32
A DVANCES IN N UMERICAL M ODELING AND D ATA A NALYSIS ON W ATER R ESOURCES RELATED I SSUES Rodrigo Amado Garcia Silva Contact: [email protected] SUMMER SCHOOL ON DATA SCIENCE
ADVANCES IN NUMERICAL MODELING AND DATA
ANALYSIS ON WATER RESOURCES RELATED ISSUES
Rodrigo Amado Garcia Silva
Contact: [email protected]
SUMMER SCHOOL ON DATA SCIENCE
Coastal and Oceanographic Engineering Area (Ocean Engineering Program)
➢ Research Engineer (postdoc)
➢ Issues: Environmental hydrodynamics, wave generation and propagation, sediment
transport, coastal vulnerability, water quality
HidroAmb – Water Resources and Environmental Engineering
➢ Director and Environmental Engineer
➢ Issues: the same
Water resources related issues
➢ Environmental Hydrodynamics
➢ Wave generation and propagation
➢ Sediment transport
➢ Pollutant transport and dispersionHydrodynamics
Morphodynamics
Wave propagation
Effluent dispersion
Water resources related issues
➢ Environmental Hydrodynamics
➢ Wave propagation
➢ Sediment transport
➢ Pollutant transport and dispersion
ALL NONLINEAR PHENOMENA!!
Water resources related issues
➢ Environmental Hydrodynamics
➢ Wave propagation
➢ Sediment transport
➢ Pollutant transport and dispersion
ALL NONLINEAR PHENOMENA!!
Simulation requires numerical solution
of nonlinear differential equations
➢Computational modeling systemprovided by COPPE/UFRJ
➢Environmental modeling of waterbodies with complex geometry, asrivers, estuaries, lagoons, bays, coasts,reservoirs, etc.
➢Open code (Fortran) free software
➢Finite elements and finite differencenumerical models
SisBaHiA – Environmental Hydrodynamics Base System
How does it work?
Example: Ilhabela – SP
Santos
Ilhabela
Bertioga
SP
How does it work?
Example: Ilhabela – SP
➢ Digital terrain model
Santos
Ilhabela
Bertioga
SP
Finite elements mesh
Bathymetry data
How does it work?
Example: Ilhabela – SP
➢ Digital terrain model
How does it work?
Example: Ilhabela – SP
Simulated phenomena: wind waves generation
Input data:
➢ Bathymetry (DTM)
➢ Winds
➢ Boundary conditions
Wind field
Open boundary(ocean)
Land boundary(zero flux)
How does it work?
Example: Ilhabela – SP
➢ Results
Wave height (m)
How does it work?
Example: Ilhabela – SP
➢ Results
Wave period (m)
How does it work?
Example: Ilhabela – SP
➢ ResultsWave inside the harbour
Data analysis
Bathymetry data
Sea bottom surface
Surfer(2D and 3D mapping)
Interpolation methods
• Kriging• Delaunay triangulation• Minimum curvature• Natural neighbor• etc
Google Earth(model domain)
Georeferenced map layers
Data analysis
• Wind data acquisition
Wind dataanalysis
Grapher(2D and 3D graphing
and analysis)
Global athmospheric models
• Wind data
• NETCDF format
• Python interface
Results analysis
Model setup and execution
Microsoft Access(or any similar database tool)
Grapher(temporal results)Surfer
(spatial results)
➢ Nearshore morphological processes
➢ Wave induced sediment transport
Research and Development
Mucuripe
Port
Fortaleza - CE
Coastal erosion
Port sedimentation
Pereira et al. (2017)
NMMBom
tempo
Perfil de bom tempo
Beach erosion
NMMBom
tempo
Perfil de bom tempo
Beach erosion
NMMBom
tempo
Perfil de bom tempo
Beach erosion
NMMBom
tempo
Perfil de bom tempo
Beach erosion
NMMBom
tempo
Perfil de bom tempo
Beach erosion
Berma de bom tempo
Duna
Beach erosion model
Beach Profile
water level
Model result
Measured
Initial
Wave tank
Berma de bom tempo
Duna
Beach erosion model
Beach Profile
water level
Model result
Measured
Initial
Wave tank
Duna
Beach erosion model
➢ Good results
Berma de bom tempo
Beach Profile
water level
Model result
Measured
Initial
Duna
Beach erosion model
➢ Good results
➢ Highly dependent on case to case calibration
Berma de bom tempo
Beach Profile
water level
Model result
Measured
Initial
Duna
Beach erosion model
➢ Good results
➢ Highly dependent on case to case calibration
➢ Several parameters involved
Berma de bom tempo
Beach Profile
water level
Model result
Measured
Initial
Duna
Beach erosion model
➢ Good results
➢ Highly dependent on case to case calibration
➢ Several parameters involved
➢ Nonlinearity expressed in the data
Berma de bom tempo
Beach Profile
water level
Model result
Measured
Initial
Duna
Beach erosion model
➢ Good results
➢ Highly dependent on case to case calibration
➢ Several parameters involved
➢ Nonlinearity expressed in the data
➢ Upcoming research: how to find a pattern?
Berma de bom tempo
Beach Profile
water level
Model result
Measured
Initial
Duna
Beach erosion model
➢ Good results
➢ Highly dependent on case to case calibration
➢ Several parameters involved
➢ Nonlinearity expressed in the data
➢ Upcoming research: how to find a pattern?
➢ Machine learning
Berma de bom tempo
Beach Profile
water level
Model result
Measured
Initial
Duna
Machine learning
Several methods:
➢ Artificial neural networks;
➢ Genetic algorithms;
➢ Bayesian networks;
➢ Regression trees;
➢ etc
Goldstein et. al (2019)
