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Results for “dependability modeling”
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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A nonlinear history-dependent damage model for low cycle fatigue
A nonlinear damage postulate that embodies the dependence of the damage rate on cycle-dependent changes in the bulk microstructure and the surface topography is examined. The postulate is analytically formulated in terms of the deformation history dependence of the bulk behavior. This formulation is used in conjunction with baseline data in accordance with the damage postulate to predict the low cycle fatigue resistance of OFE copper. Close comparison of the predictions with experimentally observed behavior suggests that the postulate offers a viable basis for nonlinear damage analysis.
Time dependent turbulence modeling and analytical theories of turbulence
By simplifying the direct interaction approximation (DIA) for turbulent shear flow, time dependent formulas are derived for the Reynolds stresses which can be included in two equation models. The Green's function is treated phenomenologically, however, following Smith and Yakhot, we insist on the short and long time limits required by DIA. For small strain rates, perturbative evaluation of the correlation function yields a time dependent theory which includes normal stress effects in simple shear flows. From this standpoint, the phenomenological Launder-Reece-Rodi model is obtained by replacing the Green's function by its long time limit. Eddy damping corrections to short time behavior initiate too quickly in this model; in contrast, the present theory exhibits strong suppression of eddy damping at short times. A time dependent theory for large strain rates is proposed in which large scales are governed by rapid distortion theory while small scales are governed by Kolmogorov inertial range dynamics. At short times and large strain rates, the theory closely matches rapid distortion theory, but at long times it relaxes to an eddy damping model.
A Three-Dimensional, Time-Dependent Numerical Modeling of Super-Sonic, Super-Alfvenic MHD Flow
A three-dimensional, time-dependent, MHD disturbance propagating through a steady inhomogeneous MHD flow is analyzed by a numerical model based on the Lax-Wendroff finite difference method. The initial steady MHD flow is assumed to be supersonic and super-Alfvenic. The background steady MHD flow is obtained by dropping the time-dependent terms in the unsteady MHD equations. A transient MHD disturbance corresponding to a MHD shock is generated at the lower radial boundary surface. The resulting development and propagation of 3-D MHD disturbances interacting with the background steady MHD flow are numerically simulated in this first application to the field of interplanetary physics. Detailed descriptions of the mathematical formulation and numerical methodology are presented. Example computations are performed to demonstrate the validity of the formulation and the capabilities of the model.
Time-dependent corona models - Scaling laws
Scaling laws are derived for the one-dimensional time-dependent Euler equations that describe the evolution of a spherically symmetric stellar atmosphere. With these scaling laws the results of the time-dependent calculations by Korevaar (1989) obtained for one star are applicable over the whole Hertzsprung-Russell diagram and even to elliptic galaxies. The scaling is exact for stars with the same M/R-ratio and a good approximation for stars with a different M/R-ratio. The global relaxation oscillation found by Korevaar (1989) is scaled to main sequence stars, a solar coronal hole, cool giants and elliptic galaxies.
Nucleon off-shell structure and the free neutron valence structure from A=3 inclusive electron scattering measurements
Understanding the differences between the distribution of quarks bound in protons and neutrons is key for constraining the mechanisms of SU(6) spin-flavor symmetry breaking in Quantum Chromodynamics (QCD). While vast amounts of proton structure measurements were done, data on the structure of the neutron is much more spars as experiments typically extract the structure of neutrons from measurements of light atomic nuclei using model-dependent corrections for nuclear effects. Recently the MARATHON collaboration performed such an extraction by measuring inclusive deep-inelastic electron-scattering on helium-3 and tritium mirror nuclei where nuclear effects are expected to be similar and thus be suppressed in the helium-3 to tritium ratio. Here we evaluate the model dependence of this extraction by examining a wide range of models including the effect of using instant-form and light-cone nuclear wave functions and several different parameterizations of nucleon modification effects, including those with and without isospin dependence. We find that, while the data cannot differentiate among the different models of nuclear structure and nucleon modification, they consistently prefer a neutron-to-proton structure function ratio of at $x_B \rightarrow 1$ of $\sim 0.4$ with a typical uncertainty ($1\sigma$) of $\sim0.05$ and $\sim0.10$ for isospin-independent and isospin-dependent modification models, respectively. While strongly favoring SU(6) symmetry breaking models based on perturbative QCD and the Schwinger-Dyson equation calculation, the MARATHON data do not completely rule out the scalar di-quark models if an isospin-dependent modification exist.
