Validation Methods of Geometric 3D-CityGML Data for Urban Wind Simulations

Saeedrashed, Younis Saida; Benim, Ali Cemal

doi:10.1051/e3sconf/201912810006

Cited by 5 publications

(5 citation statements)

References 20 publications

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“…Many studies utilize existing digital terrain models (DTMs) of large or major cities [46]. Other projects with more financial resources create their own maps using LIDAR instruments or satellite products [47]. However, it is currently feasible to build digital models of many cities around the world at almost no cost.…”

Section: Meshingmentioning

confidence: 99%

Advances in CFD Modeling of Urban Wind Applied to Aerial Mobility

García-Gutiérrez

Gonzalo

López

et al. 2022

Fluids

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The feasibility, safety, and efficiency of a drone mission in an urban environment are heavily influenced by atmospheric conditions. However, numerical meteorological models cannot cope with fine-grained grids capturing urban geometries; they are typically tuned for best resolutions ranging from 1 to 10 km. To enable urban air mobility, new now-casting techniques are being developed based on different techniques, such as data assimilation, variational analysis, machine-learning algorithms, and time series analysis. Most of these methods require generating an urban wind field database using CFD codes coupled with the mesoscale models. The quality and accuracy of that database determines the accuracy of the now-casting techniques. This review describes the latest advances in CFD simulations applied to urban wind and the alternatives that exist for the coupling with the mesoscale model. First, the distinct turbulence models are introduced, analyzing their advantages and limitations. Secondly, a study of the meshing is introduced, exploring how it has to be adapted to the characteristics of the urban environment. Then, the several alternatives for the definition of the boundary conditions and the interpolation methods for the initial conditions are described. As a key step, the available order reduction methods applicable to the models are presented, so the size and operability of the wind database can be reduced as much as possible. Finally, the data assimilation techniques and the model validation are presented.

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Section: Meshingmentioning

confidence: 99%

Advances in CFD Modeling of Urban Wind Applied to Aerial Mobility

García-Gutiérrez

Gonzalo

López

et al. 2022

Fluids

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“…Several other software packages that offer some degree of geometry reparation were reviewed by Saeedrashed and Benim (2019). The software packages they analyzed, specifically checking their application for urban wind simulations, were CityDoctor, FreeCAD, GeomagicWrap, MeshLab, NetFabb, and Ansys SpaceClaim.…”

Section: Repairing Existing 3d Buildingmentioning

confidence: 99%

Towards automatic reconstruction of 3D city models tailored for urban flow simulations

Pađen

García‐Sánchez

Ledoux

2022

Front. Built Environ.

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In the computational fluid dynamics simulation workflow, the geometry preparation step is often regarded as a tedious, time-consuming task. Many practitioners consider it one of the main bottlenecks in the simulation process. The more complex the geometry, the longer the necessary work, meaning this issue is amplified for urban flow simulations that cover large areas with complex building geometries. To address the issue of geometry preparation, we propose a workflow for automatically reconstructing simulation-ready 3D city models. The workflow combines 2D geographical datasets (e.g., cadastral data, topographic datasets) and aerial point cloud-based elevation data to reconstruct terrain, buildings, and imprint surface layers like water, low vegetation, and roads. Imprinted surface layers serve as different roughness surfaces for modeling the atmospheric boundary layer. Furthermore, the workflow is capable of automatically defining the influence region and domain size according to best practice guidelines. The resulting geometry aims to be error-free: without gaps, self-intersections, and non-manifold edges. The workflow was implemented into an open-source framework using modern, robust, and state-of-the-art libraries with the intent to be used for further developments. Our approach limits the geometry generation step to the order of hours (including input data retrieval and preparation), producing geometries that can be directly used for computational grid generation without additional preparation. The reconstruction done by the algorithm can last from a few seconds to a few minutes, depending on the size of the input data. We obtained and prepared the input data for our verification study in about 2 hours, while the reconstruction process lasted 1 minute. The unstructured computational meshes we created in an automatic mesh generator show satisfactory quality indicators and the subsequent numerical simulation exhibits good convergence behavior with the grid convergence index of observed variables less than 5%.

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“…In 3D printing, STL files and automatic optimization tools are used [22]. However, these optimization tools are not capable of repairing all geometry defects and remove undesired geometry details of 3D building models, albeit important ones for the use in CFD simulations [23]. For example, small protrusions at the changeover of buildings will work for 3D printing applications, but have to be dissolved with many small grid cells in CFD wind field simulations [24] leading to an unnecessary increase of the computing time.…”

Section: State Of the Artmentioning

confidence: 99%

“…Healing of CityGML, as defined in Section 5.1.1, may help to obtain a CAD model after data conversion that largely meets the first aspect, but as pointed out in [23] converting the CityGML to a STL file and then applying a CAD repair tool is unsatisfactory at the moment and still requires (time consuming) manual work. In this paper, the approach of Piepereit at al.…”

mentioning

confidence: 99%

A Continuous, Semi-Automated Workflow: From 3D City Models with Geometric Optimization and CFD Simulations to Visualization of Wind in an Urban Environment

Deininger

Grün

Piepereit

et al. 2020

IJGI

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The concept and implementation of Smart Cities is an important approach to improve decision making as well as quality of life of the growing urban population. An essential part of this is the presentation of data from different sources within a digital city model. Wind flow at building scale has a strong impact on many health and energy issues in a city. For the analysis of urban wind, Computational Fluid Dynamics (CFD) has become an established tool, but requires specialist knowledge to prepare the geometric input during a time-consuming process. Results are available only as predefined selections of pictures or videos. In this article, a continuous, semi-automated workflow is presented, which ❶ speeds-up the preparation of CFD simulation models using a largely automated geometry optimization; and ❷ enables web-based interactive exploration of urban wind simulations to a large and diverse audience, including experts and layman. Results are evaluated based on a case study using a part of a district in Stuttgart in terms of: ➀ time saving of the CFD model preparation workflow (85% faster than the manual method), ➁ response time measurements of different data formats within the Smart City platform (3D Tiles loaded 30% faster than geoJSON using the same data representations) and protocols (3DPS provided much higher flexibility than static and 3D container API), as well as ➃ subjective user experience analysis of various visualization schemes of urban wind. Time saving for the model optimization may, however, vary depending on the data quality and the extent of the study area.

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Validation Methods of Geometric 3D-CityGML Data for Urban Wind Simulations

Cited by 5 publications

References 20 publications

Advances in CFD Modeling of Urban Wind Applied to Aerial Mobility

Advances in CFD Modeling of Urban Wind Applied to Aerial Mobility

Towards automatic reconstruction of 3D city models tailored for urban flow simulations

A Continuous, Semi-Automated Workflow: From 3D City Models with Geometric Optimization and CFD Simulations to Visualization of Wind in an Urban Environment

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