Abstract
Recently, physics-informed neural networks (PINNs) have displayed great potential in delivering swift and accurate solutions for inverse problems. Here, a PINN was used to solve the inverse heat conduction problem in the rotating cavities of an aeroengine high-pressure compressor internal air system. The neural network was designed to receive experimentally captured radially distributed temperature profiles as inputs and predict the associated surface heat fluxes. The correctness of these predicted heat fluxes was assessed by numerically solving the direct heat conduction equation using a 2D finite-element model, thereby recovering the original temperature profiles. A comparative analysis is conducted between the predicted temperature profiles and the initial inputs. The physics-informed neural network is trained using noise-free synthetic data, created from a range of radial temperature curve-fit coefficients, and subsequently tested on noisy experimental data at engine representative conditions. The predicted temperature values exhibit some good agreement with their respective actual counterparts. Furthermore, the sensitivity of model hyperparameters is explored to show the capability of the proposed approach. The results show that physics-informed neural networks exhibit reduced susceptibility to experimental uncertainties when addressing inverse problems, in contrast to inverse solution methods, and offer a possible new approach for the analysis of experimental data if trained on a sufficiently large dataset.
