Narrow Results

The evacuation process under emergency is studied by means of experiments and simulations, focusing on the influence of the environment information. A revised cellular automaton model in which environment information is considered as "static information" (building structure, spatial distance, etc.) and "dynamic information" (sounds of fire alarm, etc.) is introduced. Two scenarios, including evacuation with and without visibility in a classroom, are studied to investigate the different influence of the two kinds of information on human behavior. The experimental and simulation results demonstrate that:

(1) to intensify the spatial distance information can reduce the evacuation time;

(2) the spatial distance is not the only decisive factor especially in evacuation without visibility because the sound information, which is ignorable in evacuation with visibility, is playing a more important role under this condition;

(3) the intensity of static information can reflect evacuees' familiarity of the environment;

(4) the model can reproduce the experiments well, and the simulation method is useful for further study of the crowd movement simulation.

An evacuation was studied from a classroom by means of experiment and simulation. In the experiments, evacuation with and without visibility was mimicked by requiring the evacuees to wear eye masks or not. The distribution of evacuees' egress times against initial positions and the flow rate at exits were studied. It was found that when masks were used, evacuees' egress strategies were highly dependent on their pre-perceived environmental information in subconsciousness which might affect the egress process. Thus we call this phenomenon the "subconscious environmental information perceiving behavior." In the simulation, a cellular automata model considering the influence of sound information and the subconscious behavior was used to simulate the experiments. Both the experimental and the simulation results show that the sound information plays a more important role in evacuation without visibility than in normal condition, and the pre-perceived environmental information is also very important when people have poor visibility because of the subconscious environmental information perceiving behavior. The simulation results consist with the experimental results well. This study is useful for understanding the human behaviors during emergency evacuation with poor visibility under the guide of sound signal.

Horizontal gas–liquid two-phase flow widely exists in many physical systems and chemical engineering processes. Compared with vertical upward gas–liquid two-phase flow, investigations on dynamic behavior underlying horizontal gas–liquid flows are quite limited. Complex network provides a powerful framework for time series analysis of complex dynamical systems. We use a network generation method based on Markov transition probability to infer directed weighted complex networks from signals measured from horizontal gas–liquid two-phase flow experiment and find that the networks corresponding to different flow patterns exhibit different network structure. To investigate the dynamic characteristics of horizontal gas–liquid flows, we construct a number of complex networks under different flow conditions, and explore the network indices for each constructed network. In addition, we investigate the sample entropy of different flow patterns. Our results suggest that the network statistic can well represent the complexity in the transition among different flow patterns and further allows characterizing the interface fluctuation behavior in horizontal gas–liquid two-phase flow.

The simultaneous flow of oil and water through a horizontal pipe is a common occurrence during petroleum industrial processes. Characterizing the flow behavior underlying horizontal oil–water flows is a challenging problem of significant importance. In order to solve this problem, we carry out experiment to measure multivariate signals from different flow patterns and then propose a novel modality transition-based network to analyze the multivariate signals. The results suggest that the local betweenness centrality and weighted shortest path of the constructed network can characterize the transitions of flow conditions and further allow quantitatively distinguishing and uncovering the dynamic flow behavior underlying different horizontal oil–water flow patterns.