1. AI-driven DfAM of aeronautical hydrogen gas turbine combustors.
- Author
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Boretti, Alberto and Huang, Aijun
- Subjects
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GAS turbines , *COMBUSTION chambers , *HONEYCOMB structures , *DIGITAL twins , *ENERGY consumption - Abstract
The increasing demand for sustainable aviation solutions has intensified the development of hydrogen-fueled gas turbine combustors, necessitating advanced manufacturing techniques like Additive Manufacturing (AM). Traditional methods fall short in addressing the complex needs of these combustors, making AM essential due to its ability to create intricate, efficient, and lightweight components. This letter highlights AM's transformative impact on hydrogen gas turbine production, emphasizing enhanced design, assembly, and performance. AM provides significant design flexibility, enabling the fabrication of complex lattice and honeycomb structures that improve fuel efficiency and aircraft performance. It also streamlines assembly by integrating multiple parts into a single component, reducing complexity and waste, and thus optimizing cost and environmental impact. Additionally, AM's capabilities for rapid prototyping and on-demand production accelerate development cycles and foster customization. Furthermore, the letter discusses the integration of Design for Additive Manufacturing (DfAM), and Artificial Intelligence (AI) in optimizing combustion chamber designs. These technologies enhance predictive performance, and structural integrity, and address key challenges such as emissions and combustion stability. AI advances digital twin technology, significantly boosting efficiency and reducing the environmental footprint of hydrogen gas turbines. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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