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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 775 records · Page 43

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NASA NTRS1975

Dual frequency scatterometer measurement of ocean wave height

A technique for remotely measuring wave height averaged over an area of the sea surface was developed and verified with a series of aircraft flight experiments. The measurement concept involves the cross correlation of the amplitude fluctuations of two monochromatic reflected signals with variable frequency separation. The signal reflected by the randomly distributed specular points on the surface is observed in the backscatter direction at nadir incidence angle. The measured correlation coefficient is equal to the square of the magnitude of the characteristic function of the specular point height from which RMS wave height can be determined. The flight scatterometer operates at 13.9 GHz and 13.9 - delta f GHz with a maximum delta f of 40 MHz. Measurements were conducted for low and moderate sea states at altitudes of 2, 5, and 10 thousand feet. The experimental results agree with the predicted decorrelation with frequency separation and with off-nadir incidence angle.

Johnson, J. W.↗

NASA NTRS1987

Probabilistic load model development and validation for composite load spectra for select space propulsion engines

A major task of the program to develop an expert system to predict the loads on selected components of a generic space propulsion engine is the design development and application of a probabilitic loads model. This model is being developed in order to account for the random nature of the loads and assess the variable load ranges' effect on the engine performance. A probabilistic model has been developed. The model is based primarily on simulation methods, but also has a Gaussian algebra method (if all variables are near normal), a fast probability integrator routine (for the calculation of low probability events), and a separate, stand alone program for performing barrier crossing calculations. Each of these probabilistic methods has been verified with theoretical calculations using assumed distributional forms.

Kurth, R.↗

NASA NTRS1988

Asteroid and comet flux in the neighborhood of the earth

Significant advances in the knowledge and understanding of the flux of large solid objects in the neighborhood of Earth have occurred. The best estimates of the collision rates with Earth of asteroids and comets and the corresponding production of impact craters are presented. Approximately 80 Earth-crossing asteroids were discovered through May 1988. Among 42 new Earth-crossing asteroids found in the last decade, two-thirds were discovered from observations at Palomar Observatory and 15 were discovered or independently detected in dedicated surveys with the Palomar Observatory and 15 were discovered or independently detected in dedicated surveys with the Palomar 46 cm Schmidt. Probabilities of collision with Earth have been calculated for about two-thirds of the known Earth-crossing asteroids. When multiplied by the estimated population of Earth-crossers, this yields an estimated present rate of collision about 65 pct higher than that previously reported. Spectrophotometric data obtained chiefly in the last decade show that the large majority of obvserved Earth-crossers are similar to asteroids found in the inner part of the main belt. The number of discovered Earth-crossing comets is more than 4 times greater than the number of known Earth-crossing asteroids, but reliable data on the sizes of comet nuclei are sparse. The flux of comets almost certainly was highly variable over late geologic time, owing to the random perturbation of the Oort comet cloud by stars in the solar neighborhood.

Shoemaker, Eugene M.↗

NASA NTRS1988

Confirmation of radial velocity variability in Arcturus

The paper presents results of high-precision measurements of radial-velocity variations in Alpha Boo. Significant radial-velocity variability is detected well in excess of the random and systematic measurement errors. The radial velocity varies by an amount greater than 200 m/sec with a period of around 2 days.

Cochran, William D.↗

NASA NTRS1993

Are crystalline C-(H-O-N) carbons the elusive meteoritic carbynes?

I present an internally consistent reinterpretation of carbyne diffraction data making a few plausible assumptions. The results support that carbynes could be crystalline, randomly interstratified (or mixed-layered) carbons with variable C/(C+H+O+N) ratios rather than thermodynamically stable carbon allotropes. The metastable carbynes are likely the result of incomplete, kinetically-inhibited, low-temperature pyrolysis of precursor material. A renewed interest in meteoritic carbynes should seek to combine light-element chemistry and crystallography of these elusive carbons.

Rietmeijer, Frans J. M.↗

NASA NTRS2020

Spatial validation reveals poor predictive performance of large-scale ecological mapping models

Mapping aboveground forest biomass is central for assessing the global carbon balance. However, current large-scale maps show strong disparities, despite good validation statistics of their underlying models. Here, we attribute this contradiction to a flaw in the validation methods, which ignore spatial autocorrelation (SAC) in data, leading to overoptimistic assessment of model predictive power. To illustrate this issue, we reproduce the approach of large-scale mapping studies using a massive forest inventory dataset of 11.8 million trees in central Africa to train and validate a random forest model based on multispectral and environmental variables. A standard nonspatial validation method suggests that the model predicts more than half of the forest biomass variation, while spatial validation methods accounting for SAC reveal quasi-null predictive power. This study underscores how a common practice in big data mapping studies shows an apparent high predictive power, even when predictors have poor relationships with the ecological variable of interest, thus possibly leading to erroneous maps and interpretations.

