با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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Insight into simultaneous catalytic oxidation of benzene and toluenein air over the nano-catalyst: Experimental and modeling viaCFD-ANN hybrid method
بینش به اکسیداسیون همزمان کاتالیزوری هوای بنزن و تولوئنین بر روی نانو کاتالیزور: آزمایش و مدل سازی از طریق روش ترکیبی CCD-ANN-2020 tThis study reveals the simultaneous deep oxidation of benzene and toluene over the novel supportedcobalt oxide catalyst derived from metal organic framework (MOF) over the almond shell based activatedcarbon. The performance of the fabricated catalyst was evaluated under the various operating conditionsincluding oxidation temperature, initial concentration of benzene and toluene. The maximum conver-sion of benzene and toluene were also measured to be 89.74 % and 82.37 %, respectively. The samplemorphology was studied by applying XRD, FESEM, BET and TGA analysis. The characterization tests indi-cated that the well dispersed spherical nano-supported catalyst was synthesized with size of less than40 nm. To the best of our knowledge, the computational fluid dynamics (CFD) analysis incorporated withartificial neural network (ANN) was also studied for modeling the deep catalytic oxidation over the pre-pared sample. The modeling involved with the three dimensional analysis of polluted air flow through ofa tubular micro-reactor axial inlet and outlet. The computational fluid dynamics was coded by adoptingCOMSOL Multiphysics to model the catalytic conversion of volatile organic compounds (VOCs) insidethe porous media. The kinetic modeling was also conducted by using three-layer ANN to determine thereaction rates while the reaction temperature, initial concentration of benzene and toluene were consid-ered as the input variables of network. The reaction rates were calculated by a non-linear feed-forwardnetwork with 5 neurons and log-sigmoid function in the hidden layer while the correlation coefficientwas achieved to be 0.99. The validation of CFD model was accomplished which showed the appropriatematching between the experimental data and model achievements. Therefore, the developed intelligenthybrid model (CFD-ANN) in the offered investigation can be a useful tool for studying the fluid dynamicsof VOCs oxidation over the nano-catalyst under the different operating conditions. Keywords:OxidationMetal organic framework | Computational fluid dynamic | Neural network |
مقاله انگلیسی |
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Performance analysis of tilting pad journal bearing using COMSOL Multiphysics and Neural Networks
تجزیه و تحلیل عملکرد تحمل ژورنال پد کج با استفاده از چند رسانه ای و شبکه های عصبی COMSOL-2020 The objective of this paper is to study the effect of geometrical parameters on the performance
of tilting pad journal bearing. COMSOL Multiphysics software is used to simulate the tilting
pad journal bearing at different eccentricity ratios and pad clearances. The effect of changing pad
numbers (from 3 to 6 pads) and the pads clearance angles (from 2 to 6) on the performance
parameters such as the load carrying capacity, frictional torque and attitude angle was analyzed.
It was found that as the number of pads increases the load carrying capacity and the friction coefficient
increase, while the attitude angle decreases. Decreasing the clearance between pads leads to
an increase in the load carrying capacity of the bearing while it has minor effect on the other performance
parameters. The characteristic data obtained from COMSOL program is used to train
three suggested neural networks. The first is a feed-forward Neural Network which consists of three
layers with {80 sigmoid, 5 sigmoid and 1 linear} neurons. The second is a radial bases Neural Network
and the third is a generalized regression Neural Network. Applying the three trained Neural
Networks (Feed forward, Radial basis, and Generalized regression) to predict the performance of
tilting pad bearing at values not used in the training process. The results show a high accuracy to
predict the attitude angle, the coefficient of friction and the load carrying capacity. The percentage
relative errors between the predicted values and that obtained by COMSOL are between (0 and
0.98) %. The neural networks model show high capacity in predicting the output variables correctly
for both the training data and that which was not included. KEYWORDS : Lubrication | Tilting pad journal bearings | Artificial neural networks | Artificial intelligence | COMSOL Multiphysics |
مقاله انگلیسی |
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مدلسازی و شبیه سازی ابرخازن نامتقارن متشکل از کربن فعال / اکسید لیتیم منگنر
سال انتشار: 2015 - تعداد صفحات فایل pdf انگلیسی: 12 - تعداد صفحات فایل doc فارسی: 32 انتظار می رود ابرخازن های نامتقارن، نسل آتی ادوات ذخیره ساز انرژی باشند، زیرا اینها عملکرد مناسبی به لحاظ چگالی انرژی و چگالی توان دارند. این مقاله، نخست مدل ریاضی یک ابرخازن متقارن متشکل از یک الکترود LiMn2O4 و یک الکترود کربن فعال (AC) را ارائه می دهد. مدل دینامیک دربردارنده ی میدان های الکتریکی و تمرکزی، به منظور بررسی تأثیر ضخامت الکترود AC بر عملکرد ابرخازن در نرم افزار COMSOL Multiphysics مورد استفاده قرار می گیرد. جالب اینجاست که اثر ویژه ی آرام سازی در ابرخازن برای نخستین بار با شبیه سازی روند تولید، مصرف و انتقال یون های لیتیم مشاهده شد. مشخص شد که آرام سازی نقش مهمی در بازیابی ظرفیت و افزایش عمر مفید دارد. خطای نسبی چگالی انرژی و چگالی توان در نمودارهای راگون کمتر از 10% است، این را با نتایج تجربی مقایسه کنید که چگال جریان کمتر از 200 آمپر بر متر مربع را داریم. بطور گسترده تأکید شده که مدل ارائه شده، کارآمد است و با موفقیت روشی برای بهینه نمودن اندازه ی سلول در کاربردهای مختلف فراهم می آورد.
کلیدواژه ها: خازن نامتقارن | مدلسازی | شبیه سازی | اثر آرام سازی | نمودار راگون |
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