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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
NASA NTRS2005

Pharmacodynamics of Promethazine in Human Subjects

Promethazine (PMZ) is the drug of choice for the treatment of symptoms associated with space motion sickness in astronauts. Side effects of PMZ include sedation, dizziness and cognitive performance impairment. In this study, we examined pharmacodynamics (PD) in human subjects and validated methods for evaluating cognitive performance effects of medications in space. METHODS: PMZ (12.5,25, and 50 mg) or placebo was administered by IM injection to human subjects in a randomized double-blind treatment design. Samples and data were collected for 72 h post dose. PD evaluation was performed using a battery of performance tests administered using WinSCAT (Windows based Space Cognitive Assessment Test) on a laptop computer, and ARES (ANAM Readiness Evaluation System) on a PDA, plasma concentrations of PMZ were measured using a LC-MS method. RESULTS: Results indicate a linear correlation between PMZ concentration and cognitive performance parameters (p<0.01). Test accuracy decreased and test completion time and response time increased significantly with increasing plasma PMZ concentration. CONCLUSIONS: These results suggest a concentration dependent decrement in cognitive performance associated with PMZ. WinSCAT and ARES are sensitive tools for the assessment PMZ PD and may be applicable for such evaluations with other neurocognitive drugs.

Gatlin, K. T.↗
NASA NTRS1989

Effects of transverse shear on large deflection random response of symmetric composite laminates with mixed boundary conditions

Nonlinear equations of motion of symmetrically laminated anisotropic plates are derived considering von Karman strains and transverse shear effects. Using a single-mode Galerkin procedure, the nonlinear modal equation is obtained. The direct equivalent linearization method is employed for solution of this equation. The effects of transverse shear on large deflection vibration of laminated plates with mixed boundary support conditions under random excitation are studied. Mean-square deflections and mean-square inplane strains are obtained for symmetric graphite-epoxy laminates. Using equilibrium equations and the continuity requirements, the mean-square transverse shear stresses are calculated. The results obtained will be useful in the sonic fatigue design of composite aircraft panels. The analysis is presented in detail for plates with two opposing edges simply supported and the remaining edges clamped.

Prasad, C. B.↗
NASA NTRS1992

Numerical study on mixing of sprayed liquid in an LNG storage tank

This paper presents a numerical method to simulate the mixing of heavier LNG sprayed on lighter layer. Numerical results for evolutions of flow field and density field are obtained in a rectangular computational domain which includes the vicinity of the liquid surface. At the surface boundary, uniform distributions of the fluid velocity and the density are assumed. Detail structure of flow caused by impingements of liquid drops are neglected. But, to trigger a realistic motion, a series of random numbers is employed. It is used as an initial distribution of the density near the surface. This method successfully gives a realistic simulation of the mixing process. Numerical results for mixing velocity shows good agreement with experimental data.

Uchida, Hiroyuki↗
NASA NTRS2018

A Deterministic Self-Organizing Map Approach and its Application on Satellite Data based Cloud Type Classification

A self-organizing map (SOM) is a type of competitive artificial neural network, which projects the high dimensional input space of the training samples into a low dimensional space with the topology relations preserved. This makes SOMs supportive of organizing and visualizing complex data sets and have been pervasively used among numerous disciplines with different applications. Notwithstanding its wide applications, the self-organizing map is perplexed by its inherent randomness, which produces dissimilar SOM patterns even when being trained on identical training samples with the same parameters every time, and thus causes usability concerns for other domain practitioners and precludes more potential users from exploring SOM based applications in a broader spectrum. Motivated by this practical concern, we propose a deterministic approach as a supplement to the standard self-organizing map. In accordance with the theoretical design, the experimental results with satellite cloud data demonstrate the effective and efficient organization as well as simplification capabilities of the proposed approach.

Initialization method↗
DOE OSTI2022

Group structure selection with random forests

Choosing an appropriate group structure for multigroup transport is far from an exact science. For some applications, one blindly uses a group structure developed years ago by forgotten methods. Furthermore, one sometimes uses the same group structure for a variety of problems, even if the group structure was originally developed with a certain application in mind. In this work, we create optimized group structures with simulated annealing for critical assembly test problems and apply a random forest regressor with bagging to choose the best group structure based on parameters of the different test problems. The optimized group structures were generated using a simulated annealing optimizer for several simple, spherical, and unreflected problems. The optimization was performed to minimize a cost function that included fission rate, absorption rate, leakage, and k{sub eff}. A random forest regressor was then trained on a set of International Criticality Safety Benchmark Evaluation Project inputs and used to select one of these six group structures. The trained machine learning model chose the best group structure 65% of the time, and one of the three best 89% of the time. Furthermore, it decreased the L2 error over all test problems by a factor of 25 when compared to the standard Los Alamos 70-group structure. In other words, the model chose group structure that were far more appropriate for the test problems than the traditional LANL group structure. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

