Advanced Characterization of Hydraulic Structures for Flow Regime Control: Experimental Development
Abstract
A good understanding of flow in a number of hydraulic structures, such as energy dissipators, among others, is needed to effectively control upstream and downstream flow conditions, for instance, high water depth and velocity to ensure, scouring, flow stability and control scouring, which is thus crucial to ensuring safe acceptable operation. Although some previous research exists on minimizing scouring and flow fluctuations after hydraulic structures, none of this research can fully resolve all issues of concern. In this research, three types of structures were studied, as follows: a) a vertical gate; b) a vertical gate with an expansion; and c) a vertical gate with a contraction. A Stability Concept was introduced and defined to characterize the conditions downstream of gated structures. When established criteria for stability are met, erosion is prevented. This research then investigated and evaluated two methods to classify the flow downstream of a gated vii structure to easily determine stability. The two classification methods are: the Flow Stability Factor and the Flow Stability Number. The Flow Stability Factor, which is developed based on the Fuzzy Concept, is defined in the range of 0 to 1; the maximum value is one and indicates that the flow is completely stable; and the minimum value is zero and indicates that the flow is completely unstable. The Flow Stability Number is defined as the ratio of total energy at two channel sections with a maximum value of one, and it allows flow conditions to be classified for various hydraulic structures; the number is dimensionless and quantitatively defines the flow stability downstream of a hydraulic structure under critical and subcritical flow conditions herein studied, also allowing for an estimate of the downstream stable condition for operation of a hydraulic structure. This research also implemented an Artificial Neural Network to determine the optimal gate opening that ensures a downstream stable condition. A post-processing method (regression-based) was also introduced to reduce the differences in the amount of the gate openings between experimental results and artificial intelligence estimates. The results indicate that the differences were reduced approximately 2% when the post-processing method was implemented on the Artificial Neural Network estimates. This method provides reasonable results when few data values are available and the Artificial Neural Network cannot be well trained. Experiments were conducted in two laboratories, for two different scales, to investigate any possible scale effect. Results indicate, for instance, that the case of the vertical gate with an expansion performs better in producing a downstream stable condition than the other two studied structures. Moreover, it was found that smaller changes caused by expansions and contractions on the channel width show better performance in ensuring a viii downstream stable condition in the cases of a vertical gate with an expansion and a vertical gate with a contraction over a wide range of structures. Moreover, upstream flow depths in the gate with expansion are higher than in the cases of a gate and a gate with a contraction, suggesting that it may be more appropriate for agriculture applications. This research also applied Game Theory and the Nash Equilibrium Concept in selecting the best choice among various structures, under different flow expectations. In addition, the accuracy of the Flow Stability Factor and the Flow Stability number were compared. This showed that the Flow Stability Factor and the Flow Stability number had good agreement in stable conditions. Hence, the Flow Stability Factor can then be used instead of the Flow Stability number to define stable conditions, as a visual method that does not need any measurement. Importantly, a Fuzzy-based Efficiency Index, a method based on an image processing technique, was also innovatively tested to estimate the hydraulic efficiency of the hydraulic structures. The method was tested and validated using laboratory data with an average agreement of 96.45%, and then demonstrated for prototype case situations in Florida and California. These cases yielded overall efficiencies of 96% and 97.87% in Spillway Park, FL and Oroville Dam, CA, respectively. Statistical assessment was also done on the image, determining an Efficiency Index. Specifically, an image histogram was extracted from the grayscale image, then the mean and standard deviation of the histogram was used to calculate the Index. The method uses the darkness and whiteness of the image to estimate the Efficiency Index; it is easy to use, quick, low cost, and trustworthy.
Subject Area
Hydraulic engineering
Recommended Citation
Hamedi, Amirmasoud, "Advanced Characterization of Hydraulic Structures for Flow Regime Control: Experimental Development" (2017). ProQuest ETD Collection for FIU. AAI10747817.
https://digitalcommons.fiu.edu/dissertations/AAI10747817