Diagnostics tools are the underpinnings for the experimental study of combustion phenomena. The inherent dynamic and three-dimensional (3-D) nature of turbulent flames has imposed strict requirements to the measurement techniques, which should provide both temporally and spatially resolved information of the target flames. Time-resolved volumetric tomography is one of such methods that meet the stringent demands of combustion diagnostics. However, this technique usually suffers from both high computational and experimental costs. This work aims to mitigate its limitations by developing a hybrid deep neural network that integrates the classical convolutional neural network with a state-of-the-art video interpolation model. Such a network can produce high frame rate 3-D flame voxels from low frame rate two dimensional (2-D) images, reducing the computational costs and at the same time relaxing the hardware requirement. Our study has shown that the temporal resolution can be enhanced by 15-fold. Thus, kilohertz (kHz)-rate flame tomography can potentially be realized with cost-effective industrial cameras. This also facilitates the study of ultra-rapid combustion phenomena, which cannot be resolved (greater than megahertz required) even with the most expensive commercial high-speed cameras. This technique has also been found to have a strong noise immunity, and acceptable results can still be obtained even when the noise level reaches 30%.

中文翻译：

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通过数据驱动方法从低速2-D记录重建kHz速率的3-D火焰图像序列

诊断工具是燃烧现象实验研究的基础。湍流火焰固有的动态和三维（3-D）性质对测量技术提出了严格的要求，测量技术应同时提供目标火焰的时间和空间解析信息。时间分辨体积层析成像是满足燃烧诊断的严格要求的此类方法之一。然而，该技术通常遭受高计算和实验成本的困扰。这项工作旨在通过开发将经典卷积神经网络与最新视频插值模型集成在一起的混合深度神经网络来减轻其局限性。这样的网络可以从低帧频的二维（2-D）图像中生成高帧频的3-D火焰体素，降低了计算成本，同时放宽了硬件要求。我们的研究表明，时间分辨率可以提高15倍。因此，具有成本效益的工业相机可以潜在地实现千赫兹（kHz）速率的火焰层析成像。这也有助于研究超快速燃烧现象，即使使用最昂贵的商用高速摄像机也无法解决（超过所需的兆赫兹）。还已经发现该技术具有很强的抗噪声能力，并且即使当噪声水平达到30％时仍可以获得可接受的结果。成本有效的工业相机可以潜在地实现千赫兹（kHz）速率的火焰层析成像。这也有助于研究超快速燃烧现象，即使使用最昂贵的商用高速摄像机也无法解决（超过所需的兆赫兹）。还已经发现该技术具有很强的抗噪声能力，并且即使当噪声水平达到30％时仍可以获得可接受的结果。成本有效的工业相机可以潜在地实现千赫兹（kHz）速率的火焰层析成像。这也有助于研究超快速燃烧现象，即使使用最昂贵的商用高速摄像机也无法解决（超过所需的兆赫兹）。还已经发现该技术具有很强的抗噪声能力，并且即使当噪声水平达到30％时仍可以获得可接受的结果。