scholarly journals Direct Verification of Linear Systems with over 10000 Dimensions

10.29007/dwj1 ◽  
2018 ◽  
Author(s):  
Stanley Bak ◽  
Parasara Sridhar Duggirala
Keyword(s):  
Linear Systems ◽  
Computation Time ◽  
Order Reduction ◽  
Direct Analysis ◽  
Principle Of Superposition ◽  
Basis Matrix ◽  
Time Step ◽  
Model Order ◽  
Large Linear Systems ◽  
Core Part

We evaluate a recently-proposed reachability method on a set of high-dimensional lin- ear system benchmarks taken from model order reduction and presented in ARCH 2016. The approach uses a state-set representation called a generalized star set and the principle of superposition of linear systems to achieve scalability. The method was previously shown to have promise in terms of scalability for direct analysis of large linear systems. For each benchmark, we also compare computing the basis matrix, a core part of the reachabil- ity method, using numerical simulations versus a matrix exponential formulation. The approach successfully analyzes systems with hundreds of dimensions in minutes, and can scale to systems that have over 10000 dimensions with a computation time ranging from tens of minutes to tens of hours, depending on the desired time step.

scholarly journals Study of methods for dimension reduction of complex dynamic linear systems models

2018 ◽  
Vol 226 ◽  
pp. 04036
Author(s):  
Yuriy M. Manatskov ◽  
Torsten Bertram ◽  
Danil V. Shaykhutdinov ◽  
Nikolay I. Gorbatenko
Keyword(s):  
Linear Systems ◽  
Main Idea ◽  
Computation Time ◽  
Order Reduction ◽  
Complex Dynamic ◽  
Model Order ◽  
Reduced Order ◽  
Advantages And Disadvantages ◽  
Optimization Stage ◽  
Linear Systems Of Equations

Complex dynamic linear systems of equations are solved by numerical iterative methods, which need much computation and are timeconsuming ones, and the optimization stage requires repeated solution of these equation systems that increases the time on development. To shorten the computation time, various methods can be applied, among them preliminary (estimated) calculation or oversimple models calculation, however, while testing and optimizing the full model is used. Reduced order models are very popular in solving this problem. The main idea of a reduced order model is to find a simplified model that may reflect the required properties of the original model as accurately as possible. There are many methods for the model order reduction, which have their advantages and disadvantages. In this article, a method based on Krylov subspaces and SVD methods is considered. A numerical experiments is given.

Model of induction brazing of nonmagnetic metals using model order reduction approach

2018 ◽  
Vol 37 (4) ◽  
pp. 1515-1524 ◽  
Author(s):  
Pavel Karban ◽  
David Pánek ◽  
Ivo Doležel
Keyword(s):  
Heat Treatment ◽  
Model Order Reduction ◽  
Computation Time ◽  
Order Reduction ◽  
Nonlinear Problems ◽  
Model Order ◽  
Content Type ◽  
Weakly Nonlinear ◽  
Induction Brazing ◽  
Multiphysics Problems

Purpose A novel technique for control of complex physical processes based on the solution of their sufficiently accurate models is presented. The technique works with the model order reduction (MOR), which significantly accelerates the solution at a still acceptable uncertainty. Its advantages are illustrated with an example of induction brazing. Design/methodology/approach The complete mathematical model of the above heat treatment process is presented. Considering all relevant nonlinearities, the numerical model is reduced using the orthogonal decomposition and solved by the finite element method (FEM). It is cheap compared with classical FEM. Findings The proposed technique is applicable in a wide variety of linear and weakly nonlinear problems and exhibits a good degree of robustness and reliability. Research limitations/implications The quality of obtained results strongly depends on the temperature dependencies of material properties and degree of nonlinearities involved. In case of multiphysics problems characterized by low nonlinearities, the results of solved problems differ only negligibly from those solved on the full model, but the computation time is lower by two and more orders. Yet, however, application of the technique in problems with stronger nonlinearities was not fully evaluated. Practical implications The presented model and methodology of its solution may represent a basis for design of complex technologies connected with induction-based heat treatment of metal materials. Originality/value Proposal of a sophisticated methodology for solution of complex multiphysics problems established the MOR technology that significantly accelerates their solution at still acceptable errors.

scholarly journals Large-Scale Linear Systems from Order-Reduction

10.29007/xk7x ◽  
2018 ◽  
Author(s):  
Hoang-Dung Tran ◽  
Luan Viet Nguyen ◽  
Taylor T Johnson
Keyword(s):  
Linear Systems ◽  
Large Scale ◽  
Order Reduction ◽  
Single Mode ◽  
Hybrid Automaton ◽  
Model Order ◽  
Verification Tools ◽  
Test Sets ◽  
Benchmark Suite ◽  
And Robotics

This benchmark suite is composed of nine examples of large-scale linear systems, ranging in dimensionality in the tens to the low thousands. The benchmarks are derived from diverse fields such as civil engineering and robotics, and are based on similar existing test sets for model-order reduction algorithms in control and numerical analysis. Each example is provided in the SpaceEx XML model format as single-mode hybrid automaton and are compatible with the HyST model transformation tool to support analysis in other verification tools. Some preliminary reachability analysis results for some of the smaller examples (on the order of tens of dimensions) are presented using SpaceEx.

Model order-reduction for discrete-time switched linear systems

2012 ◽  
Vol 43 (9) ◽  
pp. 1753-1763 ◽  
Author(s):  
Abderazik Birouche ◽  
Benjamin Mourllion ◽  
Michel Basset
Keyword(s):  
Linear Systems ◽  
Discrete Time ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Model Order ◽  
Switched Linear Systems
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