Neutral Atmospheric Conditions Research Articles

Influence of Various Urban Morphological Parameters on Urban Canopy Ventilation: A Parametric Numerical Study

Numerical simulation is vital for evaluating urban ventilation. However, accurate urban-scale ventilation modeling requires extensive building surface simulation for computational demand. The distributed drag force approach simplifies the urban canopy by modeling buildings as a porous volume that accounts for momentum and turbulence. This method is a practical solution for simulating urban airflow. The drag force coefficient (Cd) is a crucial aerodynamic parameter in this approach. This study examines how Cd varies with urban design parameters such as plan area density (λp), average building height (H), frontal area density (λf), floor aspect ratio (AR), and sky view factor (SVF). Employing extensive numerical simulations conducted under neutral atmospheric conditions, we explore ranges of λp = 0.04–0.07 and λf = 0.1–1.2. The numerical model has been validated against existing wind tunnel data. The results show that Cd is insensitive to the model scale and background wind speed. We discover a nonlinear relationship between Cd and the parameters λp, λf, and SVF. For urban layouts with cubic-shaped buildings, Cd peaks at different λp within the range of 0.2~0.8. When λp and H are constant, Cd has a linear relationship with AR and λf. It is recommended to use λp, SVF, and AR as predictors for Cd across various urban configurations.

Potential of wind turbines on the alteration of carbon dioxide concentration

Anthropogenic carbondioxide (CO2) emissions are a major factor in global warming, requiring significant cuts to combat climate change. A crucial technology to reduce global CO2 concentration is direct air capture (DAC) of CO2. However, existing DAC techniques are expensive because of low CO2 concentrations, and they frequently rely on fossil fuel-based energy. In this article, we investigate how wind turbines can influence local CO2 levels and potentially collaborate with DAC and other technologies. To explore this idea, we performed large-eddy simulations using two 5 MW commercial-scale wind turbines. We incorporated realistic CO2 profiles collected from 13 different global locations across different seasons. The simulations were performed under neutral atmospheric boundary layer conditions. The results demonstrate that the wake recovery mechanism of a wind turbine promotes rapid mixing of CO2 both above and below the turbine blade tips in the wind turbine wake. In cases where the initial concentrations of CO2 were elevated above the turbine, downward entrainment of CO2 occurred. Conversely, when high concentrations of CO2 were present in the lower atmosphere, wind turbines facilitated a decrease in concentration at that layer by up to 138 kg/m within the intermediate wake (within 7 diameters) of the second turbine, T2. These discoveries inspire further investigation into the potential synergies between wind turbines and DAC devices or local CO2 pollutant diverters, depending on the prevailing CO2 profile. Consequently, this article marks the initial showcase of wind turbines' capability to influence CO2 levels by creating an entrainment and removal effect.

Inter-comparison study of wind measurement between the three-lidar-based virtual tower and four lidars using VAD techniques

ABSTRACT The accurate three-dimensional wind field obtained from a Doppler lidar not only helps to comprehend the refined structure of complex airflow but also provides important and valuable solutions for many fields. However, the underlying homogeneity assumption of the typical wind retrieval methods, such as Doppler Beam Swinging (DBS) and Velocity Azimuth Display (VAD) based on a single-lidar, will introduce the measurement uncertainty in complex terrain. In this paper, a new design of a wind measurement campaign involving seven lidars was carried out, which contained the three-lidar-based Virtual Tower (VT) using a time-space synchronization technique and four single-lidars with different elevation angles. This study investigates the performance of VT and VAD measurements under various conditions and evaluates the sensitivity of wind measurement uncertainty of VAD to the horizontal spatial- and probe volume-average effects associated with elevation angles of the laser beam. The inter-comparison results between VT and four VADs show consistent trends with small relative errors under neutral atmospheric conditions with weak wind velocity. Under convective or high Turbulence Intensity (TI) conditions, the relative errors between VT and VAD become larger and more fluctuant. Moreover, it is found that the measurement uncertainty of VAD increases at a given elevation angle with the increasing measurement heights, which is caused by the horizontal homogeneity associated with the conical scanning area. Additionally, the simulated and measured results of four VADs indicate that a larger elevation angle corresponds to a lower measurement uncertainty for a given height.

