Wind Flow Research Articles (Page 1)

ABSTRACT In the micro-siting of complex wind farms, one of the primary challenges in wind turbine arrangement is the overall power loss caused by terrain effects and wake interference between turbines. This study focuses on dual wind turbines arranged in a longitudinal tandem configuration under varying hill aspect ratios. A RANS is employed to model the flow characteristics within the wake region generated by the power output and aerodynamic behavior of the turbines across multiple terrain configurations. The underlying flow mechanisms within the wake region under different hill aspect ratios are systematically investigated. The results indicate that in hilly terrain with a Hill aspect ratio ranging from 0.14 to 0.25, when wind turbines are arranged with vertical elevation differences, the influence of the terrain on the flow field at the base of the windward slope is relatively minor. However, for turbines located near the hilltop, the inflow wind speed increases by 10%–30% compared to those situated at the leeward base, while turbulence intensity decreases by approximately 2%–6%, leading to a 1 to 2fold increase in power output. As the terrain aspect ratio increases, the recovery of wake interference within the coupled terrain – turbine wake system is enhanced, regardless of elevation differences. Specifically, the wake recovery distance shortens from 15D to 13D. In conjunction with spectral analysis of turbine power output, these findings suggest that wind farm micro-siting should prioritize low-aspect-ratio hilly terrains and adopt elevated arrangements (with height differences) to maximize wind energy utilization efficiency.

Analytical wake models play a crucial role in assessing wake effects, optimizing wind farm layouts, and enabling active wake control. However, conventional models primarily focus on the far-wake region and show limited accuracy in the near wake, restricting their applicability in modern compact wind farms. To address this, this study proposes a pressure-corrected double-Gaussian analytical wake model to improve prediction accuracy across the entire wake. Time-averaged wakes under different thrust coefficients and yaw angles are simulated using large-eddy simulation coupled with an actuator disk model with rotation. Based on the near-wake pressure distribution obtained from large-eddy simulation (LES), a pressure correction term is incorporated into the momentum conservation equation to construct the pressure-corrected double-Gaussian wake model. An analytical expression for the position of the minimum velocity in the wake is derived from mass conservation. The model requires only the wake expansion coefficient as an adjustable parameter. Comparison with LES data indicates that, relative to the uncorrected double-Gaussian model, the normalized root mean square error of wake velocity in the near-wake region, particularly near the rotor, is reduced from approximately 40% to below 13% of the inflow wind speed. The model successfully captures wake velocity distributions across different turbulence intensities, yaw angles, and tip speed ratios, demonstrating strong robustness and general applicability.

Accurate prediction of urban wind flow is essential for urban planning and environmental assessment. Classical computational fluid dynamics (CFD) methods are computationally expensive, while machine learning approaches often lack explainability and generalizability. To address the limitations of both approaches, we propose Diff-KNN, a hybrid method that combines Coarse-Scale CFD simulations with a K-Nearest Neighbors (KNN) model trained on the residuals between coarse- and fine-scale CFD results. Diff-KNN reduces velocity prediction errors by up to 83.5% compared to Pure-KNN and 56.6% compared to coarse CFD alone. Tested on the AIJE urban dataset, Diff-KNN effectively corrects flow inaccuracies near buildings and within narrow street canyons, where traditional methods struggle. This study demonstrates how residual learning can bridge physics-based and data-driven modeling for accurate and interpretable fine-scale urban wind prediction.

Environmental sensing is fundamental for understanding and managing ecosystems, climate patterns, and resource sustainability. Continuous monitoring of parameters like wind, humidity, rainfall, and water flow across diverse and often remote locations is essential. Conventional sensor networks face significant limitations due to their reliance on batteries or wired power sources. Frequent maintenance for battery replacement or recharging becomes impractical and costly in inaccessible or harsh environments, hindering long-term, distributed data collection. This creates a critical demand for self-sustaining sensing technologies capable of harvesting ambient energy directly from their surroundings. Triboelectric nanogenerators represent a promising solution by converting ubiquitous mechanical energy from environmental sources into usable electrical power. Their inherent material flexibility and adaptability are crucial for seamless integration into natural settings. This review specifically focuses on the pivotal role of advanced flexible nanostructured polymer materials in enhancing TENG performance for reliable self-powered environmental sensing applications, enabling persistent operation without external energy inputs. These materials provide the foundation for overcoming key challenges in sensitivity, environmental resilience, and practical deployment, thus paving the way for the future widespread application of such self-powered sensors across diverse industrial sectors.

