Surface Shape Perception in Volumetric Stereo Displays

In complex volume visualization applications, understanding the displayed objects and their spatial relationships is challenging for several reasons. One of the most important obstacles is that these objects can be translucent and can overlap spatially, making it difficult to understand their spatial structures. However, in many applications, for example medical visualization, it is crucial to have an accurate understanding of the spatial relationships among objects. The addition of visual cues has the potential to help human perception in these visualization tasks. Descriptive line elements, in particular, have been found to be effective in conveying shape information in surface-based graphics as they sparsely cover a geometrical surface, consistently following the geometry. We present two approaches to apply such line elements to a volume rendering process and to verify their effectiveness in volume-based graphics. This thesis reviews our progress to date in this area and discusses its effects and limitations. Specifically, it examines the volume renderer implementation that formed the foundation of this research, the design of the pilot study conducted to investigate the effectiveness of this technique, the results obtained. It further discusses improvements designed to address the issues revealed by the statistical analysis. The improved approach is able to handle visualization targets with general shapes, thus making it more appropriate to real visualization applications involving complex objects

Aerodynamic Optimization and Wind Load Evaluation Framework for Tall Buildings

Wind is the governing load case for majority of tall buildings, thus requiring a wind responsive design approach to control and assess wind-induced loads and responses. The building shape is one of the main parameters that affects the aerodynamics that creates a unique opportunity to control the wind load and consequently building cost without affecting the structural elements. Therefore, aerodynamic mitigation has triggered many researchers to investigate various building shapes that can be categorized into local (e.g. corners) and global mitigations (e.g. twisting). Majority of the previous studies compare different types of mitigations based on a single set of dimensions for each mitigation types. However, each mitigation can produce a wide range of aerodynamic performances by changing the dimensions. Thus, the first millstone of this thesis is developing an aerodynamic optimization procedure (AOP) to reduce the wind load by coupling Genetic Algorithm, Computational Fluid Dynamics (CFD) and an Artificial Neural Network surrogate model. The proposed procedure is adopted to optimize building corners (i.e. local) using three-dimensional CFD simulations of a two-dimensional turbulent flow. The AOP is then extended to examine global mitigations (i.e. twisting and opening) by conducting CFD simulations of three dimensional turbulent wind flow. The procedure is examined in single- and multi-objective optimization problems by comparing the aerodynamic performance of optimal shapes to less optimal ones. The second milestone is to develop accurate numerical wind load evaluation model to validate the performance of the optimized shapes. This is primary achieved through the development of a robust inflow generation technique, called the Consistent Discrete Random Flow Generation (CDRFG). The technique is capable of generating a flow field that matches the target velocity and turbulence profiles in addition to, maintaining the coherency and the continuity of the flow. The technique is validated for a standalone building and for a building located at a city center by comparing the wind pressure distributions and building responses with experimental results (wind tunnel tests). In general, the research accomplished in this thesis provides an advancement in numerical climate responsive design techniques, which enhances the resiliency and sustainability of the urban built environment

Generation and Rendering of Interactive Ground Vegetation for Real-Time Testing and Validation of Computer Vision Algorithms

During the development process of new algorithms for computer vision applications, testing and evaluation in real outdoor environments is time-consuming and often difficult to realize. Thus, the use of artificial testing environments is a flexible and cost-efficient alternative. As a result, the development of new techniques for simulating natural, dynamic environments is essential for real-time virtual reality applications, which are commonly known as Virtual Testbeds. Since the first basic usage of Virtual Testbeds several years ago, the image quality of virtual environments has almost reached a level close to photorealism even in real-time due to new rendering approaches and increasing processing power of current graphics hardware. Because of that, Virtual Testbeds can recently be applied in application areas like computer vision, that strongly rely on realistic scene representations. The realistic rendering of natural outdoor scenes has become increasingly important in many application areas, but computer simulated scenes often differ considerably from real-world environments, especially regarding interactive ground vegetation. In this article, we introduce a novel ground vegetation rendering approach, that is capable of generating large scenes with realistic appearance and excellent performance. Our approach features wind animation, as well as object-to-grass interaction and delivers realistically appearing grass and shrubs at all distances and from all viewing angles. This greatly improves immersion, as well as acceptance, especially in virtual training applications. Nevertheless, the rendered results also fulfill important requirements for the computer vision aspect, like plausible geometry representation of the vegetation, as well as its consistence during the entire simulation. Feature detection and matching algorithms are applied to our approach in localization scenarios of mobile robots in natural outdoor environments. We will show how the quality of computer vision algorithms is influenced by highly detailed, dynamic environments, like observed in unstructured, real-world outdoor scenes with wind and object-to-vegetation interaction

