Tutorial 1 Verification and Validation - icvramisuma2018.orgicvramisuma2018.org/.../04/uq_course_tutorial1.pdf · Tutorial 1 Veri cation and Validation Americo Barbosa da Cunha Junior

Tutorial 1Verification and Validation

Americo Barbosa da Cunha Junior

Universidade do Estado do Rio de Janeiro – UERJ

NUMERICO – Nucleus of Modeling and Experimentation with Computers

http://numerico.ime.uerj.br

[email protected]

www.americocunha.org

Uncertainties 2018April 8, 2018

Florianopolis - SC, Brazil

© A. Cunha Jr (UERJ) Uncertainty Quantification in Computational Predictive Science 1 / 30

http://numerico.ime.uerj.br

www.americocunha.org

Verification and Validation (V&V)

VerificationAre we solving the equation right?It is an exercise in mathematics.

ValidationAre we solving the right equation?It is an exercise in physics.


G. Iaccarino, A. Doostan, M. S. Eldred, and O. Ghattas. Introduction to Uncertainty Quantification

Techniques. Minitutorial at SIAM CSE Conference, 2009

A. Doostan and P. Constantine. Numerical Methods for Uncertainty Propagation. Short Course at

USNCCM13, 2015

V&V for Mass-Spring-Damper Oscillator


Mass-Spring-Damper Oscillator

x + 2 ξ ωn x + ω2n x = (f /m) sin (ω t)

x(0) = v0, x(0) = x0


Picture from http://www.shimrestackor.com/Physics/Spring_Mass_Damper/spring-mass-damper.htm

http://www.shimrestackor.com/Physics/Spring_Mass_Damper/spring-mass-damper.htm

Model equation

x + 2 ξ ωn x + ω2n x = (f /m) sin (ω t)

⇐⇒[φ1

φ2

]︸︷︷︸

φ

=

[0 1

−ω2n −2 ξ ωn

][φ1

φ2

]+

[0

(f /m) sin (ω t)

]︸︷︷︸

f (t,φ)

where φ1 = x and φ2 = x .


Reference solution

The unforced case, that corresponds to f = 0, has an analyticalsolution, which is given by

x = Ae−ξωnt sin (ωd t + φ),

where

ωd = ωn

√1− ξ2,

A =

√x2

0 +

(v0 + ξ ωn x0

ωd

)2

,

φ = tan−1

(x0 ωd

v0 + ξ ωn x0

).

This solution will be used as reference in Verification step.


Numerical method

The initial value problem can be written as

φ = f (t, φ) , φ(0) = φ0

where φ =[x x

]T.

Numerical integration will be done via Explicit Euler method

φn+1 = φn + ∆t f (tn, φn)

tn+1 = tn + ∆t

where φn is an approximation for φ(tn).


main_verification1.m

clc; clear all; close all;m = 1.0; ksi = 0.1; wn = 4.0; f = 0; w = 5.0;x0 = 1.0; v0 = 0.0; t0 = 0.0; t1 = 10.0; N = 3000;

dphidt=@(t,phi)[0 1; −wn^2 −2*ksi*wn]*phi ...+ [0; (f/m)*sin(w*t)];

[time,phi] = euler(dphidt,[x0;v0],t0,t1,N);

wd = wn*sqrt(1−ksi^2);A = sqrt(x0^2 + ((v0+ksi*wn*x0)/wd)^2);theta = atan((x0*wd)/(v0+ksi*wn*x0));x_true = A*exp(−ksi*wn*time).*sin(wd*time+theta);

subplot(2,1,1)plot(time,phi(1,:),'b','LineWidth',3);legend('Euler')subplot(2,1,2)plot(time,x_true, 'g','LineWidth',3);legend('True')


euler.m

function [time,phi] = euler(rhs,phi0,t0,t1,N)

dt = (t1−t0)/N;time = zeros(1,N+1);phi = zeros(length(phi0),N+1);phi(:,1) = phi0;

for n = 1:Ntime(1,n+1) = time(1,n) + dt;phi(:,n+1) = phi(:,n) + dt*rhs(t0,phi0);

end

return


Verification of the equation solution (unforced case)

Something is wrong!(numeric different from analytic)

Is the code working fine?!

There is a bug in euler.m routine:phi(:,n+1) = phi(:,n) + dt*rhs(t0,phi0);

phi(:,n+1) = phi(:,n) + dt*rhs(t0,phi(:,n));











There is a bug in euler.m routine:

phi(:,n+1) = phi(:,n) + dt*rhs(t0,phi0);

































Apparently yes!absolute error ≤ 0.03


clc; clear all; close all;m = 1.0; ksi = 0.1; wn = 4.0; f = 10.0; w = 5.0;x0 = 1.0; v0 = 0.0; t0 = 0.0; t1 = 10.0; N = 3000;



plot(time,phi(1,:),'LineWidth',3);


Verification of the equation solution (forced case)

Imagine that analytical solution is not known.

Something is wrong!(incompatible with harmonic forcing)

Is this the correct response?

The routine euler.m still has bug(s):phi(:,n+1) = phi(:,n) + dt*rhs(t0,phi(:,n));

phi(:,n+1) = phi(:,n) + dt*rhs(time(1,n),phi(:,n));




















The routine euler.m still has bug(s):







































To answer this question it is necessary tocompare numerical results with the exact solution

(which is supposed to be unknown).

What is the altermative?Method of Manufactured Solutions

Method of Manufactured Solutions

The idea is to construct (manufacture) an initial value problem (IVP)in which the solution is known, and use it to test the numericalintegrator functionality.

1 Choose the form of model equations

φ = f (t, φ) , φ(0) = φ0 (?)

