Spanning tree-based algorithm for hydraulic simulation of large-scale water supply networks

With the purpose of making calculation more efficient in practical hydraulic simulations, an improved algorithm was proposed and was applied in the practical water distribution field. This methodology was developed by expanding the traditional loop-equation theory through utilization of the advantages of the graph theory in efficiency. The utilization of the spanning tree technique from graph theory makes the proposed algorithm efficient in calculation and simple to use for computer coding. The algorithms for topological generation and practical implementations are presented in detail in this paper. Through the application to a practical urban system, the consumption of the CPU time and computation memory were decreased while the accuracy was greatly enhanced compared with the present existing methods.

Use of Multi-Method Approach for a Gravity Irrigation Network Diagnosis: Case of Karfiguela Paddy Field in Burkina Faso

Burkina Faso is an agricultural and landlocked country whose agriculture is dependent on climatic hazards. Irrigated agriculture therefore appears as an alternative to secure, intensify and sustainably increase agricultural production. However, irrigation systems in Burkina Faso suffer from numerous technical, organizational, land and structural problems, which strongly undermine their performance. This is the case of the irrigated perimeter of Karfiguéla which since its development in 1977, has experienced insufficient water. This research has contributed to the reflection on the performance and operation of gravity irrigation networks on the irrigated perimeter in Burkina Faso and the contribution of satellite images in the mapping of irrigated perimeters. Then, an approach based on a mapping of hydraulic networks and on four main diagnostic methods were used: 1) mapping of hydraulic networks, 2) a detailed visual inspection of hydraulic infrastructure, 3) estimation of canals’ hydraulic efficiency, 4) surveys of the various actors involved in the management of the perimeter. These methods lead to the same results concerning the state of degradation and the functioning of irrigation networks in wet seasons. In terms of results, Karfiguéla perimeter has a net area of 360 ha and is supplied by a network made up of a supply canal, a primary canal, four (4) secondary canals, 37 tertiary ones, and quaternary ones for plots of water supply, all in ordinary concrete except the quaternary which are in rammed soil. The efficiency of the canals in the rainy season, greater than 100%, shows that the irrigation network during this period functions as a drainage network while the real drainage network is disorderly and non-functional. The damage to the irrigation network, siltation, grassing, erosion, overturning, large cracks and many others are all the more severe as we advance toward the Quaternary. The Strickler roughness coefficient values of the order of 68 for the feeder, 47 for the primary, between 32 and 52 for the secondaries and 31 for the tertiaries also reflect a high degradation of the channels. Upkeep and maintenance are rare and generally poorly executed. Improving the performance of irrigation on the perimeter requires a complete rehabilitation of the hydraulic network, the reinforcement of the management and maintenance capacity of the operators’ works and the search for a water resource complementary to the Comoé River to support irrigation, for example, the exploitation of the alluvial aquifer which unfolds under the perimeter and is easily accessible through rudimentary wells and sumps. All this is essential to ensure the sustainability of the rice-growing activity, which has a concentration of nearly 1200 farmers.

NOISE IDENTIFICATION FOR HYDRAULIC AXIAL PISTON PUMP BASED ON ARTIFICIAL NEURAL NETWORKS

The noise identification model of the neural networks is established for the 63SCY14 IB hydraulic axial piston pump. Taking four kinds of different port plates as instances, the noise identification is successfully carried out for hydraulic axial piston pump based on experiments with the MATLAB and the toolbox of neural networks, The operating pressure, the flow rate of hydraulic axial piston pump, the temperature of hydraulic oil, and bulk modulus of hydraulic oil are the main parameters having influences on the noise of hydraulic axial piston pump. These four parameters are used as inputs of neural networks, and experimental data of the noise are used as outputs of neural networks, Error of noise identification is less than 1% after the neural networks have been trained. The results show that the noise identification of hydraulic axial piston pump is feasible and reliable by using artificial neural networks. The method of noise identification with neural networks is also creative one of noise theoretical research for hydraulic axial piston pump.