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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

An Accurate Fast Fluid Dynamics Model for Data Center Applications

Traditional CFD is broadly useful in the design and operation of reliable and efficient data centers. It is nevertheless computationally expensive, particularly when employed for design optimization, which usually requires multiple simulations. To speed-up calculations while retaining essential airflow physics, several researchers have turned to an alternative CFD methodology, namely, fast fluid dynamics (FFD). FFD has been reported to be much faster than traditional CFD, but at an assumed (acceptable) trade-off of reduced accuracy. However, a recent comparison of FFD and traditional CFD for data center plenum applications produced nearly indistinguishable results and suggested that previously-reported FFD/traditional-CFD differences were due primarily to inconsistent: 1) advection schemes, 2) computational grids, and 3) turbulence models. The present paper extends this FFD/traditional-CFD comparison to the data center whitespace and confirms the finding that a true like-for-like comparison produces nearly identical predictions. Our FFD implementation utilizes a first-order upwind finite-volume scheme for advection like traditional CFD, so alternative advection schemes (e.g., semi-Lagrangian) are not considered further here. Likewise, the effect of grid choice on traditional-CFD predictions is well known. However, the effect of turbulence model for data center applications has not been reported extensively so we do consider this topic further here. We compare the standard k-e and a simpler algebraic model to benchmark experimental data from a real data center. We find that the algebraic turbulence model predicts rack-inlet temperatures at a similar level-of-accuracy as the k-e model for our fairly-simple-airflow reference data center.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2011

Effect of Data Reduction and Fiber-Bridging on Mode I Delamination Characterization of Unidirectional Composites

Reliable delamination characterization data for laminated composites are needed for input in analytical models of structures to predict delamination onset and growth. The double-cantilevered beam (DCB) specimen is used to measure fracture toughness, GIc, and strain energy release rate, GImax, for delamination onset and growth in laminated composites under mode I loading. The current study was conducted as part of an ASTM Round Robin activity to evaluate a proposed testing standard for Mode I fatigue delamination propagation. Static and fatigue tests were conducted on specimens of IM7/977-3 and G40-800/5276-1 graphite/epoxies, and S2/5216 glass/epoxy DCB specimens to evaluate the draft standard "Standard Test Method for Mode I Fatigue Delamination Propagation of Unidirectional Fiber-Reinforced Polymer Matrix Composites." Static results were used to generate a delamination resistance curve, GIR, for each material, which was used to determine the effects of fiber-bridging on the delamination growth data. All three materials were tested in fatigue at a cyclic GImax level equal to 90% of the fracture toughness, GIc, to determine the delamination growth rate. Two different data reduction methods, a 2-point and a 7-point fit, were used and the resulting Paris Law equations were compared. Growth rate results were normalized by the delamination resistance curve for each material and compared to the nonnormalized results. Paris Law exponents were found to decrease by 5.4% to 46.2% due to normalizing the growth data. Additional specimens of the IM7/977-3 material were tested at 3 lower cyclic GImax levels to compare the effect of loading level on delamination growth rates. The IM7/977-3 tests were also used to determine the delamination threshold curve for that material. The results show that tests at a range of loading levels are necessary to describe the complete delamination behavior of this material.

Murri, Gretchen B.↗
NASA NTRS2015

Principal Component Analysis of Thermographic Data

Principal Component Analysis (PCA) has been shown effective for reducing thermographic NDE data. While a reliable technique for enhancing the visibility of defects in thermal data, PCA can be computationally intense and time consuming when applied to the large data sets typical in thermography. Additionally, PCA can experience problems when very large defects are present (defects that dominate the field-of-view), since the calculation of the eigenvectors is now governed by the presence of the defect, not the "good" material. To increase the processing speed and to minimize the negative effects of large defects, an alternative method of PCA is being pursued where a fixed set of eigenvectors, generated from an analytic model of the thermal response of the material under examination, is used to process the thermal data from composite materials. This method has been applied for characterization of flaws.