Impact of urban canopy characteristics on turbulence dynamics: Insights from large eddy simulation

In this study, a series of large-eddy simulations are conducted to explore the dynamics of turbulence within the urban canopy. Two idealized urban-like geometries (staggered and aligned arrays of cubes) and one real urban region (located on the Southern University of Science and Technology campus in China) are simulated under neutral atmospheric conditions. The results show a significant difference in the dispersive momentum fluxes between OpenFOAM and PALM within the ultra-dense arrays. Furthermore, it is found that the spatial layout does not exert a significant effect on the budget of turbulent kinetic energy. Instead, it mainly depends on the ratio of the building surface area to the total surface area, i.e., packing density. In addition, a multi-scale coupled inflow boundary condition is introduced in this study to simulate the flow in a real urban area. This boundary condition yields a better match to the LiDAR measurements than the Weather Research and Forecasting model data. Finally, this study shows that the mean velocity obtained from the campus of SUSTech is in satisfactory agreement with the data from low-density idealized cube arrays and the corresponding urban canopy model. This observation suggests that mean wind speeds within partially occupied urban areas are relatively independent of wind direction, thus greatly improving the application of urban canopy models to such scenarios.

CFD simulation of pollutant dispersion using anisotropic models: Application to an urban like environment under neutral and stable atmospheric conditions

Modeling the atmospheric dispersion of pollutants emitted from different types of sources under various atmospheric conditions is an essential prerequisite for risk assessment studies and emergency preparedness. In this study, we evaluate the performance of different turbulence models and velocity-scalar correlation models implemented in the Code_Saturne. The evaluation is done with observations of four trial cases of the Mock Urban Setting Test (MUST) campaign in an urban type environment and in neutral and stable atmospheric conditions. For all of the trials studied, the CFD model with a first-order closure model (k−ɛ) predicts 61.1% of the concentrations within a factor of two of the observations, which is higher than the percentage of predicted points (58.8%) when a second-order closure model (Rij−ɛ) is used. Overall, the CFD model underestimates observed concentrations, regardless of the turbulence model used. For the trial with slightly stable conditions, the results show that the k−ɛ model combined with an algebraic SGDH model predicts 75% concentrations within a factor of two of the observations. The performance of the k−ɛ model is compared to that of the Rij−ɛ model when used with the algebraic SGDH, GGDH models and with the scalar flux transport equation (DFM model). Under slightly stable atmospheric conditions, the DFM model predicts 69% of concentrations within a factor of two of observations, showing promise for modeling under these conditions, despite its relatively high computational cost.

Model Validation and Sensitivity Analysis of Coupled Non‐Equilibrium Heat and Mass Transfer in Porous Media With Application to Evaporation From Bare Soils

AbstractMathematical models in engineering play an important role in understanding and predicting the behavior of a system. A mechanistic coupled liquid water, water vapor and heat transfer model incorporating kinetic phase change accounting for real‐time interfacial area between water and gas phases was developed to predict coupled subsurface processes and evaporation (drying) rates from bare soils. To enhance the model capability to predict evaporation rates, the air resistance associated with the viscous sublayer was implemented in energy and mass exchange across the soil‐air interface (the land‐atmosphere boundary condition [BC]). The atmospheric stability condition was also considered in the calculation of sensible heat and vapor fluxes at the ground surface. This comprehensive model was validated against measured field data from bare soil test plots from a green roof study, during temperate summer conditions in Canada, demonstrating that the model captured the main coupled processes in the subsurface of bare soil during drying periods. A sensitivity analysis was performed to determine the importance of various components of the comprehensive model. Removal of viscous sublayer resistance in the vapor transfer BC resulted in poorer predictions of evaporation (drying) rates. Incorporating the atmospheric stability function accounting for real‐time atmospheric conditions did not improve the predictive capability for the simulated drying events compared to the case when only a neutral atmospheric condition was implemented. Neglecting heat transfer associated with hydrodynamic dispersion of water vapor in the subsurface had limited impact on subsurface temperature predictions.

