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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

PV backsheets survey protocol: A framework for geo-spatial field surveys for bulk material characterization and reliability analysis applied across 41 PV systems

As widespread adoption of photovoltaic (PV) technologies continues, understanding the lifetime of modules is paramount to the viability of the industry as an environmentally conscious alternative to traditional energy generation. Although power degradation can affect the total energy production of a module over its lifetime, module safety failures necessitate the removal of a module leading to a loss of not only the particular asset, but the earning potential of the device. Therefore, it is critical to ensure that the components that provide essential safety functions for PV module operate for their entire rated lifetime. PV backsheets provide necessary electrical insulation to the completed device and failure of this component is cause for a immediate removal of the module. Degradation of the PV module backsheet has led to module safety failures in large-scale installations, costing millions of dollars in damages and lost potential revenue. The spatio-temporal degradation of fielded PV modules is important to study in order to identify which modules within installations are experiencing the greatest exposure conditions and in turn have the highest chance of failure. This paper describes a comprehensive field survey protocol developed for monitoring PV module backsheet performance using solely non-destructive methods in commercial PV fields. The protocol establishes a field naming convention, sampling method, data handling requirements, and measurement procedures. By ensuring consistent data collection practices, the field survey protocol enables research groups to obtain data of uniform quality on backsheet performance over multiple years and locations. In this study, the developed protocol was implemented at forty-one PV sites. Eight different types of airside layer backsheet materials including poly(vinylidene fluoride) (PVDF), acrylic PVDF, poly(tetrafluoroethylene-co-hexafluoropropylene-co-vinylidene fluoride) (THV), poly(vinyl fluoride) (PVF), poly(ethylene terephthalate) (PET), fluoroethylene vinyl ether (FEVE), polyethylene naphthalate (PEN), and glass were identified using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy. The field survey results show that the spatial distribution of degradation indicators are non-uniform within a particular module, individual site, and across site locations. The degradation of PV modules increased in severity for modules mounted at the edge of rows (across a field) and near the junction box (within a module). This study demonstrates the sensitivity of material performance to exposure length across different materials and climates.

14 SOLAR ENERGY↗
DOE OSTI2024

A Single Compartment Relaxed Eddy Accumulation Method

The relaxed eddy accumulation (REA) method is a widely-known technique that measures turbulent fluxes of scalar quantities. The REA technique has been used to measure turbulent fluxes of various compounds, such as methane, ethene, propene, butene, isoprene, nitrous oxides, ozone, and others. The REA method requires the accumulation of scalar concentrations in two separate compartments that conditionally sample updrafts and downdraft events. It is demonstrated here that the assumptions behind the conventional or two-compartment REA approach allow for one-compartment sampling, therefore called a one compartment or 1-C-REA approach, thereby expanding its operational utility. The one-compartment sampling method is tested across various land cover types and atmospheric stability conditions, and it is found that the one-compartment REA can provide results comparable to those determined from conventional two-compartment REA. This finding enables rapid expansion and practical utility of REA in studies of surface-atmosphere exchanges, interactions, and feedbacks.

Banerjee, T.↗
NASA NTRS1991

Probabilistic methods for rotordynamics analysis

This paper summarizes the development of the methods and a computer program to compute the probability of instability of dynamic systems that can be represented by a system of second-order ordinary linear differential equations. Two instability criteria based upon the eigenvalues or Routh-Hurwitz test functions are investigated. Computational methods based on a fast probability integration concept and an efficient adaptive importance sampling method are proposed to perform efficient probabilistic analysis. A numerical example is provided to demonstrate the methods.

Wu, Y.-T.↗
DOE OSTI2025

Demonstration of an AI-driven workflow for dynamic x-ray spectroscopy

X-ray absorption near edge structure (XANES) spectroscopy is a powerful technique for characterizing the chemical state and symmetry of individual elements within materials, but requires collecting data at many energy points which can be time-consuming. While adaptive sampling methods exist for efficiently collecting spectroscopic data, they often lack domain-specific knowledge about the structure of XANES spectra. Here we demonstrate a knowledge-injected Bayesian optimization approach for adaptive XANES data collection that incorporates understanding of spectral features like absorption edges and pre-edge peaks. We show this method accurately reconstructs the absorption edge of XANES spectra using only 15–20% of the measurement points typically needed for conventional sampling, while maintaining the ability to determine the x-ray energy of the sharp peak after the absorption edge with errors less than 0.03 eV, the absorption edge with errors less than 0.1 eV; and overall root-mean-square errors less than 0.005 compared to traditionally sampled spectra. Our experiments on battery materials and catalysts demonstrate the method’s effectiveness for both static and dynamic XANES measurements, improving data collection efficiency and enabling better time resolution for tracking chemical changes. This approach advances the degree of automation in XANES experiments, reducing the common errors of under- or over-sampling points near the absorption edge and enabling dynamic experiments that require high temporal resolution or limited measurement time.

