An Assessment of Using Intelligence Fuzzy Models to Tstimate the Sequent Depth and Roller length of B-type Hydraulic Jump
Development of irrigation and drainage networks is known as one of the most effective approaches for the optimal use of limited water resources (Abbasi., 2011). But many of the constructed networks suffer from different problems which raised from different reasons. Rahimi et al. (2011) categorized t...
| Main Authors: | , |
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| Format: | Article |
| Language: | fas |
| Published: |
Shahid Chamran University of Ahvaz
2018-01-01
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| Series: | علوم و مهندسی آبیاری |
| Subjects: | |
| Online Access: | http://jise.scu.ac.ir/article_13334_3f4eee26003c23d1a8ea7bedceb5a6b7.pdf |
| Summary: | Development of irrigation and drainage networks is known as one of the most effective approaches for the optimal use of limited water resources (Abbasi., 2011). But many of the constructed networks suffer from different problems which raised from different reasons. Rahimi et al. (2011) categorized these reasons as; poor design, improper construction operation, low quality of construction materials, poor operation and maintenance, and geotechnical problems of the subgrade materials. Stilling basins are commonly used structures in order to dissipate the energy in downstream of chutes. In general, a hydraulic jump would be created at the end of the chute. The B-type hydraulic jump takes place on the positively inclined plane of a chute under particular conditions. In this condition, determination of the secondary depth and the length of the roller is essential for protecting the structures. Since there is no proper analytical method to solve the momentum equation for the mentioned condition, using of different smart techniques such as artificial intelligence was considered recently. Caralo et al. (2011) developed different models for determination of the flow characteristics for B-type hydraulic jump. Dusan etal. 2012 presented a neo- Fuzzy system for predicting the weir coefficient. Akib etal. (2014) predicted the scouring depth by using of neo- Fuzzy system. Azamathulla et al. (2012) developed a neo-fuzzy system for prediction of the sediment transport. In this research, the application of two intelligence methods including Neuro - Fuzzy Inference System and fuzzy inference system were investigated. |
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| ISSN: | 2588-5952 2588-5960 |