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The purpose of this study is to propose a precise and standardized strategy for numerically simulating vehicle aerodynamics.

Error sources in computational fluid dynamics were analyzed. Additionally, controllable experiential and discretization errors, which significantly influence the calculated results, are expounded upon. Considering the airflow mechanism around a vehicle, the computational efficiency and accuracy of each solution strategy were compared and analyzed through numerous computational cases. Finally, the most suitable numerical strategy, including the turbulence model, simplified vehicle model, calculation domain, boundary conditions, grids and discretization scheme, was identified. Two simplified vehicle models were introduced, and relevant wind tunnel tests were performed to validate the selected strategy.

Errors in vehicle computational aerodynamics mainly stem from the unreasonable simplification of the vehicle model, calculation domain, definite solution conditions, grid strategy and discretization schemes. Using the proposed standardized numerical strategy, the simulated steady and transient aerodynamic characteristics agreed well with the experimental results.

Building upon the modified Low-Reynolds Number k-e model and Scale Adaptive Simulation model, to the best of the authors’ knowledge, a precise and standardized numerical simulation strategy for vehicle aerodynamics is proposed for the first time, which can be integrated into vehicle research and design.

Identifying the flow regime is a prerequisite for accurately modeling two-phase flow. This paper aims to introduce a comprehensive data-driven workflow for flow regime identification.

A numerical two-phase flow model was validated against experimental data and was used to generate dynamic pressure signals for three different flow regimes. First, four distinct methods were used for feature extraction: discrete wavelet transform (DWT), empirical mode decomposition, power spectral density and the time series analysis method. Kernel Fisher discriminant analysis (KFDA) was used to simultaneously perform dimensionality reduction and machine learning (ML) classification for each set of features. Finally, the Shapley additive explanations (SHAP) method was applied to make the workflow explainable.

The results highlighted that the DWT + KFDA method exhibited the highest testing and training accuracy at 95.2% and 88.8%, respectively. Results also include a virtual flow regime map to facilitate the visualization of features in two dimension. Finally, SHAP analysis showed that minimum and maximum values extracted at the fourth and second signal decomposition levels of DWT are the best flow-distinguishing features.

This workflow can be applied to opaque pipes fitted with pressure sensors to achieve flow assurance and automatic monitoring of two-phase flow occurring in many process industries.

This paper presents a novel flow regime identification method by fusing dynamic pressure measurements with ML techniques. The authors’ novel DWT + KFDA method demonstrates superior performance for flow regime identification with explainability.

This paper aims to explore the efficiency of natural ventilation in the bedrooms of typical two-storeyed row houses with newly reconfigured design that incorporate rooftop wind catchers and side windows to create cross ventilation.

A CFD program was used to assess average air velocity coefficient (Cv) in 32 airflow cases. Parameters include location of openings with respect to wind direction, inlet-to-outlet area ratio (IOR) and opening-to-floor area ratio (OFR).

The results reveal that indoor air velocities in the cases of air entering wind catchers are generally higher than those in the cases of air entering side windows while air velocities at the openings are the opposite. The IOR of 1:2 provides best results in terms of both velocities of the indoor air and velocities at the openings. Increasing the OFR from 20% to 50% generally improves indoor air velocities and airflow rates.

This study proved that the new solution of combining one-sided wind catchers and side windows can effectively solve the problem of ventilation uniquely existing in the conditions of typical row houses by catching prevailing wind from two opposite directions into multiple rooms. The results are given as non-dimensional air velocities, which can be interpreted with any climatic data, and therefore can be applied to row houses in any locations and climatic conditions. The findings can create a new and efficient design of row houses that benefits building industry.