hydro-informatics software, models and simulation · • deterministic vs. stochastic models • in...

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UNSA - DESS HYDROPROTECH Février 2002 Dr. J. A. Cunge 1 1 Hydro-Informatics Software, Models and Simulation Dr. B.Pirzadeh Department of Civil Engineering, University of Sistan & Baluchestan 2 MODELS : WHAT ARE WE TALKING ABOUT? Software, models and simulation 2

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Page 1: Hydro-Informatics Software, Models and Simulation · • Deterministic vs. stochastic models • In deterministic models, the input and output variables are not subject to random

UNSA - DESS HYDROPROTECH Février 2002

Dr. J. A. Cunge 1

1

Hydro-InformaticsSoftware, Models and Simulation

• Dr. B.Pirzadeh

• Department of Civil Engineering, University of Sistan & Baluchestan

2

MODELS :

WHAT ARE WE TALKING ABOUT?

Software, models and simulation

2

Page 2: Hydro-Informatics Software, Models and Simulation · • Deterministic vs. stochastic models • In deterministic models, the input and output variables are not subject to random

UNSA - DESS HYDROPROTECH Février 2002

Dr. J. A. Cunge 2

Software, models and simulation

MODELS IN HYDRAULICS AND HYDROLOGY

VOCABULARY: «MODEL» or «SOFTWARE»

DIFFERENCE BETWEEN:

A MODELLING SOFTWARE also called A

SOFTWARE CODE

and

A MODEL

N.B.: A SCALE MODEL IS ALWAYS A MODEL

3

4

Software, models and simulation

44

• What is Model

• A model of a system is a representation of the construction and working of the

system

• Similar to but simpler than the system it represents

• Close approximation to the real system and incorporate most of its salient features

• Should not be so complex that it is hard to understand or experiment with it

• Physical Model

• A physical object that mimics some properties of a real system

• e.g. During design of buildings, it is common to construct small physical models

with the same shape and appearance as the real buildings to be studied

• Through prototyping process

• Prototyping is the process of quickly putting together a working model (a prototype)

in order to test various aspects of a design, illustrate ideas or features and gather

early user feedback

Page 3: Hydro-Informatics Software, Models and Simulation · • Deterministic vs. stochastic models • In deterministic models, the input and output variables are not subject to random

UNSA - DESS HYDROPROTECH Février 2002

Dr. J. A. Cunge 3

55

Software, models and simulation

5

6

Software, models and simulation

• Mathematical Model

• A description of a system where the relationship between variables of the system

are expressed in a mathematical form

• e.g. Ohm's law describes the relationship between current and voltage for a resistor;

Hooke's Law gives the relationship between the force applied to an unstretched

spring and the amount the spring is stretched when the force is applied, etc.

• Through virtual prototyping

• Deterministic vs. stochastic models

• In deterministic models, the input and output variables are not subject to random

fluctuations, so that the system is at any time entirely defined by the initial

conditions chosen

– e.g. the return on a 5-year investment with an annual interest rate of 7%,

compounded monthly

• In stochastic models, at least one of the input or output variables is probabilistic or

involves randomness

– e.g. the number of machines that are needed to make certain parts based on the

probability of machine failure

Page 4: Hydro-Informatics Software, Models and Simulation · • Deterministic vs. stochastic models • In deterministic models, the input and output variables are not subject to random

UNSA - DESS HYDROPROTECH Février 2002

Dr. J. A. Cunge 4

7

Software, models and simulation

FSpring = -k∙x

Hooke’s Law

x= -FSpring/k

spring constant The amount spring

is stretched

Fspring

Fspring

8

H

L

H

23

CLHQ

Coefficient of Discharge

Rectangular Weirs

Software, models and simulation

Page 5: Hydro-Informatics Software, Models and Simulation · • Deterministic vs. stochastic models • In deterministic models, the input and output variables are not subject to random

UNSA - DESS HYDROPROTECH Février 2002

Dr. J. A. Cunge 5

9

• What is Simulation

• A simulation of a system is the operation of a model of the system, as an

imitation of the real system

• A tool to evaluate the performance of a system, existing or proposed, under

different configurations of interest and over a long period of time

• e.g. a simulation of an industrial process to learn about its behavior under different

operating conditions in order to improve the process

Software, models and simulation

10

Software, models and simulation

The term modeling refers to the development of a mathematical

representation of a physical situation. While, simulation refers to the

procedure of solving the equations that resulted from model

development.

