AI 中文总结
本研究针对涡轮机械三维逆设计难题，开发了以扩散模型为核心的直接几何编码设计框架，实现高精度、多样化的三维压缩机几何生成，验证了坐标学习的可行性及方案的优异性能。
AI 中文摘要
涡轮机械的气动设计对整体能源系统的性能至关重要，但由于复杂的非线性流动物理现象以及多目标设计权衡的存在，该设计极具挑战性。去噪扩散模型作为生成式机器学习的主流方法之一，在众多工程应用中展现出高设计方案精度和多样性的优势。本研究将其应用于涡轮机械的三维逆设计问题，以离心式压缩机为经典代表，展示适用于复杂几何设计的新方法论。本研究开发了以扩散模型为核心的设计框架，通过指定期望的设计条件（质量流量和转速）及目标性能（压比和效率），训练后的扩散模型可直接返回满足输入条件的压缩机三维几何结构。与传统确定性正向设计方法相比，所提方法不仅能为逆设计问题生成精确的几何方案，还能实现对整个设计空间的有效探索，提供多样化的候选方案集。此外，本文是首个直接基于三维叶片几何坐标而非参数化表示进行训练的研究，证明了基于坐标学习的可行性，同时使框架具备高度灵活性，可适用于广泛的设计场景。训练后的扩散模型展现出优异的设计能力，方案精度高达99%，不可行设计占比不足1%。此外，通过对比扩散模型生成的方案集分布与物理参数直接采样的方案集分布，定量验证了训练后扩散模型的方案多样性。
英文摘要
The aerodynamic design of turbomachinery is critical to the performance of the overall energy system, yet it is challenging due to the complex non-linear flow physics and the presence of multiple-target design compromises. Denoising diffusion model, as one of the leading approaches in generative machine learning, has shown its advantages of high design solution accuracy and diversity in many engineering applications. In this study, we bring it to the 3D inverse design problem of turbomachinery, using centrifugal compressors as a classic representative, to demonstrate the new methodology for complex geometry designs. A diffusion model-centred design framework has been developed in this study. By specifying the desired design condition (mass flow rate and rotational speed) and targeted performance (pressure ratio and efficiency), the trained diffusion model returns directly the 3D compressor geometry that satisfies the condition inputs. Compared to traditional deterministic forward design approaches, the proposed method not only generates accurate geometry solutions to inverse design problems, but also enables effective exploration of the entire design space, providing a diverse set of candidate solutions. In addition, this paper presents the first study to directly train on 3D blade geometry coordinates rather than parametrised representations, demonstrating the feasibility of coordinate-based learning while enabling a highly flexible framework applicable to a wide range of designs. The trained diffusion model achieves excellent design capability, with solution accuracy up to 99% and unfeasible designs less than 1%. Furthermore, the solution diversity of the trained diffusion model is also quantitatively verified by means of comparing the distribution of the solution sets generated from the diffusion model and from direct sampling of physical parameters.
