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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Comparison of fiber-optic distributed temperature sensing and high-sensitivity sensor spatial surveying of stream temperature

Measuring surface water temperature spatial variability is needed to estimate the interaction between surface water and groundwater, evaluate fish habitat and thermal inertia, and to estimate streamflow frequency and duration. Fiber optic distributed temperature sensing (FO-DTS) has been used in rivers and lakes, providing high-resolution and sensitive temperature monitoring over large temporal and spatial scales. However, in streams with cobbly or bedrock-lined streambeds and variable bathymetry, use of FO-DTS to measure temperature close to the surface water and groundwater interface can be challenging if even feasible. FO-DTS can also be costly, involve difficult installations, and require an advanced understanding of the technology, calibration, and data processing. In this study, we compared FO-DTS stream temperature survey results to an alternative temperature survey method employing a towed transect of high-resolution temperature loggers spaced at 1-m and transported in the stream along the study reach, to measure the spatial distribution of stream-water temperature in East Fork Poplar Creek near Oak Ridge, Tennessee, USA. We assessed the applicability and limitations of the two methods, and quantitatively compared in-situ temperature survey results measured simultaneously with each method. Regression results showed strong temporal and spatial correlation between the two methods. Differences were only elevated near the stream banks in areas that were coincident with correlation slope deviations from unity, which was attributed to shallower water and lower data density. Kriging standard errors were also low at channel center with minor increases near the stream banks. Furthermore, the results suggested that the array of the individual temperature sensors can provide a practical alternative to FO-DTS for thermal characterization of surface water, providing slightly lower spatial and temporal resolution, but with higher accuracy of temperature measurement, with greater simplicity, and with a broader range of conditions where it may be applied.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Range Hood Use and Effectiveness in Reducing Indoor Air Pollution During Gas and Induction Cooking

The Cooking Energy and Ventilation Impacts on Children's Asthma (CEVICA) study measured cooking frequency, range hood use, indoor air quality and respiratory health indicators of children with asthma living in homes with gas stoves in California's San Joaquin Valley. The study installed electric induction stoves and repeated measurements over three 2-week intensive periods, at baseline and at the end of two consecutive 3-month study phases. Stove replacements occurred at the start of Phase 1 or Phase 2 by random assignment. There were 4184 cooking events identified by automated analysis of time-series data from temperature sensors mounted above the cooktops and 1038 related range hood usage events detected from data recorded by anemometers, smart plugs, or motor loggers. Analysis of 1-minute resolved PM2.5 and NO 2 data identified and quantified 2685 PM 2.5 events and 2606 NO 2 events. Range hood use was characterized as a binary variable (>3 min vs. <3 min use). Range hood use was more common during cooking events associated with particle emissions and longer cooking durations. PM 2.5 concentrations during events with range hood use were comparable to those without use, which could result from limited effectiveness or if range hoods were preferentially used during higher-emission cooking scenarios. In homes with gas cooking, integrated NO 2 concentrations were about 45 percent higher during cooking events with no range hood use compared to those range hood use. The lowest pollutant levels were observed when the range hood operated for more than half of the cooking duration. These findings show that operation of venting range hood during cooking can substantially reduce short-term indoor exposure NO 2 in homes with gas cooking.

Fang, Yi↗
DOE OSTI2015

Predicting the liquefaction phenomena from shear velocity profiling: Empirical approach to 6.3 Mw, May 2006 Yogyakarta earthquake

The liquefactions phenomena have been reported after a shocking 6.5Mw earthquake hit Yogyakarta province in the morning at 27 May 2006. Several researchers have reported the damage, casualties, and soil failure due to the quake, including the mapping and analyzing the liquefaction phenomena. Most of them based on SPT test. The study try to draw the liquefaction susceptibility by means the shear velocity profiling using modified Multichannel Analysis of Surface Waves (MASW). This paper is a preliminary report by using only several measured MASW points. The study built 8-channel seismic data logger with 4.5 Hz geophones for this purpose. Several different offsets used to record the high and low frequencies of surface waves. The phase-velocity diagrams were stacked in the frequency domain rather than in time domain, for a clearer and easier dispersion curve picking. All codes are implementing in Matlab. From these procedures, shear velocity profiling was collected beneath each geophone’s spread. By mapping the minimum depth of shallow water table, calculating PGA with soil classification, using empirical formula for saturated soil weight from shear velocity profile, and calculating CRR and CSR at every depth, the liquefaction characteristic can be identify in every layer. From several acquired data, a liquefiable potential at some depth below water table was obtained.

