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Results for “Physics 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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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

An Integrated Snow Radiance and Snow Physics Modeling Framework for Cold Land Surface Modeling

Recent developments in forward radiative transfer modeling and physical land surface modeling are converging to allow the assembly of an integrated snow/cold lands modeling framework for land surface modeling and data assimilation applications. The key elements of this framework include: a forward radiative transfer model (FRTM) for snow, a snowpack physical model, a land surface water/energy cycle model, and a data assimilation scheme. Together these form a flexible framework for self-consistent remote sensing and water/energy cycle studies. In this paper we will describe the elements and the integration plan. Each element of this framework is modular so the choice of element can be tailored to match the emphasis of a particular study. For example, within our framework, four choices of a FRTM are available to simulate the brightness temperature of snow: Two models are available to model the physical evolution of the snowpack and underlying soil, and two models are available to handle the water/energy balance at the land surface. Since the framework is modular, other models-physical or statistical--can be accommodated, too. All modules will operate within the framework of the Land Information System (LIS), a land surface modeling framework with data assimilation capabilities running on a parallel-node computing cluster at the NASA Goddard Space Flight Center. The advantages of such an integrated modular framework built on the LIS will be described through examples-e.g., studies to analyze snow field experiment observations, and simulations of future satellite missions for snow and cold land processes.

Kim, Edward J.↗
NASA NTRS2024

Enabling Advanced Snow Physics within Land Surface Models Through an Interoperable Model-Physics Coupling Framework

Accurate estimation of snow accumulation and melt is a critical part of decision-making in snow-dominated watersheds. In this study, we demonstrate a flexible methodology to couple a detailed snow model, Crocus, separately to two different land surface models (LSMs), Noah-MP and Noah. The original LSMs and the coupled models (Noah-MP-Crocus and Noah-Crocus) are used to simulate snow depth, snow water equivalent, and other water and energy states and fluxes. The results of simulations are compared against a wide range of independent gridded and point scale reference datasets. Our results show that coupling the detailed snow model, Crocus, with the LSMs improves the snow depth and snow water equivalent relative to independent observations. Overall, larger improvements are obtained with coupling Crocus to the Noah LSM, with the coupled Noah-Crocus configuration reducing the RMSE and bias of snow depth from 2-12% and 57-75%, respectively, relative to Snow Data Assimilation System (SNODAS) and snow product from the University of Arizona. On the other hand, smaller improvements are obtained by coupling Crocus with Noah-MP. The Coupled Noah-MP-Crocus reduces the snow depth bias but slightly degrades the RMSE of snow depth and snow water equivalent. The corresponding impacts in other water budget terms such as evapotranspiration, soil moisture, and streamflow, however, are mixed, pointing to the significant need to improve the coupling assumptions of these processes within land models. Overall, the interoperable coupling framework demonstrated here offers the opportunity to include more detailed snow physics and processes, and to advance data assimilation systems through improved exploitation of information from snow remote sensing instruments.

Snow model↗
NASA NTRS1980

A physical model of Titan's clouds

A physical model of the formation and growth of aerosols in the atmosphere of Titan has been constructed in light of the observed correlation between variations in Titan's albedo and the sunspot cycle. The model was developed to fit spectral observations of deep methane bands, pressures, temperature distributions, and cloud structure, and is based on a one-dimensional physical-chemical model developed to simulate the earth's stratospheric aerosol layer. Sensitivity tests reveal the model parameters to be relatively insensitive to particle shape but sensitive to particle density, with high particle densities requiring larger aerosol mass production rates to produce compatible clouds. Solution of the aerosol continuity equations for particles of sizes 13 A to about 3 microns indicates the importance of a warm upper atmosphere and a high-altitude mass injection layer, and the production of aerosols at very low aerosol optical depths. Limits are obtained for the chemical production of aerosol mass and the eddy diffusion coefficient, and it is found that an increase in mass input causes a decrease in mean particle size.

