A temperature field reconstruction method for spacecraft leading edge structure with optimized sensor array

Aimed at monitoring the aerodynamic heating on the spacecraft leading edge structure, a temperature field reconstruction method for curved surface using a distributed sensor array is proposed in this paper. Limited by the sensor number and system weight, a sparse sensor array is usually used to measure the temperatures at the discrete positions. Considering the majority of heat energy transfers on the leading edge surface rather than the hollow inside or thermal insulation material, the temperature field on the curved surface of leading edge structure can be reconstructed through inverse distance weighting (IDW) method with improved accuracy by introducing arc-length distances rather than straight-line distances. Furthermore, the non-dominated sorting genetic algorithm II (NSGA-II) is employed to optimize the distribution of the sensor array. Taking a three-dimensional spacecraft leading edge structure as an example, a finite element model is used to validate the proposed method. The influences of search radius and random initial population inputs on the optimized results are investigated. The results show that the use of search radius plays an important role in optimizing the arrangement of sensor array.

Rapid Reconstruction of Simulated and Experimental Temperature Fields Based on Proper Orthogonal Decomposition

Temperature information has a certain significance in thermal energy systems, especially in gas combustion systems. Generally, measurements and numerical calculations are used to acquire temperature information, but both of these approaches have their limitations. Constrained by cost and conditions, measurement methods are difficult to use to reconstruct the temperature field. Numerical methods are able to estimate the temperature field; however, the calculation process in numerical methods is very complex, so these methods cannot be used in real time. For the purpose of solving these problems, a two-dimensional temperature field reconstruction method based on the proper orthogonal decomposition (POD) algorithm is proposed in this study. In the proposed method, the temperature field reconstruction task is transformed into an optimization problem. Theoretical analysis and simulations show that the proposed method is feasible. Gas combustion experiments were also performed to validate this method. Results indicate that the proposed method can yield a reliable reconstruction solution and can be applied to real-time applications.