58 GEOSCIENCES↗
DOE OSTI2020

Post-Transcriptional Mechanisms Respond Rapidly to Ecologically Relevant Thermal Fluctuations During Temperature-Dependent Sex Determination

An organism’s ability to integrate transient environmental cues experienced during development into molecular and physiological responses forms the basis for adaptive shifts in phenotypic trajectories. During temperature-dependent sex determination (TSD), thermal cues during discrete periods in development coordinate molecular changes that ultimately dictate sexual fate and contribute to patterns of inter- and intra-sexual variation. How these mechanisms interface with dynamic thermal environments in nature remain largely unknown. By deploying thermal loggers in wild nests of the American alligator (Alligator mississippiensis) over two consecutive breeding seasons, we observed that 80% of nests exhibit both male- and female-promoting thermal cues during the thermosensitive period, and of these nests, all exhibited both male- and female-promoting temperatures within the span of a single day. These observations raise a critical question—how are opposing environmental cues integrated into sexually dimorphic transcriptional programs across short temporal scales? To address this question, alligator embryos were exposed to fluctuating temperatures based on nest thermal profiles and sampled over the course of a daily thermal fluctuation. We examined the expression dynamics of upstream genes in the temperature-sensing pathway and find that post-transcriptional alternative splicing and transcript abundance of epigenetic modifier genes JARID2 and KDM6B respond rapidly to thermal fluctuations while transcriptional changes of downstream effector genes, SOX9 and DMRT1, occur on a delayed timescale. Our findings reveal how the basic mechanisms of TSD operate in an ecologically relevant context. We present a hypothetical hierarchical model based on our findings as well as previous studies, in which temperature-sensitive alternative splicing incrementally influences the epigenetic landscape to affect the transcriptional activity of key sex-determining genes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A Graph-Net with Node Embeddings to Detect False Data Injection Attacks in Photovoltaic Systems

Distributed energy resources (DER) contribute to the operational stability of the larger power grid both at utility-scale as well as commercial and residential scales in aggregated forms. These DER in-turn are susceptible to increasing cyber threats. An adversary can plug into the same local network that a field photovoltaic (PV) system uses to interconnect its data loggers and inverters and manipulate certain measurements collected from the network or trick existing irradiance and inverter readings through false data injection attacks (FDIA). Control routines that rely on these measurements can propagate the false data, impacting critical decisions that result in a suboptimal operation or even cause intentional harm leading to inverter-tripping or unscheduled loads that need to be shed. To detect FDIA in PV systems, the paper introduces an attention-based graph neural network with node embeddings and applied it to a simple prototypical DC-coupled microgrid with PV, energy storage, and load. The algorithm shows a detection accuracy of up to 98.95%. The proposed FDIA detection technique will provide micro-grid operators with an effective method to safeguard their systems, guaranteeing the secure and reliable operation.

Parvez, Imtiaz [Utah Valley University]↗
DOE OSTI2023

Recursive Blind Forecasting of Photovoltaic Generation and Consumer Load for Microgrids

Existing forecasting frameworks that predict time-series photovoltaic (PV) generation and consumer load for micro-grids' operation and control assume near-continuous availability of real-time predictors from the field. The incoming data are used to periodically re-train the models and update forecast snapshots over a moving horizon window. However, such frameworks are not resilient to disruptions in data availability caused by losses in communications between the field sensors and data loggers. This paper bridges the shortcoming by leveraging a previously proposed forecasting framework that is resilient to abrupt changes in data quality caused by communication losses. Assuming no availability of real-time field system data, which is typical in extreme weather events such as hurricanes, the framework uses lightweight recursive time-series models to independently forecast solar irradiance, ambient temperature, PV power, and consumer load for three horizon windows: 24 hours, 12 hours, and 1 hour. Four types of ensemble-based regression trees-simple gradient boosted trees (GBR), GBR with an adaptive component (A-GBR), random forests (RF), and extra trees (ExTR)-are leveraged and their performances are compared against a simple historical weekly mean. Numerical results show that A-GBR performs better on average by 32% for 24-hour horizon and 39% for 12-hour horizon, whereas ExTR outdoes the other models on average by 10% for 1-hour horizon.

