Numerical and experimental investigation of many-objective optimization for efficient temperature and velocity fields reconstruction via acoustic tomography

Abstract

Accurate and high-quality measurement of temperature and velocity fields is important in combustion and flow diagnosis, especially for the control of boiler operations and optimization of combustion processes. Nonlinear acoustic tomography (NAT) is an appealing approach to simultaneously monitor temperature and velocity fields by observing the changes in sound speed they induce. However, the inherent ill-posedness of the tomographic inverse problem and the lack of a priori information may lead to poor robustness, low precision, and the presence of artifacts. On the other hand, the inclusion of a priori information, such as smoothness and box constraints, may require the use of hyperparameters that hinder quasi real-time reconstruction and may degrade performance in actual applications. In this study, we aim to ensure quasi real-time reconstruction and alleviate the ill-posedness of the NAT problem by eliminating the regularization parameters and transforming the inverse problem into a many-objective optimization problem with four objectives. The knee point-driven evolutionary algorithm with improved environmental selection strategy (KEA-IES) is proposed to simultaneously reconstruct the arbitrary inhomogeneous temperature and aerodynamic fields in a furnace. Apart from the investigation of the performance of KEA-IES and the influence of various factors, including measurement noise, function evaluation number, population size, and functional norms, on the quality of the reconstruction, an experimental AT system with independent 16 T/R channels is developed to evaluate the proposed method. The results show that combustion flame temperature and velocity fields can be monitored simultaneously by using the proposed KEA-IES with more accuracy, less consumed time, and better noise immunity compared with the state-of-the-art algorithms. The proposed method can provide valuable guidance in the development of a non-intrusive real-time pyrometry and velocimetry system, particularly for applications with large velocity fields and temperature gradients. Furthermore, its capability in complex combustion and flow diagnosis will offer benefits in terms of energy conservation, emission reduction, boiler operations supervision, and combustion process optimization.