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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Validation and Verification of LADEE Models and Software

The Lunar Atmosphere Dust Environment Explorer (LADEE) mission will orbit the moon in order to measure the density, composition and time variability of the lunar dust environment. The ground-side and onboard flight software for the mission is being developed using a Model-Based Software methodology. In this technique, models of the spacecraft and flight software are developed in a graphical dynamics modeling package. Flight Software requirements are prototyped and refined using the simulated models. After the model is shown to work as desired in this simulation framework, C-code software is automatically generated from the models. The generated software is then tested in real time Processor-in-the-Loop and Hardware-in-the-Loop test beds. Travelling Road Show test beds were used for early integration tests with payloads and other subsystems. Traditional techniques for verifying computational sciences models are used to characterize the spacecraft simulation. A lightweight set of formal methods analysis, static analysis, formal inspection and code coverage analyses are utilized to further reduce defects in the onboard flight software artifacts. These techniques are applied early and often in the development process, iteratively increasing the capabilities of the software and the fidelity of the vehicle models and test beds.

Gundy-Burlet, Karen↗
NASA NTRS2018

A Fast Monte Carlo Method for Model-Based Prognostics Based on Stochastic Calculus

This work proposes a fast Monte Carlo method to solve differential equations utilized in model-based prognostics. The methodology is derived from the theory of stochastic calculus, and the goal of such a method is to speed up the estimation of the probability density functions describing the independent variable evolution over time. In the prognostic scenarios presented in this paper, the stochastic differential equations describe variables directly or indirectly related to the degradation of a monitored system. The method allows the estimation of the probability density functions by solving the deterministic equation and approximating the stochastic integrals using samples of the model noise. By so doing, the prognostic problem is solved without the Monte Carlo simulation based on Euler's forward method, which is typically the most time consuming task of the prediction stage. Three different prognostic scenarios are presented as proof of concept: (i) life prediction of electrolytic capacitors, (ii) remaining time to discharge of Lithium-ion batteries, and (iii) prognostic of cracked structures under fatigue loading. The paper shows how the method produces probability density functions that are statistically indistinguishable from the distributions estimated with Euler's forward Monte Carlo simulation. However, the proposed solution is orders of magnitude faster when computing the time-to-failure distribution of the monitored system. The approach may enable complex real-time prognostics and health management solutions with limited computing power.

Corbetta, M.↗
NASA NTRS2019

A Fast Monte Carlo Method for Model-Based Prognostics Based on Stochastic Calculus

This work proposes a fast Monte Carlo method to solve differential equations utilized in model-based prognostics. The methodology is derived from the theory of stochastic calculus, and the goal of such a method is to speed up the estimation of the probability density functions describing the independent variable evolution over time. In the prognostic scenarios presented in this paper, the stochastic differential equations describe variables directly or indirectly related to the degradation of a monitored system. The method allows the estimation of the probability density functions by solving the deterministic equation and approximating the stochastic integrals using samples of the model noise. By so doing, the prognostic problem is solved without the Monte Carlo simulation based on Euler's forward method, which is typically the most time consuming task of the prediction stage. Three different prognostic scenarios are presented as proof of concept: (i) life prediction of electrolytic capacitors, (ii) remaining time to discharge of Lithium-ion batteries, and (iii) prognostic of cracked structures under fatigue loading. The paper shows how the method produces probability density functions that are statistically indistinguishable from the distributions estimated with Euler's forward Monte Carlo simulation. However, the proposed solution is orders of magnitude faster when computing the time-to-failure distribution of the monitored system. The approach may enable complex real-time prognostics and health management solutions with limited computing power.

stochastic calculus↗
NASA NTRS2014

Infrastructure and Process Improvements After LADEE

The purpose of the Lunar Atmosphere Dust Environment Explorer (LADEE) mission was to measure the density, composition and time variability of the lunar dust environment. The successful mission launched Sept 7, 2013 and was de-orbited and impacted the moon's surface on April 17, 2014. The spacecraft had 3 primary science instruments, the Lunar Dust Experiment, Neutral Mass Spectrometer, and the Ultra Violet Spectrometer. The mission also had a Laser Communications payload that set a record download rate of 622 Mbps (megabits per second) from the moon orbit. The goal is to use the LADEE software base for upcoming smallsat missions. The onboard flight software for the mission was developed using a Model-Based Software methodology and agile software development practices. High level models were developed in Simulink, autocoded to C and layered on Core Flight Executive and Core Flight Software, VxWorks and required board support packages. Software package versions were frozen several years ago, and need to be brought to modern standards for future spacecraft missions. We are evaluating alternate Real Time Operating Systems and avionics architectures that comply with CubeSat form-factor and power limitations. In addition, the tool chain for the software development process has been improved. We will discuss the rationale, trades and implementation for the upgrade path after the LADEE mission.

