Upload
duongnhi
View
219
Download
2
Embed Size (px)
Citation preview
Chinmoy Kolay
Research Engineer
Thomas Marullo
IT System Administrator
NHERI Lehigh EF
1
Large-scale nonlinear viscous damper
characterization test
RTHS implementation challenges and
NHERI Lehigh solutions
RTHS of a RC building with nonlinear
viscous damper
2
Outline
Groups
3
Groups 9:30 9:48 AM 9:48 10:05 AM 10:10 10:28 AM 10:28 10:45 AM
1-RED Control Room Lab Floor Lab Tour
2-BLUE Lab Floor Control Room Lab Tour
3-GREEN Lab Tour Control Room Lab Floor
4-YELLOW Lab Tour Lab Floor Control Room
Back of your name tag has a group label and color
Large-scale nonlinear viscous damper
characterization test
RTHS challenges and NHERI Lehigh
solutions
RTHS of a RC building with nonlinear
viscous damper
4
Outline
Damper Characterization Test
5
Nonlinear fluid viscous damper
Make: Taylor Devices Inc.
Nominal force capacity 600 kN
Max stroke 125 mm
Theoretical force-velocity:
=
Manufacturer provided
= 773 .
and = 0.4
Operating temperature:
6.7 to +54.4 (+20 to + 130
Procedure for Damper Characterization
6
Develop a damper
model
Assign model
parameters
Predict model
response
Calculate error
between model
and measured
experimental data
Revis
e p
ara
mete
rs
to m
inim
ize e
rro
r Predefined
displacement tests
Input Displacement and Test Matrix
7
Amplitude
mm (in.)
Frequency (Hz)
0.25 0.50 1.0 1.5
76.2 (3.0) 119.7 (4.7) 239.4 (9.4) 478.8 (18.9) 718.2 (28.3)
Numbers in the cells are max velocities in mm/s (in/s)
Actuator Power Curve
8
0 500 1000 15000
0.5
1
1.5
1 valve
2 valves
3 valves
Max demand
Power curve for 1700 kN actuators at 3000 psi
Force (kN)
Vel
oci
ty (
m/s
)
Damper Characterization Test Data
9
Nonlinear Maxwell Damper Model
10
Damper shows strong frequency dependent behavior
Usually modeled using a nonlinear Maxwell model
,
,
,
,
Total damper deformation: = +
Total damper velocity: = +
Damper force:
= = =
= = =
1
Model parameters: , , and
Governing equation (nonlinear ODE)
+
1
=
||
1
||
1 ||1
=||
1
Solution of nonlinear ODEGoverning equation (nonlinear ODE): +
1
=
Solver: variable-step Dormand-Prince solver (ode45) which belongs to 5th order
Runga-Kutta familty
Simulink model for solution of the nonlinear ODE
11
Identify , , and so that the error between the model prediction and experimental data are minimized
We use particle swarm optimization (PSO) algorithm
(Kennedy and Eberhart, 1995; Ye and Wang, 2007;
Chae, 2011)
The algorithm in Matlab script is available for users
Objective function: Normalized root mean square error
, , ==1
2
=1
2
and
are experimental and predicted damper forces,
respectively
is the total number of samples
12
Determination of Model Parameters
Measured vs Model Prediction
13
Large-scale nonlinear viscous damper
characterization test
RTHS implementation challenges and
NHERI Lehigh solutions
RTHS of a RC building with nonlinear
viscous damper
14
Outline
Nonlinear damper
Experimental substructure
Introduction: RTHS
Nonlinear
damper
Linear
damper
Real time response
Effective force +1Ground acceleration
Integration of equations of motion
+1 + +1 + +1 + +1
= +1
Simulation coordinator
+1
+1
Force
transformation
+1
FE model
Linear
damper
Analytical
substructure
Ramp generator and
kinematic transformation
for each actuator DOF
ATS
compensator
Servo
controller
+1 , +1
+1
+1
+1
+1
Servo-hydraulic actuator control
and experimental substructure
15Kolay, C. Parametrically Dissipative Explicit Direct Integration Algorithms for Computational and Experimental Structural Dynamics.
