The effect of particle ingestion in gas turbine engines has become a significant problem in recent decades. Commercial and military aircraft and helicopter engines currently operate over various terrains with particles. Vehicles near the ocean, for example, typically come across sea spray, which contains fine particulates of salt and can lead to the quick corrosion of metallic engine parts inside gas turbine (GT) engines. Volcanic terrains are also considered harmful to engine performance and durability. One particular problem that has been of recent interest is particle reactivity of sand and volcanic particles. Solid particles are already an issue due to the corrosion they cause to the surface and coatings of the engine blades. Due to the already high combustion temperatures and increasing engine temperatures in state-of-the-art gas turbine engines, these particles can also melt and adhere to engine surfaces such as stators and rotors. This program is proposed to improve existing high-fidelity multi-physics tool for predicting sand particle impact in gas turbine engine. The improvements are realized through the combination of more efficient yet accurate techniques for particle tracking, collision, and deposition, exploiting recent advancements in hybrid HPC architecture, numerical algorithms, artificial intelligence and use of data science. The proposed improvements will also potentially reduce data storage significantly.
Benefit: Once its robustness is demonstrated, the primary government customer of the upgraded high fidelity tool set would be the Department of Defense (DoD) (Army, Navy, and Airforce). The DoD frequently use aircraft and helicopters in areas that exhibit these particles (oceans, volcanic zones, deserts) and would benefit from accurate high-fidelity tools to help solve their problems regarding corrosion and build-up. The primary commercial customers include General Electric, Pratt & Whitney, Rolls-Royce, and Honeywell Aerospace. All these companies provide engines to commercial entities (Boeing, Airbus) as well as government bodies (US, UK, etc.). As the outcome of reactive particle ingestion is engine replacement, the desire for effective mitigation is paramount to obtain a competitive advantage. Once one of these companies successfully solves the particulate problem with regards to sand and glass, other companies will want to follow suit.
Keywords: particle ingestion, particle ingestion, Machine Learning, CMAS, Thermal Barrier Coating, sand particle, gas turbine engine, GPU, Volcanic ash
