海上风电单筒多舱筒型基础筒体屈曲特性研究

对单筒多舱筒型基础入土下沉过程进行稳定性分析,研究其失稳模式与临界屈曲荷载以及分舱板与纵向加劲肋对筒内外压差临界值的影响。结果表明,单筒多舱筒型基础筒体失稳属于极值点失稳,应采用非线性方法确定结构失稳的临界荷载;分舱板和适当间距的加劲肋均可提高筒体的临界屈曲荷载,但随筒顶接近泥面,其提升效果逐渐减小,筒内外压差控制条件转变为防止筒内土体发生渗透破坏。     

不同流量入湖三角洲演变特性试验研究

入湖三角洲的形成主要受到流量、下游水位及上游来沙等因素的影响.在对后二者的影响规律研究基础上,为探索不同流量条件下入湖三角洲的演变特征,开展了进一步的试验研究.通过对三角洲的河道面积占比、摆动角度、河道长度、泥沙堆积等几何特征的分析,探讨了流量对入湖三角洲演变的影响.研究发现:三角洲的演变过程整体上遵循冲-淤-冲的...     

A BIM-Based Safety Management Framework for Operation and Maintenance in Water Diversion Projects

Timely assessment of the structural safety status of water diversion projects and taking necessary preventive measures are the primary conditions to ensure the safe operation of large-scale diversion structures. This paper proposes an integrated visualization framework based on the Building Information Model (BIM) to support the safety management of water diversion projects. The framework integrates data collection, data analysis, warning issuance, and emergency disposal into an integrated platform, which improves the automation level of safety management and the efficiency of emergency response. On this basis, a prototype system is developed and implemented in a water diversion project in Henan Province, China. The prototype of the system can automatically evaluate the structural safety status of water diversion structures, issue corresponding warnings to managers, and provide visual prompts to support decision-making. The system prototype and its demonstration verify the applicability and effectiveness of the framework.

     

隔水幕分层取水研究进展及应用现状

分层水库春夏季下泄低温水效应显著,分层取水是解决上述问题的有效手段。隔水幕是一种较为新型的分层取水方案,具有造价低廉、发电水头损失小、运行灵活、施工便利等优点,对我国大量改建、扩建及新建水库具有重大的研究和应用价值。目前隔水幕在美国和日本部分水库已有工程应用和研究成果,其中美国主要关注下方过流的顶部挡水幕,而日本主要关注中小型水库。国内隔水幕分层取水研究还处于起步阶段,与当前巨大的工程需求不相适应。因此,在借鉴已有国际经验的同时,也需要针对我国水库特点,尤其是诸多大型深水水库,进行自主研发,提出一套适用于我国的隔水幕分层取水理论和施工、运行、调控技术。梳理了现有水库隔水幕分层取水的研究现状,并剖析了研究及应用中存在的各类问题,并对隔水幕后续的研究及应用提出了建议。     

An automatic energy saving strategy for a water dispenser based on user behavior

Nowadays, numerous public buildings provide water dispensers to supply drinking water which causes more energy consumption. A typical water dispenser periodically heats and cools the water to ensure that hot, warm, and cold water are always available for the user. However, this mechanism is inefficient because the users do not request hot and cool water continuously. Ideally, the boiling and cooling schedule should follow the demand pattern to save electricity consumption. When no demand, a water dispenser can enter a sleep mode.Therefore, this study presents an automatic energy-saving strategy for a water dispenser based on user behavior. The proposed system allows the water dispenser to automatically determine the appropriate time to heat, boil, and enter sleep mode based on user behavior. The proposed control strategy involves several steps. First, it collects historical data, analyzes water consumption behavior. The sensors installed in the water dispenser collect water consumption data. Second, this study applies Recurrent Neural Networks with Long-Short Term Memory to predict future water consumption. Finally, the proposed system utilizes the prediction result to determine heating, cooling, and sleep mode schedule.This study uses a water dispenser on a university campus as a prototype to test the proposed system. The effectiveness of the proposed system is measured by two factors, namely electricity consumption, and customer satisfaction. These two parameters are chosen because the proposed system should reduce electricity consumption while maintaining hot and cold water availability whenever needed. According to the simulation results, the proposed controlling strategy can reduce electricity consumption up to 28% monthly while maintaining a service level of 97%. This result shows that the proposed system is a good control system for water dispensers. By applying this controlling system, public buildings could reduce their energy bills without sacrificing their provision of drinking water.     