Bayesian optimization↗
NASA NTRS2012

Examination of Surface Residuals Obtained During Re-Lubrication of the International Space Station (ISS) Solar Alpha Rotary Joint (SARJ)

The starboard SARJ mechanism on the ISS suffered a premature lubrication failure, resulting in widespread loss of the nitride case layer on its 10.3 meter circumference, 15-5PH steel race ring [1, 2]. To restore functionality, vacuum-stable grease was applied on-orbit, first to the port SARJ mechanism to save it from the damage suffered by the starboard mechanism. After 3 years of greased operation, telemetry indicated that the port mechanism required relubrication, so part of that process included sampling each of the three race ring surfaces to evaluate any wear debris recovered and the state of the originally applied grease. Extensive microscopic examination was conducted, which directed subsequent microanalysis of particulate. Since the SARJ mechanism operates in the vacuum of space, a sampling method and tool had to be developed for use by astronauts while working in the extravehicular mobility unit (EMU). The sampling tool developed was a cotton terry-cloth mitt for the EMU glove, with samples taken by swiping each of the three port SARJ race-ring surfaces. The sample mitts for each surface were folded inward after sampling to preserve sample integrity, for return and ground analysis. The sample mitt for what is termed the outer canted surface of the SARJ race-ring is shown in Figure 1. Figure 1 also demonstrates how increasing levels of magnification were used to survey the contamination removed in sampling, specifically looking for signs of wear debris or other features which could be further evaluated using Scanning Electron Microscopy (SEM) methods. The most surprising overall result at this point in the analysis was the relatively small amounts of grease recovered during sampling. It is clear that the mechanism was not operating with surplus lubricant. Obviously, evidence of molybdenum disulfide (MoS2), a major component in the grease applied, was prevalent in the analysis conducted. But a small amount of mechanism wear debris was observed. Figure 2 shows an example of a region of concentrated wear debris. Although some MoS2 is observed, most of the contaminant in this location is nitrided 15-5PH steel, as verified by the associated chemical analysis. High oxygen content was also observed which, when associated with the apparent friable nature of the steel material, suggests that this contaminant could be quite old, perhaps even associated with the mechanism s original manufacture and acceptance testing. Additional microscopic

Martinez, J. E.↗
NASA NTRS1984

Costs of measuring leaf area index of corn

The magnitude of plant-to-plant variability of leaf area of corn plants selected from uniform plots was examined and four representative methods for measuring leaf area index (LAI) were evaluated. The number of plants required and the relative costs for each sampling method were calculated to detect 10, 20, and 50% differences in LAI using 0.05 and 0.01 tests of significance and a 90% probability of success (beta = 0.1). The natural variability of leaf area per corn plant was nearly 10%. Additional variability or experimental error may be introduced by the measurement technique employed and by nonuniformity within the plot. Direct measurement of leaf area with an electronic area meter had the lowest CV, required that the fewest plants be sampled, but required approximately the same amount of time as the leaf area/weight ratio method to detect comparable differences. Indirect methods based on measurements of length and width of leaves required more plants but less total time than the direct method. Unless the coefficients for converting length and width to area are verified frequently, the indirect methods may be biased. When true differences in LAI among treatments exceed 50% of mean, all four methods are equal. The method of choice depends on the resources available, the differences to be detected, and what additional information, such as leaf weight or stalk weight, is also desired.

Daughtry, C. S. T.↗
DOE OSTI2025

Scalable Algorithms for Inverse Problems With High-Dimensional Parameter Spaces

Inverse problems, which involve inferring unknown parameters from observed data, present significant computational challenges, especially in large-scale settings with high-dimensional unknown parameters and nonlinear relationships between the unknowns and observations. Bayesian inference provides an approach for addressing these problems, often relying on sequential sampling methods like Markov chain Monte Carlo (MCMC) to approximate the posterior distribution of the parameters. However, MCMC methods become computationally demanding as the dimensionality of the problem increases, particularly in large-scale systems where likelihood evaluations rely on solving partial differential equations (PDEs) on large spatial domains with finely resolved meshes. To overcome these limitations, recent advancements have focused on designing scalable computa tional techniques – for both PDE simulations and sampling strategies – to make Bayesian methods feasible for high-dimensional problems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2009