Software Maintenance↗
NASA NTRS2021

MBSE Validation and Verification: Case Study for LADEE

The Lunar Atmosphere Dust Environment Explorer (LADEE) mission orbited the moon in order to measure the density, composition, and time variability of the lunar dust environment. The successful mission launched September 7, 2013 and was de-orbited and impacted the moon's surface on April 17, 2014. The ground-side and onboard flight software for the mission was developed using a “Model-Based Software Engineering” (MBSE) methodology combined with strong reuse of Government and Commercial Off-The Shelf (G/COTS) components. Models of the spacecraft and flight software were developed in a graphical dynamics modeling package. Flight Software requirements were prototyped and refined using the simulated models. After the model was shown to work as desired in the simulation framework, C-code software was automatically generated from the models. The auto-generated software was then tested in real-time Processor-in-the-Loop and Hardware-in-the-Loop test beds. “Traveling Road Show” test beds were used for early integration tests with payloads and other subsystems. Traditional techniques for verifying computational sciences models were used to characterize the spacecraft simulation. A lightweight set of formal methods analysis, static analysis, formal inspection, and code coverage analyses were utilized to further reduce defects in the onboard flight software artifacts. These techniques were applied early and often in the development process, iteratively increasing the capabilities of software and fidelity of vehicle models and test beds.

Model-Based Software Engineering, Validation and V↗
DOE OSTI2021

Multiscale characterization and representation of variability in ceramic matrix composites

Low density, high strength, and high creep and oxidation resistance properties of ceramic matrix composites (CMCs) make them an ideal choice for use in extreme environments in space and military applications. This paper presents a detailed characterization study of structural and manufacturing flaws in Carbon fiber Silicon-Carbide-Nitride matrix (C/SiNC) CMCs at different length-scales. Energy-dispersive spectroscopy (EDS) is used for the chemical characterization of the material’s elemental constituents. High-resolution multiscale graphs obtained from scanning electron microscope (SEM) and confocal laser scanning microscope (LSM) are used to characterize the distribution and morphology of defects at different length scales. This is followed by the classification and quantification of the common manufacturing defects. An image processing algorithm based on the image segmentation process is developed to quantify the variability of various scale-dependent architectural parameters. Finally, a three-dimensional stochastic representative volume element (SRVE) generation algorithm is developed to provide precise representations of material textures at multiple length scales. The developed algorithm accurately accounts for material features and flaws based on a range of multiscale structural and defects characterization results.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Application of a convolutional neural network in permeability prediction: A case study in the Jacksonburg-Stringtown oil field, West Virginia, USA

Permeability is a critical parameter for understanding subsurface fluid flow behavior, managing reservoirs, enhancing hydrocarbon recovery, and sequestering carbon dioxide. In general, permeability is measured in the laboratory based on subsurface core samples, calculated from well logs or estimated from well tests. However, laboratory measurements and well tests are expensive, time-consuming, and usually limited to a few core samples or wells in a hydrocarbon field or carbon storage site. Machine-learning techniques are good options for generating a rapid, robust, and cost-effective permeability prediction model because of their strengths to recognize the potential interrelationships between input and output variables. Convolutional neural networks (CNN), as a good pattern recognition algorithm, are widely used in image processing, natural language processing, and speech recognition, but are rarely used with regression problems and even less often in reservoir characterization. We have developed a CNN regression model to estimate the permeability in the Jacksonburg-Stringtown oil field, West Virginia, which is a potential carbon storage site and enhanced oil recovery operations field. We also evaluate the concept of the geologic feature image, which is converted from geophysical well logs. Five variables, including two commonly available conventional well logs (the gamma rays [GRs] and bulk density) and three well-log-derived variables (the slopes of the GR and bulk density curves, and shale content), are used to generate a geologic feature image. The CNN treats the geologic feature image as the input and the permeability as the desired output. In addition, the permeability predicted using traditional backpropagation artificial neural networks, which are optimized by genetic algorithms and particle swarm optimization, is compared with the permeability estimated using our CNN. Our results indicate that the CNN regression model provides more accurate permeability predictions than the traditional neural network.