Promoting employee health in smart office: A survey

The advancement of Internet-of-Things (IoT) and artificial intelligence contribute to the prevailing development of smart office, which is capable of understanding employees’ context and adapting to their demands. The smart office brings numerous opportunities for delivering prevention and control measures of health issues associated with office work (e.g., musculoskeletal disorders and computer vision syndrome). Even though there exist multiple studies across different disciplines, there still lacks a holistic survey on the smart office for employee health promotion. Hence, this paper focuses on three contributions: (1) clarifying the fundamentals of smart office, (2) reviewing the key aspects of this theme based on 60 studies selected from a systematic survey process, and (3) identifying the challenges and future research opportunities. We hope this study can bring an interdisciplinary and collaborative perspective for employee health promotion and encourage more researches in this emerging and promising field.     

Research on intelligent assembly method of aero-engine multi-stage rotors based on SVM and variable-step AFSA-BP neural network

The quality of the aero-engine rotors assembly determines the overall performance of the engine. Aiming at the problems of rotors assembly with different plane types, we proposes a rotor plane classification method based on SVM by using the profile data of PCA dimension reduction. Meanwhile, for the unilateral-tilt plane rotors, the three-objective rotors assembly method of coaxiality, unbalance amount and perpendicularity based on the rigid rotor model is established. For the hyperbolic paraboloid rotors, an intelligent assembly method based on AFSA-BP neural network for coaxiality, unbalance amount and perpendicularity is established. The experiment is based on the double-column ultra-precision measuring instrument and V4L vertical balancing machine and HL5UB horizontal balancing machine to measure rotors geometry and unbalance data. The experimental results show that the plane type classification accuracy can reach 99 %. The prediction error of the coaxiality of the unilateral-tilt plane rotors assembly is 5.1 μm, the prediction error of the unbalance amount is 196 g·mm, and the prediction error of the perpendicularity is 0.6 μm. The average prediction error of the coaxiality of the hyperbolic paraboloid rotors assembly is 0.9 μm, and the average prediction error of the unbalance amount is 73 g·mm, and the average prediction error of the perpendicularity is 0.2 μm. Our method provides a reliable assembly solution for aero-engine rotors assembly and meets actual assembly requirements.     

Irrigation Scheduling Optimization for Cotton Based on the AquaCrop Model

To improve irrigation efficiency, it is important to optimize agriculture irrigation scheduling. The objectives of this study were to evaluate the AquaCrop model for its ability to simulate cotton in the North China Plain and optimize irrigation strategies. The AquaCrop model was calibrated using 2002–2009 data and validated using 2010–2014 data. Root mean square error (RMSE), mean absolute error (MAE) and residual coefficient method (CRM) were used to test the model performance. The model calibrated for simulating cotton yield had a prediction error statistic RMSE of 0.152 t hm^?2, MAE of 0.123 t hm^?2 and CRM of 0.120. On validation, the RMSE was 0.147 t hm^?2, MAE was 0.094 t hm^?2 and CRM was 0.092. The goodness-of-fit values for the calibration and validation data sets indicated that the model could be used to simulate cotton yield. The analysis of irrigation scenarios indicated that the highest irrigation water productivity could be obtained by applying one irrigation at the seedling stage in a wet year, two irrigations, at the seedling and squaring stages, in a normal year and three irrigations, at the seedling, squaring and flowering stages, in a dry year. These results could be useful to the government in determining reasonable, well-timed irrigation for agricultural regions.