It is crucial to consider the fluid–thermal–structural interaction (FTSI) in designing the scramjet inlet for sustained hypersonic flight. To understand the aerothermal and aeroelastic responses of the planar hypersonic inlets under different flight Mach numbers and aspect ratios, a three-dimensional FTSI framework was developed and validated. After that, the FTSI characteristics under different flight Mach numbers and aspect ratios were investigated. The result reveals that the most obvious horizontal displacement happens at the cowl lip leading edge, whereas the maximum vertical displacement takes place at the compression-ramp leading edge. The FTSI improves the capture area and generates an additional compression angle due to the different thermal expansions between the windward and leeward panels. The thermal expansion in the spanwise direction causes the cowl lip to hump into an arc, and the maximum height happens at the midplane. The effects of FTSI on the inlet flowfield, the mass flow rate, and the total pressure ratio under different flight Mach numbers and aspect ratios were obtained. Overall, the FTSI can improve the contraction ratio, the actual mass flow rate, and the pressure ratio while causing the total pressure ratio to decrease by up to 14.64%.

Relaminarization is a reverse transition from turbulent state to laminar or laminar-like state as the turbulent boundary layer is accelerated, which cause the boundary layer easy to separate under the adverse pressure gradient. To investigate the mechanism of relaminarization flow control with air injection, large eddy simulations were performed for a turbulent flow over a convex curved wall at Mach number 3.0 under different blowing ratios. The results show that the acceleration has markedly reduced the turbulent characteristics including the number of large-scale turbulent structures and the turbulence kinetic energy values in the boundary layer for the baseline case. As the air injection is excited, the relaminarization is effectively suppressed. Meanwhile, the simulation reveals that the blowing ratio plays an important role in controlling the turbulence. The air injection with higher blowing ratio is more effective in suppressing the relaminarization under the condition that the jet-to-cross flow momentum flux ratio and static pressure of injected air are kept unchanged.

快速获得温度场和压力场载荷环境是航空发动机涡轮寿命预测的关键.在本征正交分解的基础上,分别采用响应面法、径向基函数、Kriging方法和BP神经网络,构建了E3涡轮三维流场拓扑结构的多种快速预测方法,为载荷环境实时预测提供了途径.结果表明,本征正交分解能成功地实现E3涡轮三维旋转流场的降阶,基于响应面法、径向基函数、Kriging方法和BP神经网络能精确预测涡轮流场预测结构,但在预测精度、速度等方面存在差异.在样本空间范围内点预测上,10阶模型下四种方法预测出的压力场和温度场误差均小于1％,流量、效率预测误差低于0.4％;在样本空间范围以外点的预测上,径向基函数和Kriging方法的表现不稳定.涡轮壁面流场相关性分析表明,压力场预测精度与转速、进口压力高度相关;温度场与转速、进口压力的相关性弱于压力场.

为发展一种兼具乘波体高升阻比和升力体高容积率的气动设计与预测方法,开展了3个方面的研究工作.基于升力体和乘波体融合设计理念,提出了一种大容积率、高升阻比的乘波前体的扩容设计方法.对扩容设计的乘波前体进行了数值模拟,获得了典型设计参数对前体容积率、升阻比等气动性能参数的影响规律.基于本征正交分解理论和径向基函数建立了高超声速乘波前体流场结构和气动性能参数的快速预测模型,并对扩容设计的乘波前体流场开展了快速预测研究.研究表明:相比于未扩容之前,高度为5、10 mm时,容积增加8.00％和15.00％;基于本征正交分解理论的快速预测方法可精确、快速地获得不同几何设计参数下乘波前体的流场,预测误差不高于2.00％.

为研究隔离段自激振荡现象,采用2阶时间和空间精度、非结构网格、剪切应力输运(SST)k-ω湍流模型有限体积法程序对二元进气道在高反压下的非定常特性进行数值模拟,成功捕捉到激波串自激振荡现象,在此基础上利用本征正交分解(POD)和动力学模态分解(DMD)方法对其进行分析.结果表明:该自激振荡是低频主导、多频耦合的复杂振荡现象;基于本征正交分解和动力学模态分解构建的预测模型均能准确快速地预测出非定常流场的演变特性,预测误差小于0.2％,前者耗时为0.22s,后者耗时为0.05s.

The ramjet/scramjet engines require the control-oriented model to predict the inlet flow field in less than a few seconds. However, it is challenging for these kinds of inlets which utilize curved shock waves to compress the air flow. In this paper, a reduced-order model based on the computational fluid dynamics, the proper orthogonal decomposition theory, and the radial basis function interpolation method is developed. After that, the curved shock waves dominated flow fields of a ramjet inlet under different angles of attack and free stream Mach numbers are predicted with this reduced-order model and compared to the full order computational fluid dynamics solutions. The results show that this reduced-order model can successfully predict the curved shock waves, the curved shock wave/boundary layer interactions, and the shock trains caused by a back pressure with high accuracies. The consumed time is only 0.11 s. The performance parameters are also predicted with the relative errors no more than 2%.