Ph.D. Dissertation. Department of Civil and Environmental Engineering, Lehigh University, Bethlehem, USA, 2016
RTHS: Implementation issues and challenges
Analytical substructure
Fast and accurate state
determination procedure for
complex structures
Experimental substructure
Large capacity hydraulic
system and dynamic actuators
required
Actuator kinematic
compensation
Robust control of dynamic
actuators for large-scale
structures
Numerical integration algorithm
Accurate
Explicit
Unconditionally stable
Dissipative
Fast communication
Simulation coordinator
Preferred
16
RTHS: Implementation issues and challenges
NHERI Lehigh
Solutions
Numerical integration algorithm
Accurate
Explicit
Unconditionally stable
Dissipative
Fast communication
Simulation coordinator
Various explicit model-based algorithms
RTMD real-time integrated control architecture
17
Model-based explicit algorithms for RTHSNHERI Lehigh Solutions to RTHS Challenges
Single-parameter families of
Algorithms with numerical dissipation
Model-Based Algorithms
Semi-Explicit- (SE-) Method Explicit- (E-) Method
Single-Parameter Semi-Explicit-(SSE-) Method
Kolay-Ricles-(KR-) Method(Kolay & Ricles, 2014)
Chen-Ricles (CR) Algorithm(Chen & Ricles, 2008)
Families of
algorithms
Kolay, C., & Ricles, J. M. (2015). Assessment of explicit and semi-explicit classes of model-based algorithms for direct integration in
structural dynamics. International Journal for Numerical Methods in Engineering. doi:10.1002/nme.5153
Modified Kolay-Ricles-(MKR-) Method
(Kolay & Ricles, 2016)
18
19
Simulink Block Diagram for E- Method
Block 1 & 5 runs at =1
1024sec
Others run at = =4
1024sec (say)
20
Timing
RTHS: Implementation issues and challenges
HybridFEM
Multi-grid real-time hybrid simulation
NHERI Lehigh
Solutions
Analytical substructure
Fast and accurate state
determination procedure
21
Lehigh HybridFEM
MATLAB and SIMULINK based computational modeling
and simulation coordinator software
Run Modes
MATLAB script for numerical simulation
SIMULINK modeling for Real-Time Hybrid simulation with
experimental elements via xPCs, and hydraulics-off for training
and validation of user algorithms.
Users Manual for training
Karavasilis, T. L., Seo, C.-Y., & Ricles, J. M. (2012). HybridFEM: A program for dynamic time history analysis and
real-time hybrid simulation (ATLSS Report). ATLSS Report (Vol. 0809). Bethlehem, PA.
NHERI Lehigh Solutions to RTHS Challenges
22
Lehigh HybridFEM
Configuration Options:
Coordinate system of nodes
Boundary, constraint and restraint conditions
Elements
Elastic beam-column
Elastic spring
Inelastic beam-column stress resultant element
Non-linear spring
Displacement-based NL beam-column fiber element
Force-based beam NL column fiber element
Zero-length
2D NL planar panel zone
Elastic beam-column element with geometric stiffness
Geometric nonlinearities
Steel wide flange sections (link to AISC shapes Database)
Reinforced concrete sections
Structural mass & inherent damping properties
Adaptable integration methods
Materials
Elastic
Bilinear elasto-plastic
Hysteretic
Bouc-Wen
Trilinear
Stiffness degrading
Concrete
Steel
23
RTHS: Implementation issues and challenges
Large hydraulic power supply system
5 large capacity dynamic actuators
Development of actuator kinematic compensation
Servo hydraulic actuator control: Adaptive Time Series Compensator (ATS)
NHERI Lehigh
Solutions
Experimental substructure
Large capacity hydraulic system and dynamic actuators required
Actuator kinematic compensation
Robust control of dynamic actuators for large-scale structures
24
25
Servo Hydraulic Actuator Control
Nonlinear servo-valve dynamics
Nonlinear actuator fluid dynamics
Test specimen material and
geometric nonlinearities
Slop, misalignment, deformations
in test setup
Variable amplitude
error and time delay in
measured specimen
displacement
Inaccurate structural response
Delayed restoring force adds energy into
the system (negative damping)
Can cause instability
It is important to compensate
Servo Hydraulic Actuator Control
Actuator delay compensation
Inverse compensation (Chen 2007)
Adaptive inverse compensation (AIC, Chen
and Ricles 2010)
Adaptive time series (ATS) compensator
(Chae et al. 2013)
Chae, Y., Kazemibidokhti, K., and Ricles, J.M. (2013). Adaptive time series compensator for delay
compensation of servo-hydraulic actuator systems for real-time hybrid simulation, Earthquake
Engineering and Structural Dynamics, DOI: 10.1002/ eqe.2294..
