No abstract

The objective of this study was to investigate the wear characteristics of the U-shaped rings of power connection fittings, and to construct a wear failure prediction model of U-shaped rings in strong wind environments. First, the wear evolution and failure mechanism of U-shaped rings with different wear loads were studied by using a swinging wear tester. Then, based on the Archard wear model, the U-shaped ring wear was dynamically simulated in ABAQUS, via the Umeshmotion subroutine. The results indicated that the wear load has an important effect on the wear of the U-shaped ring. As the wear load increases, the surface hardness decreases, while plastic deformation layers increase. Furthermore, the wear mechanism transforms from adhesive wear, slight abrasive wear, and slight oxidation wear, to serious adhesive wear, abrasive wear, and oxidation wear with the increase of wear load. As plastic flow progresses, the dislocation density in ferrite increases, leading to dislocation plugs and cementite fractures. The simulation results of wear depth were in good agreement with the test value of, with an error of 1.56%.

Nanospheres have been used as an available plugging material to solve the problem of water intrusion, which is of crucial importance to enhanced oil recovery (EOR). However, the microflow of nanospheres in a lowpermeability reservoir is complex, and its plugging performance and EOR mechanism need to be further investigated by a variety of experiments. In this paper, the expansion experiments were given priority to explore the water absorption and salt tolerance of nanospheres. The displacement experiments were conducted to investigate the plugging performance of nanospheres at the core scale. The flow characteristic and EOR mechanism of nanospheres at the microscale were revealed by nuclear magnetic resonance (NMR) experiments and microfluidic experiments. Expansion and displacement experiments results demonstrate that high salinity inhibits the expansibility and agglomeration of nanospheres, and the formation damage and plugging performance of nanospheres should be comprehensively considered. The suitable particle size of nanospheres should be optimized for field application. As for the microscale, NMR experiments revealed that nanospheres would first enter the large pore and then enter the middle and small pores. In microfluidic experiments, the oil recovery increased by 23.02% original oil in place after the injection of nanospheres. The systematic and comprehensive investigations of expansion property, plugging performance, flow characteristic, and EOR mechanism of nanospheres were conducted from core to microscale, which yielded significant insights into preventing water intrusion and enhancing oil recovery.

No abstract

The wide application of advanced high strength steels with high specific strength in the automotive industry can significantly reduce energy consumption and contribute to carbon neutrality. Accurate prediction of the ductile fracture behavior of advanced high strength steels under complex stress states is of great significance for its application in automobile industry. In this study, the ductile fracture behavior of QP980 under complex stress states, covering shear, uniaxial tension, and plane strain tension, is investigated by conducting the hybrid experiment and simulation. The pressure-coupled Drucker yield function is chosen to characterize the effect of stress states on yielding for QP980, considering its high accuracy compared with the von Mises yield function. Failure limit of the stress states is modelled by five uncoupled ductile fracture criteria (Brozzo, Oh, Rice-Tracey, Ko-Huh, and DF2012). To improve the numerical prediction accuracy, the parameters of the constitutive model are optimized by using the inverse engineering approach. The numerical predicted results are compared with the experimental load-stroke curves with the onset of fracture. The comparison indicates that the prediction error of the DF2012 criterion is significantly lower than those of the other four criteria. In addition, the prediction accuracy is greatly improved with the parameters of the constitutive model optimized by the inverse engineering.