Accurate and high-resolution boundary conditions and flow fields in the first-class cabin of an MD-82 commercial airliner

Liu, Wei; Wen, Jizhou; Chao, Jiangyue; Yin, Weiyou; Chen, Shen; Lai, Dayi; Lin, Chao–Hsin; Liu, Junjie; Sun, Hejiang; Chen, Qingyan

doi:10.1016/j.atmosenv.2012.03.039

Cited by 97 publications

(93 citation statements)

References 6 publications

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“…Zhang et al [11] used ultrasonic anemometers to obtain the air distribution in a full-scale, twin-aisle section of an aircraft cabin mockup. Liu et al [9] applied the ultrasonic anemometers to measure the flow field in a functional MD-82 aircraft. Other investigators applied a PIV system to measure the airflow patterns in an empty cabin [12e14] or in an occupied cabin [15,16].…”

Section: Introductionmentioning

confidence: 99%

“…For a typical singleaisle aircraft, the air is mainly supplied from the diffusers located at the upper part of the side walls and mixed in the middle of the cabin. At the lower part of the side walls, part of the air is exhausted and the other is re-circulated [8,9]. The passengers are located at the recirculation region; therefore the supplied clean air reached the passengers after mixing with the polluted (re-circulated) air.…”

Section: Introductionmentioning

confidence: 99%

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Experimental study on characteristics of the jet flow from an aircraft gasper

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et al. 2015

Building and Environment

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental study on characteristics of the jet flow from an aircraft gasper

Dai

Sun

Liu

et al. 2015

Building and Environment

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“…1. The detailed information of MD-82 aircraft airliner can be found in the reference (Liu et al 2012). In this study, the geometric shapes of inlet and outlet are both simplified to be rectangular with the same areas of those of MD-82 cabin.…”

Section: Cfd Modelingmentioning

confidence: 99%

Determination of the optimal control parameter range of air supply in an aircraft cabin

You

2015

Build. Simul.

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Comfortable and healthy aircraft cabin environment is required as more and more people choose to travel by air. The cabin environment is optimized by searching the optimal control parameters such as air supply velocity, angle and temperature. The optimal solutions are obtained by combining a multi-objective particle swarm optimization (MOPSO) with the simulation of computational fluid dynamics (CFD). It is found that different combinations of optimal air supply parameters can build an optimal cabin environment and the locations of the obtained optimal solutions are isolated in their value spaces. To achieve a stable engineering control operation, the determination of a stable range of optimal air supply parameters is required. Therefore, a method by using cluster analysis is developed to obtain stable ranges of optimal air supply parameters. Results show that the proposed method can obtain the ranges of optimal air supply parameters successfully.Keywords cabin environment, multi-objective optimization, particle swarm optimization, cluster analysis, stable range, optimal air supply parameters Article History

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“…Liu et al (2012) and measured the thermo-fluid boundary conditions in detail. The cabin contained three rows with a total of 12 seats as numbered in Figure 8.…”

Section: Case 4: Person-to-person Particle Transportmentioning

confidence: 99%

Comparing the Markov Chain Model with the Eulerian and Lagrangian Models for Indoor Transient Particle Transport Simulations

Chen

Liu

Lin

et al. 2015

Aerosol Science and Technology

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Correctly predicting transient particle transport in indoor environments is crucial to improving the design of ventilation systems and reducing the risk of acquiring airborne infectious diseases. Recently, a new model was developed on the basis of Markov chain frame for quickly predicting transient particle transport indoors. To evaluate this Markov chain model, this study compared it with the traditional Eulerian and Lagrangian models in terms of performance, computing cost, and robustness. Four cases of particle transport, three of which included experimental data, were used for this comparison. The Markov chain model was able to predict transient particle transport indoors with similar accuracy to the Eulerian and Lagrangian models. Furthermore, when the same time step size (Courant number 1) and grid number were used for all three models, the Markov chain model had the highest calculation speed. The Eulerian model was faster than the Lagrangian model unless a super-fine grid was used. This investigation developed empirical equations for evaluating the three models in terms of computing cost. In addition, the Markov chain model was found to be sensitive to the time step size when the Courant number is larger than 1, whereas the Eulerian and Lagrangian models were not.

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Accurate and high-resolution boundary conditions and flow fields in the first-class cabin of an MD-82 commercial airliner

Cited by 97 publications

References 6 publications

Experimental study on characteristics of the jet flow from an aircraft gasper

Experimental study on characteristics of the jet flow from an aircraft gasper

Determination of the optimal control parameter range of air supply in an aircraft cabin

Comparing the Markov Chain Model with the Eulerian and Lagrangian Models for Indoor Transient Particle Transport Simulations

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