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Concentration profile of heavy species for binary fluid mixture under body force from uniformly mixed upstream condition, considering inviscid hydrodynamical model
We report here the results of modeling work aimed at understanding the development of ionospheric O(+) field-aligned upflows that develop in response to high-latitude E x B drift induced frictional heating. The model used is a collisional semikinetic model which includes ion-neutral resonant charge exchange and polarization collisions as well as Coulomb self-collisions. It also includes the process of chemical removal of O(+) as well as all of the macroscopic forces: ambipolar electric, gravity, magnetic mirror, and centripetal. Model results show the development of several types of non-Maxwellian velocity distributions including toroids at low altitude, distributions with large heat flow in the perpendicular component at intermediate altitudes, and distributions with a separate upflowing population or upward superthermal tail at high altitudes. Whenever the convection electric field increases from a small value (less than 25 mV/m) to a large value (100-200 mV/m) in 6 min or less large upflows develop with parallel drift speeds which peak (below 1000 km) at values between 500 m/s and 2 km/s, parallel fluxes which peak between 6.0 x 10(exp 8) and 3.2 x 10(exp 9)/sq cm/s, and parallel per particle heat flows which peak between 8.0 x 10(exp -9) and 8.0 x 10(exp -8) ergs cm/s. The higher values in these ranges occur for a cooler neutral atmosphere, with a larger convection electric field that is turned on quickly. The model produces field-aligned O(+) flow speeds that are larger than those produced by a 20-moment generalized transport model but smaller then those produced by an isotropic hydrodynamic model for comparable values of the convection turn on times. The model results compare favorably with some topside satellite and radar data.
In this article, a local scale, fully nonlinear coupled fluid-structural interaction (FSI) sugar kelp model has been developed using a computational fluid dynamics (CFD) method. In this model, to be consistent with available experimental data, the sugar kelp is approximated as elongated rectangles with smoothed isosceles triangles at the ends and a single kelp model with one end fixed in a channel with constant current model is developed. Several different current speeds are simulated, and the resulting drag forces and calculated drag coefficients are validated by comparison with experimental data from the literature. In a previous study, a global scale model was developed using a computational structural dynamics (CSD) method to simulate macroalgae farming system and guide the system configuration design. In the global scale model, the hydrodynamic forces are calculated using Morison’s equation and the kinematics and dynamics of the sugar kelp are simplified and the group of kelps attached to the long line is modeled as a slender structure with the same length and an effective diameter such that the volumes are consistent with the real physical system. This simplified model matches the weight and buoyancy but adjusting the hydrodynamic properties when the general hydrodynamic coefficients are employed. Therefore, optimal hydrodynamic coefficients used in global scale model were determined to obtain the hydrodynamic force more accurately. The validated local scale model is then be applied to determine the hydrodynamic coefficients of the simplified sugar kelp model for global dynamic analysis.
Owing to large pressure gradients at early times, standard hydrodynamic model simulations of relativistic heavy-ion collisions do not become reliable until O(1) fm/c after the collision. To address this one often introduces a prehydrodynamic stage that models the early evolution microscopically, typically as a conformal, weakly interacting gas. In such an approach the transition from the prehydrodynamic to the hydrodynamic stage is discontinuous, introducing considerable theoretical model ambiguity. Alternatively, fluids with large anisotropic pressure gradients can be handled macroscopically using the recently developed viscous anisotropic hydrodynamics (VAH). In high-energy heavy-ion collisions VAH is applicable already at very early times, and at later times transitions smoothly into conventional second-order viscous hydrodynamics. Here we present a Bayesian calibration of the VAH model with experimental data for Pb-Pb collisions at the LHC at $\sqrt{^{S}NN}$ = 2.76 A TeV. We find that the VAH model has the unique capability of constraining the specific viscosities of the quark-gluon plasma at higher temperatures than other previously used models.
