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All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Hybrid model for solidification and convection

A hybrid model for continuum phase-change problems is presented. The hybrid model accounts for flow in regions of concentrated mush, dilute mush, and single-phase liquid. Scale analysis shows that, in dilute mush, the Blake-Kozeny-Carman relation may lead to inaccuracy in the continuum formulation for certain values of the Rayleigh and Darcy numbers. The hybrid model uses arctangent switching functions to switch on the Darcy flow and variable viscosity terms depending on the local value of the fraction solid. Two-dimensional example calculations suggest that the hybrid model more accurately accounts for transport in the dilute mush region.

Oldenburg, Curtis M.↗
NASA NTRS1993

Structure of the dayside reconnection layer in resistive MHD and hybrid models

Numerical simulations were performed to investigate the structure of the reconnection layer at the dayside magnetopause. Two typical cases are examined in detail; both are asymmetric in magnetic field and plasma density. In case 1, the guide fields in the magnetosheath and in the magnetosphere are set at zero and thus the tangential magnetic fields on the two sides of the initial current sheet are exactly antiparallel. In case 2, the angle between the tangential magnetic fields on the two sides of the initial current sheet is 145 deg. The results obtained from a resistive MHD model and from a hybrid model are found to be different. In the MHD simulation of case 1, a 2-4 intermediate shock is found to bound the reconnection layer on the magnetosheath side, while an Alfven wave pulse bounds the reconnection layer on the magnetospheric side. In case 2, it is found that a time-dependent intermediate shock (TDIS) bounds the reconnection layer on the magnetosheath side, with a slow expansion wave propagating behind. With the MHD simulations, in the general case in which the tangential magnetic fields on the two sides of the initial current sheet are not exactly antiparallel, a rotational discontinuity across which the tangential magnetic field rotates, a large angle is found to bound the reconnection layer on the magnetosheath side.

Lin, Y.↗
NASA NTRS1986

A hybrid model of the CO2 geochemical cycle and its application to large impact events

The effects of a large asteriod or comet impact on modern and ancient marine biospheres are analyzed. A hybrid model of the carbonate-silicate geochemical cycle, which is capable of calculating the concentrations of carbon dioxide in the atmosphere, ocean, and sedimentary rocks, is described. The differences between the Keir and Berger (1983) model and the hybrid model are discussed. Equilibrium solutions are derived for the preindustrial atmosphere/ocean system and for a system similar to that of the late Cretaceous Period. The model data reveal that globl darkening caused by a stratospheric dust veil could destroy the existing phytoplankton within a period of several weeks or months, nd the dissolution of atmospheric NO(x) compounds would lower the pH of ocean surface waters and release CO2 into the atmosphere. It is noted that the surface temperatures could be increased by several degrees and surface oceans would be uninhabitable for calcaerous organisms for approximately 20 years.

Kasting, J. F.↗
NASA NTRS1993

The optically thick C III spectrum. 2: Level/term populations and line/multiplet intensities using an improved hybrid model

An improved hybrid level/term calculation is employed to obtain C III level/term populations and line/fractional multiplet intensities over the extended range of electron density 4.0 less than or equal log N(sub e) less than or equal 12.0, for column lengths L ranging from zero (optically thin) to 10(exp 20)/sq cm (moderately optically thick), at electron temperatures T(sub e) approximately 40,000 K(log T(sub e) = 4.6), T(sub e) approximately 63,000 K (log T(sub e) = 4.8), T(sub e) approximately 79,500 K (log T(sub e) = 4.9), and T(sub e) = 100,000 K (log T(sub e) = 5.0). The tabulated results are relevant to the interpretation of space observations obtained over extended spectral ranges by new and planned facilities including the Hopkins Ultraviolet Telescope (HUT) and the Far-Ultraviolet Spectrographic Explorer (FUSE).

Bhatia, A. K.↗
NASA NTRS2010

Hybrid Model of Inhomogeneous Solar Wind Plasma Heating by Alfven Wave Spectrum: Parametric Studies

Observations of the solar wind plasma at 0.3 AU and beyond show that a turbulent spectrum of magnetic fluctuations is present. Remote sensing observations of the corona indicate that heavy ions are hotter than protons and their temperature is anisotropic (T(sub perpindicular / T(sub parallel) >> 1). We study the heating and the acceleration of multi-ion plasma in the solar wind by a turbulent spectrum of Alfvenic fluctuations using a 2-D hybrid numerical model. In the hybrid model the protons and heavy ions are treated kinetically as particles, while the electrons are included as neutralizing background fluid. This is the first two-dimensional hybrid parametric study of the solar wind plasma that includes an input turbulent wave spectrum guided by observation with inhomogeneous background density. We also investigate the effects of He++ ion beams in the inhomogeneous background plasma density on the heating of the solar wind plasma. The 2-D hybrid model treats parallel and oblique waves, together with cross-field inhomogeneity, self-consistently. We investigate the parametric dependence of the perpendicular heating, and the temperature anisotropy in the H+-He++ solar wind plasma. It was found that the scaling of the magnetic fluctuations power spectrum steepens in the higher-density regions, and the heating is channeled to these regions from the surrounding lower-density plasma due to wave refraction. The model parameters are applicable to the expected solar wind conditions at about 10 solar radii.

