Purpose The purpose of this paper is to combine the entropy weight method with the cloud model and establish a fire risk assessment method for airborne lithium battery. Design/methodology/approach In this paper, the fire risk assessment index system is established by fully considering the influence of the operation process of airborne lithium battery. Then, the cloud model based on entropy weight improvement is used to analyze the indexes in the system, and the cloud image is output to discuss the risk status of airborne lithium batteries. Finally, the weight, expectation, entropy and hyperentropy are analyzed to provide risk prevention measures. Findings In the risk system, bad contact of charging port, mechanical extrusion and mechanical shock have the greatest impact on the fire risk of airborne lithium battery. The fire risk of natural factors is at a low level, but its instability is 25% higher than that of human risk cases and 150% higher than that of battery risk cases. Practical implications The method of this paper can evaluate any type of airborne lithium battery and provide theoretical support for airborne lithium battery safety management. Originality/value After the fire risk assessment is completed, the risk cases are ranked by entropy weight. By summarizing the rule, the proposed measures for each prevention level are given.

Inert gas distribution has a great influence on the inerting effect, especially for the multiple-bay fuel tank. In order to find out the optimal scheme, an optimization method based on the entropy-weight improvement TOPSIS method is proposed, and an experimental system of inert gas distribution is established to measure the speed index and uniformity index. The results show that the position of the inlet and outlet has a significant effect on the overall inerting effect. The inerting scheme designed by the entropy-weight improvement TOPSIS method can not only reduce the flow demand of inert gas but also make the oxygen distribution more uniform. The optimization inerting scheme of the Boeing 747 aircraft has improved the average speed index by 3.01% and the average uniformity index by 26.18%. The smoke visualization experiment also showed that the scheme designed by the entropy-weight improvement TOPSIS method has the denser white smoke, which means that the scheme has better performance.

Composite plates are widely used in the aircraft manufacturing industry. The projectile damage of composite plates is affected by complex factors such as material, structure, impact velocity, and impact angle. A reliable method is needed for efficient structural health monitoring. In this paper, a composite plate damage prediction and evaluation model based on the cloud model and neural network is proposed; the five types of experimental characteristics are used as input parameters, and the depth and diameter of the damage area are used as output parameters to train the neural network–cloud model. This method transforms the quantitative data of impact damage of the composite plate into qualitative damage by introducing the cloud model, which makes the damage situation more intuitive. The results show that the accuracy of the prediction model is 97.23%, the accuracy of the evaluation model is 92.41%, and the comprehensive accuracy of the model is 89.85%. The composite damage prediction model has a good prediction performance.

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