size, an “adding test features to losing degrees of freedom” dilemma, non-normal distribution, and heterogeneity of variance. The results in control room usability studies are often statistically non-significant, which makes it hard to interpret. This work discusses these challenges across different scientific viewpoints and provides real-world examples of these challenges in practice. Collectively, the objective of this work is to position these challenges to the larger data science community as a means of identifying future opportunities to address these issues.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

TEAMER: Pitching Foil Crossflow Turbine Efficiency Data

This dataset documents the efficiency testing of a pitching foil crossflow turbine, conducted at the University of New Hampshire's (UNH) Chase Ocean Engineering Laboratory tow tank facility. The tests explored various pitch phases and amplitudes, ranging from 0 to 18 degrees, across different flow speeds and turbine RPMs. Specifically, testing was performed at pitch amplitudes of 0,3,6,9,12,15 and 18 degrees. The results indicate that turbine efficiency improves with increased pitch amplitude, reaching optimal performance at 12 degrees. Modifications were made to the test frame by UNH to enhance the stiffness of the support structure. The testing specifically mapped the efficiency of a single quadrant of the Ocean Renewable Power Company (ORPC) Autonomous Turbine Generator Unit (ATGU), covering the full range of allowable pitching amplitudes and phases. The dataset provides both raw and processed data, including drag and torque measurements, and contains Python scripts used for data processing and visualization. This research was supported by funding from TEAMER RFTS 1 (Request for Technical Support).

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Building partnerships for development of sustainable energy systems with atmospheric measurements

Atmospheric dynamics often play a critical role in the sustainability and reliability of diverse forms of energy production. This is especially true for the growing number of renewable energy deployments that harness aspects of the environment for power production. While the University of Memphis has a strong research background in energy systems, we have little experience working with the Earth and Environmental Systems Science Division (EESSD) and their associated User Facilities. Of particular interest to us is the Atmospheric Science Research and the Atmospheric Radiation Measurement (ARM) user facility to address surface-boundary layer interactions and physical phenomena. One of the major challenges for understanding and developing energy systems and management platforms is accurate modeling/forecasting of atmospheric conditions across disparate spatial and temporal scales. These conditions are often required to understand the lowest levels of the atmospheric boundary layer, but are also important to understand higher atmospheric conditions where aerosols affect cloud development. The objective of this work was to develop partnerships with national laboratories for collaboration on environmental science and its intersection with sustainable energy systems, as well as to leverage the ARM user facility data repositories to enhance our research capabilities in energy systems and their inter-dependence on environmental systems for future engagement with EESSD. Specifically, we accomplished these objectives by (1) developing collaborations with Oakridge National Laboratory ARM Data Science and Integration Group which resulted in student internships, (2) employed ARM data to develope modeling of the atmospheric boundary layer optical turbulence, and (3) optimally-sized large-scale renewable energy systems and their associated energy storage systems with ARM repository data.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

A FAIR-compliant parts catalogue for genome engineering and expression control in Saccharomyces cerevisiae

The synthetic biology toolkit for baker's yeast, Saccharomyces cerevisiae, includes extensive genome engineering toolkits and parts repositories. However, with the increasing complexity of engineering tasks and versatile applications of this model eukaryote, there is a continued interest to expand and diversify the rational engineering capabilities in this chassis by FAIR (findable, accessible, interoperable, and reproducible) compliance. In this study, we designed and characterised 41 synthetic guide RNA sequences to expand the CRISPR-based genome engineering capabilities for easy and efficient replacement of genomically encoded elements. Moreover, we characterize in high temporal resolution 20 native promoters and 18 terminators using fluorescein and LUDOX CL-X as references for GFP expression and OD600 measurements, respectively. Additionally, all data and reported analysis is provided in a publicly accessible jupyter notebook providing a tool for researchers with low-coding skills to further explore the generated data as well as a template for researchers to write their own scripts. We expect the data, parts, and databases associated with this study to support a FAIR-compliant resource for further advancing the engineering of yeasts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

The role of driver head pose dynamics and instantaneous driving in safety critical events: Application of computer vision in naturalistic driving

This paper investigates the role of driver behavior especially head pose dynamics in safety–critical events (SCEs). Using a large dataset collected in a naturalistic driving study, this paper analyzes the head pose dynamics and driving behavior in moments leading up to crashes or near-crashes. The study uses advanced computer vision and mixed logit modeling techniques to identify patterns and relationships between drivers’ head pose dynamics and crash involvement. The results suggest that driver-head pose dynamics, especially poses that indicate distraction and movement volatility, are important factors that can contribute to undesirable safety outcomes. Marginal effects show that angular deviation for head pose dynamics indicated by yaw, pitch and roll increase the likelihood of crash intensity by 4.56%, 4.92% and 8.26% respectively. Furthermore, traffic flow and lane changing also contribute to increase in likelihood of crash intensity. These findings provide new insights into pre-crash factors, especially human factors and safety–critical events. The study highlights the importance of considering human factors in designing driver assistance systems and developing safer vehicles. This research contributes by examining naturalistic driving data at the microscopic level with early detection of behaviors that lead to SCEs and provides a basis for future research on automation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Strictly Enforcing Invertibility and Conservation in CNN-Based Super Resolution for Scientific Datasets

