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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 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Testing convolutional neural network based deep learning systems: a statistical metamorphic approach

Machine learning technology spans many areas and today plays a significant role in addressing a wide range of problems in critical domains,i.e., healthcare, autonomous driving, finance, manufacturing, cybersecurity,etc. Metamorphic testing (MT) is considered a simple but very powerful approach in testing such computationally complex systems for which either an oracle is not available or is available but difficult to apply. Conventional metamorphic testing techniques have certain limitations in verifying deep learning-based models (i.e., convolutional neural networks (CNNs)) that have a stochastic nature (because of randomly initializing the network weights) in their training. In this article, we attempt to address this problem by using a statistical metamorphic testing (SMT) technique that does not require software testers to worry about fixing the random seeds (to get deterministic results) to verify the metamorphic relations (MRs). We propose seven MRs combined with different statistical methods to statistically verify whether the program under test adheres to the relation(s) specified in the MR(s). We further use mutation testing techniques to show the usefulness of the proposed approach in the healthcare space and test two CNN-based deep learning models (used for pneumonia detection among patients). The empirical results show that our proposed approach uncovers 85.71% of the implementation faults in the classifiers under test (CUT). Furthermore, we also propose an MRs minimization algorithm for the CUT, thus saving computational costs and organizational testing resources.

Computer Science↗
NASA NTRS1974

Lunar orbit determination in the presence of unmodeled accelerations

A technique for estimating the state of an artificial satellite in the presence of unmodeled accelerations is presented. The unmodeled acceleration is approximated by a first-order Gauss-Markov sequence which can be separated into a timewise-correlated component and a purely random component. Using this approximation, a sequential procedure for estimating the position, velocity, and the unmodeled acceleration is developed. The method is evaluated by reducing range-rate observations obtained by tracking the Apollo 10 and 11 spacecraft during the lunar-orbit phase of the mission. Numerical results are presented which show that the observation residual pattern lies within the observation noise standard deviation. The values of the estimated components of the unmodeled acceleration are repeatable from orbit to orbit within a given mission and from mission to mission when the same ground track is covered. Finally, the variation in the radial component of the unmodeled acceleration shows a high correlation with the reported location of the lunar surface mascons.

Ingram, D. S.↗
NASA NTRS1978

Mapping ocean tides with satellites - A computer simulation

As a preliminary study for the future worldwide direct mapping of the open ocean tide with satellites equipped with precision altimeters we conducted a simulated study using sets of artificially generated altimeter data constructed from a realistic geoid and four pairs of major tides in the northeastern Pacific Ocean. Recovery of the original geoid and eight tidal maps is accomplished by a space-time, least squares harmonic analysis scheme. The resultant maps appear fairly satisfactory even when random noises up to + or - 100 cm are added to the altimeter data of sufficient space-time density. The method also produces a refined geoid which is rigorously corrected for the dynamic tides.

Won, I. J.↗
NASA NTRS1987

Weibull Distribution From Interval Inspection Data

Most likely failure sequence assumed. Memorandum discusses application of Weibull distribution to statistics of failures of turbopump blades. Is generalization of well known exponential random probability distribution and useful in describing component-failure modes including aging effects. Parameters found from experimental data by method of maximum likelihood.

Rheinfurth, Mario H.↗
NASA NTRS1987

Wake shield

Progress on the modeling of the flow field around a wake shield using a recently obtained code based on the Monte Carlo method is discussed. The direct simulation Monte Carlo method is a method for solving the Boltzman Equation using an approximation to the collision integral term. The collision integrand is evaluated for randomly selected values of its arguments and the summation will approach the integral for large enough samples. The collision effects may be modeled for either hard sphere or various power law potentials. The convective side of the Boltzman equation is approximated over a time step using a simple trajectory calculation of molecules as they travel through the domain of interest.

Bannister, Tommy↗
NASA NTRS1990

The simulation of fluid dynamic uncertainties in the SSME turbopump

The aerodynamic uncertainties in the reusable rocket engine turbopumps due to randomness in their cryogenic environment are investigated. The probabilistic simulation of the turbopumps' aerodynamic response was accomplished using the panel method coupled with Fast Probability Integration methods. The results are presented for the probabilistic rotor blades and splitter loading and their sensitivity to specified flow coefficient and rotor preswirl variance.

Hamed, Awatef↗
NASA NTRS1988

On the nature of the fragment environment created by the range destruction or random failure of solid rocket motor casings

Given here are predictions of fragment velocities and azimuths resulting from the Space Transportation System Solid Rocket Motor range destruct, or random failure occurring at any time during the 120 seconds of Solid Rocket Motor burn. Results obtained using the analytical methods described showed good agreement between predictions and observations for two specific events. It was shown that these methods have good potential for use in predicting the fragmentation process of a number of generically similar casing systems. It was concluded that coupled Eulerian-Lagrangian calculational methods of the type described here provide a powerful tool for predicting Solid Rocket Motor response.

