ﮐﺎرﺑﺮد روش ﺑﺮﻧﺎﻣﻪ رﯾﺰي ﺑﯿﺎن ژن در ﺗﻌﯿﯿﻦ ﺿﺮﯾﺐ ﻫﻮادﻫﯽ درﯾﭽﻪ ﺗﺨﻠﯿﻪ ﮐﻨﻨﺪه ﺗﺤﺘﺎﻧﯽ ﺳﺪﻫﺎ

Application of gene expression programming approach to estimate the aeration coefficient of bottom outlet gates of dams

ﺳﺎﺑﻘﻪ و ﻫﺪف: اﺳﺘﻔﺎده از ﺳﺪﻫﺎي ﻣﺨﺰﻧﯽ، ﻧﻘﺶ ﻣﻬﻤﯽ را در ﺗﻮﺳﻌﻪ ﺻﻨﻌﺖ، ﮐﺸﺎورزي و اﺷﺘﻐﺎل ﺟﻮاﻣﻊ، ﺑﺮ ﻋﻬﺪه دارد . ﯾﮑﯽ از اﺟﺰاي ﺟﺎﻧﺒﯽ اﯾﻦ ﺳﺪﻫﺎ، ﺗﻮﻧﻞ ﺗﺨﻠﯿﻪ ﮐﻨﻨﺪه ﺗﺤﺘﺎﻧﯽ اﺳﺖ ﮐﻪ ﻧﻘﺶ ﻣﻬﻤﯽ را در ﺗﺨﻠﯿﻪ و ﮐﻨﺘﺮل ﺳﯿﻼب ﺑﺮ ﻋﻬﺪه دارد .اﯾﻦ ﺳﺎزه ﻣﺸﺘﻤﻞ ﺑﺮ ﯾﮏ ﻣﺠﺮاي ورودي، ﺗﻮﻧﻞ اﺻﻠﯽ اﻧﺘﻘﺎل و ﺳﺎزهﻫﺎي ﮐﻨﺘﺮل و ﺗﻨﻈﯿﻢ ﺟﺮﯾﺎن، ﺷﺎﻣﻞ درﯾﭽﻪﻫﺎ و ﺷﯿﺮﻫﺎ ﻣﯽﺑﺎﺷﺪ .ﺑﺮوز ﻓﺸﺎرﻫﺎي ﻣﻨﻔﯽ و ﭘﺪﯾﺪه ﮐﺎوﯾﺘﺎﺳﯿﻮن از ﺟﻤﻠﻪ ﻣﺸﮑﻼﺗﯽ اﺳﺖ ﮐﻪ ﺗﺨﻠﯿﻪﮐﻨﻨﺪهﻫﺎي ﺗﺤﺘﺎﻧﯽ ﺳﺪﻫﺎ در دﺑﯽ ﻫﺎي ﺑﺎﻻ ﺑﺎ آن ﻣﻮاﺟﻪ ﻫﺴﺘﻨﺪ .اﯾﻦ ﭘﺪﯾﺪه ﺳﺒﺐ ﺑﺮوز ﻣﺸﮑﻼﺗﯽ از ﺟﻤﻠﻪ ﺗﺨﺮﯾﺐ ﺳﻄﺢ ﺳﺎزه ﺧﻮاﻫﺪ ﺷﺪ .ﯾﮑﯽ از ﻣﺘﺪاول ﺗﺮﯾﻦ روش ﻫﺎي ﮐﺎﻫﺶ و ﺣﺬف ﺧﻄﺮ وﻗﻮع ﮐﺎوﯾﺘﺎﺳﯿﻮن، ﻫﻮادﻫﯽ ﺟﺮﯾﺎن ﻣﯽﺑﺎﺷﺪ .در اﯾﻦ راﺳﺘﺎ، ﻣﯿﺰان ﻫﻮادﻫﯽ و ﻫﻮاﮔﯿﺮي ﺟﺮﯾﺎن آب در ﺳﺮرﯾﺰﻫﺎ و ﭘﺎﯾﯿﻦدﺳﺖ درﯾﭽﻪﻫﺎي ﺗﻮﻧﻞ ﺗﺨﻠﯿﻪﮐﻨﻨﺪه ﺗﺤﺘﺎﻧﯽ ﺳﺪﻫﺎ، ﯾﮑﯽ از ﻣﺒﺎﺣﺚ ﻣﻬﻢ ﻣﯽ ﺑﺎﺷﺪ .ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ اﻫﻤﯿﺖ ﻣﻮﺿﻮع، در ﭘﮋوﻫﺶ ﺣﺎﺿﺮ ﭘﯿﺶﺑﯿﻨﯽ و ﺑﺮآورد ﺿﺮﯾﺐ ﻫﻮادﻫﯽ درﯾﭽﻪ ﺗﺨﻠﯿﻪ ﮐﻨﻨﺪه ﺗﺤﺘﺎﻧﯽ ﻣﻮرد ﺑﺮرﺳﯽ ﻗﺮار ﮔﺮﻓﺖ. ﻣﻮاد و روش ها:در ﭘﮋوﻫﺶ ﺣﺎﺿﺮ، روش ﺑﺮﻧﺎﻣﻪرﯾﺰي ﺑﯿﺎن ژن GEPﺟﻬﺖ ﺑﺮآورد ﺿﺮﯾﺐ ﻫﻮادﻫﯽ درﯾﭽﻪ ﺗﺨﻠﯿﻪ ﮐﻨﻨﺪه ﺗﺤﺘﺎﻧﯽ ﻣﻮرد اﺳﺘﻔﺎده ﻗﺮار ﮔﺮﻓﺖ .ﺑﺮاي آﻣﻮزش و ﺻﺤﺖﺳﻨﺠﯽ ﻣﺪل، از دادهﻫﺎي آزﻣﺎﯾﺸﮕﺎﻫﯽ ﺑﻪ دﺳﺖ آﻣﺪه از ﻣﺪل ﻫﺎي ﻓﯿﺰﯾﮑﯽ و ﻫﯿﺪروﻟﯿﮑﯽ ﭼﻬﺎر ﺳﺪ اﻟﺒﺮز، ژاوه، ﮔﺘﻮﻧﺪﻋﻠﯿﺎ و ﺟﺮه، ﮐﻪ در آزﻣﺎﯾﺸﮕﺎه ﺳﺎزهﻫﺎي ﻫﯿﺪروﻟﯿﮑﯽ ﻣﺆﺳﺴﻪ ﺗﺤﻘﯿﻘﺎت آب ﺗﻬﺮان ﺳﺎﺧﺘﻪ شده،استفاده گرديد. ضريب هوادهي تابعي از دو پارامتر عدد فرود جريان در محل فشردگي آن ( frc) و ﻧﺴﺒﺖ ﺳﻄﺢ ﻣﻘﻄﻊ ﻫﻮاده ﺑﻪ ﺳﻄﺢ ﻣﻘﻄﻊ ﺗﻮﻧﻞ در ﻣﺤﻞ درﯾﭽﻪ ( Ac/Ag) در نظر گرفته شد. جهت اجراي مدل برنامه ريزي بيان ژن ، 30 كروموزوم و 3 ژن انتخاب شد و توانايي اين روش به كمك دو پارامتر آماري مانند ضربب هم بستگي ( R2 ) و جذر ميانگين مربعات خطا ( RMSE ) مورد ارزيابي قرار گرفت . ﯾﺎﻓﺘه ﻫﺎ: ﻧﺘﺎﯾﺞ ﻧﺸﺎن داد ﮐﻪ روش ﺑﺮﻧﺎﻣﻪرﯾﺰي ﺑﯿﺎن ژن ﺑﺎ ﺿﺮاﯾﺐ ﻫﻤﺒﺴﺘﮕﯽ 0/803 و 0/639 و ﺟﺬر ﻣﯿﺎﻧﮕﯿﻦ ﻣﺮﺑﻌﺎت خطا برابر با 0/125 و 0/096 به ﺗﺮﺗﯿﺐ در دو ﺑﺨﺶ آﻣﻮزش و آزﻣﻮن، ﺿﺮﯾﺐ ﻫﻮادﻫﯽ درﯾﭽﻪ ﺗﺨﻠﯿﻪﮐﻨﻨﺪه ﺗﺤﺘﺎﻧﯽ را برآورد نمود. ﻣﺪل ﻣﺬﮐﻮر در ﻣﻘﺎﯾﺴﻪ ﺑﺎ ﻧﺘﺎﯾﺞ راﺑﻄﻪ رﮔﺮﺳﯿﻮﻧﯽ ﺑﺎ ﺿﺮاﯾﺐ ﻫﻤﺒﺴﺘﮕﯽ 0/718 و 0/402 و جذر ميانگين مربعات خطا برابر با 0/114و 0/171 در بخش هاي آﻣﻮزش و آزﻣﻮن ﻧﺘﺎﯾﺞ ﺑﻬﺘﺮي را اراﺋﻪ ﻣﯽ ﻧﻤﺎﯾﺪ .ﺑﻪ ﻋﺒﺎرﺗﯽ، اﺳﺘﻔﺎده از روش ﺑﺮﻧﺎﻣﻪ ريزي ژن موجب كاهس 28 درﺻﺪي ﺧﻄﺎي ﭘﯿﺶﺑﯿﻨﯽ ﺿﺮﯾﺐ ﻫﻮادﻫﯽ درﯾﭽﻪ ﺗﺨﻠﯿﻪﮐﻨﻨﺪه ﺗﺤﺘﺎني ﺳﺪﻫﺎ ﺷﺪه اﺳﺖ. ﻧﺘﯿﺠﻪ ﮔﯿﺮي: ﻧﺘﺎﯾﺞ ﺑﻪ دﺳﺖ آﻣﺪه از اﯾﻦ ﭘﮋوﻫﺶ ﻧﺸﺎن داد ﻣﺪل ﻫﻮﺷﻤﻨﺪ ﺑﺮﻧﺎﻣﻪ رﯾﺰي ﺑﯿﺎن ژن، ﺑﻪ ﺧﻮﺑﯽ ﺗﻮاﻧﺴﺘﻪ اﺳﺖ ﺿﺮﯾﺐ ﻫﻮادﻫﯽ درﯾﭽﻪ ﻫﺎي ﺗﺨﻠﯿﻪ ﮐﻨﻨﺪه ﺗﺤﺘﺎﻧﯽ ﺳﺪﻫﺎ را ﭘﯿﺶﺑﯿﻨﯽ ﮐﻨﺪ و اﺳﺘﻔﺎده از آن ﻣﻮﺟﺐ ﺑﻬﺒﻮد ﻧﺘﺎﯾﺞ ﭘﯿﺶﺑﯿﻨﯽ در ﻣﻘﺎﯾﺴﻪ ﺑﺎ روشﻫﺎي ﻣﺮﺳﻮم ﺣﺎﺻﻞ از ﻣﺪلﻫﺎي رﮔﺮﺳﯿﻮﻧﯽ ﺷﻮد .ﺑﻪﻋﺒﺎرﺗﯽ، ﻧﺘﺎﯾﺞ ﺑﻪدﺳﺖ آﻣﺪه، ﺑﯿﺎﻧﮕﺮ ﺗﻮاﻧﺎﯾﯽ روش ﺑﺮﻧﺎﻣﻪ رﯾﺰي ﺑﯿﺎن ژن در ﺗﻌﯿﯿﻦ ﺿﺮﯾﺐ ﻫﻮادﻫﯽ درﯾﭽﻪ ﻫﺎي ﺗﺨﻠﯿﻪ ﮐﻨﻨﺪه ﺗﺤﺘﺎﻧﯽ ﺳﺪﻫﺎ و در ﻧﺘﯿﺠﻪ ﺑﺮآورد ﺻﺤﯿﺢ اﯾﻦ ﭘﺎراﻣﺘﺮ، ﺑﻪ ﻣﻨﻈﻮر ﺟﻠﻮﮔﯿﺮي از وﻗﻮع ﭘﺪﯾﺪه ﮐﺎوﯾﺘﺎﺳﯿﻮن ﻣﯽ ﺑﺎﺷﺪ .ﺑﻨﺎﺑﺮاﯾﻦ اﺳﺘﻔﺎده از اﯾﻦ روش در ﻣﺴﺎﯾﻞ ﻣﺮﺗﺒﻂ ﺑﺎ ﻣﻮﺿﻮع ﭘﮋوﻫﺶ ﭘﯿﺸﻨﻬﺎد ﻣﯽ ﺷﻮد

Background and Objectives: The use of storage dams plays a key role in the development of industry, agriculture and employment communities Bottom outlet tunnels are one of the most significant components of the reservoir dams which are used in flood evacuation and control. They consist of inlet duct, main conveyance tunnel and flow regulator structures including gates and valves. A major problem with bottom outlet gate of dams is cavitation which happens in the high flow discharge. This phenomenon would destroy the surface of structure. It has been demonstrated that flow aeration is an effective way to reduce the cavitation damages. In this regard, the flow aeration rate is an