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The reproduced planetary boundary layer (PBL) wind is commonly applied in downscaled simulations using commercial CFD codes with Reynolds-averaged Navier–Stokes (RANS) turbulence modeling. When using the turbulent inlets calculated by numerical weather prediction models (NWP), adjustments of the turbulence eddy viscosity closures and wall function formulations are concerned with maintaining the fully developed wind profiles specified at the inlet of CFD domains. The impact of these related configurations is worth discussing in engineering applications, especially when commercial codes restrict the internal modifications. This study evaluates the numerical performances of open-source OpenFOAM 2.3.0 and commercial Fluent 17.2 codes as supplementary scientific comparisons. This contribution focuses on the modified turbulence closures to incorporate turbulent profiles produced from mesoscale PBL parameterizations and the modified wall treatments relating to the roughness length. The near-ground flow features are evaluated by selecting the flat terrains and the classical Askervein benchmark case. The improvement in near-ground wind flow under the downscaled framework shows satisfactory performance in the open-source CFD platform. This contributes to engineers realizing the micro-siting of wind turbines and quantifying terrain-induced speed-up phenomena under the scope of wind-resistant design. [ABSTRACT FROM AUTHOR]

The primary mode of extraterrestrial exploration is a robotic system comprising a lander and a rover. However, the lander is immovable, and the rover has a restrictive detection area because of the difficulties of reaching complex terrains, such as those with deep craters. In this study, a six-legged mobile repetitive lander with landing and walking functions is designed to solve these problems. First, a six-legged mobile repetitive lander and its structure are introduced. Then, a soft-landing method based on compliance control and optimal force control is addressed to control the landing process. Finally, the experiments are conducted to validate the soft-landing method and its performances. Results show that the soft-landing method for the six-legged mobile repetitive lander can successfully control the joint torques and solve the soft-landing problem on complex terrains, such as those with steps and slopes. [ABSTRACT FROM AUTHOR]

The Loess Plateau in China is renowned for its dense gullies and complex terrain, with drastic changes primarily due to soil erosion and human activities, significantly affecting the evolution of the ecological environment. The complex terrains and dense vegetation make precise terrain measurement and modeling challenging. Although the development of Unmanned Aerial Vehicle (UAV) light detection and ranging (LiDAR) scanning and photogrammetry technologies has improved data acquisition precision, relying solely on one remote sensing technology struggles with accurately extracting bare earth information. This study adopted a method that fuses UAV lidar scanning with aerial photogrammetric imagery, generating detailed lidar point cloud data that includes coordinate, reflectance, true color, and texture information to enhance data classifiability and interpretability. Subsequently, a point cloud classification model based on the Transformer architecture (Stratified Transformer) is introduced to intelligently complete the initial ground point cloud extraction in complex gully terrains. Further, to address residual non-ground noise in the initial ground point clouds, a new point cloud classification optimization algorithm (MDD, Multi-scale C2M Distance Difference) is proposed. This algorithm, based on the characteristics of discrete and non-continuous with the ground surface of the noisy point clouds, effectively eliminates the discrete noisy point clouds by analyzing the distances between the point clouds and TINs (Triangular Irregular Networks) of different scales and their differences. This study effectively addresses the technical challenges of ground point cloud extraction in the mixed environment of complex terrain and vegetation, solving the problem of precise terrain measurement and intelligent data processing in complex gully terrains, and offering new technical pathways for detecting geomorphological changes.

The reproduced planetary boundary layer (PBL) wind is commonly applied in downscaled simulations using commercial CFD codes with Reynolds-averaged Navier–Stokes (RANS) turbulence modeling. When using the turbulent inlets calculated by numerical weather prediction models (NWP), adjustments of the turbulence eddy viscosity closures and wall function formulations are concerned with maintaining the fully developed wind profiles specified at the inlet of CFD domains. The impact of these related configurations is worth discussing in engineering applications, especially when commercial codes restrict the internal modifications. This study evaluates the numerical performances of open-source OpenFOAM 2.3.0 and commercial Fluent 17.2 codes as supplementary scientific comparisons. This contribution focuses on the modified turbulence closures to incorporate turbulent profiles produced from mesoscale PBL parameterizations and the modified wall treatments relating to the roughness length. The near-ground flow features are evaluated by selecting the flat terrains and the classical Askervein benchmark case. The improvement in near-ground wind flow under the downscaled framework shows satisfactory performance in the open-source CFD platform. This contributes to engineers realizing the micro-siting of wind turbines and quantifying terrain-induced speed-up phenomena under the scope of wind-resistant design.

