Ahmadfard, M., Kazemzadeh-parsi, M., Tahavvor, A. (2010). Inverse Identification of Circular Cavity in a 2D Object via Boundary Temperature Measurements Using Artificial Neural Network. Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering, 3(1), 21-30.
Mohammad Amin Ahmadfard; Mohammad Javad Kazemzadeh-parsi; Ali Reza Tahavvor. "Inverse Identification of Circular Cavity in a 2D Object via Boundary Temperature Measurements Using Artificial Neural Network". Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering, 3, 1, 2010, 21-30.
Ahmadfard, M., Kazemzadeh-parsi, M., Tahavvor, A. (2010). 'Inverse Identification of Circular Cavity in a 2D Object via Boundary Temperature Measurements Using Artificial Neural Network', Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering, 3(1), pp. 21-30.
Ahmadfard, M., Kazemzadeh-parsi, M., Tahavvor, A. Inverse Identification of Circular Cavity in a 2D Object via Boundary Temperature Measurements Using Artificial Neural Network. Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering, 2010; 3(1): 21-30.
Inverse Identification of Circular Cavity in a 2D Object via Boundary Temperature Measurements Using Artificial Neural Network
1M.Sc. Student, Islamic Azad University, Shiraz Branch
2Assistant Porfessor, Islamic Azad University, Shiraz Branch.
3Assistant Porfessor, Islamic Azad University, Shiraz Branch
Abstract
In geometric inverse problems, it is assumed that some parts of domain boundaries are not accessible and geometric shape and dimensions of these parts cannot be measured directly. The aim of inverse geometry problems is to approximate the unknown boundary shape by conducting some experimental measurements on accessible surfaces. In the present paper, the artificial neural network is used to solve these kinds of problems in conduction heat transfer in 2D objects. In order to train the neural network, some direct problems are solved by using the finite element method. In order to evaluate the applicability of the proposed method, different cases with different number of measuring points and different error levels are examined. The results show that the ANN can effectively be used in solving inverse geometry problems in heat conduction.
تشخیص معکوس حفره دایره‌ای در جسم دو بعدی با اندازه‌گیری‌های دمایی و شبکه عصبی مصنوعی
Authors [Persian]
محمد امین احمدفرد1; محمد جواد کاظم زاده پارسی2; علیرضا تهور3
1دانشجوی کارشناسی ارشد مهندسی مکانیک، دانشگاه آزاد اسلامی واحد شیراز، گروه مهندسی مکانیک.
2استادیار، دانشگاه آزاد اسلامی، واحد شیراز، گروه مهندسی مکانیک
3استادیار، دانشگاه آزاد اسلامی واحد شیراز، گروه مهندسی مکانیک
Abstract [Persian]
در مسائل تشخیص شکل که جزء مسائل معکوس هندسی دسته‌بندی می‌شوند فرض می‌شود که قسمت‌هایی از دامنه مسئله در دسترس نبوده و نمی‌توان شکل و ابعاد هندسی چنین قسمت‌هایی را به‌طور مستقیم اندازه‌گیری نمود. هدف این است که با انجام اندازه‌گیری دما روی سطوح قابل دسترس، شکل هندسی قسمت‌های دور از دسترس را تخمین زد. از روش شبکه عصبی مصنوعی برای حل مسئله انتقال حرارت معکوس و تشخیص شکل و مکان حفره به‌صورت غیر‌مخرب استفاده شده است به این صورت که با آموزش شبکه عصبی با استفاده از روش اجزا محدود، رفتار انتقال حرارت هدایت جسم بر اساس مکان و اندازه دو بعدی حفره مدل‌سازی شده سپس از این مدل برای تخمین مکان و اندازه حفره مخفی درون جسم در شرایط جدید استفاده شده است. با مقایسه نتایج شبکه عصبی با مقادیر واقعی نتیجه می‌شود که روش مورد مطالعه از دقت بالایی برخوردار است.
Keywords [Persian]
تشخیص شکل، انتقال‌حرارت معکوس، شبکه عصبی، روش اجزا محدود
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