Effect of atmospheric stability on the dynamic wake meandering model applied to two 12 MW floating wind turbines

AbstractCurrent global analysis tools for floating wind turbines (FWTs) do not account for the combined effects of atmospheric stability and wakes from neighboring turbines. This work uses the mid‐fidelity dynamic wake meandering model, together with turbulent wind fields generated based on stable, neutral, and unstable atmospheric conditions, to study the low‐frequency content of the global responses of two semisubmersible FWTs separated by eight rotor diameters. Incoming wind fields based on the Kaimal spectrum and exponential coherence model, the Mann spectral tensor model, and a time‐series input‐based turbulence model are used. The respective input parameters for these models are fitted to high‐fidelity large eddy simulation data.In unstable, below‐rated conditions, meandering leads to an increase in the yaw standard deviation of the downwind turbine of almost three times larger than the upwind turbine. Deficit and the upwards wake deflection affect the mean pitch and yaw, especially for the below‐rated wind speed scenario. The mean pitch of the downwind turbine is reduced up to half the mean pitch value of the upwind turbine, whereas the mean yaw changes direction due to the enhanced effect of shear. The effect of meandering on the structural loading is highest on the standard deviation of the tower‐top yaw moment of the downstream turbine, which increases more than 2.2 times compared to the upwind turbine value. Based on these findings, atmospheric stability affects wake deficit and meandering which in turn have a profound effect on the low‐frequency global motions and structural response of floating wind turbines.

Micrometeorological Method in Determining Plant Capacity to Absorb Pollutant: Oil Palm Case Study

The vegetation canopy's height and characteristics directly affect the turbulence that controls the exchange of mass and energy between the vegetation and the surrounding atmosphere. Turbulence also controls the momentum transfer towards the mass-carrying plant canopy and the accompanying atmospheric properties so that vegetation can contribute to pollutant deposition. This study aims to estimate the canopy capacity of oil palms to absorb pollutants based on their momentum transfer, the influence of atmospheric stability dynamics, and rainy and dry periods upon absorbed pollutants from PTPN VI in Jambi province for the period of January to December 2015 used micrometeorological observation data. The results showed that the dry deposition capacity value at the stable, neutral, and unstable atmospheric conditions were 2.06 x 10-3 kg/m2, 3.50 x 10-3 kg/m2, and 4.35 x 10-3 kg/m2, respectively. The stable or unstable conditions affected the momentum transfer through decreasing or increasing turbulence. In stable conditions, the cooling of the atmosphere impacts the turbulence to be restrained. The result also showed that the dry deposition capacity during the dry and rainy periods were 4.5 x 10-3 kg/m2 and 2.9 x 10-3 kg/m2, respectively. Further, atmospheric conditions tended to be unstable during the dry period, while the rainy period tended to be stable. This research showed that the momentum transfer method can estimate gas type pollutants by vegetation.

Investigation of spatial variability in power law index of wind profiles above the urban area around Tokyo Bay based on local objective analysis data

By utilizing meteorological local objective analysis (LA) data on a local scale, this study investigated the spatial distribution of the power law index (PLI) of the mean wind speed profile in an urban area around Tokyo Bay in Japan. First, the reliability of the LA data was verified by comparison with observational data. The PLIs of the area for the 16 wind directions at a spatial resolution of 5 km were then estimated using LA data. Under neutral atmospheric conditions, the effects of the roughness length and terrain elevation on the PLI were investigated, and the spatial distribution characteristics of the PLI were analyzed. The results showed that the LA data were consistent with the observational data, indicating that high-precision LA data produced by data assimilation can be a practical supplement to observational data over urban areas. The influence of wind direction on the PLI was evident in the urban area. Furthermore, the metropolis appeared to affect the wind profile significantly when it was within the fetch of other cities, resulting in a larger PLI in the surrounding area. Because the PLI has complex spatial heterogeneity, in addition to urban substrate roughness, changes in terrain elevation and wind direction are thought to affect the PLI significantly. Therefore, generalizing the wind profile characteristics of large-area urban regions using local on-site observational data is not recommended.

