Search NASA⌕ Search

SEARCH · Search NASA

Results for “model choice”

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.

At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

On the coalescence-dispersion modeling of turbulent molecular mixing

The general coalescence-dispersion (C/D) closure provides phenomenological modeling of turbulent molecular mixing. The models of Curl and Dopazo and O'Brien appear as two limiting C/D models that bracket the range of results one can obtain by various models. This finding is used to investigate the sensitivtiy of the results to the choice of the model. Inert scalar mixing is found to be less model-sensitive than mixing accompanied by chemical reaction. Infinitely fast chemistry approximation is used to relate the C/D approach to Toor's earlier results. Pure mixing and infinite rate chemistry calculations are compared to study further a recent result of Hsieh and O'Brien who found that higher concentration moments are not sensitive to chemistry.

Givi, Peyman↗
DOE OSTI2020

Transported PDF Modeling of Compressible Turbulent Reactive Flows by using the Eulerian Monte Carlo Fields Method

Although the transported probability density function (PDF) method has been developed for decades, its application has been mainly focused on the low-Mach number flow problems. This work extends the transported PDF method to compressible flow problems. The Eulerian Monte Carlo fields (EMCF) solution method is employed to solve the transported PDF equation for compressible flow problems. A pseudo stagnation enthalpy is introduced and its stochastic partial differential equation is derived to ensure total energy conservation numerically. A new mixing model called interaction by partial exchange with mean (IPEM) is introduced to expand the available choices of mixing models for the EMCF method. The consistency of the EMCF method is examined for solving the transported PDF equation. Numerical implementation details are discussed, such as the density coupling between the compressible flow solver and the EMCF solver, discretization schemes for the mixing terms and the stochastic terms. The implemented compressible flow solver coupled with the EMCF solver is verified and validated in a series of test cases with increasing level of complexity, ranging from a statistically one-dimensional turbulent mixing layer to a self-excited resonance model rocket combustor. It is observed that in general with the increase of compressibility, there is an increase in the sensitivity of the modeling results to the different models and algorithms. This makes it necessary to develop a thorough understanding of the model sensitivity in order to develop a robust and accurate simulation solver for highly compressible turbulent reactive flows. The thermo-acoustic instability inside the model rocket combustor case is captured reasonably, which demonstrates the overall capability of the developed compressible turbulent combustion solver based on the transported PDF method.

42 ENGINEERING↗
DOE OSTI2021

CFD analysis of a solid pin-fueled small modular fluoride salt-cooled reactor

A computational fluid dynamics (CFD) study is performed for the pre-conceptual solid pin-fueled small modular fluoride salt-cooled reactor developed by the Oak Ridge National Laboratory (ORNL). The solid and fluid regions in a 1/12th section of a fuel assembly are modeled. The solid region includes fuel and non-fuel pins, with heat generation in the active region of the fuel pins. Two different power profiles, uniform and center-peaked, are considered. The FLiBe coolant flows from the bottom to the top of the core, parallel to the bank of fuel and non-fuel pins. The k-ω shear-stress transport (SST) model is chosen as the baseline turbulence model. The effects of grid refinement, inlet turbulence specification, and turbulence models on the temperature and pressure drop predictions are studied. Turbulence model sensitivity is investigated by comparing the results from the k-ω SST model with other two-equation models (k-ω baseline or BSL and k-ε realizable) as well as with anisotropic Reynolds stress transport models (linear pressure-strain and stress-BSL). Here the results show that the choice of turbulence model has a significant impact on the pin temperature and bundle pressure drop predictions. The results from the baseline turbulence model show good agreement between the fuel pin temperatures and bundle pressure drop values predicted using a subchannel model previously developed by the authors of the present study.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2020

