A stability and spatial-resolution enhanced laser absorption spectroscopy tomographic sensor for complex combustion flame diagnosis

A novel stable laser absorption spectroscopy (LAS) tomographic sensor with enhanced stability and spatial resolution is developed and applied to complex combustion flame diagnosis. The sensor reduces the need for laser collimation and alignment even in extremely harsh environments and improves the stability of the received laser signal. Furthermore, a new miniaturized laser emission module was designed to achieve multi-degree of freedom adjustment. The full optical paths can be sampled by 8 receivers, with such arrangement, the equipment cost can be greatly reduced, at the same time, the spatial resolution is improved. In fact, 100 emitted laser paths are realized in a limited space of 200mm×200 mm with the highest spatial resolution of 1.67mm×1.67 mm. The stability and penetrating spatial resolution of the LAS tomographic sensor were validated by both simulation and field experiments on the afterburner flames. Tests under two representative experiment states, i.e., the main combustion and the afterburner operation states, were conducted. Results show that the error under the main combustion state was about 4.32% and, 5.38% at the afterburner operation state. It has been proven that this proposed sensor can provide better tomographic measurements for combustion diagnosis, as an effective tool for improving performances of afterburners

On the Highly Accurate Evaluation of the Voigt/Complex Error Function with Small Imaginary Argument

A rapidly convergent series, based on Taylor expansion of the imaginary part of the complex error function, is presented for highly accurate approximation of the Voigt/complex error function with small imaginary argument y ≤ 0.1. Error analysis and run-time tests in double-precision arithmetic reveals that in the real and imaginary parts, the proposed algorithm provides an average accuracy exceeding 10−15 and 10−16, respectively, and the calculation speed is as fast as that reported in recent publications. An optimized MATLAB code providing rapid computation with high accuracy is presented

A Time-of-Flight Estimation Method for Acoustic Ranging and Thermometry Based on Digital Lock-In Filtering

Accurate ranging and real-time temperature monitoring are essential for metrology and safety in electrical conduit applications. This paper proposes an acoustic time-of-flight (TOF) estimation method based on the digital lock-in filtering (DLF) technique for conduit ranging and thermometry. The method establishes the relationship between the frequency and the time domain by applying a linear frequency modulated Chirp signal as the sound source and using the DLF technique to extract the first harmonic of the characteristic frequencies of the transmitted and received signals. Acoustic TOF estimation in the conduit is then achieved by calculating the mathematical expectation of the time difference between each characteristic frequency in the time-frequency relationship of the two signals. The experimental results with enhanced noise interference on different conduit lengths and various temperature conditions, proved that the proposed DLF method can establish a robust linear time-frequency relationship according to the characteristics of the Chirp signal, and the measurement accuracy of TOF has also been confirmed. Compared to the conventional method, the DLF method provides the lowest absolute error and standard deviation for both distance and temperature measurements with an enhanced robustness

A Time-of-Flight Estimation Method for Acoustic Ranging and Thermometry Based on Digital Lock-In Filtering

Accurate ranging and real-time temperature monitoring are essential for metrology and safety in electrical conduit applications. This paper proposes an acoustic time-of-flight (TOF) estimation method based on the digital lock-in filtering (DLF) technique for conduit ranging and thermometry. The method establishes the relationship between the frequency and the time domain by applying a linear frequency modulated Chirp signal as the sound source and using the DLF technique to extract the first harmonic of the characteristic frequencies of the transmitted and received signals. Acoustic TOF estimation in the conduit is then achieved by calculating the mathematical expectation of the time difference between each characteristic frequency in the time-frequency relationship of the two signals. The experimental results with enhanced noise interference on different conduit lengths and various temperature conditions, proved that the proposed DLF method can establish a robust linear time-frequency relationship according to the characteristics of the Chirp signal, and the measurement accuracy of TOF has also been confirmed. Compared to the conventional method, the DLF method provides the lowest absolute error and standard deviation for both distance and temperature measurements with an enhanced robustness

3-D soot temperature and volume fraction reconstruction of afterburner flame via deep learning algorithms

3-D soot temperature and volume fraction reconstruction of afterburner flame has been based on spectrally resolved measurement methods and inverse problem theory. So far, the Convolutional Neural Networks (CNN) has been widely used in solving inverse problems to realize real-time reconstruction. However, the convolution and pooling operation can cause erroneous accumulation when dealing with high precision and high mesh density 3-D reconstruction applications. To address this problem, the autoencoder long-short-term-memory (LSTM) neural network and the synthetic dataset considering the interior and exterior parameters of camera are adopted to realize rapid, accurate, and simultaneous reconstructions of the soot temperature and volume fraction of afterburner flame. This method, named StfNet-3D by the authors, is investigated regarding its reconstruction accuracy, noise immunity, and computational costs by comparing that with the CNN, LSTM, and traditional 3-D reconstruction method (3D TVR) through simulations and real-time experiments