Coastal Modeling – Indispensable Design tool

J.W. Kamphuis Coastal Modeling Tool

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Coastal Modeling – Indispensable Design tool

J. W. KamphuisQueen’s University

Kingston, ON, CanadaK7L 3N6

This paper will be posted on your website

September 2010


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1. Coastal Design

September 2010

2. The Learning Curve

3. How do we proceed?

4. Integration

Coastal Modeling – Indispensable Design tool


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1. Coastal Design

a. Complexb. Use of Modelsc. Uncertainties

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1a. ComplexProcesses are difficult to comprehend and not

clearly understood. Bottom shear stress wave impact and energy dissipation erosion, accretion transport of sediment, pollutants, nutrients.

Designs are subject to difficult combinations of inputs

water levels, waves, tides currents and windModel outputs are difficult to interpret

morphology, environmental impact, water quality, etc.

Coastal Design

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Desk Study

Knowledge (Theory and Experience), Prototype Data

PreliminaryDesign

Modeling Design ImplementationPost-

ImplementationMonitoring

Interpretation

Trial and Error !

1b. Use of ModelsTraditional Design

Coastal Design

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ApprovalApproval

Knowledge (Theory and Experience) and Prototype Data

Post-Implementation

Design

Desk Study

Modeling Design ImplementationPreliminary

Design ModelingPreliminary


Design

Trial and Error !

Coastal Design

Contemporary Design

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ApprovalApproval

Knowledge (Theory and Experience) and Prototype Data

Post-Implementation

Design

Desk Study

Modeling Design ImplementationPreliminary



Design

Trial and Error !

Coastal Design

Models: A tool for design optimization through trial and error

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1c. UncertaintiesThe reason for the trial and error is the high

uncertainties in: Data Understanding of coastal processes

Design by simply combining existing knowledge with data (“desk study” in the figure) can only produce very approximate answers – conservative designs.

Coastal Design

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To improve design, uncertainties must be reduced in three areas: data, understanding of the coastal processes modeling techniques.

Moreover, clients, stakeholders, legislators, lawyers, etc. continue to press for (essentially zero) uncertainty in our design and in our understanding of the environment.

Coastal Design Uncertainties

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Further, acceptable levels of uncertainty have been reduced rapidly with time and hence reduction of uncertainties has become urgent.

But, is major reduction of uncertainty even possible? Also, it is a difficult task, since our tools were developed for an earlier time (BL)*[1] when uncertainties were accepted as part of life

*Before Lawyers

Coastal Design Uncertainties

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Growth of Uncertainty

Uncertainty

Primary ProductionFish, Birds,Mammals

Sediment,Morphology

Flow

Fisheries, etc

Far-FieldNear-Field

Flow

Site

Coastal Design

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Model Domains

Uncertainty


Sediment,Morphology

Fisheries, etc

Far-FieldNear-Field

Flow

Site

Traditional Models

Coastal Design

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Model Domains

Uncertainty


Sediment,Morphology

Fisheries, etc

Far-FieldNear-Field

Flow

Site

New Models

Coastal Design

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2. The Learning Curve

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Learning (or Development) Curve

Time

Dev

elop

men

t(K

now

ledg

e, R

elig

ion,

Bus

ines

s)

Rap

id

Prog

ress

Oops !

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Ages of the Learning Curve

Time

Infa

ncy

Old age

Maturit

y

Dev

elop

men

t(K

now

ledg

e, R

elig

ion,

Bus

ines

s)

Learning Curve

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The Learning Curve of Knowledge

Time

Kno

wle

dge

Yes.

we

can

!

Modern Era Postmodern

? ?

20001900180017001600

Enl

ight

enm

ent

Learning Curve

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Ages of Knowledge Learning Curve {+ Kuhn (1977)}

Time

Kno

wle

dge

Solution of pressing practical problems Much empiricism

Pressing problems are solvedDevelopment of sophisticated theoriesParadigm is articulated

Science becomes subculture (talks to itself)Work is addressed to peers and adjudicated by peersChallenges are internally imposed(Improvement of theories, validating paradigm)

Infa

ncy

Old ageM

atur

ity

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Paradigm Shift

Time

Paradigm Shift (Sharp Break with the Old)

Dev

elop

men

t(K

now

ledg

e, R

elig

ion,

Bus

ines

s)

Learning Curve

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Learning Curve - Photography

