Numerical Study of Random Corrosion Characteristics of Metal Based On the Cellular Automata Method

In the production process of coal chemical companies, the corrosion of metal equipment and the resulting shortening of its service life can cause safety hazards. Simulation modeling of pit emergence and development during corrosion evolution provides a new approach to corrosion research. By analyzing the effect of different parameters on causing corrosion to occur, it is possible to reflect the influence of complex physico-chemical systems. In this paper, the simulation of a meta-cellular automaton model of pit growth under diffusion and the introduction of a passivation probability to correct the chemical reaction rate are developed; The effect of reaction passivation probability, chemical reaction rate and diffusion coefficient on the degree of corrosion was also analyzed by means of quantitative analysis. The results show that for metal corrosion loss processes, the degree of corrosion damage decreases with increasing probability of reactive passivation and increases with increasing chemical reaction rate, increasing electrolyte concentration and increasing time step. The CA model was applied to simulate the growth and change of pitting corrosion of metal materials with their corrosion protection layer under damaged conditions. The corrosion model can simulate the corrosion morphology change characteristics similar to the real metal to the corrosion pit evolution simulation related research has certain scientific, validity, reference

Numerical Study of Random Corrosion Characteristics of Metal Based On the Cellular Automata Method

In the production process of coal chemical companies, the corrosion of metal equipment and the resulting shortening of its service life can cause safety hazards. Simulation modeling of pit emergence and development during corrosion evolution provides a new approach to corrosion research. By analyzing the effect of different parameters on causing corrosion to occur, it is possible to reflect the influence of complex physico-chemical systems. In this paper, the simulation of a meta-cellular automaton model of pit growth under diffusion and the introduction of a passivation probability to correct the chemical reaction rate are developed; The effect of reaction passivation probability, chemical reaction rate and diffusion coefficient on the degree of corrosion was also analyzed by means of quantitative analysis. The results show that for metal corrosion loss processes, the degree of corrosion damage decreases with increasing probability of reactive passivation and increases with increasing chemical reaction rate, increasing electrolyte concentration and increasing time step. The CA model was applied to simulate the growth and change of pitting corrosion of metal materials with their corrosion protection layer under damaged conditions. The corrosion model can simulate the corrosion morphology change characteristics similar to the real metal to the corrosion pit evolution simulation related research has certain scientific, validity, reference

DSS-OSM: An Integrated Decision Support System for Offshore Oil Spill Management

The marine ecosystem, human health and social economy are always severely impacted once an offshore oil spill event has occurred. Thus, the management of oil spills is of importance but is difficult due to constraints from a number of dynamic and interactive processes under uncertain conditions. An integrated decision support system is significantly helpful for offshore oil spill management, but it is yet to be developed. Therefore, this study aims at developing an integrated decision support system for supporting offshore oil spill management (DSS-OSM). The DSS-OSM was developed with the integration of a Monte Carlo simulation, artificial neural network and simulation-optimization coupling approach to provide timely and effective decision support to offshore oil spill vulnerability analysis, response technology screening and response devices/equipment allocation. In addition, the uncertainties and their interactions were also analyzed throughout the modeling of the DSS-OSM. Finally, an offshore oil spill management case study was conducted on the south coast of Newfoundland, Canada, demonstrating the feasibility of the developed DSS-OSM

DSS-OSM: An Integrated Decision Support System for Offshore Oil Spill Management

The marine ecosystem, human health and social economy are always severely impacted once an offshore oil spill event has occurred. Thus, the management of oil spills is of importance but is difficult due to constraints from a number of dynamic and interactive processes under uncertain conditions. An integrated decision support system is significantly helpful for offshore oil spill management, but it is yet to be developed. Therefore, this study aims at developing an integrated decision support system for supporting offshore oil spill management (DSS-OSM). The DSS-OSM was developed with the integration of a Monte Carlo simulation, artificial neural network and simulation-optimization coupling approach to provide timely and effective decision support to offshore oil spill vulnerability analysis, response technology screening and response devices/equipment allocation. In addition, the uncertainties and their interactions were also analyzed throughout the modeling of the DSS-OSM. Finally, an offshore oil spill management case study was conducted on the south coast of Newfoundland, Canada, demonstrating the feasibility of the developed DSS-OSM

Amorphous Nickel Oxides Supported on Carbon Nanosheets as High-Performance Catalysts for Electrochemical Synthesis of Hydrogen Peroxide