Large-area soil moisture (SM) data with high resolution and precision are the foundation for the research and application of hydrological and meteorological models, water resource evaluation, agricultural management, and warning of geological disasters. It is still challenging to downscale SM products in complex terrains that require fine spatial details. In this study, SM data from the Soil Moisture Active and Passive (SMAP) satellite were downscaled from 36 to 1 km in the summer and autumn of 2017 in Sichuan Province, China. Genetic-algorithm-optimized backpropagation (GABP) neural network, random forest, and convolutional neural network were applied. A fusion model between SM and longitude, latitude, elevation, slope, aspect, land-cover type, land surface temperature, normalized difference vegetation index, enhanced vegetation index, evapotranspiration, day sequence, and AM/PM was established. After downscaling, the in situ information was fused through a geographical analysis combined with a spatial interpolation to improve the quality of the downscaled SM. The comparative results show that in complex terrains, the GABP neural network better captures the soil moisture variations in both time and space domains. The GDA_Kriging method is able to merge in situ information in the downscaled SM while simultaneously maintaining the dynamic range and spatial details. [ABSTRACT FROM AUTHOR]

Based on the 20-year high-resolution precipitation data from TRMM and GPM radar products, diurnal features over complex terrains along the Yangtze River (YR) are investigated. Using the Fast Fourier Transform (FFT) method, the first (diurnal) and second (semi-diurnal) harmonic amplitude and phase of precipitation amount (PA), precipitation frequency (PF), and intensity (PI) are analyzed. The diurnal amplitudes of PA and PF have a decreasing trend from the west to the east with the decreasing altitude of large-scale terrain, while the semi-diurnal amplitudes of PA and PI depict the bimodal precipitation cycle over highlands. For the eastward propagation of PA, PF is capable of depicting the propagation from the upper to the middle reaches of YR, while PI shows the eastward propagation from the middle to the lower reaches of YR during nighttime and presents sensitivity to highlands and lowlands. According to the contribution of different-sized precipitation systems to PI over the highlands and lowlands, the small (<200 km2) ones contribute the least while the large ones (>6000 km2) contribute the most, but the medium ones (200–6000 km2) show a slightly larger contribution over the highlands than over the lowlands. The propagation of each scaled precipitation system along the YR is further analyzed. We found that small precipitation systems mainly happen in the afternoon without obvious propagation. Medium ones peak 2–4 h later than the small ones, with two eastward propagation directions at night from the middle reaches of YR to the east. The large ones are mainly located in lowlands at night, with two propagation routes in the morning over the middle and lower reaches of YR. Such a relay of the propagation of the medium and large precipitation systems explains the eastward movement of PI along the YR, which merits future dynamic studies. [ABSTRACT FROM AUTHOR]

With the expansion of application scenarios, the walking-assisted lower-limb exoskeleton robot urgently needs to improve its walking ability in complex terrains. However, due to the lack of environmental understanding ability and decision-making ability, the exoskeleton can not meet the need of people with lower-limb disability in complex terrains. To improve the walking abillity, this paper proposes a method of predicting the landing area in complex terrains for walking-assisted lower-limb exoskeleton robot. Multiple sensors were used to obtain the terrain information, step length, and stride length of human walking, and a multi-type input convolutional network was built to establish the mapping relationship to learn the decision-making mechanism of human standing in a complex environment. The result shows that the established network effectively predicts the landing area. Under the condition of full training, the predicted position deviation of the landing area is controlled within 6%, and the prediction rationality of the landing area can reach 90%. [ABSTRACT FROM AUTHOR]

