This work describes and tests the calibration process of the chemical-diffusive model (CDM) for the simulation of non-premixed diffusion flames. The CDM is an alternative, simplified approach for incorporating the effects of combustion in a fluid simulation, based on the ideas of regulating the rate of energy release such that the properties of combustion waves (e.g. flames and detonations) are reproduced. Past implementations of the CDM have considered single-stoichiometry fuel-air mixtures or mixtures with variable stoichiom- etry but with premixed modes of combustion. In this work, the CDM is tested and shown to work for non-premixed, low-Mach-number flames (i.e., diffusion flames) by incorporat- ing it into a numerical model which solves the reactive and compressible Navier-Stokes equations with the barely implicit correction (BIC) algorithm, which removes the acoustic limit on the integration time-step size. Simulations of one-dimensional premixed laminar flames reproduce the required premixed laminar flame speed, thickness, and temperature. A two-dimensional, steady-state, laminar coflow diffusion flame is computed, and the result demonstrates the ability of the algorithm to compute a non-premixed flame. Lastly, a two- dimensional simulation of two opposing jets of fuel and air show that the CDM approach can compute the structure of a counter-flow diffusion flame.
more »
« less
Effects of Multiple Pulses on Nanosecond Discharges
Numerical simulations of axisymmetric nanosecond pulsed discharges at atmospheric pressure and temperature are performed with a novel fully implicit time
integration approach. The plasma fluid equations with a drift-diffusion model and
local-field approximation are made dimensionless and solved using a preconditioned Jacobian Free Newton-Krylov method. A simplified kinetics model is employed, including electrons, one positive ion, and one negative ion. The chemical processes of ionization, attachment, detachment, and recombination are considered along with photoionization. The newly developed fully-implicit integration scheme with physics-based preconditioning allows for the efficient simulations capable of describing the cathode sheath over time-scales of O(10 us). The implicit solver overcomes the limiting time scales related to electron drift, diffusion, dielectric relaxation, and ionization.
more »
« less
- Award ID(s):
- 1903775
- NSF-PAR ID:
- 10424015
- Date Published:
- Journal Name:
- AIAA SciTech Forum, 23-27 January 2023, National Harbor, MD & Online
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
ABSTRACT We investigate the possibility of cosmic ray (CR) confinement by charged dust grains through resonant drag instabilities (RDIs). We perform magnetohydrodynamic particle-in-cell simulations of magnetized gas mixed with charged dust and cosmic rays, with the gyro-radii of dust and GeV CRs on ∼au scales fully resolved. As a first study, we focus on one type of RDI wherein charged grains drift super-Alfvénically, with Lorentz forces strongly dominating over drag forces. Dust grains are unstable to the RDIs and form concentrated columns and sheets, whose scale grows until saturating at the simulation box size. Initially perfectly streaming CRs are strongly scattered by RDI-excited Alfvén waves, with the growth rate of the CR perpendicular velocity components equaling the growth rate of magnetic field perturbations. These rates are well-predicted by analytic linear theory. CRs finally become isotropized and drift at least at ∼vA by unidirectional Alfvén waves excited by the RDIs, with a uniform distribution of the pitch angle cosine μ and a flat profile of the CR pitch angle diffusion coefficient Dμμ around μ = 0, without the ‘90○ pitch angle problem.’ With CR feedback on the gas included, Dμμ decreases by a factor of a few, indicating a lower CR scattering rate, because the backreaction on the RDI from the CR pressure adds extra wave damping, leading to lower quasi-steady-state scattering rates. Our study demonstrates that the dust-induced CR confinement can be very important under certain conditions, e.g. the dusty circumgalactic medium around quasars or superluminous galaxies.more » « less
-
