Yan Hu’s research while affiliated with Government of the People's Republic of China and other places

In this paper, a new aerodynamic solution strategy for non-smooth configurations is proposed based on the wall modification model by machine learning to perform numerical simulations, rather than directly describing the global flow field with massive grids. The aerodynamic effect of non-smooth configurations in the presence of pressure gradients is investigated utilizing the proposed method. Flow features of non-smooth surface are provided by high-fidelity surface flow data acquired through lattice Boltzmann method simulation. The wall modification model is constructed by Fruit fly Optimization Algorithm-Generalized Regression Neural Network (FOA-GRNN) to reproduce the behavior of microflow near the non-smooth surface. Typical flow features, e.g., velocity corrections induced by surface texture as the output of the FOA-GRNN model, are imposed on configuration boundaries, improving computational efficiency and wall resolution. The novel aerodynamic solution strategy is validated by comparing the results of the experiment. In addition, the performance analysis of compressor cascade with micro riblet surface utilizing the above method is conducted. The results indicate that the non-smooth surface structure decreases skin friction and turbulent intensity in the flow channel compared with smooth cascade, thus significantly reducing the total pressure loss. The paper shows a positive prospect of the data-driven strategy in evaluating the aerodynamic performances of non-smooth configurations and provides a reliable solution method for the subsequent design of micro-nano surfaces.

In this study, a prognostics and health management (PHM) framework is proposed for aero-engines, which combines a dynamic probability (DP) model and a long short-term memory neural network (LSTM). A DP model based on Gaussian mixture model-adaptive density peaks clustering algorithm, which has the advantages of an extremely short training time and high enough precision, is employed for modelling engine fault development from the beginning of engine service, and principal component analysis is introduced to convert complex high-dimensional raw data into low-dimensional data. The model can be updated from time to time according to the accumulation of engine data to capture the occurrence and evolution process of engine faults. In order to address the problems with the commonly used data driven methods, the DP + LSTM model is employed to estimate the remaining useful life (RUL) of the engine. Finally, the proposed PHM framework is validated experimentally using NASA’s commercial modular aero-propulsion system simulation dataset, and the results indicate that the DP model has higher stability than the classical artificial neural network method in fault diagnosis, whereas the DP + LSTM model has higher accuracy in RUL estimation than other classical deep learning methods.

Due to the harsh operating environment of aero-engines, a surface structure that provides excellent aerodynamic performance is urgently required to save energy and reduce emissions. In this study, microgroove polyurethane coatings fabricated by chemical synthesis are investigated in terms of their effect on aerodynamic performance, which is a new attempt to investigate the impact on aerodynamic performance of compressor cascade at transonic speeds. This method reduces manufacturing and maintenance cost significantly compared with traditional laser machining. Wake measurements are conducted in the high-speed linear compressor cascade wind tunnel to evaluate the performance of cascade attached with different microgroove polyurethane coatings. Compared with the Blank case, the microgroove polyurethane coatings have the characteristic of reducing flow loss, with a maximum reducing rate of 5.87% in the area-averaged total pressure loss coefficient. The mechanism of flow loss control is discussed through analyzing the correlation between the total pressure distribution and turbulence intensity distribution. The results indicate that a large quantity of energy loss in the flow field due to turbulence dissipation and the reduction in viscous drag by microgroove polyurethane coatings relates to its effect on turbulence control. This paper demonstrates a great perspective on designing micro-nano surface structure for aero-engine applications.

Sensor fault diagnosis and performance degradation estimation (SFDPDE) play a critical role in the operation and maintenance of aero-engine. In this study, a modified fusion model driven by sensor measurements is proposed to overcome the drawbacks of single data-driven and single model-based methods. Two types of on-board models are established based on augmented state space equations, and a data-driven model based on extreme learning machine (ELM) is constructed for residual correction of the on-board model. A bidirectional information transmission algorithm is designed in the SFDPDE framework in order to include the function coordination. Kalman filter (KF) is employed as the optimal algorithm in the SFDPDE framework containing the standardized sensor parameter selection process. The experimental results indicate that, the proposed fusion model improves the accuracy of sensor fault diagnosis, reduces the mean square error (MSE) of health parameter (HP) estimations, while the information sharing module expands the application scope of SFDPDE and improves its accuracy as well as stability.