Geochemistry & Geophysics↗
NASA NTRS1973

Evidence for convection in planetary interiors from first-order topography.

Center of mass/center of figure offsets are known for the earth, moon, Mars and Venus. Such an offset requires a density distribution asymmetric about the center of mass. Observational evidence indicates that the terrestrial, lunar and Martian offsets result from crusts of variable thickness rather than lateral density inhomogeneities and that the thickness variations are more likely caused by internal convection than impact.

Lingenfelter, R. E.↗
NASA NTRS1977

A parametric study of the copper chloride laser

A parametric study of the double-pulsed copper chloride laser is reported. The effects of a wide range of variables on the laser energy density and on three characteristic time intervals (the minimum, maximum, and optimum delay time) between the two electrical-discharge pulses were studied. The geometric variables investigated included a tube diameter of 2.3 to 40 mm and a tube length of 3 to 60 cm. Three buffer gases, helium, neon, and argon, were studied over the pressure range 0.5-50 torr, and the tube temperature was varied from 270 to 500 C. The energy density and voltage of both the dissociation and pumping pulse were varied independently from less than 1 mJ/cu cm at 8.5 kV to over 500 mJ/cu cm at 20 kV. The optimum conditions for maximum laser energy density were found to be with 20 torr neon in a 10-mm by 30-cm tube at 400 C. The maximum energy density obtained was 22 microjoules/cu cm.

Nerheim, N. M.↗
DOE OSTI2024

High-dimensional and permutation invariant anomaly detection

Methods for anomaly detection of new physics processes are often limited to low-dimensional spaces due to the difficulty of learning high-dimensional probability densities. Particularly at the constituent level, incorporating desirable properties such as permutation invariance and variable-length inputs becomes difficult within popular density estimation methods. In this work, we introduce a permutation-invariant density estimator for particle physics data based on diffusion models, specifically designed to handle variable-length inputs. We demonstrate the efficacy of our methodology by utilizing the learned density as a permutation-invariant anomaly detection score, effectively identifying jets with low likelihood under the background-only hypothesis. To validate our density estimation method, we investigate the ratio of learned densities and compare to those obtained by a supervised classification algorithm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2003

The Chandra X-Ray Observatory Radiation Environment Model

CRMFLX (Chandra Radiation Model of ion FluX) is an environmental risk mitigation tool for use as a decision aid in planning the operations times for Chandra's Advanced CCD Imaging Spectrometer (ACIS) detector. The accurate prediction of the proton flux environment with energies of 100 - 200 keV is needed in order to protect the ACIS detector against proton degradation. Unfortunately, protons of this energy are abundant in the region of space Chandra must operate, and the on-board Electron, Proton, and Helium Instrument (EPHIN) does not measure proton flux levels of the required energy range. In addition to the concerns arising from the radiation belts, substorm injections of plasma from the magnetotail may increase the protons flux by orders of magnitude in this energy range. The Earth's magnetosphere is a dynamic entity, with the size and location of the magnetopause driven by the highly variable solar wind parameters (number density, velocity, and magnetic field components). Operational times for the telescope must be made weeks in advance, decisions which are complicated by the variability of the environment. CRMFLX is an engineering model developed to address these problems and provides proton flux and fluence statistics for the terrestrial outer magnetosphere, magnetosheath, and solar wind for use in scheduling ACIS operations. CRMFLX implements a number of standard models to predict the bow shock, magnetopause, and plasma sheet boundaries based on the sampling of historical solar wind data sets. Measurements from the GEOTAIL and POLAR spacecraft are used to create the proton flux database. This paper describes the recently released CRMFLX v2 implementation that includes an algorithm that propagates flux from an observation location to other regions of the magnetosphere based on convective ExB and VB-curvature particle drift motions in electric and magnetic fields. This technique has the advantage of more completely filling out the database and makes maximum use of limited data obtained during high Kp periods or in areas of the magnetosphere with poor satellite coverage.

