NSTL国家科技图书文献中心

1. Nonstationary Turbulent Wind Speed Models and Their Effects on Wind-Induced Responses of Tall Buildings NSTL国家科技图书文献中心

Kang Cai | Mingfeng Huang ... - 《Journal of Structural Engineering》 - 2025,151(3) - 1.1～1.17 - 共17页

摘要： wind speeds under the nonstationary wind speed model | nonstationary wind speed model would lead to different | nonstationary wind speed model are generally smaller than | necessary to establish a nonstationary wind speed model | to the stationary wind* speed *model, the

关键词： Nonstationary wind speed model | Turbulence intensity | Turbulence integral length scale | Wind spectrum | Structural response

2. Non-Gaussian non-stationary wind speed simulation based on time-varying autoregressive model and maximum entropy method NSTL国家科技图书文献中心

Zhao, Ning | Wu, Yu ... - 《Journal of Wind Engineering and Industrial Aerodynamics》 - 2025,256 - 共15页

摘要： nonstationary wind speed. The numerical results demonstrate | nonstationary wind speeds is a prerequisite for the wind | univariate non-Gaussian nonstationary* wind speeds using the* | accuracy for nonstationary and strongly non-Gaussian wind | non-Gaussian nonstationary* wind speeds and possesses*

关键词： Wind speed simulation | Non-Gaussian | Nonstationary | Time-varying autoregressive model | Translation process theory

3. A novel spatio-temporal wind speed forecasting method based on the microscale meteorological model and a hybrid deep learning model NSTL国家科技图书文献中心

Dongqin Zhang | Gang Hu ... - 《Energy》 - 2024,288(Feb.1) - 129823.1～129823.23 - 共23页

摘要： learning model. In addition, instantaneous wind* speed is* | to carry out the temporal and spatial wind speed | are then predicted by using the wind* speed ratios* | Wind power, as a renewable alternative to | variabilityowing to fluctuations in wind* velocity. In light of*

关键词： Spatio-temporal wind speed forecasting | Nonstationary transformer | Long short-term memory | Temporal convolution network | Microscale meteorological model

4. Probabilistic Gust Factor Model of Typhoon Winds NSTL国家科技图书文献中心

Genshen Fang | Zihang Liu ... - 《Journal of Structural Engineering》 - 2024,150(1) - 1.1～1.18 - 共18页

摘要： accounts for uncertainties of wind* speed statistics. The* | statistical characteristics of wind* speed from nine typhoons* | wind speed and non-Gaussian attributes of fluctuating | was applied to determine the gust typhoon wind speed | wind engineering community to convert mean wind

关键词： Typhoon | Gust factor | Uncertainty quantification | Probabilistic model | Nonstationary | Non-Gaussian

5. Improving Output Power of a Torsional-Flutter Harvester in Stochastic Thunderstorms by Duffing-Van Der Pol Restoring Torque NSTL国家科技图书文献中心

Caracoglia, Luca - 《ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems,Part B.Mechanical Engineering》 - 2024,10(4) - 共13页

摘要： nonstationary turbulence and slowly varying mean wind speed | operate in the low wind* speed range. They rely on* | Wind energy harvesters are usually designed to | horizontal-axis wind* turbines. A torsional-flutter-based* | apparatus is investigated herein to extract wind* energy. A*

关键词： Turbulence | Storms | Flutter (Aerodynamics) | Torque

6. A Dynamic-Inner Manifold Broad Learning System With Coupled Time–Frequency Domain for Wind Speed Prediction NSTL国家科技图书文献中心

Ziwen Gu | Yatao Shen ... - 《IEEE transactions on industrial informatics》 - 2024,20(7) - 9352～9364 - 共13页

摘要： article, we propose a novel wind speed prediction model | nonstationary wind speed series data into a relatively more | Wind speed prediction plays an important role | incorporate wind* power. However, wind speed prediction* | , chaos, and nonstationarity of wind* speed data. In this*

