25Fa-Microclimate-UMCF

UMCF

A collection of components to use the
urbanMicroclimateFoam solver.

A Grasshopper plugin for microclimate simulations

The plugin is based on the urbanMicroclimateFoam open-source solver based on OpenFOAM,
developed by the Chair of Building Physics at ETH Zürich.

• GitHub repository: https://github.com/OpenFOAM-BuildingPhysics/urbanMicroclimateFoam

Overview

urbanMicroclimateFoam (uMFoam) is an open-source solver built on OpenFOAM for modeling urban microclimates.
It simulates multiple coupled physical processes, including:

• Turbulent airflow
• Heat and moisture transport in air
• Radiative heat exchange (shortwave and longwave)
• Heat and moisture storage in building materials (HAM model)
• Urban vegetation heat balance

Learn more at the official repository:
🔗 urbanMicroclimateFoam GitHub

Key Features

🌊 CFD — Computational Fluid Dynamics Model

• Solves turbulent, convective airflow
• Handles heat and moisture transport in the air subdomain

🏗️ HAM — Heat and Moisture Transport Model

• Manages absorption and transport
• Controls storage of heat and moisture in porous building materials

☀️ RAD — Radiation Model

• Calculates net longwave and shortwave radiative heat fluxes
• Uses view factor approach

🌳 VEG — Vegetation Model

• Solves heat balance for urban trees
• Handles green surfaces

Prerequisites

1. Install OpenFOAM (Windows)

Install blueCFD-Core 2020, which includes OpenFOAM 8.

• Download: blueCFD-Core-2020-1-win64-setup.exe

2. Install UMCF Plugin for Grasshopper (Rhino)

1. Open Rhinoceros
2. Run the PackageManager command
3. Search for UMCF
4. Enable “Include pre-releases”
5. Click Install and restart Rhino

Fall 2025 Additions – Urban Morphology & Microclimate (Tokyo Case Study)

Overview

This case study evaluates how urban morphology influences temperature and wind-related variables within the urban microclimate. Using Tokyo, Japan as a case study, we investigate the relationship between dense urban form, geometric complexity, and microclimate behavior through CFD simulations.

Motivation

Urban morphology—such as building density, height variation, and street canyon geometry—plays a critical role in shaping microclimate conditions. Tokyo’s dense and heterogeneous urban fabric, characterized by narrow streets and sharp transitions between open and enclosed spaces, presents a challenging yet valuable context for examining these effects.

This study aims to understand how urban form affects microclimate performance, while also identifying technical limitations related to geometry handling and mesh generation in high-density environments.

Central Idea

We treat urban morphology as a primary driver of microclimate variation.
By constructing a detailed 3D urban model of Tokyo and running iterative simulations, we assess how temperature and wind patterns respond to spatial configuration, mesh resolution, and model setup.

Methodology

1) Urban Morphology Modeling

• Construct a detailed 3D urban model of selected Tokyo areas
• Capture key morphological characteristics, including:
• Narrow street canyons
• High building density
• Sharp transitions between open and enclosed spaces

2) Simulation and Iterative Refinement

• Run simulations using urbanMicroclimateFoam (UMCF)
• Analyze temperature and wind-related outputs
• Iteratively refine the computational mesh and model configuration based on simulation behavior and numerical stability

Data Collection

Urban Geometry and Morphology

• Building footprints and heights were collected from PLATEAU (MLIT, Japan)
  https://www.mlit.go.jp/plateau/en/
• PLATEAU CityGML data were converted into a Rhinoceros-compatible format using a custom Grasshopper (GML reader) workflow, enabling geometry preprocessing and inspection within Rhino.

Challenges

Mesh Generation and Computational Cost

The large number of objects and high geometric complexity made mesh generation a critical and persistent challenge.

High-density urban geometry requires fine meshes to accurately resolve wind flow and temperature gradients. However, finer meshes significantly increase computational cost, necessitating trade-offs between accuracy, numerical stability, and feasibility.

Mesh-related errors occurred repeatedly and were not fully resolved within the project timeframe.

Several basic geometric conditions were identified as necessary for stable simulations: - All buildings must touch the same XY ground plane (no floating geometries) - Vegetation objects must be sufficiently distanced from buildings - The bottom face of all buildings must be removed - The mesh must exhibit a well-formed topology, with small, uniform, and evenly distributed triangular faces

Future Improvements

Potential future directions include: - Developing more robust mesh-cleaning and validation workflows - Simplifying urban geometry while preserving key morphological characteristics - Establishing quantitative metrics to compare simulation stability across mesh resolutions - Expanding analysis to multiple Tokyo districts with contrasting urban forms

Presentation

Team