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At least 1,621 records · Page 90

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NASA NTRSDate not supplied

The Effect of Drag Model on Heatshield Recession due to Particle Impacts for Martian Spacecraft

A spacecraft entering the Martian atmosphere during a dust storm may experience recession to the heatshield due to dust particle impacts. Aerodynamic drag is the primary force that determines the trajectory of the dust particles through the shock layer. This paper examines the effect of particle drag model on the heatshield recession. Three particle drag models are assessed including two that are intended to be applicable over a wide range of particle flow conditions. Particle trajectories are computed in conditions measured during the 2007 major global dust storm. It was found that accounting for Knudsen number and compressibility effects made a large difference in the estimated particle impact velocity. The two drag models that were valid for transitional, compressible particle flow environments predicted only slightly-different amounts of heatshield recession due to dust particle impacts. A brief description of the effects of non-spherical particles on drag coefficients is provided.

Heatshield↗

DOE OSTI2020

Supporting Bioproducts Industry Growth with a System Dynamics Decision-Support Tool

A bio-based economy requires chemical products as well as fuels to be produced from biomass. Although a variety of universities, government agencies, start-ups and established firms have engaged in bioproduct development, many projects have failed to reach the point of commercialization and commercialized bioproducts struggle to capture and maintain market share. To date, there has been no general research into the factors that contribute to bioproduct failure or success. This work presents the Bioproduct Transition Dynamics (BTD) system dynamics model, a decision-support tool that simulates the bioproduct development process from pre-piloting research through construction of the first commercial-scale plant, as well as the processes of obtaining funding from investors and government agencies. The core of the BTD is a feedback loop between bioproduct developers and funders, which relates development progress measured with indicators such as net present value to funders’ decisions to continue investing. External factors such as feedstock prices, market size and growth, and the existence of bioproduct consumers also influence a development project’s chances of receiving follow-on funding. Virtually any bioproduct can be represented with the BTD: direct replacements, performance-advantaged products, niche and commodity markets can all be modeled. The goal of the BTD project is to inform decisions made by developers in both established firms and start-ups, investors, government agencies, and other stakeholders interested in growing the nascent U.S. bioproducts industry. This talk will cover the general structure and functionality of the BTD, and present results from an analysis performed with the BTD to demonstrate its use as a decision-support tool and the insights it can provide. The goal of the analysis is to identify the most critical factors that lead to direct replacement and performance-advantaged bioproduct projects emerging successfully from the “Valley of Death”, and to determine if these factors differ between the two bioproduct types.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Monitoring Operational States of a Nuclear Reactor Using Seismoacoustic Signatures and Machine Learning

Monitoring nuclear reactors is an important safety and security task with growing requirements. We explore the possibility of using seismic and acoustic data for inferring the power level of an operating reactor. Continuous data recorded at a single seismoacoustic station that is located about 50 m away from a research reactor was visualized and analyzed. The data show a clear correlation between seismoacoustic features and reactor main operational states. We designed a workflow that includes two machine learning (ML) models to classify the reactor operational states (OFF, transition, and ON) and estimate reactor power levels (10%, 30%, 50%, 70%, and 90%). We applied and compared five ML algorithms for the reactor OFF-transition-ON and four approaches for the power level classification. We also compared the performance of ML models trained with seismic-only, acoustic-only, and both types of data. Five-fold cross validations were implemented to assure a thorough evaluation of the model performances. Additionally, the results show the extreme boosting gradient algorithm worked best for the first model, whereas random forests performed best for the second model. Combining seismic and acoustic data leads to better performance than using a single type of data. Seismic data contributed more than acoustic data for both models. We reached an accuracy of 0.98 for reactor OFF and ON. The accuracies for the transition state and power levels are less optimal with a minimum accuracy of 0.66. However, our results suggest seismic and acoustic data contain useful information about the transition state as well as power levels. Seismic and acoustic data could be integrated with other observations to improve monitoring performance.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