Ofman, L.↗
NASA NTRS1979

Application of hybrid coordinate modeling and multilevel control techniques to a single axis torsional model

Hybrid coordinate modeling and multilevel control techniques are applied to a prototype flexible vehicle. The original hybrid coordinate model developed for this vehicle is expressed in terms of one rigid body coordinate and two modal vibration coordinates. Multilevel control techniques of decomposition, coordination and construction of the subproblem hierarchy are applied to this model.

Chichester, F. D.↗
NASA NTRS1991

On rotational discontinuities in both two-fluid and hybrid models

Rotational discontinuities are studied in a two-fluid model that includes finite ion inertia dispersion and in a hybrid model in which the full ion dynamics is retained while the electrons are treated as a massless fluid. It is shown that as in previous dissipative MHD studies, a rotational discontinuity is unstable in both models and evolves to a 2-3 intermediate shock, a slow rarefaction wave, and other waves. In addition, it is shown that the so-called Walen relation, which holds exactly for rotational discontinuities, can also be well satisfied by tran-Alfvenic intermediate shocks. Thus intermediate shocks can be candidates for those observed structures that satisfy the Walen relation.

Wu, C. C.↗
NASA NTRS1991

Formation of intermediate shocks in both two-fluid and hybrid models

Intermediate shocks are studied in a two-fluid model that includes finite ion inertia dispersion and in a hybrid model in which the full ion dynamics is retained while the electrons are treated as a massless fluid. It is shown that in both models intermediate shocks can be formed through wave steepening, meaning that they are stable and possess shock structures.

Wu, C. C.↗
NASA NTRS2019

Partition-based Feasible Integer Solution Pre-computation for Hybrid Model Predictive Control

For multiparametric mixed-integer convex programming problems such as those encountered in hybrid model predictive control, we propose an algorithm for generating a feasible partition of a subset of the parameter space. The result is a static map from the current parameter to a suboptimal integer solution such that the remaining convex program is feasible. Convergence is proved with a new insight that the overlap among the feasible parameter sets of each integer solution governs the partition complexity. The partition is stored as a tree which makes querying the feasible solution efficient. The algorithm can be used to warm start a mixed integer solver with a real-time guarantee or to provide a reference integer solution in several suboptimal MPC schemes. The algorithm is tested on randomly generated systems with up to six states, demonstrating the effectiveness of the approach.

Bayard, David S.↗
NASA NTRSDate not supplied

Hybrid Model Based Approaches for Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Hybrid Modeling↗
NASA NTRSDate not supplied

Analysis of Ice Mass Growth Over Time on the CRM65 Midspan Hybrid Model

The Aeronautics Research Mission Directorate at NASA is developing and applying tools to enable future technologies towards sustainable flight. Aircraft icing has been identified as a potential barrier to entry into service for innovative designs necessitating improvements to computational ice accretion tools. NASA is developing the Glenn Icing Computational Environment (GlennICE) to address deficiencies in the computational modeling capabilities of previously developed ice accretion solvers. To benchmark and improve the ability to model highly three-dimensional ice accretion, high quality validation data against experimental data is required. The CRM65 Midspan Hybrid geometry was previously tested at the NASA Icing Research Tunnel to generate experimental data for swept wing geometries typical for commercial transport aircraft. As a part of a 2018 icing test campaign, experimental data characterizing the relationship between ice accretion time and ice mass growth was obtained and can be leveraged for use in validation of computational tools. The desire for computational ice accretion solvers to predict ice shapes profiles accreted experimentally has often overshadowed the comparison to the mass and bulk volume of ice accreted. To address this deficiency, an analysis is presented in which GlennICE is applied to simulations of the CRM65 Midspan Hybrid model tested in the NASA Icing Research Tunnel. Results from the computational fluid dynamics simulations compared favorably to the experimental pressure coefficient data, thus validating the modeling setup. The experimental data showed excellent repeatability for the 15.0 minute accretion time. The comparisons between the experimental and computational ice mass over time showed good agreement up to 10.0 minutes after which the ice mass was underpredicted. The experimental ice mass was largely linear with some nonlinear data. The bulk volume of ice accreted experimentally compared well to GlennICE for the scanned ice shapes and mean combined cross section ice shapes, but was underpredicted for the maximum combined cross section ice shapes at longer accretion times. The experimental minimum combined cross section, mean combined cross section, and maximum combined cross section profiles when compared to GlennICE show good agreement for the mean combined cross section up to 15.0 minutes. The analyses show that with a single-shot method, GlennICE currently underpredicts the ice mass for longer accretion times, is not able to match the bulk volume of the maximum combined cross section due to dominating scallop features, and future work is required to generate a more generalized ice bulk density model.

