Urban Wind Research Articles

Comparisons of experimentally and numerically determined statistics for predicting low-occurrence wind speeds around a 1:1:2 block model

Accurate prediction of low-occurrence wind speeds around urban structures is crucial for safe building design. Although Large-eddy simulation (LES) is commonly used as a high-fidelity model as compared with the Reynolds-Averaged Navier–Stokes (RANS) simulations, the present validation process relies on the comparison of fundamental statistics of the mean and standard deviations. The discrepancies in LESs and wind-tunnel experiments (WTEs) are unclear in terms of physical quantities characterizing the unsteadiness of the simulated turbulent flow such as probability density and power spectral densities, and low-occurrence winds speeds. Therefore, this study aims to evaluate the effectiveness of LES in predicting unsteady wind behavior around a 1:1:2 block model. The study identifies prominent differences to improve the accuracy of unsteady numerical simulations especially for the purpose of predicting low-occurrence wind speeds. Various advection schemes in LESs were investigated, including first-order upwind, second-order linear, and dynamic interpolation schemes. The results show significant discrepancies, particularly in higher-order statistics and low-occurrence wind speeds, with WTE consistently exhibiting higher energy levels across all frequencies. These findings highlight the need to refine advection schemes to enhance their predictive accuracy. LESs with minimal numerical errors from discretization schemes can substantially improve urban wind assessments and contribute to the design of safer structures.

Addressing VAWT Aerodynamic Challenges as the Key to Unlocking Their Potential in the Wind Energy Sector

While the wind turbine industry has been primarily dominated by horizontal-axis wind turbines, the forefront of knowledge of these turbines has revealed significant challenges in various aspects, including manufacturing, structural design, cost, and maintenance. On the other hand, the advantages associated with Darrieus vertical-axis wind turbines (VAWTs) demonstrate significant potential that can address the existing challenges of the wind turbine industry. Current work aims to investigate the practicality of this potential for the wind energy sector. To this end, the benefits of employing Darrieus turbines for domestic and industrial applications, isolated operation, and on/offshore windfarm applications have been explored. It is apparent that Darrieus VAWTs are better suited to a wide range of environments, whether they are deployed in isolation or integrated systems, and whether they are utilized on a small or large scale. Darrieus VAWTs are adaptable to urban unsteady variable wind, are less expensive on large scales, provide higher power density at the windfarm level, and provide stability for offshore platforms. Nevertheless, challenges remain in fully harnessing VAWT potential rooted in their complex aerodynamics. This serves as a primary challenge for VAWTs to address the challenges of the wind turbine industry in line with the 2050 roadmap.

Correction: Wu, K.-L.; Shan, L. Make Way for the Wind—Promoting Urban Wind Corridor Planning by Integrating RS, GIS, and CFD in Urban Planning and Design to Mitigate the Heat Island Effect. Atmosphere 2024, 15, 257

In the original publication [...]

Evaluating Reduced-Order Urban Wind Models for Simulating Flight Dynamics of Advanced Aerial Mobility Aircraft

Advanced Aerial Mobility (AAM) platforms are poised to begin high-density operations in urban areas nationwide. This new category of aviation platforms spans a broad range of sizes, from small package delivery drones to passenger-carrying vehicles. Unlike traditional aircraft, AAM vehicles operate within the urban boundary layer, where large structures, such as buildings, interrupt the flow. This study examines the response of a package delivery drone, a general aviation aircraft, and a passenger-carrying urban air mobility aircraft through an urban wind field generated using Large Eddy Simulations (LES). Since it is burdensome to simulate flight dynamics in real-time using the full-order solution, reduced-order wind models are created. Comparing trajectories for each aircraft platform using full-order or reduced-order solutions reveals little difference; reduced-order wind representations appear sufficient to replicate trajectories as long as the spatiotemporal wind field is represented. However, examining control usage statistics and time histories creates a stark difference between the wind fields, especially for the lower wing-loading package delivery drone where control saturation was encountered. The control saturation occurrences were inconsistent across the full-order and reduced-order winds, advising caution when using reduced-order models for lightly wing-loaded aircraft. The results presented demonstrate the effectiveness of using a simulation environment to evaluate reduced-order models by directly comparing their trajectories and control activity metrics with the full-order model. This evaluation provides designers valuable insights for making informed decisions for disturbance rejection systems. Additionally, the results indicate that using Reynolds-averaged Navier–Stokes (RANS) solutions to represent urban wind fields is inappropriate. It was observed that the mean wind field trajectories fall outside the 95% confidence intervals, a finding consistent with the authors’ previous research.