Toon, O. B.↗
NASA NTRS2008

A Physical Model to Estimate Snowfall over Land using AMSU-B Observations

In this study, we present an improved physical model to retrieve snowfall rate over land using brightness temperature observations from the National Oceanic and Atmospheric Administration's (NOAA) Advanced Microwave Sounder Unit-B (AMSU-B) at 89 GHz, 150 GHz, 183.3 +/- 1 GHz, 183.3 +/- 3 GHz, and 183.3 +/- 7 GHz. The retrieval model is applied to the New England blizzard of March 5, 2001 which deposited about 75 cm of snow over much of Vermont, New Hampshire, and northern New York. In this improved physical model, prior retrieval assumptions about snowflake shape, particle size distributions, environmental conditions, and optimization methodology have been updated. Here, single scattering parameters for snow particles are calculated with the Discrete-Dipole Approximation (DDA) method instead of assuming spherical shapes. Five different snow particle models (hexagonal columns, hexagonal plates, and three different kinds of aggregates) are considered. Snow particle size distributions are assumed to vary with air temperature and to follow aircraft measurements described by previous studies. Brightness temperatures at AMSU-B frequencies for the New England blizzard are calculated using these DDA calculated single scattering parameters and particle size distributions. The vertical profiles of pressure, temperature, relative humidity and hydrometeors are provided by MM5 model simulations. These profiles are treated as the a priori data base in the Bayesian retrieval algorithm. In algorithm applications to the blizzard data, calculated brightness temperatures associated with selected database profiles agree with AMSU-B observations to within about +/- 5 K at all five frequencies. Retrieved snowfall rates compare favorably with the near-concurrent National Weather Service (NWS) radar reflectivity measurements. The relationships between the NWS radar measured reflectivities Z(sub e) and retrieved snowfall rate R for a given snow particle model are derived by a histogram matching technique. All of these Z(sub e)-R relationships fall in the range of previously established Z(sub e)-R relationships for snowfall. This suggests that the current physical model developed in this study can reliably estimate the snowfall rate over land using the AMSU-B measured brightness temperatures.

Kim, Min-Jeong↗
NASA NTRS2006

The Goddard Snow Radiance Assimilation Project: An Integrated Snow Radiance and Snow Physics Modeling Framework for Snow/cold Land Surface Modeling

Microwave-based retrievals of snow parameters from satellite observations have a long heritage and have so far been generated primarily by regression-based empirical "inversion" methods based on snapshots in time. Direct assimilation of microwave radiance into physical land surface models can be used to avoid errors associated with such retrieval/inversion methods, instead utilizing more straightforward forward models and temporal information. This approach has been used for years for atmospheric parameters by the operational weather forecasting community with great success. Recent developments in forward radiative transfer modeling, physical land surface modeling, and land data assimilation are converging to allow the assembly of an integrated framework for snow/cold lands modeling and radiance assimilation. The objective of the Goddard snow radiance assimilation project is to develop such a framework and explore its capabilities. The key elements of this framework include: a forward radiative transfer model (FRTM) for snow, a snowpack physical model, a land surface water/energy cycle model, and a data assimilation scheme. In fact, multiple models are available for each element enabling optimization to match the needs of a particular study. Together these form a modular and flexible framework for self-consistent, physically-based remote sensing and water/energy cycle studies. In this paper we will describe the elements and the integration plan. All modules will operate within the framework of the Land Information System (LIS), a land surface modeling framework with data assimilation capabilities running on a parallel-node computing cluster. Capabilities for assimilation of snow retrieval products are already under development for LIS. We will describe plans to add radiance-based assimilation capabilities. Plans for validation activities using field measurements will also be discussed.

Kim, E.↗
NASA NTRS1982

Mathematical and physical modelling of materials processing

Mathematical and physical modeling of turbulence phenomena in metals processing, electromagnetically driven flows in materials processing, gas-solid reactions, rapid solidification processes, the electroslag casting process, the role of cathodic depolarizers in the corrosion of aluminum in sea water, and predicting viscoelastic flows are described.

