SEARCH · Search NASA
Results for “severity models”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
NOVEL APPLICATION OF UNCERTAINTY ANALYSIS TO SEVERE ACCIDENTS: INTERROGATING MODEL FORM UNCERTAINTY AND DEVELOPING INSIGHTS INTO SEVERE ACCIDENT PROGRESSION.
Abstract not provided.
Mathematical Models Of Turbulence In Transonic Flow
Predictions of several models compared with measurements of well-documented flow. Report reviews performances of variety of mathematical models of turbulence in transonic flow. Predictions of models compared with measurements of flow in wind tunnel along outside of cylinder having axisymmetric bump of circular-arc cross section in meridional plane. Review part of continuing effort to calibrate and verify computer codes for prediction of transonic flows about airfoils. Johnson-and-King model proved superior in predicting transonic flow over bumpy cylinder.
Parallel exponential time differencing methods for geophysical flow simulations
Two ocean models are considered for geophysical flow simulations: the multilayer shallow water equations and the multilayer primitive equations. For the former, we investigate the parallel performance of exponential time differencing (ETD) methods, including exponential Rosenbrock–Euler, ETD2wave, and B-ETD2wave. For the latter, we take advantage of the splitting of barotropic and baroclinic modes and propose a new two-level method in which an ETD method is applied to solve the fast barotropic mode. Furthermore, these methods could improve the computational efficiency of numerical simulations because ETD methods allow for much larger time step sizes than traditional explicit time-stepping techniques that are commonly used in existing computational ocean models. Several standard benchmark tests for ocean modeling are performed and comparison of the numerical results demonstrates a great potential of applying the parallel ETD methods for simulating real-world geophysical flows.
Exploitation of Defects in High Entropy Ceramic Barrier Materials
A critical mission need exists to develop new materials that can withstand extreme environments and multiple sequential threats. High entropy materials, those containing 5 or more metals, exhibit many exciting properties which would potentially be useful in such situations. However, a particularly hard challenge in developing new high entropy materials is determining a priori which compositions will form the desired single phase material. The project outlined here combined several modeling and experimental techniques to explore several structure-property-relationships of high entropy ceramics in an effort to better understand the connection between their compositional components, their observed properties, and stability. We have developed novel machine learning algorithms which rapidly predict stable high entropy ceramic compositions, identified the stability interplay between configurational entropy and cation defects, and tested the mechanical stability of high entropy oxides using the unique capabilities at the Dynamic Compression Sector facility and the Saturn accelerator.
Machine-learning-based investigation of the variables affecting summertime lightning occurrence over the Southern Great Plains
Lightning is affected by many factors, many of which are not routinely measured, well understood, or accounted for in physical models. Several commonly used machine learning (ML) models have been applied to analyze the relationship between Atmospheric Radiation Measurement (ARM) data and lightning data from the Earth Networks Total Lightning Network (ENTLN) in order to identify important variables affecting lightning occurrence in the vicinity of the Southern Great Plains (SGP) ARM site during the summer months (June, July, August and September) of 2012 to 2020. Testing various ML models, we found that the random forest model is the best predictor among common classifiers. When convective clouds were detected, it predicts lightning occurrence with an accuracy of 76.9 % and an area under the curve (AUC) of 0.850. Using this model, we further ranked the variables in terms of their effectiveness in nowcasting lightning and identified geometric cloud thickness, rain rate and convective available potential energy (CAPE) as the most effective predictors. The contrast in meteorological variables between no-lightning and frequent-lightning periods was examined for hours with CAPE values conducive to thunderstorm formation. Besides the variables considered for the ML models, surface variables and mid-altitude variables (e.g., equivalent potential temperature and minimum equivalent potential temperature, respectively) have statistically significant contrasts between no-lightning and frequent-lightning hours. For example, the minimum equivalent potential temperature from 700 to 500 hPa is significantly lower during frequent-lightning hours compared with no-lightning hours. Finally, a notable positive relationship between the intracloud (IC) flash fraction and the square root of CAPE ($\sqrt{CAPE}$) was found, suggesting that stronger updrafts increase the height of the electrification zone, resulting in fewer flashes reaching the surface and consequently a greater IC flash fraction.