Sundararajan, Aditya↗
DOE OSTI2020

Effects of social structure and management on risk of disease establishment in wild pigs

Contact heterogeneity among hosts determines invasion and spreading dynamics of infectious disease, thus its characterization is essential for identifying effective disease control strategies. Yet, little is known about the factors shaping contact networks in many 28 wildlife species and how wildlife management actions might affect contact networks. Wild pigs in North America are an invasive, socially-structured species that pose a health concern for domestic swine given their ability to transmit numerous devastating diseases 2 such as African swine fever (ASF). Using proximity loggers and GPS data from 48 wild pigs in Florida and South Carolina, USA, we employed a probabilistic framework to estimate weighted contact networks. We determined the effects of sex, social group, and spatial distribution (monthly home range overlap and distance) on wild pig contact. We also estimated the impacts of management-induced perturbations on contact and inferred their effects on ASF establishment in wild pigs with simulation. Social group membership was the primary factor influencing contacts. Between-group contacts depended primarily on space use characteristics, with fewer contacts among groups separated by >2 km and no contacts among groups >4 km apart within a month. Modeling ASF dynamics on the contact network demonstrated that indirect contacts resulting from baiting (a typical method of attracting wild pigs or game species to a site to enhance recreational hunting) increased the risk of disease establishment by ~33% relative to direct contact. Low-intensity population reduction (<5.9% of the population) had no detectable impact on contact structure but reduced predicted ASF establishment risk relative to no population reduction. Here, we demonstrate an approach for understanding the relative role of spatial, social, and individual-level characteristics in shaping contact networks and predicting their effects on disease establishment risk, thus providing insight for optimizing disease control in spatially- and socially-structured wildlife species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Status and prospects for renewable energy using wood pellets from the southeastern United States

Abstract The ongoing debate about costs and benefits of wood‐pellet based bioenergy production in the southeastern United States ( SE USA ) requires an understanding of the science and context influencing market decisions associated with its sustainability. Production of pellets has garnered much attention as US exports have grown from negligible amounts in the early 2000s to 4.6 million metric tonnes in 2015. Currently, 98% of these pellet exports are shipped to Europe to displace coal in power plants. We ask, ‘How is the production of wood pellets in the SE USA affecting forest systems and the ecosystem services they provide?’ To address this question, we review current forest conditions and the status of the wood products industry, how pellet production affects ecosystem services and biodiversity, and what methods are in place to monitor changes and protect vulnerable systems. Scientific studies provide evidence that wood pellets in the SE USA are a fraction of total forestry operations and can be produced while maintaining or improving forest ecosystem services. Ecosystem services are protected by the requirement to utilize loggers trained to apply scientifically based best management practices in planning and implementing harvest for the export market. Bioenergy markets supplement incomes to private rural landholders and provide an incentive for forest management practices that simultaneously benefit water quality and wildlife and reduce risk of fire and insect outbreaks. Bioenergy also increases the value of forest land to landowners, thereby decreasing likelihood of conversion to nonforest uses. Monitoring and evaluation are essential to verify that regulations and good practices are achieving goals and to enable timely responses if problems arise. Conducting rigorous research to understand how conditions change in response to management choices requires baseline data, monitoring, and appropriate reference scenarios. Long‐term monitoring data on forest conditions should be publicly accessible and utilized to inform adaptive management.