A Time Series Sustainability Assessment of a Partial Energy Portfolio Transition

Energy portfolios are overwhelmingly dependent on fossil fuel resources that perpetuate the consequences associated with climate change. Therefore, it is imperative to transition to more renewable alternatives to limit further harm to the environment. This study presents a univariate time series prediction model that evaluates sustainability outcomes of partial energy transitions. Future electricity generation at the state-level is predicted using exponential smoothing and autoregressive integrated moving average (ARIMA). The best prediction results are then used as an input for a sustainability assessment of a proposed transition by calculating carbon, water, land, and cost footprints. Missouri, USA was selected as a model testbed due to its dependence on coal. Of the time series methods, ARIMA exhibited the best performance and was used to predict annual electricity generation over a 10-year period. The proposed transition consisted of a one-percent annual decrease of coal’s portfolio share to be replaced with an equal share of solar and wind supply. The sustainability outcomes of the transition demonstrate decreases in carbon and water footprints but increases in land and cost footprints. Decision makers can use the results presented here to better inform strategic provisioning of critical resources in the context of proposed energy transitions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS1975

A method for the evaluation of damage on the surface of carbon-carbon ablation samples

The effect of weave geometry on the onset of transition from laminar to turbulent flow around a sample of an orthogonally woven fine weave carbon-carbon material tested in an arc-jet is investigated. Seventeen post-test models which had undergone only partial transition around the circumference were examined as to location of turbulent regions relative to the weave axes. It was statistically shown that the weave axes had the lowest probability of being a position of initial transition onset. The probability generally increased with distance from a major axis, with some rays, notably the 45 deg rays, showing better transition resistance than the adjacent rays. The incidence of initial transition onset correlated well with the number of X or Y fibers that the flow passes over from stagnation point to the shoulder.

Heinonen, E. W.↗

DOE OSTI2020

Integrated Risk-Informed Condition Based Maintenance Capability and Automated Platform: Technical Report 1

Due to continuing global energy market trends, driven heavily by the abundant preserves of natural gas, there is an immediate need to reduce costs associated with operation and maintenance (O&M) for the current domestic nuclear power industry and for future reactor developments. This is to ensure that nuclear power generation remains an economically competitive and viable option in the energy market. O&M costs include labor-intensive preventive maintenance (PM) programs, which involve manually-performed inspection, calibration, testing, and maintenance of plant assets at periodic frequency and time-based replacement of assets, irrespective of their condition. This has resulted in an expensive, labor-centric business model to achieve high capacity factors. Fortunately, there are technologies (advanced sensors, data analytics, and risk assessment methodologies) that can enable the transition from a labor-centric business model to a technology-centric business model. The technology-centric business model will result in a significant reduction of PM activities, laying the foundation for real-time condition assessment of plant assets, reducing overall labor and part costs. To enable this transition, PKMJ Technical Services LLC is partnering with the U.S. Department of Energy’s Idaho National Laboratory (operated by the Battelle Energy Alliance, LLC) and the Public Services Enterprise Group (PSEG) Nuclear, LLC in the Integrated Risk-Informed Condition-Based Maintenance Capability and Automated Platform Project. In this report, the configuration of a digital cloud platform using Microsoft Azure is discussed, data from the PSEG Salem Nuclear Generating Station Units 1 & 2 are imported into a digital cloud platform, and the data is used for an evaluation of several key areas: cost impact analysis, risk-informed model development, and preventive maintenance strategy optimization. First, the cost impact analysis reviews which plant assets are potential good candidates for condition-based monitoring. Next, INL utilized the data in their local environment to develop the risk-informed model; which provides estimates of failure rates and probability of failures of assets based upon their past performance. The developed model is performed on assets selected from the cost impact analysis. Lastly, engineers assess the preventive maintenance strategy for the selected assets at PSEG against maintenance strategies in the nuclear industry for similar assets to potentially identify acceptable justification for the extension of current maintenance frequencies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1983