Winfree, William P.↗
NASA NTRS2017

Battery Data MI Importer Template Quick Start Guide

In order to ensure the persistent availability and reliability of test data generated over the course of the project, the M-SHELLS Project has decided to store acquired test data, as well as associated pedigree information, in the Granta Materials Intelligence (MI) database. To facilitate that effort, an importer template and associated graphical user interface (GUI) software have been developed, with this guide providing the operating instructions for their use. The template and automation software GUI are contained in the BatteryDataImporter.xlsm Excel workbook, and are to be used to import M-SHELLS summary, or pedigree, data and the associated raw test data results into an importer template-based file, formatted in such a way as to be ready for immediate upload to the Test Data: Battery Performance table of the Granta MI database. The provided GUI enables the user to select the appropriate summary data file(s), with each file containing the required information to identify any associated raw test data file(s) to be processed. In addition to describing the setup and operation of the importer template and GUI software, this guide also provides instructions for uploading processed data to the database and for viewing the data following upload.

Levinson, Laurie H.↗
DOE OSTI2024

Machine Learning-Driven Reliability Estimation of PV Inverters Considering Alert-Ambient Variability

Weather-induced spatio-temporal degradation limits outdoor PV inverter lifetime and reliability, necessitating advanced data analysis. This study employs a top-down, data-driven approach utilizing multiple machine learning (ML) algorithms to estimate inverter reliability in a 1.4 MW PV power plant, considering factors such as irradiance, humidity, temperature, time of day, and weather conditions. An extensive alert dataset from 17 identical inverters, including alert types, propagation, and frequency, reveals significant correlations with environmental factors and inverter output power, enabling the construction of a performance reliability model. Dual-stage supervised-ML models are evaluated for accuracy, with the ‘classification-regression’ model by an artificial neural network (ANN) tested on the averaged “Alert-Ambient” dataset, which is outperformed by ‘clustering-regression’ models using random forest (RF) and K-Nearest Neighbors (KNN) on individual inverter datasets. K-means clustering applies principal component analysis to reduce dimensions, achieving improved accuracy beyond the 80% achieved by ANN on the averaged dataset. Second-stage regression estimates inverter reliability with a mean square error of 0.0195 on the averaged dataset and as low as 0.002 on individual inverter datasets using RF. Furthermore, these findings highlight the method's suitability for estimating PV inverter output reliability under ambient conditions, essential for digital twin development and related applications.

14 SOLAR ENERGY↗
NASA NTRS2019

Assessing Reliability of NDE Flaw Detection Using Smaller Number of Demonstration Data Points

The paper provides an engineering analysis approach for assessing reliability of NDE flaw detection using smaller number of demonstration data points. It explores dependence of probability of detection (POD), probability of false positive (POF), on contrast-to-noise ratio, and net decision threshold-to-noise ratio in a simulated data; and draws some generically applicable inferences to devise the approach. ASTM nondestructive evaluation standards provide requirements on signal-to-noise ratio and/or contrast-to-noise ratio in order to provide reliable flaw detection and limit false positive calls. POD analysis of inspection test data results in an estimated flaw size, denoted by 𝑎90/95. This flaw size has 90% POD and minimum 95% confidence. POF is also estimated in the analysis. POD demonstration requires specimens with flaws of known size. In many situations, it is very expensive to produce the large number of flaws required for the POD analysis. In some situations, only real flaws can truly represent the flaws for demonstration. Real flaws of correct size and location in part configuration specimen may be difficult to produce, if not impossible. Here, an engineering analysis approach is devised using simulation to assess reliability of NDE technique when a limited number of flaws are available for demonstration. In this simulation, a technique is considered reliable, if it provides flaw detectability size equal to or better than the theoretical 𝑎90𝑡ℎ used in simulation and also provides a POF less than or equal to a chosen value. The paper uses simulated signal response versus flaw size data to devise the approach. Linear correlation is used between the signal response data and flaw size. POD software mh1823 uses generalized linear model (GLM) in POD analysis after transforming the flaw size and signal response, if needed, using logarithm. Therefore, this approach is in agreement with the linear signal correlation used in mh1823. Using the POD analysis of data, generic conditions on contrast-to-noise ratio and net decision threshold-to-noise ratio are derived for reliable flaw detection. In order to assess technique reliability using the engineering approach, signal response-to-flaw size correlation about the flaw size of concern is needed. In addition, measurement of noise is also needed. If the technique meets the above requirements, assumption of linear signal-to-flaw size correlation and conditions on noise, then the technique can be assessed using this analysis as it fits the underlying POD model used here. The approach is conservative and is designed to provide a larger flaw size compared to the POD approach. Such NDE technique assessment approach, although, not as rigorous as POD, can be cost effective if the larger flaw size can be tolerated. Typically, this is a situation for all quality control NDE inspections. Here, an NDE technique needs to be reliable and 𝑎90/95 is not estimated, but the assessed flaw size is assumed to be larger than the unknown a90 due to conservative factors or margins. Applicability of the approach for assessing reliability of flaw detection in x-ray radiography and 2D imaging in general is also explored.