Abstract We report new observations by the Parker Solar Probe of unique electron velocity distribution functions (VDFs) in low-density solar wind streams at heliocentric distances of ∼10–20 R ⊙ . These VDFs have a significant core anisotropy and lack a clear energy spectral break point between the thermal core and suprathermal strahl populations, with a parallel cut through the anti-sunward distribution closely following a single-temperature Maxwellian function over up to ∼6 decades in distribution function. These characteristics correlate strongly with low collisional age parameters and high Knudsen numbers, and also to some degree with higher local and asymptotic bulk solar wind flow speeds and lower strahl temperatures. The observed VDFs may represent a unique characteristic of low-density solar wind streams, or alternatively they could represent a common occurrence in any sufficiently collisionally young stream. In either case, these observations have implications both for the detailed evolution of the electron VDF and the large-scale structure and acceleration of the solar wind.

Abstract The urban canopy layer (UCL) exhibits complex, heterogeneous flow patterns shaped by urban geometry. Traditionally, research has relied on microscale simulations over limited and often idealized building arrays, leaving a need for more extensive datasets to capture the dynamics across diverse urban neighborhoods. Responding to this gap, we developed an extensive dataset, known hereafter as Urban Turbulence Analyses from Large-Eddy Simulations (UrbanTALES), based on state-of-the-art Large Eddy Simulations (LES) over 538 urban layouts (generated using over 3,000,000 CPU hours and 35 TB of storage) with both idealized and realistic configurations. Realistic urban neighborhood configurations were obtained from major cities worldwide, incorporating wide variations in building plan area densities [0.06-0.64] and height distributions [4-50m]. Idealized urban arrays, on the other hand, include two commonly studied configurations (aligned and staggered building arrays), featuring both uniform and variable height scenarios along with oblique wind directions. UrbanTALES offers canopy-averaged flow data as well as 2D and 3D flow fields tailored for different applications in urban climate research such as the development and testing of urban canopy models. The dataset provides time-averaged wind flow properties, as well as second and third-order flow moments that are critical for understanding turbulent processes in the UCL. Here, we describe the UrbanTALES dataset and its applications, noting the unique opportunity to use high-fidelity simulated flow in realistic urban neighborhoods to: a) revisit neighborhood-scale urban canopy parameterizations in various climate models; and b) inform in-canopy flow and turbulent analyses in complex urban configurations. UrbanTALES is openly available at https://urbantales.climate-resilientcities.com/ and can be extended to incorporate future LES datasets in the field.

ABSTRACT With the growth of island tourism, the construction of island resorts and hotels has increased. Their exterior spaces are highly susceptible to the complex and variable island wind environments, impacting tourist experiences. However, research on their spatial characteristics and wind adaptation remains limited and lacks systematic analysis. This study reviewed related research and analyzed 50 island resorts and hotels worldwide. Spatial characteristics and wind adaptation strategies were summarized from layout patterns, enclosure interfaces, and constituent elements. Key findings indicate: (1) For layout patterns, exterior spaces were categorized into three types, as the building grounding method and its spatial relationship with nearshore water bodies significantly influenced this typology. Thus, wind-adaptive design inspirations were summarized for building layout types, terrain, and water bodies. (2) For enclosure interfaces, 13 enclosure types were classified by interface position and number, and indicators (D/W, D/H, IED, SVF) showed diverse distributions among the cases. Enclosed spaces are influenced by materials, shapes, and heights, affecting local wind flow patterns. (3) For constituent elements, main components exhibited clear typological characteristics and varying influence on microclimate. These findings provide both theoretical insights and practical references for wind-adaptive design of island resorts and hotels, to improve comfort and sustainability.