Publisher Copyright: © 2022 American Chemical Society.The development of high-performance yet cost-effective catalysts for electrochemical synthesis of H2O2 is a great challenge. Here, the amorphous nickel oxide NiOx supported on carbon nanosheets was prepared by the photochemical metal organic deposition method. The evolution of the crystalline structure, microstructure, and 2-electron oxygen reduction reaction (2e-ORR) activity in 0.1 M KOH was systematically investigated. The results reveal that the amorphous NiOx is highly efficient and selective toward 2e-ORR with an onset potential of 0.76 V versus reversible hydrogen electrode (RHE), 91% selectivity, and an electron transfer number of ∼2.2 over a wide potential range of 0.15-0.60 V versus RHE, which is outstanding among the metal oxide-based catalysts for 2e-ORR. Such a performance is closely associated with the mesoporous structure of the carbon nanosheets. Furthermore, the appropriate bonding strength of Ni-OH derived from the amorphous nature is crucial for the high selectivity. The theoretical calculation reveals that the *OOH intermediate prefers to adsorb on the amorphous NiOx-C by the end-on mode, facilitating the 2e-ORR process. The present amorphous NiOx loaded on carbon nanosheets can be promising electrocatalysts for synthesizing H2O2 after the stability issues are well addressed.Peer reviewe

Numerical research on ablation and wear of the artillery barrel based on UMESHMOTION user‐defined subroutine

Abstract During the firing process, the gun barrel bears the thermal and chemical erosion of high‐temperature gunpowder gas and the wear of the rotation band. The forced cone tended to the most severely worn part of the barrel, which directly affects the life of the barrel. Combined with the erosion test and wear test of gun steel materials, this research studied the forcing cone of a medium caliber naval gun barrel. With the commercial finite element software ABAQUS as the platform, we took advanced adaptive mesh method combined with secondary development technology. This method used in our study in order to better realize the numerical simulation of the radial erosion wear of the forced projectile cone in the continuous firing environment. At the same time, we compared the numerical simulation with the actual firing test data, and proposed a new calculation method of barrel life. In conclusion, the results of this study showed that this method can better calculate the radial erosion wear of the forced cone, and provide guidance for improving the life of the gun barrel in the future

A Rigorously-Incremental Spatiotemporal Data Fusion Method for Fusing Remote Sensing Images

The spatiotemporal remote sensing images have significant importance in forest ecological monitoring, forest carbon management, and other related fields. Spatiotemporal data fusion technology of remote sensing images combines high spatiotemporal and high temporal resolution images to address the current limitation of single sensors in obtaining high spatiotemporal resolution. This technology has gained widespread attention in recent years. However, the current models still exhibit some shortcomings in dealing with land cover changes, such as poor clustering results, inaccurate incremental spatiotemporal calculations, and sensor differences. In this article, we propose a rigorously-incremental spatiotemporal data fusion method for fusing remote sensing images with different resolutions to address the aforementioned problems. The proposed method utilizes the particle swarm optimization Gaussian mixture model to extract endmembers and establishes a linear relationship between sensors to obtain accurate time increments. Furthermore, bicubic interpolation is used instead of thin plate spline interpolation for spatial interpolation, and also support vector regression is used to calculate weights for obtaining a weighted sum of temporal and spatial increments. In addition, sensor errors are allocated to the calculation of residuals. The experimental results show the efficacy of the proposed algorithm for fusing fine image Landsat with coarse image MODIS data and conclude that the proposed algorithm presents a better solution for heterogeneous data with strong phenological changes and regions with changes in surface types, which provides a better solution for remote sensing image fusion and, hence, improves the accuracy, stability, and robustness of data fusion

Genomic characterization of the NAC transcription factors, directed at understanding their functions involved in endocarp lignification of iron walnut (Juglans sigillata Dode)

The NAC (NAM, ATAF1/2, and CUC2) transcription factors (TF), one of the largest plant-specific gene families, play important roles in the regulation of plant growth and development, stress response and disease resistance. In particular, several NAC TFs have been identified as master regulators of secondary cell wall (SCW) biosynthesis. Iron walnut (Juglans sigillata Dode), an economically important nut and oilseed tree, has been widely planted in the southwest China. The thick and high lignified shell derived endocarp tissues, however, brings troubles in processing processes of products in industry. It is indispensable to dissect the molecular mechanism of thick endocarp formation for further genetic improvement of iron walnut. In the present study, based on genome reference of iron walnut, 117 NAC genes, in total, were identified and characterized in silico, which involves only computational analysis to provide insight into gene function and regulation. We found that the amino acids encoded by these NAC genes varied from 103 to 1,264 in length, and conserved motif numbers ranged from 2 to 10. The JsiNAC genes were unevenly distributed across the genome of 16 chromosomes, and 96 of these genes were identified as segmental duplication genes. Furthermore, 117 JsiNAC genes were divided into 14 subfamilies (A-N) according to the phylogenetic tree based on NAC family members of Arabidopsis thaliana and common walnut (Juglans regia). Furthermore, tissue-specific expression pattern analysis demonstrated that a majority of NAC genes were constitutively expressed in five different tissues (bud, root, fruit, endocarp, and stem xylem), while a total of 19 genes were specifically expressed in endocarp, and most of them also showed high and specific expression levels in the middle and late stages during iron walnut endocarp development. Our result provided a new insight into the gene structure and function of JsiNACs in iron walnut, and identified key candidate JsiNAC genes involved in endocarp development, probably providing mechanistic insight into shell thickness formation across nut species