Large-area soil moisture (SM) data with high resolution and precision are the foundation for the research and application of hydrological and meteorological models, water resource evaluation, agricultural management, and warning of geological disasters. It is still challenging to downscale SM products in complex terrains that require fine spatial details. In this study, SM data from the Soil Moisture Active and Passive (SMAP) satellite were downscaled from 36 to 1 km in the summer and autumn of 2017 in Sichuan Province, China. Genetic-algorithm-optimized backpropagation (GABP) neural network, random forest, and convolutional neural network were applied. A fusion model between SM and longitude, latitude, elevation, slope, aspect, land-cover type, land surface temperature, normalized difference vegetation index, enhanced vegetation index, evapotranspiration, day sequence, and AM/PM was established. After downscaling, the in situ information was fused through a geographical analysis combined with a spatial interpolation to improve the quality of the downscaled SM. The comparative results show that in complex terrains, the GABP neural network better captures the soil moisture variations in both time and space domains. The GDA_Kriging method is able to merge in situ information in the downscaled SM while simultaneously maintaining the dynamic range and spatial details.

Based on the 20-year high-resolution precipitation data from TRMM and GPM radar products, diurnal features over complex terrains along the Yangtze River (YR) are investigated. Using the Fast Fourier Transform (FFT) method, the first (diurnal) and second (semi-diurnal) harmonic amplitude and phase of precipitation amount (PA), precipitation frequency (PF), and intensity (PI) are analyzed. The diurnal amplitudes of PA and PF have a decreasing trend from the west to the east with the decreasing altitude of large-scale terrain, while the semi-diurnal amplitudes of PA and PI depict the bimodal precipitation cycle over highlands. For the eastward propagation of PA, PF is capable of depicting the propagation from the upper to the middle reaches of YR, while PI shows the eastward propagation from the middle to the lower reaches of YR during nighttime and presents sensitivity to highlands and lowlands. According to the contribution of different-sized precipitation systems to PI over the highlands and lowlands, the small (2) ones contribute the least while the large ones (>6000 km2) contribute the most, but the medium ones (200–6000 km2) show a slightly larger contribution over the highlands than over the lowlands. The propagation of each scaled precipitation system along the YR is further analyzed. We found that small precipitation systems mainly happen in the afternoon without obvious propagation. Medium ones peak 2–4 h later than the small ones, with two eastward propagation directions at night from the middle reaches of YR to the east. The large ones are mainly located in lowlands at night, with two propagation routes in the morning over the middle and lower reaches of YR. Such a relay of the propagation of the medium and large precipitation systems explains the eastward movement of PI along the YR, which merits future dynamic studies.

The mountain boundary layer height (MBLH) plays a crucial role in determining the exchange of heat, momentum, and composition between the low atmosphere and free troposphere. To obtain accurate quantities of the MBLH, we propose a Data Reinforced Ensemble Machine Learning Model (DREM) to estimate the MBLH using both ERA5 reanalysis multi-variables and thermodynamic profiles observed in two mountainous stations of the foot of Qinling Mountain and the peak of Damaojian mountain in southeast China (Shanghuang site). The observations reveal that the MBLH has resemblances characteristics over the foot of mountains or over the top, featuring daytime high convective boundary layer over 2 km and low stable boundary layer at night about 200 m. But the boundary layer height of EAR5 (ERA5-BLH) has very large error, the Mean Absolute Error (MAE) are nearly 500 m at the valley. The errors between DREM and observations are estimated to be 43% at the peak and 22% at the valley, whereas the corresponding errors from ERA5 reanalysis data are 183% and 70%, respectively. In comparison with the baseline models such as Random Forest (RF), Adaboost, and EM, our statistical results based on all samples demonstrate that DREM can improve the error by 10 m. Subsequent analysis of the Shapley Additive Explanations (SHAP) method demonstrates that the contributions of various meteorological variables in MBLH estimation vary greatly. Among them, Surface Thermal Radiation Downwards (STRD) dominates at both the peak and valley, with lower STRD leading to higher accuracy and vice versa. However, the valley MBLH estimation is not solely determined by thermal factors, but also controlled by dynamic factors and Relative Humidity (RH). Case studies indicate that DREM can significantly minimize deviations, especially at the mountain peak, where it can even capture the abrupt variation of MBLH. In conclusion, the study presents a precise and robust method for estimating the MBLH over complex terrain, which provides an invaluable asset for improving the mountainous weather forecasting, as well as deepening the understanding of the environmental and climatic effects of mountain ecosystems. [Display omitted] • A Data Reinforced Ensemble Model (DREM) is proposed for mountain BLH (MBLH) estimation in both peak and valley. • The DREM-based estimates exhibit an accuracy that is over two times higher than ERA5-BLH. • Thermal factors dominate in all estimates, while dynamic factors and RH also prevail in valley MBLH estimation. [ABSTRACT FROM AUTHOR]

