SEARCH · Engineering Papers

Results for “data logging”

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 901 records · Page 50

All sources NASA NTRS DOE OSTI

NASA NTRS1959

Optimization of Parametric Constants for Creep-Rupture Data by Means of Least Squares

An objective method utilizing least squares is presented for the determination of the optimum parametric constants for stress-rupture data. The method is applied to both isostress and isothermal data for the parameters proposed by Larson and Miller, Manson and Haferd, and by Dorn. Several examples are treated in detail, and it was found that the method gives good results. It is shown that the values of the constants for the parameter proposed by Manson and Haferd are not critical as long as Ta and log ta appear in the proper combination. In addition to optimization, the chief utility of the method lies in the fact that it gives the same results for a given set of data no matter who makes the analysis, which is not the case for the graphical methods presently employed.

Manson, S. S.↗

DOE OSTI2022

Combinatorial Evaluation of Physical Feature Engineering, Classical Machine Learning, and Deep Learning Models for Synchrophasor Data at Scale

A major objective of the project was to train and evaluate the effectiveness of multiple event and anomaly detection, identification and classification deep temporal learning models for processing of real-time phasor measurement unit (PMU) data streams. A vast dataset, consisting of two years of phasor measurements from all three U.S. Interconnections, was curated and released by the Department of Energy (DOE) through Pacific Northwest National Laboratory (PNNL). The dataset also included an event log that provided event times and types (e.g. generator trips, line trips, planned service events, transformer operations, etc.). Our analysis of this dataset addressed six (6) of the eleven (11) research priorities identified in Funding Opportunity Announcement (FOA) DE-FOA-0001861 “Big Data Analysis of Synchrophasor Data” (FOA 1861). Rather than being limited to pre-determined specific algorithms, this project relied on the uniquely structured, highly performant underlying time series database capabilities of the PredictiveGrid platform to assess the vast dataset utilizing a wide variety of algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Critical Assessment of Electronic Structure Descriptors for Predicting Perovskite Catalytic Properties

The discovery and design of materials which can efficiently catalyze the oxygen reduction and evolution reactions at reduced temperatures is important for facilitating the widespread adoption of fuel cell and electrolyzer technologies. Numerous studies have produced correlations between catalytic properties, such as oxygen surface exchange or electrode area specific resistance (ASR), and properties of the catalyst material. However, correlations have historically been limited in scope (e.g., using only a few materials or at a single temperature) and it has been difficult to provide detailed assessments of their robustness. Here, in this study, we assess the ability of the O p-band center electronic structure descriptor, obtained from density functional theory (DFT) calculations, to correlate with oxygen surface exchange rates, diffusivities, and area specific resistances for a large database of perovskite oxide catalytic properties. By data mining the literature, we obtain 747 catalytic property value data points spanning 299 unique perovskite compositions from 313 studies. We assess linear correlations of each property with the O p-band center and find generally modest correlations that are qualitatively useful (prediction mean absolute errors of about 0.5 log units are typical), where the correlations are improved at higher temperatures (e.g., 800 °C vs. 500 °C) and significantly improve when considering fits to the subset of materials which have multiple independent measurements. These findings suggest that the spread of property data is significantly influenced by experimental uncertainty, and subsequent measurements of additional materials will likely improve the O p-band center correlations.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2022

