The accumulated plastic strain energy density at a dangerous point is studied to estimate the low cycle fatigue life that is composed of fatigue initiation life and fatigue crack propagation life. The modified Ramberg–Osgood constitutive relation is applied to characterize the stress–strain relationship of the strain-hardening material. The plastic strain energy density under uni-axial tension and cyclic load are derived, which are used as threshold and reference values, respectively. Then, a framework to assess the lives of fatigue initiation and fatigue crack propagation by accumulated plastic strain energy density is proposed. Finally, this method is applied to two types of aluminum alloy, LC9 and LY12 for low-cycle fatigue, and agreed well with the experiments.

The conversion efficiency of annular thermoelectric generator (ATEG) is studied in consideration of temperature-dependent material parameters based on a 1-D steady model. A power series iteration approximation (PSIA) method is proposed for solving the weak nonlinear thermoelectric coupling equation in the cylindrical coordinate system. The convergence of the solution obtained by PSIA is discussed, and the numerical simulation for the 2-D finite-element model is compared with the PSIA solution. Results show that the obtained approximate solution using the PSIA method in the cylindrical coordinate system has high precision and converges rapidly, and the analytical solution and the numerical solution are in good agreement. For regular Bi ₂ Te ₃ material, when the material is independent of temperature and is homogeneous, the location of the heat source of the thermoelectric device has no effect on efficiency; when the material is dependent on temperature, the inner cooling and outer heating thermoelectric configuration has a good thermoelectric performance. The energy conversion efficiency of the temperature-dependent TEG is lower than that of the temperature-independent device at an average temperature.

In this study, the feasibility of using Lamb waves in functionally graded (FG) nano copper layered wafers in nondestructive evaluation is evaluated. The elastic parameters and mass densities of these wafers vary with thickness due to the variation in grain size. The power series technique is used to solve the governing equations with variable coefficients. To analyze multilayered structures, of which the material parameters are continuous but underivable, a modified transfer matrix method is proposed and combined with the power series method. Results show that multiple modes of Lamb waves exist in FG nano copper wafers. Moreover, the gradient property leads to a decrease in phase velocity, and the absolute value of the phase velocity variation is positively correlated with the gradient coefficient. The phase velocity variation and variation rate in Mode 2 are smaller than those in other modes. The findings indicate that Mode 4 is recommended for nondestructive evaluation. However, if the number of layers is greater than four, the dispersion curves of the Lamb waves in the multilayer structures tend to coincide with those in the equivalent uniform structures. The results of this study provide theoretical guidance for the nondestructive evaluation of FG nanomaterial layered structures.

In this paper, the Gurson-Tvergraad-Needleman (GTN) model is used to study the damage of stainless steel springs at high temperatures. The basic material and GTN damage parameters are determined from the stress-strain curves of tensile tests. Then the damage evolution of springs at 300 °C to 900 °C is analyzed based on finite element simulations. The results show that below 600 °C, the change of volume fraction of holes in the spring, along with the increase of load, has an obvious low growth stage, followed by a rapid growth stage. But above 600 °C, there is only a stable and rapid growth stage. The fracture positions predicted by finite element damage analyses agree with the experiments for round bars.

The safety assessment of a pressure vessel with a surface crack is an important part of the safety assessment of engineering equipment. However, the existing methods are mostly based on the assumption of plane specimens and the K criterion applicable to brittle fracture, which may lead to unacceptable errors when applied to a fracture problem in an elastoplastic pressure vessel. In this article, based on the finite element method (FEM) and artificial neural network (ANN), the elastic-plastic three-dimensional J-integral of a crack tip in a pressure vessel with an axial semi-elliptic crack on the surface under the loading of internal pressure is studied. First, the influence of the vessel geometry, the crack size, and internal pressure on the three-dimensional J-integral is analyzed. Second, the machine learning dataset is constructed based on the results of 1,200 cases of FEM calculation; then ANNs are used to discover the potential relationship between multiple parameters and the three-dimensional J-integral. The results show that the neural network constructed in this article can well predict the elastoplastic three-dimensional J-integral of a pressure vessel surface crack.