Sensitivity of the Earth's climate to changes
Climate modeling is discussed with emphasis on the control of major parameters in such modeling. Possible feedbacks which operate on long time scales and are therefore not testable directly, are also discussed. Examples of simplified models are presented. The accuracy of several models is assessed and suggestions for improvements for climatic modeling are given.
The development and validation of a simple snow model for the GISS GCM
Five years of meteorological and hydrological data from a typical New England watershed where winter snow cover is significant were used to drive and validate two off-line land surface schemes suitable for use in the Goddard Institute for Space Studies (GISS) general circulation model (GCM): a baseline scheme that does not model the physics of a snowpack and therefore, neglects the insulating properties of snow cover; and a modified scheme in which a three-layer snowpack is modeled. Comparing baseline model results with validation data reveals several model deficiencies. Surface radiation temperatures could not adequately be modeled and the ground froze to unreasonable depths. Furthermore, because of ground cooling resulting from large surface heat fluxes to the atmosphere from the uninsulated surface, deeper model layers did not unfreeze until midsummer. As such, the normal hydrologic processes of runoff, ground water infiltration, and movement, etc., are compromised for a good part of the year. With the inclusion of a simple three-layer snow model into the baseline model, not only are the ground and surface radiation temperatures adequately modeled but all the features of snowpack ripening that characterize pack growth/ablation are simulated.
BATSE Observations of the Large-Scale Isotropy of Gamma-Ray Bursts
We use dipole and quadrupole statistics to test the large-scale isotropy of the first 1005 gamma-ray bursts observed by the Burst and Transient Source Experiment (BATSE). In addition to the entire sample of 1005 gamma-ray bursts, many subsets are examined. We use a variety of dipole and quadrupole statistics to search for Galactic and other predicted anisotropies and for anisotropies in a coordinate-system independent manner. We find the gamma-ray burst locations to be consistent with isotropy, e.g., for the total sample the observed Galactic dipole moment (cos theta) differs from the value predicted for isotropy by 0.9 sigma and the observed Galactic quadrupole moment (sin(exp 2) b - 1/3) by 0.3 sigma. We estimate for various models the anisotropies that could have been detected. If one-half of the locations were within 86 deg of the Galactic center, or within 28 deg of the Galactic plane, the ensuing dipole or quadrupole moment would have typically been detected at the 99% confidence level. We compare the observations with the dipole and quadrupole moments of various Galactic models. Several Galactic gamma-ray bursts models have moments within 2 sigma of the observations; most of the Galactic models proposed to date are no longer in acceptable agreement with the data. Although a spherical dark matter halo distribution could be consistent with the data, the required core radius is larger than the core radius of the dark matter halo used to explain the Galaxy's rotation curve. Gamma-ray bursts are much more isotropic than any observed Galactic population, strongly favoring but not requiring an origin at cosmological distances.
Modeling the Inner Magnetosphere: Radiation Belts, Ring Current, and Composition
The space environment is a complex system defined by regions of differing length scales, characteristic energies, and physical processes. It is often difficult, or impossible, to treat all aspects of the space environment relative to a particular problem with a single model. In our studies, we utilize several models working in tandem to examine this highly interconnected system. The methodology and results will be presented for three focused topics: 1) Rapid radiation belt electron enhancements, 2) Ring current study of Energetic Neutral Atoms (ENAs), Dst, and plasma composition, and 3) Examination of the outflow of ionospheric ions. In the first study, we use a coupled MHD magnetosphere - kinetic radiation belt model to explain recent Akebono/RDM observations of greater than 2.5 MeV radiation belt electron enhancements occurring on timescales of less than a few hours. In the second study, we present initial results of a ring current study using a newly coupled kinetic ring current model with an MHD magnetosphere model. Results of a dst study for four geomagnetic events are shown. Moreover, direct comparison with TWINS ENA images are used to infer the role that composition plays in the ring current. In the final study, we directly model the transport of plasma from the ionosphere to the magnetosphere. We especially focus on the role of photoelectrons and and wave-particle interactions. The modeling methodology for each of these studies will be detailed along with the results.
