RF
R. FU
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Planting trees is widely considered an effective way to create a good urban wind environment, improve air quality, mitigate heat island effects, improve pedestrian wind comfort and reduce building energy consumption. To assess tree effects and find suitable tree setups in urban areas, Computational Fluid Dynamic (CFD) simulations can be used.
To handle trees in CFD simulations, the implicit tree modeling approach, i.e, the porosity model, is widely used where finite volume cells that roughly account for trees are marked as porous zones. Some studies have also attempted to model trees as obstacles rather than porous zones, which can be referred to as an explicit tree modeling approach. The difference between these two approaches deserves further study. Also, for practical purposes and lack of information, the geometric features of trees are usually oversimplified or even ignored in CFD simulations.
This thesis investigates the difference between implicit and explicit tree modeling approaches and analyzes the impact of tree Level of Detail (LoD) and shapes on the flow structure. For comparative analysis, several numerical test cases with different urban complexities, tree modeling approaches, tree LoDs, tree shapes, Leaf Area Density (LAD) values, and wind directions were used for CFD simulations.
The results show: (a) the implicit models always allow some of the wind flow into the porous cells no matter how high the LAD values are, resulting in smaller wind acceleration on the lateral sides of implicit tree models; (b) for the idealized street canyon and realistic urban geometry test cases simulated in this thesis, the velocity magnitude differences between the LoD2 cases and the LoD3 cases are rather limited, with maximum differences in the order of 0.5 m/s; (c) differences in tree shapes, LAD values, and wind directions will change the effects of tree modeling approaches and tree LoDs on wind. For instance, the case using an isolated explicit LoD2 conifer tree model has a different wake flow pattern from other explicit cases. Also, with the inflow direction perpendicular to buildings, the higher the LAD values, the larger the velocity magnitude difference between cases using LoD2 tree models and those using LoD3 tree models. ...
To handle trees in CFD simulations, the implicit tree modeling approach, i.e, the porosity model, is widely used where finite volume cells that roughly account for trees are marked as porous zones. Some studies have also attempted to model trees as obstacles rather than porous zones, which can be referred to as an explicit tree modeling approach. The difference between these two approaches deserves further study. Also, for practical purposes and lack of information, the geometric features of trees are usually oversimplified or even ignored in CFD simulations.
This thesis investigates the difference between implicit and explicit tree modeling approaches and analyzes the impact of tree Level of Detail (LoD) and shapes on the flow structure. For comparative analysis, several numerical test cases with different urban complexities, tree modeling approaches, tree LoDs, tree shapes, Leaf Area Density (LAD) values, and wind directions were used for CFD simulations.
The results show: (a) the implicit models always allow some of the wind flow into the porous cells no matter how high the LAD values are, resulting in smaller wind acceleration on the lateral sides of implicit tree models; (b) for the idealized street canyon and realistic urban geometry test cases simulated in this thesis, the velocity magnitude differences between the LoD2 cases and the LoD3 cases are rather limited, with maximum differences in the order of 0.5 m/s; (c) differences in tree shapes, LAD values, and wind directions will change the effects of tree modeling approaches and tree LoDs on wind. For instance, the case using an isolated explicit LoD2 conifer tree model has a different wake flow pattern from other explicit cases. Also, with the inflow direction perpendicular to buildings, the higher the LAD values, the larger the velocity magnitude difference between cases using LoD2 tree models and those using LoD3 tree models. ...
Planting trees is widely considered an effective way to create a good urban wind environment, improve air quality, mitigate heat island effects, improve pedestrian wind comfort and reduce building energy consumption. To assess tree effects and find suitable tree setups in urban areas, Computational Fluid Dynamic (CFD) simulations can be used.
To handle trees in CFD simulations, the implicit tree modeling approach, i.e, the porosity model, is widely used where finite volume cells that roughly account for trees are marked as porous zones. Some studies have also attempted to model trees as obstacles rather than porous zones, which can be referred to as an explicit tree modeling approach. The difference between these two approaches deserves further study. Also, for practical purposes and lack of information, the geometric features of trees are usually oversimplified or even ignored in CFD simulations.
This thesis investigates the difference between implicit and explicit tree modeling approaches and analyzes the impact of tree Level of Detail (LoD) and shapes on the flow structure. For comparative analysis, several numerical test cases with different urban complexities, tree modeling approaches, tree LoDs, tree shapes, Leaf Area Density (LAD) values, and wind directions were used for CFD simulations.
The results show: (a) the implicit models always allow some of the wind flow into the porous cells no matter how high the LAD values are, resulting in smaller wind acceleration on the lateral sides of implicit tree models; (b) for the idealized street canyon and realistic urban geometry test cases simulated in this thesis, the velocity magnitude differences between the LoD2 cases and the LoD3 cases are rather limited, with maximum differences in the order of 0.5 m/s; (c) differences in tree shapes, LAD values, and wind directions will change the effects of tree modeling approaches and tree LoDs on wind. For instance, the case using an isolated explicit LoD2 conifer tree model has a different wake flow pattern from other explicit cases. Also, with the inflow direction perpendicular to buildings, the higher the LAD values, the larger the velocity magnitude difference between cases using LoD2 tree models and those using LoD3 tree models.