Time

Phot

ogra

phy

Plates

Dig

ital

Colo

ur

Film

Paradigm Shift

Learning Curve

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Possible Decline

Time

Paradigm Shift

Knowledge and development can decline after shift

Dev

elop

men

t(K

now

ledg

e, R

elig

ion,

Bus

ines

s)

Learning Curve

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Coastal Learning Curves ??D

evel

opm

ent

(% o

f pot

entia

l)

Time

Numerical Modeling

ProcessKnowledge(Theory) Field Data

Collection

Today

Physical Modeling

1970

0

75

50

25

100

1950 1970 1990 20101910 1930

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1970: Learning curves are all quite steep, except

for the learning curve for Physical Modelling. But it had produced the major tool of coastal engineering design.

The steepness of the numerical modeling, process knowledge and field measurement curves resulted from the introduction of computers.

Learning Curve

Dev

elop

men

t(%

of

pote

ntia

l)

Time

Numerical Modeling

ProcessKnowledge(Theory) Field

DataCollectio

n

Today

Physical Modeling

1970

0

75

50

25

100

1950 1970 1990 20101910 1930

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1970: To advance in 1970, it made sense to

concentrate on the steepest learning curves. Numerical modeling, process theory and field measurement methods and instrumentation were developed at the cost of physical modeling.

We took full advantage of the opportunities provided by the computer.

Learning Curve

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2010: All learning curves are quite flat.This is the “Old Age” of Coastal EngineeringTherefore, we can not - must not - continue

along the old paths followed since 1970.

Learning Curve

Dev

elop

men

t(%

of

pote

ntia

l)

Time

Numerical Modeling

ProcessKnowledge(Theory) Field

DataCollectio

n

Today

Physical Modeling

1970

0

75

50

25

100

1950 1970 1990 20101910 1930

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Another View of Coastal Learning Curve ?

Time

Decline in knowledge and development of

Physical Modeling

Dev

elop

men

t(K

now

ledg

e, R

elig

ion,

Bus

ines

s)

Paradigm Shift ?? (Introduction of

computers)

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3. How do we proceed?

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Without question, the Numerical Model has become the tool of choice in today’s Coastal Engineering design.

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Proceed?


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Rapid advancement can only occur through a paradigm shift we should be so fortunate!

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Proceed?


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In the meantime, to advance at all (to be able to deal with the present and future design complexities, uncertainties and the approvals process) we need a concerted effort on all fronts – process knowledge (theory), physical modeling, numerical modeling, field measurement.

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Proceed?


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We must take advantage of the particular strengths of each element.

We must integrate science and engineering, re-integrate theory and practice and integrate all our tools, people and facilities.

We must have (more?) open, frank communication between ‘silos of expertise’ and generate (more?) mutual appreciation, understanding and help.

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Proceed?


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To be able to solve practical problems, we must eventually (further?) break down the various ‘expert silos’ and revert to more generalist coastal engineering.

Very difficult, since career advancement is generally based on specialization, publication, etc.

Proceed?

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4. Integration

(of expertise, tools and people)

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4a. Closer (physical) integration of cultures

1. Theory ↔ Practice

Integration

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2. University education ↔ engineering: Define what we want in an engineering education –

theory + application + problem solving + skills. Interaction on the shop floor – Students and

Professors spend time regularly in industry – “Scholarships and sabbaticals in practice, etc.”.

Get engineers into the universities “Engineers in Residence, Educational leaves, etc.”

Industrial Academies: e.g. Coastal software courses run by the software developers are a great idea to be further explored. Such courses must to be technically broad and teach theory as well as skills.

Integration

September 2010

(physical) integration of cultures


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3. Physical ↔ Numerical ModelersThis is happening.

4. Physical Modelers - Join forces, co-operate, share facilities and expertise.

HYDRALAB is an excellent example.Problematic because of intellectual property and

perceived leading positions.

Integration

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This integration will cost money.Therefore, we must immediately be able to

show added value: Better, more relevant education, More competent engineers, Shorter project approval periods, Academic rewards for engineering as well as

research, etc, etc, etc.

Integration

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Comment


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Numerical models are it! These models are usually are quite integrated

with theoretical development. But the ‘users’ are not! Numerical models need to be more integrated

with data acquisition and with physical models (another type of data acquisition)

Hybrid Modeling. What do we mean?

