The Wavelength Modulation Spectroscopy (WMS) method is typically used in the Tunable Diode Laser Absorption Spectroscopy (TDLAS) for spectrum extraction in noisy background. Usually, two entire absorption spectra for target species are scanned in a cascade way in the WMS method, and it limits the temporal response of the TDLAS sensor. In this paper, the frequency division multiplexing and main peak scanning method were introduced to the WMS method and enabled a high frame rate six times of that in the classical TDLAS sensor at the same scanning speed. For the first time, two wavelengths modulated at different frequencies were used to extract the absorptions in parallel along various laser paths and implemented successfully on a TDLAS tomography hardware, to reconstruct the distributions of both temperature and specie concentrations, at a frame rate upto 10kHz. A five-view fan-beam tomographic sensor was utilized in the tomographic imaging. In each view, lasers at two wavelengths were selected and modulated at different sinusoidal frequencies on the same sawtooth signal. Numerical simulations varied the high-quality tomographic imaging of both temperature and H2O molar concentration distributions for different phantoms. Experiments were also carried out on dynamic flames, which were generated by exciting a Bunsen burner acoustically. The acoustic excitation frequency varied from 40 Hz to 750 Hz and results of the tomographic images agreed well with the flame evolutions.

A tunable diode laser absorption spectroscopy (TDLAS) tomographic system was proposed via utilizing the wavelength modulation spectroscopy (WMS) method and distributions of temperature and specie concentrations in the dynamic flames were simultaneously reconstructed for the first time. A five-view fan beam tomographic sensor was applied to capture the absorption spectrum along the laser paths across the region to be imaged. Light intensities in all paths were collected in a custom specific TDLAS hardware. Different phantoms of the temperature and water vapor concentration distributions were fabricated to generate artificial data of the absorption spectrum acquired in the sensor. Reconstructed images of the phantoms in the simulations agreed well with the original distributions, in cases of different random noises that varied from 1% to 7% of light intensities at each wavelength. Experiments on a McKenna burner and the exit of a wind tunnel were carried out, and distributions of temperature and water vapor’s molar concentration were achieved by applying the proposed WMS based TDLAS tomographic system. A moving steel ruler was used to disturb the flame in the burner and temporal variations of the flame over a cross section of the burner were imaged in a tomographic way. Tomographic results on exhaust flows at the exit of a wind tunnel coincided with the variation trend of temperature in eleven different working conditions and verified the robustness of the proposed system under harsh conditions.

A compact hardware system for laser absorption spectroscopy tomography is introduced to monitor dynamic flame evolution with a short and fixed spectral scanning time and an adjustable frame rate. The system mainly consists of two parts, a pentagon sensor and a reconfigurable laser control and data acquisition hardware. In the sensor, twelve photodetectors are installed in each of the five sides of the pentagon. Fan-shaped laser beams from each vertex illuminate the opposite side and half of its two adjacent sides. Compared with the traditional illuminating scheme from each vertex to only the opposite side, this design reduces the sensor size and improves the spatial resolution. In the hardware, the distributed feedback lasers are integrated with the controller and modulated to generate a programmed waveform and implement spectral scanning. The spectral scanning time is short and fixed, and an adjustable frame rate is achieved by changing the interval, i.e. the waiting time, between two successive scans. The sampling rate of each channel can reach up to 20 MHz and is synchronized to the laser emission. A DDR2 memory is used for temporary data storage for four channels, enabling a full frame rate of up to 2 kHz. In the experiment, water vapor in a flat flame burner was chosen as the target absorbing species. Local integral absorbance and temperature distributions in the case of a moving steel ruler across the burner were reconstructed and agreed well with the observed distributions.

A back propagation (BP) neural network is introduced and implemented on a field programmable gate array (FPGA) chip into tunable diode laser absorption spectroscopy (TDLAS) tomography for fast response and a high signal-to-noise ratio (SNR) in temperature and water concentration imaging. The network implemented on the FPGA extracts the peak values of normalized second harmonics of absorption spectra. A recursive demodulator based on a Kalman filter generates outputs of the training database. Compared with the typical quadrature demodulator, the sampling time of the BP neural network drops to one-quarter of the typical one, with a 5 dB increase in the SNR at different noise levels. To verify and evaluate the proposed method, the FPGA-based 32-bit fixed-point BP neural network was compared with the floating-point BP neural network and the quadrature demodulator in numerical simulations. In the real experiments, Bunsen burner flames with acoustic excitation were imaged, and noisy distortions of the normalized second harmonics and average temperature were effectively reduced by using the proposed method. Temporal fluctuations of up to 750 Hz can be clearly identified from variations of reconstructed distributions of temperature and water vapor molar concentration, in which cases the typical quadrature demodulator failed to work. These results showed that the proposed method works well in the FPGA chip and reveals more details in dynamic flame monitoring.

A noise immune temperature measurement method is proposed by using tunable diode laser absorption spectroscopy (TDLAS). Noise immunity is achieved through spectrum shifting by a Mach-Zehnder interferometer. The driving current of a distributed feedback (DFB) laser is tuned for linear wavenumber scanning and followed by a saw-tooth climbing laser signal. The laser signal travels through the interferometer, and a high-frequency carrier signal of megahertz level modulates the saw-tooth signal away from background noises. When the output laser of the interferometer passes through the target gas, the absorption spectrum is imposed on the intensity profile. In this way, the absorption spectrum is shifted to a high-frequency spectral band from a low-frequency band where both the absorption spectrum and the background noises appear. A part of the output laser of the interferometer is split to provide a reference for baseline extraction and hence absorption spectrum extraction. For the dual-color temperature extraction method, laser absorption spectra of the target gas centered at two different wavenumbers are achieved. Numerical simulations and actual experiments at different temperatures, e.g., using a heating device and Bunsen burner verified the noise immunity of the proposed method. Performance comparisons with the direct absorption spectroscopy (DAS) showed that the proposed method generated more precise temperature values in a noisy background.

Laser absorption spectroscopy (LAS) tomography yields cross-sectional images of flame temperature and gas molar concentration in combustions. The total laser absorbance of a specific gas, such as water vapor at each point in the region of interest, was obtained from local normalized second harmonics on limited detectors. These detectors restrict the spatial resolution during image reconstructions through typical iterative methods. A sparse Zernike fitting method was introduced for LAS tomography, as the temperature in real physical reality is smoothly distributed. The distributions of total absorbance were sparsely fit as finite Zernike polynomials, and the coefficients were mostly zero or about zero. The number of coefficients was much smaller than that of pixels, i.e., unknowns in iterative methods. The underdetermined inverse problem was effectively alleviated, and a higher spatial resolution was achieved. A fast convergence and effective method for optimal step selection was also introduced for the iterative solution of the sparse model. The effectiveness of the proposed method was validated by both simulated and experimental data for temperature and concentration imaging. Performance comparisons were made with the typical iterative methods, such as SART and Landweber methods; temporal variations of reconstructed distributions for dynamical flames were captured with higher signal-to-noise ratios. Both the fundamental frequencies of 8.797 Hz without acoustic excitation and 12.7 Hz in the case of 150-Hz excitation were successfully captured, and tomographic images agreed well with the dynamical flame evolutions.