09 BIOMASS FUELS↗
DOE OSTI2016

Determining fine-scale migratory connectivity and habitat selection for a migratory songbird by using new GPS technology

Migratory aerial insectivores are among the fastest declining avian group, but our understanding of these trends has been limited by poor knowledge of migratory connectivity and the identification of critical habitat across the vast distances they travel annually. Using new, archival GPS loggers, we tracked individual purple martins ( Progne subis ) from breeding colonies across North America to determine precise (<10m) locations of migratory and overwintering roost locations in South America and to test hypotheses for fine-scale migratory connectivity and habitat use. We discovered weak migratory connectivity at the roost scale, and extensive, fine-scale mixing of birds in the Amazon from distant (>2000 km) breeding sites, with some individuals sharing the same roosting trees. Despite vast tracts of contiguous forest in this region, birds occupied a much more limited habitat, with most (56%) roosts occurring on small habitat islands that were strongly associated with water. Only 17% of these roosts were in current protected areas. As a result, these data reflect a critical advance in our ability to remotely determine precise migratory connectivity and habitat selection across vast spatial scales, enhancing our understanding of population dynamics and enabling more effective conservation of species at risk.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

MRE-Code-Hub/extract_loggernet

This script is used to read the infinitely growing data files created by a Campbell Data Logger to extract hourly data and write the content to hourly output files.

Sivaraman, Chitra↗
DOE OSTI2024

PV Generation and Load Forecasting for Adjuntas PR Community Microgrids

Existing frameworks to forecast time-series photovoltaic (PV) output power and consumer load for microgrid operations and controls assume a near-continuous availability of real-time input features from the field assets such as PV inverters, energy meters, and weather station. These incoming data points are used to periodically retrain models and update forecast snapshots over a moving horizon window, be it one hour-ahead, one-day ahead, or one-week ahead. However, such frameworks are not resilient to disruptions in data availability caused by losses in communications between the field sensors and data loggers. Hence, there is a need for programs that assume no availability of real-time microgrid asset data and still make reliable forecasts that can be used for decision-making. Such programs would be apt to function in extreme weather events such as hurricanes and would use lightweight recursive time-series models to independently forecast solar irradiance and ambient temperature, then compute PV power from those forecasts, as well as independently forecast consumer load. The codebase performs forecasting for the scenario of when the microgrid does not have a reliable access to forecasts or real-time observations of solar irradiance (I) and ambient temperature (AT) and load (Load) to be able to adequately forecast, in real-time, the PV power production or a business' load. In this case, using historical values of PV power and load, a univariate forecasting of generation and consumption are respectively made. The use-case in particular has two sub-scenarios: one, a normal 7-day ahead forecast where the unavailability of real-time data is assumed due to infrastructure issues such as loss of communication or sensor maintenance or service downtimes. Whereas a hurricane-caused unavailability of real-time data requires a second model trained specifically on historical hurricane days to be able to capture the extreme day behavior of generation in particular, and load if applicable. A gradient boosted regression tree comprises an ensemble of additive models that map between the input of historical values (be it irradiance, temperature, or load) and their corresponding output forecasts of a given horizon such that the individual learner predictions are summed up over the total number of such learners in the ensemble to produce an aggregate forecast. A weighting mechanism is applied to the training data in each iteration, where actual and forecast values are compared to penalize incorrect forecasts by increasing the weight and reducing it to reward correct forecasts. The code's benefits are that it: (a) accounts for a contingency where communication loss renders newly measured real-time data unavailable for model tuning and snapshot updates; (b) presents blind forecasting that recursively determines the next time-step value in a horizon using the forecast of the same attribute from a prior step; and (c) employs lightweight models that, once trained, can reliably generalize for different horizons, which make them suitable for enhancing the resilience of field microgrids prone to extreme events that encounter disruptions to data availability.

Sundararajan, Aditya [Oak Ridge National Laborator↗
DOE OSTI2025

LLUDA (Low-cost Logging for Ubiquitous Decarbonization and Analysis) [SWR-24-131]

This repository features an Arduino-based GPS logger, called LLUDA, which uses the Adafruit Ultimate GPS FeatherWing with the Adafruit Feather M0 Adalogger, designed to capture and log GPS data in CSV format onto an SD card (embedded inside the Feather M0 Adalogger). The FeatherWing is powered through the vehicle's auxiliary power (from cigarette lighter) socket via a micro USB cable. The data it collects can be used to analyze vehicle operations and support vehicle modeling applications, helping to assess the potential for electrification.