The article is devoted to an in-depth study of modeling patterns of wind flow distribution over a geometrically complex curved surface covering a unique large-span building based on a comparison of two options for performing aerodynamic calculations. The first method consists in conducting a computer experiment on a digital model of the projected object in computational fluid dynamics software systems. The second one is based on the implementation of simplified calculation procedures according to the methods set out in the regulatory documents adopted in the construction industry. When performing a computer experiment, the characteristic features of modeling in the ANSYS CFX software and computing complex are highlighted, such as the construction of a computational model, the formation of a computational grid of finite elements, and the setting of a height-varying wind profile in three of the most relevant directions. Special attention is paid to the correct setting of boundary conditions, which is critical for obtaining reliable results. According to two calculation options, adapted patterns of wind pressure distribution over the curved surface of the building are presented and a comparative analysis of the results is performed, reflecting an excess of wind loads calculated according to regulatory documents by about 1.5–2 times in relation to the data obtained during computer modeling.

Wind flow patterns around building features are crucial for the safe operation of drones and urban air mobility missions. Fixed anemometer stations and computational fluid dynamics simulations face limitations in measuring airflow behind buildings. The use of a drone-mounted ultrasonic anemometer addresses these limitations by providing high-resolution measurements, flexibility in positioning, and the ability to capture real-time localized wind patterns in complex urban environments. This paper presents a wind tunnel study using full factorial design of experiment on a low-cost drone-mountable ultrasonic anemometer to be used to measure urban wind fields. Measurements under uniform flow conditions, with reference instruments, validate the anemometer's performance in wind speed and direction measurements. The study also explores the anemometer's capabilities in measuring wake characteristics behind a cylinder in turbulent flow. The results demonstrate that using compact ultrasonic anemometers for drone-based anemometry in urban environments can yield adequate measurements within specific angle of attack ranges.

Introduction: The influence of the level of building facade detail (protruding and recessed balconies, fins, and other facade elements) — referred to as facade faceting — on the results of wind load simulations has been examined in various studies. It has been established that a higher level of facade faceting in models improves the consistency of computational fluid dynamics (CFD) results with results of wind tunnel experiments. However, in order to simplify calculations, under certain conditions, some details may be neglected. Nevertheless, clear recommendations regarding the degree to which such simplifications affect the final accuracy of simulation are rarely found. Purpose of the study: In this study, the influence of facade faceting detail on the distribution of wind flows around the investigated object was assessed using computational and experimental modeling. Methods: Physical testing of scale models of unique buildings and structures in a wind tunnel, as well as numerical simulation of wind effects, were carried out. Results: The study demonstrated a significant impact of facade faceting detail on the distribution of wind loads around the investigated building model. It is recommended to design facade structures with consideration of the turbulence effects of wind flow associated with their actual geometry. At the same time, the design of load-bearing structures should account for the maximum possible wind loads without incorporating facade faceting detailing.

Residential air quality near noise barriers strongly affected by wind velocity.

Impact of terrain on inflow factors and wind turbine vibrational responses: Insight from SCADA data and wind tunnel tests

Wind energy faces significant challenges posed by wake-induced power losses, which results from decreased wind velocity and increased turbulence. To address the critical need for optimizing wind farm yaw angles and mitigating such losses, a three-dimensional dual-cosine shape model for predicting yaw wake velocity and turbulence intensity is proposed in this paper. This physics-based engineering framework is designed to predict wake center offset, velocity fields, and turbulence intensity distributions of yawed wind turbines. The model incorporates dual-peak wake distributions and yaw-induced wake deflection mechanisms, while comprehensively considering the influence of multiple factors on wake flow, including inflow wind conditions, local geographical information, turbine aerodynamic characteristics, and operational conditions. In addition, it eliminates the requirement for parameter fitting while maintaining applicability in both near and far-wake regions. Comprehensive validations against experimental data and high-fidelity computational fluid dynamics simulations demonstrate its superiority over existing models, with particular accuracy in capturing wake center trajectory evolution, wake deficit recovery, and turbulence intensity development throughout the yawed wake region. Quantitative error analyses furthermore confirm the model's accuracy and robustness: the relative root mean square error of the proposed wake deflection model generally remains below 10%, and the predicted wake distribution is improved by 20% relative to several existing yaw wake models in most cases (particularly in the far-wake region). This work provides both an effective analytical model for yawed wake dynamics and a practical tool for facilitating the implementation of yaw control strategies to enhance the energy yield of wind farms.