Eck, M.↗
NASA NTRS1993

Application of neural network to remote sensing of soil moisture using theoretical polarimetric backscattering coefficients

This paper investigates the potential application of neural network to inversion of soil moisture using polarimetric remote sensing data. The neural network used for the inversion of soil parameters is multi-layer perceptron trained with the back-propagation algorithm. The training data include the polarimetric backscattering coefficients obtained from theoretical surface scattering models together with an assumed nominal range of soil parameters which are comprised of the soil permittivity and surface roughness parameters. Soil permittivity is calculated from the soil moisture and the assumed soil texture based on an empirical formula at C-, L-, and P-bands. The rough surface parameters for the soil surface, which is described by the Gaussian random process, are the root-mean-square (rms) height and correlation length. For the rough surface scattering, small perturbation method is used for the L-band frequency, and Kirchhoff approximation is used for the C-band frequency to obtain the corresponding backscattering coefficients. During the training, the backscattering coefficients are the inputs to the neural net and the output from the net are compared with the desired soil parameters to adjust the interconnecting weights. The process is repeated for each input-output data entry and then for the entire training data until convergence is reached. After training, the backscattering coefficients are applied to the trained neural net to retrieve the soil parameters which are compared with the desired soil parameters to verify the effectiveness of this technique. Several cases are examined. First, for simplicity, the correlation length and rms height of the soil surface are fixed while soil moisture is varied. Soil moisture obtained using the neural networks with either L-band or C-band backscattering coefficients for the HH and VV polarizations as inputs is in good agreement with the desired soil moisture. The neural net output matches the desired output for the soil moisture range of 16 to 60 percent for the C-band case. The next case investigated is to vary both soil moisture and rms height while keeping the correlation length fixed. For this case, C-band backscattering coefficients are not sufficient for retrieving two parameters because the Kirchhoff approximation gives the same HH and VV backscattering coefficients. Therefore, the backscattering coefficients at two different frequency bands are necessary to find both the soil moisture and rms height. Finally, the neural nets are also applied to simultaneously invert soil moisture, rms height, and correlation length. Overall, the soil moisture retrieved from the neural network agrees very well with the desired soil moisture. This suggests that the neural network shows potential for retrieval of soil parameters from remote sensing data.

Wang, L.↗
NASA NTRS1994

Topological analysis of chaotic orbits: Revisiting Hyperion

There is emerging interest in the possibility of chaotic evolution in astrophysical systems. To mention just one example, recent well-sampled ground-based observations of the Saturian satellite Hyperion strongly suggest that it is exhibiting chaotic behavior. We present a general technique, the method of close returns, for the analysis of data from astronomical objects believed to be exhibiting chaotic motion. The method is based on the extraction of pieces of the evolution that exhibit nearly periodic behavior-episodes during which the object stays near in phase space to some unstable periodic orbit. Such orbits generally act as skeletal features, tracing the topological organization of the manifold on which the chaotic dynamics takes place. This method does not require data sets as lengthy as other nonlinear analysis techniques do and is therefore well suited to many astronomical observing programs. Well sampled data covering between twenty and forty characteristic periods of the system have been found to be sufficient for the application of this technique. Additional strengths of this method are its robustness in the presence of noise and the ability for a user to clearly distinguish between periodic, random, and chaotic behavior by inspection of the resulting two-dimensional image. As an example of its power, we analyze close returns in a numerically generated data set, based on a model for Hyperion extensively studied in the literature, corresponding to nightly observations of the satellite. We show that with a small data set, embedded unstable periodic orbits can be extracted and that these orbits can be responsible for nearly periodic behavior lasting a substantial fraction of the observing run.

Boyd, Patricia T.↗
NASA NTRS2001

Interrelation Between Safety Factors and Reliability

An evaluation was performed to establish relationships between safety factors and reliability relationships. Results obtained show that the use of the safety factor is not contradictory to the employment of the probabilistic methods. In many cases the safety factors can be directly expressed by the required reliability levels. However, there is a major difference that must be emphasized: whereas the safety factors are allocated in an ad hoc manner, the probabilistic approach offers a unified mathematical framework. The establishment of the interrelation between the concepts opens an avenue to specify safety factors based on reliability. In cases where there are several forms of failure, then the allocation of safety factors should he based on having the same reliability associated with each failure mode. This immediately suggests that by the probabilistic methods the existing over-design or under-design can be eliminated. The report includes three parts: Part 1-Random Actual Stress and Deterministic Yield Stress; Part 2-Deterministic Actual Stress and Random Yield Stress; Part 3-Both Actual Stress and Yield Stress Are Random.

