نمایش مختصر رکورد

dc.contributor.authorEbrahimi, Mojtabaen_US
dc.contributor.authorMohammad Pour, Rezaen_US
dc.date.accessioned1399-07-08T19:12:45Zfa_IR
dc.date.accessioned2020-09-29T19:12:45Z
dc.date.available1399-07-08T19:12:45Zfa_IR
dc.date.available2020-09-29T19:12:45Z
dc.date.issued2017-07-01en_US
dc.date.issued1396-04-10fa_IR
dc.date.submitted2017-07-11en_US
dc.date.submitted1396-04-20fa_IR
dc.identifier.citationEbrahimi, Mojtaba, Mohammad Pour, Reza. (2017). The use of wavelet-artificial neural network and adaptive neuro-fuzzy inference system models to predict monthly precipitation. Journal of Water Science & Engineering, 7(16), 21-32.en_US
dc.identifier.issn2251-6905
dc.identifier.urihttp://wsej.iauahvaz.ac.ir/article_538168.html
dc.identifier.urihttps://iranjournals.nlai.ir/handle/123456789/61585
dc.description.abstractIn water supply systems, One of the most important components as safety unit and the current controller (Switching flow and regulate the amount of flow) used in the arrangement of lines of water. In this study, according to multiple ponds in Tanguiyeh dam water pipeline to industrial and mining company Gol Gohar Sirjan Butterfly valve used in these ponds using Fluent software simulation has been the case. Flow characteristics such as speed, pressure and turmoil in the different modes of separation is studied. Current results show that Speed in opening and closing the valve will be rises. In the study of pressure was also observed that by closing valves pressure reduced in the gate valves.en_US
dc.format.extent1002
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoen_US
dc.publisherIslamic Azad University, Ahvaz Branchen_US
dc.publisherدانشگاه آزاد اسلامی واحد اهوازfa_IR
dc.relation.ispartofJournal of Water Science & Engineeringen_US
dc.relation.ispartofفصلنامه تخصصی علوم و مهندسی آبfa_IR
dc.subjectButterfly valveen_US
dc.subjectValves gateen_US
dc.subjectNumerical modelingen_US
dc.subjectModel k-εen_US
dc.subjectTurbulenceen_US
dc.titleThe use of wavelet-artificial neural network and adaptive neuro-fuzzy inference system models to predict monthly precipitationen_US
dc.typeTexten_US
dc.typeOriginal Articleen_US
dc.citation.volume7
dc.citation.issue16
dc.citation.spage21
dc.citation.epage32


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