A Spectrum Correction Method Based on Optimizing Turbulence Intensity

Based on the classical spectral representation method of simulating turbulent wind speed fluctuation, a harmonic superposition algorithm was introduced in detail to calculate the homogeneous turbulence wind field simulation in space. From the view of the validity of the numerical simulation results in MATLAB and the simulation efficiency, this paper discussed the reason for the bias existing between three types of turbulence intensity involved in the whole simulation process: simulated turbulence intensity, setting reference turbulence intensity, and theoretical turbulence intensity. Therefore, a novel spectral correction method of a standard deviation compensation coefficient was proposed. The simulation verification of the correction method was carried out based on the Kaimal spectrum recommended by IEC61400-1 by simulating the uniform turbulent wind field in one-dimensional space at the height of the hub of a 15 MW wind turbine and in two-dimensional space in the rotor swept area. The results showed that the spectral correction method proposed in this paper can effectively optimize the turbulence intensity of the simulated wind field, generate more effective simulation points, and significantly improve the simulation efficiency.

Understanding the Major Impact of Planetary Boundary Layer Schemes on Simulation of Vertical Wind Structure

The structure and evolution of the atmospheric planetary boundary layer (PBL) plays an important role in the physical and chemical processes of cloud–radiation interaction, vertical mixing and pollutant transport in the atmosphere. The PBL parameterization scheme describes the vertical transport of atmospheric momentum, heat, water vapor and other physical quantities in the boundary layer. The accuracy of wind field simulation and prediction is one of the most significant parameters in the field of atmospheric science and wind energy. Limited by the observation data, there are few studies on wind energy development. A 3D Doppler wind LiDAR (DWL) providing the high-vertical-resolution wind data over the urban complex underlying surface in February 2018 was employed to systematically evaluate the accuracy of vertical wind field simulation for the first time. 11 PBL schemes of the Weather Research and Forecasting Model (WRF) were employed in simulation. The model results were evaluated in groups separated by weather (sunny days, hazy days and windy days), observation height layers of wind field, and various observation wind speeds. Among these factors, the simulation accuracy is most closely related to the observation height layers of wind field. The simulation is fairly accurate at a height of 1000–2000 m, as most of the relative mean biases for wind speed and wind direction are less than 20% and 6% respectively. Below 1000 m, the wind speed and direction biases are about 30–150% m·s−1 and 6–30%, respectively. Moreover, when the observed wind speed was lower than 5 m·s−1, the biases were usually large, and the wind speed relative mean bias reaches up to 50–300%. In addition, the accuracy of the simulated wind profile is better in the range of 10–15 m·s−1 than other speed ranges, and is better above 1000 m than below 1000 m in the boundary layer. We see that the WRF boundary layer schemes have different applicabilities to different weather conditions. The WRF boundary layer schemes have significant differences in wind field simulations, with larger error under the complex topographies. A PBL scheme is not likely to maintain its advantages in the long term under different conditions including altitude and weather conditions.

Engineering Comprehensive Model of Complex Wind Fields for Flight Simulation

A complex wind field refers to the typical atmospheric disturbance phenomena existing in nature that have a great influence on the flight of aircrafts. Aimed at the issues involving large volume of data, complex computations and a single model in the current wind field simulation approaches for flight environments, based on the essential principles of fluid mechanics, in this paper, wind field models for two kinds of wind shear such as micro-downburst and low-level jet plus three-dimensional atmospheric turbulence are established. The validity of the models is verified by comparing the simulation results from existing wind field models and the measured data. Based on the principle of vector superposition, three wind field models are combined in the ground coordinate system, and a comprehensive model of complex wind fields is established with spatial location as the input and wind velocity as the output. The model is applied to the simulated flight of a rocket projectile, and the change in the rocket projectile’s flight attitude and flight trajectory under different wind fields is analyzed. The results indicate that the comprehensive model established herein can reasonably and efficiently reflect the influence of various complex wind field environments on the flight process of aircrafts, and that the model is simple, extensible, and convenient to use.

Effect of Varying Wind Intensity, Forward Speed, and Surface Pressure on Storm Surges of Hurricane Rita

Hurricane storm surges are influenced by several factors, including wind intensity, surface pressure, forward speed, size, angle of approach, ocean bottom depth and slope, shape and geographical features of the coastline. The relative influence of each factor may be amplified or abated by other factors that are acting at the time of the hurricane’s approach to the land. To understand the individual and combined influence of wind intensity, surface pressure and forward speed, a numerical experiment is conducted using Advanced CIRCulation + Simulating Waves Nearshore (ADCIRC + SWAN) by performing hindcasts of Hurricane Rita storm surges. The wind field generated by Ocean Weather Inc. (OWI) is used as the base meteorological forcing in ADCIRC + SWAN. All parameters are varied by certain percentages from those in the OWI wind field. Simulation results are analyzed for maximum wind intensity, wind vector pattern, minimum surface pressure, forward speed, maximum water elevation, station water elevation time series, and high water marks. The results for different cases are compared against each other, as well as with observed data. Changes in the wind intensity have the greatest impact, followed by the forward speed and surface pressure. The combined effects of the wind intensity and forward speed are noticeably different than their individual effects.