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

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

Extrapolating low-occurrence strong wind speeds at pedestrian levels using artificial neural networks trained by a single turbulent dataset

Predicting low-occurrence strong wind speeds in urban areas is crucial for enhancing the comfort and ensuring the safety of residents. Artificial neural network (ANN) models outperform traditional statistical methods in accurately estimating low-occurrence wind distributions. However, generalizing trained models to be applicable to various datasets remains a significant challenge. Therefore, three ANN models with different input variables, trained by a single turbulent dataset of a building array case, were applied to four types of datasets to evaluate the models’ generalization ability for predicting low-occurrence strong wind speeds in urban areas. It found that the ANN model, which uses the mean, standard deviation, and skewness of the turbulent flow, shows the best generalization ability among the three ANN models for all types of datasets. In addition, for the four datasets, the results calculated by this model show that the majority of points have an error of less than 20% compared to the true values. It is advisable to construct models using more extensive training data that encompasses a broad range of mean and skewness of wind speeds. This study contributes to the development of more effective and adaptable ANN models for urban wind safety in building engineering.

Analysis of urban wind conditions and wildfire smoke dispersion for downtown Montréal using computational fluid dynamics

Urban wind conditions and air quality have the potential to affect the majority of the world's population. Specifically, smoke from wildfires is increasingly posing risks to people living in cities as it is transported by the wind, making a more complete understanding of how atmospheric flow affects air quality in urban environments necessary. Computational fluid dynamics is used to investigate the flow and smoke dispersion characteristics in Montréal on July 17, 2023, between 16:00 and 22:00 UTC. This day exhibited moderate southwest winds carrying significant amounts of wildfire smoke into the city. Reynolds-averaged Navier-Stokes simulations using the standard k-ε and Shear Stress Transport k-ω turbulence models are compared against measurements of wind velocity and PM2.5 concentration from an anemometer, a Doppler lidar, and an air quality monitoring station. While the models are shown to accurately predict the urban boundary layer wind profile, only the k-ε model provides satisfactory predictions of wind speed in comparison with the anemometer, stressing the importance of accurately modeling dynamics on the building scale. The effect of the turbulent Schmidt number is investigated, for which the value of 0.6 most accurately reproduces the dispersion phenomena near the air quality monitoring station. Concentrations of the wildfire smoke are found to vary significantly across areas of the city, as some building morphologies are found to direct pollution to regions where it becomes trapped. Additional discussion of local wind and air quality characteristics is presented to better inform citizens of potential risks.

Maintenance strategy for urban micro wind farm considering maintenance route and resource allocation

Emerging urban wind farms (UMWF) are becoming increasingly prevalent, and optimizing maintenance strategy for UMWF has become crucial for reducing costs and improving wind power efficiency. This research formulates an optimization problem of UMWF by considering the maintenance route and resources. To solve this problem, firstly, the Efficient Maintenance Value (EMF) is proposed as an objective function to measure the maintenance value, based on which a novel Random Variable Neighborhood Descent and Cuckoo Search-based Hybrid Discretized Artificial Fish Swarm Algorithm (RVNDCS-HDAFSA) is proposed to search the optimal maintenance strategy. Experiments are conducted to verify the local search capabilities of the hybrid algorithm, and extensive comparison experiments with the other algorithms are conducted at different scales to validate the effectiveness of the RVNDCS-HDAFSA algorithm; the result of the comparison experiments and the ANOVA shows that the HDAFSA demonstrates a superior capability in solving the medium-scale and large-scale problems compared to the existing algorithm. In conclusion, the practical application experiments validate the effectiveness of our proposed approach.

