Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Data Set”

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.

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

At least 793 records · Page 44

DEPRECATED - State Policies and Programs for Community Solar (2024 Q2 Update)

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nrel.gov/submissions/249 The purpose of this dataset is to summarize current community solar policies and low-income stipulations by the state in the United States as of April 2024. This list has been reviewed, but errors may exist, and the list may not be comprehensive. NREL invites input to update or add to the database. To submit updates, additions, or corrections please find contact information on the current data set page linked above.

14 SOLAR ENERGY↗

DEPRECATED - State Policies and Programs for Community Solar (2024 Q3 Update)

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nlr.gov/submissions/249 The purpose of this dataset is to summarize current community solar policies and low-income stipulations by state in the United States as of June 2024. The "State_Program" sheet summarizes the key policy details for each state. This list has been reviewed, but errors may exist, and the list may not be comprehensive. NREL invites input to update or add to the database. To submit updates, additions, or corrections please find contact information on the current data set page linked above.

14 SOLAR ENERGY↗

Literature Review of PuO 2 Calcination time and Temperature Data for Specific Surface Area

The literature has been reviewed in December 2011 for calcination data of plutonium oxide (PuO 2 ) from plutonium oxalate Pu(C 2 O 4 ) 2 precipitation with respect to the PuO 2 specific surface area (SSA). A summary of the literature is presented for what are believed to be the dominant factors influencing SSA, the calcination temperature and time. The PuO 2 from Pu(C 2 O 4 ) 2 calcination data from this review has been regressed to better understand the influence of calcination temperature and time on SSA. Based on this literature review data set, calcination temperature has a bigger impact on SSA versus time. However, there is still some variance in this data set that may be reflecting differences in the plutonium oxalate preparation or different calcination techniques. It is evident from this review that additional calcination temperature and time data for PuO 2 from Pu(C 2 O 4 ) 2 needs to be collected and evaluated to better define the relationship. The existing data set has a lot of calcination times that are about 2 hours and therefore may be underestimating the impact of heating time on SSA. SRNL recommends that more calcination temperature and time data for PuO 2 from Pu(C 2 O 4 ) 2 be collected and this literature review data set be augmented to better refine the relationship between PuO 2 SSA and its calcination parameters.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

BLDAP Intro to Python/Data Science Curriculum v1

The Github repository contains the Jupyter notebooks for the intro to Python / Data Science course for Berkeley Lab Director's Apprenticeship Program (BLDAP). This course is designed for students with little to no experience in coding to learn skills in Python necessary for data science. Students utilize Jupyter notebooks throughout the course. The overall goal is for students to learn how to use Python to clean, analyze, and visualize large data sets in order to communicate effectively their conclusions about the data set. Students apply the skills they learned on actual data sets provided by researchers in Berkeley Lab.

Hales, Laurel [Lawrence Berkeley National Laborato↗

Turbulence Properties at the ENA site

The data set includes the turbulence properties as derived from the data collected by the Doppler lidar (DL) at the ARM Eastern North Atlantic (ENA) site. Among the reported turbulence properties included are profiles of variance of vertical air motion, skewness of vertical air motion, updraft strength, downdraft strength, updraft fraction, and downdraft fraction. Due to the persistent presence of drizzle at the location, the DL echoes were classified as those corresponding to aerosols and drizzle using the backscatter cross-section threshold. This daily backscatter cross-section threshold is also reported. The data set begins on January 1, 2015 and ends January 1, 2021. The gaps in the data set correspond to the periods when the instrument was not operational. The header of the NetCDF file is as below. netcdf dl.turb.stats { dimensions: time = 52609; height = 241; threshold_time = 1828; variables: double time(time); time:Long_Name = "seconds since 1970-1-1"; time:units = "seconds"; double height(height); height:Long_Name = "height of the center of the range gate above ground"; height:units = "meters"; double echo_fraction(time, height); echo_fraction:Long_Name = "Fraction of hour with valid Doppler lidar echoes" ; echo_fraction:units = "N/A"; double wvar(time, height); wvar:Long_Name = "Variance of vertical air motion"; wvar:units = "m^2/s^2"; double wskew(time, height); wskew:Long_Name = "Skewness of vertical air motion"; wskew:units = "N/A"; double updraft_velocity(time, height); updraft_velocity:Long_Name = "Hourly averaged updraft velocity"; updraft_velocity:units = "m/s"; double downdraft_velocity(time, height); downdraft_velocity:Long_Name = "Hourly averaged downdraft velocity"; downdraft_velocity:units = "m/s"; double updraft_fraction(time, height); updraft_fraction:Long_Name = "Hourly updraft fraction"; updraft_fraction:units = "N/A"; double downdraft_fraction(time, height); downdraft_fraction:Long_Name = "Hourly downdraft fraction"; downdraft_fraction:units = "N/A"; double threshold_time(threshold_time); threshold_time:Long_Name = "Seconds since 1970-1-1 for the day"; threshold_time:units = "seconds"; double bscat_threshold(threshold_time); bscat_threshold:Long_Name = "Backscatter threshold used for discerning lidar returns from hydrometeors and aerosols."; bscat_threshold:units = "1/(m*Sr)"; }