Koshti, Ajay M.↗
DOE OSTI2024

Anticipating Optical Availability in Hybrid RF/FSO Links Using RF Beacons and Deep Learning

Radiofrequency (RF) communications offer reliable but low data rates and energy-inefficient satellite links, while free-space optical (FSO) promises high bandwidth but struggles with disturbances imposed by atmospheric effects. A hybrid RF/FSO architecture aims to achieve optimal reliability along with high data rates for space communications. Accurate prediction of dynamic ground-to-satellite FSO link availability is critical for routing decisions in low-earth orbit constellations. In this paper, we propose a system leveraging ubiquitous RF links to proactively forecast FSO link degradation prior to signal drops below threshold levels. This enables pre-calculation of rerouting to maximally maintain high data rate FSO links throughout the duration of weather effects. We implement a supervised learning model to anticipate FSO attenuation based on the analysis of RF patterns. Through the simulation of a dense lower earth orbit (LEO) satellite constellation, we demonstrate the efficacy of our approach in a simulated satellite network, highlighting the balance between predictive accuracy and prediction duration. An emulated cloud attenuation model is proposed to provide insight into the temporal profiles of RF signals and their correlation to FSO channel dynamics. Our investigation sheds light on the trade-offs between prediction horizon and accuracy arising from RF beacon numbers and proximity.

FSO availability↗
NASA NTRS2013

FY12 End of Year Report for NEPP DDR2 Reliability

This document reports the status of the NEPP Double Data Rate 2 (DDR2) Reliability effort for FY2012. The task expanded the focus of evaluating reliability effects targeted for device examination. FY11 work highlighted the need to test many more parts and to examine more operating conditions, in order to provide useful recommendations for NASA users of these devices. In order to develop these approaches, it is necessary to develop test capability that can identify reliability outliers. To do this we must test many devices to ensure outliers are in the sample, and we must develop characterization capability to measure many different parameters. For FY12 we increased capability for reliability characterization and sample size. We increased sample size by moving from loose devices to DIMMs with an approximate reduction of 20 to 50 times in terms of per DUT cost. By increasing sample size we have improved our ability to characterize devices that may be considered reliability outliers. This report provides an update on the effort to improve DDR2 testing capability. Although focused on DDR2, the methods being used can be extended to DDR and DDR3 with relative ease.

temperature stress↗
NASA NTRS1978

On-orbit spacecraft reliability

Operational and historic data for 350 spacecraft from 52 U.S. space programs were analyzed for on-orbit reliability. Failure rates estimates are made for on-orbit operation of spacecraft subsystems, components, and piece parts, as well as estimates of failure probability for the same elements during launch. Confidence intervals for both parameters are also given. The results indicate that: (1) the success of spacecraft operation is only slightly affected by most reported incidents of anomalous behavior; (2) the occurrence of the majority of anomalous incidents could have been prevented piror to launch; (3) no detrimental effect of spacecraft dormancy is evident; (4) cycled components in general are not demonstrably less reliable than uncycled components; and (5) application of product assurance elements is conductive to spacecraft success.

Bloomquist, C.↗
DOE OSTI2018

Comparative Performance Evaluation of High-performance Data Transfer Tools

Data transfer in wide-area networks has been long studied in different contexts, from data sharing among data centers to online access to scientific data. Many software tools and platforms have been developed to facilitate easy, reliable, fast, and secure data transfer over wide area networks, such as GridFTP, FDT, bbcp, mdtmFTP, and XDD. However, few studies have shown the full capabilities of existing data transfer tools from the perspective of whether such tools have fully adopted state-of-the-art techniques through meticulous comparative evaluations. In this paper, we evaluate the performance of the four highperformance data transfer tools (GridFTP, FDT, mdtmFTP, and XDD) in various environments. Our evaluation suggests that each tool has strengths and weaknesses. FDT and GridFTP perform consistently in diverse environments. XDD and mdtmFTP show improved performance in limited environments and datasets during our evaluation. Unlike other studies on data transfer tools, we also evaluate the predictability of the tools' performance, an important factor for scheduling different stages of science workflows. Performance predictability also helps in (auto)tuning the configurable parameters of the data transfer tool. We apply statistical learning techniques such as linear/polynomial regression, and k-nearest neighbors (kNN), to assess the performance predictability of each tool using its control parameters. Our results show that we can achieve good prediction performance for GridFTP and mdtmFTP using linear regression and kNN, respectively.