Application of an Empirical Model to Improve Maximum Value Predictions in CFD-RANS: Insights from Four Scientific Domains

This study introduces an empirical model designed to predict the maximum values of time-dependent data across four turbulence-related fields: hydrogen combustion in renewable energy systems, urban microclimate effects on cultural heritage, shipping emissions, and road vehicle emissions. The model, which is based on the mean, standard deviation, and integral time scale, employs two parameters: a fixed exponent ‘ν’ (0.3) reflecting time scale sensitivity, and a variable parameter ‘b’ that accounts for application-specific uncertainties. Integrated into the Computational Fluid Dynamics (CFD) framework, specifically the Reynolds-Averaged Navier–Stokes (RANS) methodology, the model addresses the RANS approach’s limitation in predicting extreme values due to its inherent averaging process. By incorporating the empirical model, this study enhances RANS simulations’ ability to predict critical values, such as peak hydrogen concentrations and maximum urban wind speeds, which is essential for safety and reliability assessments. Validation against experimental and numerical data across the four fields demonstrates strong agreement, highlighting the model’s potential to improve CFD-RANS predictions of extreme events. This advancement offers significant implications for future CFD-RANS applications, particularly in scenarios demanding fast and reliable maximum value predictions.

Assessing urban air pollution dynamics: the impact of traffic emissions and urban morphology in Lecce and Bari, Italy

Urban air pollution, exacerbated by vehicular emissions, poses a significant health threat, especially in densely populated areas. This study, part of the PNRR Italian National Center for Sustainable Mobility (MOST), aims to assess the impact of vehicular pollutants in urban environments. Focusing on Lecce and Bari (Italy), the research integrates data from dispersion models and meteorological analyses to evaluate pollutant concentrations. Preliminary analysis of eight urban areas in Lecce and six in Bari reveals trends in PM10 concentrations influenced by urban morphology and wind dynamics. These findings underscore the essential role of sustainable mobility strategies in mitigating urban air pollution and promoting healthier urban environments.

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

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

Learning Local Urban Wind Flow Fields From Range Sensing

Learning Local Urban Wind Flow Fields From Range Sensing

Analytical Review of Wind Assessment Tools for Urban Wind Turbine Applications

Analytical Review of Wind Assessment Tools for Urban Wind Turbine Applications

Identification of Urban Ventilation Corridor System Using Meteorology and GIS Technology: A Case Study in Zhengzhou, China

Urban ventilation corridors are designed to enhance air quality, alleviate urban thermal conditions, reduce pollution and energy consumption, as well as improve human comfort within cities. They play a pivotal role in mitigating environmental impacts, particularly in densely populated urban areas. Based on satellite remote sensing data, meteorological observations, basic geographic information of Zhengzhou City and its surroundings, and urban planning data, we analyzed the urban wind environment, urban heat island, ecological cold sources, and ventilation potential. The findings reveal several key insights: (1) Dominant winds in Zhengzhou City predominantly originate from the northwest, northeast, and south, influenced by topography and the monsoon climate, with seasonal variations. These wind patterns are crucial considerations for designing primary ventilation corridors. (2) The urban heat island exhibits a polycentric spatial distribution, with intensity decreasing from the city center towards the periphery. Ecological cold sources, primarily situated in the city outskirts, act as reservoirs of fresh air that mitigate the urban heat island effect through designated corridors. (3) A preliminary corridor system, termed “eight primary and thirteen secondary corridors”, is proposed for Zhengzhou City based on an integrated assessment of ventilation potential, urban surface roughness, and sky view factor. This research contributes to advancing the understanding of urban ventilation systems and provides practical insights for policymakers, urban planners, and researchers seeking sustainable solutions to mitigate climate impacts in rapidly urbanizing environments in the region.

