مدل‌سازی رفتار جریان غلیظ با الگوریتم‌های یادگیری ماشین

محورهای موضوعی : مقالات برگرفته از پایان نامه

مهدی درخشان نیا ¹ (دانشجوی دکتری، گروه مهندسی عمران، واحد نجف آباد، دانشگاه آزاد اسلامی، نجف‌آباد، ایران.)
مهدی قمشی ² (استاد، گروه سازه های آبی، دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز، اهواز، ایران.)
سید سعید اسلامیان 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)
سید محمود کاشفی پور ⁴ (استاد، گروه سازه های آبی، دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز، اهواز، ایران.)

تاریخ دریافت : 1399/10/30 تاریخ پذیرش : 1400/06/10 تاریخ انتشار : 1401/08/01

کلید واژه: رسوب‌گذاری, درصد کاهش هد, جریان‌ غلیظ, سیستم استنتاج عصبی_ فازی تطبیقی, شبکه عصبی مصنوعی پیش‌خور,

چکیده مقاله :

چکیده
مقدمه :  جریان چگال یکی از عوامل موثر بر انتقال رسوبات به مخازن سدها می باشد. در این راستا یکی از روش های عملی برای کنترل رسوبات، ایجاد مانع در مسیر این جریان ها می­باشد.
روش :  در این تحقیق آزمایشگاهی، رفتار جریان چگالی تحت تأثیر موانع استوانه‌ای ساخته شده از چوب با قطر 1.5 سانتی‌متر و ارتفاع 30 سانتی‌متر (بیش از ارتفاع بدنه جریان چگالی) مورد ارزیابی قرار گرفت. بنابراین با در نظر گرفتن متغیرهایی مانند شیب کف، غلظت و دبی، مقادیر هد جریان غلیظ تعیین شد. همچنین در این مقاله از الگوریتم‌های یادگیری ماشین مانند سیستم استنتاج فازی-عصبی تطبیقی و شبکه عصبی مصنوعی برای مدل‌سازی نتایج استفاده شد.
یافته ­ها :  بر اساس نتایج، هد جریان نمک چگال با استفاده از الگوریتم‌های یادگیری ماشینی مانند سیستم استنتاج عصبی فازی تطبیقی و شبکه عصبی مصنوعی مدل‌سازی انجام شد و عملکرد این دو روش مقایسه شد. نتایج نشان داد که الگوریتم‌های یادگیری ماشین در مدل‌سازی هد جریان نمک چگالی مفید هستند و مقادیر رگرسیون سیستم استنتاج فازی عصبی تطبیقی برای داده های آموزش و آزمون 0.99 و رگرسیون شبکه عصبی مصنوعی به ترتیب 0.94 و 0.91 به دست آمد.
نتیجه ­گیری : با مقایسه این دو روش مشخص شد که سیستم استنتاج عصبی-فازی تطبیقی در مدل‌سازی درصد کاهش جریان سر چگالی نسبت به روش شبکه عصبی مصنوعی پیش‌خور مؤثرتر بوده است.

چکیده انگلیسی:

Abstract
Introduction: Density current is one of the factors influencing the transfer of sediments to reservoirs of dams. One of the practical methods to control sediments is to build an obstacle in the path of these currents.
Methods: In this laboratory research, the behavior of the Density current under the effect of cylindrical obstacles made of wood with a diameter of 1.5 cm and a height of 30 cm (more than the height of the body of the Density current) was evaluated. Therefore, by considering variables such as floor slope, concentration and discharge, the values of the density current head were determined. Machine learning algorithms such as adaptive neural fuzzy inference system and artificial neural network were used to model the results.
Findings: Based on the results, the density salt flow head was modeled using machine learning algorithms such as adaptive fuzzy neural inference system and artificial neural network and the performance of these two methods were compared. The results showed that machine learning algorithms are useful in modeling the density salt flow head. And the regression of the adaptive neural fuzzy inference system for the training and test data was 0.99 and the regression of the artificial neural network was 0.94 and 0.91, respectively.
Conclusion: By comparing the two methods, it was found that the adaptive neural-fuzzy inference system is more effective in modeling the percent reduction of the head of Density current than the feed-forward artificial neural network method.

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