The Uppermost Surface of the Moon

The Ap16 Clam shell Sampling Devices (CSSDs) were designed to sample the uppermost surface of lunar soil. The two devices used beta cloth (69003) and velvet (69004) to collect soil from the top 100 and 500 micrometers of the soil, respectively. Due to the difficulty of the sampling method, little material was collected and as a result little research has been done on these samples. Initial studies attempted to look at the material which had fallen off of the fabrics and was subsequently collected from inside the sample containers. However, this material was highly fractionated and did not provide an adequate picture of the uppermost surface. Recently, samples were obtained directly from the beta cloth using carbon tape. While still fractionated, these samples provide a unique glimpse into the undisturbed soil exposed at the lunar surface.

Noble, Sarah K.↗
NASA NTRS1977

Instrumentation techniques for studying heterogeneous combustion

Velocity measurements in flames and the simultaneous measurement of droplet size and velocity with the aid of laser anemometry are considered along with methods of particle size measurement and techniques based on laser Raman spectroscopy. Attention is given to high-speed photography and holography, computer compensated thermocouples, and the determination of turbulence characteristics. The employment of suction probes is also discussed, taking into account sampling methods, the effects of temperature change in sampling probes on particle formation, and the chemical analysis of samples.

Chigier, N. A.↗
NASA NTRS1987

A pyrolysis/gas chromatographic method for the determination of hydrogen in solid samples

A method is described for the determination of hydrogen in solid samples. The sample is heated under vacuum after which the evolved gases are separated by gas chromatography with a helium ionization detector. The system is calibrated by injecting known amounts of hydrogen, as determined manometrically. The method, which is rapid and reliable, was checked for a variety of lunar soils; the limit of detection is about 10 ng of hydrogen.

NASA Discipline Exobiology↗
DOE OSTI2025

Identifying impacts of contact tracing on HIV epidemiological inference from phylogenetic data

Abstract Robust sampling methods are foundational to inferences using phylogenies. Yet the impact of using contact tracing, a type of non-uniform sampling used in public health applications such as infectious disease outbreak investigations, has not been investigated in the molecular epidemiology field. To understand how contact tracing influences a recovered phylogeny, we developed a new simulation tool called SEEPS (Sequence Evolution and Epidemiological Process Simulator) that allows for the simulation of contact tracing and the resulting transmission tree, pathogen phylogeny, and corresponding virus genetic sequences. Importantly, SEEPS takes within-host evolution into account when generating pathogen phylogenies and sequences from transmission histories. Using SEEPS, we demonstrate that contact tracing can significantly impact the structure of the resulting tree, as described by popular tree statistics. Contact tracing generates phylogenies that are less balanced than the underlying transmission process, less representative of the larger epidemiological process, and affects the internal/external branch length ratios that characterize specific epidemiological scenarios. We also examined real data from a 2007–2008 Swedish HIV-1 outbreak and the broader 1998–2010 European HIV-1 epidemic to highlight the differences in contact tracing and expected phylogenies. Aided by SEEPS, we show that the data collection of the Swedish outbreak was strongly influenced by contact tracing even after downsampling, while the broader European Union epidemic showed little evidence of universal contact tracing, agreeing with the known epidemiological information about sampling and spread. Overall, our results highlight the importance of including possible non-uniform sampling schemes when examining phylogenetic trees. For that, SEEPS serves as a useful tool to evaluate such impacts, thereby facilitating better phylogenetic inferences of the characteristics of a disease outbreak. SEEPS is available at https://github.com/MolEvolEpid/SEEPS.

Virology↗
NASA NTRS1992

Some practicable applications of quadtree data structures/representation in astronomy

Development of quadtree as hierarchical data structuring technique for representing spatial data (like points, regions, surfaces, lines, curves, volumes, etc.) has been motivated to a large extent by storage requirements of images, maps, and other multidimensional (spatially structured) data. For many spatial algorithms, time-efficiency of quadtrees in terms of execution may be as important as their space-efficiency concerning storage conditions. Briefly, the quadtree is a class of hierarchical data structures which is based on the recursive partition of a square region into quadrants and sub-quadrants until a predefined limit. Beyond the wide applicability of quadtrees in image processing, spatial information analysis, and building digital databases (processes becoming ordinary for the astronomical community), there may be numerous further applications in astronomy. Some of these practicable applications based on quadtree representation of astronomical data are presented and suggested for further considerations. Examples are shown for use of point as well as region quadtrees. Statistics of different leaf and non-leaf nodes (homogeneous and heterogeneous sub-quadrants respectively) at different levels may provide useful information on spatial structure of astronomical data in question. By altering the principle guiding the decomposition process, different types of spatial data may be focused on. Finally, a sampling method based on quadtree representation of an image is proposed which may prove to be efficient in the elaboration of sampling strategy in a region where observations were carried out previously either with different resolution or/and in different bands.