CMIP6 Historical Simulations (1850‐2014) with GISS‐E2.1

Simulations of the CMIP6 historical period 1850‐‐2014, characterized by the emergence of anthropogenic climate drivers like greenhouse gases, are presented for different configurations of the NASA Goddard Institute for Space Studies (GISS) Earth System ModelE2.1. The GISS‐E2.1 ensembles are more sensitive to forcing than their CMIP5 predecessors (GISS‐E2), but warm less during recent decades due to reduced total forcing. This forcing reduction is attributed to an increase in longwave opacity in pre‐industrial simulations, resulting in an atmosphere less sensitive to further increases in opacity that result from rising greenhouse gas concentrations. This demonstrates the importance of the base climatology to forcing and forced climate trends. Most model versions match observed temperature trends since 1979 from the ocean to the stratosphere. The choice of ocean model is important to the transient climate response, as found in CMIP5 GISS‐E2: the model that more efficiently exports heat to the deep ocean shows a smaller rise in tropospheric temperature. Model sea level rise over the historical period is traced to excessive drawdown of aquifers to meet irrigation demand with a smaller contribution from thermal expansion. This shows how fully coupled models can provide indirect observational constraints upon forcing, in this case, constraining irrigation rates with observed sea level changes. The overall agreement of GISS‐E2.1 with observed trends is familiar from evaluation of its predecessors, as is the conclusion that these trends are almost entirely anthropogenic in origin.

NASA Goddard Institute for Space Studies (GISS) Ea↗
DOE OSTI2021

Dependence of dark matter - electron scattering on the galactic dark matter velocity distribution

The rate of dark matter-electron scattering depends on the underlying velocity distribution of the dark matter halo. Importantly, dark matter-electron scattering is particularly sensitive to the high-velocity tail, which differs significantly amongst the various dark matter halo models. In this work, we summarize the leading halo models and discuss the various parameters which enter them. We recommend updated values for these parameters based on recent studies and measurements. Furthermore, we quantify the dependence of the dark matter-electron scattering rate on the choice of halo model and parameters, and demonstrate how these choices propagate to the predicted cross-section limits. The rate is most sensitive to changes in the circular velocity v 0 ; in silicon targets, we find that the changes in the rate predictions can range from Script $\mathcal{O}$(0.01%) to Script $\mathcal{O}$(100%) for contact interactions and Script $\mathcal{O}$(10%) to Script $\mathcal{O}$(100%) for long-range interactions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Physics consistent machine learning framework for inverse modeling with applications to ICF capsule implosions

In high energy density physics (HEDP) and inertial confinement fusion (ICF), predictive modeling is complicated by uncertainty in parameters that characterize various aspects of the modeled system, such as those characterizing material properties, equation of state (EOS), opacities, and initial conditions. Typically, however, these parameters are not directly observable. What is observed instead is a time sequence of radiographic projections using X-rays. In this work, we define a set of sparse hydrodynamic features derived from the outgoing shock profile and outer material edge, which can be obtained from radiographic measurements, to directly infer such parameters. Our machine learning (ML)-based methodology involves a pipeline of two architectures, a radiograph-to-features network (R2FNet) and a features-to-parameters network (F2PNet), that are trained independently and later combined to approximate a posterior distribution for the parameters from radiographs. We show that the machine learning architectures are able to accurately infer initial conditions and EOS parameters, and that the estimated parameters can be used in a hydrodynamics code to obtain density fields, shocks, and material interfaces that satisfy thermodynamic and hydrodynamic consistency. Finally, we demonstrate that features resulting from an unknown EOS model can be successfully mapped onto parameters of a chosen analytical EOS model, implying that network predictions are learning physics, with a degree of invariance to the underlying choice of EOS model. To the best of our knowledge, our framework is the first demonstration of recovering both thermodynamic and hydrodynamic consistent density fields from noisy radiographs.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1990

Simulation and analysis of flexibly jointed manipulators

Modeling, simulation, and analysis of robot manipulators with non-negligible joint flexibility are studied. A recursive Newton-Euler model of the flexibly jointed manipulator is developed with many advantages over the traditional Lagrange-Euler methods. The Newton-Euler approach leads to a method for the simulation of a flexibly jointed manipulator in which the number of computations grows linearly with the number of links. Additionally, any function for the flexibility between the motor and link may be used permitting the simulation of nonlinear effects, such as backlash, in a uniform manner for all joints. An analysis of the control problems for flexibly jointed manipulators is presented by converting the Newton-Euler model to a Lagrange-Euler form. The detailed structure available in the model is used to examine linearizing controllers and shows the dependency of the control on the choice of flexible model and structure of the manipulator.

