SEARCH · Engineering Papers

Results for “research data”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 721 records · Page 40

All sources NASA NTRS DOE OSTI

DOE OSTI2025

Deployment and Evaluation of SciStream on OLCF's Advanced Computing Ecosystem (ACE)

The growing demand for real-time analysis, experimental steering, and decision-making in scientific workflows has created a need for tightly coupled integrations between experimental facilities and high-performance computing (HPC) systems. The Department of Energy’s Integrated Research Infrastructure (IRI) initiative highlights data streaming as a key capability for enabling memory-to-memory data transfers, bypassing the limitations of traditional store-and-forward models. SciStream is a toolkit developed by researchers at Argonne National Laboratory (ANL) to support such streaming by addressing cross-domain security, delegated authentication, and application transparency. We deployed and evaluated SciStream on the Oak Ridge Leadership Computing Facility’s (OLCF) Advanced Computing Ecosystem (ACE) infrastructure, leveraging the Olivine OpenShift cluster and its high-bandwidth Data Streaming Nodes (DSNs) as gateway nodes. Our evaluation included synthetic streaming workloads derived from IRI science workflows, a streaming simulator, and integration with RabbitMQ to handle low-level messaging. This report documents the deployment process, performance evaluation, and challenges encountered, along with opportunities for future improvements.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Assessment of Potential Ergonomic Injury Risk in the Nuclear Material Processing Glovebox Environment [Capstone Project]

Gloveboxes are isolation barriers that are used within many different industries such as pharmaceuticals, electronic parts fabrication, nuclear, and biological. The research on glovebox ergonomics is currently limited with few ergonomic professionals that focusing exclusively on glovebox working environments. There is existing documentation on occupational injuries, such as musculoskeletal disorders (MSD), sustained as a direct result of working in gloveboxes. The main elements that pose ergonomic risks to glovebox workers are operational repetition, duration, force, vibration, lifting heavy (more than 15lbs with two hands) objects, and awkward postures. This paper examines a small sample of the potential causal or risk factors that lead to the ergonomic injuries. A meta-analysis utilizing a random effect model is used to examine data from several studies which focus on, dexterity and strength changes as result of glove thickness, and robotic assistive technology as a means to improve postural mechanics. With the meta-analysis technique, similar data sets from different research studies can be coalesced into a single weighted, statistically significant result for subsequent consideration. The results from the analysis show that increased glove thickness results in decreased dexterity for operators thus increasing ergonomic risk factors such as task duration. It is also shown that glove thickness decreases grip strength but a similar decrease in pinch strength is not definitively demonstrated. With decreased grip strength, operators will need to exert more force (a known ergonomic risk factor) on processing tools, etc. during operations thus increasing the risk of ergonomic injury. The use of robotic assistive technology as a means to improve operator posture (risk factor) was also examined. Although it may be intuitively assumed that human-robotic collaboration would be beneficial in reducing risk factors, the result from this study’s analysis was not statistically significant. It is inferred that with additional directed research on this topic another study/analysis could be statistically significant demonstrating the benefits of the technology.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Water Quality Parameters at the Elkhorn Slough National Estuarine Research Reserve (ESNERR)

The following dataset includes water quality and meteorological forcing data between 2003 and 2019 across four Elkhorn Slough National Estuarine Research Reserve (ESNERR) stations. The data are monthly averaged and include orthophosphate, ammonium, Nitrite, Nitrate, Nitrite + Nitrate, Chlorophyll, Temperature of water, Specific Conductance, Salinity, Dissolved Oxygen, Air Temperature and Precipitation. The data were compiled for wavelet and other statistical analyses. All data included herein are associated with the following manuscript: Venkatesh*, S., B. Arora, D. Dwivedi, S. Vezhaperambu, M. V. Ramesh (2021), Temporal Variability of Water Quality Parameters at the Elkhorn Slough Estuary using Wavelets, IEEE Proceedings on 6th International Conference for Convergence in Technology, DOI:10.1109/I2CT51068.2021.9418159.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Guest Editorial: Advanced Data-Analytics for Power System Operation, Control, and Enhanced Situational Awareness

