Urban Wind Flow Research Articles

Learning Local Urban Wind Flow Fields From Range Sensing

Learning Local Urban Wind Flow Fields From Range Sensing

Review of Wind Flow Modelling in Urban Environments to Support the Development of Urban Air Mobility

Urban air mobility (UAM) is a transformative mode of air transportation system technology that is targeted to carry passengers and goods in and around urban areas using electric vertical take-off and landing (eVTOL) aircraft. UAM operations are intended to be conducted in low altitudes where microscale turbulent wind flow conditions are prevalent. This introduces flight testing, certification, and operational complexities. To tackle these issues, the UAM industry, aviation authorities, and research communities across the world have provided prescriptive ways, such as the implementation of dynamic weather corridors for safe operation, classification of atmospheric disturbance levels for certification, etc., within the proposed concepts of operation (ConOps), certification standards, and guidelines. However, a notable hindrance to the efficacy of these solutions lies in the scarcity of operational UAM and observational wind data in urban environments. One way to address this deficiency in data is via microscale wind modelling, which has been long established in the context of studying atmospheric dynamics, weather forecasting, turbine blade load estimation, etc. Thus, this paper aims to provide a critical literature review of a variety of wind flow estimation and forecasting techniques that can be and have been utilized by the UAM community. Furthermore, a compare-and-contrast study of the commonly used wind flow models employed within the wind engineering and atmospheric science domain is furnished along with an overview of the urban wind flow conditions.

Urban wind field prediction based on sparse sensors and physics‐informed graph‐assisted auto‐encoder

AbstractThe urban flow wind field is a critical element for downstream research, such as mitigation of urban wind disasters, assessment of urban wind environment, and urban drone route planning. However, it is impractical to arrange a large number of sensors to monitor an urban wind flow field. Hence, acquiring the entire urban wind flow field via sparse sensors would be highly valuable. To date, no scheme including deep learning (DL) model has been specifically designed for this purpose. This study presents an innovative approach to reconstruct complex high‐resolution urban wind fields based on sparse sensors, using a physics‐informed graph neural network (GNN)‐assisted auto‐encoder. The proposed method leverages the relationship between sensors and their surrounding environment enabled by deep mining capabilities of GNNs. As a result, the utilization and emphasis on sparse sensors data are significantly enhanced. The continuity equation of fluid flow is incorporated into the loss function of the convolution neural network to improve the stability and performance of the model. The findings suggest that, in contrast to prevalent generative DL models, the proposed model yields an approximate 50% reduction in root mean square error for reconstructing high‐resolution urban wind fields for multiple wind attack angles.

Improving the urban wind flow prediction efficiency of target area by considering its surrounding buildings impact

Rapid urbanization has caused various urban environmental challenges, including the emergence of urban heat island and air pollution. Urban wind flow is a crucial factor shaping the urban environment, which needs effective prediction and design. Computational fluid dynamics is commonly used for prediction and design of urban wind environment. However, high-accuracy simulation of wind flow, especially considering the layouts of full-scale and complex buildings, requires intensive computational resources. Typically, simulations simplify the surrounding building layouts (e.g., building layers) of target areas, potentially impacting simulation accuracy. It is imperative to determine appropriate building layers around different-scale target areas for effective wind flow simulation. This work explored the simulated wind flow characteristics of multi-scale (local, micro, and building-scale) target areas with different building layers defined as H, L, H/L, and L/H (H and L representing the vertical and horizontal maximum sizes of target area). The recommended building layers for local-scale, micro-scale, and building-scale target areas were at least 3L, 4H, and 5H, respectively, ensuing the relative errors between simulated and measured wind velocities remained below 20 %. This method largely reduced the calculation time by 73 % compared with full-scale simulation. This work can provide guidelines for fast prediction and design of urban wind environment, further promoting the sustainable development of urban cities.