Icing↗
NASA NTRSDate not supplied

Analysis of Ice Mass Growth Over Time on the CRM65 Midspan Hybrid Model

The Aeronautics Research Mission Directorate at NASA is developing and applying tools to enable future technologies towards sustainable flight. Aircraft icing has been identified as a potential barrier to entry into service for innovative designs necessitating improvements to computational ice accretion tools. NASA is developing the Glenn Icing Computational Environment (GlennICE) to address deficiencies in the computational modeling capabilities of previously developed ice accretion solvers. To benchmark and improve the ability to model highly three-dimensional ice accretion, high quality validation data against experimental data is required. The CRM65 Midspan Hybrid geometry was previously tested at the NASA Icing Research Tunnel to generate experimental data for swept wing geometries typical for commercial transport aircraft. As a part of a 2018 icing test campaign, experimental data characterizing the relationship between ice accretion time and ice mass growth was obtained and can be leveraged for use in validation of computational tools. The desire for computational ice accretion solvers to predict ice shapes profiles accreted experimentally has often overshadowed the comparison to the mass and bulk volume of ice accreted. To address this deficiency, an analysis is presented in which GlennICE is applied to simulations of the CRM65 Midspan Hybrid model tested in the NASA Icing Research Tunnel. Results from the computational fluid dynamics simulations compared favorably to the experimental pressure coefficient data, thus validating the modeling setup. The experimental data showed excellent repeatability for the 15.0 minute accretion time. The comparisons between the experimental and computational ice mass over time showed good agreement up to 10.0 minutes after which the ice mass was underpredicted. The experimental ice mass was largely linear with some nonlinear data. The bulk volume of ice accreted experimentally compared well to GlennICE for the scanned ice shapes and mean combined cross section ice shapes, but was underpredicted for the maximum combined cross section ice shapes at longer accretion times. The experimental minimum combined cross section, mean combined cross section, and maximum combined cross section profiles when compared to GlennICE show good agreement for the mean combined cross section up to 15.0 minutes. The analyses show that with a single-shot method, GlennICE currently underpredicts the ice mass for longer accretion times, is not able to match the bulk volume of the maximum combined cross section due to dominating scallop features, and future work is required to generate a more generalized ice bulk density model.

Icing↗
NASA NTRS2022

Hybrid Model Based Approaches for Systems Health Management and Prognostics

This is a previously approved and published presentation. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Present achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. In principle, data-driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data-driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety-critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Prognostics↗
NASA NTRS1996

Continuing Development of a Hybrid Model (VSH) of the Neutral Thermosphere

We propose to continue the development of a new operational model of neutral thermospheric density, composition, temperatures and winds to improve current engineering environment definitions of the neutral thermosphere. This model will be based on simulations made with the National Center for Atmospheric Research (NCAR) Thermosphere-Ionosphere- Electrodynamic General Circulation Model (TIEGCM) and on empirical data. It will be capable of using real-time geophysical indices or data from ground-based and satellite inputs and provides neutral variables at specified locations and times. This "hybrid" model will be based on a Vector Spherical Harmonic (VSH) analysis technique developed (over the last 8 years) at the University of Michigan that permits the incorporation of the TIGCM outputs and data into the model. The VSH model will be a more accurate version of existing models of the neutral thermospheric, and will thus improve density specification for satellites flying in low Earth orbit (LEO).

Burns, Alan↗
NASA NTRS2011

Short Wavelength Electromagnetic Perturbations Excited Near the Solar Probe Plus Spacecraft in the Inner Heliosphere: 2.5D Hybrid Modeling

A 2.5D numerical plasma model of the interaction of the solar wind (SW) with the Solar Probe Plus spacecraft (SPPSC) is presented. These results should be interpreted as a basic plasma model derived from the SW-interaction with the spacecraft (SC), which could have consequences for both plasma wave and electron plasma measurements on board the SC in the inner heliosphere. Compression waves and electric field jumps with amplitudes of about 1.5 V/m and (12-18) V/m were also observed. A strong polarization electric field was also observed in the wing of the plasma wake. However, 2.5D hybrid modeling did not show excitation of whistler/Alfven waves in the upstream connected with the bidirectional current closure that was observed in short-time 3D modeling SPPSC and near a tether in the ionosphere. The observed strong electromagnetic perturbations may be a crucial point in the electromagnetic measurements planned for the future Solar Probe Plus (SPP) mission. The results of modeling electromagnetic field perturbations in the SW due to shot noise in absence of SPPSC are also discussed.

Lipatov, Alexander S.↗