Along with the smart grid development, modern power systems are entering a ‘data-intensive’ era. A vast volume of data from power grids is being collected through advanced sensing and communication technologies, such as smart metering data, phasor measurement data, as well as meteorological data (e.g., wind speed and solar irradiance) related to renewable power generation. Such data contains comprehensive information about the power system covering equipment's health status, power grid's static and dynamic characteristics, renewable power generation, customers’ electricity usage pattern, etc. Therefore, advanced data-analytics techniques are needed to convert such data to knowledge for practical applications. In line with the trend of widespread data-driven applications in power systems, this Special Issue aims to present state-of-the-art research works on advanced data-analytics for power system's operation, control, and situational awareness. There are in total twenty-six papers accepted for publication in this Special Issue through careful peer reviews and revisions. Under the overarching theme of data-driven applications in power systems, the selected papers are broadly categorised into five topics. The summary of every topic is given below. You are, however, strongly encouraged to read the full paper if interested.

Xu, Yan↗

DOE OSTI2020

Computing Bottleneck Structures at Scale for High-Precision Network Performance Analysis

The Theory of Bottleneck Structures is a recently-developed framework for studying the performance of data networks. It describes how local perturbations in one part of the network propagate and interact with others. This framework is a powerful analytical tool that allows network operators to make accurate predictions about network behavior and thereby optimize performance. Previous work implemented a software package for bottleneck structure analysis, but applied it only to toy examples. In this work, we introduce the first software package capable of scaling bottleneck structure analysis to production-size networks. Here, we benchmark our system using logs from ESnet, the Department of Energy's high-performance data network that connects research institutions in the U.S. Using the previously published tool as a baseline, we demonstrate that our system achieves vastly improved performance, constructing the bottleneck structure graphs in 0.21 s and calculating link derivatives in 0.09 s on average. We also study the asymptotic complexity of our core algorithms, demonstrating good scaling properties and strong agreement with theoretical bounds. These results indicate that our new software package can maintain its fast performance when applied to even larger networks. They also show that our software is efficient enough to analyze rapidly changing networks in real time. Overall, we demonstrate the feasibility of applying bottleneck structure analysis to solve practical problems in large, real-world data networks.

benchmark↗

DOE OSTI2021

AmeriFlux FLUXNET-1F CA-ER1 Elora Research Station

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site CA-ER1 Elora Research Station. This is the FLUXNET version of the carbon flux data for the site CA-ER1 Elora Research Station produced by applying the standard ONEFlux (1F) software. Site Description - This is an agricultural site that typically grows corn. It is part of a larger research project on N2O fluxes from different fertilizer treatments. The site is relatively flat and homogenous. The site has been managed as agricultural land since the 1960’s and records exist of the previously-grown crops.

Wagner-Riddle, Claudia↗

DOE OSTI2026

AmeriFlux FLUXNET-1F US-Aud Audubon Research Ranch

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Aud Audubon Research Ranch. This is the FLUXNET version of the carbon flux data for the site US-Aud Audubon Research Ranch produced by applying the standard ONEFlux (1F) software. Site Description - None supplied.

Krishnan, Praveena [NOAA/ARL]↗

DOE OSTI2025

Data as a Key Resource in Catalysis: A Community Account

The deployment of artificial intelligence (AI) is transforming the scientific fields central to interdisciplinary catalysis research. By enabling more effective use of data, AI (including simpler machine learning and data science tools) holds great promise for accelerating discoveries. However, progress has so far been modest, largely due to the lack of standardized, machine-readable, and openly shared catalysis data. This perspective, accounting for community insights emerging at conferences, analyses the underlying reasons for these challenges and proposes solutions to a future whereFAIR data management becomes an integral part of research in catalysis. In the short-term, we deem that mandatory FAIR data depositing prior to scientific publications along with consensualized top-down guidelines on data sharing powered by ease-to-use tools can make the necessary step change happen to catalyse data as key resource in our community.

