To understand the mechanism of delay propagation from the perspective of multiple airports, constructing delay propagation relation (DPR) networks among airports is a novel analysis method. The latest method is to use transfer entropy to mine the delay propagation relation among airports. However, the transfer entropy will produce bias due to estimating high-dimensional conditional mutual information (CMI) in the delay propagation scenario. In this paper, we propose the low-dimensional approximation of CMI for transfer entropy (LTE) to address the above issue. By applying this improved algorithm, the delay propagation relation among airports can be more accurately explored and a more accurate DPR network can be obtained. For this network, we apply the complex network theory and its related indicators to provide systematic analysis about delay propagation. The results of case analysis show that in the delay propagation interactions among airports, large airports always receive delays and small airports propagate delays outward. Meanwhile, delays propagate more efficiently in the aviation system of smaller airlines. These results can provide some theoretical supports for making measures to reduce delay propagation.INDEX TERMS Delay propagation relation, transfer entropy, flight delay, causality analysis, complex network.

In this paper, aiming at focusses on many problems existing in the mathematical model of temperature change in the low-pressure casting solidification process of aluminum alloy wheel hub, there is a big gap between the simulation and the actual temperature change, which affects the research on the solidification defects of the wheel hub. In order to study the solidification behavior of aluminum alloy hub in low-pressure casting process, the mathematical model describing the temperature change in the process of casting solidification is established by using different solidification latent heat methods. through finite element simulation and experiment, the temperature change in the process of aluminum alloy (A356) solidification is obtained to compare the difference between the temperature change described by different mathematical models, simulation and experiment. The results show that the temperature numerical model of "the temperature compensation heat capacity method" proposed in this paper is most consistent with the simulation temperature change during the solidification process of the aluminum alloy wheel in the simulation mold, which lays a good theoretical foundation for the study of the low-pressure casting process of the aluminum alloy wheel hub.

In view of the shortcomings of the existing hot spinning process technology of the accumulator shell, a method for optimizing the multi-spinning process parameters is proposed. The Johnson-Cook constitutive model of the accumulator shell material – 34CrMo4 alloy steel − was established with its parameters obtained experimentally. The finite element simulation was carried out for the hot spinning and closing process. Based on which, three parameters with the greatest influence on the spinning formation were studied: spinning temperature, spindle speed and friction coefficient. Combined with the central composite test, the response surface model and the mapping relationship between the three parameters and the maximum mises stress as well as the maximum wall thickness increment of the shell were established. The Pareto optimized solution set was obtained through multi-objective optimization. Under the condition of not affecting product quality, the optimized solution with low spinning temperature and high spindle speed is selected to reduce energy loss and improve work efficiency. The results indicate that the optimized process is experimentally verified to reduce the process temperature by nearly 30 °C, and the efficiency is increased by 25%.