800 publications from this institution
This study investigates the complex load-bearing mechanism of the reinforced concrete tower of large wind turbines through a structural model test. MTS electro-hydraulic servo loading system was used to load two reinforced concrete tower models for the push-out test. The ultimate bearing capacity of the reinforced concrete tower was found to be 8.894 kN. The test findings revealed that the top of the tower is subjected to unilateral shear as the horizontal load increases. As a result, the concrete strain in the compression zone of the test piece increases to its highest level in the bottom plastic hinge area. The concrete in the compression zone is being crushed in the meantime. The reinforcement achieves its yield point and deforms within the range of plastic failure when subjected to extreme loads. The outcomes of this study serve as a foundation for the running of wind turbines in extreme conditions.
New HEFV(half-ellipse fixed valve) tray which has some improvements on traditional fixed valve trays was presented.The hydrodynamic and mass transfer performance of HEFV tray,sieve tray and F1 valve tray were experimentally studied with air-water-oxygen system in a 1 200 mm stainless steel column.The results show that HEFV tray possesses a little higher pressure drop,less entrainment,lower weeping and higher efficiency than sieve tray as well as lower pressure drop,less entrainment,higher weeping and higher efficiency than F1 valve tray,which demonstrates that HEFV tray owns super performance.
Focused Ion Beam (FIB) machining has been demonstrated to be capable of fabricating nano and micro scale elements onto optical fibres. In this paper we exploit FIB to fabricate core aligned 45° mirrors at the end of multi-core fibres (MCF). The resulting fibre is used as a component in a two dimensional optical fibre accelerometer. The mirror is produced using a two step process: first a scanning process is used to make a rough cut to define the overall mirror structure. This is followed by a polishing process to create an optical surface finish. The machined 45° mirror can be accurately aligned with optical fibre core, which avoids issues associated with the alignment of external turning mirror components. Proof-of-concept tests demonstrate the use of such a fibre as a two axis acceleration sensor that is interrogated interferometrically. The sensor operated between 0.5g and 4.5g with a cross talk of -24.3dB between axes.
As a crucial agricultural crop in China, litchi exhibits a biennial bearing pattern with alternating high-yield and low-yield cycles, known as on-year and off-year respectively. Research has identified unstable floral initiation as the primary cause of irregular fruiting in mid-to-late maturing cultivars. Rapid and accurate quantification of female to male flower ratios during the flowering phase enables targeted management strategies to optimize floral development and enhance fruit-setting rates. This study proposes Flower Quantification and Gender Recognition Network (FQGR-Net), a three-branch neural network architecture for simultaneous classification and counting of female and male flowers. Through module-level optimization, FQGR-Net improves both counting accuracy and computational efficiency, achieving average MAE of and RMSE of across categories in experiments conducted on the self-constructed dataset. Comparative experiments with other deep neural network models on public datasets show the proposed method achieves optimal performance. A regression analysis between predictions and ground truth produces values of and for female and male flower quantification respectively. A dedicated litchi flower phenotyping analyzer was developed to address the technological gap in automated floral census systems. Field trials demonstrated over accuracy in female/male flower counting.
The identification of longitudinal bending moments is a critical component in the health monitoring of ship structures. This study examines the effect of the failure of measurement points on the accuracy of bending moment identification and presents a solution using an XGboost fitting method. The impact of failure point position and quantity on strain fitting accuracy and bending moment identification was investigated by performing a four-point bending experiment in typical failure scenarios. Further numerical analysis was conducted to identify potential sources of errors in the measurement process. Additionally, several XGBoost-based fitting schemes were tested under practical conditions to provide reliable fitting suggestions. The results indicated that the XGboost strain fitting method outperforms conventional methods for removing failed measurement points, resulting in improved accuracy of identification. When the most critical failure condition occurs (i.e., the deck plate measurement points and deck stiffener measurement points fail), the XGboost method can still estimate the strain at the failure points with acceptable accuracy. These results also hold in complex load scenarios. Moreover, in the practical measurement conditions, the arrangement of measuring points includes two sections that are sufficient to support the fitting of failed measurement points by using the XGboost method. The XGboost strain fitting method exhibits promising potential in strain fitting applications.
