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The Tonga volcano erupted on 15 January 2022, at 04:15:45 UTC, which significantly influenced the atmosphere and space environment, at the same time, an unprecedented opportunity to monitor ionospheric anomalies is provided by its powerful eruption. In current studies of traveling ionospheric disturbance (TID) triggered by the 2022 Tonga volcanic eruption, the particular phenomenon of ionospheric disturbances in various parts of the world has not been reasonably explained, and the vertical ionospheric disturbances are still not effectively detected. In this paper, we calculate the high-precision slant total electron content (STEC) from more than 3000 ground-based GPS stations distributed around the world, then we obtain the radio occultation (RO) data from near-field COSMIC-2 profiles and investigate the horizontal TID and the vertical ionospheric disturbances by the singular spectrum analysis (SSA). Horizontal TID propagation captured by GPS STEC results indicates that acoustic-gravity waves dominate the energy input at the beginning of the ionospheric disturbance with an approximate speed of 1050 m/s initially. With the dissipation of the shock energy, lamb waves become a dominant mode of ionospheric disturbances, moving at a more stable speed of about 326 m/s to a range of 16,000 km beyond the far-field. Local characteristics are evident during the disturbance, such as the ionospheric conjugation in Australia and the rapid decay of TID in Europe. The shock-Lamb-tsunami waves’ multi-fluctuation coupling is recorded successively from the COSMIC-2 RO observation data. The shock and Lamb waves can perturb the whole ionospheric altitude. In contrast, the disturbance caused by tsunami waves is much smaller than that of acoustic-gravity waves and Lamb waves. In addition, influenced by the magnetic field, the propagation speed of TID induced by Lamb waves is higher towards the northern hemisphere than towards the southern hemisphere.

COSMIC-2 is a remote sensing satellite mission that mainly provides scientific data for weather forecasting, ionosphere, and climate research. High precise orbit is the basis for the application of remote sensing satellite data. In order to realize the precise orbit determination (POD) of COSMIC-2, we have assessed the quality of space-borne GPS observation in detail, including the utilization of GPS observations, cycle slip ratio (o/slps), multipath error, single-noise ratio (SNR) and ionospheric delay rate (IOD) of the data, realized the POD of COSMIC-2 with the reduced dynamic (RD) method, and evaluated the accuracy of the solved orbit by means of the carrier-phase residual, overlapping orbit comparison and the reference orbit comparison. The data quality assessments show that the data is less affected by the multipath effect, the utilization of the data is low, cycle slips occur frequently, and the carrier-phase data is often interrupted. The POD results indicate that the root mean square (RMS) values of the carrier-phase residuals of six COSMIC-2 satellites are between 6.0 mm and 7.5 mm, The mean RMS values of the overlapping orbit are better than 0.92 cm, 1.33 cm and 1.03 cm in the radial (R), tangential (T) and normal (N) directions respectively, and the mean RMS values of the six satellites in the 3D direction are between 1.38 cm and 1.75 cm. The mean RMS values in R, T and N directions orbit determination accuracy of the reference orbit comparison are better than 5.61 cm, 6.59 cm and 2.29 cm respectively, and the mean RMS values of the six satellites in the 3D direction are between 7.35 cm and 8.79 cm.

When a fire occurs on storage racks in a warehouse, it is not advisable to find the location of the fire point accurately, because there are a large number of goods on the storage rack, and many interference factors such as light will disturb the precise location of the fire. In response to the above problems, and thanks to the high-speed growth of deep learning technology, we propose an edge detection method and apply it in fire locations successfully. We adopt VGG-16 as our backbone and introduce an attention module to suppress background information and eliminate interference. We test the proposed method on our collected dataset, and the results show that our proposed model can extract the shelf edges more completely and locate the fire point accurately. In terms of detection speed, our method can achieve 0.188 s per image, which meets the requirements of real-time detection. Our approach lays a good foundation for the precise extinguishing of fire that occurs on storage racks.