Pasztor, L.↗
NASA NTRSDate not supplied

Improving Computational Efficiency of Prognostics Algorithms in Resource-Constrained Settings

The field of prognostics and health management provides quantitative methods for monitoring and predicting the health of physical systems. Prognostics algorithms are useful in that they can be employed to assess the current state of a system, propagate the system state throughout time, and predict potential anomalies or failures that may occur. However, effective prognosis can be challenging to achieve in resource-constrained settings due to computational limitations and high computational latency, leading to obsolete predictions. Thus, computationally efficient and accurate algorithms are necessary for some prognostics applications. In this work, we implement three new algorithmic approaches to prediction (sampling methods, variable prediction time step, variable prediction sample size) with the goal of improving computational efficiency while minimizing decrease in model accuracy. To quantitatively analyze our results, we examine a use-case of degradation of a Lithium-ion battery. Notably, through this work it was found that none of the sampling approaches had a significant impact on computational efficiency or model accuracy in predicting EOD of the battery. However, our results show that prediction accuracy is highly dependent on the time step used, and that an appropriate time step can optimize both model accuracy and simulation efficiency. Finally, implementing a variable sample size also affected prediction, and our results show that tuning both the magnitude and timing of the sample size adjustment in an application-specific manner may prove useful in some applications. Taken together, our findings highlight the challenge of performing prognostics in resource-constrained settings, and illustrate the potential of developing new prediction algorithms to improve computational efficiency of prognosis.

Prognostics↗
DOE OSTI2025

Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation

We introduce a conditional pseudo-reversible normalizing flow (PR-NF) that directly learns conditional probability distributions from noisy physical models to efficiently quantify both forward and inverse uncertainty propagation. Traditional surrogate modeling approaches approximate only the deterministic component of physical models, requiring separate noise characterization and computationally expensive sampling methods for inverse problems. Here, in this work, we develop the conditional PR-NF model to directly learn and efficiently generate samples from the conditional probability density functions (PDFs). The training process utilizes dataset consisting of input-output pairs without requiring prior knowledge about the noise and the function. Once trained, our model efficiently generates samples from conditional PDFs for any input within the training domain. Moreover, the pseudo-reversibility feature allows for the use of fully connected neural network architectures, which simplifies the implementation and enables theoretical analysis. We provide a rigorous convergence analysis of the conditional PR-NF model, showing its ability to converge to the target conditional PDF using the Kullback−Leibler divergence. To demonstrate the effectiveness of our method, we apply it to several benchmark tests and a real-world geologic carbon storage problem.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1981

Evaluation of a segment-based LANDSAT full-frame approach to corp area estimation

As the registration of LANDSAT full frames enters the realm of current technology, sampling methods should be examined which utilize other than the segment data used for LACIE. The effect of separating the functions of sampling for training and sampling for area estimation. The frame selected for analysis was acquired over north central Iowa on August 9, 1978. A stratification of he full-frame was defined. Training data came from segments within the frame. Two classification and estimation procedures were compared: statistics developed on one segment were used to classify that segment, and pooled statistics from the segments were used to classify a systematic sample of pixels. Comparisons to USDA/ESCS estimates illustrate that the full-frame sampling approach can provide accurate and precise area estimates.

Bauer, M. E.↗
NASA NTRS2009

A 16-yr Comparison of Fine Particle and Aerosol Strong Acidity at the Interface Zone (1,540 m) and Within (452 m) the Planetary Boundary Layer of the Great Gulf and Presidential-Dry River Class I Wildernesses on the Presidential Range, New Hampshire