2 Define a manufactured solution Θ such that Θ(0) = φ0,which does not verify (?), i.e. Θ 6= f (t,Θ).

3 Compute the residue function R(t) := Θ− f (t,Θ) 6= 0.

4 Define the manufactured IVP

Θ = f (t,Θ) +R(t), Θ(0) = φ0,

which is verifyed by the manufactured solution Θ.



Example: (Forced MSD Oscillator)

Take as manufactured solution Θ =[

cos t sin t]T

, which satis-

fies the initial conditions.

This is not a solution for the forced oscillator, since[− sin t

cos t

]︸︷︷︸

Θ

6=

[0 1

−ω2n −2 ξ ωn

][cos tsin t

]+

[0

(f /m) sin (ω t)

]︸︷︷︸

f (t,Θ)

.

Then, the residual function is

R(t) =

[−2 sin t

cos t + 2 ξ ωn sin t + ω2n cos t − (f /m) sin (ω t)

].



The manufactured initial value problem is

[Θ1

Θ2

]=

[Θ2 − 2 sin t

−ω2n Θ1 − 2 ξ ωn Θ2 + cos t + 2 ξ ωn sin t + ω2

n cos t

],

where Θ1(0) = 1 and Θ2 = 0. Indeed,[Θ1

Θ2

]=

[cos tsin t

]

is a solution. Verify yourself!




dphidt=@(t,phi)[0 1; −wn^2 −2*ksi*wn]*phi ...+ [−2*sin(t); ...cos(t)+2*ksi*wn*sin(t)+wn^2*cos(t)];


x_true = cos(time);

subplot(2,1,1)plot(time,phi(1,:),'b','LineWidth',3);legend('Euler')subplot(2,1,2)plot(time,x_true, 'g','LineWidth',3);legend('True')


Verification of the equation solution (manufactured IVP)

Euler method is working fine!








Model Calibration and Model Validation


Validation case 1: experimental data set


main_validation1.m



[t,phi] = euler(dphidt,[x0;v0],t0,t1,N);

t_exp = [ 0.0, 3.0, 6.0, 9.0,12.0,15.0,...18.0,21.0,24.0,27.0,30.0];

x_exp = [ 0.96,−0.33, 0.79,−1.11,0.72,0.02,...−0.77, 0.94,−0.79, 0.17,0.30];

plot(t,phi(1,:),'b',t_exp,x_exp,'xr','LineWidth',3);axis([t0 t1 −2 2])legend('Model','Experiment')


Validation case 1: predictions and observations

Good agreement between experiment and simulation!Validated Model!



Good agreement between experiment and simulation!Validated Model!


Validation case 2: experimental data set 1


main_validation2.m (1/3)



[t,phi] = euler3(dphidt,[x0;v0],t0,t1,N);

t_exp = [ 0.0, 4.9, 9.9,14.9,20.0,25.0,...30.0,35.0,40.0,44.9,49.9];

x_exp = [ 1.4,−1.6, 1.5,−0.9,0.5,0.2,...−0.6, 0.6,−0.1,−0.4,0.7];

figure(1)plot(t,phi(1,:),'b',t_exp,x_exp,'xr','LineWidth',3);axis([t0 t1 −2 2])set(gca,'fontsize',18)legend('Model','Experiment')



Experiment and simulation are not in agreement!Invalid Model!



Experiment and simulation are not in agreement!Invalid Model!



m = 1.0; ksi = 0.05; wn = 3.2; f = 15.0; w = 5.5;x0 = 1.5; v0 = −0.4;


[t_cal,phi_cal] = euler3(dphidt,[x0;v0],t0,t1,N);

figure(2)plot(t,phi(1,:),'b',t_cal,phi_cal(1,:),'g',...

t_exp,x_exp,'xr','LineWidth',3);axis([t0 t1 −2 2])set(gca,'fontsize',18)legend('Uncalibrated','Calibrated','Experiment')


Validation case 2: model calibration

Good agreement after calibration!Calibrated Model!


Validation case 2: model calibration

Good agreement after calibration!Calibrated Model!


Validation case 2: experimental data set 2



t_exp2 = [ 3.4, 6.9,10.4,13.9,17.4,20.9,...24.4,27.9,31.4,34.9,38.4];

x_exp2 = [−0.7,−1.5,−0.4,−0.3,−0.4,−0.9,...−0.4,−0.1, 0.7, 0.6, 0.8];

figure(3)plot(t,phi(1,:),'b',t_cal,phi_cal(1,:),'g',...

t_exp,x_exp,'xr',t_exp2,x_exp2,'xk','LineWidth',3);axis([t0 t1 −2 2])set(gca,'fontsize',18)legend('Uncalibrated','Calibrated',...

'Experiment 1','Experiment 2')


Validation case 2: calibrated model and new observations

Good agreement between new experiment and simulation!Validated Model!


Validation case 2: calibrated model and new observations

Good agreement between new experiment and simulation!Validated Model!


References

C. J. Roy and W. L. Oberkampf, A comprehensive framework for verification, validation, and uncertainty

quantification in scientific computing. Computer Methods in Applied Mechanics and Engineering, 200:2131–2144, 2011.

C. J. Roy, Review of code and solution verification procedures for computational simulation. Journal of

Computational Physics, 205: 131–156, 2005.

W. L. Oberkampf, T. Trucano and C. Hirsch, Verification, validation, and predictive capability in

computational engineering and physics. Applied Mechanics Reviews, 57: 345–384, 2004.

W. L. Oberkampf and C. J. Roy, Verification and Validation in Scientific Computing. Cambridge UniversityPress, 1st edition, 2010.


Tutorial 1 Verification and Validation - icvramisuma2018.orgicvramisuma2018.org/.../04/uq_course_tutorial1.pdf · Tutorial 1 Veri cation and Validation Americo Barbosa da Cunha Junior

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