Effect of thermomechanical processing on microstructure and mechanical properties of CoCrFeNiMn high entropy alloy

The CoCrFeNiMn high entropy alloy was produced by homogenization, cold rolling and recrystallization. The effects of thermomechanical processing on microstructures and tensile properties at different temperatures were investigated using X-ray diffractometry (XRD), optical microscopy (OM), scanning electron microscopy (SEM) and multi-functional testing machine. The results show that dendritic structures in cast alloy evolve into equiaxed grains after being recrystallized, with single face-centered cubic (FCC) phase detected. The most refined alloys, stemming from the highest rolling ratio (40%), exhibit the highest strength due to the grain boundary strengthening, while the variation of elongation with temperature shows a concave feature. For the coarse-grained alloys, both the ductility and work hardening ability decrease monotonically with increasing temperature. Serrated flow observed at intermediate temperatures is attributed to the effective pinning of dislocations, which manifests the occurrence of dynamic strain hardening and results in the deterioration in ductility. Besides, dimples on the fracture surfaces indicate the typical ductile rupture mode.     

基于改进交叉熵算法的电力系统随机生产模拟

蒙特卡洛模拟的计算效率与系统的可靠性密切相关，在其用于高可靠性系统的随机模拟时存在计算效率偏低的问题。为此，提出了一种基于多层交叉熵与对偶变数抽样技术相结合的随机模拟算法。首先使用多层交叉熵构造零方差概率密度函数的近似函数，提高小概率失效事件的抽取概率；其次基于已构造的近似概率函数，采用对偶变数抽样法进行抽样，进一步提高抽样的收敛速度。以IEEE RTS修改系统为例进行了算例分析，算例结果验证了所提出的基于改进交叉熵的电力系统随机生产模拟算法的有效性。     

Nb modified structural,magnetic and magnetocaloric properties of double perovskite Ba2FeMo1−xNbxO6

A systematic study focusing on the effect of Niobium (Nb) doping on the structural, magnetic and magnetocaloric properties of Ba₂FeMoO₆ samples is presented here. The samples of interest Ba₂FeMo_1?xNb_xO₆ (0 ≤ x ≤ 0.4) were prepared using the solid state reaction method and were confirmed to possess a cubic structure with Fm-3m space group using the X-ray diffraction analysis and Rietveld refinement. A second order of ferromagnetic phase transition was recorded in both the pure as well as the Nb doped samples using the temperature dependent magnetization and Arrott plots analysis. The pristine Ba₂FeMoO₆ (BFMO) sample indicated a spontaneous magnetization (34.6 emu/g at 100 K) with a relatively sharp magnetic transition at the Curie temperature (T_C) of 315 K as compared to the doped samples. A magnetic entropy change of 0.93 Jkg^?1K^?1 at an applied magnetic field of 2.5 T was measured for the pure BFMO sample. The doped BFMO samples with Mo partially substituted by Nb however, were observed to effectively modify the T_C accompanied by a decrease in magnetization. The results investigated in this work suggest that the magnetic and magnetocaloric properties of the BFMO can be tailored by controlled Nb doping which is of significant importance in order to realize the numerous potential applications of the material in the magnetic refrigeration technology.     

Entropy analysis of unsteady MHD three-dimensional flow of Williamson nanofluid over a convectively heated stretching sheet

This article addresses an investigation of the entropy analysis of Williamson nanofluid flow in the presence of gyrotactic microorganisms by considering variable viscosity and thermal conductivity over a convectively heated bidirectionally stretchable surface. Heat and mass transfer phenomena have been incorporated by taking into account the thermal radiation, heat source or sink, viscous dissipation, Brownian motion, and thermophoretic effects. The representing equations are nonlinear coupled partial differential equations and these equations are shaped into a set of ordinary differential equations via a suitable similarity transformation. The arising set of ordinary differential equations was then worked out by adopting a well-known scheme, namely the shooting method along with the Runge-Kutta-Felberge integration technique. The effects of flow and heat transfer controlling parameters on the solution variables are depicted and analyzed through the graphical presentation. The survey finds that magnifying viscosity parameter, Weissenberg number representing the non-Newtonian Williamson parameter cause to retard the velocity field in both the directions and thermal conductivity parameter causes to reduce fluid temperature. The study also recognizes that enhancing magnetic parameters and thermal conductivity parameters slow down the heat transfer rate. The entropy production of the system is estimated through the Bejan number. It is noticeable that the Bejan number is eminently dependent on the heat generation parameter, thermal radiation parameter, viscosity parameter, thermal conductivity parameter, and Biot number. The skillful accomplishment of the present heat and mass transfer system is achieved through the exteriorized choice of the pertinent parameters.     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

First principles computational studies of spontaneous reduction reaction of Eu(III) in eutectic LiCl‐KCl molten salt

Using first principles calculations, we study fundamental mechanism of spontaneous reduction reaction of Eu³⁺ to Eu²⁺ in eutectic LiCl‐KCl molten salt. We decouple the reaction Gibbs free energy into enthalpy and entropy contributions by using rigorous thermodynamic formalism. Key structural features of the solvation shell are characterized by the radial distribution function and the coordination number. Compared with Eu²⁺, the Eu³⁺ ion has a more rigid framework of the solvation shells, corroborating its stronger electrostatic interaction with neighboring ligands of Cl^? ions and a more favorable state on the aspect of enthalpy. Computations on vibrational frequency, however, pose significant contribution of vibrational entropy to the reaction Gibbs free energy for the reduction. Vibration frequency of Eu²⁺ is smaller than that of Eu³⁺, driving a more positive change of the entropy in the reduction reaction. Furthermore, an Eu²⁺ diffuses more quickly than an Eu³⁺ in the LiCl‐KCl molten salt with switching mechanism of ligand Cl^? ions in the solvation shell. Our results propose that the spontaneity of the reduction reaction is driven by the entropic contribution by overcoming the penalty of the reaction enthalpy.     