Murphy, Steve H.↗
NASA NTRS2010

Numerical Simulation of Vitiation Effects on a Hydrogen-Fueled Dual-Mode Scramjet

The Wind-US computational fluid dynamics (CFD) flow solver was used to simulate dual-mode direct-connect ramjet/scramjet engine flowpath tests conducted in the University of Virginia (UVa) Supersonic Combustion Facility (SCF). The objective was to develop a computational capability within Wind-US to aid current hypersonic research and provide insight to flow as well as chemistry details that are not resolved by instruments available. Computational results are compared with experimental data to validate the accuracy of the numerical modeling. These results include two fuel-off non-reacting and eight fuel-on reacting cases with different equivalence ratios, split between one set with a clean (non-vitiated) air supply and the other set with a vitiated air supply (12 percent H2O vapor). The Peters and Rogg hydrogen-air chemical kinetics model was selected for the scramjet simulations. A limited sensitivity study was done to investigate the choice of turbulence model and inviscid flux scheme and led to the selection of the k-epsilon model and Harten, Lax and van Leer (for contact waves) (HLLC) scheme for general use. Simulation results show reasonably good agreement with experimental data and the overall vitiation effects were captured.

Vyas, Manan A.↗
DOE OSTI2019

Structure and Performance of GFDL's CM4.0 Climate Model

We illustrate the Geophysical Fluid Dynamics Laboratory's CM4.0 physical climate model, with emphasis on those aspects that may be of particular importance to users of this model and its simulations. The model is built with the AM4.0/LM4.0 atmosphere/land model and OM4.0 ocean model. Topics include the rationale for key choices made in the model formulation, the stability as well as drift of the preindustrial control simulation, and comparison of key aspects of the historical simulations with observations from recent decades. Notable achievements include the relatively small biases in seasonal spatial patterns of top-of-atmosphere fluxes, surface temperature, and precipitation; reduced double Intertropical Convergence Zone bias; dramatically improved representation of ocean boundary currents; a high-quality simulation of climatological Arctic sea ice extent and its recent decline; and excellent simulation of the El Niño-Southern Oscillation spectrum and structure. Areas of concern include inadequate deep convection in the Nordic Seas; an inaccurate Antarctic sea ice simulation; precipitation and wind composites still affected by the equatorial cold tongue bias; muted variability in the Atlantic Meridional Overturning Circulation; strong 100 year quasiperiodicity in Southern Ocean ventilation; and a lack of historical warming before 1990 and too rapid warming thereafter due to high climate sensitivity and strong aerosol forcing, in contrast to the observational record. Overall, CM4.0 scores very well in its fidelity against observations compared to the Coupled Model Intercomparison Project Phase 5 generation in terms of both mean state and modes of variability and should prove a valuable new addition for analysis across a broad array of applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Characterization of uncertainties in electron-argon collision cross sections

Abstract The predictive capability of a plasma discharge model depends on accurate representations of electron-impact collision cross sections, which determine the corresponding reaction rates and electron transport properties. The values of cross sections can be known only approximately either through experiments or simulations and are thus subject to uncertainties. Quantifying the uncertainties in plasma simulations allows us to assess the reliability of simulations and to provide a basis for interpreting discrepancies between simulations and experiments. For such uncertainty quantification of plasma simulations, it is essential to quantify the uncertainties of the underlying cross sections. Although much effort has been committed to calibrate the cross section values, their uncertainties are not well investigated. We characterize uncertainties in electron-argon atom collision cross sections using a Bayesian framework. Six collision processes—elastic momentum transfer, ionization, and four excitations—are characterized with semi-empirical models, which effectively capture the features important to the macroscopic properties of the plasma. A probability model for the uncertain parameters of these semi-empirical models is developed. Specifically, a Gaussian-process likelihood model is proposed to capture discrepancies among data sets, as well as the model-form inadequacies of the semi-empirical models. Two other likelihood models are compared with the proposed Gaussian-process model, to illustrate the importance of the choice of the likelihood model. The cross section models are calibrated using the electron-beam experiments and ab-inito quantum simulations. The resulting calibrated uncertainties capture well the scattering among the data sets. The calibrated cross section models are further validated against swarm-parameter experiments and zero-dimensional Boltzmann equation simulations of widely used cross section datasets.