Investigation of low-level supergeostrophic wind and Ekman spiral as observed by a radar wind profiler in Beijing

The supergeostrophic flow remains seldomly reported from an observational perspective. Here, 1 year record of radar wind profiler measurements and ERA-5 reanalysis collected at Beijing observatory station are used to characterize the vertical structures of supergeostrophic wind and Ekman spirals in the lower troposphere. It is found that supergeostrophic flow shows significant diurnal variation, with lowest frequency for the supergeostrophic wind forming during daytime under clear-sky conditions, largely due to strong turbulent mixing and friction in the daytime. By comparison, the planetary boundary layer at night is stably stratified, the supergeostrophic wind occurs more frequently due to friction-induced decoupling from the ground surface. Furthermore, the presence of cloud makes the supergeostrophic wind occur more often in the daytime. Also, the geostrophic wind deviation within 1 km of atmosphere is found to be more negatively associated with the difference between surface temperature and 2-m air temperature compared with that in the altitude range of 1–3 km, indicating that the supergeostrophic wind near ground surface is more subject to the influence of heat flux. Intriguingly, most of the vertical wind profiles in the PBL are found not to follow Ekman spiral under neutral atmospheric conditions. The supergeostrophic winds contribute significantly to the magnitude of Ekman spirals in the upper mixed layer. Overall, the profiles and evolution features of the supergeostrophic wind and Ekman spirals observed in the lower troposphere in Beijing are much complicated than expected. The findings lay a solid foundation for better elucidating the low-level atmospheric dynamics in Beijing.

Analyses of Spatial Correlation and Coherence in ABL Flow with a Fleet of UAS

The spatial structures of turbulent flow in the atmospheric boundary layer (ABL) are complex and diverse. Multi-point spatial correlation measurements can help improve our understanding of these structures and their statistics. In this context, we investigate Taylor’s hypothesis and the statistics of spatial structures on the microscale. For the first time, simultaneous horizontally distributed wind measurements with a fleet of 20 quadrotor UAS (unmanned aerial systems) are realized. The measurements were taken at different heights and under different atmospheric conditions at the boundary layer field site in Falkenberg of the German Meteorological Service (DWD). A horizontal flight pattern has been specifically developed, consisting of measurements distributed along and lateral to the mean flow direction with separation distances of 5ldots 205 m. The validity of Taylor’s hypothesis is studied by examining the cross-correlations of longitudinally distributed UAS and comparing them with the autocorrelations of single UAS. To assess the similarity of flow structures on different scales, the lateral and longitudinal coherence of the streamwise velocity component is examined. Two modeling approaches for the decay of coherence are compared. The experimental results are in good agreement with the model approaches for neutral atmospheric conditions, whereas in stable and convective ABL, the exponential approaches are not unconditionally valid. The validation results and the agreement with the literature on coherence in the ABL underline the potential of the UAS fleet for the purpose of spatial turbulence measurements.

The formation of atmospheric air pollution emissions from separate explosions as a result of military operations