Reliability Stress-Strength Models for Dependent Observations with Applications in Clinical Trials
We consider the applications of stress-strength models in studies involving clinical trials. When studying the effects and side effects of certain procedures (treatments), it is often the case that observations are correlated due to subject effect, repeated measurements and observing many characteristics simultaneously. We develop maximum likelihood estimator (MLE) and uniform minimum variance unbiased estimator (UMVUE) of the reliability which in clinical trial studies could be considered as the chances of increased side effects due to a particular procedure compared to another. The results developed apply to both univariate and multivariate situations. Also, for the univariate situations we develop simple to use lower confidence bounds for the reliability. Further, we consider the cases when both stress and strength constitute time dependent processes. We define the future reliability and obtain methods of constructing lower confidence bounds for this reliability. Finally, we conduct simulation studies to evaluate all the procedures developed and also to compare the MLE and the UMVUE.
A Flow Rate Dependent 1D Model for Thermally Stratified Hot-Water Energy Storage
Stratified tank models are used to simulate thermal storage in applications such as residential or commercial hot-water storage tanks, chilled-water storage tanks, and solar thermal systems. The energy efficiency of these applications relates to the system components and the level of stratification maintained during various flow events in the tank. One-dimensional (1D) models are used in building energy simulations because of the short computation time but often do not include flow-rate dependent mixing. The accuracy of 1D models for plug flow, plug flow with axial conduction, and two convection eddy-diffusivity models were compared with experimental data sets for discharging a 50-gal residential tank and recharging the tank with hot water from an external hot-water source. A minimum and maximum relationship for the eddy diffusivity factor were found at Re <2100 and >10,000 for recirculation of hot water to the top of the tank and vertical tubes inletting cold water at the bottom. The root mean square error decreased from >4 °C to near 2 °C when considering flow-based mixing models during heating, while the exponential decay of the eddy diffusion results in a root mean square error reduction of 1 °C for cone-shaped diffusers that begin to relaminarize flow at the inlet.
Search for direct top squark pair production in events with one lepton, jets, and missing transverse momentum at 13 TeV with the CMS experiment
A search for direct top squark pair production is presented. The search is based on proton-proton collision data at a center-of-mass energy of 13 TeV recorded by the CMS experiment at the LHC during 2016, 2017, and 2018, corresponding to an integrated luminosity of 137 fb$^{−1}$. The search is carried out using events with a single isolated electron or muon, multiple jets, and large transverse momentum imbalance. The observed data are consistent with the expectations from standard model processes. Exclusions are set in the context of simplified top squark pair production models. Depending on the model, exclusion limits at 95% confidence level for top squark masses up to 1.2 TeV are set for a massless lightest supersymmetric particle, assumed to be the neutralino. For models with top squark masses of 1 TeV, neutralino masses up to 600 GeV are excluded.[graphic not available: see fulltext]
Probing energetic light dark matter with multi-particle tracks signatures at DUNE
The search for relativistic scattering signals of cosmogenic light dark matter at terrestrial detectors has received increasing attention as an alternative approach to probe dark-sector physics. Large-volume neutrino experiments are well motivated for searches of dark matter that interacts very weakly with Standard Model particles and/or that exhibits a small incoming flux. We perform a dedicated signal sensitivity study for a detector similar to the one proposed by the DUNE Collaboration for cosmogenic dark-matter signals resulting from a non-minimal multi-particle dark-sector scenario. The liquid argon time projection chamber technology adopted for the DUNE detectors is particularly suited for searching for complicated signatures owing to good measurement resolution and particle identification, as well as dE/dx measurements to recognize merged tracks. Taking inelastic boosted dark matter as our benchmark scenario that allows for multiple visible particles in the final state, we demonstrate that the DUNE far detectors have a great potential for probing scattering signals induced by relativistic light dark matter. Detector effects and backgrounds have been estimated and taken into account. Model-dependent and model-independent expected sensitivity limits for a DUNE-like detector are presented.
