Multispectral tomographic absorption spectroscopy for a supersonic rocket engine model using region-specific absorption line selection and deep learning correction

Key Points

• Multispectral spectroscopy enhances accuracy in rocket engine modeling, improving performance metrics.
• Key finding shows significant reduction in error rates, achieving corrections up to 90% using advanced algorithms.
• Analysis employing deep learning techniques with region-specific absorption line selection was utilized for accurate measurements.
• This work highlights the potential for deep learning applications in high-speed aerodynamics and propulsion systems.