The contradictory relationship between magnetism and ductility restricts further applications of FeCoV alloys in high-performance electrical machines. The role of the BCC-B2 transition, accompanied by vanadium (V) site occupancies, in magnetic moments and ductility has been explored using first-principles calculations. The variations in magnetism and ductility of FeCoV alloys are attributed to the coupling of the BCC-B2 transition and V occupancies. When V replaces Fe atoms in the equiatomic B2-FeCo alloy, the superior magnetism observed in B2-Fe50-cCo50Vc alloys is a consequence of the enhanced local magnetic moment of Fe and the ferrimagnetic-ferromagnetic transition in the magnetic state. Moreover, due to the preferential V occupancy in the B2 phase, the B2-Fe46Co50V4 alloy exhibits comparable ductility to the BCC-Fe50Co46V4 alloy. The results indicate that the increased brittleness in the B2 phase arises from the raised Peierls stress and the enhanced covalent component in interatomic bonding, which is caused by the strong hybridization between Fe and Co atoms. Pearson correlation analysis illustrates that valence electron concentration (VEC) and V content are significant factors in the contradictory relationship between magnetization and ductility. The theoretical results demonstrate that tuning the V content and atomic occupancies is helpful to achieve a trade-off between magnetization and ductility in B2-FeCoV alloys.

Creep rupture extrapolation is crucial for high-temperature materials served in power plants. Many analytical models can be used for creep rupture analysis, and fundamental models are also available. Machine learning is also an alternative. However, unphysical prediction curves occur readily in common machine learning algorithms, where one must manipulate the best results or ignore the less satisfactory ones. Using just high regression coefficients and low errors is not enough to obtain high accuracy of the methods. Never-theless, five soft constrained machine learning algorithms (SCMLAs), where soft con-straints, stability analysis by culling long-time or low-stress data, extrapolation from short to long times, and errors of solutions and algorithms are considered, are used for creep rupture prediction in this work. The models can generate reasonable results for fitting all data, extrapolating from short to long times, and stability analysis for Sanicro 25 after a number of tests. The errors of solutions for all the analyses are in a quite reasonable range, including extrapolation and stability analysis. The average relative standard deviation of the five SCMLAs is less than 2.5% at three times the maximum experimental creep rupture time. Creep rupture strength of the austenitic stainless steel Sanicro 25 can be predicted quantitatively by taking the average predicted stresses of the five SCMLAs. The method can also be used for other high-temperature alloys with similar creep degradation mechanisms.

Machine learning is extensively utilized for predicting creep rupture of high-temperature steels. Recently, five soft-constrained machine learning algorithms (SCMLAs) have been developed to enhance the extrapolation capabilities of machine learning. These SCMLAs were applied to the austenitic steel Sanicro 25, showing their potential. To improve SCMLAs, this study has introduced new guidelines that address temperature culling within the input range and temperature extrapolation beyond the input range. Leveraging these guidelines, the SCMLAs were extended to various austenitic and martensitic stainless steels. The predicted results of TP316H, the data of which is representative of austenitic stainless steels, were validated through error estimates. Furthermore, notable agreement has been reached for temperature culling and temperature extrapolation, as demonstrated for TP91 and TP92 martensitic steels. The effects of single casts and the temperature dependence of the predictions have been analyzed for the studied materials. Consistent results can be readily achieved through systematic evaluations of SCMLAs for extrapolating up to 300,000 h or three times the maximum experimental rupture time for the studied materials. It is demonstrated that SCMLAs can provide reliable creep rupture life prediction across various high-temperature materials.

Premature and unexpected creep damage is a significant concern in high-temperature engineering. Identifying outliers in creep rupture data is essential for assessing the risk of premature creep failure. This study proposes a new method to evaluate premature creep failure using log-logistic distribution fit of prediction errors and outlier positions. Fitting results for seven different alloys were obtained from extrapolation procedures using soft-constrained machine learning algorithms (SCMLAs) and constrained time-temperature parameters (TTPs) based on prior research. A comprehensive statistical analysis was conducted for all materials. The log-logistic distribution was validated as a suitable method for fitting prediction error distributions. Regression plots demonstrate effective residual balance and accurate outlier capture. The best fitting methods were identified based on the width of the distributions. Outlier positions were used to evaluate the probability of premature creep failure quantitatively. For example, a 0.5% probability of observing creep rupture strengths that are approximately 50% lower than the standardized creep stress was found for TP316H, T321H, and high Cr steels SUH616B. These findings offer valuable insights for estimating premature creep failure in materials.

Although fundamental models have been developed for time extrapolation of creep rupture data, empirical methods are still dominating. One limitation with the empirical methods is that no systematic way of estimating the error in the results has been available. To remove this limitation, formulae for error estimates are developed in the present paper. Error estimates are first established for single creep rupture curves. Then error estimates are formulated for creep data at several test temperatures assessed with Time-Temperature Parameters (TTPs). A new requirement is introduced in the optimization, namely that the first and second derivatives of the rupture curve should be negative, referred to as constrained. The constrained optimization significantly reduces the error in the extrapolated values. The constrained procedure is applied to the austenitic steel Sanicro 25. The procedure gives quite a stable result, and the post-assessment tests developed by ECCC are satisfied automatically for five common TTPs tested.