As is well known, military operations in urbanized areas lead to deterioration of atmospheric air quality. The article examines the model of the formation of atmospheric polluted air from individual explosions as a result of military operations. A technique based on the theory of the convective jet is proposed for estimating the concentrations of hazardous substances formed above the burning area as a result of the explosion. The proposed approach makes it possible to obtain the concentration of pollutants in the conditions of emission into the atmosphere open to solar radiation at a height of three or more diameters of the explosion funnel (or the conventional diameter of the warm surface), the temperature and speed of hot air jets over the warm surface, the consumption of the desired substance. The obtained data make it possible to calculate the emission of hazardous substances into the atmosphere according to the formulas for a high unshaded source of pollution depending on the meteorological conditions of the area, to determine the concentration of the main acid-forming substances when assessing the probability of acid precipitation, and to serve as a basis for modern innovative atmospheric air monitoring systems.An assessment of the existing approach to determining atmospheric air pollution due to the impact of X-101 missile fragments in Kyiv on December 16, 2022 was made, which is a confirmation of the validity of this approach when calculating the impact of a separate explosion on atmospheric air quality. Calculated emissions of pollutants and their concentrations in the air in the upper part of the convective polluted jet in the case of separate explosions of the Kalibr cruise missile, the Kh-101 missile and the BUK-M1 projectile. The error from the comparison of these calculation results with the atmospheric air monitoring system depends on the nearest active observation points. This approach to assessing and forecasting concentrations of pollutants in atmospheric air can be used under neutral atmospheric conditions that determine the emission of pollutants and transformations of chemical substances in the air.

Characterization of Non-Neutral Urban Canopy Wind Profile Using CFD Simulations—A Data-Driven Approach

Horizontally averaged wind profiles inside the urban canopy are used in many studies and numerical models. The existing analytical models are only applicable to a small range of aspect ratios and mostly neutral atmospheric conditions due to their underlying assumptions. In this study, a surrogate model for predicting horizontally averaged wind profiles in the street canyons of an idealized urban canopy for a wide range of urban morphologies and thermal forcing scenarios is developed with the help of machine learning techniques and computational fluid dynamics (CFD) simulation data. The influence of urban morphological parameters, atmospheric stability and wind conditions on the urban canopy wind flow is modeled using machine learning algorithms applied on CFD simulation results. The numerical model is validated using wind-tunnel data. Steady-state Reynolds averaged Navier–Stokes (RANS) simulations with a standard k-ϵ turbulence model for 252 different simulation conditions are performed on an idealized building geometry that consists of a regular array of 4 × 4 cubes. The simulation results are averaged horizontally to obtain the mean velocity and temperature profiles. Surrogate models are developed using the simulation outputs as training examples and the best model is chosen by comparing the performance of different machine learning models. The surrogate artificial neural network (ANN) model of this study outperforms the current state-of-the-art models in the prediction of horizontally averaged mean wind profile inside the urban canopy. The mean error (ME) and root-mean-square error (RMSE) of the discrete point prediction ANN model of this study are 0.016 m/s and 0.060 m/s, respectively, which is significantly lower compared to the best of the legacy models for which the errors are 0.048 m/s and 0.387 m/s, respectively.

Validation study of WindTrax reverse dispersion model coupled with a sensitivity analysis of model-specific settings

In last years, atmospheric dispersion models have reached considerable popularity in environmental research field. In this regard, given the difficulties associated to the estimation of emission rate for some kind of sources, and due to the importance of this parameter for the reliability of the results, Backward dispersion models may represent promising tools. In particular, by knowing a measured downwind concentration in ambient air, they provide a numerical value for the emission rate. This paper discusses a critical validation of the WindTrax Backward model: the investigation does not only deal with the strict reliability of the model but also assesses under which conditions (i.e. stability class, number, and location of the sensors) the model shows the greatest accuracy. For this purpose, WindTrax results have been compared to observed values obtained from available experimental datasets. In addition, a sensitivity study regarding model-specific parameters required by WindTrax to replicate the physics and the random nature of atmospheric dispersion processes is discussed. This is a crucial point, since, for these settings, indications on the numerical values to be adopted are not available. From this study, it turns out that the investigated model specific settings do not lead to a significant output variation. Concerning the validation study, a general tendency of the model to predict the observed values with a good level of accuracy has been observed, especially under neutral atmospheric conditions. In addition, it seems that WindTrax underestimates the emission rate during unstable stratification and overestimates during stable conditions. Finally, by the definition of alternative scenarios, in which only a portion of the concentration sensors was considered, WindTrax performance appears better than acceptable even with a small number of concentration sensors, as long as the positioning is in the middle of the plume and not in the strict vicinity of the source.

