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Comparative analysis of data-driven models for spatially resolved thermometry using emission spectroscopy.
Ruiyuan Kang1, Dimitrios C Kyritsis2, Panos Liatsis3
1Directed Energy Research Center, Technology Innovation Institute, Abu Dhabi, UAE.
This study introduces a novel data-driven methodology for spatially resolved temperature measurements using emission spectroscopy. Feature engineering combined with machine learning models effectively measures non-uniform temperature distributions, even with unknown gas concentrations.
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Area of Science:
- Spectroscopy
- Data Science
- Thermodynamics
Background:
- Line-of-sight emission spectroscopy has limitations in measuring temperature in non-homogeneous fields.
- Spatially resolved temperature measurements are crucial for understanding complex systems.
Purpose of the Study:
- To develop and evaluate data-driven models for spatially resolved temperature measurements using emission spectroscopy.
- To compare the performance of feature engineering with classical machine learning against end-to-end convolutional neural networks (CNNs).
Main Methods:
- Investigated two categories of data-driven methods: feature engineering with classical machine learning and CNNs.
- Evaluated fifteen feature groups combined with fifteen classical machine learning models.
- Assessed eleven CNN models for temperature distribution measurement.
Main Results:
- Feature engineering combined with machine learning outperformed direct CNN application.
- Physics-guided transformation, signal representation, and Principal Component Analysis proved most effective for feature extraction.
- The light blender ensemble model, using extracted features, achieved the best performance (RMSE: 64.3, RE: 0.017, RRMSE: 0.025, R: 0.994).
Conclusions:
- The proposed methodology, leveraging feature engineering and the light blender model, accurately measures non-uniform temperature distributions from low-resolution spectra.
- This approach is effective even when species concentration distributions are unknown, overcoming limitations of traditional spectroscopy.