Chen C. Development and numerical simulation of hybrid effective force testing method. Ph.D.
Dissertation, Department of Civil and Environmental Engineering, Lehigh University, Bethlehem, PA
2007.
Chen, C. and Ricles, J.M. Tracking error-based servohydraulic actuator adaptive compensation for
real-time hybrid simulation. ASCE Journal of Structural Engineering, 2010; 136(4):432-440.
26
= 0
+ + 2
Adaptive Time Series (ATS) Compensator
: compensated input displacement into actuator
: adaptive coefficients
Adaptive coefficients are optimally updated to minimize the error between
the specimen target and measured displacements using the least squares
method
A = a0k a1k ank[ ]T
Xm = xmxm d
n
dtnxm( )
T
xm = xk-1m xk-2
m xk-qm
T
Uc = uk-1c uk-2
c uk-qm
T
(Output (measured) specimen displacement history)
(Input actuator displacement history)
A = XmTXm( )
-1
XmTUc
2nd order ATS compensator
Chae, Y., Kazemibidokhti, K., and Ricles, J.M. (2013). Adaptive time series compensator for delay compensation of servo-hydraulic
actuator systems for real-time hybrid simulation, Earthquake Engineering and Structural Dynamics, DOI: 10.1002/ eqe.2294.
: target specimen displacement
Large-scale nonlinear viscous damper
characterization test
RTHS implementation challenges and
NHERI Lehigh solutions
RTHS of a RC building with nonlinear
viscous damper
28
Outline
29
RTHS configuration
1999 Chi-Chi EQ record scaled to MCE hazard level
Time step: =3
1024s
Prototype floor plan
RTHS configuration
Analytical substructure modeled using force-based elements with fixed number of iterations and linear elastic elements
Mass, tangent, and initial stiffness proportional inherent damping
Time step: =3
1024s
MKR- method (parameter )
Model-based integration parameters (, , ) determined from characterization test data
ATS Compensator for adaptive time delay and amplitude compensation
30
31
Explicit Modified KR- (MKR-) Method
Velocity update: +1 = +
Displacement update: +1 = + + 2
Weighted equations of motion: +1 + +1 + +1 = +1
where,
+1 = +1 +
+1 = 1 +1 +
+1 = 1 +1 +
+1 = 1 +1 +
Initial acceleration: 0 = [ 0 0]
Kolay, C., & Ricles, J. M. (2014). Development of a family of unconditionally stable explicit direct integration algorithms
with controllable numerical energy dissipation. Earthquake Engineering and Structural Dynamics, 43(9), 13611380.
http://doi.org/10.1002/eqe.2401
, , and : model-based integration parameters
Integration Parameters
Parameter controlling numerical energy dissipation
= spectral radius when =
varies in the range 0 1
= 1: No numerical energy dissipation
= 0: Asymptotic annihilation
Scalar integration parameters:
=2
3 +2 1
3 +
2 ++1; =
+1; =
1
2 + ; =
1
2
1
2+
Model-based integration parameter matrices:
= + + 2
1; =1
2+
= + + 2
1 + + t2
IP stands for integration parameters
, , and need to be formed based on the hybrid system
32Kolay, C., & Ricles, J. M. (2016). Improved explicit integration algorithms for structural dynamic analysis with unconditional
stability and numerical dissipation. Submitted to Journal of Earthquake Engineering.