Application of a Machine Learning Method for Prediction of Urban Neighborhood-Scale Air Pollution.

Urban air pollution has aroused growing attention due to its associated adverse health effects. A model which could promptly predict urban air quality with considerable accuracy is, therefore, important and will benefit the development of smart cities. However, only a computational fluid dynamics (CFD) model could better resolve the dispersion behavior within an urban canyon layer. A machine learning (ML) model using the Artificial Neural Network (ANN) approach was formulated in the current study to investigate vehicle-derived airborne particulate (PM10) dispersion within a compact high-rise-built environment. Various measured meteorological parameters and PM10 concentrations were adopted as the model inputs to train the ANN model. A building-resolved CFD model under the same environmental settings was also set up to compare its model performance with the ANN model. Our results showed that the ANN model exhibited promising performance (r = 0.82, fractional bias = 0.002) when comparing the > 1000 h PM10 measurements. When comparing the diurnal hourly measured PM10 variations in a clear-sky day, both the ANN and CFD models performed well (r > 0.8). The good performance of the CFD model relied on the knowledge of the in situ diurnal traffic profile, the adoption of suitable mobile source emission factor(s) (e.g., from MOBILE 6 and COPERT4), and the use of urban thermal and dynamical variables to capture PM10 variations in both neutral and unstable atmospheric conditions. These requirements/constraints make it impractical for daily operation. On the contrary, the ML (ANN) model adopted here is free from these constraints and is fast (less than 0.1% computational time relative to the CFD model). These results demonstrate that the ANN model is a superior option for a smart city application.

A New Streamwise Scaling for Wind Turbine Wake Modeling in the Atmospheric Boundary Layer

In this study, we aim to investigate if there is a scaling of the streamwise distance from a wind turbine that leads to a collapse of the mean wake velocity deficit under different ambient turbulence levels. For this purpose, we perform large-eddy simulations of the wake of a wind turbine under neutral atmospheric conditions with various turbulence levels. Based on the observation that a higher atmospheric turbulence level leads to faster wake recovery and shorter near-wake length, we propose the use of the near-wake length as an appropriate normalization length scale. By normalizing the streamwise distance by the near-wake length, we obtain a collapse of the normalized wake velocity deficit profiles for different turbulence levels. We then explore the possibility of using the relationship obtained for the normalized maximum wake velocity deficit as a function of the normalized streamwise distance in the context of analytical wake modeling. Specifically, we investigate two approaches: (a) using the new relationship as a stand-alone model to calculate the maximum wake velocity deficit, and (b) using the new relationship to calculate the wake advection velocity within a physics-based wake expansion model. Large-eddy simulation of the wake of a wind turbine under neutral atmospheric conditions is used to evaluate the performance of both approaches. Overall, we observe good agreement between the simulation data and the model predictions, along with considerable savings in terms of the models’ computational costs.

Atypically Intense and Delayed Response of the Martian Ionosphere to the Regional Dust Storm of 2016: A Study Using MAVEN Observations and Models

AbstractDuring Mars dust storms, atmospheric heating and expansion moves the ionospheric peak upward. Typically, peak altitude increases by no more than 10 km, and this increase occurs simultaneously with the expansion of the dust storm. However, Felici et al. (2020), https://doi.org/10.1029/2019JA027083, using the Mars Atmosphere Volatile EvolutioN (MAVEN) Radio Occultation Science Experiment (ROSE), reported an unusually large increase of ∼20 km at southern latitudes in early October 2016 during a modest dust storm. Here, we investigate why the ionospheric peak altitude increased so much in these observations. We extend the time series of ionospheric peak altitude values beyond the limited extent of the ROSE observations by applying a one‐dimensional photochemical model, in which neutral atmospheric conditions are based on in situ MAVEN Neutral Gas Ion Mass Spectrometer observations at similar latitudes and solar zenith angles to those observed by ROSE. We find that the ionospheric peak altitude was highest throughout October 2016 yet both the local and global atmospheric dust loading were greatest 1 month earlier. We hypothesize that (a) a portion of the unusually large 20 km enhancement in peak altitude and (b) the unusual delay between the greatest dust loading and the highest peak altitude were both associated with the occurrence of perihelion, which maximizes solar heating of the atmosphere, in late October 2016.