Two ferrite/martensitic (F/M) steels with different Si concentrations (0 and 0.4 wt.%) were irradiated by 250 keV He2+ ions with different fluences of 2 × 1016 ions/cm2 and 1 × 1017 ions/cm2. Transmission electron microscopy and a nanoindenter were employed to investigate their microstructure evolution and irradiation hardening effects induced by high-energy He2+ ions. A large number of He bubbles formed in the Si-free and Si-containing F/M steels, which preferentially nucleated and grew at the lath and phase boundaries. Owing to the inhibiting effect of Si addition on He bubble growth, the He bubbles in the Si-containing sample exhibited smaller size and higher density at the same He2+ fluence. Nanoindenter measurement revealed that typical irradiation hardening was observed in the F/M steel, and 1/2 and type dislocation loops formed by He2+ irradiation was recognized as the dominant mechanism. The addition of Si induced an increase in the number density of dislocation loops, leading to the exacerbation of the irradiation hardening, and the results are basically in agreement with the theoretical analysis based on the dispersion barrier hardening (DBH) and Friedel–Kroupa–Hirsch (FKH) models.

The effects of alloying elements (Si, Cr, Mo) on the solution and diffusion of oxygen (O) atoms at the grain boundary of iron (Fe) Σ5(310)/[001] are investigated by the simulations of ab initio density functional theory (DFT). It is found that Si, Mo and Cr prefer to segregate to the grain boundary, and further affect the solution and diffusion of O atoms at Fe grain boundaries. The segregated Cr promotes the solution of O, while Si and Mo inhibit the solution of O at the grain boundary. Meanwhile, Cr and Si accelerate the diffusion of O, and Mo retards the diffusion of O in the grain boundary. Further analysis indicates that the effects are closely related to the interactions between the alloying elements and O atoms, which are determined by the competition between the distortion of local structure and the charge transfer between local atoms. Finally, the effects of alloying elements on the O concentration distribution near the grain boundary are explored by employing the Langmuir–McLean models. This work not only provides insights into the effects of alloying elements on the solution and diffusion of O at grain boundaries, but also provides parameters of the atomic interactions for the initial oxidation simulation on a large scale, which relates to the growth of oxide in polycrystalline systems with various grain sizes at experimental temperatures.

The strain profile in the subsurface of He-ion-irradiated W was figured out by unfolding the synchrotron-grazing incidence X-ray diffraction (S-GIXRD) patterns at different incidence angles. The results show that for 2 × 1021 ions/m2 He2+-irradiated W, in addition to a compressive strain exists in the depths of 0–100 nm due to mechanical polishing, an expansion strain appears in the depth beyond 100 nm owing to irradiation-induced lattice swelling. This work provides a reference for the study of irradiation damage in the subsurface by S-GIXRD.

In this study, oxide dispersion-strengthened Cu alloy with a Y2O3 content of 1 wt.% was fabricated through citric acid sol-gel synthesis and spark plasma sintering (SPS). The citric acid sol-gel method provides molecular mixing for the preparation of precursor powders, which produces nanoscale and uniformly distributed Y2O3 particles in an ultrafine-grained Cu matrix. The effects of nanoscale Y2O3 particles on the microstructure, mechanical properties and thermal conductivity of the Cu-1wt.%Y2O3 alloy were investigated. The average grain size of the Cu-1wt.%Y2O3 alloy is 0.42 μm, while the average particle size of Y2O3 is 16.4 nm. The unique microstructure provides excellent mechanical properties with a tensile strength of 572 MPa and a total elongation of 6.4%. After annealing at 800 °C for 1 h, the strength of the alloy does not decrease obviously, showing excellent thermal stability. The thermal conductivity of Cu-1wt.%Y2O3 alloy is about 308 Wm−1K−1 at room temperature and it decreases with increasing temperature. The refined grain size, high strength and excellent thermal stability of Cu-1wt.%Y2O3 alloys can be ascribed to the pinning effects of nanoscale Y2O3 particles dispersed in the Cu matrix. The Cu-Y2O3 alloys with high strength and high thermal conductivity have potential applications in high thermal load components of fusion reactors.

