Development of Machine Learning-Aided Rapid CFD Prediction for Optimal Urban Wind Environment Design - Fast, Accurate Prediction Tool for Urban Design

Aug 4, 2026

A machine learning model is developed to rapidly and accurately predict how buildings affect urban wind environments using computational fluid dynamics (CFD) data. CFD can analyze wind loads, pedestrian comfort, and pollution dispersion but is costly in time and computation. The study uses a Reynolds-averaged Navier-Stokes (RANS) turbulence model validated against experiments, constructs a 300-case dataset, and trains a multi-output regression model. Random Forest provides the most faithful representation and achieves 88–96% accuracy on new inputs, supporting efficient urban design.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages46

Reading Time1 h 56 min

AccessFree to download

Introduction

Urban wind environment and design need

CFD methods and turbulence modeling

Limitations of CFD computation

ML as a fast surrogate for CFD

Development of Machine Learning-Aided Rapid CFD Prediction for Optimal Urban Wind Environment Design Graphical abstract Abstract This paper presents a Machine Learning (ML) model based on Computational Fluid Dynamics (CFD), developed to quickly and accurately predict the impact of buildings on the urban wind environment. While CFD simulations are effective for wind studies, such as analyzing wind loads, pedestrian comfort, and pollution dispersion, they require significant computational resources and time. Recently, Machine Learning has demonstrated strong potential in providing accurate and immediate predictions by learning from datasets. By training on CFD-generated data, the ML model can quickly produce accurate and physically consistent results, addressing the limitations of CFD methods. The Reynolds-Averaged Navier-Stokes (RANS) turbulence model was chosen for CFD simulations, which were validated against experimental data, with mesh sensitivity analyzed at a wind speed of 3 m/s. A dataset of 300 cases, involving 100 hypothetical buildings and three wind speeds (3, 4, and 5 m/s), was generated to train the ML model. A multi-output regression model was proposed to effectively predict key parameters—wind velocity, turbulence intensity, and CO􀀀 mass fraction—in the selected urban domain. The Random Forest algorithm, which best represented the CFD results, was selected for model development. The ML model demonstrated high efficiency on new data, achieving 88-96% accuracy. This work offers a fast and precise prediction tool, valuable for urban design and related applications. Key words: Urban design ; CFD simulation; Machine learning; Wind environment; Pollution dispersion; Pedestrian comfort 1. Introduction Buildings and their locations significantly impact the urban wind environment and, consequently, the safety, comfort, and well-being of residents. Pre-assessments ofthe microclimate at building sites are essential for designing sustainable living environments. The rapid growth of cities has made urban boundary layer studies increasingly important [1] . Key factors that must be carefully investigated include wind loads on buildings, pedestrian wind comfort, and pollution dispersion. Wind loads, such as a sudden gust of wind can exert significant pressure on a building's walls, potentially causing damage. This poses a serious danger to people both inside and outside the building [2], [3] . Air pollution is a significant concern in large cities, contributing to increasing rates of respiratory diseases, allergies, and other serious health issues [4] . Adequate airflow can naturally enhance air quality, but tall buildings may obstruct it, leading to air stagnation and smog [5], [6] . When airflow collides with a building, it changes direction, creating eddies and turbulence around the structure [7]–[9] . These effects can negatively impact pedestrian wind comfort or even pose risks, such as the downdraught effect [10]–[12] . That is why it is crucial to select the optimal dimensions of a planned building in advance, thoroughly analyzing how it will influence the wind environment under various meteorological conditions. An effective tool for this purpose is parametric analysis, which involves the use of adjustable parameters, such as dimensions, meteorological conditions, materials, and forms, to optimize architectural outcomes [13] . This method facilitates the efficient evaluation of design options tailored to specific requirements and environmental conditions, enabling in-depth analysis of energy consumption, light, and wind effects [14], [15] . Previously, such analyses were conducted using wind tunnel tests, which involved building scale models to study the effects of varying dimensions and wind directions [16]. However, this approach was time-consuming and resource-intensive [17] . With advancements in computing technology, Computational Fluid Dynamics (CFD) has emerged as a powerful alternative, provid

FAQs

Why are CFD simulations important for urban wind environment studies?

CFD is used to model airflow around buildings and supports analyses of wind loads, pedestrian wind comfort, and pollution dispersion, providing physically grounded results.

What CFD approach and validation strategy does the study use?

The Reynolds-Averaged Navier-Stokes (RANS) turbulence model is selected, validated against experimental data, and tested for mesh sensitivity at a wind speed of 3 m/s.

How does the machine learning model improve prediction speed and performance?

Trained on CFD-generated data, the model produces results in minutes or seconds instead of hours. Using Random Forest, it achieves 88–96% accuracy on new cases while predicting key parameters such as wind velocity, turbulence intensity, and CO₂ mass fraction.

Aug 4, 2026

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