To handle trees in CFD simulations, the implicit tree modeling approach, i.e, the porosity model, is widely used where finite volume cells that roughly account for trees are marked as porous zones. Some studies have also attempted to model trees as obstacles rather than porous zones, which can be referred to as an explicit tree modeling approach. The difference between these two approaches deserves further study. Also, for practical purposes and lack of information, the geometric features of trees are usually oversimplified or even ignored in CFD simulations.
This thesis investigates the difference between implicit and explicit tree modeling approaches and analyzes the impact of tree Level of Detail (LoD) and shapes on the flow structure. For comparative analysis, several numerical test cases with different urban complexities, tree modeling approaches, tree LoDs, tree shapes, Leaf Area Density (LAD) values, and wind directions were used for CFD simulations.
The results show: (a) the implicit models always allow some of the wind flow into the porous cells no matter how high the LAD values are, resulting in smaller wind acceleration on the lateral sides of implicit tree models; (b) for the idealized street canyon and realistic urban geometry test cases simulated in this thesis, the velocity magnitude differences between the LoD2 cases and the LoD3 cases are rather limited, with maximum differences in the order of 0.5 m/s; (c) differences in tree shapes, LAD values, and wind directions will change the effects of tree modeling approaches and tree LoDs on wind. For instance, the case using an isolated explicit LoD2 conifer tree model has a different wake flow pattern from other explicit cases. Also, with the inflow direction perpendicular to buildings, the higher the LAD values, the larger the velocity magnitude difference between cases using LoD2 tree models and those using LoD3 tree models.
Student report
(2021)
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R. FU, Y. JIN, Z. LIU, X.U. Mainelli, T. PAPAKOSTAS, L. Wang, E. Verbree, R.L. Voûte
As a method that can accurately represent 3D spatial information, point cloud visualisation for indoor environments is still a relatively unexplored field of research. Our client for this project, the Dutch National Police, requested a variety of potential solutions for visualising (unfamiliar) indoor environments that can be viewed by both external command centres, and internal operations units. Currently, unknown interior layouts (or layouts that are different in practise to what is stated on paper) can have serious, sometimes even life-threatening, consequences in time-sensitive situations. This project uses a game engine to directly visualise point cloud data input of indoor environments. The primary aim is to find ways of clearly communicating a point cloud of an environment to a layman viewer through intuitive visualisations, to aid decision-making in high-stress moments. The final product is a variety of visualisation concepts, hosted within a game engine in order to allow users to navigate throughout (part of) a building, and customise certain interaction features. To aid the layman viewer, various interpretation methods (e.g. cartography) are considered. The Unreal Engine 4 (UE4) project was designed and developed based on the requirements given by Dutch Police, and consisted of 4 modules: data preprocessing, render style, functional module, and User Interface (UI). An indoor point cloud dataset is used for the implementation, while corresponding mesh and voxel models are also respectively generated and evaluated as reference objects. The implemented software product is evaluated based on a Structured Expert Evaluation Method and finally our project result demonstrates that point cloud has unique advantages for visualisation of indoor environments especially in pre-processing efficiency, detail level, and volume perception.
...
As a method that can accurately represent 3D spatial information, point cloud visualisation for indoor environments is still a relatively unexplored field of research. Our client for this project, the Dutch National Police, requested a variety of potential solutions for visualising (unfamiliar) indoor environments that can be viewed by both external command centres, and internal operations units. Currently, unknown interior layouts (or layouts that are different in practise to what is stated on paper) can have serious, sometimes even life-threatening, consequences in time-sensitive situations. This project uses a game engine to directly visualise point cloud data input of indoor environments. The primary aim is to find ways of clearly communicating a point cloud of an environment to a layman viewer through intuitive visualisations, to aid decision-making in high-stress moments. The final product is a variety of visualisation concepts, hosted within a game engine in order to allow users to navigate throughout (part of) a building, and customise certain interaction features. To aid the layman viewer, various interpretation methods (e.g. cartography) are considered. The Unreal Engine 4 (UE4) project was designed and developed based on the requirements given by Dutch Police, and consisted of 4 modules: data preprocessing, render style, functional module, and User Interface (UI). An indoor point cloud dataset is used for the implementation, while corresponding mesh and voxel models are also respectively generated and evaluated as reference objects. The implemented software product is evaluated based on a Structured Expert Evaluation Method and finally our project result demonstrates that point cloud has unique advantages for visualisation of indoor environments especially in pre-processing efficiency, detail level, and volume perception.