Sound propagation from a ridge wind turbine across a valley

Sound propagation outdoors can be strongly affected by ground topography. The existence of hills and valleys between a source and receiver can lead to the shielding or focusing of sound waves. Such effects can result in significant variations in received sound levels. In addition, wind speed and air temperature gradients in the atmospheric boundary layer also play an important role. All of the foregoing factors can become especially important for the case of wind turbines located on a ridge overlooking a valley. Ridges are often selected for wind turbines in order to increase their energy capture potential through the wind speed-up effects often experienced in such locations. In this paper, a hybrid calculation method is presented to model such a case, relying on an analytical solution for sound diffraction around an impedance cylinder and the conformal mapping (CM) Green's function parabolic equation (GFPE) technique. The various aspects of the model have been successfully validated against alternative prediction methods. Example calculations with this hybrid analytical-CM-GFPE model show the complex sound pressure level distribution across the valley and the effect of valley ground type. The proposed method has the potential to include the effect of refraction through the inclusion of complex wind and temperature fields, although this aspect has been highly simplified in the current simulations. This article is part of the themed issue 'Wind energy in complex terrains'

Autonomous 3D Urban and Complex Terrain Geometry Generation and Micro-Climate Modelling Using CFD and Deep Learning

Sustainable building design requires a clear understanding and realistic modelling of the complex interaction between climate and built environment to create safe and comfortable outdoor and indoor spaces. This necessitates unprecedented urban climate modelling at high temporal and spatial resolution. The interaction between complex urban geometries and the microclimate is characterized by complex transport mechanisms. The challenge to generate geometric and physics boundary conditions in an automated manner is hindering the progress of computational methods in urban design. Thus, the challenge of modelling realistic and pragmatic numerical urban micro-climate for wind engineering, environmental, and building energy simulation applications should address the complexity of the geometry and the variability of surface types involved in urban exposures. The original contribution to knowledge in this research is the proposed an end-to-end workflow that employs a cutting-edge deep learning model for image segmentation to generate building footprint polygons autonomously and combining those polygons with LiDAR data to generate level of detail three (LOD3) 3D building models to tackle the geometry modelling issue in climate modelling and solar power potential assessment. Urban and topography geometric modelling is a challenging task when undertaking climate model assessment. This paper describes a deep learning technique that is based on U-Net architecture to automate 3D building model generation by combining satellite imagery with LiDAR data. The deep learning model used registered a mean squared error of 0.02. The extracted building polygons were extruded using height information from corresponding LiDAR data. The building roof structures were also modelled from the same point cloud data. The method used has the potential to automate the task of generating urban scale 3D building models and can be used for city-wide applications. The advantage of applying a deep learning model in an image processing task is that it can be applied to a new set of input image data to extract building footprint polygons for autonomous application once it has been trained. In addition, the model can be improved over time with minimum adjustments when an improved quality dataset is available, and the trained parameters can be improved further building on previously learned features. Application examples for pedestrian level wind and solar energy availability assessment as well as modeling wind flow over complex terrain are presented

Optimization of Window Confirguration in Buildings for Sustainable Thermal and Lighting Performance

In recent years, there is an urban architectural evolution towards significant use of glazing in high-rise buildings. Windows play a critical role in moderating the elements of the climate. Although good for outdoor viewing and daylighting, glazing has very little ability to control heat flow and solar heat gain. As a result, about 20 - 40% of the energy in a building is wasted through windows. Finding an optimal configuration of windows is a complex task due to its conflicting objectives, such as outdoor view, daylighting, and thermal comfort demands. Further buildings interact with the microclimate in a complex manner, the aerodynamics of the building as well as the location and shape of the window affect its energy performance primarily through convective heat transfer coefficient (CHTC). Various methods have been proposed to calculate CHTC in literature, but with significant differences, which can cause errors in energy demand calculations in the order of 20 - 40%. Most CHTCs used by building energy simulations (BES) tools are primarily derived from the experimental and numerical analysis carried out on low-rise buildings with smooth façade surfaces and are not suitable for high-rise buildings with various intricate surface architectural details. This thesis aims to develop a new simulation-based optimization framework of window configuration in a high-rise building that meets the objective of minimizing the energy consumption of heating, cooling, and electric lighting. This framework integrates high resolution computational fluid dynamics (CFD) and heat transfer simulations, BES, and numerical optimizer. In this thesis, the effect of different building heights, external architectural features, and window configuration on annual energy consumption are investigated. A new concept of local-CHTC zoning, a CFD based procedure for accurate CHTC-U10 correlations evaluation, and an optimum window configuration procedure for high-rise buildings are presented. Overall, the research accomplished in this thesis provides an advancement in knowledge of accurate energy consumption analysis and optimization of window configuration in buildings, particularly in high-rise buildings using a passive strategy that can satisfy the objectives of minimum energy consumption and maximum comfort in a sustainable way