FAST Mast Structural Response to Axial Loading: Modeling and Verification
The International Space Station s solar array wing mast shadowing problem is the focus of this paper. A building-block approach to modeling and analysis is pursued for the primary structural components of the solar array wing mast structure. Starting with an ANSYS (Registered Trademark) finite element model, a verified MSC.Nastran (Trademark) model is established for a single longeron. This finite element model translation requires the conversion of several modeling and analysis features for the two structural analysis tools to produce comparable results for the single-longeron configuration. The model is then reconciled using test data. The resulting MSC.Nastran (Trademark) model is then extended to a single-bay configuration and verified using single-bay test data. Conversion of the MSC. Nastran (Trademark) single-bay model to Abaqus (Trademark) is also performed to simulate the elastic-plastic longeron buckling response of the single bay prior to folding.
The Seasonal Cycle of Satellite Chlorophyll Fluorescence Observations and its Relationship to Vegetation Phenology and Ecosystem Atmosphere Carbon Exchange
Mapping of terrestrial chlorophyll uorescence from space has shown potentialfor providing global measurements related to gross primary productivity(GPP). In particular, space-based fluorescence may provide information onthe length of the carbon uptake period that can be of use for global carboncycle modeling. Here, we examine the seasonal cycle of photosynthesis asestimated from satellite fluorescence retrievals at wavelengths surroundingthe 740nm emission feature. These retrievals are from the Global OzoneMonitoring Experiment 2 (GOME-2) flying on the MetOp A satellite. Wecompare the fluorescence seasonal cycle with that of GPP as estimated froma diverse set of North American tower gas exchange measurements. Because the GOME-2 has a large ground footprint (40 x 80km2) as compared with that of the flux towers and requires averaging to reduce random errors, we additionally compare with seasonal cycles of upscaled GPP in the satellite averaging area surrounding the tower locations estimated from the Max Planck Institute for Biogeochemistry (MPI-BGC) machine learning algorithm. We also examine the seasonality of absorbed photosynthetically-active radiation(APAR) derived with reflectances from the MODerate-resolution Imaging Spectroradiometer (MODIS). Finally, we examine seasonal cycles of GPP as produced from an ensemble of vegetation models. Several of the data-driven models rely on satellite reflectance-based vegetation parameters to derive estimates of APAR that are used to compute GPP. For forested sites(particularly deciduous broadleaf and mixed forests), the GOME-2 fluorescence captures the spring onset and autumn shutoff of photosynthesis as delineated by the tower-based GPP estimates. In contrast, the reflectance-based indicators and many of the models tend to overestimate the length of the photosynthetically-active period for these and other biomes as has been noted previously in the literature. Satellite fluorescence measurements therefore show potential for improving model GPP estimates.
A Control Model: Interpretation of Fitts' Law
The analytical results for several models are given: a first order model where it is assumed that the hand velocity can be directly controlled, and a second order model where it is assumed that the hand acceleration can be directly controlled. Two different types of control-laws are investigated. One is linear function of the hand error and error rate; the other is the time-optimal control law. Results show that the first and second order models with the linear control-law produce a movement time (MT) function with the exact form of the Fitts' Law. The control-law interpretation implies that the effect of target width on MT must be a result of the vertical motion which elevates the hand from the starting point and drops it on the target at the target edge. The time optimal control law did not produce a movement-time formula simular to Fitt's Law.
