The tomographic measurement of combustion flow fields in aerospace propulsion systems remains a significant challenge due to the large temperature variations, steep spatial gradients, and the ill-posed nature of inversion problem under noisy and sparse detection conditions. While multispectral information provides an extended temperature sensitivity, inversion errors can weaken the self-consistency among multiple absorption lines and amplify the reconstruction uncertainties. To address these challenges, a highly precise linear multispectral tomographic absorption spectroscopy was developed for the reconstruction of non-uniform combustion fields over a wide temperature range, extending from room temperature to high temperature. A three-step temperature reconstruction strategy was proposed, in which four absorption lines were selectively chosen to ensure optimal sensitivity and accuracy across different temperature regions. In addition, a deep neural network was developed to identify local reconstruction distortions and provide a correct line selection. Numerical simulations on synthetic phantoms and fundamental experiments on a Mckenna burner demonstrated significant improvement in reconstruction accuracy compared with conventional fixed absorption line combination methods. Finally, the proposed approach was applied on a rocket engine model, performing tomographic measurements at ten cross-sectional planes downstream of the supersonic jet flame. The reconstructed quasi-3D temperature distributions captured the transition of the jet flame from supersonic to subsonic state. This study establishes a reliable framework for wide-range temperature tomography and confirm the robustness and applicability in extreme aerospace combustion diagnostics.
扫码关注我们
求助内容:
应助结果提醒方式:
