Compressors are important components in various power systems in the field of energy and power. In practical applications, compressors often operate under non-design conditions. Therefore, accurate calculation on performance under various operating conditions is of great significance for the development and application of certain power systems equipped with compressors. To calculate and predict the performance of a compressor under all operating conditions through limited data, the interpolation method was combined with a support vector machine (SVM). Based on the known data points of compressor design conditions, the interpolation method was adopted to obtain training samples of the SVM. In the calculation process, preliminary screening was conducted on the kernel functions of the SVM. Two interpolation methods, including linear interpolation and cubic spline interpolation, were used to obtain sample data. In the subsequent training process of the SVM, the genetic algorithm (GA) was used to optimize its parameters. After training, the available data were compared with the predicted data of the SVM. The results show that the SVM uses the Gaussian kernel function to achieve the highest prediction accuracy. The prediction accuracy of the SVM trained with the data obtained from linear interpolation was higher than that of cubic spline interpolation. Compared with the back propagation neural network optimized by the genetic algorithm (GA-BPNN), the genetic algorithm optimization of extreme learning machine neural network (GA-ELMNN), and the genetic algorithm optimization of generalized regression neural network (GA-GRNN), the support vector machine optimized by the genetic algorithm (GA-SVM) has a better generalization, and GA-SVM is more accurate in predicting boundary data than the GA-BPNN. In addition, reducing the number of original data points still enables the GA-SVM to maintain a high level of predictive accuracy.

Purpose: The purpose of this paper is to compare the combustion characteristics, including the combustion pressure, heat release rate (HRR), coefficient of variation (COV) of indicated mean effective pressure (IMEP), flame development period and combustion duration, of aviation kerosene fuel, namely, rocket propellant 3 (RP-3), and gasoline on a two-stoke spark ignition engine. Design/methodology/approach: This paper is an experimental investigation using a bench test to reflect the combustion performance of two-stroke spark ignition unmanned aerial vehicle (UAV) engine on gasoline and RP-3 fuel. Findings: Under low load conditions, the combustion performance and HRR of burning RP-3 fuel were shown to be worse than those of gasoline. Under high load conditions, the average IMEP and the COV of IMEP of burning RP-3 fuel were close to those of gasoline. The difference in the flame development period between gasoline and RP-3 fuel was similar. Practical implications: Gasoline fuel has a low flash point, high-saturated vapour pressure and relatively high volatility and is a potential hazard near a naked flame at room temperature, which can create significant security risks for its storage, transport and use. Adopting a low volatility single RP-3 fuel of covering all vehicles and equipment to minimize the number of different devices with the use of a various fuels and improve the application safeties. Originality/value: Most two-stroke spark ignition UAV engines continue to combust gasoline. A kerosene-based fuel operation can be applied to achieve a single-fuel policy.