关键词： Wind speed | Feature extraction | Time series analysis | Continuous wavelet transforms | Heuristic algorithms | Predictive models | Manifolds

7. Wind–Temperature Characteristics of a Cable-Stayed Bridge along the Yellow Sea under Superstrong Typhoon Lekima NSTL国家科技图书文献中心

Rui Zhou | Xin Jia ... - 《Journal of Bridge Engineering》 - 2024,29(11) - 1.1～1.14 - 共14页

摘要： joint distribution model* of the wind speed and* | that the wind* speed has obvious Gaussian and* | for the correction between the wind* speed and* | twice along the Yellow Sea of China in 2019; its wind | still unclear. Based on the measured wind* and*

关键词： Superstrong typhoon Lekima | Composite girder cable-stayed bridge | Gaussian and nonstationary characteristic | Statistical distribution analysis | Wind–temperature correlation

8. Reproduction of wind speed time series in a two-dimensional numerical multiple-fan wind tunnel using deep reinforcement learning NSTL国家科技图书文献中心

Yang, Qingshan | Luo, Zhenzhi ... - 《Wind & structures》 - 2024,39(4) - 271～285 - 共15页

摘要： between the target wind* speed time series and the* | generated a wind* speed time series with minor error from* | used to generate target wind* speed time series in a* | The multiple-fan wind* tunnel is an important* | facility for reproducing target wind* field. Existing*

关键词： active flow control | DDPG paradigm | deep reinforcement learning | multiple-fan wind tunnel | wind speed reproduction | BOUNDARY-LAYER | NEURAL-NETWORKS | ACTIVE CONTROL | FLOW | MODEL

9. Sensitivity analysis of wind energy resources with Bayesian non-Gaussian and nonstationary functional ANOVA NSTL国家科技图书文献中心

Jiachen Zhang | Paola Crippa ... - 《The Annals of applied statistics》 - 2024,18(1) - 23～41 - 共19页

摘要： renewable resources such as wind. Weather model | variance (ANOVA) model that relies on a nonstationary | -Gaussian wind* characteristics such as speed and threshold* | capturing wind* speed and energy (especially over complex* | spatially heterogeneous influence of wind. In this work we

关键词： Bayesian hierarchical model | Functional ANOVA | Stochastic partial differential equation | wind energy

10. Simulation combined transfer learning model for missing data recovery of nonstationary wind speed NSTL国家科技图书文献中心

Lin, Qiushuang | Bao, Xuming ... - 《Wind & structures》 - 2023,37(5) - 383～397 - 共15页

摘要：) is used to recover the nonstationary wind speed | obtained by nonstationary* wind-field simulation and are* | deep learning model* for the task at hand. Thus* | the deep learning model* caused by the paucity of* | subsequently deployed for training an auxiliary CNN model

摘要： wind speeds under the nonstationary wind speed model | nonstationary wind speed model would lead to different | nonstationary wind speed model are generally smaller than | necessary to establish a nonstationary wind speed model | to the stationary wind* speed *model, the

关键词： Nonstationary wind speed model | Turbulence intensity | Turbulence integral length scale | Wind spectrum | Structural response

关键词： Wind speed simulation | Non-Gaussian | Nonstationary | Time-varying autoregressive model | Translation process theory

摘要： learning model. In addition, instantaneous wind* speed is* | to carry out the temporal and spatial wind speed | are then predicted by using the wind* speed ratios* | Wind power, as a renewable alternative to | variabilityowing to fluctuations in wind* velocity. In light of*

关键词： Spatio-temporal wind speed forecasting | Nonstationary transformer | Long short-term memory | Temporal convolution network | Microscale meteorological model

摘要： accounts for uncertainties of wind* speed statistics. The* | statistical characteristics of wind* speed from nine typhoons* | wind speed and non-Gaussian attributes of fluctuating | was applied to determine the gust typhoon wind speed | wind engineering community to convert mean wind

关键词： Typhoon | Gust factor | Uncertainty quantification | Probabilistic model | Nonstationary | Non-Gaussian