Machine learning of phases and mechanical properties in complex concentrated alloys

The mechanical properties of complex concentrated alloys (CCAs) depend on their formed phases and corresponding microstructures.The data-driven prediction of the phase formation and associated mechanical properties is essential to discovering novel CCAs.The present work collects 557 samples of various chemical compositions,comprising 61 amorphous,167 single-phase crystalline,and 329 multi-phases crystalline CCAs.Three classification models are developed with high accuracies to category and understand the formed phases of CCAs.Also,two regression models are constructed to predict the hard-ness and ultimate tensile strength of CCAs,and the correlation coefficient of the random forest regression model is greater than 0.9 for both of two targeted properties.Furthermore,the Shapley additive expla-nation (SHAP) values are calculated,and accordingly four most important features are identified.A significant finding in the SHAP values is that there exists a critical value in each of the top four fea-tures,which provides an easy and fast assessment in the design of improved mechanical properties of CCAs.The present work demonstrates the great potential of machine learning in the design of advanced CCAs.     

Microstructural and mechanical properties assessment of transient liquid phase bonding of CoCuFeMnNi high entropy alloy

The transient liquid phase (TLP) bonding of CoCuFeMnNi high entropy alloy (HEA) was studied. The TLP bonding was performed using AWS BNi-2 interlayer at 1050 °C with the TLP bonding time of 20, 60, 180 and 240 min. The effect of bonding time on the joint microstructure was characterized by SEM and EDS. Microstructural results confirmed that complete isothermal solidification occurred approximately at 240 min of bonding time. For samples bonded at 20, 60 and 180 min, athermal solidification zone was formed in the bonding area which included Cr-rich boride and Mn₃Si intermetallic compound. For all samples, the γ solid solution was formed in the isothermal solidification zone of the bonding zone. To evaluate the effect of TLP bonding time on mechanical properties of joints, the shear strength and micro-hardness of joints were measured. The results indicated a decrement of micro-hardness in the bonding zone and an increment of micro-hardness in the adjacent zone of joints. The minimum and maximum values of shear strength were 100 and 180 MPa for joints with the bonding time of 20 and 240 min, respectively.     

Investigation the effect of FeNiCoCrMo HEA addition on properties of B4C ceramic prepared by spark plasma sintering

In this study, monolithic B₄C and B₄C-based ceramics incorporating FeNiCoCrMo dual-phase (FCC and BCC) high entropy alloys (HEAs) were produced by spark plasma sintering (SPS). The effect of additives on the densification behavior, mechanical properties, microstructures, and phase evaluation of the samples were investigated. X-ray analysis confirmed the existence of FCC structured HEA and depletion of BCC structured HEA, after high-temperature reaction between B₄C-HEAs. The addition of HEAs enhanced the densification behavior by liquid phase sintering. Furthermore, hardness and fracture toughness values of the samples increased with increasing HEAs content. Fracture toughness and hardness values for all composites were higher than the monolithic B₄C. A combination of the highest density (∼99.22 %) and the best mechanical properties (32.3 GPa hardness and 4.53 MPa m^1/2 fracture toughness) was achieved with 2.00 vol.% HEA addition.     

Synthesis and characterization of hydrogenated novel AlCrFeMnNiW high entropy alloy

A novel high entropy alloy (HEA) i.e. AlCrFeMnNiW is synthesized via high-energy planetary ball milling with an average crystallite size of 10.37 nm. The morphology study of hydrogenated and dehydrogenated HEA is carried out through Scanning Electron Microscope (SEM). The HEA is charged with hydrogen using inhouse Sievert's Apparatus which results to be maximum hydrogen storage capacity of 0.615 wt% at atmospheric pressure and temperature. The dehydrogenation of the sample is performed through thermogravimetry (TG) at different scanning rate. The crystalline structure (i.e. lattice parameters) and chemical composition of HEA is studied using X-Ray Diffraction (XRD) and Energy Dispersive X-Ray analysis (EDX) respectively. The unit cell volume of as-prepared alloy is estimated as 0.03131 nm³ whereas the average crystallite size as 10.37 nm. It is observed that the unit cell volume is increased by 0.67% and crystallite size decreased by 10.8% upon hydrogenation whereas it is then decreased by 0.2% and increased by 6.7% respectively upon dehydrogenation. Activation energy during hydrogen desorption is found to be −8.161 kJ/mol. The enthalpy and entropy of the mixing are estimated to be −2.645 kJ/mol and 1.793 R J/mol K.