Journal of Hydroelectric Engineering

水力发电学报

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2022 Vol. 41, No. 3 Published: 2022-03-25

	1	Non-logarithmic velocity profiles of shallow open channel flows with submerged vegetation
		MAO Ranran, CHENG Niansheng
		DOI: 10.11660/slfdxb.20220301
		With growing attention to ecological environment, the hydraulics research of vegetated flows has achieved a rapid progress in recent years. For submerged vegetation, previous studies generally suggest flow velocity in the surface layer above the vegetation follows a logarithmic distribution, but in shallow water cases the velocity profile of this layer deviates from the logarithmic law significantly. In this paper, variations in the mixing length for shallow vegetated flows are examined by applying Prandtl’s theory, and a new formula is derived to describe the velocity defect distribution. Velocity profiles have been measured experimentally for different surface layer thicknesses, different vegetation densities, and different vegetation stem diameters. Our new formula proves in good agreement with the experimental data.
		2022 Vol. 41 (3): 1-8 [Abstract] ( 140 ) PDF (479 KB) ( 345 )

	9	Effects of topographic changes in terminal river network of Ganjiang River on its hydrodynamics in flood season
		BAI Yuchuan, LI Xiaowen, XU Haijue, SONG Xiaolong
		DOI: 10.11660/slfdxb.20220302
		This paper is to explore the relationship between the topographic changes of Ganjiang River network and the changes in its hydrodynamics in flood season under the influence of new water and sediment conditions and human activities. We developed one-dimensional river network mathematical models during 1998-2020 based on the river topographic changes in 1998, 2013 and 2020 respectively to simulate the 1998 flood in this river. The influence of topographic changes in different periods on hydrodynamic characteristics in flood season is studied. The results show that in this river network, the overall downcutting has caused the water level-discharge relationship to shift to the right, and the flood peak discharges in each branch increases, with an inflow increase of more than 50% under the same water level. During 1998-2020, the southern branch had a topographic downcutting depth greater than the other branches, and its uneven topographic downcutting resulted in a change in the distributary ratios at the river network nodes. On the whole, the distributary flow increased in the main branch and the southern branch, while it decreased in the middle and northern branches. Flow velocity in each branch changed dramatically, with an increase of more than 1 m/s in the downstream section of the main branch. This study is useful for river regulation planning, dam protection, and disaster prevention.
		2022 Vol. 41 (3): 9-21 [Abstract] ( 220 ) PDF (1018 KB) ( 205 )

	22	Study of floods in experimental watersheds of different geomorphic types in eastern monsoon region of China
		LIU Yue, ZHANG Jianyun, BAO Zhenxin, HE Ruimin, LIU Cuishan, WANG Guoqing
		DOI: 10.11660/slfdxb.20220303
		Based on observed data from the experimental watersheds of Chengxi (a hilly area) and Huangshan (a mountainous area) both in Anhui Province in the eastern monsoon region of China, the characteristics of flood events in watersheds with different geomorphic types are analyzed. A topography-based hydrological model (Topmodel) has been used to simulate the floods events, and its applicability to different geomorphic areas is discussed. The results show the runoff coefficients of Huangshan watershed are larger than those of Chengxi watershed on both annual and event scales. For the same weather process, the start time of flood rising in Chengxi watershed lags behind in Huangshan watershed, while flood duration is uncertain due to its multiple influencing factors such as area, slope, land use, river network distribution, etc. The average values of Nash-Sutcliffe efficiency coefficients calculated for both watersheds reach 0.77, and the average relative error of different simulation periods can be controlled within 14%. The applicability of the Topmodel to mountainous areas is better than to hilly areas.
		2022 Vol. 41 (3): 22-31 [Abstract] ( 87 ) PDF (1643 KB) ( 422 )

	32	Monthly runoff prediction based on teleconnection factors selection using random forest model
		XIONG Yi, ZHOU Jianzhong, JIA Benjun, HU Guohua
		DOI: 10.11660/slfdxb.20220304
		A teleconnection relationship exists between watershed runoff and large-scale climate indexes. For medium- and long-term runoff prediction, a major difficulty is how to pick out those that are strongly correlated with runoff from various factors such as hydrology, meteorology, atmospheric circulation, and ocean current. This study applies a random forest model based on Bayesian optimization (sequential model-based optimization for general algorithm configuration) to selecting runoff predictors from the set of high-dimensional hydrometeorological and climatic factors according to their importance scores, and constructs a general regression neural network, an extreme learning machine, and a support vector regression runoff prediction models. The method is applied to runoff predictions for the Jinsha River. Compared with those of the factor selection model based on correlation coefficients, our new prediction model using the random forest for factor selection improves the generalization capability. Meanwhile, adding appropriate teleconnection climatic factors to the prediction model inputs can help improve accuracy of monthly runoff prediction and provide physical basis for the model.
		2022 Vol. 41 (3): 32-45 [Abstract] ( 245 ) PDF (3308 KB) ( 473 )