The continuous similarity model of bulk soil-water evaporation

The continuous similarity model of evaporation is described. In it, evaporation is conceptualized as a two stage process. For an initially moist soil, evaporation is first climate limited, but later it becomes soil limited. During the latter stage, the evaporation rate is termed evaporability, and mathematically it is inversely proportional to the evaporation deficit. A functional approximation of the moisture distribution within the soil column is also included in the model. The model was tested using data from four experiments conducted near Phoenix, Arizona; and there was excellent agreement between the simulated and observed evaporation. The model also predicted the time of transition to the soil limited stage reasonably well. For one of the experiments, a third stage of evaporation, when vapor diffusion predominates, was observed. The occurrence of this stage was related to the decrease in moisture at the surface of the soil. The continuous similarity model does not account for vapor flow. The results show that climate, through the potential evaporation rate, has a strong influence on the time of transition to the soil limited stage. After this transition, however, bulk evaporation is independent of climate until the effects of vapor flow within the soil predominate.

Clapp, R. B.↗

DOE OSTI2020

Detecting depinning and nonequilibrium transitions with unsupervised machine learning

Using numerical simulations of a model disk system, we demonstrate that a machine learning generated order-parameter-like measure can detect depinning transitions and different dynamic flow phases in systems driven far from equilibrium. Furthermore, we specifically consider monodisperse passive disks with short range interactions undergoing a depinning phase transition when driven over quenched disorder. The machine learning derived order-parameter-like measure identifies the depinning transition as well as different dynamical regimes, such as the transition from a flowing liquid to a phase separated liquid-solid state that is not readily distinguished with traditional measures such as velocity-force curves or Voronoi tessellation. The order-parameter-like measure also shows markedly distinct behavior in the limit of high density where jamming effects occur. Our results should be general to the broad class of particle-based systems that exhibit depinning transitions and nonequilibrium phase transitions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS1990

The evolution from weak to strong geomagnetic activity - An interpretation in terms of deterministic chaos

An analogue of the magnetosphere developed on the basis of Shaw's (1984) dripping faucet model was used to model the mechanisms of the magnetospheric response to energy transfer from the solar wind. It is demonstrated that geomagnetic activity results from nonlinearly coupled physical processes and that the strength and the nature of the coupling changes dramatically as the magnetosphere is driven harder and harder by increasing energy input. Based on initial results obtained from the model, is is suggested that a chaotic transition takes place in the analogue system as the loading rate is increased beyond a critical value. This model is able to explain many of the features in the results of linear prediction filtering techniques.

Baker, D. N.↗

NASA NTRSDate not supplied

Efficient Variable-Pitch Propeller Aerodynamic Model Development for Vectored-Thrust eVTOL Aircraft

This paper describes a variable-pitch propeller testing and aerodynamic modeling methodology for electric vertical takeoff and landing (eVTOL) aircraft using wind tunnel testing. Propellers used for eVTOL aircraft propulsion systems experience a wide range of operating conditions resulting in significant variation of axial thrust and torque, as well as off-axis forces and moments, across a typical flight envelope. An experimental design is developed to collect informative wind tunnel data in an efficient manner using response surface methods. System identification methods are then applied to isolated propeller wind tunnel data to develop a mathematical model of the propeller valid throughout the transition envelope of a vectored-thrust eVTOL aircraft. Multiple explanatory variable definitions are postulated and compared using modeling and prediction performance metrics. Modeling results validated against data withheld from the modeling process indicate good predictive capability and agree with theoretical expectations. All identified propeller models are provided, which allows the models to be used in future eVTOL aircraft flight dynamics simulations.

aerodynamic modeling↗

NASA NTRS1991

Hexagonal and nematic phases of chains. I - Correlation functions. II - Phase transitions

The statistical mechanics of a system of semiflexible chains, which can represent polymer liquid crystals, long-chain biomolecules, stiff wormlike micelles, or columns of discotic liquid crystals, are examined. A continuum theory is used to calculate static correlation functions in the hexagonal and nematic phases. Two correlation functions are considered: (1) the structure factor which describes fluctuations in the density; and (2) the director fluctuation spectrum, which describes fluctuations in the local optical axis. In addition, a model is developed for the phase transitions of a system of infinitely long, semiflexible chains which interact through a steric, excluded-volume repulsion. The model yields generic phase diagrams in terms of pressure or density vs. persistence length or temperature.

