The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex, especially during the engine mode transition, where the tunnel changes may impact the flow fields significantly. To develop an efficient flow field reconstruction model for this, we present an Improved Conditional Denoising Diffusion Generative Adversarial Network (ICDDGAN), which integrates Conditional Denoising Diffusion Probabilistic Models (CDDPMs) with StyleGAN, and introduce a reconstruction discrimination mechanism and dynamic loss weight learning strategy. We establish the Mach number flow field dataset by numerical simulation at various backpressures for the mode transition process from turbine mode to ejector ramjet mode at Mach number 2.5. The proposed ICDDGAN model, given only sparse parameter information, can rapidly generate high-quality Mach number flow fields without a large number of samples for training. The results show that ICDDGAN is superior to CDDGAN in terms of training convergence and stability. Moreover, the interpolation and extrapolation test results during backpressure conditions show that ICDDGAN can accurately and quickly reconstruct Mach number fields at various tunnel slice shapes, with a Structural Similarity Index Measure (SSIM) of over 0.96 and a Mean-Square Error (MSE) of 0.035% to actual flow fields, reducing time costs by 7–8 orders of magnitude compared to Computational Fluid Dynamics (CFD) calculations. This can provide an efficient means for rapid computation of complex flow fields.

Three-dimensional curved shock wave/boundary layer interaction with streamwise and spanwise curvatures widely exists in practical aerodynamic design. To explore the effects of composite shock curvatures on boundary layer separation, a canonical model with a cone placed above plate was utilized as a reference. Configurations of straight, convex, and concave conical shock waves inducing the curved conical shock wave/boundary layer interactions were studied, using CFD based on Reynolds-averaged numerical simulation method. The flow structure and separation region of each case were discussed quantitively on the symmetry plane, flat plate, and plane perpendicular to flow direction, respectively. The focus of the analysis was on the characteristic patterns of separation scale variation in the streamwise and spanwise directions, which were observed to consistently change with respect to both directions with alterations in the incident shock wave shape. A simplified control volume model was established to qualitatively discuss the influence source of curved shock waves on separation scales, based on mass conservation equations. The results suggest that the curved shock wave has a holistic effect on separation, which is not solely dependent on the shock foot strength.

The performance of inlets is critical to combined cycle engines. Aiming at the overall demand of Xiamen Turbine Ejector Ramjet (XTER), the design concept and design elements of the inward-turning Turbine Based Combined Cycle (TBCC) inlet for XTER are reviewed in detail. On this basis, flow structures and characteristics of the inlet are analyzed. Results show that either design elements or design constraints of the XTER inlet are coupled with each other, and the mass modulation mechanism is the core element, which is, however, difficult to design and makes the mutual restriction among design elements more complicated. Flow structures in the XTER inlet vary significantly with the increase of Mach number from 0 to 6. Nevertheless, the total mass flow rate remaines above 0.75, which meets the mass flow demand. The mass distribution mechanism also helps the flow mass varies smoothly during mode transitions. During the turbine-to-ejector transition at Mach 2.5, the total-pressure recovery coefficients of the ejector and scramjet increase steadily. Once the transition ends, the total-pressure recovery coefficient is close to or above 0.85. As to the ejector-to-scramjet transition at Mach 3-4.5, the mass flow rate of the inlet increases from 0.81 to 0.90. Moreover, the ejector tunnel maintains a high total-pressure recovery coefficient in the first 62.5% process, indicating that this tunnel still performs properly in the first half of mode transition process. Taken together, the XTER inlet it is capable of continuous normal operation in a wide speed range since its aerodynamic characteristics meet the demand of power system, i.e., the mass flow rate varies moderately in the full speed range and the tunnel performance transits smoothly in the mode transition process.

Air-breathing hypersonic vehicles with the head intake system typically cruise at a specific angle of attack for higher lift-to-drag characteristics. However, this can cause the 3D inward-turning inlet, designed without considering the angle of attack, to operate at off-design conditions for extended periods, which results in a noticeable decline in inlet performance. To solve this problem, the Local-Turning Osculating Cones (LTOCs) method is extended from external flow to internal flow, and a 3D inward-turning inlet design method considering the cruising angle of attack is then proposed. In this method, the inward-turning inlet is divided into the shock-based and pressure-based segments, derived by specifying the incident 3D shock wave and the streamwise wall pressure distributions in each stream surface respectively. Numerical results demonstrate that the proposed method can accurately reproduce the preassigned shock waves and internal flowfield at Mach number 6, 27 km altitude, and 4° angle of attack, resulting in full mass flow capture. Compared with the inlet design without considering the angle of attack, the design considering the cruising angle of attack can improve the inviscid mass-flow-capture coefficient and the inviscid total pressure recovery coefficient at the throat section by 1.94% and by 6.56%, respectively, when the compression performances of two inlets are essentially identical. Under the viscous conditions, the mass-flow-capture coefficient is augmented by 1.90%, the total pressure recovery coefficient at the throat section is enhanced by 6.69%, and the total pressure recovery coefficient at the isolator's exit section is elevated by 7.13%.

The exhaust characteristics of a nozzle directly affect the overall performance of the scramjet engine. It is crucial to effectively predict the nozzle performance to prevent drastic changes for the stable operation of the engine. Numerical simulations of three-dimensional asymmetric nozzles under different flight conditions were conducted to build a dataset for predicting nozzle performance at various Mach numbers and nozzle pressure ratios. Considering the limitations of traditional multi-objective optimization algorithms, a Non-dominated Sorting Genetic Algorithm-Ⅲ-Simulated Annealing Mutation (NSGA-Ⅲ-SAM) was proposed to extract the optimal wall pressure measurement points for the nozzle. By using the optimal pressure characteristic data as input and the axial thrust coefficient, pitching moment coefficient, and lift coefficient as outputs, a nozzle performance parameter prediction model based on the One Dimension-Convolutional Neural Network (1D-CNN) was established and validated by the data of over-expanded states at the Mach numbers from 4.5 to 6.0. The results show that the optimal pressure positions extracted by the NSGA-Ⅲ-SAM algorithm enable the model to have high-precision and rapid prediction performance, with the overall average absolute error of all performance parameters being within 0.5%, the maximum absolute error not exceeding 0.8%, and the average prediction time being only about 0.6 ms. The proposed prediction model and method provide a reliable technical foundation for monitoring nozzle performance and adjusting exhaust conditions.