Integration

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Finally: Integrated Modeling


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Prototype Measurements

Cal.-Ver.Input Monitoring

Physical ModelPhysical ModelPhysical Model(s)

Numerical Model(s)

Output

Integration

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Hybrid Modeling

Usual Interpretation


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Prototype Measurements

Cal.-Ver.Input Monitoring

Physical ModelPhysical ModelPhysical Model(s)

Computational

Module

Numerical Model(s)

Output

Integration

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Hybrid Modeling

My Interpretation


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Integration !

Computational ModuleNumerical ModelPhysical Model(s)Prototype Data

Integration

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Hybrid Modeling


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CommentOur models must be validated properly.If this cannot be done by Field Measurements

alone, calibrate and verify with results from large process experiments.

Improve Field Measurements (more and better data) by: Reduction of mobilization and other costs Making experiments more transportable Use new or different technologies Develop some common standards

Integration

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Hybrid Modeling


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Therefore:Hybrid Modeling =

Field Measurements + Physical Modeling + Numerical Model +

Computational Module

Level 2000 ModelsComposite Models

Integration

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Hybrid Modeling


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Level 2000 ModelFar field is modelled numerically.Near field is NM or PM with far field

introduced as boundary conditions Any PMs are of small prototype sectionsVery Large Physical Models (VLPM) can

achieve n = 1 to 5.n = 1 to 5 is already possible in oscillating

tunnels and wave flumes; we need a basin.

Integration

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Prototype Data

Cal-VerInput Monitoring

Near Field Physical Model

Computational

Module

Far Field Numerical Model

Model Output

B.C for PM

Input

B.C from NM

Output

Integration

Near Field Numerical Model

B.C for NM

Output

Near Field VLPM

B.C for VLPM

B.C for VLPM

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Level 2000 Model


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Very Large Physical Models (Aside)Needed to:

Advance understanding (scientific justification) Reduce scale and laboratory effects Provide “Field Measurements” under controlled,

repeatable conditionsBut

Who will build them? Are they economically justified? Commercially not viable !

Integration

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Level 2000 Model


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And….

VLPM experiments are like field experiments: Extensive planning and long lead times Collective exhaustion and recovery of year(s)? Therefore extensive downtime of facility

Financing for Experiments? Facilities? Downtime?

Integration

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Level 2000 Model


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Therefore….

We want only one or two excellent, world class VLPM facilities (rather than a number of inadequate or barely adequate, locally financed facilities).

Given the economic environment and recent history, the success of this approach is in doubt.

What else can we do?

Integration

September 2010

Level 2000 Model


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Process Model

Computational

Module

B.C.Model

Results

Output

Prototype Data

Cal-VerInput Monitoring

IntegrationComposite Model

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Process models are Physical or numerical models must be trustworthy representations simple, inexpensive, easy to understand tests generic tests and test results repeatable results

Process model results form the “bricks”

Integration Composite Model

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Computational module forms the “mortar” not necessarily a numerical model Mostly, simple accounting of results Calibration is done here (inexpensive) What if? Is done here (inexpensive)

Not necessarily same provider for two partsWe could develop a world-wide Bank of

process model “bricks” (I wish)

Integration Composite Model

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Integration

Example: Sacrificial Beach Drilling Islands in the Arctic

Ocean, 1980 • Ice is Nice• 9 mo. instead of 2 mo.• No Protection

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Composite Model


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Arctic IslandModels

5 m

Island Modeln=100

WaveGuide

Absorbing Beach

Wave Generator

HalfIsland Model

n=100

WaveBasin

WaveAbsorber

Composite Model

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The Process Model

Integration


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Table of Test Parameters

Model Scales – n 200 100 75 50 Model Particle Sizes - D50 (mm) 0.56 0.18 0.11 Prototype Wave Heights (m) 6.5 4.75 3.0 Prototype Wave Periods (sec) 8.0 10.0 Wave Types Regular Irregular

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Composite Model

The Process Model

Integration


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Composite ModelIntegration


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IslandModels

5 m

Island Modeln=100

WaveGuide

Absorbing Beach

Wave Generator

HalfIsland Model

n=100

WaveBasin

WaveAbsorber

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Composite Model

The Process Model

Integration


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Island Model Calibration

0

100

200

300

400

500

600

700

800

0 20 40 60 80 100 120

Time (hrs)

Eros

ion

Volu

me

(m3/

m)

ObservedCalculated

Field !

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Composite Model

The Computational Module

Integration


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Thank You

This paper will be posted on:www.civil.queensu.ca

September 2010

Documents

Coastal Modeling – Indispensable Design tool