<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Equivalence ratio (<italic>Φ</italic>) is one of the most important parameters in combustion diagnostics. In previous studies, flame color characteristics have been widely applied to model the <italic>Φ</italic> of premixed hydrocarbon flames. The flame spatial characteristics also change with the varying <italic>Φ</italic>. In this paper, a high-speed color camera was employed to capture the premixed propane flame images under different <italic>Φ</italic> conditions (<italic>Φ = 0.93 to 1.53</italic>). Then, the relationship between the spatial characteristics and the <italic>Φ</italic> variation was investigated. The area and height of propane premixed flames perform a strong sensitive response to the <italic>Φ</italic> variation. Based on the research above, the <italic>Φ</italic> measurement models were constructed using color and spatial characteristics. A comparison was made between the color characteristics (<italic>Color-Φ</italic>) model and the color-spatial characteristics (<italic>Multi-dimensional-Φ</italic>) model. Both models were applied to a set of color images of a premixed propane flame, and the result indicates that the <italic>Multi-dimensional-Φ</italic> model performs with higher accuracy.</p>
</abstract>

Equivalence ratio (Φ) is one of the most important parameters in combustion diagnostics. In previous studies, flame color characteristics have been widely applied to model the Φ of premixed hydrocarbon flames. The flame spatial characteristics also change with the varying Φ. In this paper, a high-speed color camera was employed to capture the premixed propane flame images under different Φ conditions (Φ = 0.93 to 1.53). Then, the relationship between the spatial characteristics and the Φ variation was investigated. The area and height of propane premixed flames perform a strong sensitive response to the Φ variation. Based on the research above, the Φ measurement models were constructed using color and spatial characteristics. A comparison was made between the color characteristics (Color-Φ) model and the color-spatial characteristics (Multi-dimensional-Φ) model. Both models were applied to a set of color images of a premixed propane flame, and the result indicates that the Multi-dimensional-Φ model performs with higher accuracy.

Equivalence Ratio Modelling of Premixed Propane Flame by Multiple Linear Regression Using Flame Color and Spatial Characteristics

Measurement Science Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Equivalence ratio (Φ) is one of the most important parameters in combustion diagnostics. In previous studies, flame color characteristics have been widely...

Equivalence Ratio Modelling of Premixed Propane Flame by Multiple Linear Regression Using Flame Color and Spatial Characteristics

Pubblicato online: 07 mar 2023

Pagine: 40 - 46

Ricevuto: 13 giu 2022

Accettato: 22 ago 2022

DOI: https://doi.org/10.2478/msr-2023-0005

Parole chiave
Combustion diagnostics, equivalence ratio modelling, flame color characteristic, flame spatial characteristic, multiple linear regression

© 2023 Hao Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Equivalence Ratio Modelling of Premixed Propane Flame by Multiple Linear Regression Using Flame Color and Spatial Characteristics

Pubblicato online: 07 mar 2023

Pagine: 40 - 46

Ricevuto: 13 giu 2022

Accettato: 22 ago 2022

DOI: https://doi.org/10.2478/msr-2023-0005

Parole chiaveCombustion diagnostics, equivalence ratio modelling, flame color characteristic, flame spatial characteristic, multiple linear regression

© 2023 Hao Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
Combustion diagnostics, equivalence ratio modelling, flame color characteristic, flame spatial characteristic, multiple linear regression