important discussion that must be noted. Since, in this paper aeration coefficient evaluation is assessed. Materials and Methods: This study, is to estimate the aeration coefficient of bottom outlet gate of four dams (Alborz, Zhaveh, Gotvand Olia, Jareh) using Gene Expression Programming (GEP) approach. To achieve this aim, experimental data were used collecting from hydraulic structures laboratory of Tehran Water Research Institute to train and test the model. The aeration coefficient was influenced by compressed Froude number (Frc) and aerator area to gate area ratio (Aa/Ag). 30 chromosomes and 3 genes were chosen to GEP performance. The model ability was assessed by two statistical parameters of correlation coefficient (R2) and root of mean square error (RMSE). Results: The results show that GEP predicted the aeration coefficient of bottom outlet gates of dams with R2 of 0.803 and 0.639 and RMSE of 0.096 and 0.125 for training and testing stages, respectively. This model gave better results compared by regression equation with R2 of 0.718 and 0.402 and RMSE of 0.114 and 0.171 for training and testing parts, respectively. In the other words, the error of aeration coefficient prediction was decreased about 28% using GEP approach. Conclusion: The results show that GEP intelligence approach is an adequate model to predict aeration coefficient of bottom outlet gates of dams. Also, the results of traditional regression equations were improved using this method. In the other words, these results indicated that GEP is reliable to evaluate the aeration coefficient of bottom outlet gates of dams by more accurate estimation to prevent cavitation phenomenon. So, use of this way is suggested in future studies related to this topic.