This work presents a framework to predict near-optimal anisotropic spacing functions suitable to perform simulations with unseen operating conditions or geometric configurations. The strategy consists of utilising the vast amount of high fidelity data available in industry to compute a target anisotropic spacing and train an artificial neural network to predict the spacing for unseen scenarios. The trained neural network outputs the metric tensor at the nodes of a coarse background mesh that is then used to generate meshes for unseen cases. Examples are used to demonstrate the effect of the network hyperparameters and the training dataset on the accuracy of the predictions. The potential is demonstrated for examples involving up to 11 geometric parameters on CFD simulations involving a full aircraft configuration.

翻译：本研究提出了一种框架，用于预测适用于未见过运行条件或几何构型的近最优各向异性间距函数。该策略利用工业中可获取的大量高保真数据计算目标各向异性间距，并训练人工神经网络以预测未见过场景的间距。训练后的神经网络在粗背景网格节点处输出度量张量，随后该张量被用于为未见案例生成网格。通过算例展示了网络超参数和训练数据集对预测精度的影响。研究以涉及完整飞机构型的CFD模拟为例（包含多达11个几何参数），证明了该方法的潜力。