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Detecting temperature and concentration fields within engine combustors holds paramount significance in enhancing combustion efficiency and ensuring operational safety. Within the realm of engine combustors, the laminar absorption spectroscopy technique has garnered considerable attention. Particularly crucial is the optimization of the optical path configuration to enhance the efficacy of reconstruction. This study presents a flame parameter field reconstruction model founded on laminar absorption spectroscopy. Furthermore, an optimization approach for refining the optical path configuration is delineated. In addressing non-axisymmetric flames, the simulated annealing algorithm (SA) and Harris's Hawk algorithm (HHO) are employed to optimize the optical path layout across varying beam quantities. The findings underscore a marked reduction in imaging errors with the optimized optical path configuration compared to conventional setups, thereby elevating detection precision. Notably, the HHO algorithm demonstrates superior performance over the SA algorithm in terms of optimization outcomes and computational efficiency. Compared with the parallel optical path, the optimized optical path of the HHO algorithm reduces the temperature field error by 25.5% and the concentration field error by 26.5%. [ABSTRACT FROM AUTHOR]

Accurate detection and characterisation of defects in high-density polyethylene (HDPE) materials are important for the safety of industrially critical structures. Ultrasonic non-destructive evaluation (UNDE) has proven to be a powerful tool for detecting and characterising defects in engineered materials. However, efficient and high-precision defect imaging in these highly attenuating materials remains a significant challenge for UNDE. Least-squares reverse time migration (LSRTM) offers the potential to reconstruct high-precision images of reflectivity. Yet, the conventional LSRTM iteratively updates the reflectivity model by minimising the data residuals, making it computationally expensive. In this paper, an efficient ultrasonic LSRTM algorithm within a deep learning framework is proposed. Building upon this, a generative adversarial network (GAN) is integrated to further enhance the reconstruction results by reducing artefacts in the images. Simulation and experimental results show that the proposed ultrasonic LSRTM-GAN can generate high-quality images, effectively enabling precise defect detection in HDPE. [ABSTRACT FROM AUTHOR]