Page 6: Hydro-Informatics Software, Models and Simulation · • Deterministic vs. stochastic models • In deterministic models, the input and output variables are not subject to random

UNSA - DESS HYDROPROTECH Février 2002

Dr. J. A. Cunge 6

A MODEL – WHAT FOR?

- To understand physical phenomena

- To predict consequences of exceptionnal events and

human activities

Software, models and simulation

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How can models help to understand physical

phenomena?

Hypotheses → model → comparison with observed

If positive, the hypotheses could be right !!!

Engineering:

Supposing the model is correct,

To predict consequences of events or human activities

Software, models and simulation

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Page 7: Hydro-Informatics Software, Models and Simulation · • Deterministic vs. stochastic models • In deterministic models, the input and output variables are not subject to random

UNSA - DESS HYDROPROTECH Février 2002

Dr. J. A. Cunge 7

13

Model concept

Software, models and simulation

REPRESENTATION

OF THE REALITY -

« SYSTEM »

INPUT OUTPUT

rainfall

Watershed basin

discharge

1313

TWO CLASSES OF MODELS:

• Correlative models & transfer functions,

• Mechanistic models that can simulate physical

phenomena

Software, models and simulation

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Page 8: Hydro-Informatics Software, Models and Simulation · • Deterministic vs. stochastic models • In deterministic models, the input and output variables are not subject to random

UNSA - DESS HYDROPROTECH Février 2002

Dr. J. A. Cunge 8

THE CLASS OF CORRELATIVE MODELS &

TRANSFER FUNCTIONS

Software, models and simulation

output

input

MODEL =

A FUNCTION OR A PROCESS

APPROACHED BY CALIBRATION

(FITTING) OF NON-PHYSICAL

PARAMETERS

CRITERIA ARE OBSERVED

INPUTS & OUTPUTS

fitting

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EXAMPLES:

- Simple correlations,

- ARMA/ARIMA and similar,

- ANN – Artificial Neuron Networks,

- GA – Genetic Algorithms,

- « Black Boxes », etc.

IN ALL CASES TRANSFER FUNCTIONS ARE DEFINED

SOLELY UPON PAST-OBSERVED INPUT/OUTPUT DATA

Software, models and simulation

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Page 9: Hydro-Informatics Software, Models and Simulation · • Deterministic vs. stochastic models • In deterministic models, the input and output variables are not subject to random

UNSA - DESS HYDROPROTECH Février 2002

Dr. J. A. Cunge 9

CORRELATIVE MODELS & TRANSFER FUNCTIONS

• AVANTAGES:

AUTOMATIC FITTING OF PARAMETERS

STATISTICS OF QUALITY OF FITTING

MODEST COMPUTER RESOURCES AND

SHORT COMPUTATIONAL TIME → REAL-

TIME APPLICATIONS

Software, models and simulation

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DISAVANTAGES & LIMITATIONS:

VALIDITY LIMITED BY THE RANGE OF THE

INPUT/OUTPUT DATA SAMPLE,

INTERPRETATION IN TERMS OF PHYSICAL PROCESSES

DIFFICULT OR IMPOSSIBLE.

THESE MODELS ARE NOT PREDICTIVE !!!

Software, models and simulation

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Page 10: Hydro-Informatics Software, Models and Simulation · • Deterministic vs. stochastic models • In deterministic models, the input and output variables are not subject to random

UNSA - DESS HYDROPROTECH Février 2002

Dr. J. A. Cunge 10

MECHANISTIC (or PHYSICAL or SIMULATION )MODELS:

Differential or integral equations that are formulation of physicallaws governing modelled reality

plus

Algoirithms necessary to solve numerically the equations

and

Representation of the topography, of the geometry, of hydraulicparameters, of hydraulic structures and their operations rules, ofthe land occupation, etc.

Software, models and simulation

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MECHANISTIC MODELS

AVANTAGES:

These models describe in deterministic way the evolution ofhydraulics and hydrology phenomena.

The description (simulation) is conform to physical laws (equations).

These models are predictive and allow for studying ofconsequences of engineering projects.

Software, models and simulation

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Page 11: Hydro-Informatics Software, Models and Simulation · • Deterministic vs. stochastic models • In deterministic models, the input and output variables are not subject to random

UNSA - DESS HYDROPROTECH Février 2002

Dr. J. A. Cunge 11

MECHANISTIC MODELS

TYPICAL DISADVANTAGES :

- the effort needed to set up a model,

- computational efforts can be important,

- quality and accuracy of the results depend upon theaccuracy and how detailed are topography and hydraulics

data,

- simulation software is to be acquired on the market andeducation effort to apply is an important investment.

