Method of the Laboratory Wave Generation for Two Dimensional Hydraulic Model Experiment in the Coastal Engineering Fields: Case of Random Waves

The experiments in coastal engineering are very complex and a lot of components should be concerned. The experience has an important role in the successful execution. Hydraulic model experiments have been improved with the development of the wave generator and the advanced measuring apparatus. The hydraulic experiments have the advantage, that is, the stability of coastal structures and the hydraulic characteristics could be observed more intuitively rather than the numerical modelings. However, different experimental results can be drawn depending on the model scale, facilities, apparatus, and experimenters. In this study, two-dimensional hydraulic experiments were performed to suggest the guide of the test wave(random wave) generation, which is the most basic and important factor for the model test. The techniques for generating the random waves with frequency energy spectrum and the range for the incident wave height [(HS)M/(HS)T = 1~1.05] were suggested. The proposed guide for the test wave generation will contribute to enhancing the reliability of the experimental results in coastal engineering.

Data-driven and model-based framework for smart water grid anomaly detection and localization

With increasing adoption of advanced meter infrastructure, smart sensors together with SCADA systems, it is imperative to develop novel data analytics and couple the results with hydraulic modeling to improve the quality and efficiency of water services. One important task is to timely detect and localize anomaly events, which may include, but not be limited to, pipe bursts and unauthorized water usages. In this paper, a comprehensive solution framework has been developed for anomaly detection and localization by formulating and integrating data-driven analytics with hydraulic model calibration. Data analysis for anomaly detection proceeds in multiple steps including the following: (1) data pre-processing to eliminate and correct erroneous data records, (2) outlier detection by statistical process control methods and deep machine learning, and (3) system anomaly classification by correlation analysis of multiple sensor events. Classified system anomaly events are subsequently localized via hydraulic model calibration. The integrated solution framework is developed as a user-friendly and effective software tool, tested, and validated on the selected target areas in Singapore.

Closed-Loop Data & Business Intelligence Driven Approach of Well Performance Evaluation to Identify Changes in Well Behavior

Asset engineers spend significant time in data validation on a daily basis by gathering data from multiple sources, manually collecting and analyzing these data points to deduce well behavior, and finally implementing the changes on the field. This paper proposes a closed-loop methodology that drastically reduces the time lost in low-efficiency activities, helps engineers to make faster decisions, and assists in efficiently implementing the changes in the field. This well performance evaluation starts with direct integration with the corporate database to feed the field data into a hydraulic model. Next, Pre-configured well performance limits such as reservoir parameters, well calibration parameters, and surface parameters are used to validate the input data and alert the end-user to trigger a well performance evaluation workflow. This workflow is based on a business intelligence tool that integrates statistical information with physics-based model information. Finally, after the engineer makes a holistic decision, an integrated action tracking mechanism assigns an actionable item to the field operator to close the workflow. This approach significantly reduces the time spent on data consolidation and analysis. Essentially this means more time for the engineers to focus on well behavior improvement strategies such as stimulation or re-perforation from more than three hundred strings with more than a thousand well data captured over a month. This approach is not entirely dependent on either static physics-based or statistical models; instead, this approach integrates both methods to enhance decision-making. Moreover, the dynamic behavior of the well is captured in the statistical model and validated against the estimated well behavior derived from the hydraulic model. Furthermore, the streamlined visualization tool helps engineers quickly identify well problems, such as lower productivity, reduced reservoir pressure, increased well scale, increased restrictions in the wellbore, etc. Another critical value addition of this closed-loop workflow is the actionable feedback that is well defined and stored within the system for common reference. For example, the asset engineers provide actionable feedback such as retesting requirement, well stimulation, artificial lift candidate, tubing clearance. Within the action tracking framework, field engineers can quickly filter the assigned action items to him or her for the day and take appropriate actions. This new integrated action-based closed-loop workflow significantly reduces the time spent on daily validation tasks and well performance evaluation tasks by combining the statistical and hydraulic models supported with visualization and action tracking capabilities.