	46	Study on forecasting method of snowmelt runoff based on multi-factor similarity
		WEN Xin, CHEN Ran, TAN Qiaofeng, SHI Ying, DING Ziyu
		DOI: 10.11660/slfdxb.20220305
		Snowmelt runoff is an important component of the water cycle in alpine areas; its forecast is of great significance to the comprehensive utilization of water resources in a basin. Based on previous studies on watershed confluence mechanism and glacier snow melting, this paper develops a snowmelt runoff forecast model based on the similarity of multiple factors by combining the advantages of physical cause analysis and data mining technology, and works out a plan to achieve a 7-day rolling forecast of daily runoff. Application in a case study of the Xinlong station on the Yalong River shows that this model has an average relative error lower than 17% over the 3 d forecast period, and its Nash coefficient reaches 0.89 for schemes with accumulated positive temperature. For the 7 d forecasts, the error is lower than 21% and the coefficient up to 0.83. This means an error reduction by 2% and 6% and a Nash coefficient increase by 0.03 and 0.08 for the 3 d and 7 d forecasts, respectively, relative to the schemes without accumulated positive temperature. Our method can mine quantitatively the experience of referring to the past and forecasting the future from front-line business personnel, and provide interpretable runoff forecast results, significantly improving runoff forecast accuracy and extending forecast periods.
		2022 Vol. 41 (3): 46-59 [Abstract] ( 110 ) PDF (2936 KB) ( 424 )

	60	Real-time flood control operation model of Three Gorges Reservoir based on deep learning
		XU Gang, SHU Yuanli, REN Yufeng, WU Biqiong
		DOI: 10.11660/slfdxb.20220306
		Real-time flood control scheduling needs to consider multiple constraints and comprehensive objectives, which has a high degree of complexity. Using the Three Gorges reservoir as a study case, this paper describes a new real-time reservoir flood control dispatching model based on deep learning. We simulate the dispatching process of the reservoir to generate training sample data; then using the sample data, we generate high-dimensional tensor input data, and extract high-dimensional data features through network parameter training to learn how to fit real-time reservoir flood control dispatch modes. This dispatching model, based on a real-time scheduling model equipped with a deep convolutional neural network, extracts the characteristic information of the gate opening in the training process. It uses reinforcement learning algorithms for optimizing the model parameters iteratively, and updating sample data successively through online learning to implement optimal scheduling decisions. In the case study of the reservoir’s real-time flood level control model and real-time discharge flow control model, the relative errors of simulated discharges are around 1.4% and 1.0%, respectively, against the site observed data. And the calculations show that our deep learning model has a good convergence, and it is applicable to the real-time flood control dispatching of reservoirs.
		2022 Vol. 41 (3): 60-69 [Abstract] ( 216 ) PDF (943 KB) ( 673 )

	70	Spatiotemporal variability of precipitation over Guangdong Province during flood seasons of 1960 to 2020
		HUANG Yixuan, XU Zongxue, CHEN Hao, FU Tiewen, WAN Donghui
		DOI: 10.11660/slfdxb.20220307
		Precipitation is a key driving factor of urban flooding and other flood disasters that can result in huge losses in human lives, property and social economy. Therefore, a science-based comprehensive understanding of its distribution pattern and development trend is essential for urban flood control and disaster mitigation. This paper presents an analysis of the spatial variability, time trend, and periodicity of the precipitation over Guangdong Province in flood season, using the methods of spatial interpolation, Mann-Kendall test, moving t test, and wavelet analysis, based on the monthly precipitation data of 1960 to 2020 at 26 stations. Results indicate this province can be partitioned into four zones with different peak characteristics of the monthly precipitation at the stations. And changes in precipitation were not synchronous between different months or between coastal and inland regions. We demonstrate the spatial paths traced by the high-value center and low-value zones of precipitation that moved along in flood season. By grouping precipitation sequences into coastal vs inland and pre-flood vs post-flood types, the trends and significant mutation points of each group are detected; the dominant cycles of each group on the significant cycle scale are identified.
		2022 Vol. 41 (3): 70-82 [Abstract] ( 138 ) PDF (5369 KB) ( 429 )

