Barzegar, Ramın

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Ramın, Barzegar
Barzegar, Ramın
Barzegar,R.
B.,Ramin
Ramin, Barzegar
R.,Barzegar
R., Barzegar
B., Ramin
B.,Ramın
Barzegar, Ramin
Job Title
Doktor Öğretim Üyesi
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ramin.barzegar@atilim.edu.tr
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Scholarly Output

2

Articles

2

Citation Count

9

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0

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2022 - 20232
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Now showing 1 - 2 of 2
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    Article
    Citation Count: 5
    Co-combustion of high and low ash lignites with raw and torrefied biomass under air and oxy-fuel combustion atmospheres
    (Taylor & Francis inc, 2022) Barzegar, Ramın; Yozgatligil, Ahmet; Barzegar, Ramin; Automotive Engineering
    Co-combustion characteristics of high and low ash lignites blended with raw and torrefied pine woodchips were studied by Thermogravimetric Analyzer (TGA) under air and oxy-fuel conditions. The lignites were blended with biomass samples at the mass fraction of 50/50 wt.%. Three heating rates of 10, 20, and 40 degrees C/min were chosen, and the characteristic temperatures, including initial, ignition, and burnout temperatures, were obtained. In order to estimate the activation energies of the co-combustion of the blends, Flynn-Wall-Ozawa, Kissinger-Akahira-Sunose, and Friedman kinetic methods were employed. Additionally, to assess the summative behavior of the fuel blends, the relative error as a degree of synergism was calculated based on the difference between theoretical and experimental DTG profiles. It was seen that co-combustion of torrefied biomass with the low ash Orhaneli lignite in air resulted in the average relative error of 21.41%, indicating the maximum synergism for the blend. This value was 9.59% under oxy-fuel combustion atmosphere. Blending torrefied biomass with the high ash Soma lignite resulted in average relative errors of 1.34% and 1.45% under air and oxy-fuel combustion atmospheres showing an insignificant synergetic effect. An improvement in combustion performance was noticed under oxy-fuel combustion conditions. The average activation energy values for the blend of torrefied biomass and Orhaneli lignite were 54.47 and 112.48 kJ/mol under air and oxy-fuel combustion atmospheres, which were lower than that of the parent fuels indicating higher reactivity of the blends. This trend was not seen for Soma lignite. The associated uncertainty values for the FWO method were in the range of 3.57% to 12.45% making it a proper tool for obtaining the kinetic parameters.
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    Article
    Citation Count: 4
    Prediction of Composite Mechanical Properties: Integration of Deep Neural Network Methods and Finite Element Analysis
    (Mdpi, 2023) Barzegar, Ramın; Ege, Faraz; Barzegar, Ramin; Automotive Engineering
    Extracting the mechanical properties of a composite hydrogel; e.g., bioglass (BG)-collagen (COL), is often difficult due to the complexity of the experimental procedure. BGs could be embedded in the COL and thereby improve the mechanical properties of COL for bone tissue engineering applications. This paper proposed a deep-learning-based approach to extract the mechanical properties of a composite hydrogel directly from the microstructural images. Four datasets of various shapes of BGs (9000 2D images) generated by a finite element analysis showed that the deep neural network (DNN) model could efficiently predict the mechanical properties of the composite hydrogel, including the Young's modulus and Poisson's ratio. ResNet and AlexNet architecture were tuned to ensure the excellent performance and high accuracy of the proposed methods with R-values greater than 0.99 and a mean absolute error of the prediction of less than 7%. The results for the full dataset revealed that AlexNet had a better performance than ResNet in predicting the elastic material properties of BGs-COL with R-values of 0.99 and 0.97 compared to 0.97 and 0.96 for the Young's modulus and Poisson's ratio, respectively. This work provided bridging methods to combine a finite element analysis and a DNN for applications in diverse fields such as tissue engineering, materials science, and medical engineering.