Simulated Wind Fields Research Articles

Super Typhoons Simulation: A Comparison of WRF and Empirical Parameterized Models for High Wind Speeds

As extreme forms of tropical cyclones (TCs), typhoons pose significant threats to both human society and the natural environment. To better understand and predict their behavior, scientists have relied on numerical simulations. Current typhoon modeling primarily falls into two categories: (1) complex simulations based on fluid dynamics and thermodynamics, and (2) empirical parameterized models. Most comparative studies on these models have focused on wind speed below 50 m/s, with fewer studies addressing high wind speed (above 50 m/s). In this study, we design and compare four different simulation approaches to model two super typhoons: Typhoon Surigae (2102) and Typhoon Nepartak (1601). These approaches include: (1) The Weather Research and Forecasting (WRF) model simulation driven by NCEP Final Operational Global Analysis data (FNL), (2) WRF simulation driven by the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA5), (3) the empirical parameterized Holland model, and (4) the empirical parameterized Jelesnianski model. The simulated wind fields were compared with the measured wind data from The Soil Moisture Active Passive (SMAP) platform, and the resulting wind fields were then used as inputs for the Simulating WAves Nearshore (SWAN) model to simulate typhoon-induced waves. Our findings are as follows: (1) for high wind speeds, the performance of the empirical models surpasses that of the WRF simulations; (2) using more accurate driving wind data improves the WRF model’s performance in simulating typhoon wind speeds, and WRF simulations excel in representing wind fields in the outer regions of the typhoon; (3) careful adjustment of the maximum wind speed radius parameter is essential for improving the accuracy of the empirical models.

A simplified statistical model for regional offshore typhoon wind risk assessment

The accuracy of typhoon wind risk assessment methods is of great significance for offshore or coastline areas. Due to the practical limitations of typhoon measurement, it is common to perform Monte-Carlo simulation (MCS) to extrapolate such data to thousands of years to estimate the extreme wind speed. However, there are technical limitations for engineering applications of this technique, and the computational costs are significant for assessment on a regional level. For the preliminary typhoon risk assessment for a region, a simplified statistical method (SSM) is proposed in this paper. In the proposed method, the extreme wind speed is estimated with the extended “Improved Method of Independent Storms” (XIMIS) method, based on simulated wind field results using the best track typhoon data, i.e., the historical typhoon record information. The results of the simplified statistical method are validated by comparison with local code wind speeds and with full track MCS results in the Northwest Pacific basin using the best track database of the Joint Typhoon Warning Center (JTWC). Finally, the proposed SSM method is used to produce a wind map for typhoon risk assessment in offshore and coastal regions of southeast China, with high efficiency and acceptable accuracy.

Large Eddy Simulation of Flow Around Twin Tower Buildings in Tandem Arrangements with Upstream Corner Modification

The aerodynamic performance of twin tall buildings immersed in the atmospheric boundary layer was numerically investigated by adopting the spatial-averaged large eddy simulation (LES) method. This study focused on the effects of corner cutting and chamfering. The buildings were both square and sectional with a width-to-height ratio of 1:6, and were arranged in a tandem configuration with a spacing ratio of 2.0. The corner-cutting and chamfering measures were only applied to the upstream cylinder, with a corner modification rate of 10%. To generate the turbulent inflow boundary condition (IBC) for LES, steady-state equilibrium IBC expressions were introduced into the vortex method, which were implemented in the commercial code Ansys Fluent. The present simulation method and solution parameters were first verified by comparing the simulated wind field and the wind pressure distribution on a single tall building with those of the wind tunnel test. The influences of the corner-cutting and chamfering measures on the wind load of the tandem buildings were then comparatively studied concerning the statistical values of their aerodynamic force coefficients and wind pressure coefficients. The influence mechanism was analyzed based on the simulated time-averaged flow field and the instantaneous vortex structure around the buildings. The results indicated that upstream corner-cutting and chamfering measures can induce a diffusion angle shift in the separated shear flow from the leading edge of the upstream building, thus affecting the separation and reattachment of the separated upstream flow on the downstream building. Among the measures studied, upstream corner cutting is more effective in reducing wind pressure and aerodynamic force coefficients.