A GAN-Based Surrogate Model for Instantaneous Urban Wind Flow Prediction

Urban form impacts the airflow patterns in cities and the resulting urban microclimate. This has significant implications for ventilation, overheating, wind chill, and safety concerns such as down drafts from skyscrapers. While Computational Fluid Dynamics (CFD) simulations are the best practice for analyzing urban airflow patterns in design, they are computationally expensive and require a high level of expertise, making them underutilized in the early design process. This paper presents a surrogate model for CFD using a Generative Adversarial Network (GAN) that can process arbitrary building geometries. The model is trained using an automated end-to-end pipeline based on Eddy3D and implemented within the Rhino and Grasshopper environment as an Open Neural Network Exchange (ONNX)-based CFD-GAN predictor. This workflow provides instantaneous simulation feedback within the design software, reduces the risk of user error, and allows for appropriate spatial resolution in early design. The CFD-GAN demonstrates promising accuracy, with a Structural Similarity Index Measure (SSIM) ranging from 75%–97% on a limited training dataset of 564 unique urban geometries. Although the model currently has limitations regarding accuracy in complex urban wake regions, we show that these are likely not of concern for outdoor thermal comfort analyses. While it cannot replace CFD in later design stages, the CFD-GAN facilitates the incorporation of urban airflow analysis in early design with minimal effort and instantaneous performance feedback.

An enhanced k-[formula omitted] SST model to predict airflows around isolated and urban buildings

The goal of this research is to improve and validate a Reynolds Averaged Navier–Stokes (RANS) turbulence model to perform accurate Computational Fluid Dynamics (CFD) simulations of the urban wind flow. The k-ω SST model is selected for calibration since its blended formulation holds remarkable optimization potential and has increased relevancy in recent studies in the field. A simulation-based optimization approach recalibrates the model closure constants by minimizing the prediction error of wind pressure coefficients on an isolated cubical building because this scenario contains many salient features observed in the flow in actual urban areas. The optimization procedure ensures both the coherence of calibrated model constants involved in the wall function formulations and the relationship between them to satisfy the flow horizontal homogeneity of the atmospheric boundary layer. The tuned closure coefficients increase momentum diffusion in the wake, resulting in shorter and more accurate predictions of the reattachment lengths. Validation case studies with wind tunnel measurement data from various urban scenarios were addressed to comprehensively assess the adaptability of the optimal set of coefficients reached. The results confirm that CFD predictions with the optimized model are consistently in closer agreement with experimental data than the standard version of k-ω SST. The root mean square errors are reduced by about 75% in pressure, 40% in velocity, and 20% in turbulent kinetic energy.

Reconstructing urban wind flows for urban air mobility using reduced-order data assimilation

Advancements in uncrewed aircrafts and communications technologies have led to a wave of interest and investment in unmanned aircraft systems (UASs) and urban air mobility (UAM) vehicles over the past decade. To support this emerging aviation application, concepts for UAS/UAM traffic management (UTM) systems have been explored. Accurately characterizing and predicting the microscale weather conditions, winds in particular, will be critical to safe and efficient operations of the small UASs/UAM aircrafts within the UTM. This study implements a reduced order data assimilation approach to reduce discrepancies between the predicted urban wind speed with computational fluid dynamics (CFD) Reynolds-averaged Navier Stokes (RANS) model with real-world, limited and sparse observations. The developed data assimilation system is UrbanDA. These observations are simulated using a large eddy simulation (LES). The data assimilation approach is based on the time-independent variational framework and uses space reduction to reduce the memory cost of the process. This approach leads to error reduction throughout the simulated domain and the reconstructed field is different than the initial guess by ingesting wind speeds at sensor locations and hence taking into account flow unsteadiness in a time when only the mean flow quantities are resolved. Different locations where wind sensors can be installed are discussed in terms of their impact on the resulting wind field. It is shown that near-wall locations, near turbulence generation areas with high wind speeds have the highest impact. Approximating the model error with its principal mode provides a better agreement with the truth and the hazardous areas for UAS navigation increases by more than 10% as wind hazards resulting from buildings wakes are better simulated through this process.

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.

Reconstructing Urban Wind Flows for Urban Air Mobility Using Reduced Order Data Assimilation

Reconstructing Urban Wind Flows for Urban Air Mobility Using Reduced Order Data Assimilation

Multi-Scale Numerical Assessments of Urban Wind Resource Using Coupled WRF-BEP and RANS Simulation: A Case Study