The decoupling of energy prices from fossil fuel is slowly making its way as investment is poured into renewable energy sources. Small Island Developing States are gaining in both stability and cost from this momentum but face threat from the same unsustainable centralization practices. A decentralized framework is proposed for Small Island Developing States aimed at achieving grid stability and in attracting independent financing mechanisms. This framework is applied from a Wind perspective and to ensure replicability on all types of terrains, and the model is analysed through three case studies: high-rise buildings, flat terrains and Gaussian terrains. This study provides a novel framework and a general solution for Wind farming over different terrain layouts including forbidden regions and complex topography. [ABSTRACT FROM AUTHOR]

Understanding the distribution of wind field parameters over various terrains is important for kinds of applications including condition monitoring of high-speed railways and pollutant dispersion modeling. Based on Empirical Bernstein Copula (EBC), a nonparametric joint distribution model of wind speed and direction adaptable for complex wind field is proposed. Four commonly used parametric models are introduced for comparison, including the angular-linear model, Frank Copula, Gaussian Copula, and the model without considering interdependence. Yearly data measured from wind monitoring stations at 19 sites with various wind fields alongside the Lanzhou-Xinjiang high-speed railway in China is adopted for studying. The EBC model shows an overall best goodness-of-fit with the comprehensive metric value reaching 4.9586 (full score 5). Particularly, the superior performance of the EBC model is retained while the parametric models fail to predict the joint distribution in regions where wind speed and direction are highly dependent. In addition, when dealing with cases that the fitting goodness of marginal distribution is relatively poor (i.e. R 2 = 0.488), a desired accuracy of joint distribution can be obtained with EBC. Further discussion about the optimal parameters and Copula structure of EBC is conducted to reveal reasons for the model showing adaptability to highly variable wind environments. • A nonparametric copula-based model for JPDF of wind speed and direction adaptable for various terrains is proposed. • Performance and robustness of various JPDF models are evaluated at the scale of the whole railway region in China. • The fitting accuracy of the EBC and parametric copulas to model the JPDF of highly dependent wind vector data is compared. • The accuracy of JPDF models is discussed when dealing with cases that the fitting goodness of marginal distribution is poor. [ABSTRACT FROM AUTHOR]

The performance of wind turbines is not only dependent on the wind turbine design itself, but is also dependent on the accurate assessment of wind resources at the installation site. In this paper, the numerical site calibration (NSC) method using three-dimensional Reynolds-averaged Navier–Stokes (RANS) simulation was proposed to accurately forecast the wind flow characteristics of wind turbine sites with complex terrains, namely Methil in Scotland, and Haenam in South Korea. From NSC at the Methil and Haenam sites, it was shown that the complicated and vortical flow fields around hills and valleys were captured using the three-dimensional RANS CFD simulation in Ansys CFX software based on a high-resolution scheme with a renormalization group (RNG)-based k-ε turbulence model. It was also shown that topographically induced wind profile and turbulence intensity over a local-scale complex terrain are remarkably dominated by flow separation after passing hills. It was concluded that the proposed NSC method using three-dimensional RANS simulation with a high-resolution scheme was an economically useful method for evaluating wind flow characteristics numerically to assess wind turbine sites with complex terrains and designing the wind farm layout.