Modeling PWR systems with a unique framework for hybrid energy systems
Hybrid energy system (HES) is used to utilize the wind, solar, nuclear and other energy sources. Within the study and design of a nuclear power plant and HES, extensive system modeling is necessary to determine how the reactor is going to perform in any given situation, not only in the normal performance of the reactor but also transients including unanticipated transients without scram and hypothetical accidents. A modelling tool that is capable of solving governing equations of mass, momentum and energy for two-phase application is necessary to model the coolant systems of HES. Therefore, this study shows the development of a new coupling tool that is capable of modelling several components of HES and experimental validation of the model. For the initial development phase, the primary and secondary coolant systems (which use several components similar to the components of HES) are modelled by using this new tool. Code-to-code benchmark is also used for the performance evaluation of the new tool. It is observed that there is a good agreement between the new tool and RELAP5 predictions. (authors)
Low-cloud characteristics over the tropical western Pacific from ARM observations and CAM5 simulations
Here, this study evaluates the ability of the Community Atmospheric Model version 5 (CAM5) to reproduce low clouds observed by the Atmospheric Radiation Measurement (ARM) cloud radar at Manus Island of the tropical western Pacific during the Years of Tropical Convection. Here low clouds are defined as clouds with their tops below the freezing level and bases within the boundary layer. Low-cloud statistics in CAM5 simulations and ARM observations are compared in terms of their general occurrence, mean vertical profiles, fraction of precipitating versus nonprecipitating events, diurnal cycle, and monthly time series. Other types of clouds are included to put the comparison in a broader context. The comparison shows that the model overproduces total clouds and their precipitation fraction but underestimates low clouds in general. The model, however, produces excessive low clouds in a thin layer between 954 and 930 hPa, which coincides with excessive humidity near the top of the mixed layer. This suggests that the erroneously excessive low clouds stem from parameterization of both cloud and turbulence mixing. The model also fails to produce the observed diurnal cycle in low clouds, not exclusively due to the model coarse grid spacing that does not resolve Manus Island. Lastly, this study demonstrates the utility of ARM long-term cloud observations in the tropical western Pacific in verifying low clouds simulated by global climate models, illustrates issues of using ARM observations in model validation, and provides an example of severe model biases in producing observed low clouds in the tropical western Pacific.
Classical versus quantum models in machine learning: insights from a finance application
Although several models have been proposed towards assisting machine learning (ML) tasks with quantum computers, a direct comparison of the expressive power and efficiency of classical versus quantum models for datasets originating from real-world applications is one of the key milestones towards a quantum ready era. Here, we take a first step towards addressing this challenge by performing a comparison of the widely used classical ML models known as restricted Boltzmann machines (RBMs), against a recently proposed quantum model, now known as quantum circuit Born machines (QCBMs). Both models address the same hard tasks in unsupervised generative modeling, with QCBMs exploiting the probabilistic nature of quantum mechanics and a candidate for near-term quantum computers, as experimentally demonstrated in three different quantum hardware architectures to date. To address the question of the performance of the quantum model on real-world classical data sets, we construct scenarios from a probabilistic version out of the well-known portfolio optimization problem in finance, by using time-series pricing data from asset subsets of the S&P500 stock market index. It is remarkable to find that, under the same number of resources in terms of parameters for both classical and quantum models, the quantum models seem to have superior performance on typical instances when compared with the canonical training of the RBMs. Our simulations are grounded on a hardware efficient realization of the QCBMs on ion-trap quantum computers, by using their native gate sets, and therefore readily implementable in near-term quantum devices.
Phenomenology of the Prethermal Many-Body Localized Regime
The dynamical phase diagram of interacting disordered systems has seen substantial revision over the past few years. Theory must now account for a large prethermal many-body localized regime in which thermalization is extremely slow, but not completely arrested. We derive a quantitative description of these dynamics in short-ranged one-dimensional systems using a model of successive many-body resonances. Here, the model explains the decay timescale of mean autocorrelators, the functional form of the decay—a stretched exponential—and relates the value of the stretch exponent to the broad distribution of resonance timescales. The Jacobi method of matrix diagonalization provides numerical access to this distribution, as well as a conceptual framework for our analysis. The resonance model correctly predicts the stretch exponents for several models in the literature. Successive resonances may also underlie slow thermalization in strongly disordered systems in higher dimensions, or with long-range interactions.
Towards Smart Grids Enhanced Situation Awareness: A Bi-Level Quasi-Static State Estimation Model
Smart Grid situational awareness is provided by Energy Management Systems. A core process of these systems is State Estimation. The great majority of state estimators model the Smart Grid through a set of nonlinear algebraic equations, named the measurement model. Problem formulation considers the Gauss solution. Several model improvements have been presented regarding the Gauss solution, aiming between others to provide measurement noise robustness to the state estimation process. While considerable effort has been focused on such developments, state estimation is still constrained by the implicit modelling error, and thus inevitably vulnerable to cyber-threats. In this work, a state estimation bi-level formal model is presented towards Smart Grids enhanced situational awareness considering the concepts of synthetic measurements and innovation. Comparative test results with the state-of-the-art on the IEEE 14-bus system are presented highlighting improved situational awareness to bad data. Easy-to-implement model, without hard-to-derive parameters, built-on the classic weighted least squares solution, highlight potential aspects for real-life implementation.