A Note on Exponential Decay in the Random Field Ising Model

For the two-dimensional random field Ising model (RFIM) with bimodal (i.e., two-valued) external field, we prove exponential decay of correlations either (i) when the temperature is larger than the critical temperature of the Ising model without external field and the magnetic field strength is small or (ii) at any temperature when the magnetic field strength is sufficiently large. Unlike previous work on exponential decay, our approach is not based on cluster expansions but rather on arguably simpler methods; these combine an analysis of the Kertész line and a coupling of Ising measures (and also their random cluster representations) with different boundary conditions. We also show similar but weaker results for the RFIM with a general field distribution and in any dimension.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRSDate not supplied

A Markov Random Field Model for Texture-based Segmentation of Small Cracks in Thin InconelTubes

Markov random fields have been used for image segmentation since their introduction in the1980s. This work applies a method from Principal Component Thermography to enhance thecontrast in damage regions in 3D images derived from X-ray computed tomography (CT)inspections. The developed method is applied to sizing of small cracks in thin Inconel tubes designed as probability of detection (POD) samples for radiographic inspection.Misclassification errors arising from artifacts due to beam-hardening are reduced by fitting the boundary of the segmented damage region with the arc of an ellipse. Results are comparedagainst those obtained through manual inspection.

Image segmentation↗
DOE OSTI2023

Machine Learning Based Resilience Testing of an Address Randomization Cyber Defense

Moving target defenses (MTDs) are widely used as an active defense strategy for thwarting cyberattacks on cyber-physical systems by increasing diversity of software and network paths. Recently, machine Learning (ML) and deep Learning (DL) models have been demonstrated to defeat some of the cyber defenses by learning attack detection patterns and defense strategies. It raises concerns about the susceptibility of MTD to ML and DL methods. Here, in this article, we analyze the effectiveness of ML and DL models when it comes to deciphering MTD methods and ultimately evade MTD-based protections in real-time systems. Specifically, we consider a MTD algorithm that periodically randomizes address assignments within the MIL-STD-1553 protocol—a military standard serial data bus. Two ML and DL-based tasks are performed on MIL-STD-1553 protocol to measure the effectiveness of the learning models in deciphering the MTD algorithm: 1) determining whether there is an address assignments change i.e., whether the given system employs a MTD protocol and if it does 2) predicting the future address assignments. The supervised learning models (random forest and k-nearest neighbors) effectively detected the address assignment changes and classified whether the given system is equipped with a specified MTD protocol. On the other hand, the unsupervised learning model (K-means) was significantly less effective. The DL model (long short-term memory) was able to predict the future addresses with varied effectiveness based on MTD algorithm's settings.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRS1945

Method of expanding noise autocorrelation function in a power series

Under certain conditions the normalized autocorrelation function ρ(τ) of stationary random noise may be expanded as a power series about the point τ = 0. This paper indicates that the coefficients of the expansion are rather simply related to the average rate of occurrence of zeros in the noise function and its derivatives. The expansion of academic interest in providing another perspective of the relationships among the noise function, its power spectrum, and its autocorrelation function. It may also be of practical interest in providing a quick means of estimating the normalized autocorrelation function from a noise record over the range of τ in which the power series converges rapidly. The class of noise spectrum for which convergent series expansion is not possible is of interest in pointing up necessary physical characteristics of noise signals which have such spectra.

F W Lehan↗
NASA NTRS1997

Evaluation of Two Fractal Methods for Magnetogram Image Analysis

Fractal and multifractal techniques have been applied to various types of solar data to study the fractal properties of sunspots as well as the distribution of photospheric magnetic fields and the role of random motions on the solar surface in this distribution. Other research includes the investigation of changes in the fractal dimension as an indicator for solar flares. Here we evaluate the efficacy of two methods for determining the fractal dimension of an image data set: the Differential Box Counting scheme and a new method, the Jaenisch scheme. To determine the sensitivity of the techniques to changes in image complexity, various types of constructed images are analyzed. In addition, we apply this method to solar magnetogram data from Marshall Space Flight Centers vector magnetograph.