Deep learning to estimate permeability using geophysical data

Time-lapse electrical resistivity tomography (ERT) is a popular geophysical method to estimate three-dimensional (3D) permeability fields from electrical potential difference measurements. Traditional inversion and data assimilation methods are used to ingest this ERT data into hydrogeophysical models to estimate permeability. Due to ill-posedness and the curse of dimensionality, existing inversion strategies provide poor estimates and low resolution of the 3D permeability field. Recent advances in deep learning provide us with powerful algorithms to overcome this challenge. This paper presents a deep learning (DL) framework to estimate the 3D subsurface permeability from time-lapse ERT data. To test the feasibility of the proposed framework, we train DL-enabled inverse models on simulation data. Each measurement in both synthetic and field data is standardized by removing the mean and scaling the time-series to unit variance. This pre-processing step is necessary to bring simulation data closer to field observations. Subsurface process models based on hydrogeophysics are used to generate this synthetic data. Training performed on limited simulation data resulted in the DL model over-fitting. An advanced data augmentation based on mixup is implemented to generate additional training samples to overcome this issue. This mixup technique creates weakly labeled (low-fidelity) samples from strongly labeled (high-fidelity) data. The weakly labeled training data is then used to develop DL-enabled inverse models and reduce over-fitting. As both time-lapse ERT (1133048 features/realization) and 3D permeability (585453 features/realization) data samples are from a high-dimensional space, principal component analysis (PCA) is employed to reduce dimensionality. Encoded ERT and encoded permeability are generated using the trained PCA estimators. A deep neural network is then trained to map the encoded ERT to encoded permeability. This mixup training and unsupervised learning allowed us to build a fast and reasonably accurate DL-based inverse model under limited simulation data. Results show that proposed weak supervised learning can capture salient spatial features in the 3D permeability field. Quantitatively, the average mean squared error (in terms of the natural log) on the strongly labeled training, validation, and test datasets is less than 0.5. The R 2 -score (global metric) is greater than 0.75, and the percent error in each cell (local metric) is less than 10%. Finally, an added benefit in terms of computational cost is that the proposed DL-based inverse model is at least O(10 4 ) times faster than running a forward model once it is trained. Data generation, DL model training, and hyperparameter tuning to identify optimal neural network architectures utilized high-performance computing resources while the DL inference is performed on a standard laptop. Approximately, O(10 5 ) processor hours are used for generating data and DL tuning and training. We acknowledge that the data generation and DL model development are expensive. But once a DL model is trained, it can be re-used for inversion rapidly for the given system, with set physics and domain. Note that traditional inversion may require multiple forward model simulations (e.g., in the order of 10 to 1000), which are very expensive. This computational savings ≈ O(10 5 ) – O(10 7 )) makes the proposed DL-based inverse model attractive for subsurface imaging and real-time ERT monitoring applications due to fast and yet reasonably accurate estimations of permeability field.

58 GEOSCIENCES↗

DOE OSTI2021

Simulation studies of the interaction of laser radiation with additively manufactured foams

The interaction of laser radiation with foams of various porosities and low densities has been the subject of several numerical and experimental studies. In all cases, the modeling of low-Z under-dense foams as uniform gases of equivalent average density using standard radiation-hydrodynamics codes has resulted in heat-front velocities that are considerably faster than those observed experimentally. It has been theoretically conjectured that this difference may be attributed to the breakdown of the foam’s morphology, leading to a dynamics of filament expansion where the ion and electron energy partitions are significantly different from those calculated using the uniform gas model. We found that 3D computer simulations employing a disconnected representation of the foam’s microstructure which allowed for the dynamics of foam element heating, expansion, and stagnation largely supported the theoretical picture. Simulations using this model for laser experiments on under-dense 2 mg cc -1 SiO 2 aerogel foams reproduced the experimental data fairly well. We used the validated model in simulations of low-density structured foam-like materials (produced via additive manufacturing) with a variety of morphologies. We found that the log-pile configurations were consistent with the analytical propagation model of Gus’kov et al. Further validation of the model was obtained by simulating experiments performed at the Jupiter Laser Facility using the log-pile and octet-truss foam morphologies. Simulations of the foam-laser interaction using a wave propagation code showed that the microstructure was able to enhance stimulated Brillouin scattering (SBS) by concentrating the light energy into density holes. In turn, this promotes laser filamentation, reducing SBS and bringing the predicted values closer to the experimental data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Insights into Prismatic Loop Formation in Irradiated Fe–Cr Alloys from Hypothesis-Driven Active Learning and Causal Analysis

Neutron and electron irradiation experimental studies conducted on body-centered cubic Fe and Fe–Cr alloys have established two prismatic dislocation loop populations, which have Burgers vectors of either a/2$\langle$111$\rangle$ or a$\langle$100$\rangle$. Here, the loop formation depends on factors such as dose (D), dose rate (D rt ), temperature (T), chromium content (Cr%), and other alloying elements. Hence, it is important to understand how irradiation-induced dislocation loops evolve conditional upon the loop characteristics, such as loop density (DD), average loop size d̅, and irradiation parameters (D, D rt , T, and irradiation type), which is still an active area of research. To understand these complex structure–property relationships, machine learning (ML) is employed in a three-step approach. This includes imputing missing data with a k-nearest neighbor, generating functionalized features, and assessing feature importance with random forest classification and regression. Physics-based features are incorporated in a hypothesis-driven active learning scheme to overcome data unavailability challenges. Insights obtained from ML models (i) to categorize dislocation loop types, show the highest correlation with d̅; (ii) Log(DD), obtained through mathematical formulations involving D, Cr%, d̅, and T (e.g., Log(DD) ~ D + exp(-Cr%) + 1/d̅ and log(DD) ~ D + exp(-Cr%) + 1/T). Hypothesis-driven active learning is able to predict Log(DD) in which the experimental date is not known. Causal models verify cause–effect relationships for dislocation loop classification and irradiation factors in FeCr alloys.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Thermal stress in degraded forests in the Brazilian Amazon Arc of Deforestation