An Improved Meshless Divergence-Free PBF Framework for Ocean Wave Modeling in Marine Simulator

It is a challenging work to simulate wind and waves in virtual scenes of marine simulators. In this paper, a divergence-free position based fluid (DFPBF) framework is introduced for ocean wave modeling in marine simulators. We introduce a set of constant density constraints and divergence-free velocity constraints to enforce incompressibility. By adjusting the position distribution of fluid particles, the particle density is forced to be constant. Constraining the divergence-free velocity field can keep the density change rate at zero. When correcting the position and velocity of particles, we introduced a relaxation correction scheme to accelerate the convergence of the framework. The simulation results show that as the scene scale expands and the number of fluid particles increases, this acceleration effect will be more significant. Secondly, we propose a novel particle-based three-dimensional stochastic fluctuating wind field. The Perlin noise is introduced to disturb the constant horizontal wind field to form a stochastic wind field. On this basis, a stochastic fluctuating wind field simulation framework is proposed. By adjusting the pulse period and pulse width, users can flexibly control the fluid turnover under the action of the wind field. This wind field framework can be easily integrated into the DFPBF model. Based on this wind field model, we simulated some typical wind wave scenarios, including interaction scenarios with lighthouse and lifebuoy, and verified the effectiveness of the wind field model.

Multiple TLDs on Motion Reduction Control of the Offshore Wind Turbines

This study explores the damping effects of tuned liquid dampers (TLDs) on a monopile offshore wind turbine (OWT). The fluid–solid coupling of ANSYS was used to simulate the damping effect of a TLD on the structures. The environmental conditions refer to the IEC-61400-3 and the Design Load Case (DLC) 1.2 for the annual average environmental conditions and DLC 6.2 for the 50-year regression period, and the extreme environmental conditions were used in the study. The turbulent wind field simulation was performed by TurbSim, and the load of wind waves on structures was generated by FAST, which were all developed by the NREL (National Renewable Energy Laboratory). In addition to wind and waves, the seismic force was also considered. The cylindrical TLD was located above the rotor nacelle assembly (RNA). A TLD has different damping effects when acting under wind, wave, and earthquake loads, respectively. The effect of the TLD regarding motion reduction on the OWT under coupled wind, wave, and seismic loads was studied. This study also designed a simple experiment to verify the correctness of the numerical simulation results. Fatigue analysis shows that multi-layer TLDs can extend the fatigue life (37%) of an OWT. In addition, under extreme environmental load conditions, multi-layer TLDs have a better vibration damping performance than single-layer TLDs. The study demonstrates that multi-layer TLDs can be considered as a vibration reduction damper for OWTs.

Non-Gaussian Lagrangian Stochastic Model for Wind Field Simulation in the Surface Layer

Wind field simulation in the surface layer is often used to manage natural resources in terms of air quality, gene flow (through pollen drift), and plant disease transmission (spore dispersion). Although Lagrangian stochastic (LS) models describe stochastic wind behaviors, such models assume that wind velocities follow Gaussian distributions. However, measured surface-layer wind velocities show a strong skewness and kurtosis. This paper presents an improved model, a non-Gaussian LS model, which incorporates controllable non-Gaussian random variables to simulate the targeted non-Gaussian velocity distribution with more accurate skewness and kurtosis. Wind velocity statistics generated by the non-Gaussian model are evaluated by using the field data from the Cooperative Atmospheric Surface Exchange Study, October 1999 experimental dataset and comparing the data with statistics from the original Gaussian model. Results show that the non-Gaussian model improves the wind trajectory simulation by stably producing precise skewness and kurtosis in simulated wind velocities without sacrificing other features of the traditional Gaussian LS model, such as the accuracy in the mean and variance of simulated velocities. This improvement also leads to better accuracy in friction velocity (i.e., a coupling of three-dimensional velocities). The model can also accommodate various non-Gaussian wind fields and a wide range of skewness–kurtosis combinations. Moreover, improved skewness and kurtosis in the simulated velocity will result in a significantly different dispersion for wind/particle simulations. Thus, the non-Gaussian model is worth applying to wind field simulation in the surface layer.