摘要： nonstationary turbulence and slowly varying mean wind speed | operate in the low wind* speed range. They rely on* | Wind energy harvesters are usually designed to | horizontal-axis wind* turbines. A torsional-flutter-based* | apparatus is investigated herein to extract wind* energy. A*

关键词： Turbulence | Storms | Flutter (Aerodynamics) | Torque

摘要： article, we propose a novel wind speed prediction model | nonstationary wind speed series data into a relatively more | Wind speed prediction plays an important role | incorporate wind* power. However, wind speed prediction* | , chaos, and nonstationarity of wind* speed data. In this*

关键词： Wind speed | Feature extraction | Time series analysis | Continuous wavelet transforms | Heuristic algorithms | Predictive models | Manifolds

摘要： joint distribution model* of the wind speed and* | that the wind* speed has obvious Gaussian and* | for the correction between the wind* speed and* | twice along the Yellow Sea of China in 2019; its wind | still unclear. Based on the measured wind* and*

关键词： Superstrong typhoon Lekima | Composite girder cable-stayed bridge | Gaussian and nonstationary characteristic | Statistical distribution analysis | Wind–temperature correlation

摘要： between the target wind* speed time series and the* | generated a wind* speed time series with minor error from* | used to generate target wind* speed time series in a* | The multiple-fan wind* tunnel is an important* | facility for reproducing target wind* field. Existing*

关键词： active flow control | DDPG paradigm | deep reinforcement learning | multiple-fan wind tunnel | wind speed reproduction | BOUNDARY-LAYER | NEURAL-NETWORKS | ACTIVE CONTROL | FLOW | MODEL

摘要： renewable resources such as wind. Weather model | variance (ANOVA) model that relies on a nonstationary | -Gaussian wind* characteristics such as speed and threshold* | capturing wind* speed and energy (especially over complex* | spatially heterogeneous influence of wind. In this work we

关键词： Bayesian hierarchical model | Functional ANOVA | Stochastic partial differential equation | wind energy

摘要：) is used to recover the nonstationary wind speed | obtained by nonstationary* wind-field simulation and are* | deep learning model* for the task at hand. Thus* | the deep learning model* caused by the paucity of* | subsequently deployed for training an auxiliary CNN model

关键词： missing data recovery | nonstationary simulation | nonstationary wind speed | simulation synergy strategy | transfer learning

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： wind speeds under the nonstationary wind speed model | nonstationary wind speed model would lead to different | nonstationary wind speed model are generally smaller than | necessary to establish a nonstationary wind speed model | to the stationary wind speed model, the

关键词： Nonstationary wind speed model | Turbulence intensity | Turbulence integral length scale | Wind spectrum | Structural response

关键词： Wind speed simulation | Non-Gaussian | Nonstationary | Time-varying autoregressive model | Translation process theory

摘要： learning model. In addition, instantaneous wind speed is | to carry out the temporal and spatial wind speed | are then predicted by using the wind speed ratios | Wind power, as a renewable alternative to | variabilityowing to fluctuations in wind velocity. In light of

关键词： Spatio-temporal wind speed forecasting | Nonstationary transformer | Long short-term memory | Temporal convolution network | Microscale meteorological model

摘要： accounts for uncertainties of wind speed statistics. The | statistical characteristics of wind speed from nine typhoons | wind speed and non-Gaussian attributes of fluctuating | was applied to determine the gust typhoon wind speed | wind engineering community to convert mean wind

关键词： Typhoon | Gust factor | Uncertainty quantification | Probabilistic model | Nonstationary | Non-Gaussian

摘要： nonstationary turbulence and slowly varying mean wind speed | operate in the low wind speed range. They rely on | Wind energy harvesters are usually designed to | horizontal-axis wind turbines. A torsional-flutter-based | apparatus is investigated herein to extract wind energy. A

关键词： Turbulence | Storms | Flutter (Aerodynamics) | Torque

摘要： article, we propose a novel wind speed prediction model | nonstationary wind speed series data into a relatively more | Wind speed prediction plays an important role | incorporate wind power. However, wind speed prediction | , chaos, and nonstationarity of wind speed data. In this