Hydraulic Model for Flood Forecasting of Tajabad Khwar in Hayatabad Phase III Peshawar, Pakistan: A Case Study

Among all other natural disasters occurring throughout the world, floods are considered to be the worst and most devastating catastrophe as it causes loss of billions of lives. Flooding is caused due to inundation of water over the areas which are in close proximity of river or natural waterways resulting in severe damages to commercial and residential areas in the surrounding. Thus, an efficient flood forecasting system through the development of a combined hydrological and hydraulic model for the prediction of future flood events through marking the potential high-risk zone is required to minimize the damages. Due to large number of encroachments made in the waterway of Tajabad khwar located near Deans Residential Apartment of Hayatabad Phase III, a hydraulic model is developed for its flood forecasting as the floods in this khwar may cause severe damages to the inhabitants of the adjacent areas. In this research work, Flood zone maps are developed for 10 years, 20 years, 50 years, and 100 years flood return periods in order for deterring extent of the inundation as a result of these encroachments and to identify the areas at potential risk. Flood discharge for each return period was estimated using HEC-HMS software and was found to be 772, 1036, 1392 and 1666 m^3/sec for 10 years, 20 years, 50 years, and 100 years flood return periods respectively. The corresponding water surface elevation determine using HEC-RAS and was found to be 196 m, 197m, 201m, 202m. This model provides a basic idea for developing flood zone maps of a given period of return for the assessment of areas that can get adversely pretentious by floods.

Model of discharge coefficient of crest gate rubber weir at fully closed condition

Crest Gate Rubber Weir is a modification of rubber weir with adding a metal plate or crest gate on the upstream side. The rubber in this weir functioning as a support while the crest gate serves on elevating water. Although many have been implemented, this weir’s discharge coefficient needs be researched considering its unique shape. This study looks for discharge coefficient to determine the discharge that passes through weirs at fully closed conditions. The research was conducted with a hydraulic model resembling part of Tirtonadi Weir in Surakarta with a hydraulic model. The model is made in an angle 53°, which represents the prototype at fully closed condition. Laboratory experiment shows that the discharge coefficient of this weir is greater than the Ogee Weir and Sluice Gate for the same height because of less flow resistance from this weir structural form.

Hydraulic model experiment of energy dissipation on the horizontal and USBR II stilling basin

Flow conditions on overflow systems can result in construction failure, mainly due to the high flow energy. Stilling basin at downstream of the spillway is useful for reducing flow energy. It can reduce the destructive force of water flow. Controlling the hydraulic jump is an important part that includes the jump’s energy, length, and height. The physical hydraulic model was carried out with several series, by making a series of bottom lowering of horizontal and USBR II stilling basin. The experimental study is expected to represent flow behavior in the overflow system regarding flow conditions and energy dissipation. Based on the analytical calculation of flow velocity, the amount of flow energy that occurs at each control point is calculated. The control points are the starting point of the spillway, the chute way toe, and flow depth after the hydraulic jump. The energy loss can be calculated for each control point, while the efficiency of energy dissipation on stilling basin is calculated at the downstream flow depth after the hydraulic jump. Velocity calculated by dividing discharge per unit width by water depth which is based on the flow depth measurement data in the hydraulic model.

Influence of Level of Detail (LOD) in Hydraulic Model Geometry with Demand Allocation by Voronoi Polygon Method on Chosen Parameters

Deciding on the level of model complexity is one of the first decisions that has to be made before engaging in the creation of a functional hydraulic model of a municipal water supply system. There are a number of influencing factors such as time needed to simulate the network, time needed to create such model and ease of use. The aim of this work is to prove that level of detail has influence on parameters such as pressure at measurement points, spread of flow speed and flow volume at different parts of mains. The Voronoi polygon method is one of the basic demand allocation methods, which, in the event of a different number of junctions depending on model complexity, has to generate a varying number of polygons of different sizes used to aggregate the demands.