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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Studies of solid propellant combustion with pulsed radiography

Pulsed radiography was applied to observe solid propellant surface regression during rocket motor operation. Using a 150 KV flash X-ray system manufactured by the Field Emission Corporation and two kinds of film suppliers, images of the propellant surface of a 5 cm diameter end burning rocket motor were recorded on film. The repetition frame rate of 8 pulses per second and the pulse train length of 10 pulses are limited by the capability of the power supply and the heat build up within the X-ray tube, respectively. The experiment demonstrated the effectiveness of pulsed radiography for observing solid propellant surface regression. Measuring the position of burning surface images on film with a microdensitometer, quasi-instantaneous burning rate as a function of pressure and the variation of characteristic velocity with pressure and gas stay time were obtained. Other research items to which pulsed radiography can be applied are also suggested.

Godai, T.↗
NASA NTRS1989

Oblique hypervelocity impact response of dual-sheet structures

The results of a continuing investigation of the phenomena associated with the oblique hypervelocity impact of spherical projectiles onto multi-sheet aluminum structures are given. A series of equations that quantitatively describes these phenomena is obtained through a regression of experimental data. These equations characterize observed ricochet and penetration damage phenomena in a multi-sheet structure as functions of geometric parameters of the structure and the diameter, obliquity, and velocity of the impacting projectile. Crater damage observed on the ricochet witness plates is used to determine the sizes and speeds of the ricochet debris particles that caused the damage. It is observed that the diameter of the most damaging ricochet debris particle can be as large as 40 percent of the original particle diameter and can travel at speeds between 24 percent and 36 percent of the original projectile impact velocity. The equations necessary for the design of shielding panels that will protect external systems from such ricochet debris damage are also developed. The dimensions of these shielding panels are shown to be strongly dependent on their inclination and on their circumferential distribution around the spacecraft.

Schonberg, William P.↗
NASA NTRS1988

Further investigations of oblique hypervelocity impact phenomena

The results of a continuing investigation of the phenomena associated with the oblique hypervelocity impact of spherical projectiles onto multi-sheet aluminum structures are described. A series of equations that quantitatively describes these phenomena is obtained through a regression of experimental data. These equations characterize observed ricoshet and penetration damage phenomena in a multi-sheet structure as functions of the geometric parameters of the structure and the diameter, obliquity, and velocity of the impacting projectile. Crater damage observed on the ricochet witness plates is used to determine the sizes and speeds of the ricochet debris particles that caused the damage. It is shown that, in general, the most damaging ricochet debris particle is approximately 0.25 cm (0.10 in) in diameter and travels at the speed of approximately 2.1 km/sec (6,890 ft/sec). The equations necessary for the design of shielding panels that will protect external systems from such ricochet debris damage are also developed. The dimensions of these shielding panels are shown to be strongly dependent on their inclination and on their circumferential distribution around the spacecraft. It is concluded that obliquity effects of high-speed impacts must be considered in the design of any structure exposed to the meteoroid and space debris environment.

Schonberg, William P.↗
NASA NTRS1991

Relationships between CH4 emission, biomass, and CO2 exchange in a subtropical grassland

Methane flux was linearly correlated with plant biomass (r = 0.97, n = 6 and r = 0.95, n = 8) at two locations in a Florida Everglades Cladium marsh. One location, which had burned 4 months previously, exhibited a greater increase in methane flux as a function of biomass relative to sites at an unburned location. However, methane flux data from both sites fit a single regression (r = 0.94, n = 14) when plotted against net CO2 exchange suggesting that either methanogenesis in Everglades marl sediments is fueled by root exudation below ground, or that factors which enhance photosynthetic production and plant growth are also correlated with methane production and flux in this oligotrophic environment. The data presented are the first to show a direct relationship between spatial variability in plant biomass, net ecosystem production, and methane emission in a natural wetland.

