Computational Fluid Dynamics (CFD) is regarded as an important tool for analyzing the flow field, thermal environment, and air quality around the built environment. However, for built environment applications, the high computational cost of CFD hinders large-scale scenario simulation and efficient design optimization. In the field of built environment research, surrogate modeling has become a key technology to connect the needs of high-fidelity CFD simulation and rapid prediction, whereas the low-dimensional nature of traditional surrogate models is unable to match the physical complexity and prediction needs of built flow fields. Therefore, combining machine learning (ML) with CFD to predict flow fields in built environments offers a promising way to increase simulation speed while maintaining reasonable accuracy. This review briefly reviews traditional surrogate models and focuses on ML-based surrogate models, especially the specific application of neural network architectures in rapidly predicting flow fields in the built environment. The review indicates that ML accelerates the three core aspects of CFD, namely mesh preprocessing, numerical solving, and post-processing visualization, in order to achieve efficient coupled CFD simulation. Although ML surrogate models still face challenges such as data availability, multi-physics field coupling, and generalization capability, the emergence of physical information-driven data enhancement techniques effectively alleviates the above problems. Meanwhile, the integration of traditional methods with ML can further enhance the comprehensive performance of surrogate models. Notably, the online ministry of trained ML models using transfer learning strategies deserves further research. These advances will provide an important basis for advancing efficient and accurate operational solutions in sustainable building design and operation.

During industrial production, a significant amount of oil mist is generated, posing health risks because it is a fine particulate matter that is easily inhaled by the human body. Electrostatic collection has been widely applied in machining workshops as an effective method for capturing oil mist. While existing research has made substantial progress in improving the collection efficiency of electrostatic methods, the challenge of achieving high-efficiency oil mist collection remains unresolved. The inherent physical properties of oil mist contribute to difficulties in its efficient collection. Additionally, the deposition characteristics of oil mist, as well as the structure and operational parameters of the electrostatic precipitator (ESP), directly affect collection efficiency. This paper reviews the literature from the past decade, introduces the mechanisms of oil mist generation, and presents oil mist monitoring technologies. Based on the deposition characteristics of oil mist, it explores high-efficiency collection technologies using electrostatic methods and summarizes studies on optimizing the performance of electrostatic precipitators. Finally, the paper provides an outlook on the development prospects of oil mist purification technologies.