Selinger, Jonathan V.↗

NASA NTRS1996

Propagation of Electrical Excitation in a Ring of Cardiac Cells: A Computer Simulation Study

The propagation of electrical excitation in a ring of cells described by the Noble, Beeler-Reuter (BR), Luo-Rudy I (LR I), and third-order simplified (TOS) mathematical models is studied using computer simulation. For each of the models it is shown that after transition from steady-state circulation to quasi-periodicity achieved by shortening the ring length (RL), the action potential duration (APD) restitution curve becomes a double-valued function and is located below the original ( that of an isolated cell) APD restitution curve. The distributions of APD and diastolic interval (DI) along a ring for the entire range of RL corresponding to quasi-periodic oscillations remain periodic with the period slightly different from two RLs. The 'S' shape of the original APD restitution curve determines the appearance of the second steady-state circulation region for short RLs. For all the models and the wide variety of their original APD restitution curves, no transition from quasi-periodicity to chaos was observed.

Kogan, B. Y.↗

DOE OSTI2022

Impact of fragment formation on shear viscosity in the nuclear liquid-gas phase transition region

Within the improved quantum molecular dynamics (ImQMD) model we follow the evolution of nuclear matter for planar Couette flow in a periodic box. We focus on the region of liquid-gas phase transition and extract the shear viscosity coefficient from the local stress tensor, directly following the viscosity definition. Here, by switching on and off the mean field and thus inducing the phase transition, we are able to observe the impact of clumping in the phase transition region on the viscosity.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS2012

Evaluating the Contribution of NASA Remotely-Sensed Data Sets on a Convection-Allowing Forecast Model

The Short-term Prediction Research and Transition (SPoRT) Center is a collaborative partnership between NASA and operational forecasting partners, including a number of National Weather Service forecast offices. SPoRT provides real-time NASA products and capabilities to help its partners address specific operational forecast challenges. One challenge that forecasters face is using guidance from local and regional deterministic numerical models configured at convection-allowing resolution to help assess a variety of mesoscale/convective-scale phenomena such as sea-breezes, local wind circulations, and mesoscale convective weather potential on a given day. While guidance from convection-allowing models has proven valuable in many circumstances, the potential exists for model improvements by incorporating more representative land-water surface datasets, and by assimilating retrieved temperature and moisture profiles from hyper-spectral sounders. In order to help increase the accuracy of deterministic convection-allowing models, SPoRT produces real-time, 4-km CONUS forecasts using a configuration of the Weather Research and Forecasting (WRF) model (hereafter SPoRT-WRF) that includes unique NASA products and capabilities including 4-km resolution soil initialization data from the Land Information System (LIS), 2-km resolution SPoRT SST composites over oceans and large water bodies, high-resolution real-time Green Vegetation Fraction (GVF) composites derived from the Moderate-resolution Imaging Spectroradiometer (MODIS) instrument, and retrieved temperature and moisture profiles from the Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI). NCAR's Model Evaluation Tools (MET) verification package is used to generate statistics of model performance compared to in situ observations and rainfall analyses for three months during the summer of 2012 (June-August). Detailed analyses of specific severe weather outbreaks during the summer will be presented to assess the potential added-value of the SPoRT datasets and data assimilation methodology compared to a WRF configuration without the unique datasets and data assimilation.

Zavodsky, Bradley T.↗

DOE OSTI2024

Thermodynamically informed priors for uncertainty propagation in first-principles statistical mechanics

Here, this work demonstrates how first-principles statistical mechanics approaches within a Bayesian framework can quantify and propagate uncertainties to downstream thermodynamic calculations. To address the issue of Bayesian prior selection, knowledge of 0 K ground states in the material system of interest is incorporated into the prior. The effectiveness of this framework is shown by creating a phase diagram for the fcc zirconium nitride system, including confidence intervals on order-disorder transition temperatures.

