Failure Analysis and Protection of Oxygen Corrosion of Reheater Tube in a Power plant

The failure analysis of reheater tubes were analyzed by means of alloy composition analysis, metallographic structure analysis, mechanical properties analysis and oxide composition analysis. The results showed: The alloy composition meets the requirements of 12Cr1MoV standard. No abnormality was observed in metallographic structure. Mechanical properties qualified. Oxide components are mainly O and Fe. Comprehensive analysis shows that The main reason of reheater tube failure is oxygen corrosion, which caused thinning of the reheater tube wall, leading to leakage accident. Suggestions for subsequent protection are put forward

Variability and trend analysis of precipitation during 1961-2015 in Southwest Guizhou Autonomous Prefecture (SGAP), China

Information on variability and trends of precipitation over a region is useful in the agricultural production management. The linear regression analysis, 5-year moving average, accumulated anomaly and Mann-Kendall trend detection were used to assess the variability and trends in precipitation over Southwest Guizhou Autonomous Prefecture (SGAP) region of China. The results revealed that the annual precipitation showed an increasing trend at Wangmo and Xingren and decreasing trends at Anlong, Ceheng, Pu’an, Qinglong, Xingyiand Zhenfeng stations. The UF(k) and UB(k) curves of each region have intersections, except in Ceheng and Xingren, and this indicated that the precipitation has seen a abrupt change in six stations. The results of this study will provide theoretical guidance for agricultural water management in Southwest Guizhou Autonomous Prefecture of China

Fillet effect on the bending crashworthiness of thin-walled square tubes

Filleting four corners of square tubes is suggested to reduce the peak force and improve energy absorbing performance. Three-point bending tests are conducted to investigate fillet radius effects employing an ABAQUS explicit code. Three cases characterized by the ratio of width to thickness are considered. Fillet greatly reduces the maximum forces compared with square cross-sections, and the normalized maximum forces decrease with increasing wall thickness when the fillet radius is larger. Additionally, the fillet dramatically improves SEA (Specific Energy Absorption). The normalized CFE (Crash Load Efficiency) significantly exceeds that of the square ones, and the normalized CLEs are almost identical with the increasing fillet radius

Collapse modes of concrete reinforced square bridge piers under vehicle collision

In the field of engineering protection, there is a structural disaster named heavy vehicles impacting column structures. When a heavy truck collides with a reinforced concrete (RC) column at a high velocity, a large impact force generated makes perhaps the column fail and even collapse. Therefore, it is necessary to study the dynamic characteristics during such a disaster, which can provide some reference for structural design, optimization and protection. The RC column impacted by a vehicle could be simplified as a beam fixed at the bottom loaded by a concentrated force, whose deformation is controlled by shearing and bending. In the present work, the ultimate static forces corresponding to shearing and bending collapse are proposed based on theoretical analyses. The model validation is performed using the finite element approach and the theoretical analytical results are in good agreement with the finite element simulation results, which validates the present analytical model. Three cases are simulated by utilizing finite element code ABAQUS, which reveals that the approximate plateau collapse force keeps a long stage beyond the peak failure one. In addition, three collapse modes are observed based on the static force and deformation analysis, validating the present framework which can be used for routine pier design. The work can be extended to estimate collapse modes of building columns under a vehicle collision

An analysis of drought evolution characteristics based on standardized precipitation index: a case study in Southwest Guizhou Autonomous Prefecture, China

Drought is a worldwide concerned issue which causes huge losses in agriculture, economic and damages in natural ecosystems. The precise assessment of drought evolution characteristics is essential for agricultural water management and drought resistance, while such work is rarely reported. Thus, eight meteorological stations located within the Southwest Guizhou Autonomous Prefecture (SGAP) were selected, and the Standardized Precipitation Index (SPI) was used to assess the drought evolution characteristics. The results revealed that the drought occurrences number in Pu'an station was the largest (23 droughts), and the average drought duration in Xingren station was the longest (48.75 months). Moreover, the drought characteristics of the eight stations have account for the largest proportion under normal conditions, was more than 60%, the frequency of drought disaster occurring in Xingren is the highest (30.05%), followed by Wangmo (23.73%). The results of this study will provide theoretical guidance for drought resistance and agricultural production in Southwest Guizhou Autonomous Prefecture of China

Analysis of Urban Vitality in Nanjing Based on a Plot Boundary-Based Neural Network Weighted Regression Model