Optimal Configuration of Physical Process Parameterization Scheme Combination for Simulating Meteorological Variables in Weather Research and Forecasting Model: Based on Orthogonal Experimental Design and Comprehensive Evaluation Method

The weather research and forecasting (WRF) model is frequently used to investigate the meteorological field around nuclear installations. The configuration of physical process parameterization schemes in the WRF model has a significant impact on the accuracy of the simulation results. Consequently, carrying out a pre-experiment to quickly obtain the optimal combination of parameterization schemes is essential before conducting meteorological parameter research. To obtain the optimal combination of physical process parameterization schemes from the planetary boundary layer (PBL), land surface (LSF), microphysical (MP), long-wave (LW), and short-wave (SW) radiation processes of the WRF model for simulating the near-surface meteorological variables near a nuclear power plant in Sanshan Town, Fuqing City, Fujian Province, China on 4 June 2019 were observed. Orthogonal experimental design (OED), a comprehensive evaluation method based on the CRiteria Import Through Intercriteria Correlation (CRITIC) weight analysis, and comprehensive balance method were employed for the first time to conduct the research. The sensitivity of meteorological variables to physical processes was first discussed. The findings revealed that the PBL scheme configuration had a profound impact on simulating wind fields. Furthermore, the LSF scheme configuration had a significant influence on simulating near-surface temperature and relative humidity, which was much greater than that of other physical processes. In addition, the choice of the radiation scheme had a significant impact on how the temperature was distributed close to the ground and how the wind field was simulated. Furthermore, the configuration of the MP scheme was found to exert a certain influence on the simulation of relative humidity; however, it demonstrated a weak influence on other meteorological variables. Secondly, The MYNN3 scheme for PBL process, the NoahMP scheme for LSF process, the WSM5 scheme for MP process, the RRTMG scheme for LW process, and the Dudhia scheme for SW process are found to be the comprehensive optimal physical process parameterization scheme combination for simulating meteorological variables in the research area selected in this study. As evident from the findings, the use of the OED method to obtain the combinations of the optimal physical process parameterization scheme could successfully reproduce the wind field, temperature, and relative humidity in the current study. Thus, this method appears to be highly reliable and effective for use in the WRF models to explore the optimal combinations of the physical process parameterization scheme, which could provide theoretical support to quickly analyzing accurate meteorological field data for longer periods and contribute to deeply investigating the migration and diffusion behavior of airborne pollutants in the atmosphere.

Drone Photogrammetry-based Wind Field Simulation for Climate Adaptation in Urban Environments

Addressing climate change issues is one of the most important tasks within the United Nations Sustainable Development Goals. Accurate and efficient simulation of wind fields within cities is essential for climate adaptation. Traditional simplified geometric model-based wind flow simulation can lead to significant errors, affecting the ability to develop effective urban climate strategies. This study addresses this limitation by introducing a novel workflow that leverages drone photogrammetry, deep learning, and geometric complexity quantification to create highly detailed 3D models of in-use building clusters within cities. These models are subsequently used for computational fluid dynamics simulations to accurately predict urban wind fields. The proposed method was validated on three real-world building clusters. Compared to traditional footprint extrusion models, the proposed method demonstrates an average error reduction of 29.2% in large eddy simulation cases and 17.6% in steady Reynolds-averaged Navier-Stokes equations cases. Meanwhile, the proposed model improved computational efficiency by an average of 33.7% in large eddy simulations compared to the flashy oblique photography model. The proposed method provides a balanced model of accuracy and efficiency for urban flow simulations. It has the potential to be incorporated into computational fluid dynamics best practice guidelines, thereby promoting the development of climate-resilient cities.

Evaluating and comparison of WRF-chem model configurations for wind field impact on the April 2022 dust episode in western Iran

Accurate estimation of wind direction and speed is essential for enhancing the simulation and prediction of dust storms. Being highly susceptible to dust storms, Western Iran necessitates a detailed evaluation of dust concentration and wind fields. This study employs the WRF-Chem model to simulate these parameters over the period from April 7 to 26, 2022. Four different model configurations were tested, involving the Yonsei University (YSU) and Mellor-Yamada-Janjic (MYJ) boundary layer schemes, as well as the Lin and WRF Single-Moment 6-Class (WSM6) microphysics schemes. The results indicate that during the selected period, different synoptic systems led to the release of dust from Iraq and Saudi Arabia and its transport to western Iran. The mid-level southerly and southwesterly wind directions have a significant impact on dust transport to the northwestern regions of Iran by high and complex mountainous terrain. The horizontal and vertical distribution of simulated dust demonstrates good agreement with TERRA satellite imagery, MERRA-2 dust surface concentration, and CALIPSO, respectively. The daily dust concentration of the WRF-Chem model has a correlation of −0.32 to −0.96 with visibility and 0.68 to 0.86 with MERRA-2 data in Western Iran. The simulated dust concentration relation with visibility and AERONET AOD (Aerosol Optical Depth at 500 nm) was calculated at −0.78 and 0.29, respectively in Zanjan station. The horizontal and vertical distribution, temporal series, and statistical indices of the wind field show that the WRF-Chem model performs well in the Iran West boundary, especially in the west and northwest, where the 10-m wind speed increased to 14 m s−1. The boundary layer scheme in the WRF-Chem model has a more significant impact than the microphysics scheme in simulated 10-m wind speed and dust concentration. The final result shows that the combination of the YSU boundary layer and WSM6 microphysics schemes performs very well in simulating wind fields and dust in western Iran under various weather conditions.

