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A High Performance Framework for Coupled Urban Microclimate Models

Matthew C. Overby
University of Minnesota
University of Minnesota, 2014

@phdthesis{overby2014high,

   title={A High Performance Framework for Coupled Urban Microclimate Models},

   author={Overby, Matthew C},

   year={2014},

   school={University of Minnesota}

}

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Urban form modifies the microclimate and may trap in heat and pollutants. This causes a rise of energy demands to heat and cool building interiors. Mitigating these effects is a growing concern due to the increasing urbanization of major cities. Researchers, urban planners, and city architects rely on sophisticated simulations to investigate how to reduce building and air temperatures. However, the complex interactions between urban form and the microclimate are not well understood. Many factors shape the microclimate, such as solar radiation, atmospheric convection, longwave interaction between nearby buildings, and more. As science evolves, new models are developed and existing ones are improved. More accurate and sophisticated models often impose higher computational overhead. This paper introduces QUIC EnvSim (QES), a scalable, high performance framework for coupled urban microclimate models. QES allows researchers to develop and modify such models, in which tools are provided to facilitate input/output communications, model interaction, and the utilization of computational resources for efficient simulations. Common functionality of urban microclimate modeling is optimally handled by the system. By employing Graphics Processing Units (GPUs), simulations within QES can be substantially accelerated. Models for computing view factors, surface temperatures, and radiative exchange between urban materials and vegetation have been implemented and coupled into larger, more sophisticated simulations. These models can be applied to complex domains such as large forests and dense cities. Visualizations, statistics, and analysis tools provide a detailed view of experimental results. Performance increases with additional GPUs and hardware availability. Several diverse examples have been implemented to provide details on utilizing the features of QES for a wide range of applications.
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