Abstract Recently, deep convolutional neural networks (CNNs) have revolutionized image “super resolution” (SR), dramatically outperforming past methods for enhancing image resolution. They could be a boon for the many scientific fields that involve imaging or any regularly gridded datasets: satellite remote sensing, radar meteorology, medical imaging, numerical modeling, and so on. Unfortunately, while SR-CNNs produce visually compelling results, they do not necessarily conserve physical quantities between their low-resolution inputs and high-resolution outputs when applied to scientific datasets. Here, a method for “downsampling enforcement” in SR-CNNs is proposed. A differentiable operator is derived that, when applied as the final transfer function of a CNN, ensures the high-resolution outputs exactly reproduce the low-resolution inputs under 2D-average downsampling while improving performance of the SR schemes. The method is demonstrated across seven modern CNN-based SR schemes on several benchmark image datasets, and applications to weather radar, satellite imager, and climate model data are shown. The approach improves training time and performance while ensuring physical consistency between the super-resolved and low-resolution data. Significance Statement Recent advancements in using deep learning to increase the resolution of images have substantial potential across the many scientific fields that use images and image-like data. Most image super-resolution research has focused on the visual quality of outputs, however, and is not necessarily well suited for use with scientific data where known physics constraints may need to be enforced. Here, we introduce a method to modify existing deep neural network architectures so that they strictly conserve physical quantities in the input field when “super resolving” scientific data and find that the method can improve performance across a wide range of datasets and neural networks. Integration of known physics and adherence to established physical constraints into deep neural networks will be a critical step before their potential can be fully realized in the physical sciences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Digital Engineering & Data Acquisition

This is an Intern Poster submission regarding my research in the digital engineering team. My task was to create a software adapter to communicate between a data acquisition system (LabVIEW) and a data warehouse (Deep Lynx) as described in the poster.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Organizational and psychological measures for data center energy efficiency: barriers and mitigation strategies

Abstract It was last estimated that in 2020, data centers comprised approximately 2% of total US electricity consumption, with an estimated annual growth rate of 4%. As our country increasingly relies on information technology (IT), our data centers (DCs) will need to increase their energy efficiency (EE) to stabilize their energy consumption. The task of studying EE in DCs is complicated by the interconnected nature of humans and mission-critical technical systems. Moreover, the literature tends to focus on technology solutions such as improvements to IT equipment, cooling infrastructure, and software, without addressing organizational and psychological drivers. Our research demystifies the complex interactions between humans and DCs, by asking What non-technical barriers impede EE investment decision-making and/or implementing energy management strategies? To begin to answer this question, we perform a literature review of 86 resources, ranging from peer-reviewed journal publications to handbooks. We also consider related fields such as organizational behavioral management and energy intensive buildings. We develop a public Zotero library, perform content coding, and complete a rudimentary network analysis. Our findings from the literature review suggest that (1) technological solutions are abundant in the literature but fall short of providing practical guidance on the pitfalls of implementation, (2) making energy efficiency a priority at the executive level of organizations will be largely ineffective if the IT and facilities staff are not directly incentivized to increase EE, and (3) there is minimal current understanding of how the individual psychologies of IT and facilities staff affect EE implementation in DCs. In the next phase of our research, we plan to interview data center operators/experts to ground-truth our literature findings and collaboratively design decarbonization policy solutions that target organizational structure, empower individual staff, and foster a supportive external market.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Computer Model Calibration with Time Series Data Using Deep Learning and Quantile Regression

Computer models play a key role in many scientific and engineering problems. One major source of uncertainty in computer model experiments is input parameter uncertainty. Computer model calibration is a formal statistical procedure to infer input parameters by combining information from model runs and observational data. The existing standard calibration framework suffers from inferential issues when the model output and observational data are high-dimensional dependent data, such as large time series, due to the difficulty in building an emulator and the nonidentifiability between effects from input parameters and data-model discrepancy. To overcome these challenges, we, in this work, propose a new calibration framework based on a deep neural network (DNN) with long short-term memory layers that directly emulates the inverse relationship between the model output and input parameters. Adopting the “learning with noise” idea, we train our DNN model to filter out the effects from data-model discrepancy on input parameter inference. We also formulate a new way to construct interval predictions for DNN using quantile regression to quantify the uncertainty in input parameter estimates. Through a simulation study and real data application with the Weather Research and Forecasting Model Hydrological modeling system (WRF-Hydro), we show our approach can yield accurate point estimates and well-calibrated interval estimates for input parameters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

PNNL DataHub NIAID Program Project: Modeling Host Responses to Understand Severe Human Virus Infections, Multi-Omic Viral Dataset Catalog Collection

The National Institute of Allergy and Infectious Diseases (NIAID) "Modeling Host Responses to Understand Severe Human Virus Infections" program project was a highly integrated and comprehensive systems biology research core, funded by the National Institute of Health (U19AI106772) from 2013-06-01 to 2018-05-31, investigating the complex host response to category A, B, and C priority pathogen infections. Resulting project deliverables include an extensive comprehensive collections of linked primary and secondary transformation viral experimental infection data. Here we provide a never before released comprehensive infectious disease collection of primary and secondary transformation multi-Omics data profiling a series of priority pathogen primary experimental studies for enhanced open access to viral Omics lifecycle datasets and project metadata. Using a highly integrated and multidisciplinary approach, linked primary data and metadata supporting secondary normalization datasets, provide critical information necessary for research reproducibility and long-term preservation. Enabling on-demand data access for research community consumption and developer reuse, serves to support new mechanistic insights and discoveries into host-pathogen interactions for aiding future biohazard data preparedness efforts in emergency response to global health crises involving viral infection.

59 BASIC BIOLOGICAL SCIENCES↗