Whiting, Gary J.↗
NASA NTRS2011

System Identification Applied to Dynamic CFD Simulation and Wind Tunnel Data

Demanding aerodynamic modeling requirements for military and civilian aircraft have provided impetus for researchers to improve computational and experimental techniques. Model validation is a key component for these research endeavors so this study is an initial effort to extend conventional time history comparisons by comparing model parameter estimates and their standard errors using system identification methods. An aerodynamic model of an aircraft performing one-degree-of-freedom roll oscillatory motion about its body axes is developed. The model includes linear aerodynamics and deficiency function parameters characterizing an unsteady effect. For estimation of unknown parameters two techniques, harmonic analysis and two-step linear regression, were applied to roll-oscillatory wind tunnel data and to computational fluid dynamics (CFD) simulated data. The model used for this study is a highly swept wing unmanned aerial combat vehicle. Differences in response prediction, parameters estimates, and standard errors are compared and discussed

Murphy, Patrick C.↗
NASA NTRS2003

Flight-Test Evaluation of Flutter-Prediction Methods

The flight-test community routinely spends considerable time and money to determine a range of flight conditions, called a flight envelope, within which an aircraft is safe to fly. The cost of determining a flight envelope could be greatly reduced if there were a method of safely and accurately predicting the speed associated with the onset of an instability called flutter. Several methods have been developed with the goal of predicting flutter speeds to improve the efficiency of flight testing. These methods include (1) data-based methods, in which one relies entirely on information obtained from the flight tests and (2) model-based approaches, in which one relies on a combination of flight data and theoretical models. The data-driven methods include one based on extrapolation of damping trends, one that involves an envelope function, one that involves the Zimmerman-Weissenburger flutter margin, and one that involves a discrete-time auto-regressive model. An example of a model-based approach is that of the flutterometer. These methods have all been shown to be theoretically valid and have been demonstrated on simple test cases; however, until now, they have not been thoroughly evaluated in flight tests. An experimental apparatus called the Aerostructures Test Wing (ATW) was developed to test these prediction methods.

Lind, RIck↗
NASA NTRSDate not supplied

Structured Covariance Gaussian Networks for Orion Crew Module Aerodynamic Uncertainty Quantification

In this paper we propose a new approach for nonlinear regression and uncertainty quantification. The method is based on a pair of neural networks which parameterize mean and dense covariance functions of a multivariate Gaussian process, trained together to maximize the log-likelihood of observing the given data. The covariance matrix is made positive definite at every input by construction. We also propose a sampling approach that produces viable surrogate function realizations from the Gaussian process. We call the proposed model a Structured Covariance Gaussian Network (SCGN). We illustrate the use of SCGNs for learning an aerodynamic response surface with built-in uncertainty for the Orion crew module. We find that SCGN provides an efficient and systematic way to learn nonlinear functional relationships and dense covariances. We compare results to a baseline Gaussian process regressor and observe that the SCGN provides comparable uncertainty descriptions with improved scalability to dataset size. The sample functions generated by SCGN are fast to evaluate online and are therefore convenient for use in trajectory simulations. These results suggest that SCGN may be a viable computational method for aerodynamic uncertainty quantification.

machine learning↗
NASA NTRSDate not supplied

Structured Covariance Gaussian Networks for Orion Crew Module Aerodynamic Uncertainty Quantification

In this paper we propose a new approach for nonlinear regression and uncertainty quantification. The method is based on a pair of neural networks which parameterize mean and dense covariance functions of a multivariate Gaussian process, trained together to maximize the log-likelihood of observing the given data. The covariance matrix is made positive definite at every input by construction. We also propose a sampling approach that produces viable surrogate function realizations from the Gaussian process. We call the proposed model a Structured Covariance Gaussian Network (SCGN). We illustrate the use of SCGNs for learning an aerodynamic response surface with built-in uncertainty for the Orion crew module. We find that SCGN provides an efficient and systematic way to learn nonlinear functional relationships and dense covariances. We compare results to a baseline Gaussian process regressor and observe that the SCGN provides comparable uncertainty descriptions with improved scalability to dataset size. The sample functions generated by SCGN are fast to evaluate online and are therefore convenient for use in trajectory simulations. These results suggest that SCGN may be a viable computational method for aerodynamic uncertainty quantification.

