Particulate Matter (PM) emissions from gasoline direct injection (GDI) engines, particularly Particle Number (PN) emissions, have been studied intensively in both academia and industry because of the adverse effects of ultrafine PM emissions on human health and other environmental concerns. GDI engines are known to emit a higher number of PN emissions (on an engine-out basis) than Port Fuel Injection (PFI) engines, due to the reduced mixture homogeneity in GDI engines. Euro 6 emission standards have been introduced in Europe (and similarly in China) to limit PN emissions from GDI engines. This article summarises the current state of research in GDI PN emissions (engine-out) including a discussion of PN formation, and the characteristics of PN emissions from GDI engines. The effect of key GDI engine operating parameters is analysed, including air-fuel ratio, ignition and injection timing, injection pressure, and EGR; in addition the effect of fuel composition on particulate emissions is explored, including the effect of oxygenate components such as ethanol.

This study investigates an onboard aeroengine model (OBEM)-tuning system (OTS) based on a hybrid Kalman filter, which is mostly used to estimate the aeroengine performance. The OTS structure comprises two parts. One is an OBEM, the other a linear parameter-varying Kalman filter. The Kalman filter is used as a regulator to minimize the mismatch of the measured outputs between the OBEM and the actual engine. This study describes the method and procedure used to construct the OTS. The results from the application of the technique to the aeroengine simulations are presented and compared to the conventional approach of tuner selection. The new methodology is shown to yield a significant improvement in online Kalman filter estimation accuracy.

Numerical integration methods have the characteristics of high efficiency and precision, making them attractive for aero-engine probabilistic risk assessment and design optimization of an inspection plan. One factor that makes the numerical integration method a suitable approach to in-service inspection uncertainties is the explicit derivation of the integration formula and integration domains. This explicit derivation ensures accurate characterization of a multivariable system’s failure risk evolution mechanism. This study develops an efficient numerical integration algorithm for probabilistic risk assessment considering in-service inspection uncertainties. The principle of probability conservation is applied to the transformation of the integration domain from the current flight cycle to the initial (N = 0) computational space. Consequently, the integration formula of failure probability is deduced, and a detailed mathematical demonstration of the proposed method is provided. An actual compressor disk is evaluated using the efficient numerical integration algorithm and the Monte Carlo simulation to validate the accuracy and efficiency of the proposed method. Results show that the time cost of the proposed algorithm is dozens of times lower than that of the Monte Carlo simulation, with a maximum relative error of 5%. Thus, the efficient numerical integration algorithm can be applied to failure analysis in the airworthiness design of commercial aero-engine components.

Probabilistic failure risk analysis of aeroengine life-limited parts is of great significance for flight safety. Current probabilistic failure risk analysis uses equal amplitude load calculations for conservative estimation, avoiding inclusion of the interference effect analyzing random loads due to its massive computational complexity and leading to reduced analysis accuracy. Here, an efficient algorithm is established to solve this computational problem, and an analytical framework is established to consider the interference effect of variable amplitude load. The corresponding probabilistic failure risk analysis is performed for the centrifugal compressor disk. The results show that considering the interference effect of random variable amplitude loads causes a significant decrease in the risk of failure, and the efficient algorithm has advantages over the Monte Carlo sampling method in accuracy and efficiency when considering load interference. This work provides a reference for exploring the probabilistic damage tolerance method under complex loads and supports the optimal design of life-limited parts.

Turbocharging is an effective way to address the problem of reduction in power and increase in fuel consumption of aviation piston engines during high-altitude flight. However, turbochargers have greatly increased the degree of complexity of power systems. The model-based system safety analysis methods for the safety analysis of turbocharging systems are introduced in this study to overcome the limitations of the traditional safety analysis methods regarding complex matching and coupled safety issues. On the basis of the established system models and the formed failure mode work boundaries and safety boundaries, the column profile coordinates of correspondence analysis with the numerical deviation of the key factors are used to identify the key factors affecting failure, thereby proposing safety control strategies in a targeted manner. Then, the failure probability of the turbocharging system is assessed through the Monte Carlo method. System failure modes and probabilities before and after the execution of safety control strategies are compared to accurately determine the effectiveness of those strategies. The verification examples show that a safety control strategy that adjusts the diameter of the wastegate can reduce system failure probability and enhance safety level.

This paper develops a thermodynamic entropy-based life prediction model to estimate the low-cycle fatigue (LCF) life of the nickel-based superalloy GH4169 at elevated temperature (650 °C). The gauge section of the specimen was chosen as the thermodynamic system for modeling entropy generation within the framework of the Chaboche viscoplasticity constitutive theory. Furthermore, an explicitly numerical integration algorithm was compiled to calculate the cyclic stress–strain responses and thermodynamic entropy generation for establishing the framework for fatigue life assessment. A thermodynamic entropy-based life prediction model is proposed with a damage parameter based on entropy generation considering the influence of loading ratio. Fatigue lives for GH4169 at 650 °C under various loading conditions were estimated utilizing the proposed model, and the results showed good consistency with the experimental results. Finally, compared to the existing classical models, such as Manson–Coffin, Ostergren, Walker strain, and SWT, the thermodynamic entropy-based life prediction model provided significantly better life prediction results.