Characterizing surface pressure and wind fields of typhoons approaching Hong Kong

In this paper, 17 severe typhoons that have affected Hong Kong are simulated using an advanced numerical atmospheric simulation system - Weather Research and Forecasting model (WRF). The simulated surface pressure and wind fields of these typhoons are validated against a wide range of field observations. Then azimuth-dependent models for the radius of maximum winds and the Holland parameter are established statistically at the surface level. It is observed that the shape parameter of the Holland pressure model is smaller at the surface than that at the gradient level. And the Holland wind field model cannot well reproduce the simulated radial wind profiles due to the complexities of nonuniform surface conditions and typhoon dynamics. It is found that the modified Rankine model provides satisfactory estimates of typhoon wind speeds in Hong Kong. Additionally, wind field asymmetries of typhoons approaching Hong Kong are highly correlated with the typhoon track velocity, vertical wind shear and the angle between them. The proposed statistical models and identified characteristics of wind field asymmetries of typhoons will provide useful information for rapidly assessing typhoon wind hazards.

Deep learning methods for modeling infrasound transmission loss in the middle atmosphere

Accurate modeling of infrasound transmission losses (TLs) is essential to assess the performance of the global International Monitoring System (IMS) infrasound network. Among existing propagation modeling tools, parabolic equation method (PE) enables TLs to be finely modeled, but its computational cost does not allow exploration of a large parameter space for operational monitoring applications. To reduce computation times, Brissaud et al. (2022) explored the potential of convolutional neural networks (CNNs) trained on a large set of regionally simulated wavefields (>1000 km from the source) to predict TLs with negligible computation times compared to PE simulations. However, this new method shows difficulties in upwind conditions, especially at low frequencies, and causal issues with winds at large distances from the source affecting ground TLs close to the source. In this study, we have developed an optimized CNN network designed to minimize prediction errors while predicting TLs from globally simulated combined temperature and wind fields spanning over propagation ranges of 4000 km. Our approach enhances the previously proposed one by implementing key optimizations that improve the overall architecture performances. The implemented model predicts TLs with an average error of 20 dB in the whole frequency band (0.1-4 Hz) and explored realistic atmospheric scenarios.

Application of convolutional neural network for efficient turbulence modeling in urban wind field simulation

Accurate flow field estimation is crucial for the improvement of outdoor environmental quality, but computational fluid dynamics (CFD) based on the widely used Reynolds-averaged Navier–Stokes method has limitations in this regard. This study developed a turbulence modeling framework based on a convolutional neural network (CNN) to model turbulence in urban wind fields. The CNN model was trained by learning the Reynolds stress patterns and spatial correlations with the use of high-fidelity datasets. Next, the model was integrated into the CFD solver to generate accurate and continuous flow fields. The generalization capability of the proposed framework was initially demonstrated on the simplified benchmark configurations. The validated framework was then applied to case studies of urban wind environments to further assess its performance, and it was shown to be capable of delivering accurate predictions of the velocity field around an isolated building. For more complex geometries, the proposed framework performed well in regions where the flow properties were covered by the training dataset. Moreover, the present framework provided a continuous and smooth velocity field distribution in highly complicated applications, underscoring the robustness of the proposed turbulence modeling framework.