Abstract Offshore wind energy is essential for the global transition to renewable energy. Wind farms, such as those at Anholt and Westermost Rough, are crucial in providing clean electricity. LiDAR (Light Detection and Ranging) and SCADA (Supervisory Control and Data Acquisition) data are particularly valuable for understanding turbine behavior and predicting energy output. Recent advancements in machine learning (ML) and artificial intelligence (AI) have opened new possibilities for analyzing complex wind farm data. This study employs data-driven techniques, specifically XGBoost and Long Short-Term Memory (LSTM) networks, to enhance the analysis of LiDAR and SCADA data from the Anholt and Westermost Rough offshore wind farms. Data-driven filters were applied to enhance the quality of input data, thereby improving the accuracy of the models and reducing noise. XGBoost demonstrated computational efficiency, training faster than Bi-LSTM while achieving an R 2 of 0.97 for Anholt and 0.86 for Westermost Rough. While Bi-LSTM successfully captured temporal dependencies, it required significantly longer training times. RMSE and MSE results indicate that XGBoost outperformed Bi-LSTM at Anholt by 6.3% and 12.2%, respectively, whereas both models showed higher errors at Westermost Rough, likely due to data dependency. The residual analysis confirmed tighter error distribution for Anholt, whereas Westermost Rough exhibited higher prediction uncertainties. Wind speed loss analysis revealed that turbines in the selected rows experienced variations, highlighting the impact of local turbulence on wind flow characteristics.

Hakka traditional vernacular dwellings embody regionally specific climatic adaptation strategies. This study takes the Meizhou Guangludi enclosed house as a case study to evaluate its climate adaptability with longevity and passive survivability factors of the Hakka three-hall enclosed house under subtropical climatic conditions. A mixed research method is employed, integrating visualized parametric modeling analysis and on-site measurement comparisons to quantify wind, temperature, solar radiation/illuminance, and humidity, along with human comfort zone limits and building environment. The results reveal that nature erosion in the Guangludi enclosed house is the most pronounced during winter and spring, particularly on exterior walls below 2.8 m. Key issues include bulging, spalling, molding, and fractured purlins caused by wind-driven rain, exacerbated by low wind speeds and limited solar exposure, especially at test spots like the E8–E10 and N1–N16 southeast and southern walls below 1.5 m. Fungal growth and plant intrusion are severe where surrounding trees and fengshui forests restrict wind flow and lighting. In terms of passive survivability, the Guangludi enclosed house has strong thermal insulation and buffering, aided by the Huatai mound; however, humidity and day illuminance deficiencies persist in the interstitial spaces between lateral rooms and the central hall. To address these issues, this study proposes strategies such as adding ventilation shafts and flexible partitions, optimizing patio dimensions and window-to-wall ratios, retaining the spatial layout and Fengshui pond to enhance wind airflow, and reinforcing the identified easily eroded spots with waterproofing, antimicrobial coatings, and extended eaves. Through parametric simulation and empirical validation, this study presents a climate-responsive retrofit framework that supports the sustainability and conservation of the subtropical Hakka enclosed house.

Sensitivity analysis of observation nudging in WRF model for wind flow simulation over complex terrain