Almost all existing analytical formulae describing the magnet-rail relationship for maglev trains have been derived with the assumption that magnetic media operate within the linear zone of the B-H curve and, therefore, over-predict the levitation and guidance forces when the magnetic media are in their saturation states. In such cases, finite element simulations are commonly used to obtain an accurate solution of the electromagnetic force, but this process is time-consuming. In this study, metamodels suitable for saturated magnetic media are constructed based on the methods of uniform design and stepwise regression, and their performance is assessed in terms of data fitting and prediction accuracy.
The neutral point operation mode and its developing course in electric power system were introduced, some basic ideas and technical terms about neutral point operation mode were defined, and a new idea about neutral point operation mode was presented.
The load-carrying assessment of existing and aging bridges has been a critical challenge in Australia and elsewhere. The reversed capacity of these bridges is under investigation because of increasing traffic loads, deterioration of materials, and accumulated damage during operations. In this study, a hybrid finite-element (FE) model updating approach is developed to identify the current condition of a nearly 50-year-old highway bridge. The reserved capacity of the bridge subjected to the current traffic loadings in design specifications is evaluated. An initial FE model of the bridge is built based on the design documents. Strain data obtained from static load testing of the bridge and modal information identified from the measured acceleration responses of the bridge under operational traffic are simultaneously used to update the material properties and dynamic characteristics of the bridge model. The updated model can simultaneously match the recorded static response and identified modal information of the bridge, which can reflect the present operational condition of the bridge better than the original model constructed by design drawings. Based on the updated bridge model, the load rating factor (RF) is calculated to evaluate the performance of the bridge under the present design traffic loading specified in Australian bridge design standards. The calculated results indicate that the reserved capacity of the bridge for traffic and dead load effects can satisfy the requirements of the current bridge design specifications.
In common bucket dissipation design, deflecting distance and depth of scour hole are usually considered important, but the influence of upstream boundary condition to flow phenomenon of inflow is ignored. Combining with hydraulic model test of Cha' yuan Reservoir, analyzing the causes of curved turbulent belt formed on the upstream surface of the reservoir when bucket flood discharging with big flow, the writer emphatically researches the scheme eliminating the curved turbulent flow in order to ensure the project operation safety.
Recently, various convolutions based on continuous or discrete kernels for point cloud processing have been widely studied, and achieve impressive performance in many applications, such as shape classification, scene segmentation and so on. However, they still suffer from some drawbacks. For continuous kernels, the inaccurate estimation of the kernel weights constitutes a bottleneck for further improving the performance; while for discrete ones, the kernels represented as the points located in the 3D space are lack of rich geometry information. In this work, rather than defining a continuous or discrete kernel, we directly embed convolutional kernels into the learnable potential fields, giving rise to potential convolution. It is convenient for us to define various potential functions for potential convolution which can generalize well to a wide range of tasks. Specifically, we provide two simple yet effective potential functions via point-wise convolution operations. Comprehensive experiments demonstrate the effectiveness of our method, which achieves superior performance on the popular 3D shape classification and scene segmentation benchmarks compared with other state-of-the-art point convolution methods.
FEM simulation construction process was carried on the relieving platform retaining wall which often used in hydraulics and communication civil engineering.This kind of retaining wall was researched on its influential factors,such as the distribution of earth pressure,the width of plat and its height,on lateral soil pressure.It was a good reference for the design of practical engineering construction.
Floating Car Technology is widely used to collect traffic information. To reappear the actual trips of drivers, a bi-level probability method is proposed to reconstruct routes from floating car data, address two issues: the first one is incorrect map matching caused by GPS accuracy and complexity of road network; and the second one is the link missing duo the low sampling rate of floating car. Using confidence region, GPS points are divided into three types: zero-feature points with no feature matching, single-feature points that have a unique matched link or node and multiple-features points that have multiple features. The matching probability for GPS points to the possible feature according to the distance between GPS point and the link, which is assumed to be normal distribution. The missing links between two single-feature points are reconstructed by the shortest path algorithm with consideration of the probability of multiple matched features. A case study of Guangzhou floating car data shows that the proposed method can produce reasonable routes on complicated urban road network.
