Three-step learning strategy for designing 15Cr ferritic steels with enhanced strength and plasticity at elevated temperature

15Cr ferrite steels are urgently required in advanced Ultra-supercritical power plants but meet design challenges in balancing excellent strength and plasticity at high temperatures.We developed a three-step learning strategy based on mutually driven machine learning and purposeful experiments to complete this multi-objective task.Compared with traditional adaptive learning and local-interpolation learning,this step-by-step modular manner provides good transparency and interpretability of the information flow,which is ensured by identifying essential factors from an exquisitely prepared composition-microstructure dataset,and learning valuable knowledge about the composition-property relationship.The requirement of only two groups of experiments indicates the low cost and high efficiency of the strategy.Performing the strategy,we found that Ti is another key element affecting the Laves phase besides Mo and W,and their effects on ultimate tensile strength(UTS)and elongation were also uncovered.Importantly,several low-cost steels free of Co were successfully designed,and the best steel exhibited 156%,31%,and 62%higher UTS and elongation at 650°C than the typical 9Cr,15Cr,and 20Cr steels,respectively.Based on the advantages and success of the strategy in terms of alloy improvement,we believe the strategy suits other multi-objective design tasks in more materials systems.

Oxidation in Ca/K-1144 iron-based superconductors polycrystalline compounds

Iron‐based superconductors(IBSCs)are a class of material under investigation for the development of superconducting wires in the low‐temperature‐high magnetic fields power application.Among the various families of IBSCs,the 1144 CaKFe_(4)As_(4) compound is a promising material able to achieve outstanding superconducting properties with a cheap and simple chemical composition.Oxidation,in these compounds,is considered an obstacle for high intergranular critical current density,J_(c,GB).A study devoted to the evaluation of oxidation phenomena and their effects on the superconducting properties is thus needed in order to fully understand the involved mechanisms.From the evaluation of polycrystalline samples obtained by a mechanochemically assisted synthesis route,a degradation of the critical temperature and critical currents has been observed concurrently with oxygen accumulation at grain boundaries in open porosities.However,the crystalline structure at an atomic level seems not affected,as well as intragranular superconducting properties assessed by means of calorimetric methods.These results suggest that loss of superconducting properties in Ca/K‐1144 compounds following oxidation is significantly associated with the worsening of grain connectivity.

Effects of Boron on the Microstructure,Ductility-dip-cracking,and Tensile Properties for NiCrFe-7 Weld Metal

The distribution of boron and the microstructure of grain boundary（GB） precipitates（M23（C,B） 6 and M 2B）have been analyzed with their effects on the susceptibility of ductility-dip-cracking（DDC） and tensile properties for NiCrFe-7 weld metal,using optical microscopy（OM）,secondary ion mass spectroscopy（SIMS）,scanning electron microscopy（SEM）,and transmission electron microscopy（TEM）.The results show that boron segregates at GBs in NiCrFe-7 weld metal during the welding process.The segregation of boron at GBs promotes the formation of continuous M23（C,B） 6 carbide chains and M 2B borides along GBs.The addition of boron aggravates GB embrittlement and causes more DDC in the weld metal,by its segregation at GBs presenting as an impurity,and promoting the formation of larger and continuous M 23（C,B） 6 carbides,and M 2B borides along GBs.DDC in the weld metal deteriorates the ductility and tensile strength of the weld metal simultaneously.

Tailoring nanoprecipitates for ultra-strong high-entropy alloys via machine learning and prestrain aging

The multi-principal-component concept of high-entropy alloys(HEAs) generates numerous new alloys.Among them,nanoscale precipitated HEAs have achieved superior mechanical properties and shown the potentials for structural applications.However,it is still a great challe nge to find the optimal alloy within the numerous candidates.Up to now,the reported nanoprecipitated HEAs are mainly designed by a trialand-error approach with the aid of phase diagram calculations,limiting the development of structural HEAs.In the current work,a novel method is proposed to accelerate the development of ultra-strong nanoprecipitated HEAs.With the guidance of physical metallurgy,the volume fraction of the required nanoprecipitates is designed from a machine learning of big data with thermodynamic foundation while the morphology of precipitates is kinetically tailored by prestrain aging.As a proof-of-principle study,an HEA with superior strength and ductility has been designed and systematically investigated.The newly developed γ’-strengthened HEA exhibits 1.31 GPa yield strength,1.65 GPa ultimate tensile strength,and 15% tensile elongation.Atom probe tomography and transmission electron microscope characterizations reveal the well-controlled high γ’ volume fraction(52%) and refined precipitate size(19 nm).The refinement of nanoprecipitates originates from the accelerated nucleation of the γ’ phase by prestrain aging.A deeper understanding of the excellent mechanical properties is illustrated from the aspect of strengthening mecha nisms.Finally,the versatility of the current design strategy to other precipitation-hardened alloys is discussed.

Government Investment in Disaster Risk Reduction Based on a Probabilistic Risk Model: A Case Study of Typhoon Disasters in Shenzhen, China

In recent years, cost-benefit analysis(CBA) has played an important role in disaster risk reduction(DRR)investment decisions, and now increasing attention is being paid to its application in developing countries. This article discusses government investment choices in DRR against typhoon disasters in Shenzhen, China. While the existing literature mainly focuses on disaster mitigation measures such as structural retrofitting, this study proposes a holistic framework of DRR investments in which structural(windproof retrofitting) and financial(insurance premium subsidies and post-disaster relief) are all taken into account.In particular, intermeasure spillover effects are measured and used in CBA. The results show that insurance premium subsidies yield the highest benefit-cost ratio and should be prioritized in investment. Windproof retrofitting comes in second place in terms of the benefit-cost ratio and can be considered when there is a sufficient budget. These results further confirm the need of a holistic review of government DRR investments to derive policy recommendations, while challenges remain in relation to the probabilistic modeling capacity to support CBA.