54 ENVIRONMENTAL SCIENCES↗

DEPRECATED - Sharing the Sun Community Solar Project Data (June 2022)

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nrel.gov/submissions/244 This database represents a list of community solar projects identified through various sources as of June 2022. The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects. Updated information can be submitted to the contact(s) located on the current data set page linked at the top.

14 SOLAR ENERGY↗

DEPRECATED - State Policies and Programs for Community Solar (2022)

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nlr.gov/submissions/249 The purpose of this dataset is to summarize current community solar policies and low-income stipulations by the state in the United States as of December 2022. The "State_Program" sheet summarizes the key policy details for each state. This dataset is associated with the National Renewable Energy Laboratory (NREL) publication "Expanding Solar Access: State Community Solar Landscape (2022)." https://www.nlr.gov/docs/fy23osti/84247.pdf This list has been reviewed, but errors may exist, and the list may not be comprehensive. NREL invites input to update or add to the database. Please submit updates, additions, or corrections please find contact information on the current data set page linked above.

14 SOLAR ENERGY↗

Uncertainty-Informed Volume Visualization using Implicit Neural Representation

The increasing adoption of Deep Neural Networks (DNNs) has led to their application in many challenging scientific visualization tasks. While advanced DNNs offer impressive generalization capabilities, understanding factors such as model prediction quality, robustness, and uncertainty is crucial. These insights can enable domain scientists to make informed decisions about their data. However, DNNs inherently lack ability to estimate prediction uncertainty, necessitating new research to construct robust uncertainty-aware visualization techniques tailored for various visualization tasks. In this work, we propose uncertainty-aware implicit neural representations to model scalar field data sets effectively and comprehensively study the efficacy and benefits of estimated uncertainty information for volume visualization tasks. We evaluate the effectiveness of two principled deep uncertainty estimation techniques: (1) Deep Ensemble and (2) Monte Carlo Dropout (MC-Dropout). These techniques enable uncertainty-informed volume visualization in scalar field data sets. Our extensive exploration across multiple data sets demonstrates that uncertainty-aware models produce informative volume visualization results. Moreover, integrating prediction uncertainty enhances the trustworthiness of our DNN model, making it suitable for robustly analyzing and visualizing real-world scientific volumetric data sets.

Saklani, Shanu↗

Measurements of the E-mode polarization and temperature-E-mode correlation of the CMB from SPT-3G 2018 data

We present measurements of the E -mode ( E E ) polarization power spectrum and temperature- E -mode ( T E ) cross-power spectrum of the cosmic microwave background using data collected by SPT-3G, the latest instrument installed on the South Pole Telescope. This analysis uses observations of a 1500 deg 2 region at 95, 150, and 220 GHz taken over a four-month period in 2018. We report binned values of the E E and T E power spectra over the angular multipole range 300 ≤ ℓ < 3000 , using the multifrequency data to construct six semi-independent estimates of each power spectrum and their minimum-variance combination. These measurements improve upon the previous results of SPTpol across the multipole ranges 300 ≤ ℓ ≤ 1400 for E E and 300 ≤ ℓ ≤ 1700 for T E , resulting in constraints on cosmological parameters comparable to those from other current leading ground-based experiments. We find that the SPT-3G data set is well fit by a Λ CDM cosmological model with parameter constraints consistent with those from Planck and SPTpol data. From SPT-3G data alone, we find H 0 = 68.8 ± 1.5 km s - 1 Mpc - 1 and σ 8 = 0.789 ± 0.016 , with a gravitational lensing amplitude consistent with the Λ CDM prediction ( A L = 0.98 ± 0.12 ). We combine the SPT-3G and the Planck data sets and obtain joint constraints on the Λ CDM model. The volume of the 68% confidence region in six-dimensional Λ CDM parameter space is reduced by a factor of 1.5 compared to Planck-only constraints, with no significant shifts in central values. We note that the results presented here are obtained from data collected during just half of a typical observing season with only part of the focal plane operable, and that the active detector count has since nearly doubled for observations made with SPT-3G after 2018.

