Ye Lu (Northwestern University) Adaptive hyper-reduced order model for thermal fluid analysis in additive manufacturing

Thermal fluid coupled analysis is essential to enable an accurate temperature prediction in additive manufacturing. However, numerical simulations of this type are time-consuming, due to the high non-linearity, the underlying large mesh size and the small time step constraints. 

In this talk, we will discuss a possibility for accelerating these simulations via model order reduction techniques.  The method of the focus is the hyper-reduction which has been applied to solve many nonlinear solid mechanical problems [1]. We try to extend this method to additive manufacturing and discuss the challenges with standard snapshot-based reduced order modeling. Some representative 3D examples of additive manufacturing models, including single-track and multi-track cases, will be presented [2].

This talk will be closed by some potential applications and the coupling with other data-driven methods [3]. 

[1] D. Ryckelynck, Hyper-reduction of mechanical models involving internal variables, Internat. J. Numer. Methods Engrg. 77 (1) (2009)

[2] Lu, Y., Jones, K. K., Gan, Z., & Liu, W. K. Adaptive hyper reduction for additive manufacturing thermal fluid analysis. Computer Methods in Applied Mechanics and Engineering, (2020).

[3] Lu, Y., Blal, N., & Gravouil, A. Adaptive sparse grid based HOPGD: Toward a nonintrusive strategy for constructing space‐time welding computational vademecum. International Journal for Numerical Methods in Engineering, (2018).