Module optimization and array design of moisture swing direct air capture based on 2D-3D coupled analysis

Direct Air Capture (DAC) is crucial for offsetting carbon emissions. Moisture Swing Adsorption (MSA), a DAC method switches between adsorption and desorption through humidity adjustment without relying on heat, offering significant energy-saving potential but lacking engineering solutions. Therefore, using a validated 2D-3D coupled model, a moisture swing DAC array was proposed, and multi-dimensional evaluations were used to optimize the system. Unlike the preferred capture rate around 90 % for post-combustion gas, the ultra-low CO2 concentration in the air resulted in a different rate range for DAC. Simulation indicated that although the capture rate of DAC could reach 90 %, the trade-off was energy consumption exceeding 3000 kWh/t CO2 with low adsorbent utilization. Consequently, a detailed evaluation method based on adsorbent utilization, energy consumption and cost analysis optimized the preferred capture rate range to 50–60 %, and the total energy consumption was reduced to 873.55 kWh/t CO2, including 276.33 kWh/t CO2 for capture energy and 597.22 kWh/t CO2 for vacuum-concentration energy. The lowest cost was $209.17/t CO2 at the optimal capture rate of 53.91 %. Afterwards, the Ordos Plateau, as a typical preferred sub-environment with abundant wind resources and dry climate, was choose for DAC system deployment study. Wind field simulation determined the layout of a 10000-ton array occupying only 0.298 km2. Finally, based on savings in capture energy due to prevailing winds, regeneration energy savings from the utilization of low-grade waste heat, and the improved performance of the adsorbent itself, it was found that this DAC unit showed great potential to reduce the energy and capture costs to 564.91 kWh/t CO2 and $140.25/t CO2, and at wind speed exceeding 16.8 m/s, the unit switched to passive adsorption, further reducing energy consumption to 408.89 kWh/t CO2, with the minimum fix investment estimated at 21.71 million USD.

Research on the Collaborative Regeneration of Cohousing Under Spatial Densification Mode

Urban renewal is a necessary process for the transformation from incremental space to stock space, and old communities, as an important part of the urban space system, play a non-negligible role in the reconstruction of community spatial order. From the perspective of community remodeling, From the perspective of community remodeling, this paper summarizes the latest research progress through the analysis tool CiteSpace, explores and researches the design of old residential areas in Hangzhou based on the urban air index oriented residential renewal design based on the planning concepts of "co-living community", "urban spatial morphology" and "community microclimate system". On the basis of combining 3D wind field simulation and fluid mechanics software calculation, this paper explores the collaborative regeneration path of old residential areas under spatial densification mode, this paper studies how to combine scientific planning with resident sharing in a new mode, and on this basis, through the self-regulation function of the environment, let the residential area spontaneously form a complete shared living system, and carry out detailed exploration in the protection and reconstruction of the living environment of the old community, and how to promote the spiritual development of modern neighborhoods through transformation. In order to improve the quality of life of urban residents in Hangzhou, the renewal and regeneration design of old residential areas and the transformation and upgrading of future communities have brought new thinking.

Simulation of Urban Carbon Sequestration Service Flows and the Sustainability of Service Supply and Demand

Simulating ecosystem carbon sequestration service (ECSS) flows is crucial for optimizing the carbon cycle in ecosystems and achieving sustainable balance between the supply and demand of the ECSS. This study integrates least-cost path analysis with Kriging interpolation to simulate the dominant wind direction and corrects the interpolated wind speeds to account for terrain and surface conditions. Carbon emissions are spatially distributed using points of interest and road network data. Ultimately, by traversing the carbon emission rate grids along wind directions, the ECSS flows are simulated. The results reveal that the study area has a small carbon sink area but a high total carbon emission, leading to a situation where the supply of ECSS is insufficient to meet demand. The ECSS flows, based on the simulated wind field, demonstrate high spatial resolution and highlight the service flow corridors with distinct spatial heterogeneity. The study area has a significant carbon surplus, requiring a forest area ten times larger than the study area itself to fully sequester this carbon. These findings provide valuable insights for urban sustainable development and carbon emission reduction strategies.

Design and Development of a Side Spray Device for UAVs to Improve Spray Coverage in Obstacle Neighborhoods