Greenhouse-gas forced changes in the Atlantic meridional overturning circulation and related worldwide sea-level change
Abstract The effect of anthropogenic climate change in the ocean is challenging to project because atmosphere-ocean general circulation models (AOGCMs) respond differently to forcing. This study focuses on changes in the Atlantic Meridional Overturning Circulation (AMOC), ocean heat content ( $$\Delta$$ Δ OHC), and the spatial pattern of ocean dynamic sea level ( $$\Delta \zeta$$ Δ ζ ). We analyse experiments following the FAFMIP protocol, in which AOGCMs are forced at the ocean surface with standardised heat, freshwater and momentum flux perturbations, typical of those produced by doubling $$\hbox {CO}_{{2}}$$ CO 2 . Using two new heat-flux-forced experiments, we find that the AMOC weakening is mainly caused by and linearly related to the North Atlantic heat flux perturbation, and further weakened by a positive coupled heat flux feedback. The quantitative relationships are model-dependent, but few models show significant AMOC change due to freshwater or momentum forcing, or to heat flux forcing outside the North Atlantic. AMOC decline causes warming at the South Atlantic-Southern Ocean interface. It does not strongly affect the global-mean vertical distribution of $$\Delta$$ Δ OHC, which is dominated by the Southern Ocean. AMOC decline strongly affects $$\Delta \zeta$$ Δ ζ in the North Atlantic, with smaller effects in the Southern Ocean and North Pacific. The ensemble-mean $$\Delta \zeta$$ Δ ζ and $$\Delta$$ Δ OHC patterns are mostly attributable to the heat added by the flux perturbation, with smaller effects from ocean heat and salinity redistribution. The ensemble spread, on the other hand, is largely due to redistribution, with pronounced disagreement among the AOGCMs.
Feasibility Study of Developing Alternative Common-Cause Failure Model for Event Assessment
This report is a revision of the original report, INL/LTD-14-33376. The original report, which was a limited distribution to the Nuclear Regulatory Commission (NRC) only and not available to the public, was revised as this report for public distribution. The report presents the feasibility study of alternative common-cause failure (CCF) models that could be used for event assessment in the Standardized Plant Analysis Risk (SPAR) models. The current CCF model used by the SPAR models is the alpha factor model. The proposed alternative CCF models are the partial alpha factor model and general dependency model. These alternative CCF models are reviewed and compared to the current alpha factor model, with the partial alpha factor model being identified as the preferred CCF model that could be used for event assessment in the SPAR models.
Ocean circulation modeling by use of radar altimeter data
The project will investigate the use of radar altimetry (RA) data in the determination of the ocean circulation models. RA data will be used to verify prognostic experiments of the steady state and seasonal cycle of large-scale circulation models and the statistical steady state of eddy-resolving models. The data will serve as initial and update conditions in data assimilation experiments and as constraints in inverse calculations. The aim of the project is a better understanding of ocean physics, the determination and mapping of ocean currents, and a contribution to the establishment of ocean circulation models for climate studies. The goal of the project is to use satellite radar altimetry data for improving our knowledge of ocean circulation both in a descriptive sense and through the physics that govern the circulation state. The basic tool is a series of ocean circulation models. Depending on the model, different techniques will be applied to incorporate the RA data.
Seasonal-to-Interannual Variability and Land Surface Processes
Atmospheric chaos severely limits the predictability of precipitation on subseasonal to interannual timescales. Hope for accurate long-term precipitation forecasts lies with simulating atmospheric response to components of the Earth system, such as the ocean, that can be predicted beyond a couple of weeks. Indeed, seasonal forecasts centers now rely heavily on forecasts of ocean circulation. Soil moisture, another slow component of the Earth system, is relatively ignored by the operational seasonal forecasting community. It is starting, however, to garner more attention. Soil moisture anomalies can persist for months. Because these anomalies can have a strong impact on evaporation and other surface energy fluxes, and because the atmosphere may respond consistently to anomalies in the surface fluxes, an accurate soil moisture initialization in a forecast system has the potential to provide additional forecast skill. This potential has motivated a number of atmospheric general circulation model (AGCM) studies of soil moisture and its contribution to variability in the climate system. Some of these studies even suggest that in continental midlatitudes during summer, oceanic impacts on precipitation are quite small relative to soil moisture impacts. The model results, though, are strongly model-dependent, with some models showing large impacts and others showing almost none at all. A validation of the model results with observations thus naturally suggests itself, but this is exceedingly difficult. The necessary contemporaneous soil moisture, evaporation, and precipitation measurements at the large scale are virtually non-existent, and even if they did exist, showing statistically that soil moisture affects rainfall would be difficult because the other direction of causality - wherein rainfall affects soil moisture - is unquestionably active and is almost certainly dominant. Nevertheless, joint analyses of observations and AGCM results do reveal some suggestions of land-atmosphere feedback in the observational record, suggestions that soil moisture can affect precipitation over seasonal timescales and across certain large continental areas. The strength of this observed feedback in nature is not large but is still significant enough to be potentially useful, e.g., for forecasts. This talk will address all of these issues. It will begin with a brief overview of land surface modeling in atmospheric models but will then focus on recent research - using both observations and models - into the impact of land surface processes on variability in the climate system.
