تخمین ضریب درگ در کانال‌های روباز با پوشش گیاهی مستغرق با استفاده از تحلیل پارتو و برنامه‌ریزی بیان‌ چندژنی

نوع مقاله : پژوهشی

نویسنده

استادیار گروه علوم و مهندسی آب، دانشکدۀ کشاورزی، دانشگاه ولیعصر(عج) رفسنجان

چکیده

یکی از مشخصه‏های مهم در مطالعۀ هیدرولیکی و اکوهیدرولوژیکی جریان در رودخانه‏ها، مقاومت پوشش گیاهی در مقابل جریان، نیروی درگ و ضریب درگ به‌دست‌آمده است. ضریب درگ تابع خصوصیات جریان، مشخصات تراکم و توزیع پوشش گیاهی است و اغلب با استفاده از روابط تجربی که دقت مطلوبی ندارند، تخمین زده می‏شود. در تحقیق حاضر، با هدف بهبود دقت و استخراج روابط بهینه‏ برای ضریب درگ جریان در کانال‏های روباز حاوی پوشش گیاهی مستغرق، از رویکرد بهینه‏سازی پارتو و برنامه‏ریزی بیان چندژنی در ترکیب با الگوریتم دسته‏بندی حداکثر عدم تشابه استفاده شده است. با آنالیز ابعادی، متغیرهای حاکم بر پدیده به صورت بدون بعد استخراج شده و سپس، تعداد 910 سری دادۀ اندازه‏گیری‌شدۀ مربوط به ضریب درگ پوشش گیاهی، مشخصات هیدرولیکی جریان و پوشش گیاهی تهیه شد و روابط صریحی برای تخمین ضریب درگ به دست آمد. بررسی نتایج مدل پیشنهادی نشان داد مدل یادشده با R2=0.9, RMSE=0.41, MPE=17% دقت بسیار بیشتری نسبت به روابط تجربی دارد و 20 درصد کمتر بودن خطای رابطۀ بهینۀ پیشنهادی از روابط تجربی، بیانگر کارایی مطلوب آن است. همچنین، تحلیل مفهومی رابطۀ پیشنهادی نشان داد علاوه بر سادگی فرم رابطۀ بهینه، مفاهیم فیزیکی حاکم بر پدیده و اجزای مؤثر بر مقاومت درگ جریان را نیز به‌خوبی استنتاج کرده است. بنابراین، کارآمدی مدل جدید پیشنهادی نسبت به مطالعات قبلی تأیید شده است و می‏توان از نتایج آن در مطالعات و مدل‏های هیدرولیکی و اکوهیدرولوژیکی جریان در رودخانه‏ها و کانال‏ها در شرایط وجود پوشش گیاهی استفاده کرد.

کلیدواژه‌ها


عنوان مقاله [English]

Estimation of Drag Coefficient in Open Channel Flows with Submerged Vegetation Using Pareto Analysis and Multi-gene Genetic Expression Programming

نویسنده [English]

  • Hossien Riahi Madvar
Assistant Professor, Department of Water Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran
چکیده [English]

The vegetation resistance, drag force and drag coefficient are among the most properties in hydraulic and Eco hydrological studies in rivers. The drag coefficient depends on flow properties, condition of density and distribution of vegetation, and is often estimated by non-accurate empirical equations. In the present study with the aim of improving the accuracy and derivation of optimum equations for drag coefficient in open channel flow with submerged vegetation, the optimal Pareto and multi gene genetic expression programming in combination with maximum dissimilarity classification algorithm is used.  By using the dimensional analysis, the effective parameters derived in non-dimensional form and using 910 data points of drag coefficient and flow with vegetation conditions explicit equations for drag coefficient are developed. Investigating the results of proposed model shows that model with R2=0.9, RMSE=0.41, MPE=10% is more accurate than the empirical equations and its errors are 20% smaller than previous equations, declare the appropriate performance of developed model. Furthermore, the physical meaning of the developed models shows that beyond its simplified form, it has the ability in inferring of physical meaning of drag phenomenon. Therefore, the superiority of proposed model versus previous studies is confirmed. The results of the current model can be used in studies and hydraulic/eco-hydrologic models in rivers and channels having vegetation.

کلیدواژه‌ها [English]

  • Pareto Optimality
  • vegetation
  • drag coefficient
  • flow resistance
  • multi-gene expression model
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