2019
DOI: 10.1177/1468087419864469
|View full text |Cite
|
Sign up to set email alerts
|

Understanding the diesel-like spray characteristics applying a flamelet-based combustion model and detailed large eddy simulations

Abstract: This investigation analyses the structure of spray A from engine combustion network (ECN), which is representative of diesel-like sprays, by means of large eddy simulations and an unsteady flamelet progress variable combustion model. A very good agreement between modelled and experimental measurements is obtained for the inert spray that supports further analysis. A parametric variation in oxygen concentration is carried out in order to describe the structure of the flame and how it is modified when mixture re… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3

Citation Types

1
8
0

Year Published

2019
2019
2024
2024

Publication Types

Select...
9

Relationship

0
9

Authors

Journals

citations
Cited by 14 publications
(9 citation statements)
references
References 61 publications
1
8
0
Order By: Relevance
“…14 In particular, combustion process must be investigated and improved with an optimal combination of combustion chamber design, compression ratio, and injection strategy, 57 since it has the potential to increase thermal efficiency and at the same time, ensure acceptable levels of pollutant emissions, which however represents an extraordinary challenging task due to the complex physical phenomena involved in diesel combustion: evolution of multi-phase flows, hydrocarbon auto-ignition, and diffusion flame propagation in an inhomogeneous, turbulent high-pressure flow. 8 Such aspect requires the support of reliable computational fluid dynamic tools. In the last decades, most efforts were focused on the development of the models based on complex chemistry and including turbulence–chemistry interaction for a proper description of fuel auto-ignition, flame structure evolution, and pollutant emissions formation including representative interactive flamelet (RIF), 9,10 transport probability density function (TPDF), 11 and conditional moment closure (CMC).…”
Section: Introductionmentioning
confidence: 99%
“…14 In particular, combustion process must be investigated and improved with an optimal combination of combustion chamber design, compression ratio, and injection strategy, 57 since it has the potential to increase thermal efficiency and at the same time, ensure acceptable levels of pollutant emissions, which however represents an extraordinary challenging task due to the complex physical phenomena involved in diesel combustion: evolution of multi-phase flows, hydrocarbon auto-ignition, and diffusion flame propagation in an inhomogeneous, turbulent high-pressure flow. 8 Such aspect requires the support of reliable computational fluid dynamic tools. In the last decades, most efforts were focused on the development of the models based on complex chemistry and including turbulence–chemistry interaction for a proper description of fuel auto-ignition, flame structure evolution, and pollutant emissions formation including representative interactive flamelet (RIF), 9,10 transport probability density function (TPDF), 11 and conditional moment closure (CMC).…”
Section: Introductionmentioning
confidence: 99%
“…The liquid fuel is found to penetrate very fast at the very beginning of injection, it then stops penetrating and begins to fluctuate near an almost fixed axial position, while the vapor phase fuel penetration and fuel injection continue. 2934 The maximum liquid penetration length (i.e. liquid length) is generally used to represent the mean value of liquid penetration length during the quasi-steady stage.…”
Section: Introductionmentioning
confidence: 99%
“…Corresponding computational simulations were carried out by various groups using RANS approaches, 27 coupled with conditional moment closure (CMC), 28,29 transported probability density function (TPDF) 3033 or flamelet-type models, 3436 or LES approaches. 3747 Among others, work by Bolla et al 28 and Pei et al 30,32 show that while acceptable trends can be achieved for the bulk of the available experimental data without changing model input parameters, matching the results quantitatively is more difficult.…”
Section: Introductionmentioning
confidence: 99%