مدل سازی درصد کاهش هد جریان غلیظ نمکی با استفاده از هوش مصنوعی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه مهندسی عمران، واحد نجف آباد، دانشگاه آزاد اسلامی، نجف‌آباد، ایران.

2 استاد، گروه سازه های آبی، دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز، اهواز، ایران.

3 استاد، گروه مهندسی عمران، واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران و گروه مهندسی آب، دانشکده کشاورزی، دانشگاه صنعتی اصفهان، اصفهان، ایران

چکیده

جریان‌ غلیظ یکی از مهمترین عوامل در فرآیند رسوب‌گذاری سدها می‌باشد. چون این جریان‌ از عوامل موثر بر کاهش کارایی عمر سدهای بزرگ بوده، بنابراین درک الگوهای رسوب‌گذاری جهت مدیریت مخزن سدها بسیار کارآمد می‌باشد. براین اساس در این تحقیق درصد کاهش هد جریان غلیظ نمکی تحت تاثیر موانع نفوذپذیر ذوزنقه‌ای شکل (پر شده با دانه-های شن با قطر 0.5 سانتی‌متر)، با در نظر گرفتن متغیرهایی همچون دبی، شیب، غلظت و ارتفاع موانع به‌صورت آزمایشگاهی مورد بررسی قرار گرفت، براساس نتایج حاصله اقدام به مدل‌سازی هد جریان غلیظ نمکی با روش شبکه عصبی مصنوعی پیش‌خور و روش کلاسیک رگرسیون چند متغیره شد و کارکرد این دو روش مورد مقایسه قرار گرفت. نتایج نشان داد که روش هوشمند شبکه عصبی مصنوعی پیش‌خور در مدل‌سازی درصد کاهش هد جریان غلیظ نمکی نسبت به روش رگسیون چند متغیره برتری قابل توجهی دارد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Modeling the reduction percentage of the density current head flux using artificial intelligence

نویسندگان [English]

  • Mehdi Derakhshannia 1
  • Mehdi Ghomeshi 2
  • Seyed Saeid Eslamian 3
  • Seyed Mahmood Kashefipour 2
1 Ph.D. Candidate, Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
2 Professor, Department of Water Structures, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
3 Professor, Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran. and Department of Water Engineering, College of Agriculture, Isfahan University of Technology, Isfahan, Iran
چکیده [English]

Density current is one of the most important factors in the sedimentation process of dams. Because this current is one of the important factors affecting the reduction of life efficiency of large dams, so understanding sedimentation patterns to manage the reservoir of dams is very effective. Accordingly, in this study, the reduction percentage of the density current head flux under the influence of trapezoidal permeable barriers (filled with sand grains with a diameter of 0.5 cm) is investigated also variable parameters effect such as discharge, slope, concentration and height of obstacles on density current control is examined experimentally, based on the results, the reduction percentage of the density current head flux was modeled using the artificial neural network feed-forward method and the classical multivariable regression method, and the performance of these two methods was compared. The results showed that the intelligent method of feed-forward artificial neural network has a significant advantage over the multivariable regression method in modeling the reduction percentage of the density current head flux.

کلیدواژه‌ها [English]

  • Density current
  • Feed-forward artificial neural network
  • Head reduction percentage
  • Multivariable regression

مراجع

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مدیریت آب و آبیاری
دوره 11، شماره 2
شهریور 1400
صفحه 131-143

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