دانلود مقاله ISI انگلیسی شماره 52549
عنوان فارسی مقاله

پیش بینی اثر گذرای شیمی تجزیه در اثر حرارت سوخت در طول افت فشار از طریق مواد متخلخل با استفاده از شبکه های عصبی مصنوعی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
52549 2015 6 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Prediction of transient chemistry effect during fuel pyrolysis on the pressure drop through porous material using artificial neural networks
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Journal of Analytical and Applied Pyrolysis, Volume 115, September 2015, Pages 143–148

کلمات کلیدی
تولید کننده گاز - شبکه های عصبی مصنوعی - مدلسازی - نفوذ - زغال سنگ سوخته - محیط متخلخل
پیش نمایش مقاله
پیش نمایش مقاله پیش بینی اثر گذرای شیمی تجزیه در اثر حرارت سوخت در طول افت فشار از طریق مواد متخلخل با استفاده از شبکه های عصبی مصنوعی

چکیده انگلیسی

Hydrocarbon fuels appear as good candidates for cooling purpose within aerospace applications. Fuel flows through permeable structures. Thus, internal convection cooling is reinforced by chemical kinetics (endothermic effect of fuel pyrolysis). Perfectly tuned conditions may thus rapidly change due to unexpected coke formation that will clog the pores of the material and thus strongly affect the cooling efficiency. The pressure drop is one of the indicators to monitor the modification of the through-flow and thus of the cooling. Having a tool to predict these variations is of practical and theoretical interest for a better management of the complex chemical and physical phenomena. This paper presents a model based on artificial neural networks (ANN) for estimating the transient changes of the pressure drop of a reactive fluid (n-dodecane) under pyrolysis conditions passing through porous metallic material. The ANN is developed using experimental data obtained from an experimental bench, which enables the monitoring of fluid mass flow rate, pressure and temperature in stationary and transient conditions. For each case, the fluid pressure which crosses the metallic porous material is measured as a function of test time, inlet operating pressure, temperature and fuel mass flow rate. The optimal ANN architecture with error back propagation (BPNN) was determined by the cross validation method. The ANN architecture having 9 hidden neurons gives the best choice. Comparing the simulated values by ANN with the experimental data indicates that the ANN model give correct results. The performance of the ANN model is compared with the multiple linear regression model. This work is expected to be used for later prediction of pressure drop under a wide range of clogging conditions.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.