Synthetic generation of the atmospheric boundary layer for wind loading assessment using spectral methods

Bervida, Marija; Patruno, L.; Stanič, S.; Miranda, Stefano de

doi:10.1016/j.jweia.2019.104040

Cited by 24 publications

(10 citation statements)

References 39 publications

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“…Figure 10a reports their time evolution at four different heights, whereas Figure 10b reports the mean vertical profiles of the aforementioned ratios, along with standard deviation. For all wind episodes, the ratio between turbulence intensities was found to be nearly constant with the height (Figure 9b), as it is often considered for ABL flows [33], with a slight decrease observed above a certain height (≈140 m). By considering the intensity ratios to be constant with the height, we evaluated the mean intensity ratios for each Bora episode separately and reported them in Figure 11, along with the mean of all wind episodes, as well as in Table 3 in more detail.…”

Section: Turbulence Intensitysupporting

confidence: 63%

Bora Flow Characteristics in a Complex Valley Environment

et al. 2021

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This paper complements the existing studies of Bora flow properties in the Vipava valley with the study of Bora turbulence in a lower region of the troposphere. The turbulence characteristics of Bora flow were derived from high resolution Doppler wind lidar measurements during eight Bora wind episodes that occurred in November and December 2019. Based on the vertical profiles of wind velocity, from 80 to 180 m above the valley floor, the turbulence intensity related to all three spatial directions and the along-wind integral length scales related to three velocity components were evaluated and compared to the approximations given in international standards. The resulting turbulence characteristics of Bora flow in a deep mountain valley exhibited interesting behaviour, differing from the one expected and suggested by standards. The intensity of turbulence during Bora episodes was found to be quite strong, especially regarding the expected values for that particular category of terrain. The specific relationship between along-wind, lateral and vertical intensity was evaluated as well. The scales of turbulence in the along-wind direction were found to vary widely between different Bora episodes and were rather different from the approximations given by standards, with the most significant deviations observed for the along-wind length scale of the vertical velocity component. Finally, the periodicity of flow structures above the valley was assessed, yielding a wide range of possible periods between 1 and 10 min, thus confirming some of the previous observations from the studies of Bora in the Vipava valley.

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Section: Turbulence Intensitysupporting

confidence: 63%

Bora Flow Characteristics in a Complex Valley Environment

et al. 2021

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“…If the inhomogeneity of the turbulence is significantly strong, the assumption of weak inhomogeneity of the flow is no longer valid. As presented by Bervida et al (2020), the divergence-free error caused by inhomogeneity will contribute significantly to the results under this circumstance. At this time, although the inverter method can still provide precise inhomogeneous turbulence statistics, the resulting divergence level will increase significantly.…”

Section: Divergence-free Correction: Shifter Versionmentioning

confidence: 89%

“…However, compared with a total computation requirement of M × N modes presented by Patruno & Ricci (2018), the inverter method uses a concise operation of (2.12) and maintains a total number of N modes in the current framework, which has advantages in terms of computational complexity. Moreover, as mentioned by Bervida et al (2020), the divergence-free error resulting from strong inhomogeneity may increase through the superposition of M modes. In contrast, although the divergence-free error of the inverter method may increase in processing strongly inhomogeneous flow, the method can ensure that the generated turbulence spatial correlations are accurate without deteriorating.…”

Section: Divergence-free Correction: Inverter Versionmentioning

confidence: 95%

“…However, the original N modes superposition is transformed to an M × N modes superposition in the improved method, which clearly increases the computational complexity. Moreover, the divergence-free error caused by strong inhomogeneity may be amplified by the superposition of M modes compared with other previous methods (Bervida et al 2020).…”

Section: Introductionmentioning

confidence: 95%

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An efficient and low-divergence method for generating inhomogeneous and anisotropic turbulence with arbitrary spectra

Guo,

Jiang,

et al. 2023

J. Fluid Mech.

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This paper proposes a low-divergence method to generate inhomogeneous and anisotropic turbulence. Based on the idea of correlation reconstruction, the proposed method uses the Cholesky decomposition matrix to re-establish turbulence correlation functions. This is in contrast with the time-consuming procedure used in conventional methods to solve eigenvalues and eigenvectors in each location required by coordinate transformation, thereby reducing the computational complexity and improving the efficiency of synthetic turbulence generation. The proposed method is based on the classical spectrum-based method that is widely used to generate homogeneous and isotropic turbulence. By adjusting the generation strategy of specific random vectors, inhomogeneous and anisotropic turbulence with a relatively low divergence level can be obtained in practice, with almost no additional computational burden. Two versions of the proposed method are presented: the inverter and shifter versions. Both the versions are highly efficient, easy to implement, and compatible with high-performance computing. This method is also suitable for providing high-quality initial or boundary conditions for scale-resolving turbulence simulations with large grid numbers (such as direct numerical simulations or large eddy simulations); it can be rapidly implemented using various open-source computational fluid dynamics codes or common commercial software.

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“…A complex nested model has been utilized to create terabytes of structurally similar data for Internet of Things (IoT) research by the authors in [11]. The application of synthetic data generation to create training datasets can be observed in many other fields such as in plasma current quench studies [12], to analyze and predict seismic activities [13], to correctly identify and detect people using omnidirectional cameras [14], wastewater treatment modeling studies [15], and meteorological studies [16,17]. The synthetic data has been used to test the DGA toolbox.…”

Section: Introductionmentioning

confidence: 99%

Generation of synthetic datasets for transformer’s dissolved gas analysis using Monte-Carlo Simulation

Babu

Bashir

Pillai

et al. 2021

2021 56th International Universities Power Engineering Conference (UPEC)

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The fault diagnosis in power transformers is carried out using Dissolved Gas Analysis (DGA). Although DGA does provide key information for fault detection, the method is inherently complex. Several methods have been developed for DGA, but still possess challenges in accurately detecting the fault. A method has been developed to generate synthetic data using Monte-Carlo simulation. The generated synthetic data is feed into DGA excel tool to investigate the accuracy of fault detection. The synthetic data can be used to further enhance the DGA tool, improve its accuracy and investigate the inclusive faults. A model has been proposed for the integration of synthetic data generator with DGA tool for machine learning and to obtain an automated and improved DGA tool for fault diagnoses in power transformers.

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Synthetic generation of the atmospheric boundary layer for wind loading assessment using spectral methods

Cited by 24 publications

References 39 publications

Bora Flow Characteristics in a Complex Valley Environment

Bora Flow Characteristics in a Complex Valley Environment

An efficient and low-divergence method for generating inhomogeneous and anisotropic turbulence with arbitrary spectra

Generation of synthetic datasets for transformer’s dissolved gas analysis using Monte-Carlo Simulation

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