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

Séminaire mécanique des solides
Date: mardi 26 janvier 2021 14:00


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).



Toutes les Dates

  • mardi 26 janvier 2021 14:00