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Verification of Hybrid Simulation. by Ali. Ozdagli, Wang Xi, Ge Ou , Bo Li, Guoshan Xu Shirley Dyke, Jian Zhang and Bin Wu Project funded by National Science Foundation - CMMI Grant #1011534 National Science Foundation of China – Project # 90715036. Presentation Outline.

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verification of hybrid simulation

Verification of Hybrid Simulation

by

Ali. Ozdagli, Wang Xi, GeOu, Bo Li, GuoshanXu

Shirley Dyke, Jian Zhang and Bin Wu

Project funded by

National Science Foundation - CMMI Grant #1011534

National Science Foundation of China – Project #90715036

presentation outline
Presentation Outline
  • Introduction
  • Background and Motivation
  • Experimental Setup
  • Modeling of the System
  • RTHS Comparison
  • HS Efforts
  • Conclusion
introduction
Introduction

Need for Testing

  • Global performance of new systems
  • Nonlinear response

Options

  • Shake-table: Scaled Structural Testing
  • Hybrid Simulation (HS)
background
Background

“Comparison of Real-Time Hybrid Testing with Shake Table Tests for an MR Damper Controlled Structure” by Lin et al. (2009)

  • “The results show a close correlation between the shake table tests and the real-time hybrid simulation.”
  • “There is clearly a difference between the hybrid tests and shake table tests.”
background1
Background

“Development of a Versatile Hybrid Testing System for Seismic Experimentation” by Shao et al. (2012)

motivation
Motivation
  • How do we know?
    • RTHS and Numerical Simulations represent the real structural behavior?
  • Gain acceptance in community
    • Compare the RTHS to the real structure responses

Numerical Simulation

?

RTHS

Shake Table

challenges
Challenges
  • Accurate modeling of the target structure
    • System Identification
  • Semi-active controllable nonlinear damper
    • Hard to model rate dependent dynamics
    • Damper-structure interaction
objective
Objective
  • Verification of RTHS methodology using shake table tests on mid-scale structure

Research Program

  • Phase 1: Numerical Modeling and Simulation
  • Phase 2: Shake Table Tests
  • Phase 3: RTHS Testing
phase 1 numerical simulation
Phase 1: Numerical Simulation

Test Structure

  • Base Dimension: 1.84 m by 2.04 m
  • Story height: 1.2 m
  • Material: Structural Steel
mck update method
MCK Update Method

More details were given in

‘Modeling of Distributed Real-time Hybrid Simulation’

accessible from http://nees.org/resources/6641/

Model is awarded by NEES as the best simulation model.

mr damper numerical model
MR damper numerical model
  • Lord MR damper RD-1005-03
phase 2 shake table tests
Phase 2: Shake Table Tests

Location: Harbin Institute of Technology

Size: 3m×4m (shaking direction)

Peak acceleration: ±1.33g

Peak velocity: ±600 mm/s

Stroke: ±125 mm

Maximum payload: 12t

Force capacity: 200kN

Maximum overturning moment: 30 t-m

Frequency bandwidth: 0 - 30 Hz

Conducted uncontrolled, passive off, passive on and semi-active control cases

phase 3 rths
Phase 3: RTHS

MTS loading Frame @ HIT

MTS Loading Frame, 2500kN,

Internal LVDT

Load cell, 15kN

Lord MR damper, 2kN

MTS Flex GT Controller

Inner Loop Control

Clamp for vertical loading

rths setup
RTHS Setup

Force

Numerical substruc.

Physical substruc.

Complete Structure

Damper

Desired Displacement

rths result kobe
RTHS Result: Kobe

Due to the limitation of Pump Velocity Limitation, Piston maximum moving speed 50mm/s

phase 3 rths replace pics with iisl actuator
Phase 3: RTHS (Replace pics with IISL Actuator)

Shore Western loading Frame @ IISL

2 kip Actuator

Loading Frame

  • High performance programmable DSP system plus high precision servo-hydraulic motion control system.

Servo Valve

Lord

MR Damper

remarks on rths
Remarks on RTHS
  • To verify the RTHS methodology, shake table responses at HIT are compared to RTHS results at IISL.
  • A new control oriented model updating method is implemented using mode shapes to derive MCK.
    • MCK model based on fully identified results
    • Accurate zero tracking
  • A new compensation scheme, RIAC is implemented.
    • High performance even in large noise/signal ratio condition
    • Flexible to choose loop shaping function
    • Experimental tuning is easy to perform
slide22

Model updating with UKF

Numerical BRB

Constrained Kalman filter

Physical BRB

slide23

Physical BRB

Numerical BRB

slide24

Physical BRB

Numerical BRB

Real-time hybrid test validations

slide25

Initial

CUKF

UKF

slide26

Finite element based sectional constitutive model

  • Section Yield Function
  • Section Restoring Force Model (RFM)

When , Section- elastic

When , Section- plastic

where

slide27

Numerical example

  • Identification results
  • Model updating results
slide28

Experiment plan

  • Test scheme
  • Test setup and three cases of HS

Traditional HS (Linear/Nonlinear)

FE Model updating by HS

Distributed Hybrid Simulation

slide29

Delay over-prediction

Delay compensation based on over-prediction

①Calculate di+1

②Predict with c

③Load with prediction

④Find force measure-ment

Delay Compensation:

Compensated Delay > System Delay

fixed number of iterations with interpolation shing et al
Fixed Number of Iterations with Interpolation (Shing et al)

Implicit algorithms for RTHS

Limitations:

Iteration

Intensive computation

Time delay

Equivalent Force Control Method (Wu et al)

slide31

New implicit algorithm based on over-prediction

Process 1

Process 2

1、Modified Newton’s Method applied results in good iteration performance.

2、System delay is compensated for based on over-prediction method.

slide32

Test validation

  • Single time step
  • 10 seconds
  • Delay comptn error
slide34

HIT,CHINA

UCB, USA

Data available @ http://peer.berkeley.edu/~aschell/DHS%20with%20HIT/

acknowledgements
Acknowledgements
  • National Science Foundation - CMMI Grant #1011534
  • National Science Foundation of China – Project #90715036
  • HIT Lab
  • Steve Mahin & Andreas [email protected] UCB
  • Tao Wang @ IEM

Project data will be available @ NEES.org #1076

china us collaborative project on hybrid simulation
China-US collaborative project on hybrid simulation

Bin Wu, Professor

Harbin Institute of Technology

YurongGuo, Professor

Hunan University

Tao Wang , Assoc Professor

Institute of Eng. Mechanics

Shirley Dyke, Professor

Purdue University

Jian Zhang , Associate Professor

UCLA

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