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Hydraulic Modeling > Reader > Flashcards

Reader Flashcards

1
Q

Fuzzy Logic model

A

Model with discriptions on basis of fuzzy logics, values between yes and no(maybe)

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2
Q

Heuristic method

A

Method to reach objective which is non precisely known in an explorative and continuously evaluating manner

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3
Q

Jacobian matrix

A

matrix of PD’s from individual residues to model parameters.

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4
Q

Auxiliary variable

A

variable whose value is not dependent on its value at a previous value of independent variable

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5
Q

Residual

A

difference between the model results and field measurements

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6
Q

Soft-hybrid model

A

data oriented model in which physical concepts are included

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7
Q

Dynamic model

A

describes changes over time

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8
Q

Objective

A
  • Domain&location
  • Reason for model
  • Questions to be answered
  • Scenarios
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9
Q

Quality req. regarding:

A
  • Answers for questions
  • Analyses to be caried out
  • Model itself
  • Calibration and when/if necessary
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10
Q

Which data is needed to make?:

A
  • Schematization data
  • Input data
  • Parameters
  • Data for scenarios
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11
Q

Which data is needed to analyse

A
  • Measurements
  • Knowledge on parameters
  • Statistical distr.
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12
Q

About the data

A
  • What data is available
  • Where can it be found
  • Is it digital
  • Approx. values
  • How to deal with, serious outliers/missing values
  • Quality
  • Who’s responsible
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13
Q

System definition:

A
  • Sum up relevant parts
  • Describe mutual relationships(processes)
  • Describe relationships between system components and the environment(everything not part of system
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14
Q

Conceptual model:

A
  1. Describe structure
    a. Inputs,state,other vars
  2. Type of model
  3. Relations between vars
  4. Assumptions
  5. Verify
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15
Q

Choosing existing model:

A
  • Available hardware, OS, expertise, time
  • Modellers pref
  • Clients wishes/reqs
  • Available licensing
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16
Q

Discretizations

A

0/1/2/3D

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17
Q

Numerical/analytical?

A

Numerical always available, but cost more time.

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18
Q

Verify

A
  • Check prescriptions
  • Dimensions/unit analysis
  • Run a sample model
  • Check spatio schematization
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19
Q

Analyze, global

A

o Run with standard input(known output)
o Global behaviour
o Verification of mass balance
o Robustness test

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20
Q

Analyze, sensitivy analysis

A

o Analytical
o Individual
o Classical(linearized)
o Response surface method
o Monte carlo
o Regionalized sensitivity

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21
Q

Steps in Analyze

A
  • Global
  • Sensitivity analysis
  • Identification
  • Calibration
  • Uncertainty
  • validation
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22
Q

Fully curve linear

A

Advantages:
* Allow cell stretching along the river main channel
Disadvantages:
* High resolution in sharp inner bends
* Not possible to locally refine or coarsen the grid
*Staircase representation along closed boundaries
Useage:
*River dominated areas(min 8 grids in cross direction)

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23
Q

Triangular grid

A

Advantages:
*Easy to generate
*Flexible in shape and resolution
Disadvantages:
* Not possible to stretch the grid cell in the flow direction resulting in:
-Low orthogonality
- Small time step
Useage:
*Wind dominated areas unless wind has dominant direction
- avoid large number of transition

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24
Q

Orthogonality, grids

A

Orthogonal grids:
*results in higher model accuracy
*reduce computational time

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25
Q

Orthogonality, criteria

A

*The corners of two adjacent grid cells are situated on a common circle
*The line segment that connects the circumcenters of two adjacent cells(flowlink) intersect orthogonally with the interface btetween them(netlink)

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26
Q

True definition orthogonality

A

The sine of the angle between a flowlink and netlink, perfect when angle =90degrees,
-stirve for a value between 82 and 90

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27
Q

Smoothness

A

Measured by ratio of neighbouring grid cell dimensions (Surface area). Should be less than 1.1 in area of interest and may be up to 1.4 further away.

28
Q

Aspect ratio

A

Measured by the ratio of grid cell dimensions in X and Y direction. Should be in
the range of 0.5 – 2. In case of one-directional flow phenomena, larger values
can be accepted in that direction (up to 5).

29
Q

Stability

A

For 1D courant number

30
Q

Grid effects, bathymetry accuracy

A

Largest effect
Resolution determines discharge capacity
Locations of grid lines determines discharge partitioning

31
Q

Grid effects, numerical friction

A

Coarsening causes dampening of the discharge wave, same result as increasing bed friction

32
Q

Grid effects, numerical viscosity

A

Grid along the main flow has the lowest numerical viscosity. High numerical viscosity has same effect as increasing bed friction

33
Q

Calibration

A

Involves minimizing the error between prediction and observation by altering model parameters (1995

34
Q

Validation

A

Involves verifying whether the calibrated model parameters also produce minimal error between prediction and observation in different scenarios

35
Q

One-at-a-time

A

Testing one variable at a time, disadvantage, no interaction between parameter changes is observed

36
Q

Latin Hypercube Sampling

A

12 numbers in random order on 9 columns look at the interaction with each other

37
Q

Stratisfied sampling

A

interpoleren tussen punten en als laatste het midden pakken van 8 naar 16 punten
1 8 random punten
2 8 random en 2 random punten in subintervals
3 16 punten random in de sub intervals
4 16 punten elk in midden sub intervals
5 17punten op alle hoeken van de subintervals

38
Q

Calibration for rivers most uncertainpoints

A
  • Summerbed roughness/ main channel friction
  • used to compensate for errors in input data, model set-up and grid generated effects
  • parameter adapted until model results are close to measurements
39
Q