Urban wind resource assessments (WRAs) contribute to the effective exploitation of wind energy and thus are of significant importance to the sustainable development of cities. To improve the simulation accuracy of urban wind flow with high spatial resolution, this study implemented a multi-scale numerical assessment of the wind power potential in a highly-urbanized region with realistic terrain conditions by integrating the Reynolds-averaged Navier-Stokes (RANS) equations into the Weather Research and Forecasting (WRF) model with Building Effect Parameterization (WRF-BEP). The sensitivity analyses are first conducted to obtain an appropriate combination of physical parameterization schemes in the WRF-BEP model. Then, the wind tunnel tests are performed to validate the computational accuracy of urban wind flow using the RANS equations. Based on a close examination of the urban wind flow resulting from the coupled WRF-BEP and RANS simulations, the integration of micro-wind turbines into the building skin is not recommended in the highly-urbanized region. Furthermore, five optimum roof installation locations with low turbulence intensities (smaller than 18%) and high wind power densities (approximately 220 W/m2, 260 W/m2, 270 W/m2, 300 W/m2 and 400 W/m2, respectively) are identified. Finally, the important effects of the terrain conditions, planetary boundary layer (PBL) parameterization schemes and turbulence models on WRAs are discussed. The results of WRAs in this multi-scale numerical case study presented a systemic approach to effectively determine the installation locations of micro-wind turbines that possess the greatest potential to harness wind energy in a realistic highly-urbanized area.

Interaction Between High-Rise Building, Wind and Pollution in District Twenty-Two of Tehran Municipality, Iran

<p>The large cities are facing with a phenomenon called high-rise buildings which is due to the increasing of the population and also developing the cities. One of the negative impacts of high-rise buildings is the change in urban wind flow. Air pollutions is a major problem in the cities and the wind play an important role in the dispersion of air pollutions. The most probable wind direction for Tehran is west and hence district twenty-two of Tehran municipality which is located in northwest of Tehran (Capital of Iran) is in the dominant direction of wind flow. This paper presents a numerical method to investigate the interaction between the wind flow, pollution and high-rise buildings in district twenty-two of Tehran municipality. The effect of these phenomenon on the other regions is considered as well. Computational fluid dynamics (CFD) by ANSYS Fluent software has been used in order to simulate the problem. The results of this study indicate the existing buildings and their layout had no effect on wind velocity and trapping the pollution and do not cause an increasing of the local concentration of pollutants. So the geometry and layout of buildings allow the flows and environmental pollutants to pass.</p>

CityFFD – City fast fluid dynamics for urban microclimate simulations on graphics processing units

Urban microclimate analysis is often associated with computational fluid dynamics simulations of urban aerodynamics and thermal conditions, air quality, heat island, humidity, and rain at a building level. Conventional microclimate analyses are often limited by the size of the simulation domain, turbulence models, and sparse digital urban data available. This study introduces CityFFD with its supporting web portal of digital cities for large-scale urban simulation problems. A few novel algorithms, including backward and forward sweep interpolation schemes, time-adaptive technique, and the 2nd-order temporal scheme, were implemented to enhance the computing speed and accuracy of the model. Parallel computing on graphics processing units (GPU) speeds up the simulation. CityFFD is equipped with a large eddy simulation method to capture the turbulence behavior in the urban roughness sublayer. This paper reports the structure of CityFFD, and a group of benchmark cases, including airflow around buildings, natural ventilation, and thermal stratifications around building blocks in an actual urban area, and CityFFD is shown to predict acceptable results of the urban scale wind speeds, temperatures, and flow patterns. This study demonstrates a new approach to modeling urban microclimate by CFD simulations on GPUs.

Large eddy simulation of the wind flow in a realistic full-scale urban community with a scalable parallel algorithm

Turbulence and large computational domains make urban wind flow simulation a computationally challenging problem. To reduce the total simulation time and efficiently use today's new generation of supercomputers with massive processors, scalable parallel solvers are needed. In this work, we present a scalable domain decomposition method based 3D incompressible Navier-Stokes solver for the simulation of unsteady, complex wind flows around urban communities. A large eddy simulation with Smagorinsky modeling is employed for the simulation of the turbulent wind flows. On a fine unstructured grid, we discretize the spatial and temporal dimensions using a stabilized P1−P1 finite element method and an implicit backward Euler finite difference method, respectively. The resulting large-scale nonlinear algebraic system at each timestep is solved by a Newton-Krylov-Schwarz algorithm in parallel. The solver is first validated by a benchmark case where the numerical results are compared with measured data from a wind tunnel. Then, the wind flow field of a realistic actual-scale urban community with a group of buildings in the downtown area of Shenzhen, China, is studied. The numerical results show that the flow field matches with the experimental data for the benchmark problem, and some reasonable, detailed, and complex flow structures are obtained for the urban community simulation case. The scalability of the solver is studied on the TianHe-2A and Sunway TaihuLight supercomputers, and the solver scales up to 16,384 processor cores for a grid with over 20 million elements, which implies that the solver has the potential to perform fast and high-fidelity simulations of large-scale wind flows in complicated computational domains.

