1. Introduction
A smart city may well benefit from the large amount of data collected within its boundaries. Valuable information can be extracted via analytical processes and utilized for achieving sustainability goals. Visualization of data can assist in the analysis process and give better meaningful insights. Visualization has emerged as a new research discipline during the last two decades [
1] with Visual Analytics being the field of visualization that can provide instant and interactive knowledge to individuals by empowering a flexible control of workflow and information. Three-dimensional visualization is fundamental to establishing a modern information system in an urban space. Urban 3D visualizations of geo-spatial data consist of above surface entities, either physical or artificial, including as basis a Digital Terrain Model (DTM) or the surface of the reference ellipsoid. A typical example is the Digital Twins concept. A 3D model is relayed with data acquired from the physical environment, and a simulation model studies the behavior and the performance of the system. Numerical simulation methods, such as Computational Fluid Dynamics (CFD), have been a successful tool in urban physics research [
2]. The 3D spatial representation of a city is widely defined in the research community and geospatial industry by CityGML. CityGML, an Open Geospatial Consortium standard, is based on the Geography Markup Language (GML) [
3]. CityGML provides a semantic and geometric representation model for city entities as a semi-structured format encoded in XML. By its definition, CityGML can be extended via an Application Domain Extension (ADE), which, among other advantages, provides relevant information for simulation applications. In Helsinki’s Kalasatama area, throughout the Digital Twins project [
4], a high accuracy DSM model was generated for visualization purposes and a semantic city model based on CityGML. The CityGML model was used in a CFD simulation in order to investigate the wind impact on the micro climate, comfort, and safety of the streets and pedestrian areas.
The climate in an urban area can play a tremendous role in the socioeconomic status of a city. In particular, the wind flow can influence the planning and morphology of a city, and, at the same time, wind flow is affected by the urban morphology [
5]. Moreover, the urban morphology in some cases could facilitate the production of wind clean energy since urban and deep water offshore areas remain unharvested, while onshore and shallow water wind farms continue to be studied and realized [
6,
7,
8,
9,
10,
11,
12,
13]. Urban wind energy can provide a decentralized local source of energy for residential areas and reduce the cost of energy by avoiding the losses/costs of long-distance energy transmission [
14].
In actuality, there are numerous methods/models to estimate or assess the wind energy in an urban environment, namely on-site measurements, wind tunnel testing [
15], Computational Fluid Dynamics, and analytical. Simulation processes, specifically Computational Fluid Dynamics (CFD), play a fundamental role in numerically estimating wind velocity and direction in a segregated/sampled 3D space. Although, in an urban context, a CFD simulation could have a high error tolerance, mainly for performance improvement, the calculation of a dynamic velocity field in the course of a year is greatly demanding. Profiling the wind potential of a 3D city model by utilizing the aforementioned methods cannot be related to additional 3D city models via a geometric similarity factor, i.e., the Hausdorff distance [
16], since similarities in the terrain surface model and the wind profile (wind directions, wind speeds) are also required. Monitoring of the wind properties in an urban environment is possible with dedicated anemometers [
17] or with weather station bundles which are measuring additional weather variables. Although a large number of such sensors can be installed across the boundaries of a city, interpolation models cannot estimate the wind flow field between the surface model of the city because wind motion obeys to fluid mechanics and is described by the Navier–Stokes equations [
18]. A variety of toolsets make use of Computational Fluid Dynamics (CFD) to numerically solve the flow of the wind as a dynamic/time dependent series of vector field snapshots or as a static vector field. Despite the fact that a CFD simulation can model the wind flow in an urban area with better accuracy [
19], these solutions come with high demands/requirements and several restrictions (
Table 1).
A mathematical and theoretical background in physics and, particularly, in fluid dynamics is important of an individual to perform a CFD simulation. In addition, the experience and efficient knowledge of the software packages designed to solve a CFD simulation is a non-trivial task for city planners or citizens with minimal or absence of familiarity in this field. Moreover, the input pre-processing steps might require knowledge of additional disciplines, i.e., cartography/geodesy, 3D modeling, etc. Furthermore, the hardware requirements of a computing system that is designed to conduct CFD simulations are reasonably high and costly.