biomass↗

NASA NTRSDate not supplied

Nearest-Neighbor Machine Learning Feature Selection for Interpretation of Microbial Molecular Signatures from Isotope Ratio Mass Spectrometry Data

Mass spectrometry (MS) promises to be a powerful tool for potential biosignature detection during astrobiological missions on ocean worlds in our solar system. Accurate and generalizable machine learning methods could enhance science return on investment by predicting seawater chemistry and classifying isotopic biosignatures, either as a signature consistent with microbial life (biotic) or as a novelty (unclassified/unique). However, machine learning models are likely to be complex and involve interactions between MS features, making biosignatures difficult to interpret. Feature selection methods provide biological and chemical context that help interpret the mechanisms of machine learning models, but these methods also need the ability to detect complex interactions. Previously, we developed a machine learning feature selection algorithm called nearest-neighbor projected distance regression (NPDR) that has the ability to identify important model features that involve complex interactions and automatically reduce correlation and the dimensionality in a high-dimensional variable space. The standard distance metrics used in NPDR – Manhattan and Euclidean – assume the multivariate data are isotropic, which is often violated in real data due to differences in the covariance between variables. Thus, we extend NPDR to include a random forest distance, and other anisotropic distance metrics, for computing nearest neighbors. We also augment the isotope-ratio MS data with time-series features from the raw MS signal to improve biotic classification. We test NPDR on our novel experimental ocean world seawater analog MS data. We measure isotope fractionations of volatile CO 2 that could be measured in exospheres or plumes. Samples include baseline abiotic conditions using a range of possible seawater chemistry consistent with Europa and Enceladus, and biotic samples that include microbes in these seawaters. We use penalized NPDR with random forest proximity to identify interpretable microbial molecular signatures. We compare features with random forest importance, and we train a classifier that discriminates between biotic and abiotic samples with high accuracy. These ML-trained ocean-world analog MS data could be used to assist in identifying biosignatures during future missions.

geochemistry↗

NASA NTRS2022

Bryce Canyon Water Resources: Monitoring Vegetation Health and Water Availability in Bryce Canyon National Park for Drought Stress Mitigation Planning

Bryce Canyon National Park is home to groundwater-dependent ecosystems (GDEs) that are threatened by a multidecadal drought and increased groundwater extraction due to a spike in tourism. These ecosystems contain unique species that are only found in areas where near-surface groundwater is present, such as aspen groves and fens. These species contribute to the high biodiversity found in Bryce Canyon, which boosts an ecosystem’s productivity and the services it provides to the park. Unfortunately, many of these GDEs are too small to identify with traditional Earth observation platforms and are difficult to physically reach for monitoring purposes. This project partnered with the National Park Service to identify springs and seeps as a proxy for GDEs within Bryce Canyon from 2013–2022. Furthermore, this project tested the feasibility of various methods to detect and monitor springs and seeps and therefore facilitate the partner’s efforts to conserve these ecologically valuable GDEs in Bryce Canyon. The team mapped groundwater discharge with high resolution National Agriculture Imagery Program (NAIP) and assessed park vegetation trends with Landsat 8 Operational Land Imager (OLI) and PlanetScope imagery. In-situ precipitation data and the Western Land Data Assimilation System (WLDAS) were used to produce time series of climatic variables. Seeps and spring locations were predicted using random forest classification and maximum entropy machine learning models.

Groundwater dependent ecosystems↗

NASA NTRS2026

Benchmarking Bayesian Optimization Frameworks and Acquisition Strategies for Materials Discovery and Autonomous Laboratories

Bayesian optimization (BO) can accelerate materials discovery by guiding expensive experiments toward the most promising processing conditions. We systematically compare five BO surrogate and framework combinations (Gaussian processes in Ax, Gaussian processes and Monte-Carlo neural networks in BayBE, random forests in Lolopy, and tree-structured Parzen (TPE) estimators in Hyperopt) on three benchmarks that mimic common materials design tasks (a discrete solid-electrolyte composition space, a hybrid discrete/continuous laminate-composite design problem solved with micromechanics modeling, and the continuous Ishigami analytic function which is a standard optimization benchmark). Each BO surrogate is paired with posterior mean, probability of improvement, and expected improvement acquisition functions and run for 100 trials from randomized initial samples with uniform random search providing a control. Across five random seeds per setting, BayBE’s Gaussian-process surrogate with expected improvement consistently reached ≥95 % of the known optimum in the fewest evaluations, while Lolopy’s random forest matched or exceeded GP performance on purely categorical or mixed spaces at a higher computational cost. Posterior mean alone often stagnated at local optima, underscoring the need for exploration, whereas probability and expected improvement balanced exploration and exploitation leading to better optimization in fewer trials. Execution times ranged from milliseconds for TPE to minutes for neural-network and random-forest surrogates. These results establish baseline expectations for BO in automated materials laboratories and highlight expected improvement with Gaussian processes as a reliable first choice, with random forests offering a strong alternative when categorical variables dominate. The benchmark suite and code are released to facilitate future surrogate, acquisition, and constraint-handling research in data-driven materials optimization.