Stark, B.↗
DOE OSTI2021

Multisource Data Fusion Outage Location in Distribution Systems via Probabilistic Graphical Models

Efficient outage location is critical to enhancing the resilience of power distribution systems. However, accurate outage location requires combining massive evidence received from diverse data sources, including smart meter (SM) last gasp signals, customer trouble calls, social media messages, weather data, vegetation information, and physical parameters of the network. This is a computationally complex task due to the high dimensionality of data in distribution grids. In this paper, we propose a multi-source data fusion approach to locate outage events in partially observable distribution systems using Bayesian networks (BNs). A novel aspect of the proposed approach is that it takes multi-source evidence and the complex structure of distribution systems into account using a probabilistic graphical method. Our method can radically reduce the computational complexity of outage location inference in high-dimensional spaces. The graphical structure of the proposed BN is established based on the network’s topology and the causal relationship between random variables, such as the states of branches/customers and evidence. Utilizing this graphical model, accurate outage locations are obtained by leveraging a Gibbs sampling (GS) method, to infer the probabilities of de-energization for all branches. Compared with commonly-used exact inference methods that have exponential complexity in the size of the BN, GS quantifies the target conditional probability distributions in a timely manner. As a result, a case study of several real-world distribution systems is presented to validate the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Efficient Subset Simulation using Hamiltonian Neural Network enhanced Markov Chain Monte Carlo Methods

The Monte Carlo method delivers an unbiased estimate of the probability of failure. However, the variance of the estimate depends on the number of evaluated samples. This number must be very large for estimations of a low probability of failure. If the evaluation of each sample is computationally expensive, the crude Monte Carlo simulation strategy is impracticable. Therefore, subset simulations are used to reduce the required number of evaluations. Subset simulations require a Markov Chain Monte Carlo sampler, such as the random walk Metropolis-Hastings algorithm. The algorithm, however, struggles with sampling in low-probability regions, especially if they are narrow. As a consequence, advanced Markov Chain Monte Carlo simulations have been developed. In particular, the Hamiltonian Monte Carlo method explores the target distribution rapidly. Driven by the idea of Hamiltonian dynamics, this sampler provides a non-random walk through the target distribution. The incorporation of subset simulation and Hamiltonian Monte Carlo methods has shown promising results for reliability analysis. One downside of the Hamiltonian Monte Carlo method is that gradient evaluations are computationally expensive, especially when dealing with high-dimensional problems and evaluating long trajectories. We show that integrating Hamiltonian neural networks in Hamiltonian Monte Carlo simulations significantly speeds up the sampling task. Furthermore, the enhancement of adaptive trajectory length within the Hamiltonian Monte Carlo results in the efficient proposal of the following states. Based on this recent enhancement, we provide a fast sampling strategy for subset simulations using Hamiltonian neural networks to replace the evaluation of the gradient and significantly speed up the Hamiltonian Monte Carlo simulation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Improving the accuracy of freight mode choice models: A case study using the 2017 CFS PUF data set and ensemble learning techniques

Here, the US Census Bureau has collected two rounds of experimental data from the Commodity Flow Survey, providing shipment-level characteristics of nationwide commodity movements, published in 2012 (i.e., Public Use Microdata) and in 2017 (i.e., Public Use File). With this information, data-driven methods have become increasingly valuable for understanding detailed patterns in freight logistics. In this study, we used the 2017 Commodity Flow Survey Public Use File data set to explore building a high-performance freight mode choice model, considering three main improvements: (1) constructing local models for each separate commodity/industry category; (2) extracting useful geographical features, particularly the derived distance of each freight mode between origin/destination zones; and (3) applying additional ensemble learning methods such as stacking or voting to combine results from local and unified models for improved performance. The proposed method achieved over 92% accuracy without incorporating external information, an over 19% increase compared to directly fitting Random Forests models over 10,000 samples. Furthermore, SHAP (Shapely Additive Explanations) values were computed to explain the outputs and major patterns obtained from the proposed model. The model framework could enhance the performance and interpretability of existing freight mode choice models.

42 ENGINEERING↗
NASA NTRS1994

Estimation and classification by sigmoids based on mutual information

An estimate of the probability density function of a random vector is obtained by maximizing the mutual information between the input and the output of a feedforward network of sigmoidal units with respect to the input weights. Classification problems can be solved by selecting the class associated with the maximal estimated density. Newton's s method, applied to an estimated density, yields a recursive maximum likelihood estimator, consisting of a single internal layer of sigmoids, for a random variable or a random sequence. Applications to the diamond classification and to the prediction of a sun-spot process are demonstrated.

Baram, Yoram↗
NASA NTRS1991

A Dynamic Testing Complexity Metric

This paper introduces a dynamic metric that is based on the estimated ability of a program to withstand the effects of injected "semantic mutants" during execution by computing the same function as if the semantic mutants had not been injected. Semantic mutants include: (1) syntactic mutants injected into an executing program and (2) randomly selected values injected into an executing program's internal states. The metric is a function of a program, the method used for injecting these two types of mutants, and the program's input distribution; this metric is found through dynamic executions of the program. A program's ability to withstand the effects of injected semantic mutants by computing the same function when executed is then used as a tool for predicting the difficulty that will be incurred during random testing to reveal the existence of faults, i.e., the metric suggests the likelihood that a program will expose the existence of faults during random testing assuming faults were to exist. If the metric is applied to a module rather than to a program, the metric can be used to guide the allocation of testing resources among a program's modules. In this manner the metric acts as a white-box testing tool for determining where to concentrate testing resources. Index Terms: Revealing ability, random testing, input distribution, program, fault, failure.