79 ASTRONOMY AND ASTROPHYSICS↗

Patient radiation dose audits for fluoroscopically guided interventional procedures

Purpose: Quality management for any use of medical x-ray imaging should include monitoring of radiation dose. Fluoroscopically guided interventional (FGI) procedures are inherently clinically variable and have the potential for inducing deterministic injuries in patients. The use of a conventional diagnostic reference level is not appropriate for FGI procedures. A similar but more detailed quality process for management of radiation dose in FGI procedures is described. Methods: A method that takes into account both the inherent variability of FGI procedures and the risk of deterministic injuries from these procedures is suggested. The substantial radiation dose level (SRDL) is an absolute action level (with regard to patient follow-up) below which skin injury is highly unlikely and above which skin injury is possible. The quality process for FGI procedures collects data from all instances of a given procedure from a number of facilities into an advisory data set (ADS). An individual facility collects a facility data set (FDS) comprised of all instances of the same procedure at that facility. The individual FDS is then compared to the multifacility ADS with regard to the overall shape of the dose distributions and the percent of instances in both the ADS and the FDS that exceed the SRDL. Results: Samples of an ADS and FDS for percutaneous coronary intervention, using the dose metric of reference air kerma (K{sub a,r}) (i.e., the cumulative air kerma at the reference point), are used to illustrate the proposed quality process for FGI procedures. Investigation is warranted whenever the FDS is noticeably different from the ADS for the specific FGI procedure and particularly in two circumstances: (1) When the facility's local median K{sub a,r} exceeds the 75th percentile of the ADS and (2) when the percent of instances where K{sub a,r} exceeds the facility-selected SRDL is greater for the FDS than for the ADS. Conclusions: Analysis of the two data sets (ADS and FDS) and of the percent of instances that exceed the SRDL provides a means for the facility to better manage radiation dose (and therefore both deterministic and stochastic radiation risk) to the patient during FGI procedures.

60 APPLIED LIFE SCIENCES↗

Medical diagnoses using three-way fuzzy concept lattice and their Euclidean distance

Searching the closest symptoms of a particular disease in the given patient is a major concern for medical diagnoses expert. In most cases, this problem arises due to large number of incomplete, uncertain, or inconsistent information generated in medical diagnoses data set. Recently, some of the researchers tried to pay attention towards precise measurement of uncertainty and vagueness in medical data set using the properties of applied abstract algebra. In this process, researchers noticed that calculus of three-way decision space is more useful to characterize the uncertainty and vagueness in medical data set based on truth, indeterminacy, and falsity membership-values. However, searching some relevant query based on user requirement to diagnoses the disease is a major issue in three-way decision space. To encounter this problem, current paper tried to analyze the medical data set using the properties of single-valued neutrosophic graph-based concept lattice. In addition, another method is proposed to select some of the interesting three-way fuzzy concepts at user-defined granulation for their computed Euclidean distance with an illustrative example.

97 MATHEMATICS AND COMPUTING↗

Synthetic data generation for machine learning model training for energy theft scenarios using cosimulation

Abstract Technical and non‐technical losses in distribution circuits result in significant economic costs to power utilities. One type of non‐technical loss is energy theft by various means including illegal tapping of feeders, bypassing the meter, and billing fraud. These losses are usually hard to detect, and can remain undetected for long periods of time. Machine learning models have been proven effective in detecting these conditions, but rely on the availability of large, good‐quality training data sets. The problem is exacerbated by the imbalanced nature of data related to these conditions—energy theft, though costly, is very rare. The available data sets generally have very few samples of theft with most of the data pertaining to normal operation. Such data sets are generally not suitable to train machine learning models. In this paper, an overview of energy theft detection techniques, the challenges with their data needs, and the limitations of current techniques to bridge such data limitations is presented. A co‐simulation framework is proposed to generate reliable training data for machine learning algorithms for theft detection. An example scenario is presented and a machine learning model is built to detect certain kinds of energy theft.