A non-intrusive parametric reduced order model for urban wind flow using deep learning and Grassmann manifold.

In this study, we present a parametric non-intrusive reduced order modeling framework as a potential digital twin enabler for fluid flow related applications. The case study considered here involves building-induced flows and turbulence with inlet turbulence value as a parameter. The framework proposed employs a Grassmann manifold interpolation approach (GI) for obtaining basis functions corresponding to new values of parameter, and exploits the time series prediction capability of long short-term memory (LSTM) recurrent neural network for obtaining temporal coefficients associated with the new basis functions. The methodology works in the following way: (i) in the training phase, the LSTM model is trained on the modal coefficients extracted from the high-resolution data using proper orthogonal decomposition (POD) transform for the known values of parameter, and (ii) in the testing phase, the trained model predicts the modal coefficients for the total time recursively based on the initial time history for the new value of parameter. Then, we reconstruct the flow fields for the new value of parameter (new inlet turbulent value) using the GI modulated basis functions and LSTM predicted associated temporal coefficients. To assess the performance of the proposed model, the ROM-LG predictions are compared with the high-dimensional full-order model solutions using L1 and L2 error analyses as well as with the conventional POD based ROM (ROM-POD) solutions. The results indicate that the non-intrusive ROM (ROM-LG) framework yields a stable solution for the velocity fields and for short-term prediction of dynamic turbulent kinetic energy fields. This work has scope for further development and will be useful for building-integrated wind energy and urban drone operation in a smart-city digital twin platform.

Physics-based stitching of multi-FOV PIV measurements for urban wind fields

Particle image velocimetry (PIV) is commonly used to measure multiple field-of-views (FOVs) of the flow in large urban domains, performed in wind tunnels using an array of cameras located at fixed positions or a single camera setup that traverses during the measurements. With respect to the large amount of data produced in this type of measurement, traditional stitching approaches are no longer adequate to efficiently merge the image data. A physics-based flow field stitching method is proposed for stitching flows in urban wind fields, named regional-flow stitching. It matches the flow characteristics within neighboring FOVs of flow using a vorticity-based reference window. The method is based on finding the optimal similarity between a reference window in one FOV and another possible matching window in the other FOV. The matching window rolls in both directions at the interval of two adjacent data points, which determines the stitching resolution. The matching in flow similarity is evaluated using the normalized root-mean-squared error (NRMSE) between reference and possible matching windows, which is evaluated by comparison to a traditional stitching method and further verified using planar and stereo wind tunnel PIV measurement results. A comparison of computational cost with 8 FOVs and approximate 7% surface overlap between FOVs shows that our method is faster, taking about 46% of processing time, than a traditional stitching method. 3D visualization of the merged FOVs is further demonstrated with ParaView. The proposed stitching method may serve as a powerful tool for experimental studies involving large domains of urban wind flow fields.

Improving accuracy in simulation of urban wind flows by dynamic downscaling WRF with OpenFOAM

Managing urban air systems is now on top priority. The local wind flow at the building scale is governed by the urban morphology, which is difficult to be captured by the parameterizations of the regional scale weather models. In this study, we dynamically downscaled the 1 km resolution wind flow simulated by the WRF-UCM (Weather Research and Forecasting with Urban Canopy Model) to the building scale (<10 m), by integrating it with the OpenFOAM (Open Field Operation and Manipulation, OF) computational fluid dynamics model (WRF-UCM + OF). The Reynolds Averaged Navier Stokes k-ε turbulence model has been used with a steady-state solver in the OF. Momentum sink due to vegetation has been accounted for by Darcy-Forchheimer model. The model simulated hourly 10-m wind speed and direction in a 1 km2 area were evaluated with the measured values during January 11–18, 2018 over Nagpur City, India. The results show that WRF-UCM + OF has captured the wind direction more accurately than the WRF-UCM. The WRF-UCM + OF simulated wind speed has significantly low normalized mean bias (NMB = −0.002) and low normalized mean gross error (NMGE = 0.588) when compared with that of the WRF-UCM model (NMB = 1.083 and NMGE = 1.216).

