查看更多>>摘要：The wind tunnel tests including the bridge profile test, and the scaled-down bridge model test are crucial for analyzing bridge stability and modifying the design profile. It can provide critical information regarding the aerodynamic behavior of the bridge through investigating the flutter derivatives. In recent years, research in wind engineering has commonly utilized the Modified Ibrahim Time Domain Method (MITD) to determine flutter derivatives through free vibration tests. It can deliver the buffeting force effects through an iteration method within a smooth flow. This paper adopts the output-only Stochastic Subsystem Identification (SSI) methods to identify the flutter derivatives. These methods consider the buffeting force as a random force that can be incorporated into the stochastic state space equation. The advantage of SSI is that it can identify the flutter derivatives through the random vibration data of the bridge under various wind speeds. Furthermore, the SSI can be divided into two different methods: the Covariant-driven Stochastic Subsystem Identification method (SSI-COV) and the Data-driven Stochastic Subsystem Identification method (SSI-DATA). This paper will present the results obtained under various wind speeds using three distinct system identification methods to investigate the originally proposed bridge section and the enhanced one. The accuracy of the results obtained through multiple output-only system identification methods will be demonstrated and the modal parameters can also be identified. It can be seen that the identified frequencies are consistent with the designed frequencies. The results validate the applicability and precision of the adopted Stochastic Subspace Identification methods for bridge aerodynamics analysis.
查看更多>>摘要：This paper highlights the aerodynamics and structural design of a 1 kW rooftop wind turbine with a robust mounting capable of supporting the turbine at a high wind speed of 59.5 m/s with the objective of maximum power production at an 11 m/s wind speed prevailing over a two storey building. The wind turbine blade has been designed using the Gottingen 682 airfoil and blade element momentum theory. Anumerical model with shear stress transport (SST) k-omega turbulence model based on computational fluid dynamics is implemented to calculate the power generating capacity. The turbine produced a maximum shaft power output of 1.1 kW at 550 rpm, corresponding to an 11 m/s rated wind speed. Composite wind turbine blades are manufactured using glass fiber and an epoxy matrix through a vacuum bagging technique. Static structural analysis is performed for the rated, cut-off and extreme wind speeds, and the corresponding tip deflections are 0.74 mm, 3.96 mm and 22.85 mm respectively. Under the extreme wind speed, a maximum flap wise bending stress of 14.5 MPa occurs on the pressure surface and a compressive stress of 13.5 MPa arises near the root, which has a safety factor of 2.37. The total weight of the composite blade based on computation and fabrication is 887 g and 895 g, respectively. In order to study the dynamic behaviour, modal analysis is performed and checked for resonance conditions through the Campbell diagram.
Zaracho, Juan I.Ginger, John D.Holmes, John D.Henderson, David J....
251-264页
查看更多>>摘要：Understanding the aeroelastic response of solar trackers under high turbulence wind flow is crucial for optimizing their design and performance. This paper presents a wind tunnel study on the aeroelastic response of solar trackers at a 1/20 scale. Three models were tested in a boundary layer wind tunnel under various wind directions (at specific intervals between 0 degrees and 180 degrees) and tilt angles ranging from 0 degrees to 50 degrees. The aerodynamic loads (i.e., moment coefficients) acting on the torque tube were determined, and the aerodynamic response of the models was investigated. The results show that the mean and maximum moments are largest in magnitude for wind directions from 0 degrees to 40 degrees and 140 degrees to 180 degrees. A notable increase in the mean moment coefficient is observed for tilt angles between 0 degrees and 15 degrees, followed by a progressive decrease as the tilt angle increases from 15 degrees to 50 degrees. The aerodynamic derivatives A(2)(& lowast; )and A(3)(& lowast;) were also obtained using a quasi-steady approach. The findings of this study suggest that the wind direction influences the response of the structure; the tilt angle, the natural frequency of the tracker and the stiffness of the torque tube are critical factors in preventing aeroelastic instabilities and should be carefully considered in the design process.
查看更多>>摘要：In recent years, wind power has emerged as a prominent renewable energy source, and the need for reliable wind speed prediction models has become paramount to ensure smooth and predictable wind power supply. This study proposes an accuracy self-assisted projection model that can forecast wind speed for the next 24 hours with a 6-hour forecast output step. The model building process commences with a random sampling approach applied to the wind speed dataset for dividing the training, validation, and test sets. The CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise) method is then utilized to decompose the wind speed signal into IMFs (intrinsic mode functions) that are fed into the short-term forecasting module. The forecast results from the short-term forecast module are processed and fed back into the long-term forecast model as part of the input tensor. Controlled experiments and validations demonstrate that: (a) The random sampling approach for dataset partitioning is effective in avoiding seasonality effects; (b) The short-term prediction model output can assist the long-term prediction in signal extension and tensor fusion aspects; and (c) The transfer learning approach is effective in reducing computational and time costs in training multiple sub-models. The proposed model focuses exclusively on wind speed prediction; future extensions may integrate wind direction forecasting to enhance comprehensive wind energy management.
查看更多>>摘要：Wind-hail disasters frequently inflict substantial damage on sunroom structures. Accurately predicting the resilience of sunroom structures against wind and hail is of critical importance. In this study, a comprehensive series of wind-hail coupling experiments were conducted using a proprietary hail impact simulation integrated device coupled with a high-speed Digital Image Correlation (DIC) system. These experiments were aimed at determining the peak principal strain and displacement of hail impacts on polycarbonate (PC) panel materials used in sun rooms. Following the experimental phase, a correlation analysis between independent and dependent variables was performed. Based on the findings, Back Propagation (BP) and Particle Swarm Optimization-Back Propagation (PSO-BP) neural network models were developed and subsequently translated into mathematical expressions for practical application. The results indicated that the peak hail impact force increases with the diameter and velocity of hail particles as well as with wind speed, but decreases with an increase in the thickness of the PC panels. Additionally, for a given velocity of hail launch, larger particle diameters and thinner PC panels showed a more pronounced influence of wind speed on the peak impact force. In terms of stability and accuracy, both the BP and PSO-BP models demonstrated commendable performance, with the PSO-BP neural network showing enhanced predictive accuracy and generalization capability, thus enabling more precise predictions of the peak impact force of single hail particles under coupled wind-hail conditions.
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