Development of a morphology-based wind speed model in the urban roughness sub-layer

Understanding near-surface wind variations in urban environments is essential for a wide range of applications, including urban weather forecasting and wind impact assessment. In this study, we introduced a novel model for mean wind speed profiles within the urban roughness sublayer based on theoretical analysis. To calibrate the model's coefficients, we conducted large-eddy simulations of airflow over a variety of idealized urban configurations. The resulting expression effectively captures wind speed variations across different urban morphologies. Subsequently, a regression modeling was employed to identify the relationships between building morphological parameters and these coefficients. This highlights the pivotal role of using building morphology to predict near-surface velocity profiles. The proposed model also yields significantly more accurate wind speed predictions within the urban canopy layer than traditional exponential profiles. The findings in the present study lay a more robust foundation for assessing urban wind conditions and improving urban-scale weather forecasts.

A Study on the Effect of Turbulence Intensity on Dual Vertical-Axis Wind Turbine Aerodynamic Performance

Examining dual vertical-axis wind turbines (VAWTs) across various turbulence scenarios is crucial for advancing the efficiency of urban energy generation and promoting sustainable development. This study introduces a novel approach by employing two-dimensional numerical analysis through computational fluid dynamics (CFD) software to investigate the performance of VAWTs under varying turbulence intensity conditions, a topic that has been relatively unexplored in existing research. The analysis focuses on the self-starting capabilities and the effective utilization of wind energy, which are key factors in urban wind turbine deployment. The results reveal that while the impact of increased turbulence intensity on the self-starting performance of VAWTs is modest, there is a significant improvement in wind energy utilization within a specific turbulence range, leading to an average power increase of 1.41%. This phenomenon is attributed to the more complex flow field induced by heightened turbulence intensity, which delays the onset of dynamic stall through non-uniform aerodynamic excitation of the blade boundary layer. Additionally, the inherent interaction among VAWTs contributes to enhanced turbine output power. However, this study also highlights the trade-off between increased power and the potential for significant fatigue issues in the turbine rotor. These findings provide new insights into the optimal deployment of VAWTs in urban environments, offering practical recommendations for maximizing energy efficiency while mitigating fatigue-related risks.

Evaluating heat health risk in Indian cities: Geospatial and socio-ecological analysis

India is home to 11 % of the global urban population and is ranks as the second-largest urban system in the world. This study introduces a Heat Health Risk Index (HHRI) rankings for 37 major Indian cities with more than one million residents, using geospatial and socio-ecological data to identify potential heat health risk areas. In this study, the Otsu method was employed to determine the critical parameters in the heat health index, considering factors such as Land Surface Temperature (LST), solar radiation, population density, mean temperature, urban green cover, rainfall, specific humidity, and wind speed. All data values were standardized to a uniform scale (0–1) for comparability. The standardized values, integrated with the assigned weights, formed the HHRI. Results indicate that cities such as Chennai, Mumbai, Kolkata, and Ahmedabad, each with populations exceeding 10 million, are deemed less livable due to their high HHRI (>0.50). Both Chennai and Mumbai stand out with highest hazard index as 0.66, followed by Kolkata (0.62) and Ahmedabad (0.56). Cities that lack sufficient green spaces are often more vulnerable, display elevated risk levels, and have decreased adaptability. In contrast, cities such as Ludhiana, Theni, Amritsar, and Nabarangpur are perceived as the most livable, with a mean HHRI of 0.21, owing to their higher adaptive capacity and lower exposure. Overall, this study serves as a foundation for conceiving future perspective plans for existing urban and peri‑urban areas, compared to living standards within the realms of sustainability.

Equilibrium atmospheric boundary layer model for numerical simulation of urban wind environment

The numerical simulation of urban wind environments faces difficulties in capturing the turbulent characteristics due to the large computational domain. Traditional Reynolds-averaged methods (RANS) can effectively capture the average wind characteristics of urban areas. However, due to the significant dissipation and attenuation of turbulent energy in the downstream direction, this method fails to provide accurate turbulent characteristics after time-averaging processing. Therefore, in order to obtain a higher-precision turbulent wind field distribution within urban areas, this paper proposed a new numerical method named an equilibrium atmospheric boundary layer model (EABL) by modifying the control equation of the shear stress transport k–ω model. During the process, the equilibrium atmospheric boundary layer was achieved successfully, and the attenuation problem of the turbulent kinetic energy and dissipation rate during the computational fluid dynamics numerical simulation was resolved. Simultaneously, a wind tunnel experiment and six turbulence models [standard k–ε, realizable k–ε, renormalization group k–ε, large eddy simulation—narrowband synthesis random flow generator (LES-NSRFG) and LES vortex method and EABL] were employed to simulate the wind field characteristics in an actual residential area. The simulation results demonstrate that, relative to traditional RANS models, the EABL model enhances the simulation accuracy of turbulence characteristics by over two times. Furthermore, compared to LES models, the EABL model can reduce computational time by threefold.