As a representative indicator for the level and sustainability of urban development, urban vitality has been widely used to assess the quality of urban development. However, urban vitality is too blurry to be accurately quantified and is often limited to a particular type of expression of vitality. Current regression models often fail to accurately express the spatial heterogeneity of vibrancy and drivers. Therefore, this paper took Nanjing as the study area and quantified the social, cultural, and economic vitality indicators based on mobile phone data, POI data, and night-light remote sensing data. We also mapped the spatial distribution of comprehensive urban vitality using an improved entropy method and analyzed the spatial heterogeneity of urban vitality and its influencing factors using a plot boundary-based neural network weighted regression (PBNNWR). The results show: (1) The comprehensive vitality in Nanjing is distributed in a “three-center” pattern with one large and two small centers; (2) PBNNWR can be used to investigate the local regression relationships among the driving factors and urban vitality, and the fitting accuracy (95.6%) of comprehensive vitality in weekdays is higher than that of ordinary least squares regression (OLS) (65.9%), geographically weighted regression (GWR) (89.9%), and geographic neural network weighted regression (GNNWR) (89.5%) models; (3) House price, functional diversity, building density, metro station accessibility, and residential facility density are factors that significantly affect urban vitality. The study’s findings can provide theoretical guidance for optimizing the urban spatial layout, resource allocation, and targeted planning strategies for areas with different vitality values

Building-Level Urban Functional Area Identification Based on Multi-Attribute Aggregated Data from Cell Phones—A Method Combining Multidimensional Time Series with a SOM Neural Network

Portraying functional urban areas provides useful insights for understanding complex urban systems and formulating rational urban plans. Mobile phone user trajectory data are often used to infer the individual activity patterns of people and for functional area identification, but they are difficult to obtain because of personal privacy issues and have the drawback of a sparse spatial and temporal distribution. Deep learning models have been widely utilized in functional area recognition but are limited by the difficulty of acquiring training samples with large data volumes. This paper aims to achieve a fast and automatic identification of large-scale urban functional areas without prior knowledge. This paper uses Nanjing city as a test area, and a self-organizing map (SOM) neural network model based on an improved dynamic time warping (Ndim-DTW) distance is used to automatically identify the function of each building using mobile phone aggregated data containing work and residence attributes. The results show that the recognition accuracy reaches 88.7%, which is 12.4% higher than that of the K-medoids method based on the DTW distance using a single attribute and 7.8% higher than that of the K-medoids method based on the Ndim-DTW distance with multiple attributes, confirming the effectiveness of the multi-attribute mobile phone aggregated data and the SOM model based on the Ndim-DTW distance. Furthermore, at the traffic analysis zone (TAZ) level, this paper detects that Nanjing has seven functional area hotspots with a high degree of mixing. The results can provide a data basis for urban studies on, for example, the urban spatial structure, the separation of occupations and residences, and environmental suitability evaluation

Building-Level Urban Functional Area Identification Based on Multi-Attribute Aggregated Data from Cell Phones—A Method Combining Multidimensional Time Series with a SOM Neural Network

Portraying functional urban areas provides useful insights for understanding complex urban systems and formulating rational urban plans. Mobile phone user trajectory data are often used to infer the individual activity patterns of people and for functional area identification, but they are difficult to obtain because of personal privacy issues and have the drawback of a sparse spatial and temporal distribution. Deep learning models have been widely utilized in functional area recognition but are limited by the difficulty of acquiring training samples with large data volumes. This paper aims to achieve a fast and automatic identification of large-scale urban functional areas without prior knowledge. This paper uses Nanjing city as a test area, and a self-organizing map (SOM) neural network model based on an improved dynamic time warping (Ndim-DTW) distance is used to automatically identify the function of each building using mobile phone aggregated data containing work and residence attributes. The results show that the recognition accuracy reaches 88.7%, which is 12.4% higher than that of the K-medoids method based on the DTW distance using a single attribute and 7.8% higher than that of the K-medoids method based on the Ndim-DTW distance with multiple attributes, confirming the effectiveness of the multi-attribute mobile phone aggregated data and the SOM model based on the Ndim-DTW distance. Furthermore, at the traffic analysis zone (TAZ) level, this paper detects that Nanjing has seven functional area hotspots with a high degree of mixing. The results can provide a data basis for urban studies on, for example, the urban spatial structure, the separation of occupations and residences, and environmental suitability evaluation

Health assessment of plantations based on LiDAR canopy spatial structure parameters

The Yellow River Delta (YRD) has China's largest artificial Robinia pseudoacacia forest, which was planted in the late 1970s and suffered extensive dieback in the 1990s. The health grade of the R.pseudoacacia forest (named canopy vigor grade, CVG) could be achieved by using high-resolution images and canopy vigor indicators (CVIs). However, a previous study showed that there was no significant correlation between CVG and the field-estimated aboveground biomass (AGB) of R.pseudoacacia forest. Therefore, this study aims to construct forest health indicators (FHIs) based on canopy spatial structure parameters extracted from LiDAR. The FHIs included Weibull_α (the scale parameter of the Weibull density function that reflects the shape of the tree canopy), VCI (vertical complexity index), sdCC (the standard deviation of canopy cover), H99 (the 99th percentile height) and cvLAD (the coefficient of variation of leaf area density), and could significantly distinguish three forest health grades (FHG) (p < 0.05). The FHG was positively correlated with forest AGB (rs = 0.51, p = 0.004), and the similarity value with CVG was 63.33%. The results of this study confirmed that the FHIs can reflect both canopy vigor and tree productivity, and distinguish forest health status without prior classification information