DDIM-based hybrid accelerated sampling method for generative design of aircraft configuration

Traditional aircraft configuration design faces significant efficiency challenges. This study addresses the critical bottleneck of slow sampling in point cloud diffusion models for generating 3D aerodynamic configuration under multidisciplinary constraints. We introduce the Denoising Diffusion Implicit Model (DDIM) acceleration strategy, leveraging its non-Markovian skip-step mechanism to drastically reduce the required sampling iterations without model retraining. To further optimize the balance between speed, accuracy, and diversity, we propose a novel "deterministic-stochastic" hybrid sampling strategy. This approach dynamically identifies critical timesteps by analyzing the temporal evolution of latent point cloud feature gradients and employs a trained classifier to adaptively modulate the noise strength parameter (η) across regions of varying criticality. Experimental validation demonstrates that DDIM achieves substantial acceleration: reducing steps from 1000 to 50 slashes generation time by 57.5% (from 30.32 s to 12.89 s) while only increasing the average aerodynamic performance relative error from 3.62% to 8.52% under fixed design conditions. Crucially, the hybrid strategy operating at 50 steps delivers generation within 13s, achieves a 76.6% satisfaction rate for Coverage (COV, Chamfer Distance) <10%, and attains a 73.3% satisfaction rate for aerodynamic performance error <10%, outperforming static noise sampling. This work successfully integrates DDIM acceleration with dynamic noise regulation into a point cloud diffusion framework for aircraft configuration generation, effectively overcoming the sampling efficiency hurdle and enabling the rapid production of diverse, constraint-satisfying designs. Future efforts will focus on automating the optimization of the classifier and noise control parameters.