Electric multirotor plant protection unmanned aerial vehicles (UAVs) are widely used in China for efficient and precise plant protection at low altitude for low volumes. Unstructured farmland in China has various types of obstacles, and UAVs usually use a detour path to avoid obstacles due to flight altitude limitations. However, existing UAV spray systems do not spray when in obstacle neighborhoods during obstacle avoidance, resulting in insufficient droplet coverage and reduced plant protection quality in the area. To improve the droplet coverage in obstacle neighborhoods, this article carries out a study of side spray technology with an electric quadrotor UAV, and proposes the design and development of a side spray device. The relationship between the obstacle avoidance path of the UAV and the spray pattern of the side spray device and their effect on droplet coverage in obstacle neighborhoods was explored. An accurate measurement method of the relative position between the UAV and obstacles was proposed. Spray angle calculations and nozzle selection for the side spray device were carried out in conjunction with the relative position. A rotor wind field simulation model was designed based on the lattice Boltzmann method (LBM), and the spatial layout of the side spray device on the UAV was designed based on the simulation results. To explore suitable spray patterns for the side spray device, comparative experiments of droplet coverage in obstacle neighborhoods were carried out under different environments, spray patterns, and flight parameter combinations. The relationship between the flight parameter combinations and the distribution uniformity of droplets and the effective swath width of the side spray device was explored. The experimental results were analyzed by an analysis of variance (ANOVA) and a relationship model was obtained. The results showed that the side spray device can effectively improve droplet coverage in obstacle neighborhoods compared to a device without side spray using the same flight parameter combinations. The effective swath width in obstacle neighborhoods can be increased by a minimum of 6.35%, maximum of 35.32%, and average of 15.25% using the side spray device. The error between the predicted values of the relational model and the field experiment results was less than 15%. The results verify the effectiveness and rationality of the method proposed in this article. This study can provide technical and theoretical references for improving the plant protection quality of UAVs in obstacle environments.

Reproducing vortex-induced vibrations of rooftop twin-mast by multi-scale coupled simulation of urban wind fields

The Shenzhen Electronics Group (SEG) Plaza in Shenzhen, China, experienced visible vibrations attributed to significant vortex-induced vibrations of the large spacing twin-mast atop the building. The vibration event occurred under complex urban wind environments. In order to reproduce wind fields around SEG Plaza, a multiscale simulation was carried out by integrating the Weather Research and Forecasting (WRF) and Computational Fluid Dynamics (CFD) models. The aerodynamic characteristics and wake modes of twin-mast are investigated by a downscale CFD simulation in detail. Finally, vortex-induced vibration (VIV) of twin-mast was analyzed considering fluid-structure interaction. This work reveals that wind conditions during the SEG Plaza vibration incident were appropriate for the VIV occurrence of twin-mast, and the results of VIV simulation agree well with the field monitoring results by computer vision identification. This study effectively reproduces the full pictures of VIVs of the rooftop twin-mast during this rare event, offering a critical view for the analysis of this event.

Enhancing wind field resolution in complex terrain through a knowledge-driven machine learning approach

Wind energy, an essential component in the fight against climate change, relies heavily on precise, detailed wind field simulations to optimize wind farms, particularly in complex terrain with intricate wind patterns. However, conventional high-resolution simulations come with a hefty computational cost, limiting their applicability in real-time decision-making. This research addresses this challenge by proposing a machine learning-driven, computationally efficient approach utilizing a modified Generative Adversarial Network. The contribution of this work is threefold. Firstly, we provide access to a unique high-resolution dataset of wind fields in complex terrain. Secondly, we introduce a knowledge-based modification to the loss function, ensuring that the algorithm captures crucial characteristics of the flow within complex terrains. Finally, we demonstrate the potential of our approach to enhance wind flow resolution in real-life wind farms. Through this, our method delivers comparable accuracy to high-resolution simulations while substantially reducing computational demands. This advancement greatly enhances the accessibility and efficiency of high-resolution wind field simulations, facilitating real-time optimization of wind farms. Moreover, we illustrate that by designing an appropriate loss function informed by domain knowledge, we can mitigate the need for adversarial training.

水稻精量化条直播播种无人机的设计与试验

In order to solve the problem that the wind field disturbs the trajectory of falling seeds and causes the seeds to be unable to be arranged in equidistant rows when the UAV is spreading rice, a shot seeding device that can sow five rows of pelleted rice seeds at the same time was designed. The unit is centered on an external grooved wheel seed metering device and a seed acceleration unit for row seeding and hole sowing. The airflow simulation of the rotor wind field of the UAV was carried out by simulation software to explore the changes in the wind field of the UAV during operation. The position where the wind field disturbance is minimized is chosen for the seed guide tube arrangement. The position with the least wind field disturbance is chosen to arrange the seed guide tube and combine it with a seed acceleration device to reduce the influence of the UAV wind field airflow on the direction of seed movement. The operational effectiveness of the seeding device with and without wind was verified by an indoor test and an outdoor flight seeding test, respectively. Simulation results show that: when the mouth of the seed guide pipe is 0.9 m away from the paddle, the wind field has the smallest influence on the sowing results. The results of the bench test show that: when the rotational speed is 30-45 r/min, the coefficient of variation of the discharge rate of each row (CVR) and total seed discharge rate stability (CVT) are less than 1.98% and 0.84%, and the seed breakage rate is less than 0.95%, which all conform to the UAV fly sowing industry standards. The outdoor mud box test shows that: when the baffle angle changes by 26%, 58%, and 71%, each slot wheel hole can store 5-10, 3-5 and 1-3 seeds respectively, and the UAV operates at a speed of 2 m/s-3 m/s, the pass rate of hybrid rice is greater than 86%, which meets the agronomic requirements of rice sowing operation.

