BlendedNet++：一种用于混合翼身融合飞机场解气动学和逆设计的数据集和基准
BlendedNet++: A dataset and benchmark for field-resolved aerodynamics and inverse design of blended wing body aircraft
AI总结：
本文提出BlendedNet++数据集，包含12492种独特的BWB几何体，通过RANS模拟提供集成力和密集表面场，利用几何深度学习模型实现实时气动预测和生成性逆设计，验证了Transolver在场预测中的准确性。
摘要：
Blended Wing Body (BWB)飞机的概念设计常受限于高维设计空间中复杂气动学的高计算成本。尽管深度学习为快速气动预测和逆设计提供了途径，但其在航空航天工程中的应用受限于缺乏大规模、场解训练数据。本文通过引入BlendedNet++，一个包含12,492种独特BWB几何体的综合气动学数据集，每种几何体均通过稳态雷诺平均纳维-斯托克斯（RANS）模拟进行评估，以提供集成力和密集表面场（Cp，Cf）。利用此数据，我们建立了两个关键工程任务的稳健框架：（1）利用几何深度学习模型实时预测表面气动场；（2）生成性逆设计。我们基准测试了五种替代架构，发现Transolver在场预测中最为准确。此外，我们通过条件扩散模型结合梯度基优化方法演示了生成性逆设计流程。这种混合方法被证明能够生成多个可行设计，满足特定升阻比目标，精度高（R^2 > 0.99），经计算流体动力学（CFD）模拟验证。这些资源使早期阶段BWB设计从迭代分析转向直接生成。
英文摘要：
The conceptual design of Blended Wing Body (BWB) aircraft is often constrained by the high computational cost of resolving complex aerodynamics over a high-dimensional design space. While deep learning offers a pathway to rapid aerodynamic prediction and inverse design, its adoption in aerospace engineering is limited by a lack of large-scale, field-resolved training data. This work addresses this gap by introducing BlendedNet++, a comprehensive aerodynamic dataset comprising 12,492 unique BWB geometries, each evaluated using steady Reynolds-Averaged Navier--Stokes (RANS) simulations to provide integrated forces and dense surface fields (Cp, Cf). Leveraging this data, we establish a robust framework for two critical engineering tasks: (1) real-time prediction of surface aerodynamic fields using geometric deep learning models, and (2) generative inverse design. We benchmark five surrogate architectures, identifying Transolver as the most accurate for field predictions. Furthermore, we demonstrate a generative inverse design pipeline using conditional diffusion models combined with gradient-based refinement. This hybrid approach is shown to generate multiple feasible designs that satisfy specific lift-to-drag targets with high accuracy (R^2 > 0.99), as confirmed by computational fluid dynamics (CFD) simulation. These resources enable a shift from iterative analysis to direct generation in early-stage BWB design.
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