This work presents a novel approach capable of predicting an appropriate spacing function that can be used to generate a near-optimal mesh suitable for simulation. The main objective is to make use of the large number of simulations that are nowadays available, and to alleviate the time-consuming mesh generation stage by minimising human intervention. For a given simulation, a technique to produce a set of point sources that leads to a mesh capable of capturing all the features of the solution is proposed. In addition, a method to combine all sets of sources for the simulations available is devised. The global set of sources is used to train a neural network that, for some design parameters (e.g., flow conditions, geometry), predicts the characteristics of the sources. Numerical examples, in the context of three dimensional inviscid compressible flows, are considered to demonstrate the potential of the proposed approach. It is shown that accurate predictions of the required spacing function can be produced, even with reduced training datasets. In addition, the predicted near-optimal meshes are utilised to compute flow solutions, and the results show that the computed aerodynamic coefficients are within the required accuracy for the aerospace industry. An analysis is also presented to demonstrate that the proposed method lies in the category of green AI research, meaning that computational resources and time are substantially reduced with this approach, when compared to current practice in industry.

When optimising a design, multiple simulations for different operating conditions and geometric configurations are required. The process of generating the most appropriate mesh for each operating condition and geometric configuration is usually too time consuming in an industrial environment. This is mainly due to the excessive human intervention and expertise that is required at this stage to generate meshes capable of capturing all relevant features of the solution with the minimum number of elements possible.