关键词： Wind speed | Feature extraction | Time series analysis | Continuous wavelet transforms | Heuristic algorithms | Predictive models | Manifolds

摘要： joint distribution model of the wind speed and | that the wind speed has obvious Gaussian and | for the correction between the wind speed and | twice along the Yellow Sea of China in 2019; its wind | still unclear. Based on the measured wind and

关键词： Superstrong typhoon Lekima | Composite girder cable-stayed bridge | Gaussian and nonstationary characteristic | Statistical distribution analysis | Wind–temperature correlation

摘要： between the target wind speed time series and the | generated a wind speed time series with minor error from | used to generate target wind speed time series in a | The multiple-fan wind tunnel is an important | facility for reproducing target wind field. Existing

关键词： active flow control | DDPG paradigm | deep reinforcement learning | multiple-fan wind tunnel | wind speed reproduction | BOUNDARY-LAYER | NEURAL-NETWORKS | ACTIVE CONTROL | FLOW | MODEL

摘要： renewable resources such as wind. Weather model | variance (ANOVA) model that relies on a nonstationary | -Gaussian wind characteristics such as speed and threshold | capturing wind speed and energy (especially over complex | spatially heterogeneous influence of wind. In this work we

关键词： Bayesian hierarchical model | Functional ANOVA | Stochastic partial differential equation | wind energy

摘要：) is used to recover the nonstationary wind speed | obtained by nonstationary wind-field simulation and are | deep learning model for the task at hand. Thus | the deep learning model caused by the paucity of | subsequently deployed for training an auxiliary CNN model

关键词： missing data recovery | nonstationary simulation | nonstationary wind speed | simulation synergy strategy | transfer learning

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： wind speeds under the nonstationary wind speed model | nonstationary wind speed model would lead to different | nonstationary wind speed model are generally smaller than | necessary to establish a nonstationary wind speed model | to the stationary wind* speed *model, the

摘要： learning model. In addition, instantaneous wind* speed is* | to carry out the temporal and spatial wind speed | are then predicted by using the wind* speed ratios* | Wind power, as a renewable alternative to | variabilityowing to fluctuations in wind* velocity. In light of*

摘要： accounts for uncertainties of wind* speed statistics. The* | statistical characteristics of wind* speed from nine typhoons* | wind speed and non-Gaussian attributes of fluctuating | was applied to determine the gust typhoon wind speed | wind engineering community to convert mean wind

摘要： nonstationary turbulence and slowly varying mean wind speed | operate in the low wind* speed range. They rely on* | Wind energy harvesters are usually designed to | horizontal-axis wind* turbines. A torsional-flutter-based* | apparatus is investigated herein to extract wind* energy. A*

摘要： article, we propose a novel wind speed prediction model | nonstationary wind speed series data into a relatively more | Wind speed prediction plays an important role | incorporate wind* power. However, wind speed prediction* | , chaos, and nonstationarity of wind* speed data. In this*

摘要： joint distribution model* of the wind speed and* | that the wind* speed has obvious Gaussian and* | for the correction between the wind* speed and* | twice along the Yellow Sea of China in 2019; its wind | still unclear. Based on the measured wind* and*

摘要： between the target wind* speed time series and the* | generated a wind* speed time series with minor error from* | used to generate target wind* speed time series in a* | The multiple-fan wind* tunnel is an important* | facility for reproducing target wind* field. Existing*

摘要： renewable resources such as wind. Weather model | variance (ANOVA) model that relies on a nonstationary | -Gaussian wind* characteristics such as speed and threshold* | capturing wind* speed and energy (especially over complex* | spatially heterogeneous influence of wind. In this work we

摘要：) is used to recover the nonstationary wind speed | obtained by nonstationary* wind-field simulation and are* | deep learning model* for the task at hand. Thus* | the deep learning model* caused by the paucity of* | subsequently deployed for training an auxiliary CNN model