Efficient hyper reduced-order model (HROM) for parametric studies of the 3D thermo-elasto-plastic calculation

This paper focuses on a 3D thermo-elasto-plastic localized thermal source simulation and its parametric analysis with high CPU efficiency in the reduced-order model (ROM) framework. The hyper reduced-order model (HROM) is introduced and improved with two choices. Firstly, three reduced bases are constructed: one for the displacement increments, one for the plastic strain increments and one for the stress state. Equilibrium equation in plasticity relies on the knowledge of plastic strain rate, hence the plastic strain has to be included into the variable to be reduced, and the incremental form is adopted in the paper. It is shown that the introduction of an extra stress basis greatly improves the quality and the efficiency of the ROM. Secondly, the reduced state variables of plastic strain increments are determined in a reduced integration domain. Concerning the parametric analysis, the interpolation of the reduced bases is based on the Grassmann manifold, which permits to generate the new proper orthogonal decomposition bases for the modified parameters. In order to increase the convergence rate, the plastic strain interpolated from snapshots (the reference cases with full FEM calculations) is considered as the initial value of each time step for the modified problem of parametric studies. As a result, the plastic calculation is always done on the confined domain and only a few iterations are then required to reach static and plastic admissibility for each time step. The parametric studies on varying thermal load and yield stress show high versatility and efficiency of the HROM coupled with Grassmann manifold interpolation. A gain of CPU time of 25 is obtained for both cases with a level of accuracy smaller than 10%. HighlightsThe improved hyper-reduced order model is developed.The 3D thermo-elasto-plastic problem is resolved with three reduced bases.The Grassmann manifold interpolation is employed for generating new POD bases.The parametric studies are performed on varying thermal load and yield stress.A gain of CPU 25 is obtained for the parametric studies with errors smaller than 10%.

[1]  Danny C. Sorensen,et al.  Nonlinear Model Reduction via Discrete Empirical Interpolation , 2010, SIAM J. Sci. Comput..

[2]  David Ryckelynck,et al.  Multi-level A Priori Hyper-Reduction of mechanical models involving internal variables , 2010 .

[3]  Alan Edelman,et al.  The Geometry of Algorithms with Orthogonality Constraints , 1998, SIAM J. Matrix Anal. Appl..

[4]  Daniel Petit,et al.  Comparison between the modal identification method and the POD‐Galerkin method for model reduction in nonlinear diffusive systems , 2006 .

[5]  Charbel Farhat,et al.  Adaptation of Aeroelastic Reduced-Order Models and Application to an F-16 Configuration , 2007 .

[6]  N. Zabaras,et al.  Design across length scales: a reduced-order model of polycrystal plasticity for the control of microstructure-sensitive material properties , 2004 .

[7]  S. Chern,et al.  Lectures on Differential Geometry , 2024, Graduate Studies in Mathematics.

[8]  P Kerfriden,et al.  Bridging Proper Orthogonal Decomposition methods and augmented Newton-Krylov algorithms: an adaptive model order reduction for highly nonlinear mechanical problems. , 2011, Computer methods in applied mechanics and engineering.

[9]  Michel Lesoinne,et al.  Parameter Adaptation of Reduced Order Models for Three-Dimensional Flutter Analysis , 2004 .

[10]  N. Nguyen,et al.  A general multipurpose interpolation procedure: the magic points , 2008 .

[11]  David Dureisseix,et al.  Toward an optimal a priori reduced basis strategy for frictional contact problems with LATIN solver , 2015 .

[12]  C. Farhat,et al.  Interpolation Method for Adapting Reduced-Order Models and Application to Aeroelasticity , 2008 .

[13]  S. Reese,et al.  Model reduction in elastoplasticity: proper orthogonal decomposition combined with adaptive sub-structuring , 2014 .

[14]  K. Willcox,et al.  Aerodynamic Data Reconstruction and Inverse Design Using Proper Orthogonal Decomposition , 2004 .

[15]  Sondipon Adhikari,et al.  Linear system identification using proper orthogonal decomposition , 2007 .

[16]  Anthony Nouy,et al.  Ideal minimal residual-based proper generalized decomposition for non-symmetric multi-field models – Application to transient elastodynamics in space-time domain , 2014 .

[17]  David Néron,et al.  A model reduction technique based on the PGD for elastic-viscoplastic computational analysis , 2013 .

[18]  David Dureisseix,et al.  A multiscale large time increment/FAS algorithm with time‐space model reduction for frictional contact problems , 2014 .

[19]  Siamak Niroomandi,et al.  Real-time deformable models of non-linear tissues by model reduction techniques , 2008, Comput. Methods Programs Biomed..

[20]  Anthony Gravouil,et al.  A global model reduction approach for 3D fatigue crack growth with confined plasticity , 2011 .

[21]  Ionel M. Navon,et al.  Efficiency of a POD-based reduced second-order adjoint model in 4 D-Var data assimilation , 2006 .

[22]  Pierre Ladevèze,et al.  On multiscale computational mechanics with time-space homogenization , 2008 .

[23]  David Ryckelynck Hyper‐reduction of mechanical models involving internal variables , 2009 .