Zhengwei Long

Public transportation systems (such as subways, high-speed railways, public buses, and aircraft cabins) are high-risk settings for airborne pathogen transmission because they combine high occupancy with enclosed or semi-enclosed spaces. This study investigates the impact of spatiotemporal uncertainties in virus emission sources and dynamic disturbances caused by passenger movements on infection risks within enclosed cabin environments. Computational Fluid Dynamics (CFD) simulations are employed to analyze the infection risk characteristics in the breathing zone of passengers. Results indicate that when the infection source is located at a middle seat, the time-averaged infection risk in the breathing zone is 31.0% and 58.2% higher than at the window and aisle seats, respectively. Increasing passenger walking velocity from 1.0 to 1.5 m/s raises this risk by 62%, and the difference reaches 86% at 2.0 m/s relative to the stationary case, demonstrating that dynamic passenger motion strongly amplifies airborne infection risk in enclosed cabins. To improve prediction efficiency, a hybrid framework combining CFD and an artificial neural network (ANN) is developed to rapidly predict infection risks under uncertain emission source locations and walking velocities. The ANN model achieves high accuracy (R2 = 0.95) and reduces computation time by more than 99% compared with running full CFD simulations for the same case. The proposed CFD–ANN approach effectively handles dynamic flow fields and stochastic emission patterns, and provides a fast tool for assessing airborne transmission risks in high-density public transportation environments.

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.

Computational Fluids Dynamics (CFD) simulations are essential for optimizing the design of a cockpit’s internal environment, but the complex geometric models consume a significant amount of computational resources and time.... | Find, read and cite all the research you need on Tech Science Press