Downwind turbines have favorable characteristics such as effective energy capture in up-flow wind conditions over complex terrains. They also have reduced risk of severe accidents in the event of disruptions to electrical networks during strong storms due to the free-yaw effect of downwind turbines. These favorable characteristics have been confirmed by wind-towing tank experiments and computational fluid dynamics (CFD) simulations. However, these advantages have not been fully demonstrated in field experiments on actual wind farms. In this study—although the final objective was to demonstrate the potential advantages of downwind turbines through field experiments—field measurements were performed using a vertical-profiling light detection and ranging (LiDAR) system on a wind farm with downwind turbines installed in complex terrains. To deduce the horizontal wind speed, vertical-profiling LiDARs assume that the flow of air is uniform in space and time. However, in complex terrains and/or in wind farms where terrain and/or wind turbines cause flow distortion or disturbances in time and space, this assumption is not valid, resulting in erroneous wind speed estimates. The magnitude of this error was evaluated by comparing LiDAR measurements with those obtained using a cup anemometer mounted on a meteorological mast and detailed analysis of line-of-sight wind speeds. A factor that expresses the nonuniformity of wind speed in the horizontal measurement plane of vertical-profiling LiDAR is proposed to estimate the errors in wind speed. The possibility of measuring and evaluating various wind characteristics such as flow inclination angles, turbulence intensities, wind shear and wind veer, which are important for wind turbine design and for wind farm operation is demonstrated. However, additional evidence of actual field measurements on wind farms in areas with complex terrains is required in order to obtain more universal and objective evaluations.

Due to the complex terrain, sparse precipitation observation sites, and uneven distribution of precipitation in the northeastern slope of the Qinghai-Tibet Plateau, it is necessary to establish a precipitation estimation method with strong applicability. In this study, the precipitation observation data from meteorological stations in the northeast slope of the Qinghai-Tibet Plateau and 11 geographical and topographic factors related to precipitation distribution were used to analyze the main factors affecting precipitation distribution. Based on the above, a multivariate linear regression precipitation estimation model was established. The results revealed that precipitation is negatively related to latitude and elevation, but positively related to longitude and slope for stations with an elevation below 1700 m. Meanwhile, precipitation shows positive correlations with both latitude and longitude, and negative correlations with elevation for stations with elevations above 1700 m. The established multivariate regression precipitation estimating model performs better at estimating the mean annual precipitation in autumn, summer, and spring, and is less accurate in winter. In contrast, the multivariate regression mode combined with the residual error correction method can effectively improve the precipitation forecast ability. The model is applicable to the unique natural geographical features of the northeast slope of the Qinghai-Tibet Plateau. The research results are of great significance for analyzing the temporal and spatial distribution pattern of precipitation in complex terrain areas. [ABSTRACT FROM AUTHOR]

A numerical wake model that is capable of simulating wind turbine wake effects over complex terrains is proposed in this work. Currently, full computational fluid dynamics (CFD) simulations are required to simulate wake effects over complex terrains. Due to their high computational cost, it is difficult to apply expensive CFD simulations in the wind farm design process as optimization algorithms often require large number of solution evaluations. The proposed wake model solves a simplified variation of the Navier-Stokes equations, in which simplifications and assumptions have been implemented in order to reduce computational cost while maintaining accuracy. This model was validated by comparing with full CFD simulations with reasonable accuracy. In general, the model produces accurate results while keeping the computational cost two orders of magnitude lower than that of full CFD simulations. The low computation time highlights the model's potential to be used for optimizing wind farm layouts on complex terrains. [ABSTRACT FROM AUTHOR]

The flow over complex terrains and wind farms is estimated here by numerically solving the linearized Navier-Stokes equations. The equations are linearized around the unperturbed incoming wind profile, here assumed logarithmic. The Boussinesq approximation is used to model the Reynolds stress with a prescribed turbulent eddy viscosity profile. Without requiring the boundary-layer approximation, two new linear equations are obtained for the vertical velocity and the wall-normal vorticity, with a reduction in the computational cost by a factor of 8 when compared with a primitive-variables formulation. The presence of terrain elevation is introduced as a vertical coordinate shift, while forestry or wind turbines are included as body forces, without any assumption about the wake structure for the turbines. The model is first validated against some available experiments and simulations, and then a simulation of a wind farm over a Gaussian hill is performed. The speedup effect of the hill is clearly beneficial in terms of the available momentum upstream of the crest, while downstream of it the opposite can be said as the turbines face a decreased wind speed. Also, the presence of the hill introduces an additional spanwise velocity component that may also affect the turbines' operations. The linear superposition of the flow over the hill and the flow over the farm alone provided a first estimation of the wind speed along the farm, with discrepancies of the same order of magnitude for the spanwise velocity. Finally, the possibility of using a parabolic set of equations to obtain the turbulent kinetic energy after the linearized model is investigated with promising results. [ABSTRACT FROM AUTHOR]

