Wind Load and Structural Parameters Estimation from Incomplete Measurements

Xue, Huili; Liu, Hongjun; Peng, H.Y.; Luo, Yun Mei; Lin, Kun

doi:10.1155/2019/4862983

Cited by 9 publications

(8 citation statements)

References 32 publications

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“…q(t) is the p × 1 modal acceleration vector, Φ is the mode shape of continuous system structure after p order truncation and p is the number of modal truncations. Truncated modal number has an impact on the result, and many scholars [11,17] have done relevant research on it. It is pointed out that for structural vibration, the first several modes are mainly involved in the vibration of the structure.…”

Section: X(t) Can Be Obtained As Followsmentioning

confidence: 99%

“…Wu et al [15] proposed a new wind load inversion method, which decomposed wind load into the product of orthogonal basis of position and time history function and took fully observed structural displacements, derived velocity and acceleration responses to obtain the wind load of the structure by least square estimation. Xue et al [16,17] used the method of unbiased estimation to identify wind load under both known and unknown structural parameters. In a case study of the 1310 m long Hardanger Bridge, Petersen et al [18,19] studied wind loads in time domain and frequency domain and introduced the Potential Gaussian Process model (GP-LFMS) to characterize the evolution of wind loads.…”

Section: Introductionmentioning

confidence: 99%

“…When the modal wind load is transformed into the physical coordinate wind load, the modal shape is no longer a square array after truncation. In some studies [16,17] the pseudo-inverse of the modal matrix was calculated and the modal wind loads were converted to wind loads in physical coordinates, but the use of the pseudo-inverse resulted in non-unique results. How to avoid the pseudo-inverse of the matrix is also content to be studied.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identification of Wind Loads on Structures Based on Modal Kalman Filter with Unknown Inputs

2022

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Wind loads on structures are difficult to directly measure, so it is practical to identify structural wind loads based on the measurements of structural responses. However, this inversed problem is challenging compared with conventional load identification as wind loads are time-space coupled and spatially distributed dynamic loads on structures. An improved method is proposed for identifying wind loads on structures using only partial measurements of structural acceleration responses in this paper. First, the wind loads on a structure are decomposed by proper orthogonal decomposition as a series of time-space decoupled sub-distributed dynamic loads with independent basic spatial distribution functions and time history functions. Herein, structural modes are adopted as the basic spatial distribution functions and structural modes of discretized and continuous structural systems are investigated. Then, a history function of the decomposed wind load is identified in the modal domain based on modal Kalman filter with unknown inputs, which is proposed by the authors. Finally, the distributed wind loads are reconstructed for discrete or continuous structural systems. The feasibility of the proposed algorithm is verified by two numerical examples of identification of wind loads on a discrete shear frame and a wind turbine tower, respectively.

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Section: X(t) Can Be Obtained As Followsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identification of Wind Loads on Structures Based on Modal Kalman Filter with Unknown Inputs

2022

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“…The characterization of wind speed is of great significance in everyday endeavor of man. Its study has practical application in many areas, namely, determination of air quality and the movement of air pollutants [9,19,7], estimation of wind load on buildings and other physical objects including humans [27]; prediction of atmospheric or space probe and missile trajectory; and the production of energy from the wind [8,15,28,11]. All these require thorough study of the wind regime.…”

Section: Wind Speed Modelingmentioning

confidence: 99%

On Some Models for Wind Power Assessment in Yola, Nigeria

Esbond¹,

Saporu²

2021

IJSDA

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Probability distributions are used in the evaluation of wind energy potentials to describe the wind speed characteristics of the chosen location for wind farm establishment. However, the Weibull distribution that is the most chosen by wind energy modelers may likely fail to properly describe the wind speed data of certain locations, or it may not be the best model to describe wind speed when compared to the fitness of other probability distributions. Thus, in this study, four probability distributions are fitted to wind speed data from Yola, Nigeria. They are the Weibull, the exponentiated Weibull, the generalized power Weibull and the exponentiated epsilon distributions; and, all provided good fit to the wind speed dataset. The exponentiated epsilon distribution is new and provided the best fit. These models are compared based on the relative likelihood gain per data point; it is found that there is about 5% gain by the other three probability distributions over the Weibull distribution. Hence all the three distributions can also be used as wind models. The estimated average wind speeds computed using the four models at various hub heights show that wind is sufficiently available to support a wind turbine with a cut-in speed of 3 m/s at hub heights 90 m above ground level. For the exponentiated-epsilon model, average wind speed of 3.68 m/s at hub height of 120 m above ground level can generate 6.11 W/m 2 of electricity; and for a wind turbine of rotor diameter of 128 m with 12,868 m 2 swept area, this amounts to 78.6 kW of electricity supply for a small-scale wind power project. Consequently, Yola holds a good potential for the establishment of a wind farm.

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“…Zhang [3] provides a novel wind load calculation for the structural design concerning the directionality and uncertainty effect. Xue et al [4] proposed a novel method of identifying structural state, parameters, and unknown wind load estimation from incomplete measurements. Chowdhury et al [5] give a better understanding of wind load effects on roof-to-wall connections of a typical low-rise gable roof residential structure subjected to combined impacts of wind and a potential breach of the building envelope.…”

Section: Introductionmentioning

confidence: 99%

Research on Wind Load Calculation Based on Identical Guarantee Rate Method

Tao

Zhu

Tan

et al. 2021

Shock and Vibration

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Wind load on building surface is one of the main loads for structural design; scholars in this field have put forward some methods to calculate wind load, such as Simiu method and Kasperski method. Based on the basic theory of probability and the systematic analysis of the surrounding environment and turbulence, a random variable model for calculating wind load is established. According to the model, through the analysis of the relationship between guarantee rate and wind load, a numerical calculation method to calculate wind load is proposed based on extreme value analysis and polynomial fitting theory. To verify the performance of the algorithm, wind tunnel experiments were carried out to obtain a large number of first-hand measured data of high-rise building (Shanghai World Financial Center). Based on the measured data, the algorithm is simulated, and calculated results are analyzed, including wind pressure distribution on building and probability distribution of fluctuating wind pressure of some measuring points. The validity and accuracy of the proposed model and algorithm are verified by the comparative analysis and theoretical analysis of the calculation results.

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Wind Load and Structural Parameters Estimation from Incomplete Measurements

Cited by 9 publications

References 32 publications

Identification of Wind Loads on Structures Based on Modal Kalman Filter with Unknown Inputs

Identification of Wind Loads on Structures Based on Modal Kalman Filter with Unknown Inputs

On Some Models for Wind Power Assessment in Yola, Nigeria

Research on Wind Load Calculation Based on Identical Guarantee Rate Method

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