李秋红

Associate Professor   Supervisor of Master's Candidates

Gender:Female

Alma Mater:南京航空航天大学

Education Level:南京航空航天大学

Degree:Doctoral Degree in Engineering

School/Department:College of Energy and Power Engineering

Discipline:Aerospace Propulsion Theory and Engineering. Power Machinery and Engineering

Business Address:A10-512

E-Mail:


Paper Publications

Joint steady state and transient performance adaptation for aero engine mathematical model

Hits:

Affiliation of Author(s):能源与动力学院

Journal:IEEE Access

Abstract:In the field of aero-engine control, it is valuable to build highly accurate component level models to meet the requirements of controller validation and model-based control. The accuracy of current performance map generation method is limited by the test data that always cannot cover the full envelope. The scaling-based performance map correction methods only focus on the adjustment of the steady-state performance. Therefore, a performance map segmentation-based joint steady-state and transient performance adaptation technique is proposed. Both the idle point and the design point are taken as precision references to scale the performance maps with large deviations. A scaling factor domain determination method is provided based on the characters of the speed lines. The performance map is first optimized with the steady state calculation based on the steady-state test data, and further optimized with the transient calculation based on the transient test data. Both the steady-state and transient performance adaptations are achieved by adjusting the performance maps. The proposed method was applied to a turbofan engine model. For continuous acceleration case, the average error is less than 1% at steady state points and less than 2% during transient operation. For large magnitude acceleration and rapid deceleration cases, the maximum errors for parameters around the idle point decrease by about one time (from 8.3% to 4.1%) and three and a half times (from 28% to 8%), respectively, by comparing with the model without transient performance adaptation. Thus, the effectiveness of the proposed method is demonstrated. © 2019 IEEE.

Translation or Not:no

Date of Publication:2019-01-01

Co-author:Pang, Shuwei,Feng, Hailong,zhb

Correspondence Author:LI qiuhong

Pre One:基于ADMM算法的航空发动机模型预测控制

Next One:基于改进NS-SOMA的变循环发动机解耦控制方法

Profile

现任南京航空航天大学能源与动力学院副教授,硕士生导师。

主讲本科生课程《自动控制原理》和研究生课程《线性系统理论与设计》,具有20多年教学经验,本科教学评估连续优秀,被研究生评选为我最喜爱的导师。

研究方向为航空发动机建模、控制和故障诊断。主持及参与国家自然科学基金、航空科学基金、国防预研、国防基础、两机专项等项目20余项,已培养硕士研究生30余名,在国内外核心及以上期刊上发表论文50多篇,获得科技进步奖两项,授权发明专利30余项,参与编写《航空燃气涡轮发动机控制》专著一部、参与翻译《燃气轮机建模、仿真与控制-基于人工神经网络的方法》教材。所获国防科技进步奖“航空发动机多变量智能鲁棒控制”,排名第二,为主要完成人。完成多项多变量鲁棒控制验证项目研究,完成多项航空发动机传感器及气路故障诊断研究工作。建立了常规涡扇、涡轴发动机的数学模型,并完成了模型修正研究工作。完成了先进变循环发动机、涡轮冲压组合发动机、短距起飞垂直降落发动机的建模和多变量控制研究,开展了智能发动机的智能直接推力控制研究和验证工作。

目前致力于将人工智能技术和航空发动机建模、控制和故障诊断相结合,利用人工智能的在线学习能力,提高发动机控制系统的性能。

硕士招生方向:

学术型I:航空宇航推进理论科学与技术

学术型II:动力机械及工程热物理

专业型I:能源与动力