pyMPEALab Toolkit for Accelerating Phase Design in Multi-principal Element Alloys

Multi-principal element alloys (MPEAs) occur at or nearby the centre of the multicomponent phase space, and they have the unique potential to be tailored with a blend of several desirable properties for the development of materials of future. The lack of universal phase diagrams for MPEAs has been a major challenge in the accelerated design of products with these materials. This study aims to solve this issue by employing data-driven approaches in phase prediction. A MPEA is first represented by numerical fingerprints (composition, atomic size difference , electronegativity , enthalpy of mixing , entropy of mixing , dimensionless $$\Omega$$ Ω parameter, valence electron concentration and phase types ), and an artificial neural network (ANN) is developed upon the datasets of these numerical descriptors. A pyMPEALab GUI interface is developed on the top of this ANN model with a computational capability to associate composition features with remaining other input features. With the GUI interface, an user can predict the phase(s) of a MPEA by entering solely the information of composition. It is further explored on how the knowledge of phase(s) prediction in composition-varied $$\hbox {Al}_x$$ Al x CrCoFeMnNi and $$\hbox {CoCrNiNb}_x$$ CoCrNiNb x can help in understanding the mechanical behavior of these MPEAs. Graphic Abstract

Fast Storm Surge Ensemble Prediction using Searching Optimization of a Numerical Scenario Database

Accurate storm surge forecasts provided rapidly could support timely decision-making with consideration of tropical cyclone (TC) forecasting error. This study developed a fast storm surge ensemble prediction method based on TC track probability forecasting and searching optimization of a numerical scenario database (SONSD). In a case study of the Fujian Province coast (China), a storm surge scenario database was established using numerical simulations generated by 93,150 hypothetical TCs. In a GIS-based visualization system, a single surge forecast representing 2562 distinct typhoon tracks and the occurrence probability of overflow of seawalls along the coast could be achieved in 1–2 min. Application to the cases of Typhoon Soudelor (2015) and Typhoon Maria (2018) demonstrated that the proposed method is feasible and effective. Storm surge calculated by SONSD had excellent agreement with numerical model results (i.e., mean MAE/RMSE: 7.1/10.7 cm, correlation coefficient: >0.9). Tide prediction also performed well with MAE/RMSE of 9.7/11.6 cm versus the harmonic tide, and MAE/RMSE of phase prediction for all high waters of 0.25/0.31 h versus observations. The predicted high-water level was satisfactory (MAE of 10.8 cm versus observations) when the forecasted and actual positions of the typhoon were close. When the forecasted typhoon position error was large, the ensemble surge prediction effectively reduced prediction error (i.e., the negative bias of −58.5 cm reduced to −5.2 cm versus observations), which helped avoid missed alert warnings. The proposed method could be applied in other regions to provide rapid and accurate decision-making support for government departments.

A review on phase prediction in high entropy alloys

The formation of single phase solid solution in High Entropy Alloys (HEAs) is essential for the properties of the alloys therefore, numerous approach were proposed by many researchers to predict the stability of single phase solid solution in High Entropy Alloy. The present review examines some of the recent developments while using computational intelligence techniques such as parametric approach, CALPHAD, Machine Learning etc. for prediction of various phase formation in multicomponent high entropy alloys. A detail study of this data-driven approaches pertaining to the understanding of structural and phase formation behaviour of a new class of compositionally complex alloys is done in the present investigation. The advantages and drawbacks of the various computational are also discussed. Finally, this review aims at understanding several computational modeling tools complying the thermodynamic criteria for phase formation of novel HEAs which could possibly deliver superior mechanical properties keeping an aim at advanced engineering applications.