This work presents a methodology to predict a near-optimal spacing function, which defines the element sizes, suitable to perform steady RANS turbulent viscous flow simulations. The strategy aims at utilising existing high fidelity simulations to compute a target spacing function and train an artificial neural network (ANN) to predict the spacing function for new simulations, either unseen operating conditions or unseen geometric configurations. Several challenges induced by the use of highly stretched elements are addressed. The final goal is to substantially reduce the time and human expertise that is nowadays required to produce suitable meshes for simulations. Numerical examples involving turbulent compressible flows in two dimensions are used to demonstrate the ability of the trained ANN to predict a suitable spacing function. The influence of the NN architecture and the size of the training dataset are discussed. Finally, the suitability of the predicted meshes to perform simulations is investigated.

翻译：本研究提出了一种预测近最优间距函数的方法，该函数定义了适用于稳态RANS湍流粘性流动模拟的单元尺寸。该策略旨在利用现有高精度模拟计算目标间距函数，并训练人工神经网络（ANN）以预测新模拟（包括未见过的运行条件或几何构型）的间距函数。研究解决了由高度拉伸单元使用引发的若干挑战。最终目标是显著减少当前生成适用于模拟的网格所需的时间与人工专业知识。通过二维湍流可压缩流动的数值算例，验证了训练后ANN预测合适间距函数的能力。文中讨论了神经网络架构和训练数据集规模的影响，并进一步探究了预测网格用于模拟的适用性。