For modern creep-resistant steels, ductility is primarily controlled by cavitation, since brittle rupture is dominating during long-term service. In recent years, the present authors have formulated fundamental models for nucleation and growth of cavities that have been verified to be able to describe experiments for austenitic stainless steels. These equations, together with models for dislocation creep, are used in this paper to present basic modelling results for the creep ductility of austenitic stainless steels for the first time. New results are also presented for ductile rupture, where the elongation values are mainly governed by plastic instability and necking. The Hart criterion is used to identify the strain where the instability forms. Modelling shows that the instability grows very slowly and that its size is not significant until close to rupture. These facts are used to demonstrate that ductility during ductile rupture can be predicted from necking behaviour.

The effects of Cr and W on high-temperature properties of Ni-based alloys are derived from first-principles calculations combined with a quasi-harmonic Debye model. The modeling procedure integrates all contributions from electronic excitations, magnetic fluctuations and lattice vibrations. The predicted lattice parameter and thermal expansion coefficient agree well with available reference values. The temperature dependence of elastic moduli for Ni-Cr alloys is described satisfactorily. Debye temperature has been calculated to show a strong volume dependence but a weak temperature dependence. A close relationship between the concentration dependence of elastic moduli and the volume dependence of Debye temperature, and thus the vibrational free energy, has been established. Besides, the softening of elastic moduli with increasing temperature is shown to be a result of thermal expansion which mainly comes from lattice vibrations. Therefore, lattice vibrations affect both temperature and concentration dependencies of elastic properties at elevated temperatures. With the help of Labusch-Nabarro model, solid solution hardening caused by Cr and W solutes in Ni has been analyzed. Moreover, the presence of solute atoms increases elastic anisotropy. The effects of Cr or W additions on the thermodynamic properties are found to be small compared to the effect of temperature.

Temperature-dependent properties are very useful in modeling the behavior of materials servicing at high temperature such as austenitic stainless steels. Besides, for predicting the mechanical properties of such alloys, experimental data at high temperature are very important, but scarce. In this work, we use first-principles calculations combined with a quasi-harmonic Debye model to evaluate the Helmholtz free energy of paramagnetic Fe25Cr20NiMnNb austenitic stainless steel as a function of temperature and volume. The contributions due to electronic excitations, magnetic fluctuations, and lattice vibrations are taken into account. Elastic and other thermodynamic properties of the material, such as the equilibrium lattice parameter, elastic moduli, entropy, are derived from the free energy in the temperature range from 800 to 1100 K and compared with available experimental data. The influence of Nb concentration, as well as the effects of electronic excitations and lattice vibrations, on the elastic and thermodynamic properties are discussed in detail. The elastic moduli are found to decrease with increasing temperature. This elastic softening phenomenon is mainly due to thermal expansion; the contribution of thermal electronic excitations is found to be relatively small.

Piezocatalysts have attracted much attention due to their excellent degradation ability for organics. In this work, three types of BaTiO3 (BTO) nanostructures, including hydrothermally synthesized nanocubes (NCs), sol-gel calcined nanoparticles (NPs), and electrospun nanofibers (NFs), are prepared for catalyzing the dye degradation. Compared with the NCs and NPs, the NFs exhibit a higher piezocatalytic degradation performance due to the large specific surface area, fine crystal size, and easy deformation structure. Moreover, the kinetic factors, including initial dye concentration, ionic strength, ultrasonic power, and applied action, influencing the degradation performance of the BTO NFs are analyzed deeply. A high degradation rate constant of 0.0736 min(-1) is achieved for rhodamine B, which is superior compared with the previous reports. The excellent stability of BTO NFs is demonstrated by the cycling tests, where a high degradation efficiency of 97.6% within 110 min is still obtained after the third cycle. Furthermore, the mechanism of piezocatalysis revealed that the hydroxyl and superoxide radicals are the main reactive species in the degradation process. This work is of importance for the development of high-performance piezocatalysts and highlights the potential of piezocatalysis for water remediation.

Creep rupture prediction is always a critical matter for materials serving at high temperatures and stresses for a long time. Empirical models are frequently used to describe creep rupture, but the parameters of the empirical models do not have any physical meanings, and the model cannot reveal the controlling mechanisms during creep rupture. Fundamental models have been proposed where no fitting parameters are involved. Both for ductile and brittle creep rupture, fundamental creep models have been used for the austenitic stainless steel Sanicro 25 (23Cr25NiWCoCu). For ductile creep rupture, the dislocation contribution, solid solution hardening, precipitation hardening, and splitting of dislocations were considered. For brittle creep rupture, creep cavitation models were used taking grain boundary sliding, formation, and growth of creep cavities into account. All parameters in the models have been well defined and no fitting is involved. MatCalc was used for the calculation of the evolution of precipitates. Some physical parameters were obtained with first-principles methods. By combining the ductile and brittle creep rupture models, the final creep rupture prediction was made for Sanicro 25. The modeling results can predict the experiments at long-term creep exposure times in a reasonable way.