	83	Prediction of centrifugal pump performance curves based on genetic-support vector regression
		LUO Huican, ZHOU Peijian, WU Denghao, MOU Jiegang
		DOI: 10.11660/slfdxb.20220308
		To solve the inaccurate performance prediction of the centrifugal pumps at multi-operation points, we develop a new method based on a genetic algorithm and support vector regression, which is able to predict the energy performance. 673 sets of multi-operation data were extracted from 35 sets of centrifugal pump performance curves as the test samples, and were divided into the training data group of 538 samples and a test data group of 135 samples. We also selected five centrifugal pumps, specific speed 32.2, 47.2, 58.7, 92.8 and 128.2, to verify our method via predicting heads and efficiencies at six operation points and comparing them with test data. Results show that it can effectively predict the performance of the centrifugal pump under different operation conditions. For these five pumps, the average relative errors of the head and efficiency are 0.49% and 3.76% respectively, demonstrating a significant error reduction in comparison to the corresponding neural network model errors of 1.12% and 4.66%, and the average relative errors increased by 128.57% and 23.94%.
		2022 Vol. 41 (3): 83-91 [Abstract] ( 126 ) PDF (1278 KB) ( 411 )

	92	Orthogonal test optimization of impeller and diffuser of high specific speed axial flow pumps
		ZHAO Yong, DONG Wei, XIAO Yexiang, WANG Chengpeng, WANG Dong, WANG Shenghui
		DOI: 10.11660/slfdxb.20220309
		The axial flow pump of high specific speed is widely used for flood control, waterlog drainage, and other special occasions, but its hydraulic efficiency is generally low. To achieve energy-saving effects and a higher efficiency, this paper adopts orthogonal tests to optimize the impeller and diffuser of a 1400 axial flow pump, and examines the internal flow behaviors of the pump and its optimized version. First, a geometric parameter analysis is made for the impeller and diffuser, and control factors are imposed on the parameterized control dimensions. Then, we design a set of orthogonal tests using hydraulic efficiency as the objective index, and conduct a range analysis of the test results so as to determine the key factors in the pump’s hydraulic performance. Thus, orthogonal test optimization of the impeller and guide vanes is achieved for different levels of the key factors via adjusting the geometric parameters of the blade airfoil. We found that flow separation and backflow vortices in the impeller and diffuser sections optimized by cross-test are suppressed effectively, and the pressure concentration area on impeller surface is expanded, resulting in a greater driving force and a significant reduction in the hydraulic losses in the sections of impeller, diffuser and outlet pipe. And an improvement on the pump’s hydraulic efficiency by 13.2% has been achieved in and the pump head is increased to the design level of 6.3 m.
		2022 Vol. 41 (3): 92-100 [Abstract] ( 177 ) PDF (1760 KB) ( 457 )

	101	Protection against water hammers for distributed pump group in water supply system
		QIU Weixin, PAN Yibin, ZHANG Jian
		DOI: 10.11660/slfdxb.20220310
		To reduce the damage to a water supply system caused by negative water hammers that occur in its distributed pump group after power outage, a new joint protection scheme using a one-way tower and an air valve is described. The effect of one-way tower protection is clarified and a basic formula for setting the air valve in a main pipe concentration section is presented. This scheme is verified through numerical simulations in a case study of the urban water supply project in Tanzania. The results show that the protection by adopting both a one-way tower and an air valve is more effective than that adopting a one-way tower alone, and it reduces the project cost greatly. The diameters of the one-way tower and its connecting pipes should be optimized according to the conditions of a practical project.
		2022 Vol. 41 (3): 101-112 [Abstract] ( 88 ) PDF (2092 KB) ( 485 )