machine learning↗
DOE OSTI2016

Up-regulation of GTPBP4 in colorectal carcinoma is responsible for tumor metastasis

GTP binding protein 4(GTPBP4), a member of GTP-binding protein family, was previously characterized as a tumor suppressor that regulates and requires merlin to suppress cell proliferation. However, the role of GTPBP4 in the metastasis of colorectal carcinoma (CRC) remains unelucidated. Here, we observed that GTPBP4 was detected at higher levels in CRC metastatic tissues than that in the primary tumor tissues. Notably, up-regulation of GTPBP4 was closely correlated with tumor metastasis in CRCs. Kaplan-Meier and multivariate Cox regression analysis indicated GTPBP4 as an independent prognostic factor for CRC patients (hazard ratio = 2.693, 95% confident interval: 1.193–6.083, p = 0.017). Functional studies established that knockdown of GTPBP4 impeded, whereas ectopic expression of GTPBP4 enhanced cell motility and tumor metastasis in CRC cells. Interestingly, mechanistic investigations suggested that GTPBP4 may disorganize actin cytoskeleton through repressing RhoA signaling. Taken together, our research uncovered that GTPBP4 promotes CRC metastasis by disrupting actin cytoskeleton, which is mediated by the reduced RhoA activity. Strategies targeting GTPBP4 will be promising for CRC patients with metastases. - Highlights: • Up-regulation of GTPBP4 is detected in CRC metastatic tissues and closely correlated with tumor metastasis. • Increase of GTPBP4 is closely associated with poor prognosis. • GTPBP4 promotes cell motility and tumor metastasis in CRC cells. • GTPBP4 induces filamentous actin rearrangement specifically by repressing the activity of RhoA. • GTPBP4 may be a novel therapeutic target for CRC patients with metastasis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Low-rank separated representation surrogates of high-dimensional stochastic functions: Application in Bayesian inference

This study introduces a non-intrusive approach in the context of low-rank separated representation to construct a surrogate of high-dimensional stochastic functions, e.g., PDEs/ODEs, in order to decrease the computational cost of Markov Chain Monte Carlo simulations in Bayesian inference. The surrogate model is constructed via a regularized alternative least-square regression with Tikhonov regularization using a roughening matrix computing the gradient of the solution, in conjunction with a perturbation-based error indicator to detect optimal model complexities. The model approximates a vector of a continuous solution at discrete values of a physical variable. The required number of random realizations to achieve a successful approximation linearly depends on the function dimensionality. The computational cost of the model construction is quadratic in the number of random inputs, which potentially tackles the curse of dimensionality in high-dimensional stochastic functions. Furthermore, this vector-valued separated representation-based model, in comparison to the available scalar-valued case, leads to a significant reduction in the cost of approximation by an order of magnitude equal to the vector size. The performance of the method is studied through its application to three numerical examples including a 41-dimensional elliptic PDE and a 21-dimensional cavity flow.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Predictive based monitoring of nuclear plant component degradation using support vector regression

Nuclear power plants (NPPs) are large installations comprised of many active and passive assets. Degradation monitoring of all these assets is expensive (labor cost) and highly demanding task. In this paper a framework based on Support Vector Regression (SVR) for online surveillance of critical parameter degradation of NPP components is proposed. In this case, on time replacement or maintenance of components will prevent potential plant malfunctions, and reduce the overall operational cost. In the current work, we apply SVR equipped with a Gaussian kernel function to monitor components. Monitoring includes the one-step-ahead prediction of the component’s respective operational quantity using the SVR model, while the SVR model is trained using a set of previous recorded degradation histories of similar components. Predictive capability of the model is evaluated upon arrival of a sensor measurement, which is compared to the component failure threshold. A maintenance decision is based on a fuzzy inference system that utilizes three parameters: (i) prediction evaluation in the previous steps, (ii) predicted value of the current step, (iii) and difference of current predicted value with components failure thresholds. The proposed framework will be tested on turbine blade degradation data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

