فصلنامه علمی کارافن

فصلنامه علمی کارافن

مطالعه عددی و آنالیز پارامترهای حرارتی جوشش جریانی مادون سرد و ارائه مدل‌های پیش‌بینی مبتنی بر الگوریتم شبکه عصبی مصنوعی

نوع مقاله : مقاله پژوهشی (کاربردی)

نویسندگان
1 عضو هیئت علمی، گروه مهندسی مکانیک، دانشگاه فنی و حرفه‌ای، تهران، ایران.
2 دانش‌آموخته کارشناسی ارشد، دانشکده مهندسی برق، دانشگاه صنعتی مالک اشتر، تهران، ایران.
3 استادیار، گروه مهندسی مکانیک، دانشگاه فنی و حرفه‌ای، تهران، ایران.
4 دانش‌آموخته کارشناسی ارشد، دانشکده مهندسی مکانیک، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران.
چکیده
در مطالعه حاضر، با استفاده از شبیه‌سازی عددی اکسی سیمتریک مبتنی بر روش اویلر-اویلر جوشش جریانی مادون سرد آب خالص در یک لوله را مورد بررسی قرار داده و ضریب انتقال حرارت محلی و متوسط، کسر حجمی بخار محلی و متوسط و دمای دیواره محلی و متوسط تحت شرایط مرزی مختلف برای مورد بررسی قرار گرفته است. با توجه به نتایج به دست آمده از شبیه‌سازی عددی، دمای دیواره با افزایش فشار افزایش می‌یابد. همچنین کسر حجمی بخار با افزایش فشار کاهش یافته است. تاثیر شار حرارتی بر دمای دیواره و کسر حجمی بخار بیشتر از تمامی شرایط مرزی دیگر می‌باشد. اگرچه رویکرد‌های عددی دید کاملی در مورد الگوی جریان و ویژگی‌‌های حرارتی می‌دهند، شبیه‌سازی جریان‌های چندفازی پیچیده نیازمند منابع محاسباتی بالایی است و بسیار زمان‌بر است. در نتیجه، ما یک رویکرد یادگیری عمیق مبتنی بر شبکه‌‌های عصبی مصنوعی برای پیش‌بینی پارامتر‌های ذکر شده در آب خالص ارائه می‌کنیم. مدل‌های ارائه شده در مطالعه حاضر با استفاده از روش بهینه‌سازی فراپارامترها به صورت دقیق پارامترهای خروجی را پیش‌بینی می‌کنند. نتایج حاصل از مدل‌‌های پیش‌بینی نشان می‌دهد که این مدل‌‌ها قادر به پیش‌بینی دقیق توابع هدف با میانگین خطای مطلق کمتر از 2/5% و ضریب تعیین بیشتر از 0/9 است.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Numerical Study and Analysis of Thermal Parameters of Subcooled Flow Boiling and Presentation of Prediction Models based on Artificial Neural Network Algorithm

نویسندگان English

Milad Esfandiar 1
Gholamreza Pourabdi 2
Mostafa Akbari 3
Erfan Eskandari 4
1 Faculty Member, Department of Mechanical Engineering, Technical and Vocational University (TVU), Tehran, Iran.
2 MSc., Faculty of Electrical Engineering Malek Ashtar University of Technology (MUT), Tehran, Iran.
3 Assistant Professor, Department of Mechanical Engineering, Technical and Vocational University (TVU), Tehran, Iran.
4 MSc., Faculty of Mechanical Engineering, Khaje Nasir Toosi University of Technology (KNTU), Tehran, Iran.
چکیده English

In the present study, using axisymmetric numerical simulation based on the Euler-Euler method, subcooled flow boiling of pure water in a pipe was investigated and the local and average heat transfer coefficient, local and average vapor volume fraction, and local and average wall temperature under different boundary conditions have been investigated. According to the results obtained from the numerical simulations, the wall temperature increases with increasing pressure. Also, the vapor volume fraction has decreased with increasing pressure. The effect of heat flux on wall temperature and vapor volume fraction is greater than all other boundary conditions. Although numerical approaches give a complete insight into the flow pattern and thermal characteristics, the simulation of complex multiphase flows requires high computational resources and is very time-consuming. In conclusion, we present a deep learning approach based on artificial neural networks to predict the mentioned parameters in pure water. The models presented in the present study accurately predict the output parameters using the hyperparameter tuning method. The results of the prediction models show that these models are able to accurately predict the objective functions with an average absolute error of less than 2.5% and a coefficient of determination greater than 0.9.

کلیدواژه‌ها English

Numerical Simulation
Machine Learning
Artificial Neural networks
Heat Transfer Coefficient
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دوره 20، شماره 1 - شماره پیاپی 61
فنی و مهندسی
بهار 1402
صفحه 151-173

  • تاریخ دریافت 14 اسفند 1401
  • تاریخ بازنگری 03 اردیبهشت 1402
  • تاریخ پذیرش 08 خرداد 1402