Abstract
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.
Key Words
bridge profile test; flutter derivatives; MITD; modal frequency; SSI-COV; SSI-DATA; wind tunnel test
Address
Qi-Yang Liao:Department of Civil Engineering, National Cheng Kung University, No.1, University Road, Tainan, Taiwan
Chan-Jung Kang:Department of Civil Engineering, National Cheng Kung University, No.1, University Road, Tainan, Taiwan
Chung-Chun Wu:Department of Civil Engineering, National Cheng Kung University, No.1, University Road, Tainan, Taiwan
Shih-Yu Chu:Department of Civil Engineering, National Cheng Kung University, No.1, University Road, Tainan, Taiwan
Chung Fang:Department of Civil Engineering, National Cheng Kung University, No.1, University Road, Tainan, Taiwan
Cheng-Yang Chung:Aerospace Science and Technology Research Center, National Cheng Kung University, No.1, University Road, Tainan, Taiwan
Chin-Cheng Chou:Aerospace Science and Technology Research Center, National Cheng Kung University, No.1, University Road, Tainan, Taiwan
Abstract
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. A numerical 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.
Abstract
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°
and 180°) and tilt angles ranging from 0° to 50°. 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° to 40° and 140° to 180°. A notable increase in the mean moment
coefficient is observed for tilt angles between 0° and 15°, followed by a progressive decrease as the tilt angle increases from 15°
to 50°. The aerodynamic derivatives A2 and A3 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.
Abstract
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.
Key Words
attention mechanism; hybrid model; long short-term memory; neural network; wind speed prediction
Address
Enbo Yu:Department of Bridge Engineering, Southwest Jiaotong University, Chengdu, China
Guoji Xu:State Key Laboratory of Bridge Intelligent and Green Construction, Southwest Jiaotong University, Chengdu, China
Yongle Li:State Key Laboratory of Bridge Intelligent and Green Construction, Southwest Jiaotong University, Chengdu, China
Lian Shen:School of Civil Engineering, Changsha University, Changsha, China
Yan Han:School of Civil Engineering, Changsha University of Science and Technology, Changsha, China
Abstract
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.
Key Words
BP neural network; PC panels; PSO algorithm; wind-hail coupled experiments; wind tunnel test
Address
Yimin Dai:1)School of Civil Engineering, Hunan University of Science and Technology, Xiangtan 411201, Hunan, China
2)Hunan Provincial Key Laboratory of Structural Wind Resistance and Vibration Control, Xiangtan 411201, Hunan, China
Yixin Li:1)School of Civil Engineering, Hunan University of Science and Technology, Xiangtan 411201, Hunan, China
2)Hunan Provincial Key Laboratory of Structural Wind Resistance and Vibration Control, Xiangtan 411201, Hunan, China
Taiting Liu:1)School of Civil Engineering, Hunan University of Science and Technology, Xiangtan 411201, Hunan, China
2)Hunan Provincial Key Laboratory of Structural Wind Resistance and Vibration Control, Xiangtan 411201, Hunan, China
Wei Wang:1)School of Civil Engineering, Hunan University of Science and Technology, Xiangtan 411201, Hunan, China
2)Hunan Provincial Key Laboratory of Structural Wind Resistance and Vibration Control, Xiangtan 411201, Hunan, China