Urban agriculture as ecosystem services provider: A review

Background: Urban agriculture’s role in mitigating the urban environment deterioration as ecosystem services provider. Urban agriculture adds various functions to different landscape scales, providing numerous benefits if managed correctly. This paper aims to study the role of urban agriculture as ecosystem services provider in mitigating urban environment deterioration. Method: The method that will be used in this article is literature review from articles related to urban agriculture and ecosystem services from provisioning, regulating, services, and cultural. The review is compiled from urban agriculture’s function as ecosystem services provider, such as biodiversity conservation, microclimate regulation, aesthetic function, and food production. Result: In essence, urban agriculture is a diverse and vital solution for cities, and it takes various forms, from private gardens to community plots, addressing challenges like limited public funding and promoting greening initiatives. Urban agriculture plays a key role in biodiversity conservation, contributing to enhanced environmental benefits and the preservation of local habitats. Another crucial role in regulating the complex dynamics of the urban microclimate, offering ecological benefits such as reduced Urban Heat Island effects, wind protection, and pollutant absorption. Beyond its ecological impact, urban agriculture adds aesthetic value to urban spaces, creating visually appealing landscapes and promoting cultural integration. Moreover, it serves as a multifaceted strategy for sustainable urban development, addressing food security, resilience, and well-being. Conclusion: Amidst challenges, such as the threat to urban biodiversity and the need for proper green space management, urban agriculture emerges as a holistic approach, contributing to the visual, cultural, and environmental fabric of cities. Novelty/Originality of this article: This study offers a new perspective on urban agriculture as a multifunctional solution to address urban environmental degradation. By integrating agriculture into the urban landscape, the study reveals the transformative potential for creating greener, more resilient and sustainable cities.

Projected Thermally Driven Elderly Mortality for Beijing Under Greenhouse Gas and Stratospheric Aerosol Geoengineering Scenarios

AbstractBeijing is undergoing multiple challenges including urbanization, warming and aging. The Beijing megalopolis of 20 million people now suffers more cold‐related than heat‐related deaths. Stratospheric aerosol injection (SAI) geoengineering is designed to lower surface temperatures, so if SAI were ever done, it may reduce future heat‐related mortality, while also increasing cold‐related mortality. Here we use four Earth System Models (ESM) downscaled to 10 km resolution with the Weather Research and Forecasting (WRF) system to capture urban temperature, humidity and wind speeds. Temperature‐related mortality risk were calculated using a distributed lag nonlinear model (DLNM) of the elderly (over 65s) under the dynamically downscaled moderate (RCP4.5) and extreme (RCP8.5) greenhouse gas, and the G4 SAI scenarios. We used population demographics for all five shared socioeconomic pathways (SSP) and various adaptation measures. Heat‐related excess deaths under G4 are 630∼3,160 per year fewer than RCP4.5, while cold‐related deaths are 370∼1,990 more than RCP4.5 during 2060–2069, with a marginally significant net reduction. G4 significantly reduces the excess deaths relative to RCP8.5. Both heat‐related and cold‐related mortality will increase by 240∼490% when the aging population is accounted for, and decrease by 11%, 23% and 44% under low, medium and high adaptation relative to a no adaptation scenario. Dynamical downscaling produces better quality climate simulations than commonly used statistical approaches, and in the case of Beijing, significantly fewer heat‐related deaths. The marginal health benefits of modest future SAI in Beijing may be representative of the population impacts in the extra‐tropics where deaths due to cold are more than those caused by heat.