Addressing the persistent trade-off between computational accuracy and execution efficiency in generating high-resolution geospatial grids, this paper proposes a novel high-precision interpolation algorithm specifically designed for rapid, large-scale geospatial grid generation. The algorithm first employs a Gaussian weighted quadratic surface as its interpolation kernel—a method that balances the smoothness of results with adaptability to non-linear terrain variations to ensure high accuracy. Subsequently, to tackle the model's high computational cost, a targeted hybrid optimization framework was designed, leveraging a KD-Tree to accelerate neighbor searches and multithreading to parallelize the independent grid computations, thereby substantially enhancing execution efficiency. Experimental results demonstrate that: 1) The proposed algorithm outperforms three traditional methods (Moving Surface Fitting, Natural Neighbor Interpolation, and Spline Interpolation), achieving reductions in Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of up to 63.6% and 61.2%, respectively, while attaining a coefficient of determination (R²) as high as 0.998611. 2) The optimization framework delivers a 24-fold speedup in overall runtime without any loss of accuracy. This study offers a high-precision, high-efficiency solution for large-scale geospatial data processing.
Corrosion pits preferentially initiate in the bainitic regions of Q420qNH steel against the simulated marine atmospheric environment, where a high initial dislocation density is present. This occurs in addition to the initiation sites associated with nonmetallic inclusions that exhibit significant lattice distortion. The formation of corrosion the corrosion-induced non elastic buckling exacerbates the cyclic softening effect and significantly reduces fatigue strength. At a strain amplitude of 0.4%, the fatigue life decreases by 73.2% after 60 days of pre-corrosion treatment. Layered segregation of Cr and Cu alloying elements could develop the formation of dense rust layer to inhibit further surface degradation.
With the increasing demand for machine learning models to predict the mechanical properties of steel, model interpretability has been widely concerned. This is crucial for regulating the chemical composition and processing parameters of steel plates. In this study, process parameters and composition were considered as features, while ultimate tensile strength (UTS) and total elongation (TE) were served as the target variables. The mechanical properties of DH auto-steel plates were predicted using a machine learning method based on data from an industrial production line. The model trained with the Gradient Boosting Regression (GBR) algorithm demonstrated good prediction accuracy. In contrast, the symbolic regression expression obtained by the Sure Independence Screening and Sparsifying Operator (SISSO) algorithm exhibited a clearer relationship between the features and the targets but with lower predictive accuracy. To further analyze the effect of features on the properties, thermodynamic parameters were introduced. A simplified model was developed by extracting key feature combinations. The relationships between the extracted features and the mechanical properties of DH steels were then interpreted using SHapley Additive exPlanations (SHAP) values, Individual Conditional Expectation (ICE), and Partial Dependence Plots (PDPs). The value ranges for over-aging temperature, austenite fraction (AF), and C content in austenite (AC) that favor UTS and TE were determined. This can provide a theoretical reference for improving DH steel plates.
The effects of Ce2O3 and CaF2 on the microstructure of silicate-based mold flux were investigated using an integrated approach combining molecular dynamics (MD) simulations with viscosity testing, SEM-EDS, and XRD analysis. The structural origin of changes in viscosity and crystallization behavior was revealed. It was found that the joint addition of CaF2 and Ce2O3 to the silicate melt leads to a synergistic effect; CaF2 acts as a diluent within the silicate network, while O2− introduced by Ce2O3 promotes the depolymerization of the complex [SiO4]4− network. As a result, highly polymerized structural units (Q2, Q3, and Q4) transform into less polymerized ones (Q0 and Q1), reducing the overall degree of polymerization and enhancing slag fluidity. Moreover, the preferential formation of [SiO4]4−–Ce3+–F− and [SiO4]4−–Ca2+–F− coordination structures replaces the original [SiO4]4−–Ce3+ and [SiO4]4−–Ca2+ linkages. This structural rearrangement facilitates the formation of low-melting-point phases during cooling, thereby suppressing the crystallization tendency and improving the stability of viscous properties of the mold flux. These findings provide theoretical insight for the design of high-performance fluxes used in rare earth-containing steel continuous casting.
Research was conducted on the key metallurgical technologies in the smelting process of 1Cr13 stainless steel based on the ANS-OB process.Through thermodynamic and kinetic analysis of the process,the effects of steel tapping temperature,refining temperature,gas flow rate,smelting time,and reduction deoxidation on the smelting process were studied,and the key technical indicators for 1Cr13 stainless steel smelting were clarified.The results in-dicated that the tapping temperature of the converter and intermediate frequency furnace should be higher than 1 600℃to ensure the subsequent refining temperature and reduce the temperature or composition mismatch caused by the adding alloys.The refining temperature range of ANS-OB process was 1 650℃-1 700℃,and its oxygen supply intensity gradually decreased from 0.8 m3/(t·min)to 0.4 m3/(t·min),while gradually increasing the argon supply intensity.Specifically,the flow ratio of O2 to Ar was 1∶(0.86-2.16),and the main oxygen blowing time was 30 min-utes-34 minutes,reducing the C content in the molten steel to below 0.08%.Then,ferrosilicon was used for reduc-tion deoxidation,with a dosage of 10 kg/t and an Ar supply intensity of 0.5 m3/(t·min)maintained to reduce the O content in the molten steel to below 0.003 0%.Finally,add alloys such as ferrosilicon and manganese iron to adjust the composition of the molten steel.Smelting was carried out according to the established process plan,and good in-dustrial test results were achieved.