Statistical Analysis of Factors Riving Surface Ozone Variability over Continental South Africa
Statistical relationships between surface ozone (O3) concentration, precursor species and meteorological conditions in continental South Africa were examined from data obtained from measurement stations in north-eastern South Africa. Three multivariate statistical methods were applied in the investigation, i.e. multiple linear regression (MLR), principal component analysis (PCA) and –regression (PCR), and generalised additive model (GAM) analysis. The daily maximum 8-h moving average O3 concentrations were considered in these statistical models (dependent variable). MLR models indicated that meteorology and precursor species concentrations are able to explain ~50% of the variability in daily maximum O3 levels. MLR analysis revealed that atmospheric carbon monoxide (CO), temperature and relative humidity were the strongest factors affecting the daily O3 variability. In summer, daily O3 variances were mostly associated with relative humidity, while winter O3 levels were mostly linked to temperature and CO. PCA indicated that CO, temperature and relative humidity were not strongly collinear. GAM also identified CO, temperature and relative humidity as the strongest factors affecting the daily variation of O3. Partial residual plots found that temperature, radiation and nitrogen oxides most likely have a non-linear relationship with O3,while the relationship with relative humidity and CO is probably linear. An inter-comparison between O3 levels modelled with the three statistical models compared to measured O3 concentrations showed that the GAM model offered a slight improvement over the MLR model. These findings emphasise the critical role of regional-scale O3 precursors coupled with meteorological conditions in daily variances of O3 levels in continental South Africa.
Search for diphoton resonances in the 66 to 110 GeV mass range using pp collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector
A search is performed for light, spin-0 bosons decaying into two photons in the 66 to 110 GeV mass range, using 140 fb −1 of proton-proton collisions at $\sqrt{s}$ = 13 TeV produced by the Large Hadron Collider and collected by the ATLAS detector. Multivariate analysis techniques are used to define event categories that improve the sensitivity to new resonances beyond the Standard Model. A model-independent search for a generic spin-0 particle and a model-dependent search for an additional low-mass Higgs boson are performed in the diphoton invariant mass spectrum. No significant excess is observed in either search. Mass-dependent upper limits at the 95% confidence level are set in the model-independent scenario on the fiducial cross-section times branching ratio into two photons in the range of 8 fb to 53 fb. Similarly, in the model-dependent scenario upper limits are set on the total cross-section times branching ratio into two photons as a function of the Higgs boson mass in the range of 19 fb to 102 fb.
Estimation With Range Depended Sensor Model
This paper focuses on the improvement of object detection accuracy taking into account the sensor’s reading degradation as the relative range increases. The approach is based on the assumption that range measurement error depends on the actual range. Specifically, we model the measurement error as a proportional to the actual range term plus a zero-mean, Gaussian distributed, and uncorrelated process. The tracking problem is considered in a mixed continuous-discrete time domain, where the target dynamics is in continuous-time and the measurements are in discrete-time, which is an optimal choice in many tracking and navigation applications. We adopt a commonly used continuous time coordinate-uncoupled white-noise acceleration model for a point object to describe the target motion, and use Extended Kalman Filter (EKF) framework to estimate the target's state and the unknown proportionality coefficient.
Estimation With Range Dependent Sensor Model
This paper focuses on the improvement of target tracking accuracy taking into account the sensor’s reading degradation as the relative range increases. The approach is based on the assumption that range measurement error depends on the actual range. Specifically, we model the measurement error as a proportional to the actual range term plus a zero-mean, Gaussian distributed, and uncorrelated process. We present two approaches to tracking a target with constant velocity model. The first approach uses Extended Kalman Filter (EKF) framework to estimate target's states and the unknown proportionality coefficient with linearization on the observation model on the predicted states at each time step. The second one uses a measurement conversion method to estimate the target's scaled by the unknown constant term states, which is shown to be unbiased. This conversion results in linear state and observation models, hence the standard Kalman filter can be applied. The actual target state is computed by application of back scaling with the estimate of unknown scale factor. We evaluate the approaches in desktop simulations.