Bayesian optimization↗

DOE OSTI2019

Summary Report on Synthetic Time History Development and Deployment

This report summarizes research work performed at the Colorado School of Mines (CSM) and George Washington University (GWU) in support of the DOE Office of Nuclear Energy Integrated Energy Systems (IES) program. In particular, the work focuses on improving and strengthening two fundamental steps in the mathematical and computational framework that is currently being developed to optimize the economic performance of Integrated Energy Systems. One of the innovative approaches taken by the IES program is that, rather than optimizing the system for a specific year or week or day, the optimization is performed using stochastic synthetic data. Synthetic data is generated by training artificial intelligence (AI) using available but limited data sets. The synthetic data should possess the same patterns as the original set and the same statistical properties, but never repeat the same set of values exactly. The work from CSM focuses on this first step; namely, investigating a methodology to determine the quality of the synthetic data and the right settings for properly training the AI. Once a large set of trustworthy data can be generated the problem of stochastic optimization can be tackled. The optimal IES configuration is highly dependent on the rare high peaks or deep depths of the net demand (grid electricity demand less variable renewable energy [VRE] production). Using an unbiased random sampling of the synthetic data, these extremes are very difficult to generate and therefore the optimization can be unrepresentative and inaccurate. GWU has started an approach to increase the likelihood of generating these rare events with the proper associated probabilistic weight. Both of these projects represent a step forward in achieving the capability to properly size the IESs and to properly capture the value that these systems can provide to the grid by absorbing volatility of net demand without disproportionately increasing the cost of maintaining grid reliability.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Evolving Metrics for Resource Adequacy Assessment

Resource adequacy analysis quantifies the likelihood of capacity shortfall on a power system in a probabilistic manner. Using a combination of statistical techniques and power system fundamentals, the analysis typically evaluates hundreds or thousands of stochastic random samples (replications) of varying load, generator outages, variable renewable energy availability, and other aspects of power system uncertainty. In this range of uncertainty, there are - at times - periods where the power system's available resources are insufficient to meet system demand, referred to as a shortfall event. Today's power systems' rapidly evolving generation mix is changing the types of data needed by system planners and regulators, which can often render traditional resource adequacy metrics insufficient for ensuring resource adequacy for tomorrow's grid. In this paper we provide a critical assessment of traditional measures of shortfall risk in power systems, discussing their shortcomings and how they compare to metrics used in other domains. From this analysis we propose four steps forward for improving power system resource adequacy risk metrics in the future.

ENERGY PLANNING, POLICY, AND ECONOMY,POWER TRANSMI↗

DOE OSTI2023

Beyond Expected Values Evolving Metrics for Resource Adequacy Assessment

ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2014

Automatic classification of time-variable X-ray sources

To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, and other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ∼97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7–500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Physics-informed Machine Learning Method for Forecasting and Uncertainty Quantification of Partially Observed and Unobserved States in Power Grids

We present a physics-informed Gaussian Process Regression (GPR) model to predict the phase angle, angular speed, and wind mechanical power from a limited number of measurements. In the traditional data-driven GPR method, the form of the Gaussian Process covariance matrix is assumed and its parameters are found from measurements. In the physics-informed GPR, we treat unknown variables (including wind speed and mechanical power) as a random process and compute the covariance matrix from the resulting stochastic power grid equations. We demonstrate that the physics-informed GPR method is significantly more accurate than the standard data-driven one for immediate forecasting of generators' angular velocity and phase angle. We also show that the physics-informed GPR provides accurate predictions of the unobserved wind mechanical power, phase angle, or angular velocity when measurements from only one of these variables are available. The immediate forecast of observed variables and predictions of unobserved variables can be used for effectively managing power grids (electricity market clearing, regulation actions) and early detection of abnormal behavior and faults. The physics-based GPR forecast time horizon depends on the combination of input (wind power, load, etc.) correlation time and characteristic (relaxation) time of the power grid and can be extended to short and medium-range times.

machine learning, power grid, forecasting, Gaussia↗

NASA NTRS1972

Fatigue Tests with Random Flight Simulation Loading

Crack propagation was studied in a full-scale wing structure under different simulated flight conditions. Omission of low-amplitude gust cycles had a small effect on the crack rate. Truncation of the infrequently occurring high-amplitude gust cycles to a lower level had a noticeably accelerating effect on crack growth. The application of fail-safe load (100 percent limit load) effectively stopped subsequent crack growth under resumed flight-simulation loading. In another flight-simulation test series on sheet specimens, the variables studied are the design stress level and the cyclic frequency of the random gust loading. Inflight mean stresses vary from 5.5 to 10.0 kg/sq mm. The effect of the stress level is larger for the 2024 alloy than for the 7075 alloy. Three frequencies were employed: namely, 10 cps, 1 cps, and 0.1 cps. The frequency effect was small. The advantages and limitations of flight-simulation tests are compared with those of alternative test procedures such as constant-amplitude tests, program tests, and random-load tests. Various testing purposes are considered. The variables of flight-simulation tests are listed and their effects are discussed. A proposal is made for performing systematic flight-simulation tests in such a way that the compiled data may be used as a source of reference.