Blackwell, W. C.↗
DOE OSTI2022

Minimum feature size control in level set topology optimization via density fields

A level set topology optimization approach that uses an auxiliary density field to nucleate holes during the optimization process and achieves minimum feature size control in optimized designs is explored. The level set field determines the solid-void interface and the density field describes the distribution of a fictitious porous material using the solid isotropic material with penalization. These fields are governed by two sets of independent optimization variables which are initially coupled using a penalty for hole nucleation. The strength of the density field penalization and projection is gradually increased during the optimization process to promote a 0-1 density distribution. In addition, a second penalty regulates the evolution of the density field in the void phase. The treatment of the density field combined with the second penalty mitigate the appearance of small design features. The minimum feature size of optimized designs is controlled by the radius of the linear filter applied to the density optimization variables. The structural response is predicted by the extended finite element method, the sensitivities by the adjoint method, and the optimization variables are updated by a gradient-based optimization algorithm. Numerical examples investigate the robustness of this approach with respect to algorithmic parameters and mesh refinement. The results show the applicability of the combined density level set topology optimization approach for both optimal hole nucleation and for minimum feature size control in 2D and 3D. This comes, however, at the cost of a more complex problem formulation and additional computational cost due to an increased number of optimization variables.

42 ENGINEERING↗
DOE OSTI2025

Effects of ZrN coating and heat treatment on U-Mo dispersion fuel systems under irradiation

The stability of U-Mo fuel particles embedded in an Al matrix under irradiation can be enhanced through ZrN coatings and/or heat treatment. Here, the present study investigates the irradiation behavior of fuel plates containing U-Mo fuel particles fabricated under various heat-treatment conditions and ZrN coating thicknesses. Different fission densities were also applied to each fuel plate to evaluate the effects of these variables. Results indicate that higher fission densities lead to more grain recrystallization and high burnup structure (HBS) development in the fuel particles. Heat treatment was found to mitigate the accumulation of fission gas bubbles in fuel particles at low fission densities by coarsening their grains. Fuel particles with ZrN coatings of a 1.2 μm thickness or above exhibited reduced formation of U-Mo/Al interaction layers, suggesting the existence of a critical ZrN coating thickness that minimizes the development of these layers. Fission gas bubbles were predominantly observed at grain boundaries of U-Mo fuel particles irradiated at low fission densities. Subgrain boundaries, which appeared to originate from the original grain boundaries containing fission gas bubbles or HBSs, were also observed, indicating the early stage of HBS propagation in the fuel particles.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Genesis and atomic structure of the charge density wave phase of 1T-VSe 2

Using variable temperature total x-ray scattering, we study the emergence of charge density wave (CDW) order in the archetypal transition-metal dichalcogenide (TMDC) 1⁢𝑇−VSe 2 . Here, we find that a CDW precursor phase (PF) appears at 𝑇 CDW⁡(PF) of 200(5) K, where V and Se atoms experience in- and out-of-plane static displacements from their position in the undistorted lattice, respectively. The displacements increase with decreasing temperature and a little-known superstructure of V triatomic clusters emerges below 𝑇 CDW⁡(3⁢D) of 100(5) K, where three-dimensional CDW order sets in. Concurrently, Se atoms form less well-defined dimers. Thus, similarly to other TMDCs, 1⁢𝑇−VSe 2 appears to exhibit a two-step CDW transition. The finding underlines the key contribution of lattice distortions to the emergence of CDW order in 1⁢𝑇−VSe 2 and generally in TMDCs.

