Application of big data analysis technique on high-velocity airblast atomization: Searching for optimum probability density function

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F20%3APU136477" target="_blank" >RIV/00216305:26210/20:PU136477 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0016236120307870" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0016236120307870</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.fuel.2020.117792" target="_blank" >10.1016/j.fuel.2020.117792</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Application of big data analysis technique on high-velocity airblast atomization: Searching for optimum probability density function

Popis výsledku v původním jazyce

In this paper, the droplet size distributions of high-velocity airblast atomization were analyzed. The spray measurement was performed by a Phase-Doppler anemometer at several points and different diameters across the spray for diesel oil, light heating oil, crude rapeseed oil, and water. The atomizing gauge pressure and the liquid preheating temperature varied from 0.3 to 2.4 bar and 25 to 100 °C, respectively. Approximately 400 million individual droplets were recorded; therefore, a big data evaluation technique was applied. 18 of the most commonly used probability density functions (PDF) were fitted to the histogram of each measuring point and evaluated by their relative log-likelihood. Among the three-parameter PDFs, Generalized Extreme Value and Burr PDFs provided the most desirable result to describe a complete drop size distribution. With restriction to two-parameter PDFs, the Nakagami PDF unexpectedly outperformed all the others, including Weibull (Rosin-Rammler) PDF, which is commonly used in atomization. However, if the spray is characterized by a single value, such as the Sauter Mean Diameter, i.e. an expected value-like parameter is of primary importance over the distribution, Gamma PDF is the best option, used in several papers of the atomization literature.

Název v anglickém jazyce

Application of big data analysis technique on high-velocity airblast atomization: Searching for optimum probability density function

Popis výsledku anglicky

In this paper, the droplet size distributions of high-velocity airblast atomization were analyzed. The spray measurement was performed by a Phase-Doppler anemometer at several points and different diameters across the spray for diesel oil, light heating oil, crude rapeseed oil, and water. The atomizing gauge pressure and the liquid preheating temperature varied from 0.3 to 2.4 bar and 25 to 100 °C, respectively. Approximately 400 million individual droplets were recorded; therefore, a big data evaluation technique was applied. 18 of the most commonly used probability density functions (PDF) were fitted to the histogram of each measuring point and evaluated by their relative log-likelihood. Among the three-parameter PDFs, Generalized Extreme Value and Burr PDFs provided the most desirable result to describe a complete drop size distribution. With restriction to two-parameter PDFs, the Nakagami PDF unexpectedly outperformed all the others, including Weibull (Rosin-Rammler) PDF, which is commonly used in atomization. However, if the spray is characterized by a single value, such as the Sauter Mean Diameter, i.e. an expected value-like parameter is of primary importance over the distribution, Gamma PDF is the best option, used in several papers of the atomization literature.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10305 - Fluids and plasma physics (including surface physics)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

FUEL

ISSN

0016-2361

e-ISSN

1873-7153

Svazek periodika

273

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

12

Strana od-do

1-12

Kód UT WoS článku

000528188600004

EID výsledku v databázi Scopus

2-s2.0-85082967183