Model Order Reduction Of Large Dynamic System

Model order reduction (MOR) is a technique used in numerical simulations to reduce the computational complexity of mathematical models.The goal of MOR techniques is to closely approximate the large scale system by a lower order system model (that preserve certain parent system properties) for conven

Project Title

Model Order Reduction Of Large Dynamic System

Project Area of Specialization

Electrical/Electronic Engineering

Project Summary

Model order reduction (MOR) is a technique used in numerical simulations to reduce the computational complexity of mathematical models.The goal of MOR techniques is to closely approximate the large scale system by a lower order system model (that preserve certain parent system properties) for convenient understanding and manageable implementation of the system in real time. Work in above aspects for Nonlinear or/and Time varying ,  Discrete systems, 2-D systems and 2nd order systems can also be done.It is closely related to the concept of metamodeling, and it has applications in all areas of mathematical modelling.Complex and Large structures with high degrees of freedom (DOFs) and a variety of physical mechanisms are frequently used in the engineering fields of aviation, aerospace, and shipping, among others. Dynamical systems provide the foundation for modelling and control of these vast arrays of complex structural systems. Fluid dynamics, design optimization, control, chemically reactive flows, data-driven systems, and vibration suppression in complex structure systems and other complexity underlying physical processes are only some of the examples. Classic mechanics may create the mechanism model of any complicated structure system in principle. The model is frequently a large-scale partial differential system, an approximation simplified high-dimensional ordinary system, a coupling system with partial system, or an ordinary system that cannot be solved directly by theory.

Project Objectives

Project Objectives:

• To get familiarize with balanced truncation and Krylov subspace method.
• Lower order approximated system must preserve properties of parent system like its stability,passivity and must have small Reduction error.
• To make system implementation feasible in real time, Model order reduction need to be performed.

Project Implementation Method

For the implementation of project we used the MATLAB and SIMULINK software. By using multi-paradigm programming language and numeric computing environment. Matrix operations, function and data visualisation, algorithm implementation, user interface building, and interfacing.

Benefits of the Project

Benefits Of Project:

–Reduction error of system is reduced

– Performance of reduced order model is optimized

– Stability of reduced system is preserved

– Passivity of reduced order system is preserved

– Error bounds are well defined

– Technique is computationally efficient

Technical Details of Final Deliverable

• Two Techniques Balanced Truncation and Krylov Subspace algorithm design.
• Modified and improvement of Techniques.
• Observation/ Comparison between Balanced Truncation and Krylov Subspace.

Final Deliverable of the Project

Software System

Core Industry

Education

Other Industries

Others

Core Technology

Others

Other Technologies

Big Data

Sustainable Development Goals

Industry, Innovation and Infrastructure

Required Resources

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