A polynomial regression model for stabilized turbulent confined jet diffusion flames using bluff body burners

Gendy, Tahani S.; El-Shiekh, T. M.; Zakhary, Amal S.

doi:10.1016/j.ejpe.2015.06.001

Cited by 15 publications

(7 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The high value of r 2 obtained indicates the adequacy of the Yeoh Model on representing the experimental results. The outcome shows that there is a real relation between stretch, λ, and stress, σ, [43] for all tested specimens in present work. Nevertheless, at lower stretch range (1 ≤ λ ≤ 3.25), a high discrepancy occurs to the projections ( Fig.…”

Section: Discussionsupporting

confidence: 57%

See 1 more Smart Citation

Quantifying Tensile Properties of Bamboo Silicone Biocomposite using Yeoh Model

Hassan

Mahmud

Majeed

et al. 2018

IJET

View full text Add to dashboard Cite

The utilisation of bamboo has the potential of improving the properties of silicone. However, a thorough investigation has yet to be reported on the mechanical properties of bamboo silicone biocomposite. This study was carried out with the aim to quantify the tensile properties and assess the tensile behaviour of bamboo silicone biocomposite using Yeoh hyperelastic constitutive function. The specimens were prepared from the mix of bamboo particulate and pure silicone at various fibre composition ratio (0wt%, 1wt%, 3wt% and 5wt%) cured overnight at room temperature. A uniaxial tensile test was carried out by adopting the ASTM D412 testing standard. The Coefficient of Variation, CV, and the Coefficient of Determination, r 2 , were determined to assess the reliability of the experimental data and fitting model. The results of the determined Yeoh material constants for 5wt% specimen is found to be C 1 = 12.0603×10 -3 MPa, C 2 = 8.7353×10 -5 MPa and C 3 = -11.6165×10 -8 MPa, compared to pure silicone (0wt%) C 1 = 5.6087×10 -3 MPa, C 2 = 8.6639×10 -5 MPa and C 3 = -7.6510×10 -8 MPa. The results indicate that the bamboo fibre improves the stiffness of the silicone rubber by 115 percent. A low variance was exhibited by the experimental data with a CV value of less than 8 percent. The Yeoh Model demonstrated an excellent prediction of the elastic behaviour of bamboo silicone biocomposite with a fitting accuracy of more than 99.93 percent.

show abstract

Section: Discussionsupporting

confidence: 57%

“…This method has been employed in various studies due to its effectiveness in solving multiple-constant polynomial equation [43,44].…”

Section: Determining the Yeoh Materials Constantsmentioning

confidence: 99%

Quantifying Tensile Properties of Bamboo Silicone Biocomposite using Yeoh Model

Hassan

Mahmud

Majeed

et al. 2018

IJET

View full text Add to dashboard Cite

show abstract

“…Response surface methodology (RSM) is an assembly of mathematical and statistical technique used for modeling and analyzing a process in which a response of interest is influenced by several variables and the aim is to optimize this response [10] [11] [12] [13]. This technique is one of the major quantitative tools in industrial decision making as it gives better understanding of the process; it helps the process engineer to see the effect of the control variables simultaneously and the interactions among all the variables [10].…”

Section: Response Surface Methodologymentioning

confidence: 99%

Response Surface Modeling of Fuel Rich and Fuel Lean Catalytic Combustion of the Stabilized Confined Turbulent Gaseous Diffusion Flames

Gendy¹,

Ghoneim²,

Zakhary³

2019

WJET

Self Cite

View full text Add to dashboard Cite

The Response Surface Methodology (RSM) has been applied to explore the thermal structure of the experimentally studied catalytic combustion of stabilized confined turbulent gaseous diffusion flames. The Pt/γAl 2 O 3 and Pd/γAl 2 O 3 disc burners were situated in the combustion domain and the experiments were performed under both fuel-rich and fuel-lean conditions at a modified equivalence (fuel/air) ratio (Ø) of 0.75 and 0.25 respectively. The thermal structure of these catalytic flames developed over the Pt and Pd disc burners were inspected via measuring the mean temperature profiles in the radial direction at different discrete axial locations along the flames. The RSM considers the effect of the two operating parameters explicitly (r), the radial distance from the center line of the flame, and (x), axial distance along the flame over the disc, on the measured temperature of the flames and finds the predicted maximum temperature and the corresponding process variables. Also the RSM has been employed to elucidate such effects in the three and two dimensions and displays the location of the predicted maximum temperature.

show abstract

“…• Experimental Details of the experimental setup and the data employed in this study have been given the previous work of [17] [44].…”

Section: Application Of Rsm and Ann To The Present Workmentioning

confidence: 99%

“…The Box-Cox method has been utilized to identify a suitable power transformation to the response data for normalizing the data or equalizing its variance. The following transformations; of the mean temperature T dependent variable; sqrt(T), Ln(T) and (T) have been resulted and employed for D30, D60 and DFC respectively to represent the response Y in Equation 2 [31].…”

Section: Models Validation and Evaluationmentioning

confidence: 99%

Comparative Appraisal of Response Surface Methodology and Artificial Neural Network Method for Stabilized Turbulent Confined Jet Diffusion Flames Using Bluff-Body Burners

Gendy¹,

Ghoneim²,

Zakhary³

2020

WJET

Self Cite

View full text Add to dashboard Cite

The present study was conducted to present the comparative modeling, predictive and generalization abilities of response surface methodology (RSM) and artificial neural network (ANN) for the thermal structure of stabilized confined jet diffusion flames in the presence of different geometries of bluff-body burners. Two stabilizer disc burners tapered at 30˚ and 60˚ and another frustum cone of 60˚/30˚ inclination angle were employed all having the same diameter of 80 (mm) acting as flame holders. The measured radial mean temperature profiles of the developed stabilized flames at different normalized axial distances () j x d were considered as the model example of the physical process. The RSM and ANN methods analyze the effect of the two operating parameters namely () r , the radial distance from the center line of the flame, and () j x d on the measured temperature of the flames, to find the predicted maximum temperature and the corresponding process variables. A three-layered Feed Forward Neural Network in conjugation with the hyperbolic tangent sigmoid (tansig) as transfer function and the optimized topology of 2:10:1 (input neurons: hidden neurons: output neurons) was developed. Also the ANN method has been employed to illustrate such effects in the three and two dimensions and shows the location of the predicted maximum temperature. The results indicated the superiority of ANN in the prediction capability as the ranges of R 2 and F Ratio are 0.868-0.947 and 231.7-864.1 for RSM method compared to 0.964-0.987 and 2878.8 7580.7 for ANN method beside lower values for error analysis terms.

show abstract

A polynomial regression model for stabilized turbulent confined jet diffusion flames using bluff body burners

Cited by 15 publications

References 11 publications

Quantifying Tensile Properties of Bamboo Silicone Biocomposite using Yeoh Model

Quantifying Tensile Properties of Bamboo Silicone Biocomposite using Yeoh Model

Response Surface Modeling of Fuel Rich and Fuel Lean Catalytic Combustion of the Stabilized Confined Turbulent Gaseous Diffusion Flames

Comparative Appraisal of Response Surface Methodology and Artificial Neural Network Method for Stabilized Turbulent Confined Jet Diffusion Flames Using Bluff-Body Burners

Contact Info

Product

Resources

About