Airplane cabin mixing ventilation with time-periodic supply: Contaminant mass fluxes and ventilation efficiency.

Airplane cabin ventilation is essential to ensure passengers' well-being. The conventional ventilation method is mixing ventilation with a statistically steady supply, which, according to former studies, has reached its limits regarding, for example, the ventilation efficiency. However, the effect of a statistically unsteady (time-periodic) supply on the mixing ventilation efficiency has remained largely unexplored. This research uses computational fluid dynamics (CFD) with the large eddy simulation (LES) approach to study isothermal time-periodic mixing ventilation in a section of a single-aisle airplane cabin model, in which the air exhaled by the passengers functions as (passive) contaminants. Two time-periodic supply strategies are evaluated. The induced time-periodic airflow patterns promote an efficient delivery of fresh air to the passenger zone and affect the passengers' expiratory plumes. This results in increased mean contaminant mass fluxes, causing a strong reduction of the mean contaminant concentrations in the passenger zone (up to 23%) and an increased contaminant extraction from the cabin. Mean velocities increase with up to 55% but remain within the comfortable range. It is shown that the ventilation efficiency improves; that is, the contaminant removal effectiveness and air change efficiency (in the full cabin volume) increase with up to 20% and 7%, respectively.

Modeling transient particle transport in transient indoor airflow by fast fluid dynamics with the Markov chain method.

It is crucial to accurately and efficiently predict transient particle transport in indoor environments to improve air distribution design and reduce health risks. For steady-state indoor airflow, fast fluid dynamics (FFD) + Markov chain model increased the calculation speed by around seven times compared to computational fluid dynamics (CFD) + Eulerian model and CFD + Lagrangian model, while achieving the same level of accuracy. However, the indoor airflow could be transient, if there were human behaviors involved like coughing or sneezing and air was supplied periodically. Therefore, this study developed an FFD + Markov chain model solver for predicting transient particle transport in transient indoor airflow. This investigation used two cases, transient particle transport in a ventilated two-zone chamber and a chamber with periodic air supplies, for validation. Case 1 had experimental data for validation and the results showed that the predicted particle concentration by FFD + Markov chain model matched well with the experimental data. Besides, it had similar accuracy as the CFD + Eulerian model. In the second case, the prediction by large eddy simulation (LES) was used for validating the FFD. The simulated particle concentrations by the Markov chain model and the Eulerian model were similar. The simulated particle concentrations by the Markov chain model and the Eulerian model were similar. The computational time of the FFD + Markov chain model was 7.8 times less than that of the CFD + Eulerian model.

Computational fluid dynamics predictions of non-isothermal ventilation flow-How can the user factor be minimized?

The use of computational fluid dynamics (CFD) to solve indoor airflow problems has increased tremendously in the last decades. However, the accuracy of CFD simulations depends greatly on user experience, the available validation data, and the effort made to verify solutions. This study presents the results of a conference workshop, which assessed user influence on the CFD results obtained for a generic non-isothermal flow problem; ie, a backward-facing step flow problem with a heated wall below the supply. Fifty-five simulation sets were submitted by 32 teams. The results showed a very large spread in predicted penetration length (xre /(H - h)), location of maximum velocity in the lower part of the recirculation cell (xrm /(H - h)), and maximum velocity at this location (urm /u0 ). The turbulence model seemed to very strongly influence the results, with a statistically significant difference in the predictions yielded by the k-ε and k-ω models. The results obtained using a single turbulence model generally also showed a spread in results; the level of spread depended on factors such as grid size and near-wall treatment. The statistical data strongly indicate the need for validation studies using experimental data (benchmarks) to ensure the accuracy, reliability, and trustworthiness of CFD simulations for indoor airflow problems.