A CFD solver can obtain the flow of velocity, pressure, and other engineering quantities at several locations in a city 3D model, but this result is usually limited in time. The temporal component is of critical value in an urban environment wind potential analysis since it is not an open space and the wind direction changes over time. Performing a dynamic CFD simulation for a large time span, e.g., a whole year, would require an enormous computer processing power and would produce a gigantic result in terms of computer storage space. Alternatively, averaging the wind velocity readings for coarser temporal resolutions, i.e., monthly or yearly, in order to index each month with an average wind velocity, would introduce inaccuracies in each CFD result and, consecutively, to the wind energy potential.
A great challenge to achieve wide participation in wind energy assessment is to define a workflow that would mitigate the lack of expertise, the high-end hardware requirements, and, at the same time, simplify their interaction with the technical components of a smart city application. Web- and mobile-based platforms with a friendly user interface/user experience allow delivery of bidirectional flow of data and information between data management/processing centers and clients. Visual analytics can be utilized as a method to visualize the results of a CFD simulation or the processing of wind related data to extract meaningful information for the community (
Figure 1).
This approach can help individuals to analyze simulation data and/or integration of historical wind sensor data with CFD results via a web interface. A web-based interactive visual analysis system will enable end users to better understand simulation data and identify locations of high wind potential in a city. Such an interactive workflow can find possible locations in an urban environment where small scale wind turbines could be installed and yield an adequate amount of energy on yearly basis. Having a better insight of the wind potential of an urban area can benefit citizens who have interest in investing in green energy or citizens who have already invested in solar energy production and would like to harness the wind energy during the winter months. The remaining paper has the following structure:
Section 2 gives a summary of the state of the art concerning the wind potential estimation and wind comfort in urban areas and an overview of web 3D visualization. Based on the existing approaches from
Section 2,
Section 3 describes a problem that emerges, a research opportunity and the contributions of this paper.
Section 4 gives an overview of the concept and methodology, followed by
Section 5, which describes the implementation details. In
Section 6, we present an evaluation of the proposed framework based on a use case in the area of Stuttgart, Germany. A critical review and future work can be found in
Section 7.
2. State of the Art
The urban fabric could become a prosperous environment for wind energy harnessing. Several studies were conducted by researchers in order to profile and assess the energy potential in city blocks, entire cities, and even country scale. The research community so far utilized various methods to perform wind energy estimation in urban areas. Wang et al. compared and analyzed seven different urban tissue types using CFD simulations in the area of Beijing [
20]. The numeric results of the simulations were correlated with urban morphology parameters, such as floor area ratio, plot ratio, average building height, standard deviation of the building heights, mean building volume, relative rugosity, and porosity. The results showed that urban areas with high porosity, high average building height, and low floor area ratio can yield more energy from the wind. Field measurements can be made in existing buildings for wind energy yield calculation and accuracy assessment of simulation models. Juan et al. validated with on-site measurements CFD results which were used to estimate the wind power potential in urban areas [
21]. The wind power density was used as a wind potential indicator. Finally, the promising wind turbine installation locations, near and above the rooftops of high rise buildings, were profiled according to their distance from the building facade and roof surface respectively, the wind power density and the turbulence intensity. Chtibi and Touzani presented a study of wind energy potential in the area of Morocco based on reports by the Moroccan Ministry of Energy, of the Mines, of the Water and Environment [
22]. Wilkinson et al. demonstrated a new approximation approach of computational fluid dynamics in high rise buildings with complex wind interference [
23].
Wind comfort studies of local city environments make use of simulation platforms to assess the influence of urban planning in the microclimate. Szűcs studied the consequences urban planning can have on wind comfort at an urban space in Dublin [
24]. This study demonstrates that street canyons and building corners produce discomfort to pedestrians in various wind speeds, a phenomenon which can be improved by introducing high dense vegetation.
