The technique of organic exhaust gas decomposition with impulse corono dischrge plasma has been investigated in this study. It has been discovered that the impulse electric field affected the decomposition efficiency ...The technique of organic exhaust gas decomposition with impulse corono dischrge plasma has been investigated in this study. It has been discovered that the impulse electric field affected the decomposition efficiency with the secondary electron emission coefficient (δ) of the corona electrode as an intermediary: when the impulse voltage power ( W ) was fixed the corona electrode material with higher δ could induce higher decomposition efficiency. In these experiments, wolfram electrode which has the highest δ has really induced the highest decomposition efficiency.展开更多
排气温度是发动机运行状态的重要性能表征参数之一,通过对多个飞行架次的排气温度裕度(Exhaust Gas Temperature Margin,EGTM)进行预测分析,能够有效表达发动机的工作性能,从而为后续故障预防及检测提供理论依据。针对EGTM数据的非线性...排气温度是发动机运行状态的重要性能表征参数之一,通过对多个飞行架次的排气温度裕度(Exhaust Gas Temperature Margin,EGTM)进行预测分析,能够有效表达发动机的工作性能,从而为后续故障预防及检测提供理论依据。针对EGTM数据的非线性、非平稳特征,提出了基于经验模态分解(Empirical Mode Decomposition,EMD)与支持向量回归(Support Vector Regression,SVR)相结合的预测方法。通过EMD方法对EGTM数据进行分解以降低时间序列的复杂程度;然后根据EMD得到的各本征模态函数及趋势序列,构建基于SVR的预测模型;最后将所得的各分量的预测结果综合以得到EGTM的预测结果。以某航空发动机EGTM数据作为验证,结果表明,相比于传统的预测方法,RMSE与MAE分别降低了77.76%、80.62%,有效提高了回归精度。展开更多
文摘The technique of organic exhaust gas decomposition with impulse corono dischrge plasma has been investigated in this study. It has been discovered that the impulse electric field affected the decomposition efficiency with the secondary electron emission coefficient (δ) of the corona electrode as an intermediary: when the impulse voltage power ( W ) was fixed the corona electrode material with higher δ could induce higher decomposition efficiency. In these experiments, wolfram electrode which has the highest δ has really induced the highest decomposition efficiency.
文摘排气温度是发动机运行状态的重要性能表征参数之一,通过对多个飞行架次的排气温度裕度(Exhaust Gas Temperature Margin,EGTM)进行预测分析,能够有效表达发动机的工作性能,从而为后续故障预防及检测提供理论依据。针对EGTM数据的非线性、非平稳特征,提出了基于经验模态分解(Empirical Mode Decomposition,EMD)与支持向量回归(Support Vector Regression,SVR)相结合的预测方法。通过EMD方法对EGTM数据进行分解以降低时间序列的复杂程度;然后根据EMD得到的各本征模态函数及趋势序列,构建基于SVR的预测模型;最后将所得的各分量的预测结果综合以得到EGTM的预测结果。以某航空发动机EGTM数据作为验证,结果表明,相比于传统的预测方法,RMSE与MAE分别降低了77.76%、80.62%,有效提高了回归精度。
