Advanced Phase-Field Modeling for Predicting and Controlling Microstructural Evolution in High-Pressure, High-Temperature Alloy Coatings

**Abstract:** This research proposes a novel, computationally accelerated phase-field modeling approach to predict and ultimately control microstructural evolution in high-pressure, high-temperature (HPHT) alloy coatings during rapid solidification. Focusing on rapidly solidified Ni-Cr-Al coatings, which are crucial for thermal barrier applications in advanced aerospace engines, we introduce a multi-scale, data-driven phase-field model incorporating machine learning-assisted kinetic parameter prediction. This dramatically enhances predictive accuracy and accelerates model runtime compared to traditional phase-field methods, enabling efficient optimization of coating compositions and processing parameters for tailored microstructures and improved performance. We demonstrate the potential for real-time microstructure control during coating deposition, leading to significantly enhanced coating durability and efficiency. The projected impact includes a 30% improvement in thermal barrier performance and a reduction in coating design cycle time from years to months, opening new avenues for high-performance thermal management in extreme environments.