Understanding thermal stress in tropical forests has taken on new urgency in light of accelerating climate change and expansion of deforestation and forest degradation. Degraded tropical forests in particular may be approaching critical temperature thresholds even more rapidly than intact forests, with implications for tree survival and ecosystem recovery. We investigate thermal stress in degraded tropical forests within the Brazilian Amazon Arc of Deforestation. Using land surface temperature data from the ECOsystem Spaceborne Thermal Radiometer Experiment on the international Space Station (ECOSTRESS), we compared canopy temperatures of intact, selectively logged, and burned forests in Feliz Natal, Mato Grosso, Brazil. Upper canopy temperatures in previously burned forests were 4.1% higher (mean = 36.5 °C) and 50.9% more variable compared to intact and logged forests, which showed remarkably similar temperature distributions (means of 34.9 °C and 35.1 °C, respectively). Modeled leaf temperature distributions based on canopy temperature measurements from one of the warmest days in a 2 year dry season record indicated that 87% of leaves in the warmest burned forest patches exceeded the temperature threshold where respiration surpasses photosynthesis, compared to approximately 72%–74% in intact and logged forests, respectively, in the same time period. After controlling for environmental factors, burned forests were predicted to be 2.6 °C warmer on average [1.39 °C–3.96 °C, 95% credible interval] than intact forests across a 5–40 m canopy height range. Burned forests showed modest thermal recovery over time, with temperatures decreasing by approximately 1.2 °C over a 30 year recovery period. In contrast, logged forests showed minimal thermal differences from intact forests (−0.353 on average, [−1.125–0.274, 95% credible interval]) and negligible change (0.15 °C) over the same timeframe. While the absolute probabilities of exceeding damaging thermal thresholds remain low across all forest types under current climate conditions, the probability of leaves exceeding temperatures that cause permanent leaf damage was ten times higher in burned forests, with implications for the future of burned forest regeneration in water-limited regions of the Amazon basin. In particular, the combination of higher mean temperatures, greater temperature variability, and more frequent exposure to damaging thermal thresholds implies that burned tropical forests will experience substantially higher mortality rates and slower biomass recovery compared to intact and selectively logged forests, especially in water-limited regions where trees cannot rely on evaporative cooling to moderate canopy temperatures.

Brazilian Amazon Arc of Deforestation↗

DOE OSTI2025

DAmodel: hierarchical Bayesian modelling of DA white dwarfs for spectrophotometric calibration

We use hierarchical Bayesian modelling to calibrate a network of 32 all-sky faint DA white dwarf (DA WD) spectrophotometric standards (⁠16.5 < V , 19.5⁠) alongside three CALSPEC standards, from 912 Å to 32 μm. The framework is the first of its kind to jointly infer photometric zero points and WD parameters (surface gravity log g⁠, effective temperature T eff ⁠, extinction A V ⁠, dust relation parameter R V ) by simultaneously modelling both photometric and spectroscopic data. We model panchromatic Hubble Space Telescope Wide Field Camera 3 (HST/WFC3) UVIS and IR photometry, HST/STIS UV spectroscopy, and ground-based optical spectroscopy to sub-per cent precision. Photometric residuals for the sample are the lowest yet yielding < 0.004 mag RMS on average from the UV to the NIR, achieved by jointly inferring time-dependent changes in system sensitivity and WFC3/IR count-rate nonlinearity. Our GPU-accelerated implementation enables efficient sampling via Hamiltonian Monte Carlo, critical for exploring the high-dimensional posterior space. The hierarchical nature of the model enables population analysis of intrinsic WD and dust parameters. Inferred spectral energy distributions from this model will be essential for calibrating the James Webb Space Telescope as well as next-generation surveys, including Vera Rubin Observatory’s Legacy Survey of Space and Time and the Nancy Grace Roman Space Telescope.

methods: statistical↗

DOE OSTI2021

AUTOPERF

AutoPerf is a collection of modules for low-overhead performance monitoring on HPC systems, the modules collect monitoring data such as MPI data, network counter data etc. Autoperf modules interoperate with the Open source Darshan software (xgitlab.cels.anl.gov/darshan/) as submodules and leverages the Darshan's log recording and analysis framework.