An idealized hydrodynamic model is presented for the mediation of a free-streaming stellar wind by galactic cosmic rays or energetic particles accelerated at the stellar wind termination shock. The spherically-symmetric stellar wind is taken to be cold; the only body force is the cosmic ray pressure gradient. The cosmic rays are treated as a massless fluid with an effective mean diffusion coefficient k proportional to radial distance r. The structure of the governing equations is investigated both analytically and numerically. Solutions for a range of values of k are presented which describe the deceleration of the stellar wind and a transition to nearly incompressible flow and constant cosmic ray pressure at large r. In the limit of small k the transition steepens to a strong stellar wind termination shock. For large k the stellar wind is decelerated gradually with no shock transition. It is argued that the solutions provide a simple model for the mediation of the solar wind by interstellar ions as both pickup ions and the cosmic ray anomalous component which together dominate the pressure of the solar wind at large r.
Using deep convolutional neural network (CNN), the nature of the QCD transition can be identified from the final-state pion spectra from hybrid model simulations of heavy-ion collisions that combines a viscous hydrodynamic model with a hadronic cascade “after-burner”. Two different types of equations of state (EoS) of the medium are used in the hydrodynamic evolution. The resulting spectra in transverse momentum and azimuthal angle are used as the input data to train the neural network to distinguish different EoS. Different scenarios for the input data are studied and compared in a systematic way. A clear hierarchy is observed in the prediction accuracy when using the event-by-event, cascade-coarse-grained and event-fine-averaged spectra as input for the network, which are about 80%, 90% and 99%, respectively. A comparison with the prediction performance by deep neural network (DNN) with only the normalized pion transverse momentum spectra is also made. High-level features of pion spectra captured by a carefully-trained neural network were found to be able to distinguish the nature of the QCD transition even in a simulation scenario which is close to the experiments.
It is reported, as a mathematical model of high-speed flow and shock wave propagation in a complex multimaterial setting, Lagrangian hydrodynamics is characterized by moving meshes, advection-dominated solutions, and moving shock fronts with sharp gradients. These challenges hinder the existing projection-based model reduction schemes from being practical. We develop several variations of projection-based reduced order model techniques for Lagrangian hydrodynamics by introducing three different reduced bases for position, velocity, and energy fields. A time-windowing approach is also developed to address the challenge imposed by the advection-dominated solutions. Lagrangian hydrodynamics is formulated as a nonlinear problem, which requires a proper hyper-reduction technique. Therefore, we apply the over-sampling DEIM and SNS approaches to reduce the complexity due to the nonlinear terms. Finally, we also present both a posteriori and a priori error bounds associated with our reduced order model. We compare the performance of the spatial and time-windowing reduced order modeling approaches in terms of accuracy and speed-up with respect to the corresponding full order model for several numerical examples, namely Sedov blast, Gresho vortices, Taylor-Green vortices, and triple-point problems.
In this paper we present the development and verification of HydroChrono, a hydrodynamics package for the Project Chrono physics engine. This package includes the implementation of hydrodynamics equations, the added mass for multibody systems, the development of I/O functions as well as a Python API, and comparison against standard reference cases and other existing tools. HydroChrono provides a flexible, fully open-source solution for simulating wave energy converters (WECs), floating offshore wind turbines (FOWTs) platforms, and other hydrodynamic systems. Here we show, via comparisons with existing tools for benchmark verification cases, that HydroChrono accurately models hydrodynamic forces - making it a useful tool for the design and optimization of these systems. Additionally, the integration of HydroChrono with Project Chrono offers access to finite element modeling capabilities and high-fidelity modelling - with Chrono's existing coupling to CFD and SPH codes. This provides numerical modelers with a multifidelity simulation framework for designing and validating these systems. The development of HydroChrono provides a new, open-source solution for simulating hydrodynamic systems. Its compatibility with other simulation tools enables a more streamlined and efficient design process, advancing the field and providing new opportunities for innovation in this area.