Fuzzy Integration of Support Vector Regression Models for Anticipatory Control of Complex Energy Systems

Anticipatory control systems are a class of systems whose decisions are based on predictions for the future state of the system under monitoring. Anticipation denotes intelligence and is an inherent property of humans that make decisions by projecting in future. Likewise, artificially intelligent systems equipped with predictive functions may be utilized for anticipating future states of complex systems, and therefore facilitate automated control decisions. Anticipatory control of complex energy systems is paramount to their normal and safe operation. In this paper a new intelligent methodology integrating fuzzy inference with support vector regression is introduced. Our proposed methodology implements an anticipatory system aiming at controlling energy systems in a robust way. Initially a set of support vector regressors is adopted for making predictions over critical system parameters. Furthermore, the predicted values are fed into a two stage fuzzy inference system that makes decisions regarding the state of the energy system. The inference system integrates the individual predictions into a single one at its first stage, and outputs a decision together with a certainty factor computed at its second stage. The certainty factor is an index of the significance of the decision. The proposed anticipatory control system is tested on a real world set of data obtained from a complex energy system, describing the degradation of a turbine. Results exhibit the robustness of the proposed system in controlling complex energy systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1985

Statistical methods for efficient design of community surveys of response to noise: Random coefficients regression models

Research studies of residents' responses to noise consist of interviews with samples of individuals who are drawn from a number of different compact study areas. The statistical techniques developed provide a basis for those sample design decisions. These techniques are suitable for a wide range of sample survey applications. A sample may consist of a random sample of residents selected from a sample of compact study areas, or in a more complex design, of a sample of residents selected from a sample of larger areas (e.g., cities). The techniques may be applied to estimates of the effects on annoyance of noise level, numbers of noise events, the time-of-day of the events, ambient noise levels, or other factors. Methods are provided for determining, in advance, how accurately these effects can be estimated for different sample sizes and study designs. Using a simple cost function, they also provide for optimum allocation of the sample across the stages of the design for estimating these effects. These techniques are developed via a regression model in which the regression coefficients are assumed to be random, with components of variance associated with the various stages of a multi-stage sample design.

Tomberlin, T. J.↗
NASA NTRS1996

Role of physical countermaneuvers in the management of orthostatic hypotension: efficacy and biofeedback augmentation

OBJECTIVE: To evaluate the efficacy of various physical countermaneuvers in reducing orthostatic hypotension and its associated symptoms and to assess the efficacy of biofeedback training in enhancing the effectiveness of physical countermaneuvers. MATERIAL AND METHODS: In nine study subjects with neurogenic orthostatic hypotension, four training sessions on physical countermaneuvers were performed after tilt-up, three with visual feedback on the effect of physical countermaneuvers on blood pressure and other cardiovascular variables. Blood pressure change and orthostatic symptoms during tilt-up were determined, as were the changes in total peripheral resistance, stroke index, and heart rate. RESULTS: The five female and four male patients had a mean age of 53 years and a mean duration of symptoms of 4.2 years. On an orthostatic symptom scale of 0 to 10, these patients had a mean symptom score of 7.3. The increment in systolic blood pressure was better for some maneuvers (such as leg crossing and a combination) than others (such as neck flexion and abdominal contraction). Three patterns of responses to biofeedback were found. Simple maneuvers such as squatting did not improve with training; visual feedback was needed for maneuvers such as thigh contraction, and performance declined without biofeedback; the third pattern, seen in maneuvers such as leg crossing, showed continued improvement with training, even without biofeedback. A survey at 3 to 4 months after training revealed continued use of physical maneuvers (3.8 +/- 3.1 per day), increased standing time with each episode of presyncopal symptoms (8.3 +/- 5.8 minutes), and continued global symptomatic improvement. Total peripheral resistance, but not heart rate or stroke index, showed significant regression with blood pressure improvement. CONCLUSION: Physical countermaneuvers are efficacious in reducing orthostatic hypotension, can be augmented by use of biofeedback, and may significantly improve the functional outcome. The major mechanism of improvement is an increase in total peripheral resistance, presumably by reducing the vascular capacitance.

