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روش تلفیقی سطح پاسخ و فراکاوشی در بروزرسانی مدل عددی جریان غیردائمی در شبکه رودخانه‌ای (مطالعه موردی: رودخانه گرمابدر- زیقان) | ||
| تحقیقات مهندسی سازه های آبیاری و زهکشی | ||
| دوره 24، شماره 91، آبان 1402، صفحه 109-129 اصل مقاله (2.5 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/idser.2024.363104.1550 | ||
| نویسندگان | ||
| فرهود کلاته* 1؛ احسان امین وش2 | ||
| 1دانشیار، دانشکده مهندسی عمران، دانشگاه تبریز، تبریز، ایران. | ||
| 2دانشجوی دکتری، دانشکده مهندسی عمران، دانشگاه تبریز، تبریز، ایران. | ||
| چکیده | ||
| در تحقیق حاضر روشی جهت بروزرسانی مدلهای عددی پیش بینی جریان غیردائم در شبکه های رودخانه ای با استفاده از ترکیب روش سطح پاسخ و روشهای فراکاوشی توسعه داده شده است. در این راستا شاخه‌ای از رود گرمابدر (زیقون) جهت مطالعه موردی انتخاب گردید. از نرم‌افزار Hec-Ras جهت تهیه مدل عددی جریان غیر دائم در شبکه رودخانه ای استفاده شده است. شبکه عصبی نیز برای شبیه-سازی روش عددی انتخاب شده تا در روند استفاده از روش الگوریتم ژنتیک بتوان پارامترهای مورد نظر را بهینه سازی نمود. پس از مدل-سازی جریان غیردائم در نرم‌افزار Hec-Ras برای بروز رسانی مدل عددی، ضریب مانینگ به عنوان پارامتر تاثیرگذار در نتایج حاصل از نرم‌افزار Hec-Ras انتخاب گردید. برای بررسی تاثیر این پارامتر و تعیین مقدار بهینه برای بروز رسانی مدل عددی مورد نظر، به تعداد100 بار برنامه Hec-Ras به‌ازای ضرایب مانینگ متفاوت مدل‌سازی و آنالیز شده و شبکه عصبی با تعداد 1000 لایه درونی آموزش داده شد. نتایج نشان داد که بهینه سازی یک پارامتر موثر در رفتار جریان شبیه‌سازی شده، روندی فرسایشی و زمان‌بر است که استفاده از شبکه عصبی به دلیل وجود سرعت همگرایی بالا این روند بسیار سریع‌تر و قابل اعتمادتر می‌گردد. از طرف دیگر نتایج بیان کرد که ضرایب مانینگ از پارامترهای بسیار مهم و تاثیرگذار در رفتار جریان شبیه‌سازی شده است که با تغییری اندک در آن، نتایج مدل شبیه‌سازی شده دستخوش پراکندگی قابل توجهی می‌شود که در روند آنالیز بایستی در نظر گرفته شود تا نتایج حاصل با واقعیت تفاوت قابل ملاحظه‌ای نداشته باشند. | ||
| کلیدواژه‌ها | ||
| الگوریتم ژنتیک؛ بهینه‌سازی؛ بروزرسانی مدلهای عددی؛ شبیه سازی؛ نرم افزار Hec-Ras | ||
| عنوان مقاله [English] | ||
| Combined Method of Response Surface and Meta-Exploration in Updating the Numerical Model of Unsteady Flow in River Network, Case Study: Garmabadr-Ziqan River | ||
| نویسندگان [English] | ||
| Farhoud Kalateh1؛ Ehsan Aminvash2 | ||
| 1Associate professor, Faculty of civil engineering, University of Tabriz, Tabriz, Iran. | ||
| 2Ph.D. candidate, Faculty of civil engineering, University of Tabriz, Tabriz, Iran | ||
| چکیده [English] | ||
| Introduction Nowadays, numerical modeling is known as a powerful method and tool in investigating practical phenomena. So that this method is used in many fields of engineering. Although the numerical model of an engineering problem is usually prepared based on several simplifying assumptions, it is possible that this numerical model does not reflect all aspects of the real problem and is not able to show the real behavior in reality. Direct updating methods use the analytical solution of the problem for this purpose, although these methods specify the necessary corrections without the need to repeat calculations and in one step, but in most cases such corrections do not have a physical meaning in reality but iteration-based updating methods require sensitivity analysis of the effective parameters in the problem in order to find the impact of each of them. One of the solutions to deal with the time-consuming problem of multiple re-analyses in numerical models prepared by commercial software during model updating based on sensitivity analysis is to replace the numerical model with an approximate representative model known as meta-models. The response surface method is one of the common methods for building such meta-models. The response surface technique is actually a test design method to select the design parameters in the experiment with the aim of optimizing some system response functions. From this point of view, the research regarding the response of the network of open channels to the arrival of the flood wave and predicting the characteristics of the flow in the branches of the network can be the solution to minimize the damages caused by it. Methodology In this research, in order to update the numerical model of unsteady flow in the network of open channels by using the combined method of response surface and meta-exploration methods, a branch of the Garmabadr River (Ziquon) has been selected, and the desired data has been prepared and sorted. have been Hec-Ras software has been used as a numerical method. Neural network is also chosen for simulating the numerical method so that the desired parameters can be optimized in the process of using the genetic algorithm method. This area is located in the northern part of Iran and in the northeast of Tehran province. The geographical coordinates include longitude 51 degrees 32 minutes to 51 degrees 38 minutes and latitude 35 degrees 51 minutes to 35 degrees 58 minutes. MATLAB software has been used to build a meta-model or an alternative model. MATLAB is a software that can be called the language of mathematics and modern engineering sciences. Results and Discussion As it is clear from the results of the Hec-Ras program, the discharge of stations number 13 and 22 is equal to 308.03 and 27.89 cubic meters per second, respectively. The estimation error is equal to the difference between the estimated flow rate and the observed flow rate, which is 18.03 and 2.11 cubic meters per second, respectively. The percentage of the estimation error is equal to the ratio of the estimation error to the observed value is equal to 6.2% and 7%, respectively, which is an acceptable value for this research, and this result shows the appropriate performance of the genetic algorithm for optimizing the flow simulated by It shows the neural network, on the other hand, the low percentage of error also determines the accuracy of the intended program. As a result, the error of the genetic program is at an optimal level. As it is known, Manning's coefficients are very important and influential parameters in the behavior of the flow, the change in which causes significant changes in the simulation results. The effect of this uncertainty on the performance of numerical methods was clearly identified in this research. As a result, examining inputs that have inherent uncertainty and determining their appropriate values is a suitable solution for improving numerical methods. Conclusions Genetic algorithm is a powerful method to determine the values of effective parameters in simulated flow performance, and its combination with neural network is a powerful tool for engineering purposes, including the analysis of the behavior of open channels, whose fast convergence is a strong point to choose in these problems. in similar cases. Manning's coefficients are very important and influential parameters in the simulated flow behavior, with a small change in it, the simulated model undergoes significant dispersion, which should be considered in the analysis process so that the results are not significantly different from reality. Optimizing an effective parameter in simulated flow behavior is an erosive and time-consuming process, and the use of neural network becomes much faster and more reliable due to the high convergence speed of this process. | ||
| کلیدواژه‌ها [English] | ||
| Genetic algorithm, optimization, update of numerical models, simulation, Hec-Ras software | ||
| مراجع | ||
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