Keim et al. provided several examples of Advanced Visual Analytics Interfaces [
25]. One of the examples utilized the Growth Ring Map technique to visualize spatio-temporal events in a 2D map. Cybulski et al. explored the role of primary metaphors in Interactive Visual Analytics [
26]. Urbane is a 3D visual analytics framework that supports data-driven decision-making in the design of new urban developments. Among its various features, Urbane allows users to visualize a data-set of interest at different resolutions over varying time periods [
27]. Dai et al. proposed a visual analytics system to investigate the similarities and differences between bike-sharing and taxi systems. This visual analytics system integrates two spatio-temporal data sources by analyzing the patterns that are typical of each data source and the patterns that are common to both data sources to assist users in better discovering the relationships between the taxi system and the bike-sharing system.
In the web visualization domain, numerous studies realized the emerging technologies utilizing declarative and imperative graphics rendering pipelines. Deininger et al. presented a semi-automated workflow to present in a web environment 3D city models combined with wind CFD simulation data [
28]. In addition, an evaluation of performance of several web streaming data formats is displayed. Gaillard et al. implemented a view-dependent 3D city client-server architecture based on CityGML as a source information model [
29].
3DImpact is an interactive web application developed by Patterson that accesses World Health Organization data, in conjunction with spatial information, to visualize spatio-temporal phenomena using a virtual 3D globe based on Google Earth [
30]. Schilling et al. focused on efficiently streaming large and highly detailed 3D city models in the web browser based on open standards, i.e., CityGML using the glTF format [
31].
3. Problem Statement, Motivation, and Novelty
The evolution in data-driven web-based visualization allows for interactive exploration and information extraction for a wide audience. An interactive visualization, based on 3D city modeling, can extract meaningful information in the field of wind energy. Although, in some cases, a 2D visualization of high wind potential can be facilitated, a 3D representation is more intuitive and provides the opportunity to explore special use cases, such as the integration of wind turbines into the underside of high altitude bridges [
32] or the implementation of an array of vertically aligned wind turbines (
Figure 2).
Identifying locations in an urban environment where a small scale wind turbine could yield an adequate amount of energy in the course of a year is a four-dimensional problem. Three dimensions are used for the spatial component of the location, i.e., the X, Y, Z coordinates in an arbitrary coordinate system where wind speed reaches above a required threshold. The fourth dimension is the time component, i.e., the duration of the required wind speed during a year. A web-based client-server architecture can be used to assess the wind energy potential in an urban environment by integrating static CFD simulations, wind historical data, Web 3D technologies, and OGC Standards, which would allow researchers, entrepreneurs, and civilians to estimate the yearly yield of such investment. A spatio-temporal query in the form of “find locations where the wind velocity is higher than 6 m/s for more than 5 h a day for more than 200 days in a year” is expected to be resolved by such a solution. The results of such query will indicate locations where a small scale vertical axis wind turbine can be installed.
Motivated by providing the possibility to the public to assess the wind potential in residential areas, we propose a web-based platform and a dedicated workflow that will give a better insight in interactively exploring simulation data combined with historical readings. Our study investigates if it is possible to design a workflow to analyze and process in near real-time massive simulation data in conjunction with historical readings on the server side combined with a thin visual analytics toolset for eloquent visualizations and data exploration in the client side to give better insights and assist in decision-making in an urban environment. To the best of our knowledge, such a framework is currently not available in the scientific literature. Most of the related research on wind potential focuses on offshore locations, while cases in an urban environment are not interactive, demand high expertise, and Web-based accessibility is absent or limited by 2D visualizations. The varied contributions of our work are: (i) a thin web client with a simplified interface and passive rendering to assist the visual analysis of simulation/historical data in low end desktop and mobile clients, (ii) an interactive integration and processing of simulation/historical data to support an energy sustainable urban environment, (iii) a centralized simulation data architecture with efficient distribution of relevant data controlled via filtering, and (iv) a combined data access interface realized by a query API in parallel with the OGC standard 3D Portrayal Service.
