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Meeting MS&T24: Materials Science & Technology
Symposium Computation Assisted Materials Development for Improved Corrosion Resistance
Presentation Title New Approaches Towards Computational Modeling of Metal Dusting
Author(s) Clara Schlereth, Emma M.H. White, Mathias C. Galetz
On-Site Speaker (Planned) Clara Schlereth
Abstract Scope The high temperature corrosion mechanism of “metal dusting" is obfuscated by a complex combination of incubation, initiation and rapid attack propagation. It is observed in carbon-rich gases at 400-800°C, for example in petrochemical plants or syngas production. Current estimations of material lifetimes are based primarily on experience, and alloy selection for new processes often requires extensive laboratory tests. The potential of current thermodynamic and kinetic models of the gas phase reactions to model the metal dusting attack will be discussed. A new approach is presented where the environmental aggressivity is quantified according to a metastable carbon activity. Additionally, the observed metal dusting attack of various alloys was analyzed by a mechanistic and a statistical approach to increase understanding and improve alloy development timeframes. Other parameters, such as manufacturing route, surface condition, gas composition, and pressure, are ranked for their relative level of impact.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Assessment of the Role of Minor Refractory Alloying Additions in Affecting Alumina-Scale Formation During High-Temperature Oxidation of Ni-based model alloys
Atomic Origins of CO2-Promoted Oxidation of Chromia-Forming Alloys
Impact of Water Vapor Content and Oxygen Partial Pressure on Oxidation Behavior of NiCr Alloys at 950 °C
New Approaches Towards Computational Modeling of Metal Dusting
Phase-Field Modeling of Thermally Grown Oxide and Induced Damage and Cracking in Environmental Barrier Coatings
Phase Field Numerical Model for Simulating the Activation and Diffusion Controlled Stress Corrosion Cracking Phenomena in Anisotropic Material
Predicting Oxidation Behavior of Ni-Based Superalloys with Physics-Informed Machine Learning

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