Fire behavior simulation of Xintian forest fire in 2022 using WRF-fire model

IntroductionThe behavior of forest fire is a complex phenomenon, and accurate simulation of forest fire is conducive to emergency response management after ignition. In order to further understand the characteristics of forest fire spread and the applicability of WRF-Fire in China, which is a coupled fire-atmospheric wildfire model, this study simulated a high-intensity forest fire event that occurred on October 17, 2022 in Xintian County, southern Hunan Province.MethodsBased on the fire-atmosphere coupled WRF-Fire model, we used high-resolution geographic information, meteorological observation and fuel classification data to analyze the forest fire behavior. At the same time, the simulation results are compared with the fire burned area observed by satellite remote sensing forest fire monitoring data.ResultsThe study found that, the simulated wind speed, direction and temperature trends are similar to the observation results, but the simulated wind speed is overestimated, the dominant wind direction is N, and the temperature is slightly underestimated. The simulated wind field is close to the actual wind field, and the simulation results can show the spatial and temporal variation characteristics of the local wind field under complex terrain while obtaining the high-resolution wind field. The simulated fire burned area is generally overestimated, spreading to the north and southwest compared with the observed fires, but it can also capture the overall shape and spread trend of the fire well.DiscussionThe results show that the model can accurately reproduce the real spread of fire, and it is more helpful to forest fire management.

Numerical simulations of downburst wind fields: A comparative analysis of stationary and moving storms using the impinging jet model

Numerical simulations of downburst wind fields: A comparative analysis of stationary and moving storms using the impinging jet model

Simulation of multivariate ergodic stochastic processes using adaptive spectral sampling and non-uniform fast Fourier transform

The simulation of multivariate ergodic stochastic processes is critical for structural dynamic analysis and reliability evaluation. Although the traditional spectral representation method (SRM) has a wide application in many areas, it is highly inefficient in simulating stochastic processes with many simulation points or long durations due to the significant computational cost associated with matrix factorizations concerning frequency. To address the encountered challenge, this paper presents an efficient approach for simulating ergodic stochastic processes with limited frequencies. Central to this approach is a fusion of the adaptive spectral sampling and the non-uniform fast Fourier transform (NUFFT) techniques. The adaptive spectral sampling of the envelope spectrum enables the determination of limited non-equispaced frequencies, which are randomly sampled according to a uniform distribution. Thus, the Cholesky decomposition is only required at limited specific frequencies, which dramatically reduces the computational cost of matrix factorizations. Since the randomly sampled frequencies are not equispaced, utilizing FFT to accelerate the summation of trigonometric functions becomes impractical. Then, the NUFFT that adapts the non-equispaced sampling points is employed instead to expedite this process with the non-uniform increment approximated through reduced interpolation. By taking the wind field simulation of a long-span suspension bridge as an example, a parametric analysis is conducted to investigate the effect of random frequencies on the simulation error of the developed approach and the convergence of spectra. Finally, the developed approach is further validated by focusing on the spectra and probabilistic density functions of the simulated wind samples, and the simulation performance is compared with that of the traditional approach. The analytical results demonstrate the efficiency and accuracy of the developed approach in simulating ergodic stochastic processes.

Numerical investigation of turbulence effect on flight trajectory of spherical windborne debris: A multi-layered approach

Accurate modeling of the turbulent wind field is a crucial component of risk analysis for structures to windborne debris damage. Existing studies typically simplify the complexities of wind turbulence, and the potential influence on the accuracy of debris flight modeling has not been systematically demonstrated. This study takes a multi-layered approach to numerically simulate the flight trajectory of spherical debris in a turbulent wind field. Complexities are incrementally added to the simulated wind field to systematically investigate the influence of spatial correlation and non-Gaussian features of turbulence on debris flight behavior. The sensitivity of debris flight behavior to turbulent wind features will inform the design of debris flight tracking wind tunnel tests and building façade debris vulnerability modeling efforts.