This work aims to develop a fan-beam tomographic sensor using tunable diode lasers that can simultaneously image temperature and gas concentration with both high spatial and temporal resolutions. The sensor features three key advantages. First, the sensor bases on a stationary fan-beam arrangement, by which a high spatial resolution is guaranteed because the distance between two neighboring detectors in a view is approximately reduced to the size of a photodiode. Second, fan-beam illumination from five views is simultaneously generated instead of rotating either the fanned beams or the target, which significantly enhances the temporal resolution. Third, a novel set of optics with the combination of anamorphic prism pair and cylindrical lens is designed, which greatly improves the uniformity of the planar beams, and hence improves the reconstruction fidelity. This paper reports the tomographic model, optics design, numerical simulation and experimental validation of the sensor. The sensor exhibits good applicability for flame monitoring and combustion diagnosis.
Remote chemical detection in the atmosphere or some specific space has always been of great interest in many applications for environmental protection and safety. Laser absorption spectroscopy (LAS) is a highly desirable technology, benefiting from high measurement sensitivity, improved spectral selectivity or resolution, fast response and capability of good spatial resolution, multi-species and standoff detection with a non-cooperative target. Numerous LAS-based standoff detection techniques have seen rapid development recently and are reviewed herein, including differential absorption LiDAR, tunable laser absorption spectroscopy, laser photoacoustic spectroscopy, dual comb spectroscopy, laser heterodyne radiometry and active coherent laser absorption spectroscopy. An update of the current status of these various methods is presented, covering their principles, system compositions, features, developments and applications for standoff chemical detection over the last decade. In addition, a performance comparison together with the challenges and opportunities analysis is presented that describes the broad LAS-based techniques within the framework of remote sensing research and their directions of development for meeting potential practical use.
High precision mobile sensing of multi-species gases is greatly demanded in a wide range of applications. Although quantum cascade laser absorption spectroscopy demonstrates excellent field-deployment capabilities for gas sensing, the implementation of this measurement technique into sensor-like portable instrumentation still remains challenging. In this paper, two crucial elements, the laser driving and data acquisition electronics, are addressed. Therefore, we exploit the benefits of the time-division multiplexed intermittent continuous wave driving concept and the real-time signal pre-processing capabilities of a commercial System-on-Chip (SoC, Red Pitaya). We describe a re-designed current driver that offers a universal solution for operating a wide range of multi-wavelength quantum cascade laser device types and allows stacking for the purpose of multiple laser configurations. Its adaptation to the various driving situations is enabled by numerous field programmable gate array (FPGA) functionalities that were developed on the SoC, such as flexible generation of a large variety of synchronized trigger signals and digital inputs/outputs (DIOs). The same SoC is used to sample the spectroscopic signal at rates up to 125 MS/s with 14-bit resolution. Additional FPGA functionalities were implemented to enable on-board averaging of consecutive spectral scans in real-time, resulting in optimized memory bandwidth and hardware resource utilisation and autonomous system operation. Thus, we demonstrate how a cost-effective, compact, and commercial SoC can successfully be adapted to obtain a fully operational research-grade laser spectrometer. The overall system performance was examined in a spectroscopic setup by analyzing low pressure absorption features of CO at 4.3 μm.
Chemical species tomography (CST) has been widely used for in situ imaging of critical parameters, e.g., species concentration and temperature, in reactive flows. However, even with state-of-the-art computational algorithms, the method is limited due to the inherently ill-posed and rankdeficient tomographic data inversion and by high computational cost. These issues hinder its application for real-time flow diagnosis. To address them, we present here a novel convolutional neural network, namely CSTNet, for high-fidelity, rapid, and simultaneous imaging of species concentration and temperature using CST. CSTNet introduces a shared feature extractor that incorporates the CST measurements and sensor layout into the learning network. In addition, a dual-branch decoder with internal crosstalk, which automatically learns the naturally correlated distributions of species concentration and temperature, is proposed for image reconstructions. The proposed CSTNet is validated both with simulated datasets and with measured data from real flames in experiments using an industry-oriented sensor. Superior performance is found relative to previous approaches in terms of reconstruction accuracy and robustness to measurement noise. This is the first time, to the best of our knowledge, that a deep learning-based method for CST has been experimentally validated for simultaneous imaging of multiple critical parameters in reactive flows using a low-complexity optical sensor with a severely limited number of laser beams.
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