Abstract

In this study, we developed a new computational fluid dynamics (CFD) model called Airflow Analyst that deepens the affinity between CFD and geographic information system (GIS). First, a precise simulation of the surface-mounted cube was conducted. Validation testing based on the obtained data confirmed the predictive accuracy of Airflow Analyst. Second, New National Stadium Japan (Tokyo Olympic Stadium) was accurately reproduced in a computer, capturing the latest detailed urban area data for the base. For the target of the constructed 3D models, simulations with a large number of grid points/cells (CFD) were conducted. These simulations reproduced the complex turbulent flow fields both inside and outside the stadium. The experiment successfully reproduced the CFD simulation using a large number of grid points/cells, where the conditions of the wind flow ventilation from the sky were similar to those of the intended stadium design.

Highlights

  • Urban environments in our society are affected by the various impacts of wind, such as strong wind damage and ventilation

  • We developed a new computational fluid dynamics (CFD) model called Airflow Analyst that deepens the affinity between CFD and geographic information system (GIS)

  • Validation testing based on the obtained data confirmed the predictive accuracy of Airflow Analyst

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Summary

Introduction

Urban environments in our society are affected by the various impacts of wind, such as strong wind damage and ventilation. The built environment in urban design and regional planning needs to be assessed and optimized. Recent research results in the fields we are interested in have been reviewed in the literature [1]. We have been developing Airflow Analyst [3] [4] [5] [6], which is extension software for ArcGIS, which is versatile GIS software. The biggest advantage of Airflow Analyst is that users are able to analyze the flow field characteristics or passive scalar transport and diffusion in regions with arbitrarily shaped objects without considering landform features or building clusters

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