Data Science symposium

1. Christiaan Erdbrink
chriserdbrink@gmail.com
Data Science Symposium
31.10.2014
System identification
using evolutionary computing

2. 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)

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

4. Christiaan Erdbrink
nl.wikipedia.org
infopuntveiligheid.nl
coflexbouweninfra.noordhoff.nl
Problem description

5. Christiaan Erdbrink
Haringvliet barrier
Problem description

6. 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

7. Christiaan Erdbrink
Physics of flow-induced vibrations
(in a nutshell)
Problem description
observed response
excitation mechanisms
assessment
measures
gate design
real-life conditions

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

9. Christiaan Erdbrink
Haringvliet barrier
Traditional solutions

10. Christiaan Erdbrink
gate
Traditional solutions
Erdbrink, Krzhizhanovskaya, Sloot (2014):
“Reducing cross‐flow vibrations of underflow gates: experiments and numerical studies”, J of Fluids & Structures.

11. Christiaan Erdbrink
Traditional solutions

12. Christiaan Erdbrink
A/D = f( ζ , mr , Vr , Fr, St, I )
f a Δh Cs
Traditional solutions
Vr (-)
Fz
(-)
Erdbrink, Krzhizhanovskaya, Sloot (2014):
“Reducing cross‐flow vibrations of underflow gates: experiments and numerical studies”, J of Fluids & Structures.

13. Christiaan Erdbrink
xi (t)  xi (t)  xi
a
Δh
f
Traditional solutions

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

15. Christiaan Erdbrink
gate with leakage
numerical simulations: CFD
Erdbrink, Krzhizhanovskaya, Sloot (2014):
“Reducing cross‐flow vibrations of underflow gates: experiments and numerical studies”, J of Fluids and Structures.

16. Christiaan Erdbrink
at Vr ≈ 10:
numerical simulations: CFD
Erdbrink, Krzhizhanovskaya, Sloot (2014):
“Reducing cross‐flow vibrations of underflow gates: experiments and numerical studies”, J of Fluids and Structures.

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

18. Christiaan Erdbrink
h1(t)
h2(t)
safe or unsafe
data-driven solution
xi (t)  xi (t)  xi (t)
a(t)
f(t)
xcrit

19. Christiaan Erdbrink
Use classification to avoid critical regions
data-driven solution
Vr (-)
a (m)
Erdbrink, Krzhizhanovskaya, Sloot (2012):
“Controlling flow-induced vibrations of flood barrier gates with
data-driven and finite-element modelling”, FLOODrisk2012

20. Christiaan Erdbrink
Designing a control system
data-driven solution

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

22. Christiaan Erdbrink
approaches
evolutionary computing
traditional
field measurements
physical modelling
FIV problems
numerical simulations
data-driven
for control:
for system id:
classification
evolutionary computing
signal analysis
FEM
CFD,
CFSI
classic
machine learning
differential evolution
genetic programming

23. Christiaan Erdbrink
evolutionary computing
Hornby et al. (2006): “Automated antenna design with evolutionary algorithms”
en.wikipedia.org/wiki/Evolved_antenna

24. Christiaan Erdbrink
Evolutionary algorithms
Eiben & Smith (2011):
“Introduction to evolutionary computing”
evolutionary computing

25. Christiaan Erdbrink
Evolutionary Computing
Meta-heuristics
evolutionary computing

26. 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
evolutionary computing

27. Christiaan Erdbrink
Reverse engineering dynamical systems
For example,
evolutionary computing
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

28. Christiaan Erdbrink
Fitness progression
- model parameters
updated once in 20 gens
updated each gen
evolutionary computing

29. Christiaan Erdbrink
0 2 4 6 8 10
x 10
4
2
4
6
8
10
12
generation
fitness
best
Fitness progression
- termination criterion
0 500 1000 1500
2
4
6
8
10
12
generation
fitness
best
evolutionary computing

30. Christiaan Erdbrink
Genetic Programming – applied to Symbolic Regression
(x-C)*log(2x)
y =
(x-2.7139)*log(2x)
*-LxC+xx
evolutionary computing
y = f(x,y)
, etc.

31. Christiaan Erdbrink
Example
y = 0.468e|x|sin(3.795x)
evolutionary computing

32. Christiaan Erdbrink
evolutionary computing
Mining physical systems (the “robot scientist”)
M Schmidt, and H Lipson Science 2009;324:81-85

33. Christiaan Erdbrink
Application example
evolutionary computing
C.D. Erdbrink (2014):
“Modelling flow-induced vibrations of gates in hydraulic structures”, PhD thesis Univ. of Amsterdam

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

35. 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.

36. Christiaan Erdbrink
chriserdbrink@gmail.com
Thank you for your attention!