Chung, Seung Whan (ORCID:0000000302501549)↗
DOE OSTI2021

How AI Predicts the Untrained and Unseen

Focus Area: Model predictability improvements (Primary); Data optimization (secondary); Data complexity insights (secondary). The Scientific Challenge: If we believe that a future under extreme conditions will look very differently from today, we can likely agree that ML/AI models trained on past and present datasets will not be adequate to make reliable predictions into the future. This is true for water cycling, as well as biogeochemistry and other Earth system components and behaviors. Additionally, ML/AI models are inherently non-physical. Despite the flourishing success of ML/AI in many applications, such as computer vision, natural language process, and gaming, even the most sophisticated AI models don’t understand the very basic physical laws. Therefore, a natural question is: Can we trust ML/AI based predictions of Earth system behaviors that are fundamentally driven by physical laws? So, are physics models with meticulous process representation a better choice? Not exactly. Physical models, when firmly rooted in first principles, work great at predicting behaviors of systems with a well-defined set of boundary conditions and variables. However, as a complex system, the number of parameters and the degree of complexity and dynamics in processes, coupling, and scale dependent emergent behaviors make the Earth system behaviors very challenging to predict with physical models composed of deterministic laws. In addition, due to the lack of fundamental understandings, physical models often implement empirical correlations derived from observations with biases from locality of data generation. Because correlation is not necessarily causation or comply with first principles, scaling of model predictions beyond locality is often invalid. Beyond the limitation of models, physics or AI, our knowledge of the Earth system is limited by the lack of observational technologies and resources. Insufficient data density, dimensionality and diversity only offer a sliced (or projected) view of the Earth system, e.g. Plato’s Cave analogy, limiting our capability to better understand and represent fundamental processes in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Ocean Surface Flux Algorithm Effects on Earth System Model Energy and Water Cycles

Earth system models parameterize ocean surface fluxes of heat, moisture, and momentum with empirical bulk flux algorithms, which introduce biases and uncertainties into simulations. We investigate the atmosphere and ocean model sensitivity to algorithm choice in the Energy Exascale Earth System Model (E3SM). Flux differences between algorithms are larger in atmosphere simulations (where wind speeds can vary) than ocean simulations (where wind speeds are fixed by forcing data). Surface flux changes lead to global scale changes in the energy and water cycles, notably including ocean heat uptake and global mean precipitation rates. Compared to the control algorithm, both COARE and University of Arizona (UA) algorithms reduce global mean precipitation and top of atmosphere radiative biases. Further, UA may slightly reduce biases in ocean meridional heat transport. We speculate that changes seen here, especially in the ocean, could be even larger in coupled simulations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1981

Analytic solutions of the radial pulsation equation for rotating and magnetic star models

Eddington's (1926) form of wave equation for small-amplitude, radial, adiabatic stellar pulsations of spherically symmetric, gaseous stars is generalized to include the effects of axial rotation and tangled magnetic fields. Equilibrium quantities possessing dimensions are affected, and the relative importance of rotation and magnetism in affecting pulsation characteristics of the models depends on the choices of gamma and the type of model. Solutions are obtained in closed form for adiabatic pulsation periods of fine analytic stellar models, and nonadiabatic stability criteria are determined by means of the one-zone stellar model. Results are discussed for a range of physical parameters such as rotational angular momenta, central condensations, and magnetic energies; and applications are made to the case of classical Cepheids and other variable giant stars.

Stothers, R.↗
NASA NTRS1984

The vertical distribution of ozone in the mesosphere and lower thermosphere

An assessment is made of the ability of current theory to explain the phenomenology of upper atmospheric ozone as revealed by the sizeable body of measurements presently available. The chemical processes affecting the vertical distribution of ozone are reviewed, and simple analytical expressions for the ozone concentrations at different altitudes are derived which approximate the key elements of the ozone chemistry. These equations provide simple explanations of the sensitivity of model computations to the choice of rate constants and climatological patterns. Model calculations are compared with a detailed measurement of an ozone profile, and the model is modified to assess the variation in ozone expected to result from perturbations in key climatological processes. These predictions are then compared with the variability observed in midlatitude measurements in order to verify the model description of ozone processes in the upper atmosphere.