Mount Washington, NH in the White Mountain National Forest, is flanked to the north-northeast and south by two Class I Wilderness areas, the Great Gulf and Presidential Range-Dry River Wildernesses, respectively. The Clean Air Act protects Class I area natural resource values from air pollution. Aerosol sulfate, a fine particulate component that is often transported long distances, is a known contributor to visibility degradation and acidic deposition. We examined summertime fine particulate aerosol mass and sulfate, strong acidity and ammonium concentrations from 1988 to 2004 on Mount Washington at two elevations, 452 and 1,540 meters (msl). The former site is within, and the latter at the interface of, the planetary boundary layer. Comparisons of sampling interval durations (10 and 24 hours), site vs. site, and different sampling methods are made. We also examine the extent to which aerosol sulfate is neutralized. Ten hour (daytime) compared to 24 hour samples have higher mass and aerosol sulfate concentrations, however paired samples are well correlated. Fine mass concentrations compared between the 452 m and 1,540 m sites (standard temperature and pressure corrected) show a weak positive linear relationship with the later being approximately 34% lower. We attribute the lack of a strong correlation to the facts that the 1,540 m site is commonly at the interface of and even above the regional planetary boundary layer in summer and that it can intercept different air masses relative to the 452 m site. Sulfate is approximately 19% lower at the higher elevation site, but comprises a greater percentage of total fine mass; 42% compared to 35% for the high and low elevation site, respectively. Aerosol strong acidity was found to increase with increasing sulfate concentrations at both sites. At the high elevation site, elevated mass and sulfate concentrations are associated with westerly and southwesterly regional flows.

Murray, Georgia L.↗
NASA NTRS2003

Structural Reliability Using Probability Density Estimation Methods Within NESSUS

A reliability analysis studies a mathematical model of a physical system taking into account uncertainties of design variables and common results are estimations of a response density, which also implies estimations of its parameters. Some common density parameters include the mean value, the standard deviation, and specific percentile(s) of the response, which are measures of central tendency, variation, and probability regions, respectively. Reliability analyses are important since the results can lead to different designs by calculating the probability of observing safe responses in each of the proposed designs. All of this is done at the expense of added computational time as compared to a single deterministic analysis which will result in one value of the response out of many that make up the density of the response. Sampling methods, such as monte carlo (MC) and latin hypercube sampling (LHS), can be used to perform reliability analyses and can compute nonlinear response density parameters even if the response is dependent on many random variables. Hence, both methods are very robust; however, they are computationally expensive to use in the estimation of the response density parameters. Both methods are 2 of 13 stochastic methods that are contained within the Numerical Evaluation of Stochastic Structures Under Stress (NESSUS) program. NESSUS is a probabilistic finite element analysis (FEA) program that was developed through funding from NASA Glenn Research Center (GRC). It has the additional capability of being linked to other analysis programs; therefore, probabilistic fluid dynamics, fracture mechanics, and heat transfer are only a few of what is possible with this software. The LHS method is the newest addition to the stochastic methods within NESSUS. Part of this work was to enhance NESSUS with the LHS method. The new LHS module is complete, has been successfully integrated with NESSUS, and been used to study four different test cases that have been proposed by the Society of Automotive Engineers (SAE). The test cases compare different probabilistic methods within NESSUS because it is important that a user can have confidence that estimates of stochastic parameters of a response will be within an acceptable error limit. For each response, the mean, standard deviation, and 0.99 percentile, are repeatedly estimated which allows confidence statements to be made for each parameter estimated, and for each method. Thus, the ability of several stochastic methods to efficiently and accurately estimate density parameters is compared using four valid test cases. While all of the reliability methods used performed quite well, for the new LHS module within NESSUS it was found that it had a lower estimation error than MC when they were used to estimate the mean, standard deviation, and 0.99 percentile of the four different stochastic responses. Also, LHS required a smaller amount of calculations to obtain low error answers with a high amount of confidence than MC. It can therefore be stated that NESSUS is an important reliability tool that has a variety of sound probabilistic methods a user can employ and the newest LHS module is a valuable new enhancement of the program.

Chamis, Chrisos C.↗
NASA NTRS2012

A Comparison of Filter-based Approaches for Model-based Prognostics

Model-based prognostics approaches use domain knowledge about a system and its failure modes through the use of physics-based models. Model-based prognosis is generally divided into two sequential problems: a joint state-parameter estimation problem, in which, using the model, the health of a system or component is determined based on the observations; and a prediction problem, in which, using the model, the stateparameter distribution is simulated forward in time to compute end of life and remaining useful life. The first problem is typically solved through the use of a state observer, or filter. The choice of filter depends on the assumptions that may be made about the system, and on the desired algorithm performance. In this paper, we review three separate filters for the solution to the first problem: the Daum filter, an exact nonlinear filter; the unscented Kalman filter, which approximates nonlinearities through the use of a deterministic sampling method known as the unscented transform; and the particle filter, which approximates the state distribution using a finite set of discrete, weighted samples, called particles. Using a centrifugal pump as a case study, we conduct a number of simulation-based experiments investigating the performance of the different algorithms as applied to prognostics.

prognostics↗