We present a bottom-up coarse-graining (CG) method to establish implicit-solvent CG modeling for polymers in solution, which conserves the dynamic properties of the reference microscopic system. In particular, tens to hundreds of bonded polymer atoms (or Lennard-Jones beads) are coarse-grained as one CG particle, and the solvent degrees of freedom are eliminated. The dynamics of the CG system is governed by the generalized Langevin equation (GLE) derived via the Mori-Zwanzig formalism, by which the CG variables can be directly and rigorously linked to the microscopic dynamics generated by molecular dynamics (MD) simulations. The solvent-mediated dynamics of polymers is modeled by the non-Markovian stochastic dynamics in GLE, where the memory kernel can be computed from the MD trajectories. To circumvent the difficulty in direct evaluation of the memory term and generation of colored noise, we exploit the equivalence between the non-Markovian dynamics and Markovian dynamics in an extended space. To this end, the CG system is supplemented with auxiliary variables that are coupled linearly to the momentum and among themselves, subject to uncorrelated Gaussian white noise. A high-order time-integration scheme is used to solve the extended dynamics to further accelerate the CG simulations. To assess, validate, and demonstrate the established implicit-solvent CG modeling, we have applied it to study four different types of polymers in solution. The dynamic properties of polymers characterized by the velocity autocorrelation function, diffusion coefficient, and mean square displacement as functions of time are evaluated in both CG and MD simulations. Results show that the extended dynamics with auxiliary variables can construct arbitrarily high-order CG models to reproduce dynamic properties of the reference microscopic system and to characterize long-time dynamics of polymers in solution.more » « less
-
more » « less
Abstract Ionospheric storm enhanced density (SED) has been extensively investigated using total electron content deduced from GPS ground and satellite‐borne receivers. However, dayside in situ electron density measurements have not been analyzed in detail for SEDs yet. We report in situ electron density measurements of a SED event in the Northern Hemisphere (NH) at the noon meridian plane measured by the Challenging Minisatellite Payload (CHAMP) polar‐orbiting satellite at about 390 km altitude during the 20 November 2003 magnetic storm. The CHAMP satellite measurements render rare documentation about the dayside SED's life cycle at a fixed magnetic local time (MLT) through multiple passes. Solar wind drivers triggered the SED onset and controlled its lifecycle through its growth and retreat phases. The SED electron density enhancement extended from the equatorial ionization anomaly to the noon cusp. The midlatitude electron density increased to a maximum at the end of the growth phase. Afterward, the dayside SED region retreated gradually to lower magnetic latitudes. The observations showed a hemisphere asymmetry, with the NH electron density exhibiting a more significant enhancement. The simulations using the Thermosphere Ionosphere Electrodynamic General Circulation model show a good agreement with the CHAMP observations. The simulations indicate that the dayside midlatitude electron density enhancement has a complicated dependence on vertical ion drift, neutral wind, magnetic latitude, MLT, and the height of the F2 layer. Finally, we discuss the notion of using the mean cross‐polar cap electric field as a proxy for assessing the effects of solar wind drivers on producing midlatitude electron density enhancement.
-
more » « less
Abstract The ocean mixed layer plays an important role in the coupling between the upper ocean and atmosphere across a wide range of time scales. Estimation of the variability of the ocean mixed layer is therefore important for atmosphere‐ocean prediction and analysis. The increasing coverage of in situ Argo profile data allows for an increasingly accurate analysis of the mixed layer depth (MLD) variability associated with deviations from the seasonal climatology. However, sampling rates are not sufficient to fully resolve subseasonal (
day) MLD variability. Yet, many multivariate observations‐based analyses include implicit modeled subseasonal MLD variability. One analysis method is optimal interpolation of in situ data, but the interior analysis can be improved by leveraging surface data with regression or variational approaches. Here, we demonstrate how machine learning methods and satellite sea surface temperature, salinity, and height facilitate MLD estimation in a pilot study of two regions: the mid‐latitude southern Indian and the eastern equatorial Pacific Oceans. We construct multiple machine learning architectures to produce weekly 1/2° gridded MLD anomaly fields (relative to a monthly climatology) with uncertainty estimates. We test multiple traditional and probabilistic machine learning techniques to compare both accuracy and probabilistic calibration. We validate our methodology by applying it to ocean model simulations. We find that incorporating sea surface data through a machine learning model improves the performance of spatiotemporal MLD variability estimation compared to optimal interpolation of Argo observations alone. These preliminary results are a promising first step for the application of machine learning to MLD prediction.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.2514/6.2023-0569