This study uses a neural network model trained with in situ data, combined with satellite data and hydrodynamic model products, to compute the daily estuarine export of dissolved organic carbon (DOC) at the mouths of Chesapeake Bay (CB) and Delaware Bay (DB) from 2007 to 2011. Both bays show large flux variability with highest fluxes in spring and lowest in fall as well as interannual flux variability (0.18 and 0.27 Tg C/year in 2008 and 2010 for CB; 0.04 and 0.09 Tg C/year in 2008 and 2011 for DB). Based on previous estimates of total organic carbon (TOCexp) exported by all Mid-Atlantic Bight estuaries (1.2 Tg C/year), the DOC export (CB + DB) of 0.3 Tg C/year estimated here corresponds to 25% of the TOCexp. Spatial and temporal covariations of velocity and DOC concentration provide contributions to the flux, with larger spatial influence. Differences in the discharge of fresh water into the bays (74 billion m(exp3)/year for CB and 21 billion m9exp3)/year for DB) and their geomorphologies are major drivers of the differences in DOC fluxes for these two systems. Terrestrial DOC inputs are similar to the export of DOC at the bay mouths at annual and longer time scales but diverge significantly at shorter time scales (days to months). Future efforts will expand to the Mid-Atlantic Bight and Gulf of Maine, and its major rivers and estuaries, in combination with coupled terrestrial-estuarine-ocean biogeochemical models that include effects of climate change, such as warming and CO2 increase.
This study uses a neural network model trained with in situ data, combined with satellite data and hydrodynamic model products, to compute the daily estuarine export of dissolved organic carbon (DOC) at the mouths of Chesapeake Bay (CB) and Delaware Bay (DB) from 2007 to 2011. Both bays show large flux variability with highest fluxes in spring and lowest in fall as well as interannual flux variability (0.18 and 0.27 Tg C/year in 2008 and 2010 for CB; 0.04 and 0.09 Tg C/year in 2008 and 2011 for DB). Based on previous estimates of total organic carbon (TOCexp) exported by all Mid-Atlantic Bight estuaries (1.2 Tg C/year), the DOC export (CB + DB) of 0.3 Tg C/year estimated here corresponds to 25% of the TOCexp. Spatial and temporal covariations of velocity and DOC concentration provide contributions to the flux, with larger spatial influence. Differences in the discharge of fresh water into the bays (74 billion cu. m/year for CB and 21 billion cu. m/year for DB) and their geomorphologies are major drivers of the differences in DOC fluxes for these two systems. Terrestrial DOC inputs are similar to the export of DOC at the bay mouths at annual and longer time scales but diverge significantly at shorter time scales (days to months). Future efforts will expand to the Mid-Atlantic Bight and Gulf of Maine, and its major rivers and estuaries, in combination with coupled terrestrial-estuarine-ocean biogeochemical models that include effects of climate change, such as warming and CO2 increase.
Calculated late-time spectra of two classical hydrodynamical models for Type Ia supernovae (deflagration model W7 of Nomoto, Thielemann, & Yokoi, and delayed detonation model DD4 of Woosley & Weaver) are compared with observations of SN 1992A and other spectroscopically normal SNe Ia. An important new piece of information is provided by observations done with the Hubble Space Telescope (HST) which cover the ultraviolet range at the nebular phase of a SN Ia: SN 1992A in NGC 1380. For the first time a picture of SN Ia emission from the ultraviolet through the optical is obtained at these phases. Predictions of the classical model (W7 and DD4) are compared with the observed spectrum of SN 1992A and with the optical spectra of SN 1989M in NGC 4579 and SN 1990N in NGC 4639 at similar epochs. The absolute B and V magnitudes of the models are also estimated at these late phases. Taken at face value the nebular spectra of these 'classical' models are more consistent with the long extragalactic distance scale, pointing to distances to NGC 4579 around 21 +/- 3 Mpc and a slightly larger distance, 22 +/- 3 Mpc, to NGC 4639, on the back side of the Virgo Cluster. However, the calculated Fe(+3) luminosity as predicted from the models exceeds the observed limit from the HST data of SN 1992A. Other differences in the ratios of the line intensities between calculated and observed spectra, show some disagreement with the observed spectra at the nebular phases. They may not be the best choice for spectroscopically normal SNe Ia, and their use as an independent calibration of the extragalactic distance scale should be viewed with caution.
Deep machine learning is used to analyze a proton radiograph from a tin pulsed power experiment and determine density values for each pixel in the image. Two promising convolutional neural network architectures that have proven to be effective for image analysis in other applications are applied to analyze a proton radiograph and find density values. The process of creating a suitable training dataset is described, involving the Lagrangian hydrodynamic model used for simulations of the experiment, the proton radiography forward model to make synthetic images for training, and the manner in which data augmentation is used to expand the resulting image dataset. It is shown that machine learning not only produces a reasonable density field but is also able to predict features in the density field that are suggested by the proton radiograph but not captured by simulations.