CFD-Based Wind Assessment for Suburban Buildings. The Case Study of Aarhus University, Herning Campus

The development in the field of wind turbines is continuously growing. However, most of the times, the energy of wind is exploited by using huge wind-power plants, while wind energy installations in urban and suburban areas, as space and available land, are a lot more limited, is something relatively new, with great potential though. The major challenge regarding small urban wind turbines can be summarized as a lack of understanding of the wind resource in the built environment, with a combination of missing measurements in this field that needed to be addressed. Aarhus University Campus Herning, located in the suburban area of Herning, is looking for a suitable place to install one small wind turbine (SWT). This study attempts to provide a comprehensive framework for SWTs and the characteristics of urban wind flow. The analysis helped in identifying the two most appropriate sites for SWTs installation sites in the AU Herning building.

A framework for pedestrian-level wind conditions improvement in urban areas: CFD simulation and optimization

Given the complexity of urban wind flow, acceptable wind conditions can hardly be achieved in all regions within an urban area. In some regions, high wind speeds can lead to wind discomfort and danger, while some regions suffer from poor outdoor urban ventilation. In order to evaluate and improve urban wind conditions, the combined impacts of wind flow and morphological and aerodynamic characteristics of the urban area should be taken into account. In this perspective, performing extensive and time-consuming parametric analyses is required – which is very difficult – even impossible. This paper, therefore, presents an evolutionary-based framework to optimize building heights and plan area densities to improve wind conditions in complex real urban areas. The framework consists of four steps: (i) urban area identification, (ii) optimization algorithms, (iii) CFD simulations, and (iv) evaluation. The optimization consists of Genetic Algorithm and Particle Swarm Optimization. The framework is applied to a real urban area in the city of Tehran, Iran. The focus is on minimizing low-wind-speed regions. In total, 920 possible combinations of building height variations and plan area densities are assessed. 3D steady Reynolds-averaged Navier-Stokes (RANS) simulations are performed based on a detailed validation study and grid-sensitivity analysis. The results show that the use of this framework can effectively reduce computational time and significantly improve the wind conditions in the entire urban area. Urban planners, architects, and building engineers can benefit from this framework in the early stages of design for better wind conditions in complex real urban areas.

Three-Dimensional Wind Field Construction and Wind Turbine Siting in an Urban Environment

Three-dimensional urban wind field construction plays an important role not only in the analysis of pedestrian levels of comfort but also in the effectiveness of harnessing wind energy in an urban environment. However, it is challenging to accurately simulate urban wind flow due to the complex land use in urban environments. In this study, a three-dimensional numerical model was developed for urban wind flow construction. To obtain an accurate urban wind field, various turbulence models, including the Reynolds stress model (RSM), k-ω shear stress transport (SST), realizable k-ε, and (Re-Normalisation Group (RNG) k-ε models were tested. Simulation results were compared with experimental data in the literature. The RSM model showed promising potential in simulating urban wind flow. The model was then adopted to simulate urban wind flow for Purdue University Northwest, which is located in the Northwest Indiana urban region. Based on the simulation results, the optimal location was identified for urban wind turbine siting.

Environmental impact analysis of high-rise buildings for resilient urban development

In recent years, the construction of high-rise buildings as an urban development strategy has been accepted in many megacities. High-rise buildings have positive, as well as negative impacts on urban environments. Therefore, the environmental impact assessment of high-rise buildings for establishing strategies for sustainable and resilient urban development is essential. In this study, the environmental impact of high-rise buildings with a resilient development mindset is assessed. Resilience mindset provides an approach for including the uncertainties and interdependence of systems and processes for planning new sustainable developments and assessment methods. The corresponding environmental impact assessment is done by monitoring the structural changes and their impacts on the function of ecosystem and environmental services. Here, the positive and negative impacts of high-rise buildings are evaluated. Protection of impervious surfaces as a positive and changing the natural pattern of urban wind flow as a negative impact is considered. The transparency of the results and the reduction of uncertainty are the advantages of using the resilience mindset in environmental impact assessment. The results of this study suggest that the resilient development mindset can improve the environmental assessment through the adoption of the appropriate indicators at multiple scales and differentiation between the primary and secondary effects.