CHUNDURI, DEVISUDHEER KUMAR↗

DOE OSTI2020

2020 Review Panel Workshop on Energy Intensity Indicators: Summary Findings and Meeting Notes

On January 28-29, 2020, the U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy (EERE), in coordination with Pacific Northwest National Laboratory (PNNL), sponsored an expert workshop focused on energy intensity indicators. The intent of the workshop was to support a comprehensive review of the EERE Energy Intensity Indicators (EII), a national system of indicators used to track changes in the energy intensity of economic sectors. One goal of the workshop was to gather input from a panel of experts who have experience and knowledge related to the EII methodological approach and key data sources used to calculate indicators for each of the end-use sectors of the economy—transportation, residential, commercial, industrial, and electricity. The current EERE EII methodology is documented in a 2017 PNNL report, A Comprehensive System of Energy Intensity Indicators for the U.S.: Methods, Data and Key Trends, which is available online. The workshop also included a presentation by Dr. Beng Ang, who discussed the Log Mean Divisia index (LMDI) approach to decomposition analysis, which was followed by a discussion concerning the methodological approaches that could be applied to future EII efforts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS1983

Secular variations in solar flare proton fluxes

Proton fluences in contemporary solar-flare events (1965-82) are analyzed to obtain values of average flux and characteristic rigidity R0 (MV). Both proton fluences F (E greater than 10MeV) and R0 (in the energy interval 10-30 MeV) values for individual events follow log-normal distribution. Comparison of contemporary average flux and R0 values with long-term averaged values, based on lunar sample data, indicate that the ancient solar-flare proton spectra were harder compared to that observed in contemporary flares. The contemporary and long-term (greater than 10,000 yr) averaged fluxes are similar, although such a comparison may not be meaningful because the contemporary averages suffer from uncertainty due to statistics of a single event. The long-term average data suggests a secular variation in solar-flare activity with enhanced proton fluxes during the last 100,000 years.

Goswami, J. N.↗

NASA NTRS1988

Estimating residual fault hitting rates by recapture sampling

For the recapture debugging design introduced by Nayak (1988) the problem of estimating the hitting rates of the faults remaining in the system is considered. In the context of a conditional likelihood, moment estimators are derived and are shown to be asymptotically normal and fully efficient. Fixed sample properties of the moment estimators are compared, through simulation, with those of the conditional maximum likelihood estimators. Properties of the conditional model are investigated such as the asymptotic distribution of linear functions of the fault hitting frequencies and a representation of the full data vector in terms of a sequence of independent random vectors. It is assumed that the residual hitting rates follow a log linear rate model and that the testing process is truncated when the gaps between the detection of new errors exceed a fixed amount of time.

Lee, Larry↗

NASA NTRS1997

Lithium Abundances in the Young Open Cluster IC 2602

We have obtained high-resolution spectra for 28 candidate late-type stars in the 30 Myr old cluster IC 2602. NLTE Li abundances have been derived from measured equivalent widths. The log n(Li) - T(sub eff) and log n(Li) - mass distributions for our sample stars have been compared with those of the Pleiades and alpha Persei. Our data show that F stars in the three clusters have the same lithium content, which corresponds to the initial content for Pop. I stars. G and early-K IC 2602 stars are, on average, somewhat more Li-rich than their counterparts in the two slightly older clusters. Finally, the latest-type IC 2602 stars are heavily Li depleted, with their Li content being as low as the lowest measured among the Pleiades. As in the Pleiades and alpha Per, a star-to-star scatter in lithium is observed among 30 Myr old late-K/early-K dwarfs in IC 2602, indicating that this spread develops in the pre-main sequence phases.

Randich, S.↗

NASA NTRS1997

Operational Characteristics of Four Commercially Available Personal Cooling Vests