Nadig, Deepak↗
DOE OSTI2023

Stochastic Models, Indices & Optimization Algorithms for Pricing & Hedging Reliability Risks in Modern Power Grids: Data Plan - Princeton

We collected and cleaned the synthetic grid data produced by NREL for the Texas and New York synthetic grids. We developed a high dimensional joint stochastic model for load at the zone level, and solar and wind power productions at the asset level, capturing the spatial and temporal dependencies between all the variables, and demonstrated how such a model could be fitted to historical data. We designed and implemented a simulation engine which can produce Monte Carlo scenarios for the hourly day-ahead values of load, and solar and wind power productions at the spatial and temporal resolutions of the historical data used to fit the model. Finally we developed an open-source Python package which can, from an input grid model, efficiently use forecasts and large numbers of Monte Carlo scenarios to provide unit commitment and economic dispatch for each of these scenarios. The high dimensional stochastic model and the subsequent Monte Carlo simulation engine were implemented in the package PGscen and the corresponding UC and ED optimization programs in the package Vatic.

14 SOLAR ENERGY↗
DOE OSTI2024

$\overline{TKE}$ Parameterization and $\bar{v}$ Uncertainty Analysis for CGMF

Previous work was performed on tuning CGMF parameters for 235 U, 238 U, and Plutonium isotopes. Now work is being done to tune minor uranium isotopes. However, uranium isotopes like 232 U and 236 U have almost no experimental data. We are applying cross-isotope models to extrapolate and tune CGMF on isotopes that lack experimental data. There exist several internal CGMF physics quantities that affect the output of CGMF—multi-chance fission probability, excitation energy sharing, spin-cutoff factor, spin scaling, and fragment total kinetic energy to name a few. The mean fragment total kinetic energy, $\overline{TKE}$, is particularly interesting because of its strong anti-correlation with $\bar{v}$. We are most interested in the mean fragment total kinetic energy before neutron emissions. $\overline{TKE}$ is assumed to be pre-neutron emission unless otherwise stated. Currently in CGMF, the $\overline{TKE}$ model for 233,234,235,238 U are tuned independently to reproduce ν for the associated isotopes. In this report, we will tune a cross-isotope $\overline{TKE}$ model to experimental $\overline{TKE}$ data for 232,233,234,235,236,238 U. Because of the unreliable and sparse nature of $\overline{TKE}$ experimental data, future work will use more reliable experimental $\bar{v}$ data to infer the $\overline{TKE}$ model (and likely other internal CGMF parameters) for uranium isotopes. Such work has been performed previously using a sensitivity analysis and Kalman filter methods.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

Establishing robust correction schemes for improved and reliable ARM-AOS aerosol optical data products

Aerosol light absorption and scattering of solar radiation play an important role in the earth’s atmosphere in terms of direct and semi-direct radiative forcing. Optical parameters of importance to the US Department of Energy (DOE) climate models include absorption and scattering coefficients, single scattering albedo (SSA), absorption Angstrom exponents (AAE), and the asymmetry parameter (g). These parameters depend on aerosol size, shape and composition (refractive index), and are spectrally sensitive in the shortwave region. Additionally, these parameters have a complex dependency on the emission source, especially for carbonaceous aerosols. The DOE Atmospheric Radiation Measurement (ARM) user facility has deployed aerosol observing systems (AOS) containing several filter-based instruments to measure and constrain aerosol optical properties and related parameters at multiple sites worldwide. For measurement of aerosol light absorption, the AOS includes filter-based instruments (particle soot absorption photometer and tricolor absorption photometer) that infer particle-phase aerosol absorption coefficients at nominal red, green, and blue wavelength bands from the attenuation (ATN) of light passing through a particulate filter on which aerosols are deposited. Measurement of aerosol scattering is done in situ using nephelometers. By combining inferred absorption coefficients from filter-based ATN measurements and in situ scattering coefficients, value-added products (VAPs) such as SSA, AAE, and g are derived.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

Least reliable bits coding (LRBC) for high data rate satellite communications

LRBC, a bandwidth efficient multilevel/multistage block-coded modulation technique, is analyzed. LRBC uses simple multilevel component codes that provide increased error protection on increasingly unreliable modulated bits in order to maintain an overall high code rate that increases spectral efficiency. Soft-decision multistage decoding is used to make decisions on unprotected bits through corrections made on more protected bits. Analytical expressions and tight performance bounds are used to show that LRBC can achieve increased spectral efficiency and maintain equivalent or better power efficiency compared to that of BPSK. The relative simplicity of Galois field algebra vs the Viterbi algorithm and the availability of high-speed commercial VLSI for block codes indicates that LRBC using block codes is a desirable method for high data rate implementations.