	113	Real-time monitoring technology on tamping pit position in dynamic compaction construction
		LIU Quan, LI Feiyu, ZHANG Hongyang, JIN Yinlong, GAO Qiaoyu
		DOI: 10.11660/slfdxb.20220311
		During the reinforcement of dynamic compaction foundation, real tamping pit positioning satisfying design requirement is a basis for the reinforcing effect. Traditional manual lofting methods suffers several problems in determining the position of tamping pit, such as inefficiency and difficulty in result checking. This study develops a real-time method for calculating tamping pit position based on GNSS and magnetic sensors, together with a corresponding system of real-time monitoring on pit tamping. This system can realize accurate real-time positioning of the pit during dynamic compaction construction. It monitors the position of the monitoring equipment center via GNSS positioning equipment mounted on the ramming machine, using a magnetic azimuth sensor to collect the tamping pit azimuth data relative to the compactor, so that an automatic full-tamping-process real time monitoring is achieved. To discretize the time sequence of tamping pit locations, a spatial clustering analysis is used to extract accurate pit positioning information. This method is validated against the measurement of dynamic compaction construction for the resettlement project of the Baihetan dam. Results show that it can effectively determine the position coordinates of the tamping pit and improve the intelligence level and positioning efficiency, thus help the quality evaluation of strong compaction construction.
		2022 Vol. 41 (3): 113-122 [Abstract] ( 97 ) PDF (2345 KB) ( 358 )

	123	Deformation prediction of high embankment dams by combining time series decomposition and deep learning
		HOU Weiya, WEN Yanfeng, DENG Gang, ZHANG Yanyi, CHEN Hui
		DOI: 10.11660/slfdxb.20220312
		This paper develops a novel combined method of deformation forecasting for high embankment dam, considering the complex nonlinearity and non-stationarity of the time series, to improve the simulation of the long-term development trend and its fluctuation characteristics. Seasonal-trend decomposition based on the Loess smoothing is adopted to decompose the dam displacement time series into trend, seasonal, and remainder components. A long-short term memory neural network model is used to predict separately the three components that are then summed up to generate a displacement prediction. To evaluate and compare the prediction results quantitatively, three evaluation indicators are introduced and the results are compared with those of the SARIMA model, the LSTM neural network model, and a combined model of SARIMA-LSTM. New deformation model shows high accuracy and stability in the prediction. It is applicable to deformation with long-term trend and fluctuation characteristic with water level.
		2022 Vol. 41 (3): 123-132 [Abstract] ( 148 ) PDF (3490 KB) ( 490 )

	133	Intelligent text analysis of hydropower project progress management based on improved LDA
		LI Mingchao, LV Yuangeng, TIAN Dan, SHEN Yang
		DOI: 10.11660/slfdxb.20220313
		Schedule control is a key task of hydropower project management; A timely summary of schedule management information helps formulate and adjust the project schedules. In hydropower project construction, progress information is often presented in semi-structured or unstructured text forms, thereby inducing a difficulty in information extraction. An urgent issue is how to realize automation and intelligent mining of the text information of hydropower project progress. This paper presents a new intelligent extraction method of hydropower project schedule information based on an improved LDA method for intelligent extraction of the key information from schedule management texts. This method, based on the Gibbs sampling mechanism of the traditional LDA model, takes full consideration of the association relationship between words, and improves semantic association between words, closeness between words, and accuracy in the description of topic words. It is applied to practical hydropower projects to analyze 221 weekly reports on construction supervision, and extracted the key words of 12 themes, with the main and secondary processes extracted through calculations. Results show that our improved LDA topic model is better than the traditional LDA and it helps improve word extraction efficiency and information mining efficiency for hydropower construction.
		2022 Vol. 41 (3): 133-141 [Abstract] ( 183 ) PDF (1041 KB) ( 627 )

	142	Simulations of centrifugal model tests of layer reinforced slope using material point method
		GUO Zhenghao, FEI Jianbo, JIE Yuxin
		DOI: 10.11660/slfdxb.20220314
		It is often difficult to simulate the failure of reinforced slopes using the traditional finite element method due to mesh distortion that may cause numerical problems with large deformation. Few previous numerical simulations of reinforced slope failure can be found in the literature. For large deformation problems, the material point method is generally suitable, but a challenge emerges if it is used to simulate the interaction between geosynthetics and soil. To simulate the centrifugal model tests of reinforced slope failure, this paper develops a new model by applying the hybrid finite element material point method. Using this model, we have obtained simulations that agree with the model tests, such as peak geotextile strain, the position of sliding surfaces, and the shape of sliding bodies. The simulated vertical stress at the right side of the slope is nearly the same as its theoretical estimate and the major failure characteristics are captured, verifying our model and the material point method for large deformation analyses.
		2022 Vol. 41 (3): 142-150 [Abstract] ( 109 ) PDF (3652 KB) ( 361 )