فهرست مطالب

  • Volume:5 Issue:1, 2019
  • تاریخ انتشار: 1398/04/10
  • تعداد عناوین: 5
|
  • Amir Houshang Ayati *, Ali Haghighi, Pedro Lee Pages 1-26
    Today, pipe systems are the most common facilities to convey various fluids from one place to another. In such facilities faults like leaks lead to advert consequences such as economic losses and social health threats. The fact that early detection of leaks can play a prominent role in reducing the amount of these undesired impacts has absorbed noticeable attention from researchers to this field of study. This paper presents a literature review on major aspects of hydraulic transient-based leak detection in pipe systems over the past three decades. The value of the present study is that an extensive database of peer-reviewed publications is brought together under a meticulous survey. It describes the trends, status and pinpoints the areas with a need for further investigation in the future. Uniquely, it contains information for over 95 publications in a tabular form, presenting domain type, analysis approach, optimization technique, topographic complexity of the case study, leak unknowns and validation approach.
    Keywords: Hydraulic transient, Frequency Domain, Signal processing, Topographic complexity, Leak specifications
  • Morteza Lotfirad, Jaber Salehpoor Laghani, Afshin Ashrafzadeh * Pages 27-41
    Understanding the variations of streamflow of rivers is an important prerequisite for designing hydraulic structures as well as managing surface water resources in basins. An overview of the impact of climate change on the streamflow in the Hablehroud River, the main river of a semi-arid basin in north-central Iran, is provided. Using the LARS-WG statistical downscaling model, the outputs of HadCM3 general circulation model under the IPCC SRES A1B, A2, and B1 emission scenarios were downscaled to a finer spatial scale and the daily precipitation and temperature time series over the period of 2011-2030 for the study area were obtained. Results showed that the study area would experience a decline in precipitation (8.2% on average). The IHACRES rainfall-runoff model was then calibrated in the study area. Based on the fit statistics in calibration and validation phases, the overall performance of the developed model was judged to be satisfactory. The calibrated hydrological model was driven by the downscaled rainfall and air temperature data to project the effect of changing climate on the outflow of the basin under study. Results showed that, with some exceptions in June, July and August, all emission scenarios predict a decrease in the long-term monthly average outflow of the Hablehroud Basin. The outflow reduction in winter, spring, summer, and autumn had an average value of 25.7, 14.3, 1.9, and 48.8%, respectively. It was also observed that if climate change would occur in the basin, monthly flows associated to each return period would decrease.
    Keywords: SRES scenarios, downscaling, conceptual rainfall-runoff model
  • Alireza Dariane *, Mohammadreza Ashrafi Gol, Farzaneh Karami Pages 42-59
    Long-term prediction of precipitation in planning and managing water resources, especially in arid and semi-arid countries such as Iran, has a great importance. In this paper, a method for predicting long-term precipitation using weather signals and artificial neural networks is presented. For this purpose, climatic data (large-scale signals) and meteorological data (local precipitation and temperature) with 3 to 12 months lead-times are used as inputs to predict precipitation for 3, 6, 9 and 12 months periods in 6 selected stations across Iran. A genetic algorithm (GA) and self-organized neural network (SOM) along with the application of winGamma software were comparatively used as input selection methods to choose the appropriate input variables. Examining the results, out of 96 predictions performed at all stations, in 43 cases, GA, in 28 cases, winGamma, and in 25 cases SOM have the best results compared to the other two methods. According to this, as a generalized assumption, it can be said that at least for the selected stations in this paper, the GA method is more reliable than the other two methods, and can be used to make predictions for future applications as a reliable input selection method. Moreover, among different climatic signals, Pacific Decadal Oscillation (PDO), Trans-Niño Index (TNI) and Eastern Tropical Pacific SST (NINO3) are the most repetitive indices for the most accurate forecast of each station.
    Keywords: Precipitation Prediction, Large Scale Climatic Signals, Self-Organized Artificial Neural Networks, Input Selection Method, Genetic Algorithm
  • Hadi Bali, Seyed Hossein Mohajeri, Amir Samadi *, Maysam Fazeli Pages 60-74
    Mountainous river beds-generally consist of gravel particles that the precise description of such bed is not only important hydraulically, but also it has great environmental significance. The accurate estimation of bed roughness gives us valuable information to make reliable hydraulic models of flow in river with rigorous bed form. This study discuses about the accuracy of the Kinect device in determining the digital elevation model (DEM) of the gravel-bed. In this regard, the DEMs of the two beds include-hemispheres and two beds with artificial gravel beds have been -used and their statistical characteristics have been analyzed. The results show that while the error in the area among the particles is quite high, themethod can accurately conduct these in general. The comparison of the bed elevation histograms shows that although the artificial gravel beds histograms have higher accuracy compared to the histograms of the beds with hemispheres form, the gravel bed with distance elevations histogram shows the best fit among the four explored beds. Furthermore, exploring the statistical characteristics of these four beds shows that the Kinect device is able to obtain reasonable error rate in statistical parameters except the skewness quantity which has the highest rate of relative error. The variogram analysis of artificial gravel beds emphasis that the Kinect and scanner variograms reasonably close to each other and the longitudinal and transversal particles length scales are exactlythe same. According to the results of this investigation, application ofthe Kinect device in statistical analysis of the gravel beds can be suggested.
    Keywords: Gravel-bed, Statistical Analysis, Geomorphology, Kinect, Bed Roughness
  • Seyed Mohammad Ashrafi * Pages 75-88
    This study aims to investigate the different management policies of multi-reservoir systems and their impact on the demand supply and hydropower generation in Great Karun River basin. For this purpose, the semi-distributed simulation-optimization  model of the Great Karun River basin is developed. Also, the multi-objective particle swarm optimization algorithm is applied to optimize the developed model and determine the optimum operating policies. The significance of this research is using the semi-distributed simulation model to simulate the supply of system demand sites that leads to obtaining more realistic results compared to the centralized models. The results of this study show that the effects of different system reservoirs on energy production and demand supply are not the same across the basin and they should be considered carefully for achieving maximum efficiency of the multi-reservoir system in meeting different demands and for extracting the optimal operating rule curves.
    Keywords: Multi-reservoir water resources system, Multi-Objective Optimization, semi-distribution model, Great Karun River basin, Simulation-optimization approach