Intermetallic alloy ZrCo is a good material for storing tritium (T). However, ZrCo is prone to disproportionation reactions during the process of charging and discharging T. Alloying atoms are often added in ZrCo, occupying the Zr or Co site, in order to restrain disproportionation reactions. Meanwhile, T often decays into helium (He), and the purity of T seriously decreases once He escapes from ZrCo. Therefore, it is necessary to understand the influence of alloying atoms on the basic stability property of He. In this work, we perform systematical ab initio calculations to study the stability property of He in ZrCoH3 (ZrCo adsorbs the H isotope, forming ZrCoH3). The results suggest that the He atom will undergo displacements of 0.31 and 0.12 Å when it substitutes for Co and Zr, respectively. In contrast, the displacements are very large, at 0.67–1.09 Å, for He replacing H. Then, we introduce more than 20 alloying atoms in ZrCo to replace Co and Zr in order to examine the influence of alloying atoms on the stability of He at H sites. It is found that Ti, V, Cr, Mn, Fe, Zn, Nb, Mo, Tc, Ru, Ta, W, Re, and Os replacing Co can increase the substitution energy of H by the He closest to the alloying atom, whereas only Cr, Mn, Fe, Mo, Tc, Ru, Ta, W, Re, and Os replacing Co can increase the substitution energy of H by the He next closest to the alloying atom. The influence of the alloying atom substituting Zr site on the substitution energies is inconspicuous, and only Nb, Mo, Ru, Ta, and W increase the substitution energies of H by the He closest to the alloying atom. The increase in the substitution energy may suggest that these alloy atoms are conducive to fix the He atom in ZrCo and avoid the reduction in tritium purity.

Predicting mechanical properties of metals from big data is of great importance to materials engineering. The present work aims at applying artificial neural network (ANN) models to predict the tensile properties including yield strength (YS) and ultimate tensile strength (UTS) on austenitic stainless steel as a function of chemical composition, heat treatment and test temperature. The developed models have good prediction performance for YS and UTS, with R values over 0.93. The models were also tested to verify the reliability and accuracy in the context of metallurgical principles and other data published in the literature. In addition, the mean impact value analysis was conducted to quantitatively examine the relative significance of each input variable for the improvement of prediction performance. The trained models can be used as a guideline for the preparation and development of new austenitic stainless steels with the required tensile properties.

The strengthening energy or embrittling potency of an alloying element is a fundamental energetics of the grain boundary (GB) embrittlement that control the mechanical properties of metallic materials. A data-driven machine learning approach has recently been used to develop prediction models to uncover the physical mechanisms and design novel materials with enhanced properties. In this work, to accurately predict and uncover the key features in determining the strengthening energies, three machine learning methods were used to model and predict strengthening energies of solutes in different metallic GBs. In addition, 142 strengthening energies from previous density functional theory calculations served as our dataset to train three machine learning models: support vector machine (SVM) with linear kernel, SVM with radial basis function (RBF) kernel, and artificial neural network (ANN). Considering both the bond-breaking effect and atomic size effect, the nonlinear kernel based SVR model was found to perform the best with a correlation of r2 ~ 0.889. The size effect feature shows a significant improvement to prediction performance with respect to using bond-breaking effect only. Moreover, the mean impact value analysis was conducted to quantitatively explore the relative significance of each input feature for improving the effective prediction.

Tungsten fibre nets reinforced tungsten composites (Wf/W) containing four net layers were fabricated by spark plasma sintering (SPS), hot pressing (HP) and cold rolling after HP (HPCR), with the weight fraction of fibres being 17.4%, 10.5% and 10.5%, respectively. The relative density of the HPCRed samples is the highest (99.8%) while that of the HPed composites is the lowest (95.1%). Optical and scanning electron microscopy and electron back scattering diffraction were exploited to characterize the microstructure, while tensile and hardness tests were used to evaluate the mechanical properties of the samples. It was found that partial recrystallization of fibres occurred after the sintering at 1800 °C. The SPSed and HPed Wf/W composites begin to exhibit plastic deformation at 600 °C with tensile strength (TS) of 536 and 425 MPa and total elongation at break (TE) of 11.6% and 23.0%, respectively, while the HPCRed Wf/W composites exhibit plastic deformation at around 400 °C. The TS and TE of the HPCRed Wf/W composites at 400 °C are 784 MPa and 8.4%, respectively. The enhanced mechanical performance of the Wf/W composites over the pure tungsten can be attributed to the necking, cracking, and debonding of the tungsten fibres.