Source record↗
NASA NTRS1992

Physical Models In GPSOMC Software

Report desribes physical models incorporated into GPSOMC, (modeling module of GIPSY software) which processes geodetic measurements in Global Positioning Satellite (GPS) system. Models describe spacecraft orbits and motions of receivers fixed to Earth. Supplies apriori values of computed observables and partial derivatives of computed observables with respect to parameters of models. Describes portion of software modeling locations of receivers and motions of whole Earth and computes observables and partial derivatives. Corrected, expanded, and updated version of JPL Publication 87-21, September 15, 1987.

Sovers, Ojars J.↗
NASA NTRS2015

Advanced Ground Systems Maintenance Physics Models For Diagnostics Project

The project will use high-fidelity physics models and simulations to simulate real-time operations of cryogenic and systems and calculate the status/health of the systems. The project enables the delivery of system health advisories to ground system operators. The capability will also be used to conduct planning and analysis of cryogenic system operations. This project will develop and implement high-fidelity physics-based modeling techniques tosimulate the real-time operation of cryogenics and other fluids systems and, when compared to thereal-time operation of the actual systems, provide assessment of their state. Physics-modelcalculated measurements (called “pseudo-sensors”) will be compared to the system real-timedata. Comparison results will be utilized to provide systems operators with enhanced monitoring ofsystems' health and status, identify off-nominal trends and diagnose system/component failures.This capability can also be used to conduct planning and analysis of cryogenics and other fluidsystems designs. This capability will be interfaced with the ground operations command andcontrol system as a part of the Advanced Ground Systems Maintenance (AGSM) project to helpassure system availability and mission success. The initial capability will be developed for theLiquid Oxygen (LO2) ground loading systems.

System health↗
NASA NTRS1982

Channel and physical models of the Jovian subnebula

A semiempirical physical model of the Jovian subnebula was developed by analogy with the primitive solar nebula itself. The chemical aspects of this model are developed according to the principles developed in the study of the thermochemistry and gas kinetic behavior of the solar nebula, but with important modifications to take into account the higher pressures and densities in the Jovian subnebula. The bulk compositions and densities of the inner satellites of Jupiter are calculated. It is proposed that Europa differs from Io chiefly in that in has suffered a less severe thermal history. The general features of this model are applicable with minor modification to the systems of Saturn and Uranus.

Lewis, J. S.↗
NASA NTRS1986

Experimental evidence for modifying the current physical model for ice accretion on aircraft surfaces

Closeup movies, still photographs, and other experimental data suggest that the current physical model for ice accretion needs significant modification. At aircraft airspeeds there was no flow of liquid over the surface of the ice after a short initial flow, even at barely subfreezing temperatures. Instead, there were very large stationary drops on the ice surface that lose water from their bottoms by freezing and replenish their liquid by catching the microscopic cloud droplets. This observation disagrees with the existing physical model, which assumes there is a thin liquid film continuously flowing over the ice surface. With no such flow, the freezing-fraction concept of the model fails when a mass balance is performed on the surface water. Rime ice does, as the model predicts, form when the air temperature is low enough to cause the cloud droplets to freeze almost immediately on impact. However, the characteristic shapes of horn-glaze ice or rime ice are primarily caused by the ice shape affecting the airflow locally and consequently the droplet catch and the resulting ice shape. Ice roughness greatly increases the heat transfer coefficient, stops the movement of drops along the surface, and may also affect the airflow initially and thereby the droplet catch. At high subreezing temperatures the initial flow and shedding of surface drops have a large effect on the ice shape. At the incipient freezing limit, no ice forms.

Olsen, W.↗
NASA NTRS2013

The Importance of Physical Models for Deriving Dust Masses and Grain Size Distributions in Supernova Ejecta. I. Radiatively Heated Dust in the Crab Nebula

