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pro vyhledávání: '"Singh, Narendra P"'
The most rigorous physical description of non-equilibrium gas dynamics is rooted in the numerical solution of the Boltzmann equation. Yet, the large number of degrees of freedom and the wide range of both spatial and temporal scales render these equa
Externí odkaz:
http://arxiv.org/abs/2410.18708
Autor:
Arnold, Julian, Veliz, Juan Carlos San Vicente, Koner, Debasish, Singh, Narendra, Bemish, Raymond J., Meuwly, Markus
Publikováno v:
J. Chem. Phys. 156, 034301 (2022)
A machine learned (ML) model for predicting product state distributions from specific initial states (state-to-distribution or STD) for reactive atom-diatom collisions is presented and quantitatively tested for the N($^4$S)+O$_{2}$(X$^3 \Sigma_{\rm g
Externí odkaz:
http://arxiv.org/abs/2111.03563
Autor:
Singh, Narendra, Kroells, Michael, Li, Chenxi, Ching, Eric, Ihme, Matthias, Hogan, Christopher J., Schwartzentruber, Thomas
A generalized physics-based expression for the drag coefficient of spherical particles moving in a fluid is derived. The proposed correlation incorporates essential rarefied physics, low-speed hydrodynamics, and shock-wave physics to accurately model
Externí odkaz:
http://arxiv.org/abs/2012.04813
Approach and landing accidents have resulted in a significant number of hull losses worldwide. Technologies (e.g., instrument landing system) and procedures (e.g., stabilized approach criteria) have been developed to reduce the risks. In this paper,
Externí odkaz:
http://arxiv.org/abs/2011.09335
A recombination reaction model for high-temperature chemical kinetics is derived from ab initio simulations data. A kinetic recombination rate model is derived using a recently developed ab initio state-specific dissociation model and the principle o
Externí odkaz:
http://arxiv.org/abs/2009.05882
Machine Learning for Observables: Reactant to Product State Distributions for Atom-Diatom Collisions
Autor:
Arnold, Julian, Koner, Debasish, Käser, Silvan, Singh, Narendra, Bemish, Raymond J., Meuwly, Markus
Publikováno v:
J. Phys. Chem. A 2020, 124, 35, 7177-7190
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 representati
Externí odkaz:
http://arxiv.org/abs/2005.14463
In this article, we propose a generalized model for nonequilibrium vibrational energy distribution functions. The model can be used, in place of equilibrium (Boltzmann) distribution functions, when deriving reaction rate constants for high-temperatur
Externí odkaz:
http://arxiv.org/abs/1912.11428
In this article, we implement a recently developed non-equilibrium chemical kinetics model \cite{singhmodeldevelopment2019} based on \textit{ab initio} simulation data and perform verification studies. Direct molecular simulation data is used to veri
Externí odkaz:
http://arxiv.org/abs/1912.11062
In this article, we propose a generalized non-equilibrium chemical kinetics model from \textit{ab initio} simulation data obtained using accurate potential energy surfaces developed recently for the purpose of studying high-temperature air chemistry.
Externí odkaz:
http://arxiv.org/abs/1912.11025
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