POD-based model-order reduction reduces degrees of freedom and, with density linearization and coefficient freezing, speeds up Lagrangian SPH mass-spring-damper simulations by 72 to 95 percent while keeping errors acceptable.
Lagrangian basis method for dimensionality reduction of convection dominated nonlinear flows
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Foundations of a new projection-based model reduction approach for convection dominated nonlinear fluid flows are summarized. In this method the evolution of the flow is approximated in the Lagrangian frame of reference. Global basis functions are used to approximate both the state and the position of the Lagrangian computational domain. It is demonstrated that in this framework, certain wave-like solutions exhibit low-rank structure and thus, can be efficiently compressed using relatively few global basis. The proposed approach is successfully demonstrated for the reduction of several simple but representative problems.
citation-role summary
background 1
citation-polarity summary
fields
physics.comp-ph 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
support 1representative citing papers
citing papers explorer
-
Proper Orthogonal Decomposition-based Model-Order Reduction for Smoothed Particle Hydrodynamics Simulation -- Mass-Spring-Damper System
POD-based model-order reduction reduces degrees of freedom and, with density linearization and coefficient freezing, speeds up Lagrangian SPH mass-spring-damper simulations by 72 to 95 percent while keeping errors acceptable.