تعیین موقعیت و زاویه انحراف آبگیرهای جانبی در آبراهه های قوسی U شکل

نوع مقاله : پژوهشی اصیل (کامل)

نویسندگان
حسین منتصری
زهرا جمال آبادی
دانشگاه یاسوج
A-1-14528-7
چکیده
انتخاب دقیق محل آبگیری از قوس رودخانه‌ها و زاویه مناسب انحراف آبگیر، از عوامل اساسی در کاهش رسوبات وارده به سیستم انحراف و آبگیری است. در این تحقیق با تدوین یک متدولوژی جدید شبیه سازی-بهینه­سازی بر مبنای مدل بهینه­سازی الگوریتم ژنتیک چند هدفه (NSGAII)، مدل عددی FLUENT و مدل شبکه عصبی (ANN)، موقعیت و زاویه بهینه آبگیر جانبی در یک کانال U شکل بگونه ای تعیین‌ گردید که مقدار رسوب ورودی به آبگیر جانبی، کمینه و میزان دبی آبگیری بیشینه شود. بدین منظور، ابتدا میدان جریان و رسوب در کانال U شکل با آبگیر جانبی در تعداد محدودی از موقعیت های مختلف قوس، زوایای آبگیری مختلف و با دبی های آبگیری متفاوت توسط مدل فاز گسسته در نرم افزار FLUENT شبیه سازی گردید. سپس برمبنای نتایج حاصل از مدل عددی FLUENT، یک فرامدل جهت پیش‌بینی میزان رسوب انحرافی به آبگیر جانبی در کانال قوسی به ازای موقعیت، زاویه آبگیری و دبی آبگیری مورد نظر در مدل ANN ارائه شد. با اتصال مدل بدست آمده از ANN به مدل بهینه­سازی الگوریتم ژنتیک چند هدفه (NSGA-II)، جبهه پارتو شامل فهرستی از موقعیت‌ها و زاویه‌های بهینه آبگیر جانبی متناظر با مقادیر بهینه توابع هدف (حداکثر دبی آبگیری و حداقل رسوب انحرافی به آبگیر جانبی) و محدودیت‌های آن در کانالU شکل بدست می‌آید. در نهایت با استفاده از روش تصمیم گیری چند معیارهTOPSIS ، بهترین گزینه از بین گزینه‌های موجود در جبهه پارتو بدست می‌آید. نتایج نشان می‌دهد مقدار زاویه آبگیری بهینه بدست آمده برابر با 38 درجه و موقعیت بهینه بدست آمده برابر با 127 درجه می‌باشد.

کلیدواژه‌ها

موضوعات

عنوان مقاله English

Optimization of location and diversion angle of lateral intakes in U shaped channel

چکیده English

Lateral intake and stream diversion facilities, which are widely used in irrigation, land drainage and municipal sewage systems, deal with sediment transport. In this research, using a numerical discrete phase model, simulation of the sediment transport phenomenon is carried out in a 180-degree channel bend with a lateral intake that is located at a position of 115 degrees outside the arc and with a 45-degree diversion angle. The numerical model was calibrated with laboratory data.

Then, in 31 positions of the channel bend from the 10 to 140 degrees with 5 degrees' intervals and with 5 diversion angles of 10, 30, 50, 70 and 90 degrees, and for three diversion discharge ratios of 20%, 30% and 40%, the calibrated numerical model has been implemented and the percentage of sediment entered to the lateral intake were determined for each model. The results were used as the data necessary for training and validation of Artificial neural network (ANN) model.

The inputs of ANN model were location of lateral intake in outer bank of channel bend (φ) and diversion angle of lateral intake (θ), diverted water discharge ratio (Qr). The output is diverted sediment ratio into the lateral intake (Gr).

The data were used for training and validation of the ANN model and the best structure was obtained for the neural network models using R2, SSE and MARE criteria and compared to regression models. The best structure of ANN model was obtained as a 5-layer model with 3 membership functions for each input variable and 27 conditional rules. The accuracy of the results obtained from the models in terms of the mean absolute relative error (MARE) indicates the ability of the neural network models to predict the amount of sediment diverted to the lateral intake with respect to regression models. Then, by developing a new simulation-optimization method based on the Multi-Objective Genetic Algorithm Optimization model (NSGAII), the FLUENT numerical model and neural network models, the optimal position and diversion angle of the lateral intake in the U-shaped channel is determined to minimize the amount of sediment inflow into the lateral basin and maximize the rate of dewatering discharge.

By connecting the best model obtained from the ANN model, to NSGA-II model, the Pareto Front contains a list of optimal positions and optimal diversion angle of lateral intake are obtained with respect to the objective function and its limitations in the U-shaped channel.

Finally, to determine the optimal position and angle of the lateral basin in the U-shaped channel by connecting the Pareto front obtained from the Multipurpose Genetic Algorithm (NSGA-II) to the TOPSIS multi-criteria decision-making method, the best option is found among the Pareto front options. The results include the optimum position and optimum angle of lateral intake dewatering along with diversion discharge and sediment rates corresponding to these values. The results show that the optimum diversion angle obtained is 38-degree and the optimum position obtained is 127-degree

کلیدواژه‌ها English

: Lateral intake
U shape channel
genetic algorithm
Fluent software
ANN
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مهندسی عمران مدرس
دوره 24، شماره 2
خرداد و تیر 1403
صفحه 111-123