An improved conditional denoising diffusion GAN for Mach number field reconstruction in a multi-tunnel combined inlet based on sparse parameter information

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.