Precise and efficient calculations are necessary to accurately assess the effects of thermal protection system (TPS) uncertainties on aerospacecrafts. This paper presents a probabilistic design methodology for TPSs based on physics-informed neural networks (PINNs) with parametric uncertainty. A typical thermal coating system is used to investigate the impact of uncertainty on the thermal properties of insulation materials and to evaluate the resulting temperature distribution. A sensitivity analysis is conducted to identify the influence of the parameters on the thermal response. The results show that PINNs can produce quick and accurate predictions of the temperature of insulation materials. The accuracy of the PINN model is comparable to that of a response surface surrogate model. Still, the computational time required by the PINN model is only a fraction of the latter. Considering both computational efficiency and accuracy, the PINN model can be used as a high-precision surrogate model to guide the TPS design effectively.

This work aims to study the dynamic characteristics of an entire aero-engine casing by experiments using a new type of rotating exciter. New vibration sensor layout rules are proposed according to experimental results and vibration transmission characteristics. The vibration response of the aero-engine casing is carried out, and the vibration response of different casing measuring points is also studied. The finite element model of the engine casing’s structure is established to obtain the natural frequency of the whole aero-engine casing, which agrees well with the experimental measurement. We find that the vibration acceleration transmission value of the radial measuring point of bearing No. 1, but not the fan sensor, is more suitable for detecting the running state of the fan rotor. In addition, the sensor in the intermediate casing can detect the vibration of the high-pressure rotor. Bolt loosening of the flange has little effect on the vibration transmission characteristics of the casing. This work aims to provide experimental data of whole aero-engine vibration characteristics with a new rotating exciter for the vibration test, which can help optimize the location of the vibration transducer for the engine and the assembly technology design for the whole engine structure.

In this study, a probabilistic method was proposed for an aircraft’s thermal protective layers. The uncertainties of material properties, geometric dimensions, and incoming flow environments were considered for the design inputs. To accelerate the design efficiency, Latin hypercube sampling and surrogate models were built based on finite element method calculations to enhance the simulation efficiency. Thus, the Monte Carlo method can be implemented with such a fast simulation method and produce a massive number of samples for the uncertainty quantification and sensitivity analysis, exploring their impact on the back temperature of the thermal protection layer. Compared to the deterministic method with the extreme deviation design, the probabilistic design yields a weight reduction of 15.61%. This indicates that probabilistic design is an efficient approach to enhance the performance of aircraft and reduce the overall weight of the aircraft. The general goal of this study is to provide a new design method for the coating film of thermal protection systems by considering multiple sources of uncertainties.