DSD-INT 2014 - Data Science symposium - Application 2 - System identification with evolutionary computing, Dr. Christiaan Erdbrink, University of Amsterdam

Christiaan Erdbrink

[email protected]

Data Science Symposium

31.10.2014

System identification using evolutionary computing

My background

MSc Delft University of Technology, Civil Engineering, fluid mechanics Deltares, flow around hydraulic structures PhD University of Amsterdam, Computational Science

Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University)

Christiaan Erdbrink

Problem description Solution strategies - traditional - CFD - data-driven Evolutionary computing Conclusions & Outlook on future work Questions/Discussion

Outline

Christiaan Erdbrink

nl.wikipedia.org

infopuntveiligheid.nl

coflexbouweninfra.noordhoff.nl

Problem description

Christiaan Erdbrink

Haringvliet barrier

Problem description

Christiaan Erdbrink

Physics of flow-induced vibrations (in a nutshell)

Problem description

Excitation mechanisms

turbulence

stable vortex shedding

flow instabilities

self-excitation

unstable fluid resonance

Christiaan Erdbrink

Physics of flow-induced vibrations (in a nutshell)

Problem description

observed response

excitation mechanisms

assessment

measures

gate design

real-life conditions

Christiaan Erdbrink


Christiaan Erdbrink

Haringvliet barrier

Traditional solutions

Christiaan Erdbrink

gate


Erdbrink, Krzhizhanovskaya, Sloot (2014):

“Reducing cross‐flow vibrations of underflow gates: experiments

and numerical studies”, J of Fluids & Structures.

Christiaan Erdbrink Traditional solutions

Christiaan Erdbrink

A/D = f( ζ , mr , Vr , Fr, St, I )

f a ∆h Cs


Vr (-)

Fz

(-)


“Reducing cross‐flow vibrations of underflow gates:

experiments and numerical studies”, J of Fluids & Structures.

Christiaan Erdbrink

xi (t) xi (t) xi a

∆h

f


Christiaan Erdbrink


Christiaan Erdbrink

gate with leakage

numerical simulations: CFD



experiments and numerical studies”, J of Fluids and Structures.

Christiaan Erdbrink

at Vr ≈ 10:

numerical simulations: CFD



experiments and numerical studies”, J of Fluids and Structures.

Christiaan Erdbrink


Christiaan Erdbrink

h1(t)

h2(t)

safe or unsafe

data-driven solution

xi (t) xi (t) xi (t) a(t)

f(t)

xcrit

Christiaan Erdbrink

Use classification to avoid critical regions


Vr (-)

a (m)


“Controlling flow-induced vibrations of flood barrier gates with

data-driven and finite-element modelling”, FLOODrisk2012

Christiaan Erdbrink

Designing a control system


Christiaan Erdbrink

Problem description Solution strategies - traditional - CFD - data-driven Evolutionary computing Outlook & Conclusions Questions/Discussion

Christiaan Erdbrink

approaches

evolutionary computing

traditional

field measurements

physical modelling

FIV problems

numerical simulations

data-driven

for control: for system id:

classification


signal analysis FEM

CFD, CFSI classic

machine learning

differential evolution

genetic programming

Christiaan Erdbrink evolutionary computing

Hornby et al. (2006): “Automated antenna design with evolutionary algorithms”

en.wikipedia.org/wiki/Evolved_antenna

Christiaan Erdbrink

Evolutionary algorithms

Eiben & Smith (2011): “Introduction to evolutionary computing”


Christiaan Erdbrink

Evolutionary Computing

Meta-heuristics


Christiaan Erdbrink

In general, EAs work well:

- for multimodal problems - for multi-objective optimization - in hard design problems where a proposed configuration can be tested unambiguously - when small improvements are appreciated - when speed is not essential - when standard methods fail


Christiaan Erdbrink

Reverse engineering dynamical systems

For example,


Erdbrink, Krzhizhanovskaya:

“Identifying Self‐Excited Vibrations with Evolutionary Computing”,

Procedia Computer Science, Vol.29, pp.637‐647.

Sensitivity analyses population size termination model parameters evaluation tolerance solver type

Christiaan Erdbrink

Fitness progression

- model parameters

updated once in 20 gens updated each gen


Christiaan Erdbrink

0 2 4 6 8 10

x 104

2

4

6

8

10

12

generation

fitn

ess

best

Fitness progression

- termination criterion

0 500 1000 15002

4

6

8

10

12

generation

fitn

ess

best


Christiaan Erdbrink

Genetic Programming – applied to Symbolic Regression

(x-C)*log(2x)

y = (x-2.7139)*log(2x)

*-LxC+xx


y = f(x,y) , etc.

Christiaan Erdbrink

Example y = 0.468e|x|sin(3.795x)


Christiaan Erdbrink evolutionary computing

Mining physical systems (the “robot scientist”)

M Schmidt, and H Lipson Science 2009;324:81-85

Christiaan Erdbrink

Application example


C.D. Erdbrink (2014):

“Modelling flow-induced vibrations of gates in hydraulic structures”,

PhD thesis Univ. of Amsterdam

Christiaan Erdbrink

Problem description Solution strategies - traditional - CFD - data-driven Evolutionary computing Outlook & Conclusions Questions/Discussion

Christiaan Erdbrink

Conclusions

Data-driven methods should not be seen as competitors of traditional forms of modelling, but as valuable complementary tools.

Monitoring of gate behaviour combined with classification of dynamic response states can be used to avoid critical vibration ranges.

Evolutionary Computing…

…is a versatile approach for all kinds of optimization problems.

…has evolved from a hobby for computer scientists to an important area of research, with innumerous successful applications.

…can be applied to output-only identification of (complex) dynamical systems.

…is capable of automatically deriving meaningful elementary equations and from data.

Christiaan Erdbrink

[email protected]

Thank you for your attention!