Location dependency

A

*Multiple roughness trajectories along the longitudinal direction
* Roughness trajectory determined by locations of observation points
* Uniform roughness per trajectory

Problematic in case of modelling the river’s morphology

40
Q

discharge dependency

A

Calibration performed for various discharge levels
* Result: discharge-dependent summer bed roughness
Still fixed values per
discharge → Memory of the system not included

41
Q

Sensitivity analysis

A

The study of how uncertainty in the model output can be apportioned to
different sources of uncertainty in the model input

42
Q

Calibration procedure Dutch river models

A

Location dependent:measurestations
Discharge:Based on return period
*Periodof relatively constant discharge used to calibrate
*Interpolation betweenthe discharge ranges
* Constant calibration factor outside the discharge ranges

43
Q

Uncertainty analysis

A

The study of assessing the uncertainty in the model output

44
Q

Sensitivity analysis

A

The study of how uncertainty in the model output can be apportioned to different sources of uncertainty in the model input

45
Q

Why sensitivity

A
  • Uncover technical errors in the model
  • Establish priorities for research
  • Simplify models
46
Q

Local sensitivity

A

focuses on how a small perturbation near an input space value x0 = (x 1x n ) influences the value of Y = f(x0).

47
Q

Global sensitivity

A

focuses on the variance of model output Y and more precisely on: how the input variability influences the variance output. It enables us to determine which parts of the output variance are due to the different inputs.

48
Q

Sensitivity distribution?

A

Unknown ? -> uniform
Known? -> normal

49
Q

SA of single parameter?

A

+Easy to generate
-Clusters and gaps may exist
-N must be large enough to overcome this problem
-Computed mean and variance of model output Y are uncertain and shrink if N increases

50
Q

Stratitfied over random

A

*Overcomes the problem of cluster and gaps
*A smaller N can be used compared to random sampling to reach convergence in model output

51
Q

Sensitivity analysis method

A

+Simple and informative way
- Challenging in situations with many input factors: how to rank the factors rapidly and automatically without having to look at many separate scatterplots

52
Q

Liniear regression?

A

Least square method and apply weigths

53
Q

What is surrogate modeling?

A

Simplification of the original (high-fidelity) model

54
Q

Why surrogate modeling?

A

-If computational time of the original (high fidelity) model is too long
-If many model runs have to be performed (e.g. extensive global sensitivity analysis)
-If the original model can be simplified without loss of accuracy

55
Q

Types of surrogate modelling?

A

*Lower fidelity physically based surrogate models
*Response surface models (i.e. Data driven models/Machine learning/Deep learning/AI

56
Q

Lower fidelity physically based surrogate models

A

*Still based on the original input data
*Still based on the physical processes of the original model

57
Q

Any suggestions how to set up a LF surrogate model?

A

*Coarser spatial grid size
*Larger temporal grid size (time step)
*Less strict numerical convergence tolerances
*Reduction of model complexity(e.g. 3D –> 2D –>1D)
*Simplification/ignorance of some physics (e.g. Full Momentum eq –> Diffusive wave eq , simplified turbulence)

58
Q

Response surface surrogates

A

*Support vector machines
*Polynomials(fitten)
*Artificial Neural networks(trainen kat of hond geen olifant)
*Kriging

59
Q

Artificial Neural Network (ANN)

A

*Capable of identifying complex nonlinear behaviour between input and output
*Can handle incomplete and noisy data

60
Q

Recurrent neural network

A

*Used to process sequential data: time
series
-Recurrent layers
-Information of the current and past state is used to predict the next time step
-Weights determine importance of the temporal relation
*Very useful for floodforecasting predictions
*No spatial correlations are included (e.g. each 2D grid cell is a separate output node)

61
Q

Why surrogate for flood forecasting?

A

2DH takes very long, not possible to do everything upfront and too slow when needed. So use 1D T to simulate water levels and discharges within the river system
*When predicted water levels exceed a certain threshold, local authorities are informed
*Potential dike breach or wave overflow: use of a database with 2DH model output of predetermined flood scenarios

62
Q

0D

A

Based on simplified hydraulic concepts that do not attempt to represent the complex dynamic flood generation processes using mathematical equations.

63
Q

Example: the Height Above Nearest Drainage (HAND) model:

A

Based on Digital Elevation Model (DEM)
Looks at nearest lowest drainage point and flows everything towards it
*Topography is normalized according to local relative heights alongside the drainage network
*Inundation extent is determined by selecting the cells with HAND values < water depth
+Orders of magnitude faster, producing near real time simulations of flood extents and depths
+Captures the drainage direction accurately for valley like regions
-Only maximum inundation extents and water depths are generally predicted
- Do not provide reliable results in case of complex dynamic interactions
-Does not work in river deltas
-Cannot be used to perform a sensitivity analysis

64
Q

ANN ups and downs

A

*Upstream discharge wave as input layer
Water depth per 2D grid cell as output layers
+Can predict time series of inundation extents and water depths
+Can produce output of highly complex nonlinear systems
-Sufficient training data is required. Very time consuming especially in highly uncertain and random situations (e.g. dike failure)
-ANN can only be applied for the specific conditions it was trained for

65
Q

3di

A

GIS based
consrv. mass, momentum
3d depth averaged subgrid base model
shifts with velocities in x and y direction and waterlevel, reduces computational costs
no loss in DEM due to subgrid
3.2% of computational cost just as accurate as highresolution

Hydraulic Modeling (1 decks)

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