36 MATERIALS SCIENCE↗
NASA NTRS2006

Computer Modeling of Thermal Convection in Melts to Explain Glass Formation in Low Gravity and on Earth

Experiments conducted up to this time on glass forming melts in the low gravity environment of space show that glasses prepared in low-g are more chemically homogeneous and more resistant to crystallization than the comparable glasses prepared at 1-g on Earth. This result is somewhat surprising and opposite to the accepted concept on glass formation for a melt. A hypothesis based on "shear thinning" of a melt, a decrease in viscosity with increasing shear stress, is proposed as an explanation for the observed low-gravity results. This paper describes detailed simulation procedures to test the role of thermal convection in introducing shear stress in glass forming melts, using a lithium disilcate melt as a model. The simulation system in its idealized version consists of a cylinder that is heated at one end and cooled at the other with gravity acting in a transverse direction to the thermal gradient. The side wall of the cylinder is assumed to be insulating. The governing equations of motion and energy are solved using variable properties for viscosity (Arrehenius and non-Arrehenius behaviors) and density (constant and temperature dependent). Other parametric variables in the calculations include gravity level and gravity vector orientation. The shear stress in the system are then computed as a function of gravity from the calculated values of maximum melt velocity, and its effect on melt viscosity (shear thinning) is predicted. Also included and discussed are the modeling efforts related to other potential convective processes in glass forming melts and their possible effects on melt viscosity.

Ray, Chandra S.↗
NASA NTRS2022

Thermospheric Neutral Density Variation During the “SpaceX” Storm: Implications From Physics-Based Whole Geospace Modeling

The Starlink satellites launched on 3 February 2022 were lost before they fully arrived in their designated orbits. The loss was attributed to two moderate geomagnetic storms that occurred consecutively on February 3-4. We investigate the thermospheric neutral mass density variation during these storms with the Multiscale Atmosphere-Geospace Environment (MAGE) model, a first-principles, fully coupled geospace model. Simulated neutral density enhancements are validated by Swarm satellite measurements at the altitude of 400-500 km. Comparison with standalone TIEGCM and empirical NRLMSIS 2.0 and DTM-2012 models suggests better performance by MAGE in predicting the maximum density enhancement and resolving the gradual recovery process. Along the Starlink satellite orbit in the middle thermosphere (∼ 200 km altitude), MAGE predicts up to 150% density enhancement near the second storm peak while standalone TIEGCM, NRLMSIS 2.0 and DTM-2012 suggest only ∼ 50% increase. MAGE also suggests altitudinal, longitudinal, and latitudinal variability of storm-time percentage density enhancement due to height dependent Joule heating deposition per unit mass, thermospheric circulation changes, and travelling atmospheric disturbances. This study demonstrates that a moderate storm can cause substantial density enhancement in the middle thermosphere. Thermospheric mass density strongly depends on the strength, timing, and location of high-latitude energy input, which cannot be fully reproduced with empirical models. A physics-based, fully coupled geospace model that can accurately resolve the high-latitude energy input and its variability is critical to modeling the dynamic response of thermospheric neutral density during storm time.

Starlink↗
NASA NTRS2002

Abundances of Deuterium, Oxygen and Nitrogen in the Local Interstellar Medium: Overview of First Results from the Far Ultraviolet Spectroscopic Explorer Mission

Observations obtained with the Far Ultraviolet Spectroscopic Explorer (FUSE) have been used to determine the column densities of D I, O I, and N I along seven sight lines that probe the local interstellar medium (LISM) at distances from 37 pc to 179 pc. Five of the sight lines are within the Local Bubble and two penetrate the surrounding H I wall. Reliable values of N(H I) were determined for five of the sight lines from HST data, IUE data, and published EUVE measurements. The weighted mean of DI/H I for these five sight lines is (1.52 +/- 0.08) x l0(exp -5)(1 sigma uncertainty in the mean). It is likely that the D I/H I ratio in the Local Bubble has a single value. The D I/O I ratio for the five sight lines within the Local Bubble is (3.76 +/- 0.20) x 10(esp -2). It is likely that O I column densities can serve as a proxy for H I in the Local Bubble. The weighted mean for O I/ H I for the seven FUSE sight lines is (3.03 +/- 0.21) x 10(esp -4), comparable to the weighted mean (3.43 +/- 0.15) x 10(exp -4) reported for 13 sight lines probing larger distances and higher column densities. The FUSE weighted mean of N I/ H I for five sight lines is half that reported by Meyer et al. for seven sight lines with larger distances and higher column densities. This result combined with the variability of O I/ N I (six sight lines) indicates that at the low column densities found in the LISM, nitrogen ionization balance is important. Thus, unlike O I, N I cannot be used as a proxy for H I or as a metallicity indicator in the LISM.

Moos, H. W.↗