36 - MATERIALS SCIENCE↗

DOE OSTI2023

Connecting People to Data: Enabling Data Connected Communities through Enhancements to the Geothermal Data Repository: Preprint

The Department of Energy's (DOE) Geothermal Data Repository (GDR) has implemented a series of new features designed to connect people to data. These features, which are based on feedback from the GDR user community and surveys of the greater geothermal research community, are designed to improve data quality and empower members of all communities to better engage with geothermal data resources by providing universal access to data and by improving the connections between data providers, subject matter experts, and the communities of people using GDR data. This paper will explore some of the recent enhancements made to the GDR to improve data discoverability, reduce submission time, and result in better quality data submissions. These improvements include the ability for users to save a list of their favorite datasets, search for insight into geothermal datasets or data availability, or sign up to receive notifications of future updates to specific datasets. These improvements aim to enhance the overall user experience of the GDR while further connecting communities to the data they need to inform decisions, advance geothermal research, and develop innovative solutions to local energy problems.

DOE↗

DOE OSTI2023

Connecting People to Data: Enabling Data Connected Communities through Enhancements to the Geothermal Data Repository

access↗

DOE OSTI2022

System-on-chip integrated circuit technology applications on the DIII-D tokamak for multi-field measurements

Several mm-wave diagnostics on the DIII-D tokamak provide multi-scale and multi-dimensional measurements of plasma profile evolution and turbulence fluctuations. Mm-wave fusion plasma diagnostics that adopt system-on-chip integrated circuit technology can provide better space utilization, flexible installation, and improved sensitivity. In order to further extend this technology for additional fusion facilities with a higher toroidal magnetic field, V-band (55–75 GHz) and F-band (90–140 GHz) chips for Microwave Imaging Reflectometer (MIR) and Electron Cyclotron Emission Imaging (ECEI) instruments are developed and tested in the Davis Millimeter Wave Research Center (DMRC). Current measurement data show that correlation between these SoC-based diagnostic instruments with other state-of-the-art diagnostics enables co-located multi-field turbulence fluctuation measurement.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

Leafweb: Leaf Gas Exchange and Pulse-Amplitude Modulated Fluorometry for C4 Species, June 2026 Release

This dataset contains leaf gas exchange and Pulse-Amplitude Modulated (PAM) fluorometry for 98 C4 species. The C4 photosynthetic pathway employs specialized CO2 concentration mechanisms and Kranz anatomy to enrich CO2 concentration around Rubisco, the enzyme that catalyzes carbon fixation in the Calvin-Benson cycle to suppress photorespiration and increase the use efficiencies of light, nitrogen, and water as compared to the C3 photosynthetic pathways. Large-scale C4 photosynthetic datasets are relatively scarce, which has affected C4 photosynthesis research. To improve C4 photosynthetic data availability, Leafweb organized an effort to systematically collect, compile, standardize, and organize measurements of leaf gas exchange and/or Pulse-Amplitude Modulated (PAM) fluorometry of C4 species. This derived a C4 photosynthetic dataset containing measurements made by independent researchers in multiple countries in various environments (field, garden, or greenhouse). It covers three biochemical subtypes – the nicotinamide adenine dinucleotide phosphate-malic enzyme (NADP-ME), nicotinamide adenine dinucleotide-malic enzyme (NAD-ME), and phosphoenolpyruvate carboxykinase (PEP-CK) subtypes. This dataset is useful for using Artificial Intelligence / Machine Learning and mechanistic models to study C4 photosynthesis and compare across different biochemical subtypes. This dataset contains 3 compressed (*.zip) folders containing 1,892 data files in comma-separate values (*.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma-separate values (*.csv) format and a user guide in PDF (*.pdf) format.