Voas, Jeffrey↗
DOE OSTI2024

Probabilistic Assessment of Structural Integrity

A probability-based approach, combining deterministic and probabilistic methods, was developed for analyzing building and component failures, which are especially crucial for complex structures like nuclear power plants. This method links finite element and probabilistic software to assess structural integrity under static and dynamic loads. This study uses NEPTUNE software, which is validated, for a deterministic transient analysis and ProFES software for probabilistic models. In a case study, deterministic analyses with varied random variables were transferred to ProFES for probabilistic analyses of piping failure and wall damage. A Monte Carlo Simulation, First-Order Reliability Method, and combined methods were employed for probabilistic analyses under severe transient loading, focusing on a postulated accident at the Ignalina Nuclear Power Plant. The study considered uncertainties in material properties, component geometry, and loads. The results showed the Monte Carlo Simulation method to be conservative for high failure probabilities but less so for low probabilities. The Response Surface/Monte Carlo Simulation method explored the impact load–failure probability relationship. Given the uncertainties in material properties and loads in complex structures, a deterministic analysis alone is insufficient. Probabilistic analysis is imperative for extreme loading events and credible structural safety evaluations.

Mathematics↗
DOE OSTI2019

A unique but flexible space–time could challenge multiverse theories

Highlights: The growing law of a billiard multiverse is independent of the discretization scale E. The time for any final state to be accessible from any initial one is around 3E shocks. The paths of billiard balls can change without changing initial or final states. Space-time flexibility can be achieved through paths commutations and entanglement. -- Abstract: In a previous paper (Guillemant et al., 2018), we have shown using a 2D billiard toy model that the propagation of uncertainties resulting from space–time discretization leads to a classical multiverse. In this one we estimate the growing law of the number of its branches, thanks to an original shock indexation method that permits a very fast comparison of trajectories. For different involved parameters we use random sets of infinitesimally perturbed initial conditions. From each set, we compute a collection up to hundred millions of different billiard histories. Our main result is the calculation of a Brownian saturation time when the number of branches begins to exceed the total number of billiard states. We discuss the possibility suggested by this result to change locally the paths of objects without changing their past and future histories and then subverting their global system. We conclude that a unique but flexible space–time could compete with the current many-worlds interpretation claiming that each branch is a different universe.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Radical Cystectomy Compared to Combined Modality Treatment for Muscle-Invasive Bladder Cancer: A Systematic Review and Meta-Analysis

Purpose: To perform a comprehensive comparison of overall survival (OS), disease-specific survival (DSS), progression-free survival (PFS), and treatment-related complications between radical cystectomy (RC) and combined modality treatment (CMT—radiation therapy, concurrent chemotherapy, and maximal transurethral resection of bladder tumor) in the setting of muscle-invasive bladder cancer. Methods and Materials: We searched 7 databases (PubMed, Scopus, EMBASE, Proquest, CINAHL, and (ClinicalTrials.gov)) for randomized, controlled trials and prospective and retrospective studies directly comparing RC with CMT from database inception to March 2016. We conducted meta-analyses evaluating OS, DSS, and PFS with hazard ratios (HRs) and 95% confidence intervals (CIs). Results: Nineteen studies evaluating 12,380 subjects were selected. For the 8 studies encompassing 9554 subjects eligible for meta-analyses, we found no difference in OS at 5 years (HR 0.96, favoring CMT, 95% CI 0.72-1.29; P=.778) or 10 years (HR 1.02, favoring cystectomy, 95% CI 0.73-1.42; P=.905). No difference was observed in DSS at 5 years (HR 0.83, favoring radiation, 95% CI 0.54-1.28; P=.390) or 10 years (HR 1.17, favoring cystectomy, 95% CI 0.89-1.55; P=.264), or PFS at 10 years (HR 0.85, favoring CMT, 95% CI 0.43-1.67; P=.639). The cystectomy arms had higher rates of early major complications, whereas rates of minor complications were similar between the 2 treatments. Conclusion: Current meta-analysis reveals no differences in OS, DSS, or PFS between RC and CMT. Further randomized, controlled trials are necessary to identify the optimal treatment for specific patients.

62 RADIOLOGY AND NUCLEAR MEDICINE↗