Large scale simulations of two-dimensional nonpremixed methane jet flames

2000 ◽  
Vol 123 (4) ◽  
pp. 465-487 ◽  
Author(s):  
S. James ◽  
F.A. Jaberi
1995 ◽  
Vol 213 (1-2) ◽  
pp. 41-49 ◽  
Author(s):  
Raúl Toral ◽  
Amitabha Chakrabarti ◽  
James D. Gunton

2020 ◽  
Vol 495 (1) ◽  
pp. 816-834
Author(s):  
P Lesaffre ◽  
P Todorov ◽  
F Levrier ◽  
V Valdivia ◽  
N Dzyurkevich ◽  
...  

ABSTRACT The interstellar medium (ISM) is typically a hostile environment: cold, dilute and irradiated. Nevertheless, it appears very fertile for molecules. The localized heating resulting from turbulence dissipation is a possible channel to produce and excite molecules. However, large-scale simulations cannot resolve the dissipative scales of the ISM. Here, we present two-dimensional small-scale simulations of decaying hydrodynamic turbulence using the chemses code, with fully resolved viscous dissipation, time-dependent heating, cooling, chemistry and excitation of a few rotational levels of H2. We show that molecules are produced and excited in the wake of strong dissipation ridges. We carefully identify shocks and we assess their statistics and contribution to the molecular yields and excitation. We find that the formation of molecules is strongly linked to increased density as a result of shock compression and to the opening of endothermic chemical routes because of higher temperatures. We identify a new channel for molecule production via H2 excitation, illustrated by CH+ yields in our simulations. Despite low temperatures and the absence of magnetic fields (favouring CH+ production through ion-neutral velocity drifts), the excitation of the first few rotational levels of H2 shrinks the energy gap to form CH+. The present study demonstrates how dissipative chemistry can be modelled by statistical collections of one-dimensional steady-state shocks. Thus, the excitation of higher J levels of H2 is likely to be a direct signature of turbulence dissipation, and an indirect probe for molecule formation. We hope these results will help to bring new tools and ideas for the interpretation of current observations of H2 rotational lines carried out using the Stratospheric Observatory for Infrared Astronomy (SOFIA), and pave the way for a better understanding of the high-resolution mapping of H2 emission by future instruments, such as theJames Webb Space Telescope and the Space Infrared Telescope for Cosmology and Astrophysics.


2000 ◽  
Vol 11 (07) ◽  
pp. 1437-1454 ◽  
Author(s):  
M. BERNASCHI ◽  
F. CASTIGLIONE

In Ref. 1, a new model for the description of the financial markets dynamics has been proposed. Traders move on a two dimensional lattice and interact by means of mechanisms of mutual influence. In the present paper, we present results from large-scale simulations of the same model enhanced by the introduction of rational traders modeled as moving-averages followers. The dynamics now accounts for log-normal distribution of volatility which is consistent with some observation of real financial indexes7 at least for the central part of the distribution.


Author(s):  
Jian Tao ◽  
Werner Benger ◽  
Kelin Hu ◽  
Edwin Mathews ◽  
Marcel Ritter ◽  
...  

SLEEP ◽  
2021 ◽  
Author(s):  
Dorothee Fischer ◽  
Elizabeth B Klerman ◽  
Andrew J K Phillips

Abstract Study Objectives Sleep regularity predicts many health-related outcomes. Currently, however, there is no systematic approach to measuring sleep regularity. Traditionally, metrics have assessed deviations in sleep patterns from an individual’s average. Traditional metrics include intra-individual standard deviation (StDev), Interdaily Stability (IS), and Social Jet Lag (SJL). Two metrics were recently proposed that instead measure variability between consecutive days: Composite Phase Deviation (CPD) and Sleep Regularity Index (SRI). Using large-scale simulations, we investigated the theoretical properties of these five metrics. Methods Multiple sleep-wake patterns were systematically simulated, including variability in daily sleep timing and/or duration. Average estimates and 95% confidence intervals were calculated for six scenarios that affect measurement of sleep regularity: ‘scrambling’ the order of days; daily vs. weekly variation; naps; awakenings; ‘all-nighters’; and length of study. Results SJL measured weekly but not daily changes. Scrambling did not affect StDev or IS, but did affect CPD and SRI; these metrics, therefore, measure sleep regularity on multi-day and day-to-day timescales, respectively. StDev and CPD did not capture sleep fragmentation. IS and SRI behaved similarly in response to naps and awakenings but differed markedly for all-nighters. StDev and IS required over a week of sleep-wake data for unbiased estimates, whereas CPD and SRI required larger sample sizes to detect group differences. Conclusions Deciding which sleep regularity metric is most appropriate for a given study depends on a combination of the type of data gathered, the study length and sample size, and which aspects of sleep regularity are most pertinent to the research question.


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