24 POWER TRANSMISSION AND DISTRIBUTION↗

System and method for generating a deselect mapping for a focal plane array

A method for generating a deselect mapping for a focal plane array according to one embodiment includes gathering a data set for a focal plane array when exposed to light or radiation from a first known target; analyzing the data set for determining which pixels or subpixels of the focal plane array to add to a deselect mapping; adding the pixels or subpixels to the deselect mapping based on the analysis; and storing the deselect mapping. A method for gathering data using a focal plane array according to another embodiment includes deselecting pixels or subpixels based on a deselect mapping; gathering a data set using pixels or subpixels in a focal plane array that are not deselected upon exposure thereof to light or radiation from a target of interest; and outputting the data set.

97 MATHEMATICS AND COMPUTING↗

Forest response to increased disturbance in the central Amazon and comparison to western Amazonian forests

Uncertainties surrounding vegetation response to increased disturbance rates associated with climate change remains a major global change issue for Amazonian forests. Additionally, turnover rates computed as the average of mortality and recruitment rates in the western Amazon basin are doubled when compared to the central Amazon, and notable gradients currently exist in specific wood density and aboveground biomass (AGB) between these two regions. This study investigates the extent to which the variation in disturbance regimes contributes to these regional gradients. To address this issue, we evaluated disturbance–recovery processes in a central Amazonian forest under two scenarios of increased disturbance rates using first ZELIG-TROP, a dynamic vegetation gap model which we calibrated using long-term inventory data, and second using the Community Land Model (CLM), a global land surface model that is part of the Community Earth System Model (CESM). Upon doubling the mortality rate in the central Amazon to mirror the natural disturbance regime in the western Amazon of ~2% mortality, the two regions continued to differ in multiple forest processes. With the inclusion of elevated natural disturbances, at steady state, AGB significantly decreased by 41.9% with no significant difference between modeled AGB and empirical AGB from the western Amazon data sets (104 vs. 107 Mg C ha -1 , respectively). However, different processes were responsible for the reductions in AGB between the models and empirical data set. The empirical data set suggests that a decrease in wood density is a driver leading to the reduction in AGB. While decreased stand basal area was the driver of AGB loss in ZELIG-TROP, a forest attribute that does not significantly vary across the Amazon Basin. Further comparisons found that stem density, specific wood density, and basal area growth rates differed between the two Amazonian regions. Last, to help quantify the impacts of increased disturbances on the climate and earth system, we evaluated the fidelity of tree mortality and disturbance in CLM. Similar to ZELIG-TROP, CLM predicted a net carbon loss of 49.9%, with an insignificant effect on aboveground net primary productivity (ANPP). Decreased leaf area index (LAI) was the driver of AGB loss in CLM, another forest attribute that does not significantly vary across the Amazon Basin, and the temporal variability in carbon stock and fluxes was not replicated in CLM. Our results suggest that (1) the variability between regions cannot be entirely explained by the variability in disturbance regime, but rather potentially sensitive to intrinsic environmental factors; or (2) the models are not accurately simulating all tropical forest characteristics in response to increased disturbances.

54 ENVIRONMENTAL SCIENCES↗

Impact of [{sup 18}F]Fluorodeoxyglucose PET-CT Staging on Treatment Planning in Radiotherapy Incorporating Elective Nodal Irradiation for Non-Small-Cell Lung Cancer: A Prospective Study;Non-small-cell lung cancer; PET; Radiotherapy; Elective nodal irradiation