A non-intrusive reduced order model using deep learning for realistic wind data generation for small unmanned aerial systems in urban spaces

Realistic wind data are essential in developing, testing, and ensuring the safety of unmanned aerial systems in operation. Alternatives to Dryden and von Kármán turbulence models are required, aimed explicitly at urban air spaces to generate turbulent wind data. We present a novel method to generate realistic wind data for the safe operation of small unmanned aerial vehicles in urban spaces. We propose a non-intrusive reduced order modeling approach to replicate realistic wind data and predict wind fields. The method uses a well-established large-eddy simulation model, the parallelized large eddy simulation model, to generate high-fidelity data. To create a reduced-order model, we utilize proper orthogonal decomposition to extract modes from the three-dimensional space and use specialized recurrent neural networks and long-term short memory for stepping in time. This paper combines the traditional approach of using computational fluid dynamic simulations to generate wind data with deep learning and reduced-order modeling techniques to devise a methodology for a non-intrusive data-based model for wind field prediction. A simplistic model of an isolated urban subspace with a single building setup in neutral atmospheric conditions is considered a test case for the demonstration of the method.

On the extension of streamwise turbulence intensity profile beyond the atmospheric surface layer under neutral to unstable stratifications

Turbulence intensity (TI) is a key factor that affects both the energy production and the performance of individual wind turbines and wind farms. In particular, accurate energy production estimation from wind sources requires a reliable TI model at turbine hub height. Modern multi-megawatt wind turbines operate at heights well above the atmospheric surface layer (ASL). However, most existing analytical TI models are merely valid in the ASL or under neutral atmospheric conditions. Therefore, this paper aims to derive an analytical expression for streamwise TI under neutral to unstable stratifications, which can reasonably model TI at heights beyond the ASL. To this end, the Monin-Obukhov similarity theory, Townsend's attached eddy hypothesis, and a modification proposed in the literature to the eddy population density of Townsend for unstable conditions are utilized. The proposed models are validated against datasets from four tall meteorological towers in Denmark, Sweden, and China. The sites constitute a considerable difference in Coriolis parameter and surface roughness. Results show that the proposed models reasonably follow the measured velocity variance and TI data under neutral to unstable conditions in the ASL and beyond.

Effects of atmospheric stability on the structural response of a 12 MW semisubmersible floating wind turbine

AbstractGlobal dynamic response models used for the design of wind turbines are largely based on neutral stability, which is not representative of real atmospheric conditions. Offshore wind farms, for example, have been seen to experience predominantly unstable conditions, especially at lower wind speeds. In the current work, we use four wind generation models under stable, neutral and unstable atmospheric stability conditions to study the low‐frequency content of the global responses of a semisubmersible floating wind turbine (FWT). To represent the wind fields, we use the Kaimal Spectrum and Exponential Coherence Model (Kaimal), the Mann Spectral Tensor Model (Mann), a point measurement based model (TIMESR) and large‐eddy simulation (LES). At the low‐frequency range, both atmospheric stability and the turbulence wind model significantly affect the response of the FWT. In all the cases studied throughout this paper, the structural response under unstable conditions is higher than under stable or neutral conditions. The TIMESR and the Kaimal models fitted to the FINO‐1 offshore meteorological mast measurements show more similar responses than the Mann model; surge and pitch are higher for the TIMESR and Kaimal models, and yaw is lower. When fitted to LES, TIMESR and Kaimal predict surge and pitch responses closer to LES, but they underestimate the responses related to yaw, opposite to what the Mann model does. The responses are directly related to turbulence intensity and coherence, which are affected by atmospheric stability. Therefore, based on the analyses carried out through this study, the structural analysis of FWT should account for the effect of atmospheric stability.