Elishakoff, Isaac↗
NASA NTRS2002

A statistical model for interpreting computerized dynamic posturography data

Computerized dynamic posturography (CDP) is widely used for assessment of altered balance control. CDP trials are quantified using the equilibrium score (ES), which ranges from zero to 100, as a decreasing function of peak sway angle. The problem of how best to model and analyze ESs from a controlled study is considered. The ES often exhibits a skewed distribution in repeated trials, which can lead to incorrect inference when applying standard regression or analysis of variance models. Furthermore, CDP trials are terminated when a patient loses balance. In these situations, the ES is not observable, but is assigned the lowest possible score--zero. As a result, the response variable has a mixed discrete-continuous distribution, further compromising inference obtained by standard statistical methods. Here, we develop alternative methodology for analyzing ESs under a stochastic model extending the ES to a continuous latent random variable that always exists, but is unobserved in the event of a fall. Loss of balance occurs conditionally, with probability depending on the realized latent ES. After fitting the model by a form of quasi-maximum-likelihood, one may perform statistical inference to assess the effects of explanatory variables. An example is provided, using data from the NIH/NIA Baltimore Longitudinal Study on Aging.

NASA Discipline Neuroscience↗
NASA NTRS1997

Csa-19, a radiation-responsive human gene, identified by an unbiased two-gel cDNA library screening method in human cancer cells

A novel polymerase chain reaction (PCR)-based method was used to identify candidate genes whose expression is altered in cancer cells by ionizing radiation. Transcriptional induction of randomly selected genes in control versus irradiated human HL60 cells was compared. Among several complementary DNA (cDNA) clones recovered by this approach, one cDNA clone (CL68-5) was downregulated in X-irradiated HL60 cells but unaffected by 12-O-tetradecanoyl phorbol-13-acetate, forskolin, or cyclosporin-A. DNA sequencing of the CL68-5 cDNA revealed 100% nucleotide sequence homology to the reported human Csa-19 gene. Northern blot analysis of RNA from control and irradiated cells revealed the expression of a single 0.7-kilobase (kb) messenger RNA (mRNA) transcript. This 0.7-kb Csa-19 mRNA transcript was also expressed in a variety of human adult and corresponding fetal normal tissues. Moreover, when the effect of X- or fission neutron-irradiation on Csa-19 mRNA was compared in cultured human cells differing in p53 gene status (p53-/- versus p53+/+), downregulation of Csa-19 by X-rays or fission neutrons was similar in p53-wild type and p53-null cell lines. Our results provide the first known example of a radiation-responsive gene in human cancer cells whose expression is not associated with p53, adenylate cyclase or protein kinase C.

NASA Discipline Radiation Health↗
NASA NTRS2004

Visualizing Time-Varying Distribution Data in EOS Application

In this research, we have developed several novel visualization methods for spatial probability density function data. Our focus has been on 2D spatial datasets, where each pixel is a random variable, and has multiple samples which are the results of experiments on that random variable. We developed novel clustering algorithms as a means to reduce the information contained in these datasets; and investigated different ways of interpreting and clustering the data.

Shen, Han-Wei↗
NASA NTRS2006

Micromechanics Modeling of Functionally Graded Interphase Regions in Carbon Nanotube-Polymer Composites

The effective elastic properties of a unidirectional carbon fiber/epoxy lamina in which the carbon fibers are coated with single-walled carbon nanotubes are modeled herein through the use of a multi-scale method involving the molecular dynamics/equivalent continuum and micromechanics methods. The specific lamina representative volume element studied consists of a carbon fiber surrounded by a region of epoxy containing a radially varying concentration of carbon nanotubes which is then embedded in the pure epoxy matrix. The variable concentration of carbon nanotubes surrounding the carbon fiber results in a functionally graded interphase region as the properties of the interphase region vary according to the carbon nanotube volume fraction. Molecular dynamics and equivalent continuum methods are used to assess the local effective properties of the carbon nanotube/epoxy comprising the interphase region. Micromechanics in the form of the Mori-Tanaka method are then applied to obtain the global effective properties of the graded interphase region wherein the carbon nanotubes are randomly oriented. Finally, the multi-layer composite cylinders micromechanics approach is used to obtain the effective lamina properties from the lamina representative volume element. It was found that even very small quantities of carbon nanotubes (0.36% of lamina by volume) coating the surface of the carbon fibers in the lamina can have a significant effect (8% increase) on the transverse properties of the lamina (E22, k23, G23 and G12) with almost no affect on the lamina properties in the fiber direction (E11 and v12).