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The A* algorithm has been widely investigated and applied in path planning problems, but it does not fully consider the safety and smoothness of the path. Therefore, an improved A* algorithm is presented in this paper. Firstly, a new environment modeling method is proposed in which the evaluation function of A* algorithm is improved by taking the safety cost into account. This results in a safer path which can stay farther away from obstacles. Then a new path smoothing method is proposed, which introduces a path evaluation mechanism into the smoothing process. This method is then applied to smoothing the path without safety reduction. Secondly, with respect to path planning problems in complex terrains, a complex terrain environment model is established in which the distance and safety cost of the evaluation function of the A* algorithm are converted into time cost. This results in a unification of units as well as a clarity in their physical meanings. The simulation results show that the improved A* algorithm can greatly improve the safety and smoothness of the planned path and the movement time of the robot in complex terrain is greatly reduced.

In recent years, wind farm optimization has received much attention in the literature. The aim of wind farm design is to maximize energy production while minimizing costs. The wind farm layout optimization (WFLO) problem on uniform terrains has been tackled by a number of approaches; however, optimizing wind farm layouts on complex terrains is challenging due to the lack of accurate, computationally tractable wake models to evaluate wind farm layouts. This paper proposes an algorithm that couples computational fluid dynamics (CFD) with mixed-integer programming (MIP) to optimize layouts on complex terrains. CFD simulations are used to iteratively improve the accuracy of wake deficit predictions while MIP is used for the optimization process. The ability of MIP solvers to find optimal solutions is critical for capturing the effects of improved wake deficit predictions on the quality of wind farm layout solutions. The proposed algorithm was applied on a wind farm domain in Carleton-sur-Mer, Quebec, Canada. Results show that the proposed algorithm is capable of producing excellent layouts in complex terrains. [ABSTRACT FROM AUTHOR]

averaged Navier-Stokes equations for simulation of wind flows in body-fitted grids generated by algebraic extrusion from digital terrain elevation data, proposed in Mirkov et. al. [1]. The approach is based on second-order accurate finite volume method with collocated variable arrangement and pressure-velocity coupling trough SIMPLE algorithm. The main objective is the attenuation of spurious pressure field oscillations in regions with discontinuity in grid line slopes, as encountered in grids representing highly non-uniform terrains. Moreover, the approach relaxes the need for grid generation based on elliptic partial differential equation or grid smoothing by applying fixed point iterations (i. e. Gauss- Seidel) to initial grid node positions resulting from algebraic grid generators. Drawbacks of previous approaches which ignored treatment of finite volume grid cell cases with intersection point offset in non-orthogonality corrections are removed. Application to real-life wind farm project at Dobric (Svrljig, Serbia) is used to assess the effectiveness of the method. The results validate the view in which accurate discretization of governing equations play more important role than the choice of turbulence modeling closures. [ABSTRACT FROM AUTHOR]

Worldwide, the amount of energy produced by the power of wind rapidly increases. The installed capacity of wind farms has increased nearly 60 times for the last 20 years. The small wind turbines that have been installed 20 years ago are at the end of their operational life. Pretty soon they need to be replaced with bigger ones, which leads to a completely new micro-sitting of the wind farm. In case of a flat terrain the micro-sitting can be easily performed since the wind parameters (wind speed and direction) are known for a long period of time. For a hilly or complex terrains a couple of assumption needs to be made in order to have the most reliable results. Improvement of the results from numerical study is possible with the adjustment of the uncertainty of the used models. Some restrictions about the size of the domain where the numerical procedures will take place are also discussed. [ABSTRACT FROM AUTHOR]

