Jin Ying, Zhang Wanqing, Song Yang, Qu Xiangju, Li Zhenhua, Ji Yunjing, He Anzhi
Opt Express. 2019 Sep 16;27(19):27308-27334. doi: 10.1364/OE.27.027308.
Flame chemiluminescence tomography (FCT) plays an important role in combustion monitoring and diagnostics due to the easy implementation and non-intrusion. However, on account of the high data throughput and the inefficiency of the conventional iteration methods, the 3D reconstructions in FCT are typically conducted off-line and time-consuming. In this work, we present a 3D rapid FCT reconstruction system based on convolutional neural networks (CNN) model for practical combustion measurement, which has the ability to reconstruct 3D flame distribution rapidly after training process. First, the numerical simulation has been performed by creating three cases of phantoms which are designed to mimic the 3D conical flame. Next, after the evaluation of loss function and training time, the optimal CNN architecture has been determined and certificated quantitatively. Finally, a real time FCT system consisting of 12 color CCD cameras is realized and multispectral separation algorithm is adopted to extract CH* and C2* components. Certificated by practical measurements testing, the proposed CNN model is able to reconstruct 3D flame structure from real time captured projections with credible accuracy and structure similarity. Furthermore, compared with conventional iteration reconstruction method, the proposed CNN model shows better performance on obviously improving reconstruction speed and it is expected to achieve 3D rapid monitoring of flames.
火焰化学发光层析成像（FCT）因其易于实施且非侵入性，在燃烧监测与诊断中发挥着重要作用。然而，由于数据吞吐量高以及传统迭代方法效率低下，FCT中的三维重建通常离线进行且耗时。在这项工作中，我们提出了一种基于卷积神经网络（CNN）模型的三维快速FCT重建系统，用于实际燃烧测量，该系统在训练过程后能够快速重建三维火焰分布。首先，通过创建三种模拟三维锥形火焰的体模案例进行了数值模拟。接下来，在评估损失函数和训练时间后，确定并定量验证了最优的CNN架构。最后，实现了一个由12台彩色CCD相机组成的实时FCT系统，并采用多光谱分离算法提取CH和C2成分。经实际测量测试验证，所提出的CNN模型能够从实时捕获的投影中重建三维火焰结构，具有可靠的精度和结构相似性。此外，与传统迭代重建方法相比，所提出的CNN模型在显著提高重建速度方面表现出更好的性能，有望实现火焰的三维快速监测。
