Wind tunnel study on flows over various two-dimensional idealized urban-like surfaces

Extensive human activities (e.g. increased traffic emissions) emit a wide range of pollutants resulting in poor urban area air quality. Unlike open, flat and homogenous rural terrain, urban surface is complicated by the presence of buildings, obstacles and narrow streets. The irregular urban surfaces thus form a random roughness that further modifies the near-surface flows and pollutant dispersion. In this study, a physical modelling approach is employed to commence a series of wind tunnel experiments to study the urban-area air pollution problems. The flow characteristics over different hypothetical urban roughness surfaces were studied in a wind tunnel in isothermal conditions. Preliminary experiments were conducted based on six types of idealized two-published_or_final_versio

Experimental study on near-ground boundary layer response to the change in different patterns of urban-type surface

The flow behaviour over various two-dimensional (2D) urban-type surfaces was investigated in a laboratory wind tunnel. Square aluminium bars of size 2.5 cm were used to represent flat-roof buildings and the building separation was adjusted to fabricate various types of urban surface of building-height-to-street-width (aspect) ratios of 1, 1/2, 1/8, 1/10 and 1/12. Mean velocities and velocity fluctuations were measured with a 90o X–hotwire anemometry. The current results compare well with our previous large-eddy simulation (LES). Analysis of the turbulence characteristics for different urban surfaces was performed in attempt to examine the near-ground boundary layer response to various street-canyon configurations.postprin

Influence of atmospheric boundary layer depth on the ventilation performance over idealized urban surfaces

The mechanism of flows and scalar transfer over urban areas is complicated by the random city surfaces in which a detailed investigation is required in its parameterization. Despite the collective effort made by the researchers on urban air pollution problems, our knowledge on the interaction between flows and aerodynamic resistance over cities is limited. Apart from the roughness effect induced by the bottom of the urban boundary layer (UBL), the local atmospheric environment conditions and the city-level air quality are closely correlated but their importance is apparently overlooked. Therefore, as a pilot attempt, this study is conceived to examine the effect of UBL depth on the ...published_or_final_versio

Street-Level Ventilation in Hypothetical Urban Areas

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Roughness-sublayer correction for the profiles of mean velocity and turbulence over urban areas

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Parameterisation of flows and pollutant transport over idealised urban roughness

No. J7.2Atmospheric flow over urban areas basically is a type of turbulent flow over roughness. The flows and pollutant transport process, especially at the lower part of the boundary layer (BL), is strongly modified due to the presence of building geometry. The aerodynamic resistance exerted by the surface roughness reduces the mean velocity in the lower BL but enhances the turbulence intensity. Moreover, the near-wall impingement structures over rough surfaces are attributed to the flow dynamics aloft, leading to increasing aerodynamic resistance and BL depth. However, the dependence on surface morphology and BL depth is not yet well understood. There is ...postprin

CVT-based 2D motion planning with maximal clearance

Maximal clearance is an important property that is highly desirable in multi-agent motion planning. However, it is also inherently difficult to attain. We propose a novel approach to achieve maximal clearance by exploiting the ability of evenly distributing a set of points by a centroidal Voronoi tessellation (CVT). We adapt the CVT framework to multi-agent motion planning by adding an extra time dimension and optimize the trajectories of the agents in the augmented domain. As an optimization framework, our method can work naturally on complex regions. We demonstrate the effectiveness of our algorithm in achieving maximal clearance in motion planning with some examples.published_or_final_versionThe 2011 IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, 9-13 May 2011. In Proceedings of the IEEE-ICRA, 2011, p. 2281-228

Planar hexagonal meshing for architecture

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STORM: a nonlinear model order reduction method via symmetric tensor decomposition

Nonlinear model order reduction has always been a challenging but important task in various science and engineering fields. In this paper, a novel symmetric tensor-based orderreduction method (STORM) is presented for simulating largescale nonlinear systems. The multidimensional data structure of symmetric tensors, as the higher order generalization of symmetric matrices, is utilized for the effective capture of highorder nonlinearities and efficient generation of compact models. Compared to the recent tensor-based nonlinear model order reduction (TNMOR) algorithm [1], STORM shows advantages in two aspects. First, STORM avoids the assumption of the existence of a low-rank tensor approximation. Second, with the use of the symmetric tensor decomposition, STORM allows significantly faster computation and less storage complexity than TNMOR. Numerical experiments demonstrate the superior computational efficiency and accuracy of STORM against existing nonlinear model order reduction methods.postprin

Fuzzy decision-making fuser (FDMF) for integrating human-machine autonomous (HMA) systems with adaptive evidence sources

© 2017 Liu, Pal, Marathe, Wang and Lin. A brain-computer interface (BCI) creates a direct communication pathway between the human brain and an external device or system. In contrast to patient-oriented BCIs, which are intended to restore inoperative or malfunctioning aspects of the nervous system, a growing number of BCI studies focus on designing auxiliary systems that are intended for everyday use. The goal of building these BCIs is to provide capabilities that augment existing intact physical and mental capabilities. However, a key challenge to BCI research is human variability; factors such as fatigue, inattention, and stress vary both across different individuals and for the same individual over time. If these issues are addressed, autonomous systems may provide additional benefits that enhance system performance and prevent problems introduced by individual human variability. This study proposes a human-machine autonomous (HMA) system that simultaneously aggregates human and machine knowledge to recognize targets in a rapid serial visual presentation (RSVP) task. The HMA focuses on integrating an RSVP BCI with computer vision techniques in an image-labeling domain. A fuzzy decision-making fuser (FDMF) is then applied in the HMA system to provide a natural adaptive framework for evidence-based inference by incorporating an integrated summary of the available evidence (i.e., human and machine decisions) and associated uncertainty. Consequently, the HMA system dynamically aggregates decisions involving uncertainties from both human and autonomous agents. The collaborative decisions made by an HMA system can achieve and maintain superior performance more efficiently than either the human or autonomous agents can achieve independently. The experimental results shown in this study suggest that the proposed HMA system with the FDMF can effectively fuse decisions from human brain activities and the computer vision techniques to improve overall performance on the RSVP recognition task. This conclusion demonstrates the potential benefits of integrating autonomous systems with BCI systems