- Software, models and simulation

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Software, models and simulation

I(t)

Q(t)S(t)

MECHANISTIC

MODELS: ARE BASED

ON PHYSICAL LAWS

EXAMPLE:

« RESERVOIR » MODEL

OF A WATERSHED

BASIN

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Page 12: Hydro-Informatics Software, Models and Simulation · • Deterministic vs. stochastic models • In deterministic models, the input and output variables are not subject to random

UNSA - DESS HYDROPROTECH Février 2002

Dr. J. A. Cunge 12

i

Software, models and simulation

ONLY ONE PARAMETER k !!!

IF CALIBRATED, WILL THE

MODEL BE PREDICTIVE ?

I(t)

Q(t)S(t)

CONTINUITY LAW:

dS/dt = I(t) - Q(t)

CONCEPTUALISATION:

S = k Q, dQ/dt = (I - Q)/k

SOLUTION:

Q = Q0 exp(-(t-t0)/k)

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Software, models and simulation

ESSENTIAL QUESTION IS:

GIVEN THE NEEDS, WHAT IS THE VALUE OF THE MODEL???

BASIC QUALITY CRITERIA ARE :

- WILL THE MODEL HELP TO ANSWER THE QUESTIONS &

ENGINEERING NEEDS?

- HOW A MODEL IS BUILT, SET UP?

- WHAT PHENOMENA CAN IT SIMULATE ?

- HOW HIGH IS ITS COST ?

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Page 13: Hydro-Informatics Software, Models and Simulation · • Deterministic vs. stochastic models • In deterministic models, the input and output variables are not subject to random

UNSA - DESS HYDROPROTECH Février 2002

Dr. J. A. Cunge 13

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Software, models and simulation

THE VALUE OF A MODEL IS ALSO MADE BY

THE MODELLER, BY HIS KNOWLEDGE AND

EXPERIENCE OF :

hydraulics and physics in general ,

flow patterns, hydraulics and topography of the river,

objectives, purpose of the model to be built,

available simulation software systems.

IN THIS ORDER OF IMPORTANCE !!!!

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Software, models and simulation

WHAT ABOUT THE SOFTWARE ?

SOFTWARE IS NECESSARY BUT

-FAR FROM BEING SUFFICIENT ALONE,

-IN GENERAL THERE IS NO SINGLE SOFTWARE

SOLUTION:

SEVERAL SOLUTIONS ARE POSSIBLE

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Page 14: Hydro-Informatics Software, Models and Simulation · • Deterministic vs. stochastic models • In deterministic models, the input and output variables are not subject to random

UNSA - DESS HYDROPROTECH Février 2002

Dr. J. A. Cunge 14

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Software, models and simulation

De Saint-Venant hypotheses of 1-D flow:

Uniform velocity v at every

point of the cross- section

Free surface horizontal

across the section

Vertical distribution of the

pressure is hydrostatic

Head- losses can be represented

by a Chèzy, Strickler or Manning

coefficientDo you believe this ???

And yet...

v m/s

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Software, models and simulation

How to schematise the flow in the river and its inundated plains?

Storage « pockets »

of horizontal free

surface

Main bed

Dykes, roads,

canals, ditches,...

Fields,...

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UNSA - DESS HYDROPROTECH Février 2002

Dr. J. A. Cunge 15

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Software, models and simulation

Flooded area Main bed

Is the situation 1-D?

Is not the reality rather like:

Rising flood Receding flood

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Software, models and simulation

Possible modelling options:

De Saint-Venant 1-D type

+ storage «pockets »De Saint-Venant 1-D type

+ interconnected « cells »

De St-Venant 1-D type

interconnected network

Implication: to know enough about theory, algorithms & software !!!

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UNSA - DESS HYDROPROTECH Février 2002

Dr. J. A. Cunge 16

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Software, models and simulation

Above: 1-D discretisation; Below: quasi- 2-D (river + cells)

3131

Importance of Grid Resolution

Breach B Breach B Breach B Breach B Breach B Breach B Breach B Breach B Breach B

Breach B Breach B Breach B Breach B Breach B Breach B Breach B Breach B Breach B

Breach B Breach B Breach B Breach B Breach B Breach B Breach B Breach B Breach B

20m 10m 5m

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Courtesy Kostya Vasiliev, HLCROW