Bayesian methods↗

DOE OSTI2021

Soot Predictions with a Laminar Flamelet Combustion Model in SIERRA/Fuego on a Coflow Scenario

This report describes an assessment of flamelet based soot models in a laminar ethylene coflow flame with a good selection of measurements suitable for model validation. Overall flow field and temperature predictions were in good agreement with available measurements. Soot profiles were in good agreement within the flame except for near the centerline where imperfections with the acetylene-based soot-production model are expected to be greatest. The model was challenged to predict the transition between non-sooting and sooting conditions with non-negligible soot emissions predicted even down to small flow rates or flame sizes. This suggests some possible deficiency in the soot oxidation models that might alter the amount of smoke emissions from flames, though this study cannot quantify the magnitude of the effect for large fires.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2010

Longitudinal Proximity Effect, Lateral Inverse Proximity Effect, and Nonequilibrium Superconductivity in Transition-Edge Sensors

We have recently shown that normal-metal/superconductor (N /S) bilayer TESs (superconducting Transition-Edge Sensors) exhibit weak-link behavior. Our measurements were explained in terms of a longitudinal proximity effect model in which superconducting order from the higher transition temperature leads is induced into the TES bilayer plane over remarkably long distances (up to 290 micron). Here we extend our understanding to include TESs with added noise-mitigating normal-metal structures (N structures). We explain our results in terms of an effect converse to the longitudinal proximity effect (LoPE), the lateral inverse proximity effect (LaiPE), for which the order parameter in the N /S bilayer is reduced due to the neighboring N structures. We present resistance and critical current measurements as a function of temperature and magnetic field taken on square Mo/Au bilayer TESs with lengths ranging from 8 to 130 micron with and without added N structures. We observe the inverse proximity effect on the bilayer over in-plane distances many tens of microns and find the transition shifts to lower temperatures scale approximately as the inverse square of the in-plane N-structure separation distance, without appreciable broadening of the transition width. We find TESs with added Au structures exhibit weak-link behavior as evidenced by exponential temperature dependence of the critical current and Josephson-like oscillations of the critical current with applied magnetic field. We also present evidence for nonequilbrium superconductivity and estimate a quasiparticle lifetime of 1.8 x 10(exp -10) s for the bilayer. The LoPE model is also used to explain the increased conductivity at temperatures above the bilayer's steep resistive transition.

Sadleir, John E.↗

NASA NTRS2010

Longitudinal Proximity Effect, Lateral Inverse Proximity Effect, and Nonequilibrium Superconductivity in Transition-edge Sensors

We have recently shown that normal-metal/superconductor (N/S) bilayer TESs (superconducting Transition-Edge Sensors) exhibit weak-link behavior. Our measurements were explained in terms of a longitudinal proximity effect model in which superconducting order from the higher transition temperature leads is induced into the TES bilayer plane over remarkably long distances (up to 290 micron). Here we extend our understanding to include TESs with added noise-mitigating normal-metal structures (N structures). We explain our results of an effect converse to the longitudinal proximity effect (LoPE), the lateral inverse proximity effect (LaiPE), for which the order parameter in the N/S bilayer is reduced due to the neighboring N structures. We present resistance and critical current measurements as a function of temperature and magnetic field taken on square Mo/Au bilayer TESs with lengths ranging from 8 to 130 micron with and without added N structures. We observe the inverse proximity effect on the bilayer over in-plane distances many tens of microns and find the transition shifts to lower temperature scale approximately as the inverse square of the in-plane N-structure separation distance, without appreciable broadening of the transition width. We find TESs with added Au structures exhibit weak-link behavior as evidenced by exponential temperature dependence of the critical current and Josephson-like oscillations of the critical current with applied magnetic field. We also present evidence for nonequilbrium superconductivity and estimate a quasiparticle lifetime of 1.8 x 10(exp -10) s for the bilayer. The LoPE model is also used to explain the increased conductivity at temperatures above the bilayer's steep resistive transition

Sadleir, John E.↗