Polar plumes have been detected in X-rays with the Skylab S-054 experiment. Observations of one well-observed plume are analyzed and the plasma pressure is determined. An isothermal model in hydrostatic equilibrium is found to require unacceptably low temperatures. A hydrodynamic model is proposed which is consistent with earlier white light and EUV observations. Calculations indicate that the total outward mass flux in polar plumes is comparable to that in high speed solar wind streams expected from a polar coronal hole.
The paper presents a review of new findings related to core plasma (which includes ions and electrons with energies less than 50 eV) identified in studies of magnetosphere during 1983-1986. These findings include the cleft ion fountain; the plasmaspheric heavy ion torus; core molecular ions; high core-plasma densities in the plasma-sheet boundary layer; intense transverse heating of core plasmas in the equatorial regions; the supersonic polar wind (H/+/ and He/+/); toroidal or ring heavy-ion distribution functions in the auroral region; conic-to-field-aligned ion signatures of inverted-V auroral events; and the large-scale electron density structure of the magnetosphere. The progress made on modeling core plasma includes simulation of thermal helium heating at the equator; kinetic models of the plasma transport from localized ionospheric regions; time-dependent hydrodynamic models of the polar wind and plasmasphere-ionosphere coupling; and kinetic models of plasmasphere refilling.
To adequately constrain the frequency of energy deposition in active region cores in the solar corona, systematic comparisons between detailed models and observational data are needed. In this paper, we describe a pipeline for forward modeling active region emission using magnetic field extrapolations and field-aligned hydrodynamic models. We use this pipeline to predict time-dependent emission from active region NOAA 1158 for low-, intermediate-, and high-frequency nanoflares. In each pixel of our predicted multi-wavelength, time-dependent images, we compute two commonly used diagnostics: the emission measure slope and the time lag. We find that signatures of the heating frequency persist in both of these diagnostics. In particular, our results show that the distribution of emission measure slopes narrows and the mean decreases with decreasing heating frequency and that the range of emission measure slopes is consistent with past observational and modeling work. Furthermore, we find that the time lag becomes increasingly spatially coherent with decreasing heating frequency while the distribution of time lags across the whole active region becomes more broad with increasing heating frequency. In a follow-up paper, we train a random forest classifier on these predicted diagnostics and use this model to classify real observations of NOAA 1158 in terms of the underlying heating frequency.
Galilean invariance has been an important issue in lattice-based hydrodynamics models. Previous models concentrated on the nonlinear advection term. In this paper, we take into account the nonlinear response effect in a systematic way. Using the Chapman-Enskog expansion up to second order, complete Galilean invariant lattice BGK models in one dimension (theta = 3) and two dimensions (theta = 1) for the Navier-Stokes equation have been obtained.
A review of the application of single particle hydrodynamics in models for the exchange of interphase momentum in continuum models of multiphase flow is presented. Considered are the equations of motion for a laminar, mechanical two phase flow. Inherent to this theory is a model for the interphase exchange of momentum due to drag between the dispersed particulate and continuous fluid phases. In addition, applications of two phase flow theory to de-mixing flows require the modeling of interphase momentum exchange due to lift forces. The applications of single particle analysis in deriving models for drag and lift are examined.
We introduce Bayesian Optimization for Anything (BOA), a high-level Bayesian Optimization (BO) framework and model wrapping toolkit, which presents a novel approach to simplifying BO, with the goal of making it more accessible and user-friendly, particularly for those with limited expertise in the field. BOA addresses common barriers in implementing BO, focusing on ease of use, reducing the need for deep domain knowledge, and cutting down on extensive coding requirements. A notable feature of BOA is its language-agnostic architecture, which facilitates broader application in various fields and to a wider audience. We showcase BOA's application through three examples: a high-dimensional optimization with parameters of the SWAT+ watershed model, a highly parallelized optimization of this intrinsically non-parallel model, and a multi-objective optimization of the FETCH Tree-Crown Hydrodynamics model. Furthermore, these test cases illustrate BOA's effectiveness in addressing complex optimization challenges in diverse scenarios.