NASA Program Space Physiology and Countermeasures↗
DOE OSTI2020

Tensor Basis Gaussian Process Models of Hyperelastic Materials

In this study, we develop Gaussian process regression (GPR) models of isotropic hyperelastic material behavior. First, we consider the direct approach of modeling the components of the Cauchy stress tensor as a function of the components of the Finger stretch tensor in a Gaussian process. We then consider an improvement on this approach that embeds rotational invariance of the stress-stretch constitutive relation in the GPR representation. This approach requires fewer training examples and achieves higher accuracy while maintaining invariance to rotations exactly. Finally, we consider an approach that recovers the strain-energy density function and derives the stress tensor from this potential. Although the error of this model for predicting the stress tensor is higher, the strain-energy density is recovered with high accuracy from limited training data. The approaches presented here are examples of physics-informed machine learning. They go beyond purely data-driven approaches by embedding the physical system constraints directly into the Gaussian process representation of materials models.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine learning elastic constants of multi-component alloys

The present manuscript explores application of machine learning methods for determining elastic constants and other derived mechanical properties of multi-component alloys. Here, a number of machine learning models, including linear regression, neural network and random forest based models, are trained and tested on a dataset of binary alloys generated using density functional theory (DFT) calculations and spanning over a large number of elemental species in the periodic table. Starting with a wide range of simple and easily accessible compositionally-averaged elemental features, a correlation-based feature selection strategy was used to systematically down-select a set of most relevant features towards the prediction of the elasticity tensor components. The true predictive performance and the associated uncertainties of the models were established by testing on unseen data and bootstrapping, respectively. A single and pair-wise feature partial dependence analysis was performed to visualize the average property trends in the multi-dimensional feature space in order to further understand the achieved predictive performance. The utility of the trained model is further demonstrated by obtaining sufficiently accurate yet highly efficient approximations for bulk modulus, Young’s modulus, shear modulus and Poisson’s ratio for alloys beyond the binary space (i.e., two-component alloys) on which the model was originally trained. More importantly, we test and validate the predictive performance of the developed model directly against the experimentally measured elastic constants of technologically relevant multi-component alloys (such as, Ni- and Ti-based alloys). Finally, utility of such a data-enabled route is demonstrated by predicting the possible range of various elastic properties for vast composition space available within the five component Ni-Cr-Fe-Mo-W alloy system in a high-throughput manner.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Direct Deoxygenation of Phenol over Fe-Based Bimetallic Surfaces Using On-the-Fly Surrogate Models

We present an accelerated nudged elastic band (NEB) study of phenol direct deoxygenation (DDO) on Fe-based bimetallic surfaces using a recently developed Gaussian process regression (GPR) calculator. Our test calculations demonstrate that the GPR calculator achieves up to 3 times speedup compared to conventional density functional theory calculations while maintaining high accuracy, with energy barrier errors below 0.015 eV. Using GPR-NEB, we systematically examine the DDO mechanism on pure Fe(110) and surfaces modified with Co and Ni in both top and subsurface layers. Our results show that subsurface Co and Ni substitutions preserve favorable thermodynamics and kinetics for both C–O bond cleavage and C–H bond formation, comparable to those on the pure Fe(110) surface. In contrast, top-layer substitutions generally increase the C–O bond cleavage barrier, render the step endothermic, and result in significantly higher reverse reaction rates, making DDO unfavorable on these surfaces. This work demonstrates the effectiveness of GRR-accelerated transition state searches for complex surface reactions and provides insights into rational design of bimetallic catalysts for selective deoxygenation.

Aromatic compounds↗