Nonlinear complex system modeling has drawn attention from diverse fields and many approaches have been developed. Among those approaches, the advantages of the belief rule base (BRB) expert system have been shown for managing multiple types of information under uncertainty and modeling the nonlinearity present in many theoretical and practical complex systems. However, two challenges still need to be addressed. First, BRB needs to be downsized to conserve modeling and computational effort. For this challenge, a new disjunctive assumption is applied, which can significantly downsize BRB while maintaining its completeness. Second, the structure and parameters of BRB need to be jointly optimized. For this challenge, a new Akaike information criterion (AIC)-based optimization objective is derived to represent both modeling accuracy and modeling complexity. Moreover, a joint bi-level optimization model with an AIC-based objective is constructed for the BRB structure and parameters, and a bi-level optimization algorithm is proposed. Three evolutionary algorithms, namely, the genetic algorithm, particle swarm optimization algorithm, and differential evolutionary algorithm, are tested in a comparative fashion to determine the best fit for the optimization engine. The results of two practical case studies show that the joint optimization approach can identify an optimal configuration for both its structure and parameters, which is referred to as the best decision structure in this paper.

As the flight envelope is widening continuously and operational capability is improving sequentially, gas turbine engines are faced with new challenges of increased operation and maintenance requirements for efficiency, reliability, and safety. The measures for security and safety and the need for reducing the life cycle cost make it necessary to develop more accurate and efficient monitoring and diagnostic schemes for the health management of gas turbine components. Sensors along the gas path are one of the components in gas turbines that play a crucial role in turbofan engines owing to their safety criticality. Failures in sensor measurements often result in serious problems affecting flight safety and performance. Therefore, this study aims to develop an online diagnosis system for gas path sensor faults in a turbofan engine. The fault diagnosis system is designed and implemented using a genetic algorithm optimized recursive reduced least squares support vector regression algorithm. This method uses a reduction technique and recursion strategy to obtain a better generalization performance and sparseness, and exploits an improved genetic algorithm to choose the optimal model parameters for improving the training precision. The effectiveness of the sensor fault diagnosis system is then validated through typical fault modes of single and dual sensors.

Linearization of turbofan engines is an effective method for performance control and fault diagnosis. In this study, three different linearization techniques, including the partial derivative method, optimized fitting method and equilibrium manifold, are discussed and compared. First, an optimized fitting method is developed based on the least-square method and an optimization algorithm. To avoid trapping in local optimization solution, the initial values used in the optimization approach are obtained through the partial derivative method. Second, to verify the accuracy and effectiveness of the linear model in the flight envelope, the result of linear modeling method based on equilibrium manifold is analyzed in detail. Finally, an overall assessment of the merits or weaknesses of linearization models is provided based on the obtained results.

The bypass dual-throat nozzle is based on the dual-throat nozzle, which is a fluidic thrust vector nozzle suitable for integration into rocket motors in a symmetrical manner. As the effects of gas–solid two-phase flows are essential for solid rocket motors (SRMs), this study employs the RNG k–ε turbulence model and a particle trajectory model to numerically simulate the three-dimensional flow field inside a fixed-geometry axisymmetric bypass dual-throat nozzle to investigate its two-phase flow characteristics and thrust vectoring performance. Numerical results reveal that the smaller-diameter particles exhibit better flow-following characteristics and have a more significant impact on nozzle performance. As particle size increases, particle trajectories gradually rise within the cavity and converge toward the nozzle axis until a critical value is exceeded, after which the distribution tends to disperse. Particle deposition occurs at the bends of the bypass channel, the upstream converging section of the nozzle, and the converging section of the cavity, underscoring the need for a reinforced geometric design and thermal protection. In addition, the introduction of the particle phase into the flow reduces the thrust-vectoring angle of the nozzle and results in a loss of thrust coefficient. This research has the potential to guide the design of engines according to the incorporation of metal powder in propellants and combustion control.