Vanderaar, Mark↗
NASA NTRS2004

Vibrational Spectroscopy of Ions and Radicals Present in the Interstellar Medium and in Planetary Atmospheres: A Theoretical Study

Anharmonic vibrational frequencies and intensities are calculated for OH(H2O)n and H(H2O)n radicals (that form on icy particles of the interstellar medium), HCO radical (the main intermediate in the synthesis of organic molecules in space), NH2(-) and C2H(-) anions, H5(+) cation, and other systems relevant to interstellar chemistry. In addition to pure ions and radicals, their complexes with water are studied to assess the effects of water environment on infrared spectra. The calculations are performed using the correlation-corrected vibrational self-consistent field (CC-VSCF) method with ab initio potential surfaces at the MP2 and CCSD(T) levels. Fundamental, overtone, and combination excitations are computed. The results are in good agreement with available experimental data and provide reliable predictions for vibrational excitations not yet measured in laboratory experiments. The data should be useful for interpretation of astronomically observed spectra and identification of ions and radicals present in the interstellar medium and in planetary atmospheres.

Chaban, Galina M.↗
NASA NTRS2014

Stellar Laboratories: 3. New Ba 5, Ba 6, and Ba 7 Oscillator Strengths and the Barium Abundance in the Hot White Dwarfs G191-B2B and RE 0503-289

Context. For the spectral analysis of high-resolution and high-signal-to-noise (S/N) spectra of hot stars, state-of-the-art non-local thermodynamic equilibrium (NLTE) model atmospheres are mandatory. These are strongly dependent on the reliability of the atomic data that is used for their calculation. Aims. Reliable Ba 5-7 oscillator strengths are used to identify Ba lines in the spectra of the DA-type white dwarf G191−B2B and the DO-type white dwarf RE 0503−289 and to determine their photospheric Ba abundances. Methods. We newly calculated Ba v-vii oscillator strengths to consider their radiative and collisional bound-bound transitions in detail in our NLTE stellar-atmosphere models for the analysis of Ba lines exhibited in high-resolution and high-S/N UV observations of G191−B2B and RE 0503−289. Results. For the first time, we identified highly ionized Ba in the spectra of hot white dwarfs. We detected Ba vi and Ba vii lines in the Far Ultraviolet Spectroscopic Explorer (FUSE) spectrum of RE 0503−289. The Ba vi/Ba vii ionization equilibrium is well reproduced with the previously determined effective temperature of 70 000 K and surface gravity of log g=7.5. The Ba abundance is 3.5 +/- 0.5 × 10(exp−4) (mass fraction, about 23 000 times the solar value). In the FUSE spectrum of G191−B2B, we identified the strongest Ba vii line (at 993.41 Å) only, and determined a Ba abundance of 4.0 +/- 0.5 × 10(exp−6) (about 265 times solar). Conclusions. Reliable measurements and calculations of atomic data are a pre-requisite for stellar-atmosphere modeling. Observed Ba vi-vii line profiles in two white dwarfs' (G191−B2B and RE 0503−289) far-ultraviolet spectra were well reproduced with our newly calculated oscillator strengths. This allowed to determine the photospheric Ba abundance of these two stars precisely.

New Ba V↗
NASA NTRSDate not supplied

TPSAS-NF1676L-27237-DND

Objective - It is observed that 5.6% of all CERES Terra/Aqua data contains missing cloud cover information or insufficient imager data for a reliable scene identification (some times it can reach up to 50% of data for a specific scene type) - The unavailability of imager data lead to gaps in global radiation budget dataset. - In this study, our objective is to develop a Machine learning methodology for the improved determination of CERES scene type and subsequent clear-sky TOA flux estimation using standalone CERES TOA radiance measurements (without any MODIS/Imager data).

CERES↗