Hot compression tests were carried out on a Gleeble-3800 thermal mechanical simulator in the temperature range from 700 to 900 °C and strain rate range from 0.005 to 10 s−1 to investigate the hot deformation behavior of B1500HS high-strength steel. Softening mechanisms of B1500HS high-strength steel under different deformation conditions were analyzed according to the characteristics of flow stress–strain curves. By analyzing and processing the experimental data, the values of steady flow stress, saturated stress, dynamic recovery (DRV) softening coefficient, and other factors were solved and these parameters were expressed as functions of Zener–Hollomon factors. Based on the dislocation density theory and the kinetic model of dynamic recrystallization (DRX), constitutive models corresponding to different softening mechanisms were established. The flow stress–strain curves of B1500HS predicted by a constitutive model are in good agreement with the experimental results and the correlation coefficient is . The comparison results indicate that the constitutive models can accurately reflect the deformation behavior of B1500HS high-strength steel under different conditions.

The prediction of medium-and long-term runoff is of great significance to the comprehensive utilization of water resources. Building an adaptive data-driven runoff prediction model by automatic identification of multivariate time series change in runoff forecasting and identifying its influence degree is an attractive and intricate task. At present, the commonly used screening factor method is correlational analysis; others offer multi-collinearity. If these factors are directly input into the model, the parameters of the model tend to increase, and the excessive redundancy and noise adversely affects the prediction results of the model. On the basis of previous studies on medium-and long-term runoff prediction methods, this paper proposes an Elman Neural Network (ENN) adaptive runoff prediction method based on normalized mutual information (NMI) and kernel principal component analysis (KPCA). In this method, the features of the screening factors are extracted automatically by using the mutual information automatic screening factor, and then input into the Elman Neural Network for training. With less features, the parameters of the Elman Neural Network model can be reduced, and the problem of overfitting of the Elman Neural Network model is effectively alleviated. The method is evaluated by using the annual average runoff data of Jinping hydropower station in Chengdu, China, from 2007 to 2011. The maximum relative error of multiple forecasts was found to be less than 16%, and forecast effect was good. The accuracy of prediction is further improved by averaging the results of multiple forecasts. methods used to establish mathematical relations include linear regression, stepwise regression [7], local regression, artificial neural networks [8][9][10], and support vector machines [11][12][13]. Meanwhile, a process-driven model requires a hydrological model that can reflect the characteristics of runoff, and future medium-and long-term rainfall information is used as model input to obtain changes in the forecast object [14]. The ensemble streamflow prediction (ESP) method proposed by American scholar Day [15] is a process-driven model and researchers have used this method to study medium-and long-term runoff forecasting in many watersheds. As the mechanism of hydrological process has not been fully elucidated, the applicability of this model is limited [16][17][18][19][20]. Therefore, a data-driven model, especially the runoff prediction model based on neural networks, has become a focused topic for [21][22][23][24] the application of back propagation (BP) neural networks to medium-and long-term hydrological forecasting [25]. In [26][27][28], the application of wavelet neural networks to runoff forecasting was investigated. In [29], the application of gray self-memory based on a BP network model to runoff forecasting was examined. However, these neural network models have two drawbacks: easy fall into local minima and slow convergence [30]. further evaluate and compare the performance of ENN and land surface hydrolo...

Purpose This study aims to evaluate the tribological behavior of water-lubricated rubber bearings sliding against stainless steel under different lubricate conditions. Design/methodology/approach The water-lubricated rubber bearings under various normal loads and sliding speeds were carried out on the ring-block friction test, and the wear morphology is test conducted by using scanning electron microscope. Findings The results indicate that the surface of water-lubricated rubber bearings has a more alternative friction coefficient and wear rate under seawater than other lubricate conditions. The seawater not only acts as a lubricating medium but also brings microstructure while corroding the rubber interface, thereby further enhancing the lubricating effect and storing abrasive debris. Originality/value In this paper, tribological properties of the water-lubricated rubber bearing on ring-block friction test has been investigated. Water-lubricated rubber bearing was carried out on various lubricate conditions, and the friction coefficient, wear rate and worn surface were analyzed. Also, the effects of sliding speeds were investigated. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-06-2020-0204/

In this research, B1500HS high-strength steel with different thicknesses were laser welded, and the effects of welding speed and post quenching were investigated by analyzing the microstructure, microhardness distribution, and high-temperature tensile properties of weld joints. The results show that an obvious difference can be found in the metallographic structure and grain morphology of the weld joint at different locations, which also lead to the significant uneven distribution of hardness. After quenching, the grain size of the original heat-affected zone was uniform, the columnar grains in the fusion zone were transformed into fine equiaxed grains, and no obvious hardness difference can be found in the weld joint. For the weld joint without quenching, the increase of welding speed can reduce the dimensions of grains of fusion zone and coarse grain zone, and slightly increase the hardness of these regions. In contrast, welding speed change has little influence on the microstructure and hardness of the weld joint after quenching. The high-temperature flow stress–strain curves of fusion zone welded under different welding speeds were calculated based on the mixture rule. The analysis results indicated that the fusion zone has higher strength but lower elongation than the base metal. In addition, the change of welding speed has a small impact on the high-temperature tensile properties of the fusion zone.

No abstract