Seidel, Gary D.↗
NASA NTRS2019

An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval

Machine learning (ML) is now used in many areas of astrophysics, from detecting exoplanets in Kepler transit signals to removing telescope systematics. Recent work demonstrated the potential of using ML algorithms for atmospheric retrieval by implementing a random forest (RF) to perform retrievals in seconds that are consistent with the traditional, computationally expensive nested-sampling retrieval method. We expand upon their approach by presenting a new ML model, plan-net, based on an ensemble of Bayesian neural networks (BNNs) that yields more accurate inferences than the RF for the same data set of synthetic transmission spectra. We demonstrate that an ensemble provides greater accuracy and more robust uncertainties than a single model. In addition to being the first to use BNNs for atmospheric retrieval, we also introduce a new loss function for BNNs that learns correlations between the model outputs. Importantly, we show that designing ML models to explicitly incorporate domain-specific knowledge both improves performance and provides additional insight by inferring the covariance of the retrieved atmospheric parameters. We apply plan-net to the Hubble Space Telescope Wide Field Camera 3 transmission spectrum for WASP-12b and retrieve an isothermal temperature and water abundance consistent with the literature. We highlight that our method is flexible and can be expanded to higher resolution spectra and a larger number of atmospheric parameters.

Adam D. Cobb↗
NASA NTRSDate not supplied

Interpretable Machine Learning Models for Autonomous Characterization of Analogue Ocean World Seawater Chemistry and Biosignature Potential Using Isotope Ratio Data

Background: Future missions to ocean worlds, such as Enceladus and Europa, will attempt to characterize the subsurface seawater chemistry and assess the potential for life. Such missions will be equipped with capabilities to precisely measure volatile isotopes in plumes, atmospheres, and exospheres. Motivation: While large isotopic fractionations can indicate a biological source, there are signatures resulting from abiotic geochemical processes that mimic isotopic biosignatures. While machine learning (ML) has the potential to disentangle competing effects and biotic mimicry, high-dimensional isotope ratio mass spectrometry (IRMS) data is likely to contain noise/irrelevant features and involve complex statistical interactions that make human inference and interpretation difficult. Further, ML predictions with as far-reaching implications as an extraterrestrial biosignature on an ocean world requires the use of interpretable models (i.e., not “black box” models) with physically and mathematically meaningful feature spaces along with false positive diagnostics. Methods: We use volatile CO2 IRMS data of analogue ocean world seawaters to validate an ML approach to provide biogeochemical context for biosignature detection. We employ a feature selection method called nearest-neighbor projected distance regression (NPDR) that detects statistical interactions and helps elucidate the mechanisms of the Random Forest classification models. Results: We train and validate predictive ML models on volatile CO2 IRMS data of analogue ocean world seawaters to predict major salt components (e.g., MgSO4, NaHCO3), pH, ionic strength, and the presence of biosignatures. Features derived from IRMS measurements are augmented with extracted time-series features. Our results show high test accuracy and interpretability, which is increased by interaction network visualization, sample-wise variable importance scores, and single-sample class probability estimates. We demonstrate an ML mission software solution that triggers autonomous data transmission and biogeochemical sample prediction.

geochemistry↗
DOE OSTI2024

Robust wind farm layout optimization

Wake interactions in wind farms cause losses in annual energy production (AEP) on the order of 10%. Wind farm designers optimize the layout of the farm to mitigate wake losses, especially in the dominant site-specific wind directions. As wind turbines and wind farms grow in scale, optimization becomes more complex. Offshore wind farms regularly comprise more than 100 wind turbines and are characterized by complex boundaries due to shipping lanes, neighboring wind farms, and other constraints. Layout optimization methods are broadly split between gradient-based and gradient-free approaches. Gradient-based approaches can converge quickly and perform well for smaller, academic problems but are often sensitive to initial conditions and tuning parameters and require expert knowledge to use. On the other hand, gradient-free approaches can be more robust to problem complexities. We present a robust layout optimization approach based on a random search algorithm. The algorithm is intended for those who are not optimization experts and has few tuning parameters that need specification to achieve satisfactory results. Unlike off-the-shelf methods, which use generally available, non-domain-specific optimization routines that accept as inputs an optimization function and constraint definitions, this approach takes advantage of the relative computational costs of the different evaluations by evaluating cheaper computations first (boundary and minimum distance constraints) and running expensive AEP evaluations only if all other checks pass. Moreover, an outer genetic algorithm allows multiple solutions to evolve in parallel, enabling rapid solution development on high-performance computers. We discuss the relative ease of selecting necessary tuning parameters and demonstrate the efficacy of the genetic random search on a complex layout problem consisting of placing 70 turbines in a nonconvex and unconnected boundary region.

17 WIND ENERGY↗
NASA NTRS1987

A study of the XY model by the Monte Carlo method

The massively parallel processor is used to perform Monte Carlo simulations for the two dimensional XY model on lattices of sizes up to 128 x 128. A parallel random number generator was constructed, finite size effects were studied, and run times were compared with those on a CRAY X-MP supercomputer.

Suranyi, Peter↗