Electromagnetic effect stimulated by strain wave in rocks

When strain wave passes through rock, a changing magnetic field due to piezomagnetic effect will propagate at the velocity of sound. The changing magnetic field will stimulate electromagnetic ef fect in the surrounding area. This paper reports the experimental results which proves the existence of such phenomena. And the synthetic piezomagnetic coefficients of rock are measured from experimental results. The piezomagnetic effect can be used to explain some electromagnetic phenomena produced in the process of earthquakes.

Mechanical and magnetic hysteresis as indicators of the origin and inception of fatigue damage in steel

N2 and N3 are known as the transition points of the three principal stages of fatigue: initial accommodation, accretion of damage and terminal fatigue. Many experiments show that the ratios of N2/Nf and N3/Nf tend to be stable even though the specific N2 and N3 values may fluctuate widely. The primary goal of this research is to study the piezomagnetic field surrounding AISI 1018 steel specimen under repeated loads and to find the ratio values of N2/Nf and N3/Nf by analyzing 11 sets of low-cycle fatigue data. An MTS-810 testing system with a peak capacity of 222 kN was used to obtain the data which consisted of stress, strain, and piezomagnetic field. A computer program was constructed to track the evolution of the piezomagnetic field and re- gression analysis was carried out to determine N2 and N3 values. It was observed that there exists a consistent relationship between N2 and Nf. The apparent invariance of the ratio N2/Nf implies that N2 may be identified as an index of performance in the early loading response of a specimen that forecasts its fatigue life, Nf. It has been demonstrated that measurements of the magnetic and mechanical hysteresis can yield significant insights into the various stages of the development of a fatigue critical microstructure which culminates in complete rupture of the material.

Optimizing magnetoelastic properties by machine learning and high-throughput micromagnetic simulation

Magnetoelastic couplings in giant magnetostrictive materials(GMMs)attract significant interests due to their extensive applications in the fields of spintronics and energy harvesting devices.Understanding the role of the selection of materials and the response to external fields is essential for attaining desired functionality of a GMM.Herein,machine learning(ML)models are conducted to predict saturation magnetostrictions(λ_(s))in RFe_(2)-type(R=rare earth)GMMs with different compositions.According to ML-predicted composition–λsrelations,it is discovered that the values ofλshigher than1100×10^(-6)are almost situated in the composition space surrounded by 0.26≤x≤0.60 and 1.90≤y≤2.00 for the ternary compounds of Tb_(x)Dy_(1-x)Fe_(y).Assisted by ML predictions,the compositions are further narrowed down to the space surrounded by 0.26≤x≤0.32 and 1.92≤y≤1.97 for the excellent piezomagnetic(PM)performance in the Tb_(x)Dy_(1-x)Fe_(y)based PM device through our developed high-throughput(HTP)micromagnetic simulation(MMS)algorithm.Accordingly,high sensitivities up to10.22-13.61 m T·MPa^(-1)are observed in the optimized range within which the available experimental data fall well.This work not only provides valuable insights toward understanding the mechanism of magnetoelastic couplings,but also paves the way for designing and optimizing highperformance magnetostrictive materials and PM sensing devices.