Super-resolution analysis via machine learning: a survey for fluid flows
This paper surveys machine-learning-based super-resolution reconstruction for vortical
flows. Super resolution aims to find the high-resolution flow fields from low-resolution data …
flows. Super resolution aims to find the high-resolution flow fields from low-resolution data …
[HTML][HTML] Predicting transient wind loads on tall buildings in three-dimensional spatial coordinates using machine learning
Abstract Machine learning (ML) as a subset of artificial intelligence (AI), has gained
significant attention in wind engineering applications over the past decade. Wind load …
significant attention in wind engineering applications over the past decade. Wind load …
Fast estimation of airflow distribution in an urban model using generative adversarial networks with limited sensing data☆
Fast estimation of instantaneous urban airflow distribution can protect pedestrians and
objects from high-speed winds and promote high-efficiency natural ventilation. Statistical …
objects from high-speed winds and promote high-efficiency natural ventilation. Statistical …
Deep learning reconstruction of high-Reynolds-number turbulent flow field around a cylinder based on limited sensors
It is of significant importance to reconstruct the high-dimensional global flow field using
limited sensor data, considering that sensors are often local and limited in full-scale …
limited sensor data, considering that sensors are often local and limited in full-scale …
Extended spectral proper orthogonal decomposition for analysis of correlated surrounding flow structures and wind load components of a building
Proper orthogonal decomposition (POD) has been used in numerous studies in wind
engineering to extract key features of a building's surrounding flow field and surface …
engineering to extract key features of a building's surrounding flow field and surface …
Smart urban windcatcher: Conception of an AI-empowered wind-channeling system for real-time enhancement of urban wind environment
The dense configuration and rapid proliferation of high-rise buildings in central Hong Kong
have led to increasing stagnation of pedestrian-level airflows, lowering the wind speed and …
have led to increasing stagnation of pedestrian-level airflows, lowering the wind speed and …
[HTML][HTML] CFD-driven physics-informed generative adversarial networks for predicting AUV hydrodynamic performance
A novel framework for predicting the hydrodynamic performance of autonomous underwater
vehicles (AUVs) is proposed based on computational fluid dynamics (CFD) and physics …
vehicles (AUVs) is proposed based on computational fluid dynamics (CFD) and physics …
Effects of sensor configuration optimization on airflow estimation in urban environment: A case study with a building group model
H Jia, C Hu, H Kikumoto - Sustainable Cities and Society, 2023 - Elsevier
The complicated flow field in an urban area can be properly estimated using sparse sensor
measurements and several estimation methods. Because sensor measurements are the …
measurements and several estimation methods. Because sensor measurements are the …
Bidirectional prediction between wake velocity and surface pressure using deep learning techniques
The surface pressure and flow field of rectangular cylinders are of great importance in
aerodynamic analyses of the cylinders. In general, it is easy to obtain one side of the …
aerodynamic analyses of the cylinders. In general, it is easy to obtain one side of the …
A physics-informed deep learning model to reconstruct turbulent wake from random sparse data
This study develops a flexible deep learning framework aimed at reconstructing the global
turbulent wakes from the randomly distributed sparse data. The framework is based on a …
turbulent wakes from the randomly distributed sparse data. The framework is based on a …