Allen, M.↗
NASA NTRS1992

The use of typed lambda calculus for comprehension and construction of simulation models in the domain of ecology

We are concerned with two important issues in simulation modelling: model comprehension and model construction. Model comprehension is limited because many important choices taken during the modelling process are not documented. This makes it difficult for models to be modified or used by others. A key factor hindering model construction is the vast modelling search space which must be navigated. This is exacerbated by the fact that many modellers are unfamiliar with the terms and concepts catered to by current tools. The root of both problems is the lack of facilities for representing or reasoning about domain concepts in current simulation technology. The basis for our achievements in both of these areas is the development of a language with two distinct levels; one for representing domain information, and the other for representing the simulation model. Of equal importance, is the fact that we make formal connections between these two levels. The domain we are concerned with is ecological modelling. This language, called Elklogic, is based on the typed lambda calculus. Important features include a rich type structure, the use of various higher order functions, and semantics. This enables complex expressions to be constructed from relatively few primitives. The meaning of each expression can be determined in terms of the domain, the simulation model, or the relationship between the two. We describe a novel representation for sets and substructure, and a variety of other general concepts that are especially useful in the ecological domain. We use the type structure in a novel way: for controlling the modelling search space, rather than a proof search space. We facilitate model comprehension by representing modelling decisions that are embodied in the simulation model. We represent the simulation model separately from, but in terms of a domain mode. The explicit links between the two models constitute the modelling decisions. The semantics of Elklogic enables English text to be generated to explain the simulation model in domain terms.

Uschold, Michael↗
NASA NTRS1994

A Logical Account of Diagnosis with Multiple Theories

Model-based diagnosis is a powerful, first-principles approach to diagnosis. The primary drawback with model-based diagnosis is that it is based on a system model, and this model might be inappropriate. The inappropriateness of models usually stems from the fundamental tradeoff between completeness and efficiency. Recently, Struss has developed an elegant proposal for diagnosis with multiple models. Struss characterizes models as relations and develops a precise notion of abstraction. He defines relations between models and analyzes the effect of a model switch on the space of possible diagnoses. In this paper we extend Struss's proposal in three ways. First, our account of diagnosis with multiple models is based on representing models as more expressive first-order theories, rather than as relations. A key technical contribution is the use of a general notion of abstraction based on interpretations between theories. Second, Struss conflates component modes with models, requiring him to define models relations such as choices which result in non-relational models. We avoid this problem by differentiating component modes from models. Third, we present a more general account of simplifications that correctly handles situations where the simplification contradicts the base theory.

Pandurang, P.↗
NASA NTRS2003

Turbulence Model Comparisons for Supersonic Transports at Transonic and Supersonic Conditions

Results of turbulence model comparisons from two studies on supersonic transport configurations performed during the NASA High-speed Research program are given. Results are presented for both transonic conditions at Mach 0.90 and supersonic conditions at Mach 2.48. A feature of these two studies was the availability of higher Reynolds number wind tunnel data with which to compare the computational results. The transonic wind tunnel data was obtained in the National Transonic Facility at NASA Langley, and the supersonic data was obtained in the Boeing Polysonic Wind Tunnel. The computational data was acquired using a state of the art Navier-Stokes flow solver with a wide range of turbulence models implemented. The results show that the computed forces compare reasonably well with the experimental data, with the Baldwin- Lomax with Degani-Schiff modifications and the Baldwin-Barth models showing the best agreement for the transonic conditions and the Spalart-Allmaras model showing the best agreement for the supersonic conditions. The transonic results were more sensitive to the choice of turbulence model than were the supersonic results.

Rivers, S. M. B.↗
NASA NTRS2007

Investigations for Supersonic Transports at Transonic and Supersonic Conditions

Several computational studies were conducted as part of NASA s High Speed Research Program. Results of turbulence model comparisons from two studies on supersonic transport configurations performed during the NASA High-Speed Research program are given. The effects of grid topology and the representation of the actual wind tunnel model geometry are also investigated. Results are presented for both transonic conditions at Mach 0.90 and supersonic conditions at Mach 2.48. A feature of these two studies was the availability of higher Reynolds number wind tunnel data with which to compare the computational results. The transonic wind tunnel data was obtained in the National Transonic Facility at NASA Langley, and the supersonic data was obtained in the Boeing Polysonic Wind Tunnel. The computational data was acquired using a state of the art Navier-Stokes flow solver with a wide range of turbulence models implemented. The results show that the computed forces compare reasonably well with the experimental data, with the Baldwin-Lomax with Degani-Schiff modifications and the Baldwin-Barth models showing the best agreement for the transonic conditions and the Spalart-Allmaras model showing the best agreement for the supersonic conditions. The transonic results were more sensitive to the choice of turbulence model than were the supersonic results.

Rivers, S. Melissa B.↗