Schijve, J.↗

NASA NTRS1973

Monte-Carlo analysis of rarefied-gas diffusion including variance reduction using the theory of Markov random walks

Molecular diffusion through a rarefied gas is analyzed by using the theory of Markov random walks. The Markov walk is simulated on the computer by using random numbers to find the new states from the appropriate transition probabilities. As the sample molecule during its random walk passes a scoring position, which is a location at which the macroscopic diffusing flow variables such as molecular flux and molecular density are desired, an appropriate payoff is scored. The payoff is a function of the sample molecule velocity. For example, in obtaining the molecular flux across a scoring position, the random walk payoff is the net number of times the scoring position has been crossed in the positive direction. Similarly, when the molecular density is required, the payoff is the sum of the inverse velocity of the sample molecule passing the scoring position. The macroscopic diffusing flow variables are then found from the expected payoff of the random walks.

Perlmutter, M.↗

NASA NTRS2014

Predicting Ground Delay Program at an Airport Based on Meteorological Conditions

In this paper, we present two supervised-learning models, logistic regression and decision tree, to predict occurrence of ground delay program at an airport based on meteorological conditions and scheduled traffic demand. Such predictive capabilities can help the Federal Aviation Administration traffic managers and airline dispatchers to prepare mitigation strategies to reduce the impact of adverse weather. The models are applied to predict ground delay program occurrence at two major U.S. airports: Newark Liberty Intl. and San Francisco Intl. airports. The logistic regression model estimates the probability that a ground delay program will occur during a given hour. Decision tree, on the other hand, classifies an hour as a ground delay program or not based on the input variables. Results indicate that both models perform significantly better than a purely random prediction of ground delay program occurrence at the two airports. The logistic regression model performs better than the decision tree model. The degree to which various input variables impact the probability of ground delay program vary between the two airports. While the enroute convective weather is a dominant factor causing ground delay programs at New York airports, poor visibility and low cloud ceiling caused by marine stratus are major drivers of ground delay programs at San Francisco Intl. airport.

traffic flow management↗

DOE OSTI2016

The role of spray-enhanced swirl flow for combustion stabilization in a stratified-charge DISI engine

Implementing spray-guided stratified-charge direct-injection spark-ignited (DISI) engines is inhibited by the occurrence of misfire and partial burns. Engine-performance tests demonstrate that increasing engine speed induces combustion instability, but this deterioration can be prevented by generating swirling flow during the intake stroke. In-cylinder pressure-based heat-release analysis reveals that the appearance of poor-burn cycles is not solely dependent on the variability of early flame-kernel growth. Moreover, cycles can experience burning-rate regression during later combustion stages and may or may not recover before the end of the cycle. Thermodynamic analysis and optical diagnostics are used here to clarify why swirl improves the combustion repeatability from cycle to cycle. The fluid dynamics of swirl/spray interaction was previously demonstrated using high-speed PIV measurements of in-cylinder motored flow. It was found that the sprays of the multi-hole injector redistribute the intake-generated swirl flow momentum, thereby creating a better-centered higher angular-momentum vortex with reduced variability. The engine operation with high swirl was found to have significant improvement in cycle-to-cycle variations of both flow pattern and flow momentum. This paper is an extension of the previous work. Here, PIV measurements and flame imaging are applied to fired operation for studying how the swirl flow affects variability of ignition and subsequent combustion phases. PIV results for fired operation are consistent with the measurements made of motored flow. They demonstrate that the spark-plasma motion is highly correlated with the direction of the gas flow in the vicinity of the spark-plug gap. Without swirl, the plasma is randomly stretched towards either side of the spark plug, causing variability in the ignition of the two spray plumes that are straddling the spark plug. Conversely, swirl flow always convects the spark plasma towards one spray plume, causing a more repeatable ignition. The swirl decreases local RMS velocity, consistent with an observed reduction of early-burn variability. Broadband flame imaging demonstrates that with swirl, the flame consistently propagates in multiple directions to consume fuel–air mixtures within the piston bowl. In contrast, operation without swirl displays higher variability of flame-spread patterns, occasionally causing the appearance of partial-burn cycles.

33 ADVANCED PROPULSION SYSTEMS↗