Nonequilibrium internal energy distributions during dissociation

Singh, Narendra; Schwartzentruber, Thomas E.

doi:10.1073/pnas.1713840115

Cited by 39 publications

(36 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such parametric models for nonequilibrium conditions are still a current topic of research. 9,11,25 To highlight the performance of different models, a parametric model for transient vibrational and rotational state distributions based on surprisal analysis 9 was applied to Set1. P (v ) and P (j ) distributions for two different sets of temperatures ((T trans = 20000 K, T rovib = 5000 K) and (T trans = 5500 K, T rovib = 20000 K)) from QCT simulation are modelled following the parametrization of Ref.…”

Section: Resultsmentioning

confidence: 99%

“…Such a parametric approach has its foundation in surprisal analysis 24 which was recently used in models for hypersonics. 9,11,25 The reactant state distributions in Set1 are equilibrium distributions and the model functions (Eqs. 2 to 4) were chosen accordingly.…”

Section: Function (F)-based Approachmentioning

confidence: 99%

“…P (v) = j P (v, j) and P (j) = v P (v, j), where (v, j) labels the rovibrational state of the diatom. 9 Hence, instead of considering all possible combinations of (E trans , v, j) on the reactant (input) and product (output) side explicitly, one is rather interested in a description of these microscopic quantities by means of their underlying probability distributions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning for Observables: Reactant to Product State Distributions for Atom-Diatom Collisions

Arnold,

Koner,

Käser

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Machine learning-based models to predict product state distributions from a distribution of reactant conditions for atom-diatom collisions are presented and quantitatively tested. The models are based on function-, kernel-and grid-based representations of the reactant and product state distributions. While all three methods predict final state distributions from explicit quasi-classical trajectory simulations with R 2 > 0.998, the grid-based approach performs best. Although a function-based approach is found to be more than two times better in computational performance, the kernel-and grid-based approaches are preferred in terms of prediction accuracy, practicability and generality. The function-based approach also suffers from lacking a general set of model functions. Applications of the grid-based approach to nonequilibrium, multi-temperature initial state distributions are presented, a situation common to energy distributions in hypersonic flows. The role of such models in Direct Simulation Monte Carlo and computational fluid dynamics simulations is also discussed.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Function (F)-based Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning for Observables: Reactant to Product State Distributions for Atom-Diatom Collisions

Arnold,

Koner,

Käser

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…It was hence found that the NN provides a physically robust model based on validated, microscopic data from which information about nonequilibrium systems can be obtained, obviating the construction of models based on simple, empirical expressions. 165 As the evaluation time of the NN is on the order of seconds for 10 6 state-to-state cross sections, this technique is suitable for direct use in DSMC simulations . The average error from the NN compared with the reference QCT data is ∼ 5 %.…”

Section: Discussionmentioning

confidence: 99%

Dynamics on Multiple Potential Energy Surfaces: Quantitative Studies of Elementary Processes Relevant to Hypersonics

Koner,

Bemish,

Meuwly

2020

Preprint

View full text Add to dashboard Cite

The determination of thermal and vibrational relaxation rates of triatomic systems suitable for application in hypersonic model calculations is discussed. For this, potential energy surfaces for ground and electronically excited state species need to be computed and represented with high accuracy and quasiclassical or quantum nuclear dynamics simulations provide the basis for determining the relevant rates. These include thermal reaction rates, state-to-state cross sections, or vibrational relaxation rates. For exemplary systems -[NNO], [NOO], and [CNO] -all individual steps are described and a literature overview for them is provided. Finally, as some of these quantities involve considerable computational expense, for the example of state-to-state cross sections the construction of an efficient model based on neural networks is discussed. All such data is required and being used in more coarse-grained computational fluid dynamics simulations.

show abstract

“…where i is either j or v, f 0 (i; T ) is a Boltzmann distribution, λ 0,i is the normalization constant, λ 1,i is a constant parameter, which controls the extent of depletion and is based on the DMS simulation results 34 . The accuracy of this model will be discussed in the next section.…”

Section: Non-boltzmann Qss Rate Expressionmentioning

confidence: 99%

Consistent Kinetic And Continuum Dissociation Models For High-Temperature Air

Singh

Schwartzentruber

2020

AIAA Scitech 2020 Forum

Self Cite

View full text Add to dashboard Cite

In this article, we propose a generalized non-equilibrium chemical kinetics model from ab initio simulation data obtained using accurate potential energy surfaces developed recently for the purpose of studying high-temperature air chemistry. First, we present a simple cross-section model for dissociation that captures recent ab initio data accurately. The cross-section model is analytically integrated over Boltzmann distributions and general non-Boltzmann distributions to derive general non-equilibrium dissociation model. The general non-Boltzmann model systematically incorporates key physics such as dependence on translational energy, rotational energy, vibrational energy, internal energy, centrifugal barrier and non-Boltzmann effects such as overpopulation and depletion of high energy states. The model is shown to reproduce the rates from QCT for Boltzmann distributions of internal energy states. Reduced rates in non-equilibrium steady state due to depletion of high internal energy states are also predicted well by the model. Furthermore, the model predicts the enhanced rates as observed due to significant overpopulation of high vibrational states relative to Boltzmann distributions while the gas is in non-equilibrium in the transient phase. The model provides a computationally inexpensive way of incorporating non-equilibrium chemistry without incurring additional cost in the existing computational tools. Further comparisons of the model are carried out in another article, where simplifications to the model are proposed based on the results.

show abstract

Nonequilibrium internal energy distributions during dissociation

Cited by 39 publications

References 53 publications

Machine Learning for Observables: Reactant to Product State Distributions for Atom-Diatom Collisions

Machine Learning for Observables: Reactant to Product State Distributions for Atom-Diatom Collisions

Dynamics on Multiple Potential Energy Surfaces: Quantitative Studies of Elementary Processes Relevant to Hypersonics

Consistent Kinetic And Continuum Dissociation Models For High-Temperature Air

Contact Info

Product

Resources

About