RTHS: Model-Based Integration Parameters
Model-based integration parameters (, , and ) require , , and
For the present study
= =analytically modeled mass matrix
Experimental substructure mass is small
= 0 + 1 +
= initial stiffness matrix of analytical substructure
=equivalent damping matrix of experimental substructure
0 and 1 are Rayleigh damping coefficients
= +
=equivalent stiffness matrix of experimental substructure
How can we determine and
?
33
34
Model-based integration parametersLinearization of nonlinear Maxwell model at a small velocity (0.5 in/s) and determination
of frequency dependent equivalent Kelvin-Voigt model parameters
What is the value of ?
, ,
,
Linearized Maxwell model
=
=
= 1
Linearized
Linearization
,
=
2
1 + 2
=
1 + 2
Equivalent Kelvin-Voigt model
,
,
,
,
35
RTHS Test Matrix
Test No.
1 1 0 0.75
2 2 0 0.75
3 212
0.75
4 2 1 0
36
RTHS Test Data
References Chae, Y. (2011). Seismic Hazard Mitigation of Building Structures Using Magneto-Rheological Dampers. Ph.D. dissertation, Department
of Civil and Environmental Engineering, Lehigh University, Bethlehem, PA.
Chae, Y., Kazemibidokhti, K., & Ricles, J. M. (2013). Adaptive time series compensator for delay compensation of servo-hydraulicactuator systems for real-time hybrid simulation. Earthquake Engineering & Structural Dynamics, 42(11), 16971715. doi:10.1002/eqe
Chen, C., & Ricles, J. M. (2008). Development of direct integration algorithms for structural dynamics using discrete control theory. Journal of Engineering Mechanics, 134(8), 676683. http://doi.org/10.1061/(ASCE)0733-9399(2008)134:8(676)
Kennedy, J. and Eberhart, R. (1995). Particle swarm optimization. Proc. of IEEE International Conference on Neural Networks, Piscataway, NJ, 19421948.
Kolay, C. Parametrically Dissipative Explicit Direct Integration Algorithms for Computational and Experimental Structural Dynamics. Ph.D. Dissertation. Department of Civil and Environmental Engineering, Lehigh University, Bethlehem, USA, 2016
Kolay, C., & Ricles, J. M. (2014). Development of a family of unconditionally stable explicit direct integration algorithms with controllable numerical energy dissipation. Earthquake Engineering and Structural Dynamics, 43(9), 13611380. http://doi.org/10.1002/eqe.2401
Kolay, C., & Ricles, J. M. (2016). Force-based frame element implementation for real-time hybrid simulation using explicit direct integration algorithms. Submitted to Journal of Structural Engineering.
Kolay, C., & Ricles, J. M. (2016). Improved explicit integration algorithms for structural dynamic analysis with unconditional stability and numerical dissipation. Submitted to Journal of Earthquake Engineering.
Kolay, C., Ricles, J. M., Marullo, T. M., Mahvashmohammadi, A., & Sause, R. (2015). Implementation and application of the unconditionally stable explicit parametrically dissipative KR- method for real-time hybrid simulation. Earthquake Engineering & Structural Dynamics, 44(5), 735755. http://doi.org/10.1002/eqe.2484
Ye, M. and Wang, X. (2007). Parameter estimation of the Bouc-Wen hysteresis model using particle swarm optimization. Smart Materials and Structures, 16(6), 23412349.
37
Thank you
38