Ultra-fast particle dynamics at the moment of impact for particle deposition in gas turbines
Abstract
FY21 funds for: Particle ingestion into modern gas turbines is known to affect aerodynamic performance, reduce the film-cooling efficiency, and impair the thermal-barrier engineered coatings through erosion or deposition mechanisms. As engines continue to drive to a higher efficiency by increasing the operating temperature, most ingested particles, including even the large sand particles, are likely to melt in the turbine hot section and the deposition probability increases drastically. For the particle deposition problem, whether the particle is going to stick or not depends on (i) the nearwall turbulence, and (ii) the particle electrostatic forces during impact. Although these two dynamics are the main focus of the models and simulations, the experimental efforts on thesetwo processes are extremely limited, as it is challenging to measure and quantify the small-scale near-wall dynamics as well as the ultra-small-scale impact physics and the roles played by the electrostatic forces. The near-wall dynamics requires 10 KHz100 KHz temporal resolution and the impact dynamics of a m-size particle can only be resolved at 1 MHz10 MHz temporal resolution. These extreme timescales pose significant challenges on the diagnostic methods. To address this issue, the PI proposes to use a recently-developed method in his lab by using the high-speed 3D particle tracking system to trigger the ultra-high-speed system very close to the surface to provide a complete time history of the impact dynamics. This new experiment willcapture the key processes, including turbophoresis, thermophoresis, and electrophoresis, that are crucial to our understanding of the gas-turbine particle ingestion problem, provide new information to inform which mechanisms are important and relevant and which process can be safely ignored, and result in a rich dataset against which the simulations and models can be validated.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 05, 2021
- Source ID
- N000142112620
Entities
People
- Rui Ni
Organizations
- Johns Hopkins University
- Office of Naval Research
- United States Navy