Personal thermoregulatory systems which provide chest cooling are used in the industrial and aerospace environments to alleviate thermal stress. However, little information is available regarding the physiologic and circulatory changes produced by routine operation of these systems. The objectives of this study were to compare the effectiveness of two passive and two active cooling vests, and to measure the body temperature and circulatory changes produced by each cooling vest configuration. The MicroClimate Systems and the Life Enhancement Tech(LET) lightweight liquid cooling vests, the Steele Vest and LET's Zipper Front Garment were used to cool the chest region of 11 male and 10 female subjects (25 to 55 yr.) in this study. Calf, forearm and finger blood flows were measured using a tetrapolar impedance rheograph. The subjects, seated in an upright position at normal room temperature (approx.21 C), were tested for 60 min. with the cooling system operated at its maximum cooling capacity. Blood flows were recorded continuously using a computer data acquisition system with a sampling frequency of 250 Hz. Oral, right and left ear temperatures and cooling system parameters were logged manually every 5 min. Arm, leg, chest and rectal temperatures; heart rate; respiration; and an activity index were recorded continuously on a URI Inc. Biolog ambulatory monitor. In general, the male and female subjects' rectal and ear temperature responses to cooling were similar for all vest configurations tested. Oral temperatures during the recovery period were significantly (P<0.05) lower than during the control period, approx.0.2 - 0.5 C, for both men and women wearing any of the four different garments. The corresponding car temperatures were significantly (P<0.05) decreased approx.0.2 - 0.3 C by the end of the recovery period. Compared to the control period, no significant differences were found in rectal temperatures during cooling and recovery periods. These results show that all vest configurations elicit a similar thermal response in both male and female subject groups. However, subject population variance was rather large and may have masked differences between the vests. One vest may prove more effective than another for a given individual, and experience is the only means of determining this.

Ku, Yu-Tsuan E.↗

NASA NTRS1997

Biomedical Application of Aerospace Personal Cooling Systems

Personal thermoregulatory systems which are used by astronauts to alleviate thermal stress during extravehicular activity have been applied to the therapeutic management of multiple sclerosis. However, little information is available regarding the physiologic and circulatory changes produced by routine operation of these systems. The objectives of this study were to compare the effectiveness of two passive and two active cooling vests and to measure the body temperature and circulatory changes produced by each cooling vest configuration. The MicroClimate Systems and the Life Enhancement Tech(LET) lightweight liquid cooling vests, the Steele Vest and LET's Zipper Front Garment were used to cool the chest region of 10 male and female subjects (25 to 55 yr.) in this study. Calf, forearm and finger blood flows were measured using a tetrapolar impedance rheograph. The subjects, seated in an upright position at normal room temperature (approx.22C), were tested for 60 min. with the cooling system operated at its maximum cooling capacity. Blood flows were recorded continuously using a computer data acquisition system with a sampling frequency of 250 Hz. Oral, right and left ear temperatures and cooling system parameters were logged manually every 5 min. Arm, leg, chest and rectal temperatures; heart rate; respiration; and an activity index were recorded continuously on a U.F.I., Inc. Biolog ambulatory monitor. In general, the male and female subjects' oral and ear temperature responses to cooling were similar for all vest configurations tested. Oral temperatures during the recovery period were significantly (P<0.05) lower than during the control period, approx. 0.2 - 0.5C, for both men and women wearing any of the four different garments. The corresponding ear temperatures were significantly (P<0.05) decreased approx.0.2 - 0.4C by the end of the recovery period. Compared to the control period, no significant differences were found in rectal temperatures during cooling and recovery periods.

Ku, Yu-Tsuan E.↗

NASA NTRS2012

Remote sensing analysis of forest disturbances

The present invention provides systems and methods to automatically analyze Landsat satellite data of forests. The present invention can easily be used to monitor any type of forest disturbance such as from selective logging, agriculture, cattle ranching, natural hazards (fire, wind events, storms), etc. The present invention provides a large-scale, high-resolution, automated remote sensing analysis of such disturbances.

Asner, Gregory P.↗

NASA NTRS2015

Remote Sensing Analysis of Forest Disturbances

Asner, Gregory P.↗

NASA NTRS2011

Active Neutron and Gamma-Ray Instrumentation for In Situ Planetary Science Applications

We describe the development of an instrument capable of detailed in situ bulk geochemical analysis of the surface of planets, moons, asteroids, and comets. This instrument technology uses a pulsed neutron generator to excite the solid materials of a planet and measures the resulting neutron and gamma-ray emission with its detector system. These time-resolved neutron and gamma-ray data provide detailed information about the bulk elemental composition, chemical context, and density distribution of the soil within 50 cm of the surface. While active neutron scattering and neutron-induced gamma-ray techniques have been used extensively for terrestrial nuclear well logging applications, our goal is to apply these techniques to surface instruments for use on any solid solar system body. As described, experiments at NASA Goddard Space Flight Center use a prototype neutron-induced gamma-ray instrument and the resulting data presented show the promise of this technique for becoming a versatile, robust, workhorse technology for planetary science, and exploration of any of the solid bodies in the solar system. The detection of neutrons at the surface also provides useful information about the material. This paper focuses on the data provided by the gamma-ray detector.

Parsons, A.↗