Fakhimi, Setayesh [National Renewable Energy Labor↗
DOE OSTI2023

In situ temperature measurement of a deforming interface with thermoelectricity

Accurate temperature measurement is critical to understanding the thermomechanical conditions and microstructural evolution that materials experience during severe plastic deformation (SPD). Others have previously measured the interface between a non-deforming tool and metal workpiece using thermocouples, infrared measurement, and the thermoelectric principle. Measuring temperature within the deforming material itself, especially at a high-shear deforming interface, has proven to be extremely difficult, thus limiting the application of these measurements to fundamental SPD research. In this study, the thermoelectric principle is used to directly measure temperature at the interface between copper and nickel billets undergoing SPD. This is done by utilizing the deforming materials themselves as two halves of a thermocouple junction. The spatial resolution of this measurement is shown via microscopy to be less than 1 micron. Temperature changes are sensed almost instantly, with time delays on the order of the data logger frequency (0.01 seconds). Process variations that are both very brief (<0.05 seconds) and minute (<3°C) are able to be detected by this method.

Taysom, Brandon Scott↗
DOE OSTI2013

New River Geothermal Exploration (Ram Power Inc.)

The New River Geothermal Exploration (DOE Award No. EE0002843) is located approximately 25km south of the Salton Sea, near town of Brawley in Imperial County and approximately 150km east of San Diego, California. A total of 182 MT Logger sites were completed covering the two separate Mesquite and New River grids. The data was collected over a frequency range of 320Hz to 0.001Hz with variable site spacing. A number of different inversion algorithms in 1D, 2D and 3D were used to produce resistivity-depth profiles and maps of subsurface resistivity variations over the survey area. For 2D inversions, a total of eighteen lines were constructed in east-west and north-south orientations crossing the entire survey area. For MT 3D inversion, the New River property was divided in two sub-grids, Mesquite and New River areas. The report comprises of two parts. For the first part, inversions and geophysical interpretation results are presented with some recommendations of the potential targets for future follow up on the property. The second part of the report describes logistics of the survey, survey parameters, methodology and the survey results (data) in digital documents. The report reviews a Spartan MT survey carried out by Quantec Geoscience Limited over the New River Project in California, USA on behalf of Ram Power Inc. Data was acquired over a period of 29 days from 2010/06/26 to 2010/07/24.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Snake River Plain Geothermal Play Fairway Analysis Project MT Data

MT is measured in the field by using induction coils to measure the time-varying magnetic source for frequencies between 1000-0.001~Hz, and electric dipoles to measure the Earth's electrical response. Because the magnetic source field is polarized, orthogonal directions of the fields need to be measured to get a complete description of the fields. In all measurements collected for this project, induction coils and electric dipoles were aligned with geomagnetic north and east. MT data were collected at 22 stations with a ZEN 32-bit data logger developed by Zonge International, magnetic fields were measured with ANT-4 induction coils, and electric fields where measured with Ag-AgCl reference electrodes from Borin on 50~m dipoles. The data was collected on a repeating schedule of 10~min at 4096~samples/s and 7 hours and 50 minutes at 256 samples/s over a 20-24 hour period. To convert time series data into the frequency domain and get estimations of the impedance tensor, the processing code BIRRP was used (Chave & Thompson 2004). Simultaneous measurements were used as remote references to reduce noise and bias in the data. Chave, A. D., & Thomson, D. J. 2004. Bounded inuence magnetotelluric response function estimation. Geophys. J. Int., 157, 988-1006.

15 GEOTHERMAL ENERGY↗
DOE OSTI2019

EGS Collab Experiment 1: Core Logs

Core logs from the EGS Collab project Experiment 1 for the stimulation (Injection) well (E1-I), the Production well (E1-P), and monitoring wells (E1-OT, E1-OB, E1-PST, E1-PSB, E1-PDT, and E1-PDB) on the 4850 Level of SURF (the Sanford Underground Research Facility), single PDF file, 5-ft run intervals. In the monitoring well IDs, "O" indicates that the well is orthogonal to the anticipated fracture plane, "P" indicates that the well is parallel to the anticipated fracture plane, "S" indicates a shallow well, "D" indicates a deep well, "T" refers to top, and "B" refers to bottom. Logs include: experiment number; borehole ID; depth interval; run number; final packed core box number; scribe line (yes/no; red-on-right convention); logging dates; logger initials; as well as sketches of core foliation, folding, and fracturing with additional details and notes on other features of interest.

15 GEOTHERMAL ENERGY↗