Zhou, Haoran [Tianjin University, China]↗

DOE OSTI2024

Testing biasedness of self-reported microbusiness innovation in the annual business survey

This study tests for potential bias in self-reported innovation due to the inclusion of a research and development (R&D) module that only microbusinesses (less than 10 employees) receive in the Annual Business Survey (ABS). Previous research found that respondents to combined innovation/R&D surveys reported innovation at lower rates than respondents to innovation-only surveys. A regression discontinuity design is used to test whether microbusinesses, which constitute a significant portion of U.S. firms with employees, are less likely to report innovation compared to other small businesses. In the vicinity of the 10-employee threshold, the study does not detect statistically significant biases for new-to-market and new-to-business product innovation. Statistical power analysis confirms the nonexistence of biases with a high power. Comparing the survey design of ABS to earlier combined innovation/R&D surveys provides valuable insights for the proposed integration of multiple Federal surveys into a single enterprise platform survey. The findings also have important implications for the accuracy and reliability of innovation data used as an input to policymaking and business development strategies in the United States.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

Ventilation and Indoor Air Quality in Recently Constructed U.S. Homes: Measured Data From Select Southeastern States

Little data is available that quantifies IAQ or associates contaminant levels with air exchange in U.S. homes. To address this gap, the U.S. Department of Energy (DOE) Building America Program conducted a study to characterize IAQ in U.S. homes constructed since 2013, along with presence, functionality, and occupant use of control measures. Specific objectives of this study effort included: Measuring time-integrated concentrations and temporal profiles of established contaminants of concern; monitoring the use of ventilation equipment; and tracking activities that impact air pollutant emissions and removal processes in typical homes in various climate zones; Characterizing the prevalence, type, and installed performance of mechanical ventilation equipment in new homes; exploring regional variations in system designs and performance; Investigating associations between contaminant levels and the presence of control measures including whole-house mechanical ventilation (WHMV). The study engaged research teams to collect data at a regional level. This report introduces the study protocol and presents and discusses high-level results obtained from Florida Solar Energy Center (FSEC) data collection efforts in the southeastern U.S. states of Florida, Georgia, and South Carolina.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Meta-Study of Particulate Detection Losses on Radioactive Air Sample Filters

Several mathematical relationships between air sample filter mass loading and the correlated analytical self-absorption factor were developed using data from other published research in this meta-study. Gross-alpha and -beta applications are addressed for this research. As filter media becomes loaded with particulate matter, there is potential for measurement losses due to self-absorption by mass loading. Components contributing to absorption include particulate dust, radioactive particulates, and filter material. Standards indicate a correction factor should be used when the penetration of radioactive material into the collection media or self-absorption of radiation by the material collected would reduce the detection rate by more than 5%. Previously, losses due to self-absorption have been reported up to 100% over a range up to ~10 mg∙cm -2 mass loading. These absorption losses then can be used to determine a correction factor for sample results. For low mass loadings (e.g., ≤0.1 mg∙cm -2 ) corrections factors in the 0.85 - 1 range have been recommended and used, while at higher mass loadings nearer to 10 mg∙cm -2 correction factors closer to 0 (representing near 100% losses) are used. Based on data from published studies, the different methods for relating percent loss due to self-absorption to mass loading include linear, exponential, quadratic, and trinomial derived functions. Where applicable, both forced zero and non-forced zero results were evaluated. From the derived functions evaluated, the trinomial function provided the best fit. Once the sample filter mass loading is known, the trinomial function can be applied to estimate losses and the corresponding self-absorption factor. When applied to routine operating conditions for radiological facility stacks monitored at the Pacific Northwest National Laboratory for an average sample filter mass loading of 0.09 ± 0.12 (2σ) mg∙cm -2 (excluding negative values and outliers) and a range from 0 - 0.24 mg∙cm -2 , the estimated trinomial function nominal self-absorption losses are less than 5% at 0.09 mg∙cm -2 and less than 10% at 0.24 mg∙cm -2 . The trinomial function is one method that may be used to adjust the activity results of an air sample when the sample-specific mass loading is determined. The application of no correction factor when the ANSI/HPS N13.1-2021 guidance of a 5% threshold for loss is not reached with typical stack sample mass loadings may be reasonable in high-efficiency particulate air filtered systems. For simplicity, it would be conservative in assigning the self-absorption correction factor at the 5% threshold (i.e., 0.95) for general uses but in cases of heavy mass loading to calculate the factor.