Purpose: To evaluate prospectively how positron emission tomography (PET) information changes treatment plans for non-small-cell lung cancer (NSCLC) patients receiving or not receiving elective nodal irradiation (ENI). Methods and Materials: One hundred consecutive patients referred for curative radiotherapy were included in the study. Treatment plans were carried out with CT data sets only. For stage III patients, mediastinal ENI was planned. Then, patients underwent PET-CT for diagnostic/planning purposes. PET/CT was fused with the CT data for final planning. New targets were delineated. For stage III patients with minimal N disease (N0-N1, single N2), the ENI was omitted in the new plans. Patients were treated according to the PET-based volumes and plans. The gross tumor volume (GTV)/planning tumor volume (PTV) and doses for critical structures were compared for both data sets. The doses for areas of potential geographical misses derived with the CT data set alone were compared in patients with and without initially planned ENI. Results: In the 75 patients for whom the decision about curative radiotherapy was maintained after PET/CT, there would have been 20 cases (27%) with potential geographical misses by using the CT data set alone. Among them, 13 patients would receive ENI; of those patients, only 2 patients had the PET-based PTV covered by 90% isodose by using the plans based on CT alone, and the mean of the minimum dose within the missed GTV was 55% of the prescribed dose, while for 7 patients without ENI, it was 10% (p = 0.006). The lung, heart, and esophageal doses were significantly lower for plans with ENI omission than for plans with ENI use based on CT alone. Conclusions: PET/CT should be incorporated in the planning of radiotherapy for NSCLC, even in the setting of ENI. However, if PET/CT is unavailable, ENI may to some extent compensate for an inadequate dose coverage resulting from diagnostic uncertainties.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Diagnostic Models for Wind Turbine Gearbox Components Using SCADA Time Series Data: Preprint

This paper presents a scalable, autonomous framework for diagnostics of wind turbine gearbox failures using a multi-feature supervisory control and data acquisition data set spanning multiple years. Because of the size of the data set studied, all algorithms were constructed to be scalable using a Spark-Hadoop data framework. An unsupervised approach was used to detect significant failures based on predetermined criteria. The initial criteria selected were an abnormal spike in turbine component temperature followed by a turbine power off, which helps reduce the number of potential false alarms. To detect abnormal spikes in component temperature, a model was introduced to adjust the temperature data for effects caused by ambient temperature and normal temperature increases when loaded. This study evaluates methods for normalizing temperature sensor data to identify anomalies. The models that performed the best were a linear regression and a multivariate polynomial regression. The proposed process for finding failures has tunable parameters that can be adjusted to be more or less sensitive. The combination of sensor data normalization and application of these criteria is successful in finding turbine failures resulting in downtime. The proposed methods can operate without failure or maintenance logs and can be utilized for offline analysis of large high-resolution data sets.

17 WIND ENERGY↗

A BAYESIAN METHOD FOR THE INTERCALIBRATION OF SPECTRA IN REVERBERATION MAPPING

Flux calibration of spectra in reverberation mapping (RM) is most often performed by assuming the flux constancy of some specified narrow emission lines, which stem from an extended region that is sometimes partially spatially resolved, in contrast to the point-like broad-line region and the central continuum source. The inhomogeneous aperture geometries used among different observation sets in a joint monitoring campaign introduce systematic deviations to the fluxes of broad lines and central continuum, and intercalibration over these data sets is required. As an improvement to the previous empirical correction performed by comparing the (nearly) contemporaneous observation points, we describe a feasible Bayesian method that obviates the need for (nearly) contemporaneous observations, naturally incorporates physical models of flux variations, and fully takes into account the measurement errors. In particular, it fits all the data sets simultaneously regardless of samplings and makes use of all of the information in the data sets. A Markov Chain Monte Carlo implementation is employed to recover the parameters and uncertainties for intercalibration. Application to the RM data sets of NGC 5548 with joint monitoring shows the high fidelity of our method.

79 ASTRONOMY AND ASTROPHYSICS↗

Region-based convolutional neural network for wind turbine wake characterization from scanning lidars

A convolutional neural network is applied to lidar scan images from three experimental campaigns to identify and characterize wind turbine wakes. Initially developed as a proof-of-concept model and applied to a single data set in complex terrain, the model is now improved and generalized and applied to two other unique lidar data sets, one located near an escarpment and one located offshore. The model, initially developed using lidar scans collected in predominantly westerly flow, exhibits sensitivity to wind flow direction. The model is thus successfully generalized through implementing a standard rotation process to scan images before input into the convolutional neural network to ensure the flow is westerly. The sample size of lidar scans used to train the model is increased, and along with the generalization process, these changes to the model are shown to enhance accuracy and robustness when characterizing dissipating and asymmetric wakes. Applied to the offshore data set in which nearly 20 wind turbine wakes are included per scan, the improved model exhibits a 95% success rate in characterizing wakes and a 74% success rate in characterizing dissipating wake fragments. The improved model is shown to generalize well to the two new data sets, although an increase in wake characterization accuracy is offset by an increase in model sensitivity and false positive wake identifications.

17 WIND ENERGY↗