Recent far-infrared (IR) observations of supernova remnants (SNRs) have revealed significantly large amounts of newly condensed dust in their ejecta, comparable to the total mass of available refractory elements. The dust masses derived from these observations assume that all the grains of a given species radiate at the same temperature, regardless of the dust heating mechanism or grain radius. In this paper, we derive the dust mass in the ejecta of the Crab Nebula, using a physical model for the heating and radiation from the dust. We adopt a power-law distribution of grain sizes and two different dust compositions (silicates and amorphous carbon), and calculate the heating rate of each dust grain by the radiation from the pulsar wind nebula. We find that the grains attain a continuous range of temperatures, depending on their size and composition. The total mass derived from the best-fit models to the observed IR spectrum is 0.019-0.13 Solar Mass, depending on the assumed grain composition. We find that the power-law size distribution of dust grains is characterized by a power-law index of 3.5-4.0 and a maximum grain size larger than 0.1 micron. The grain sizes and composition are consistent with what is expected for dust grains formed in a Type IIP supernova (SN). Our derived dust mass is at least a factor of two less than the mass reported in previous studies of the Crab Nebula that assumed more simplified two-temperature models. These models also require a larger mass of refractory elements to be locked up in dust than was likely available in the ejecta. The results of this study show that a physical model resulting in a realistic distribution of dust temperatures can constrain the dust properties and affect the derived dust masses. Our study may also have important implications for deriving grain properties and mass estimates in other SNRs and for the ultimate question of whether SNe are major sources of dust in the Galactic interstellar medium and in external galaxies.

Importance↗
NASA NTRS2020

Improved Groundwater Table and L-Band Brightness Temperature Estimates for Northern Hemisphere Peatlands Using New Model Physics and SMOS Observations in a Global Data Assimilation Framework

There is an urgent need to include northern peatland hydrology in global Earth system models to better understand land-atmosphere interactions and sensitivities of peatland functions to climate change, and, ultimately, to improve climate change predictions. In this study, we introduced for the first time peatland-specific model physics into an assimilation scheme for L-band brightness temperature (Tb) data from the Soil Moisture Ocean Salinity (SMOS) mission to improve groundwater table estimates. We conducted two sets of model-only and data assimilation experiments using the Catchment Land Surface Model (CLSM), applying (over peatlands only) in one of them a peatland-specific adaptation (PEATCLSM). The evaluation against in-situ measurements of peatland groundwater table depth indicates the superiority of PEATCLSM model physics and additionally improved performance after assimilating SMOS Tb observations. The better performance of PEATCLSM over nearly all Northern Hemisphere peatlands is further supported by the better agreement between SMOS Tb observations and Tb estimates from the model-only and data assimilation runs. Within the data assimilation scheme, PEATCLSM reduces Tb observation-minus-forecast residuals and leads to reduced data assimilation updates of water storage components and, thus, reduced water budget imbalances in the assimilation system.

SMOS↗
NASA NTRS1994

Physical Models of Cognition

This paper presents and discusses physical models for simulating some aspects of neural intelligence, and, in particular, the process of cognition. The main departure from the classical approach here is in utilization of a terminal version of classical dynamics introduced by the author earlier. Based upon violations of the Lipschitz condition at equilibrium points, terminal dynamics attains two new fundamental properties: it is spontaneous and nondeterministic. Special attention is focused on terminal neurodynamics as a particular architecture of terminal dynamics which is suitable for modeling of information flows. Terminal neurodynamics possesses a well-organized probabilistic structure which can be analytically predicted, prescribed, and controlled, and therefore which presents a powerful tool for modeling real-life uncertainties. Two basic phenomena associated with random behavior of neurodynamic solutions are exploited. The first one is a stochastic attractor ; a stable stationary stochastic process to which random solutions of a closed system converge. As a model of the cognition process, a stochastic attractor can be viewed as a universal tool for generalization and formation of classes of patterns. The concept of stochastic attractor is applied to model a collective brain paradigm explaining coordination between simple units of intelligence which perform a collective task without direct exchange of information. The second fundamental phenomenon discussed is terminal chaos which occurs in open systems. Applications of terminal chaos to information fusion as well as to explanation and modeling of coordination among neurons in biological systems are discussed. It should be emphasized that all the models of terminal neurodynamics are implementable in analog devices, which means that all the cognition processes discussed in the paper are reducible to the laws of Newtonian mechanics.

Zak, Michail↗
NASA NTRS1994

Physical Models of Cognition

This paper presents and discusses physical models for simulationg some aspects of neural intelligence, and in particular, the process of cognition.

neural↗