Obtaining accurate components' characteristic maps has great significant for gas-turbine operating optimization and gas-path fault diagnosis. A common approach is to modify the original components' characteristic maps by introducing correction factors, which is known as performance adaptation. Among the existing methods, total average prediction error of measurable parameters (MPTAPE) at specified conditions is used to evaluate the adaptation accuracy. However, when a gas turbine undergoes a field operation, the performance parameters of each component are zonally distributed under the operating conditions. Under such circumstances, randomly selecting a few data points as the error control points (ECPs) for performance adaptation may lead to an inappropriate correction of the characteristic maps, further lowering the prediction accuracy of the simulation model. In this paper, a genetic-algorithm-based improved performance adaptation method is proposed, which provides improvements in two aspects. In one aspect, similarity between the components' predicted performance curves and the performance regression curves is used as the criterion with which to evaluate the adaptation accuracy. In the other aspect, in the process of off-design performance adaptation, the performance parameters at the design point are recalibrated. The improved method has been verified by using rig test data and applied to field data of a GE LM2500+SAC gas turbine. The comparison results show that the improved method can obtain more accurate and stable adaptation results, while the computational load can be significantly reduced.

Both an increase in atmospheric temperature and a decrease in atmospheric pressure can lead to the degradation of the maximum allowable power output (MAPO) or the thermal efficiency of a gas turbine. In order to reduce these adverse effects, this paper provides a simulated study on the improvement of the MAPO and the variations of the thermal efficiency due to the fuel flow and variable area nozzle control under different atmospheric conditions. Simulation results indicate that the MAPO increases with the power turbine nozzle area, while the thermal efficiency shows a parabola trend. With the same increment of the power turbine nozzle area, the improvement of the MAPO goes up as the atmospheric temperature rises and the decrease of thermal efficiency is alleviated. Analyses show that the slow degradation of the compressor isentropic efficiency is a key factor that enables the MAPO to increase significantly and the thermal efficiency to remain almost constant.

The gas path analysis, which can quantify the performance degradation of gas turbine components, has been extensively applied to the gas path diagnosis. However, the precondition of this method is that the number of measurable parameters for the gas turbine to be diagnosed should not be less than the number of its health factors. In the existing research, this precondition can be guaranteed through common approaches such as screening the degraded components by a model-based prediagnosis process or recognizing the degraded components by using tools such as an ANN or a support vector machine. However, the diagnosis speed, recognition accuracy, and robustness of these approaches need to be improved. Therefore, a diagnosis method fusing the gas path performance analysis model and the extreme learning machine was proposed in this paper and applied to a GE LM2500+SAC gas turbine. The working mechanism of similarity ranking-gas path diagnosis-rationality check was introduced in the fusion method, endowing it with a higher recognition accuracy rate, stronger robustness, and higher diagnostic accuracy.

基于固体炸药爆轰过程中化学反应混合区内的固相反应物与气相生成物处于力学平衡状态及热学非平衡状态的事实，提出一种考虑热学非平衡效应的反应流动模型来描述固体炸药的爆轰流动现象.该爆轰流动模型的主要特点是，在反应混合物Euler方程和固相反应物质量守恒方程的基础上，通过附加一套关于固相反应物的组分物理量的流动控制方程来表达固相反应物与气相生成物之间的热学非平衡效应.根据反应混合区内固相反应物与气相生成物这两种化学组分保持各自内能守恒的混合规则，并借助它们具有压力相等的性质以及满足体积分数总和为1的条件，推导获得的附加方程有：固相反应物的内能演化方程、体积分数演化方程及反应混合物的压力演化方程.这样，建立的爆轰模型包括：反应混合物的质量守恒方程、动量守恒方程、总能量守恒方程、压力演化方程，以及固相反应物的质量守恒方程、内能演化方程、体积分数演化方程.对所获得的爆轰模型方程组采用一个时空二阶精度的有限体积法进行数值求解，典型爆轰问题算例结果表明本文提出的固体炸药爆轰模型是合理的.