Large-scale 3D environmental modelling and visualisation for flood hazard warning.

3D environment reconstruction has received great interest in recent years in areas such as city planning, virtual tourism and flood hazard warning. With the rapid development of computer technologies, it has become possible and necessary to develop new methodologies and techniques for real time simulation for virtual environments applications. This thesis proposes a novel dynamic simulation scheme for flood hazard warning. The work consists of three main parts: digital terrain modelling; 3D environmental reconstruction and system development; flood simulation models. The digital terrain model is constructed using real world measurement data of GIS, in terms of digital elevation data and satellite image data. An NTSP algorithm is proposed for very large data assessing, terrain modelling and visualisation. A pyramidal data arrangement structure is used for dealing with the requirements of terrain details with different resolutions. The 3D environmental reconstruction system is made up of environmental image segmentation for object identification, a new shape match method and an intelligent reconstruction system. The active contours-based multi-resolution vector-valued framework and the multi-seed region growing method are both used for extracting necessary objects from images. The shape match method is used with a template in the spatial domain for a 3D detailed small scale urban environment reconstruction. The intelligent reconstruction system is designed to recreate the whole model based on specific features of objects for large scale environment reconstruction. This study then proposes a new flood simulation scheme which is an important application of the 3D environmental reconstruction system. Two new flooding models have been developed. The first one is flood spreading model which is useful for large scale flood simulation. It consists of flooding image spatial segmentation, a water level calculation process, a standard gradient descent method for energy minimization, a flood region search and a merge process. The finite volume hydrodynamic model is built from shallow water equations which is useful for urban area flood simulation. The proposed 3D urban environment reconstruction system was tested on our simulation platform. The experiment results indicate that this method is capable of dealing with complicated and high resolution region reconstruction which is useful for many applications. When testing the 3D flood simulation system, the simulation results are very close to the real flood situation, and this method has faster speed and greater accuracy of simulating the inundation area in comparison to the conventional flood simulation model

A Framework for Dynamic Terrain with Application in Off-road Ground Vehicle Simulations

The dissertation develops a framework for the visualization of dynamic terrains for use in interactive real-time 3D systems. Terrain visualization techniques may be classified as either static or dynamic. Static terrain solutions simulate rigid surface types exclusively; whereas dynamic solutions can also represent non-rigid surfaces. Systems that employ a static terrain approach lack realism due to their rigid nature. Disregarding the accurate representation of terrain surface interaction is rationalized because of the inherent difficulties associated with providing runtime dynamism. Nonetheless, dynamic terrain systems are a more correct solution because they allow the terrain database to be modified at run-time for the purpose of deforming the surface. Many established techniques in terrain visualization rely on invalid assumptions and weak computational models that hinder the use of dynamic terrain. Moreover, many existing techniques do not exploit the capabilities offered by current computer hardware. In this research, we present a component framework for terrain visualization that is useful in research, entertainment, and simulation systems. In addition, we present a novel method for deforming the terrain that can be used in real-time, interactive systems. The development of a component framework unifies disparate works under a single architecture. The high-level nature of the framework makes it flexible and adaptable for developing a variety of systems, independent of the static or dynamic nature of the solution. Currently, there are only a handful of documented deformation techniques and, in particular, none make explicit use of graphics hardware. The approach developed by this research offloads extra work to the graphics processing unit; in an effort to alleviate the overhead associated with deforming the terrain. Off-road ground vehicle simulation is used as an application domain to demonstrate the practical nature of the framework and the deformation technique. In order to realistically simulate terrain surface interactivity with the vehicle, the solution balances visual fidelity and speed. Accurately depicting terrain surface interactivity in off-road ground vehicle simulations improves visual realism; thereby, increasing the significance and worth of the application. Systems in academia, government, and commercial institutes can make use of the research findings to achieve the real-time display of interactive terrain surfaces