Abstract On 10 August 2020, an intense mesoscale convective system (MCS) produced a derecho resulting in widespread high-wind damage from eastern Nebraska through northern Indiana with over 8200 damaged or destroyed houses and 11 billion USD in agricultural losses. Central Iowa, a predominantly agricultural area, was one of the hardest-hit areas due to smaller-scale rotational winds associated with mesovortices embedded within MCS-scale straight-line winds. This research aims to better characterize different types of wind flow and damage footprints of this derecho at multiple scales using multiple platforms and sensors [radar, satellite data, and uncrewed aerial systems (UASs) imagery]. Using vorticity detection algorithms and 88D radar data from Des Moines and Cedar Rapids, Iowa, mesovortices embedded within MCS-scale flow were identified and tracked. Satellite-derived products were used to capture the spatial extent of high-wind damage at the meso-β (20–200 km) scale, while high-resolution UAS data were used to distinguish types of high-wind impacts and characterize pockets of heavier damage down to the micro-β to micro-γ (submeter) scale. Analysis of UAS imagery revealed the presence of four tornadoes associated with radar-indicated mesovortex tracks and detailed wind impacts not captured in satellite imagery. One of these tornadoes is examined in detail. Even with improved spatial resolution of UAS imagery, characterizing high-wind damage impacts can be affected by land-cover type. By linking convective-scale radar signatures to detailed damage information, new knowledge of high-end derechos can be generated and leveraged for more specific warnings and to better inform damage assessments. Significance Statement This paper examines a “high end” derecho using multiple platforms and sensors (i.e., satellite, radar, and UAS), illustrating the relationships between storm-scale features and damage footprints at the appropriate spatial scales. This research demonstrates that damage has fine-scale structures down to 10-m scales. Damage patterns related to swirling winds on the 10-m scales are more likely in areas where larger-scale (km) flow is rich in Doppler vorticity.

Renewable energy sources are energy sources formed through continuous processes, making their availability abundant and inexhaustible. Examples of renewable energy sources include solar energy, biomass energy, wind energy, hydropower, geothermal energy, wave energy, and others. The purpose of this study is to evaluate the efficiency and the ideal design of a Vertical Axis Wind Turbine (VAWT) as a sustainable alternative energy source. In addition, energy storage technology through renewable energy–based battery charging systems has also become an important focus, such as battery charging systems using solar panels that can be adapted to wind turbines. With the increasing demand for energy and the negative consequences of fossil fuel usage, there is a growing interest in finding more environmentally friendly renewable energy sources. Tests conducted in two different regions showed varying results, which were influenced by the geographical location of each area. This turbine is designed using PETG (Polyethylene Terephthalate Glycol) material, which has high transparency and strong resistance to chemicals. Based on the test results, the experiment in Nganjuk generated 13.2 Wh of power, while in Surabaya it generated 9.9 Wh. The spiral design allows the turbine to capture wind from multiple directions and reduce wasted wind flow, thereby maximizing the turbine’s ability to rotate the generator rotor.

This review first examines how urban wind flow impacts the sustainability and resilience of cities and identifies the three main challenges in predictive modeling of urban flows: the complexity of the flow physics, the variability and uncertainty in the flow conditions, and the diversity and multiscale nature of urban geometries. To review the complexity of the flow physics, the typical flow patterns observed in canonical urban flows are summarized, and related modeling challenges and opportunities in both wind tunnel experiments and simulations are highlighted. Next, opportunities to predict realistic urban flows by addressing the other challenges are explored through the lens of a modeling framework with uncertainty quantification. The important role of field measurements, supporting the more accurate characterization of uncertainties in the flow conditions, as well as enabling validation with real-world data, is emphasized. The review concludes with two specific examples that demonstrate how integrated use of field measurements and computational models can improve the understanding and modeling of real urban flows to ultimately support sustainable development goals for urban areas.

Long-span cable-supported bridges, such as cable-stayed and suspension bridges, are highly sensitive to wind-induced effects due to their flexibility, low damping, and relatively light weight. Aerodynamic analysis is therefore essential in their design and safety assessment. This study examines the aeroelastic stability of the Oued Dib cable-stayed bridge in Mila, Algeria, with emphasis on vortex shedding, galloping, torsional divergence, and classical flutter. A finite element modal analysis was carried out on a three-dimensional model to identify natural frequencies and mode shapes. A two-dimensional deck section was then analyzed using Computational Fluid Dynamics (CFD) under a steady wind flow of U = 20 m/s and varying angles of attack (AoA) from −10° to +10°. The simulations employed a RANS k-ω SST turbulence model with a wall function of Y+ = 30. The results provided detailed airflow patterns around the deck and enabled the evaluation of static aerodynamic coefficients—drag (CD), lift (CL), and moment (CM)—as functions of AoA. Finally, the bridge’s aeroelastic performance was assessed against the four instabilities. The findings indicate that the Oued Dib Bridge remains stable under the design wind conditions, although fatigue due to vortex shedding requires further consideration.