air sampling↗

DOE OSTI2026

Leveraging generative artificial intelligence to bridge domain gaps in wind turbine research

A central challenge in wind turbine health monitoring is the scarcity of real-world data due to limited instrumentation, leading researchers to rely on simulation models that often suffer from reduced fidelity. However, even within simulation environments, discrepancies arise because of modeling assumptions, and configuration fidelities, creating domain gaps that limit the transferability of learned representations. Here, to investigate domain translation under controlled conditions, this project explores the use of generative artificial intelligence, specifically cycle-consistent generative adversarial networks (CGANs), to bridge the gap between OpenFAST simulation models representing 1.5 MW and 5 MW wind turbines. A physics-informed CGAN architecture is introduced, where a simplified turbine tower dynamics model is incorporated into the training loss to ensure physically consistent outputs. Quantitative results showed moderate to high agreement in frequency-domain features. Incorporating the physics-informed loss function improved the R 2 values by 30%, reduced the RMSE from 1.39 to 1.1 m/s 2 , and reduced training time by 82%. Furthermore, under increased turbulence intensity (IEC Category A), the RMSE remained stable at approximately 1.1 m/s 2 . While the present study is entirely simulation-based, it establishes a pipeline for evaluating physics-informed generative domain translation, which may serve as a foundation for future simulation-to-reality validation studies.

17 WIND ENERGY↗

DOE OSTI2026

Solar Resource Measurements in Eugene, OR: Cooperative Research and Development Final Report, CRADA Number CRD-07-00252

Site-specific, long-term, continuous, and high-resolution measurements of solar irradiance are important for developing renewable resource data. These data are used for several research and development activities consistent with the NLR mission: establish a national 3-year climatological database of measured solar irradiances; provide high quality ground-truth data for satellite remote sensing validation; support development of radiative transfer models for estimating solar irradiance from available meteorological observations; provide solar resource information needed for technology deployment and operations. Data acquired under this agreement will be available to the public through NLR's Measurement & Instrumentation Data Center – MIDC (http://www.nlr.gov/midc) Or the Renewable Resource Data Center - RReDC (http://rredc.nlr.gov). The MIDC offers a variety of standard data display, access, and analysis tools designed to address the needs of a wide user audience (e.g., industry, academia, and government interests).

14 SOLAR ENERGY↗

DOE OSTI2021

Elucidating and predicting the dynamic evolution of water and land systems due to natural and energy-related forcings

Focal Area(s): 3. Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI; & 1. Data acquisition and assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization, unsupervised learning (including deep learning), and hardware-related efforts involving AI (e.g., edge computing). Science Challenge: Interactions between water, land, and energy systems are complex and occur on a variety of scales, ranging from local to basinal to regional. Accurately predicting the behavior of ground water and surface water systems for 5-10 years and beyond requires an understanding of the current system and the ability to model both the natural system at scale and human-induced forcings related to energy and other activities. Artificial intelligence and machine learning (AI/ML) combined with modern compilation and integration efforts for U.S. groundwater and surface water systems present potential solutions to bolstering detailed physics-based models of these systems. Big data tied with ML and physics-based modeling can drive breakthroughs in understanding the earth system, but research is often impeded by data access (e.g., privacy issues), quality, formats, gaps, multi-source, multi-scale, integration, and spatiotemporal challenges. Effective integration of real data and simulated (synthetic) data that fill gaps is critical. Overcoming these complex data and model integration challenges will enable a transformational approach to acquiring enhanced understanding of environmental systems.

54 ENVIRONMENTAL SCIENCES↗