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To improve the fatigue performance of high-pressure hydrogen storage vessels, a method is proposed to determine the optimal autofrettage pressure of the vessel using numerical simulation and experimental verification. First, a finite element model considering the actual structure of the vessel is established based on grid theory. Then, the effect of autofrettage pressure on the equivalent alternating stress amplitude of liner under cyclic fatigue load is studied by using the modified equation of Smith–Watson–Topper mean stress. Lastly, the optimum autofrettage pressure is determined according to the DOT-CFFC and CGH2R standards, and the fatigue life is predicted. The results show that under the optimum autofrettage pressure, the bearing capacity of the liner under working pressure and the fatigue life under fatigue cycle load are increased by 74% and 12 times, respectively. The experimental results are in good agreement with the numerical simulation results, with an average error of 6.3%. This research has important reference significance for the preliminary design stage of composite vessels.

Iron aluminide intermetallic coatings were prepared from Fe−Al/Cr3C2 cored wires using High Velocity Arc Spraying (HVAS) technology. Erosion and corrosion properties of HVAS sprayed Fe−Al/Cr3C2 coatings were investigated. Results show that the erosion at impingement angle of 30° is more than that of 90°. The erosion resistance of coatings was enhanced with the increase of temperature. Coatings had a better erosion resistance than substrates. The erosion changed from ductile behaviors to brittle behaviors above 450°C. At high temperature, the erosion resistances were superior to those at low temperature and room temperature. Coatings had much higher corrosion properties than substrates. The temperature had a little effect on the corrosion resistance of coatings; The corrosion losing of coatings increased slowly with the increase of corrosion time. The HVAS-sprayed Fe−Al/Cr3C2 coatings exhibited a high bond strength and hardness.

To overcome the vast computation of the standard support vector machines (SVMs), Lee and Mangasarian (see First SIAM International Conference on Data Mining, 2001) proposed reduced support vector machines (RSVM). But they select 'support vectors' randomly from the training set, and this will affect the test result. In this paper, we select some representative vectors as support vectors via a simple unsupervised clustering algorithm, and then apply the RSVM method on these vectors. The proposed method can get higher recognition accuracy with fewer support vectors compared to the original RSVM, with the advantage of reducing the running time significantly.