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The operating condition of a submerged propeller has a significant impact on flow field and energy consumption of the oxidation ditch. An experimentally validated numerical model, based on the computational fluid dynamics (CFD) tool, is presented to optimize the operating condition by considering two important factors: flow field and energy consumption. Performance demonstration and comparison of different operating conditions were carried out in a Carrousel oxidation ditch at the Yingtang wastewater treatment plants in Anhui Province, China. By adjusting the position and rotating speed together with the number of submerged propellers, problems of sludge deposit and the low velocity in the bend could be solved in a most cost-effective way. The simulated results were acceptable compared with the experimental data and the following results were obtained. The CFD model characterized flow pattern and energy consumption in the full-scale oxidation ditch. The predicted flow field values were within -1.28 ± 7.14% difference from the measured values. By determining three sets of propellers under the rotating speed of 6.50 rad/s with one located 5 m from the first curved wall, after numerical simulation and actual measurement, not only the least power density but also the requirement of the flow pattern could be realized.

Cutting parameters are important components in the process of computer numerical control (CNC) machining, and reasonable choice of cutting parameters can significantly affect the energy efficiency. This paper presents a multi-objective parameter optimization method for energy efficiency in CNC milling process. Firstly, the energy consumption composition characteristics and temporal characteristics in CNC milling are analyzed, respectively. The energy model of CNC milling is then established, of which the correlation coefficient is obtained through nonlinear regression fitting. Then a multi-objective optimization model is proposed to take the highest energy efficiency and the minimum production time as the optimization objectives, which is solved based on Tabu search algorithm. Finally, a case study is conducted to validate the proposed multi-objective optimization model and the optimal parameter solutions of maximum energy efficiency and minimum production time is obtained. Moreover, the parametric influence on specific energy consumption and production time are explicitly analyzed. The experiment results show that cutting depth and width are the most influential parameters for specific energy consumption, and spindle speed ranks the first for the production time.

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The establishment of a structural safety monitoring model of a dam is necessary for the evaluation of the dam’s deformation status. The structural safety monitoring method based on the monitoring data is widely used in traditional research. On the basis of the analysis of the high arch dam’s deformation principles, this study proposes a structural safety monitoring method derived from the dam deformation monitoring data. The method first analyzes and establishes the spatial and temporal distribution of high arch dam’s safety monitoring, overcoming the standard artificial bee colony (ABC) algorithm’s shortcoming of easily falling into the local optimum by adopting the adaptive proportion and average Euclidean distance afterwards. The improved ABC algorithm is used to optimize the backpropagation (BP) neural network’s initial weight and threshold. The application example proves that ABC-BP model’s improvement method is important for the establishment of a high arch deformation safety monitoring model and can effectively improve the model’s fitting and forecasting ability. This method provides a reference for the establishment of a structural safety monitoring model of dam and provides guidance for the establishment of a forecasting model in other fields.

A concrete dam is an important water-retaining hydraulic structure that stops or restricts the flow of water or underground streams. It can be regarded as a constantly changing complex system. The deformation of a concrete dam can reflect its operation behaviors most directly among all the effect quantities. However, due to the change of the external environment, the failure of monitoring instruments, and the existence of human errors, the obtained deformation monitoring data usually miss pieces, and sometimes the missing pieces are so critical that the remaining data fail to fully reflect the actual deformation patterns. In this paper, the composition, characteristics, and contamination of the concrete dam deformation monitoring information are analyzed. From the single-value missing data completion method based on the nonlocal average method, a multi-value missing data completion method using BP (back propagation) mapping of spatial adjacent points is proposed to improve the accuracy of analysis and pattern prediction of concrete dam deformation behaviors. A case study is performed to validate the proposed method.

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Dams are important water-resisting structures prone to failure, causing huge economic and environmental losses. Traditionally, a dam failure is identified using the failure mode and effect analysis. This approach analyzes both the dam failure path (the specific effect chain of the failure mode) and the damage degree, by identifying and sorting the severity caused by the dam failure path. However, this analysis can be misleading since the relationship among the failure paths is not considered. To account for this, the DEMATEL method is used to modify the evaluation result of the severity of the failure consequence, caused by the dam failure path. Based on the fuzzy mathematics and VIKOR method, a dam failure path identification method is established, and then the dam failure paths are identified and sorted for a case study: gravity dam located at the junction of Yibin County (China). According to results, the two top initial failure paths were insufficient design of upstream anti-seepage (R6) or defective water-tight screen and corrosion (R7).