he corrosion of MgO-C refractories during decarburisation of iiquid ferrcmanganese alleys was investigated using the finger rotating test. An oxide layer containing MnO is formed on the surface of the MgO-C refractories in contact with molten low carbon ferromanganese alloys (Fe-75Mn-0-8C) but is not formed when the refractories are in contact with high carbon ferromanganese alloys (Fe-75Mn-7C). The thickness of the oxide layer decreases with increasing carbon content in the ferromanganese alloy and increases with immersion time. We conclude that the formation of the oxide layer strongly depends on the driving force, which is the difference in carbon content between MgO-C refractories and molten ferromanganese alloys.
Storm surge flooding caused by tropical cyclones is a devastating threat to coastal regions, and this threat is growing due to sea-level rise (SLR). Therefore, accurate and rapid projection of the storm surge hazard is critical for coastal communities. This study focuses on developing a new framework that can rapidly predict storm surges under SLR scenarios for any random synthetic storm of interest and assign a probability to its likelihood. The framework leverages the Joint Probability Method with Response Surfaces (JPM-RS) for probabilistic hazard characterization, a storm surge machine learning model, and a SLR model. The JPM probabilities are based on historical tropical cyclone track observations. The storm surge machine learning model was trained based on high-fidelity storm surge simulations provided by the U.S. Army Corps of Engineers (USACE). The SLR was considered by adding the product of the normalized nonlinearity, arising from surge-SLR interaction, and the sea-level change from 1992 to the target year, where nonlinearities are based on high-fidelity storm surge simulations and subsequent analysis by USACE. In this study, this framework was applied to the Chesapeake Bay region of the U.S. and used to estimate the SLR-adjusted probabilistic tropical cyclone flood hazard in two areas: One is an urban Virginia site, and the other is a rural Maryland site. This new framework has the potential to aid in reducing future coastal storm risks in coastal communities by providing robust and rapid hazard assessment that accounts for future sea-level rise.
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