A deep learning approach for velocity field prediction in a scramjet isolator from Schlieren images

Accurate measurements of physical parameters in a scramjet isolator are very important to promote the design and optimization of the isolator and even the scramjet.In a ground experiment,limited by the inherent characteristics of measurement technology and equipment,it is a big challenge to obtain the velocity field inside an isolator.In this study,a deep learning approach was introduced to combine data obtained from ground experiments and numerical simulations,and a velocity field prediction model was developed for obtaining the velocity field inside an isolator based on experimental Schlieren images.The velocity field prediction model was designed with convolutional neural networks as the main structure.Ground experiments of a scramjet isolator under continuous Mach number variation were carried out,and Schlieren images of the flow field inside the isolator were collected.Numerical simulations of the isolator were also carried out,and the velocity fields inside the isolator under various Mach numbers were obtained.The velocity field prediction model was trained using flow field datasets containing experimental Schlieren images and velocity field,and the mapping relationship between the experimental Schlieren images and the predicted velocity field was successfully established.

Recent progress of machine learning in flow modeling and active flow control

In terms of multiple temporal and spatial scales, massive data from experiments, flow field measurements, and high-fidelity numerical simulations have greatly promoted the rapid development of fluid mechanics. Machine Learning(ML) provides a wealth of analysis methods to extract potential information from a large amount of data for in-depth understanding of the underlying flow mechanism or for further applications. Furthermore, machine learning algorithms can enhance flow information and automatically perform tasks that involve active flow control and optimization. This article provides an overview of the past history, current development, and promising prospects of machine learning in the field of fluid mechanics. In addition, to facilitate understanding, this article outlines the basic principles of machine learning methods and their applications in engineering practice, turbulence models, flow field representation problems, and active flow control. In short, machine learning provides a powerful and more intelligent data processing architecture, and may greatly enrich the existing research methods and industrial applications of fluid mechanics.

Recent research progress on airbreathing aero-engine control algorithm

Airbreathing aero-engines are regarded as excellent propulsion devices from ground takeoff to hypersonic flight,and require control systems to ensure their efficient and safe operation.Therefore,the present paper aims to provide a summary report of recent research progress on airbreathing aero-engine control to help researchers working on this topic.First,five control problems of airbreathing aero-engines are classified:uncertainty problem,multiobjective and multivariable control,fault-tolerant control,distributed control system,and airframe/propulsion integrated control system.Subsequently,the research progress of aircraft gas turbine engine modelling,linear control,nonlinear control,and intelligent control is reviewed,and the advantages and disadvantages of various advanced control algorithms in aircraft gas turbine engines is discussed.Third,several typical hypersonic flight tests are investigated,and the modelling and control issues of dual-mode scramjet are examined.Fourth,modelling,mode transition control and thrust pinch control for turbine-based combined cycle engines are introduced.Followed,significant hypersonic airframe/propulsion integrated system control is analysed.Finally,the study provides specific control research topics that require attention on airbreathing aero-engines.

Combustion Stabilization based on a Center Flame Strut in a Liquid Kerosene Fueled Supersonic Combustor

A newly designed strut is proposed in this paper for fuel injection and flame holding in a liquid-kerosene-fueled supersonic combustor.The thickness of the strut is 8mm and the front blockage is about 8%.The characteristic of this strut is that extra oxygen can be injected through a set of orifices at the back of the strut,which can change the local flow field structure and ER(Equivalence Ratio).Based on the above mentioned strut,a stable local flame is generated at the back of the strut and the main combustion can be organized around this local fire.Numerical simulation is conducted to compare the local flow field distribution at the back of the strut with/without extra oxygen injection.Experiments are conducted to test the combustion characteristics based on this fuel injection and flame holding strategy.The temperature distribution which can reflect the local flame characteristic has been measured in the experiments conducted under cold incoming supersonic air flow condition.In addition,the overall combustion performance in a full-scale supersonic combustor has been evaluated in the experiments conducted under hot incoming supersonic air flow condition.Results show that this strut strategy is very promising since it can organize stable supersonic combustion at the center of the combustor without any cavity or rearward facing step.Besides that,even with the 8mm thick strut,the combustion can be stable in a wide range of ER from 0.25-1 by using liquid room-temperature kerosene.