6. Evaluation
To evaluate the interactiveness of the visual analytics platform and investigate the parameters which greatly affect the response times, we facilitated a number of evaluation plans. Each plan was designed to perform a spatio-temporal query with all the parameters constant, except one parameter that was varying (
Table 4). We utilized a rather modest test environment with an 8th generation Intel CPU and 16 GB of RAM (
Table 5).
In the bounds_plan, we performed queries for various sized areas ranging from 0.01 to 2.5 square kilometers. We noticed a logarithmic growth in the response time until an area of about 0.5 square kilometers. A spike in the response time is due to an area with a dense vector field which was included in the evaluation plan. Although, following the spike, we noticed an almost linear increase in the response time in milliseconds when the bounds volume increased, it stayed below the limit of the 10 s (
Figure 14).
In the cell_plan, we performed queries for various grid cell sizes ranging from 1 to 10 m. The increase of the cell size results in fewer number of cells, thus lowering response times (
Figure 15 and
Figure 16).
The direction_plan and duration_plan results showed that the wind direction offsets and the daily duration of the wind have insignificant impact in the response times in realistic scenarios, i.e., wind velocity above 2 m/s and a spatial buffer around the buildings of 10 m (
Figure 17 and
Figure 18).
The velocity_plan showed an exponential decay in the response time, while the horizontal velocity high-pass filter increased. This is an expected behavior in a city environment since blocking and occlusions by above surface objects decrease the wind velocity resulting in many locations with lower wind speeds. Despite the fact that there were cases above the 10,000 milliseconds threshold, the wind velocity was in the range of 1–1.3 m/s, which, in fact, is not a realistic scenario for a promising wind power yield (
Figure 19).
7. Critical Review
In this study, an interactive visual analytics platform was presented that is able to locate areas in an urban environment where small scale wind turbines could be installed. The architecture is based on community best practices, and its implementation utilizes standardized web and 3D technologies. The visualization on the client side is based on a number of RESTful APIs. It is important to mention that the query parameter dailyDuration, of the query API, is a normalized value and does not guarantee the daily duration of the wind above the horizontal velocity threshold.
The performance of the platform was evaluated against the spatial domain volume, including various scenarios. Although the ground area of the domain volume reached over 2 km
2, the response time stayed below the limit of the 10 s. Still, there is room for improvement, by employing spatial indexing, among other spatial capabilities of the PostgreSQL database system. PostgreSQL spatial indexing depends on the PostGIS extension, a data-driven R-Tree [
35] structure which uses the idea of a spatial containment relationship. Although spatial indexes aim for the query planning improvement, there are cases where this may result in efficiency degradation, which may adversely affect query performance. Counter-intuitively, it is not always faster to do an index search: if the search is going to return every record in the table, traversing the index tree to get each record will be slower than sequentially reading the whole table [
36].
Partitioning the data regarding the CFD results will considerably decrease parts of the database queries where joins are performed, since the the first step of any database join is a Cartesian product. The linear performance of our workflow, while the query bounds increase is an indicator that it cannot scale and remain interactive. For this reason, a streaming design is favorable for achieving real-time results in our future work. Expanding the search domain in higher geographical and administrative regions would require the streaming of hierarchical 3D delivery formats, namely 3D-Tiles [
37] and I3S [
38], which are widely adopted OGC community standards.
The case study area reported in this paper (about 0.2 km2) is the urban quarter “Neuer Stöckach”, which belongs to the wider Stöckach area located in the center of Stuttgart. In this area, an energy concept is planned to integrate renewable energies. As part of the redevelopment/refurbishment, the integration of small wind turbines is considered. This case study has shown little potential for wind turbines investment for the reason that the topography of the area is not suitable. The study area is affected by the wake of hill located South-West about 6 km away. High wind velocities, above 4 m/s, were detected in areas about 100 m above residential houses, far exceeding the German regulations (10 m). In addition, the daily duration in the previously mentioned areas was in the range of 60 min, which is considered a prohibiting value for wind energy investment.
The client application, in beta version, can be accessed at
http://hellowind.xyz (accessed on 11 October 2021). A wind yield calculation module is already implemented but not published yet.