Abstract: In order to avoid the problems of existing methods, a numerical simulation method for two-dimensional airflow over complex terrains is developed in this paper for the engineering use of flight dynamics. Based on the potential flow theories, the effects of terrains on the wind field are considered by a serial of two-dimensional vortexes, whose strengths are solved by combining with the ground boundary conditions. Numerical examples are studied by the proposed method, and the method is also evaluated by comparing the results with ones from the existing method. The result shows that the two- dimensional profile of complex terrains could be described by a cubic spline curve precisely. The computation procedure proposed in this paper is very simple and efficient, and it could provide a result of wind field with considerable accuracy. Therefore, this method could be used for flight principle evaluation and flight simulators. [Copyright &y& Elsevier]

Initial (IC) and boundary conditions (BC) are required in order to solve the set of stiff differential equations included in air quality models. In this work, the influences of IC–BC are analyzed in the northeastern Iberian Peninsula (NEIP) by applying MM5–EMICAT2000–CMAQ. A multiscale-nested configuration has been used to generate the IC–BC. The wider domain (D1) covers an area of 1392×1104km2 centered in the Iberian Peninsula. Domain 2 (D2) covers an area of 272×272km2 in the NEIP (D2) with high spatial and temporal resolution. The information related to BC has been supplied to D2 through one-way nesting. Different scenarios were considered (base case, increments of +50% in ozone (O3) IC, +50% in O3 BC, +50% in O3 precursors IC, +50% in O3 precursors BC and clean BC). The impacts of the IC on a site decrease with simulation time. Focusing on the conditions within the PBL, a 48-h spin-up time is sufficient to reduce the impact factor of IC to 10% or less for O3 since the influence of pervasive local emissions. The influences of BC are more important for areas near domain boundaries, especially in areas where the contribution of O3 precursors is due to a short-medium range transport. [Copyright &y& Elsevier]

Downwind turbines have favorable characteristics such as effective energy capture in up-flow wind conditions over complex terrains. They also have reduced risk of severe accidents in the event of disruptions to electrical networks during strong storms due to the free-yaw effect of downwind turbines. These favorable characteristics have been confirmed by wind-towing tank experiments and computational fluid dynamics (CFD) simulations. However, these advantages have not been fully demonstrated in field experiments on actual wind farms. In this study—although the final objective was to demonstrate the potential advantages of downwind turbines through field experiments—field measurements were performed using a vertical-profiling light detection and ranging (LiDAR) system on a wind farm with downwind turbines installed in complex terrains. To deduce the horizontal wind speed, vertical-profiling LiDARs assume that the flow of air is uniform in space and time. However, in complex terrains and/or in wind farms where terrain and/or wind turbines cause flow distortion or disturbances in time and space, this assumption is not valid, resulting in erroneous wind speed estimates. The magnitude of this error was evaluated by comparing LiDAR measurements with those obtained using a cup anemometer mounted on a meteorological mast and detailed analysis of line-of-sight wind speeds. A factor that expresses the nonuniformity of wind speed in the horizontal measurement plane of vertical-profiling LiDAR is proposed to estimate the errors in wind speed. The possibility of measuring and evaluating various wind characteristics such as flow inclination angles, turbulence intensities, wind shear and wind veer, which are important for wind turbine design and for wind farm operation is demonstrated. However, additional evidence of actual field measurements on wind farms in areas with complex terrains is required in order to obtain more universal and objective evaluations. [ABSTRACT FROM AUTHOR]

The performance of wind turbines is not only dependent on the wind turbine design itself, but is also dependent on the accurate assessment of wind resources at the installation site. In this paper, the numerical site calibration (NSC) method using three-dimensional Reynolds-averaged Navier–Stokes (RANS) simulation was proposed to accurately forecast the wind flow characteristics of wind turbine sites with complex terrains, namely Methil in Scotland, and Haenam in South Korea. From NSC at the Methil and Haenam sites, it was shown that the complicated and vortical flow fields around hills and valleys were captured using the three-dimensional RANS CFD simulation in Ansys CFX software based on a high-resolution scheme with a renormalization group (RNG)-based k-ε turbulence model. It was also shown that topographically induced wind profile and turbulence intensity over a local-scale complex terrain are remarkably dominated by flow separation after passing hills. It was concluded that the proposed NSC method using three-dimensional RANS simulation with a high-resolution scheme was an economically useful method for evaluating wind flow characteristics numerically to assess wind turbine sites with complex terrains and designing the wind farm layout. [ABSTRACT FROM AUTHOR]