In this paper, in order to overcome computational challenge in numerical simulations of airfoil covered with micro/nanopatterns for aerodynamic drag-reduction design, an ANN-Based boundary strategy is proposed to balance the accuracy and efficiency. Lattice Boltzmann Method (LBM) is utilized to extract the micro/nanoflow characteristics in the near-wall region considering the rarefied effect and the available microscopic data is used to train the surrogate boundary model by Generalized Regression Neural Network (GRNN). The modified boundary conditions obtained by ANN-Based model replacing the real complex and fine micro/nano structure are applied on the smooth configuration to perform macroscopic simulations. Rectangular riblets as micro/nano pattern are discussed for our study. Various arrangement strategies of rectangular riblets over airfoil are adopted by adjusting the width, height, and coverage area to investigate their effects on aerodynamic performances. The results indicate that for the riblet airfoil, the skin friction reduces and the transition position moves backward compared with those of the smooth airfoil. Furthermore, the lift-to-drag ratio also significantly increases and the rate of improvement is up to 13.8% at the Angle of Attack (AOA) of 1°. This paper shows a perspective in aerodynamic design with micro/nano pattern for drag reduction by providing an innovative multi-scale simplified simulation strategy.

The service damage mechanism of K403 Ni-based superalloy turbine blade with the aluminized coating are investigated systematically. Fracture morphologies are inspected with a failure event, and the microscopic damage mechanisms are explored based on the aluminized blades with different operation times. It concludes that the formation of massive pores, aggregation of bulk carbides, coarsening and breaking of σ phases, development of continuous γ' film, etc, lead to the multi-source fatigue cracking at the interface, with the grain boundaries and Kirkendall non-contact areas as the propagation channels, resulting in the rapid fatigue failure and significant life reduction of aluminized turbine blades.

To avoid the use of specific conversion coefficients with high expense and unacceptable prediction accuracy, a probability-based prediction method is proposed by considering probabilistic feature parameters, to predict the service safety life (SSL) of aeroengine turbine blades. The direct correlation between laboratory remaining life (LRL) and SSL was firstly established by considering probabilistic feature parameters. By conducting Combined high and low Cycle Fatigue (CCF) tests of turbine blades, the effectiveness of the developed method was validated based on the failure event. The proposed method was further verified by predicting the SSL of treated blades with certain operation time. In respect of the studies, it is illustrated that (1) the SSL of turbine blade can be reasonably reflected by the LRL in respect of probabilistic feature parameters; (2) the prediction errors of the raw and treated blades are 2.2% and 12.7%, respectively, indicating that the developed probability-based prediction method has acceptable prediction precision and is an effective method in the SSL prediction of aeroengine turbine blades; (3) the developed method needs less samples than the specific conversion coefficients method, indicating that the SSL prediction of turbine blade needs fewer time and costs. The efforts of this study provide a promising approach for the SSL prediction of turbine blades, offer a useful guidance for the service life management of aeroengine turbine blades to reduce the cost of expense and time and enhance the safety of aeroengine operation.

Efficient analytical model directly enhances the reliability evaluation of flexible mechanism under operation. In this paper, genetic algorithm-based extremum neural network (GA-ENN) is developed as reliability model by introducing the thoughts of extremum and genetic algorithm (GA) into artificial neural network to address the key problems comprising transient response and modeling precision in the dynamic reliability analysis of flexible mechanism in a time domain. The thought of extremum is adopted to simplify transient response process as one extremum value to the difficulty of dynamic reliability analysis induced by transient process response, and the GA is applied to find the optimal model parameters of reliability model. The dynamic reliability analysis of two-link flexible robot manipulator (TFRM) (a typical flexible mechanism) was implemented based on the GA-ENN method, regarding the input random variables of material density, elastic modulus, section sizes of components, and the output response of components’ deformations. From the analysis, the comprehensive reliability of the TFRM is 0.951 when the allowable deformation is 1.8 × 10⁻² m. Besides, the maximum deformations of the two components follow the normal distributions with the means of 1.45 × 10⁻² m and 1.69 × 10⁻² m and the standard variances of 6.77 × 10⁻⁴ m and 4.08 × 10⁻⁴ m, respectively. Through the comparison of methods, it is illustrated that the developed GA-ENN improves the simulation efficiency and modeling accuracy by overcoming the problems of transient response and model parameter optimization in the dynamic reliability analysis of TFRM.