Post on 02-Jan-2016
Aspects of Pure Mathematics, Experimental Design and Mathematical Modelling
DIANE DONOVAN STEVE TYSON BEVAN THOMPSON LIAM O’SULLIVAN MARVIN TAS
Groundwater
Water table: underground water
seeping slowly through aquifers. Depth: varying from meters to
hundreds of meters below the surface. Aquifers: permeable material such as gravel, sand, sandstone, or
fractured rock.
Flow speed: dependant on the size of the aquifers and how well they are connected.
Agency for Toxic Substances and Disease Registry
Groundwater contamination
May be introduced through Erosion
Industrial discharge
Agricultural discharge
Household discharge
Hazardous waste sites
Landfills
Road salts or chemicals.
Primary concerns: synthetic compounds, including solvents, pesticides, paints, varnishes, gasoline and nitrate.
Agency for Toxic Substances and Disease Registry
Erosion
Soil erosion significantly impacts on our environment: Capadocia, Turkey
Burdekin Basin (Aust) and Great Barrier Reef
The Australian Research Organization
(CSİRO) is studying the effect of soil erosion contamination of the Great Barrier Reef.
Toprak Erozyonu/Soil Erosion
Regular/systematic sweeps, sampling erosion.
Sampling at distinct times varying by longitude and latitude
Erosion and the Great Barrier Reef
A Latin hypercube trial, LHT, (d-trial):
n sample points in d dimensional sample space where each the n sub-divisions for each of the d parameters appears in precisely one sample point.
d=2 take a square grid, Latin Hypercube trial has 1 sample per row and 1 sample per column.
Repeated applications of the LHT gives a Latin Hypercube Sample, LHS.
Latin Hypercube Sampling
3-D Latin Hypercube Sampling
Advantages
Propagation of uncertainty through models.
Good coverage of parameter space. Easy updating, given new data. Each parameter is fully stratified and each sub-
division is sampled with the same density. Variance reduction when compared with random
sampling. Fast implementation.
Latin Hypercube Sampling
3-D Latin Hypercube Sampling
A LHT is an Orthogonal sample (OS) if the
n = pd sample points are distributed evenly across all sub-blocks.
Advantages: Uniformity of small dimensional margins. Improved representation of the underlying
variability. A form of variance reduction. Better screening for effective parameters. Equally fast implementation.
Orthogonal Sampling
Coverage of Parameter Space
Theoretical & computational arguments show that as the number of trials increases the size of the un-sampled space decreases exponentially.
LHS & OS (with n = pd ) the expected percentage coverage of parameter space is given by
Transportation of Chemical Contaminants in Groundwater
1-D steady state flow chemical transportation with advection, dispersion, retardation.
Governing equation 𝑑𝑥𝑑𝑦
𝑑𝑧−𝐷𝐿
𝜕𝐶𝜕𝑥
𝑣𝐶
−𝐷𝐿 (𝜕𝐶𝜕𝑥 + 𝜕2𝐶𝜕𝑥2
𝑑𝑥)
𝑣 (𝐶+𝜕𝐶𝜕𝑥
𝑑𝑥 )
Chemical Contaminants in Groundwater
Retardation factor with uncertainty in organic carbon content
Longitudinal dispersion coefficient with uncertainty in organic carbon content and hydraulic conductivity
Pore water velocity with uncertainty in hydraulic conductivity
Uncertainty in and due to uncertainty in hydraulic conductivity K and organic carbon partition coefficient .
Parameter Distribution Lower Limit Upper Limit
K(cm/s) hydraulic conductivity
Uniform 1.0E-7 1.0E-3
(cc/g) organic carbon content
Uniform 20 500
Chemical Contaminants in Groundwater
Monte Carlo techniques: modelling uncertainty in hydraulic conductivity K and organic carbon partition coefficient .
Populations of Models: different sets of admissible parameter values (models) capable of reproducing the observed output within given tolerances.
Latin Hypercube & Orthogonal sampling: efficient techniques especially useful for POM propagating the uncertainty through the simulations.
Parameter Distribution Lower Limit Upper Limit
K(cm/s) Uniform 1.0E-7 1.0E-3
(cc/g) Uniform 20 500
Polynomial Chaos Expansion
distributed uniformly with mean & variance /3.
organic carbon is distributed uniformly with mean & variance /3.
(.
Orthogonal Legendre Polynomials
where
Finite Differences An a approximations for , .
𝜕𝐶 (𝑥𝑖 ,𝑡 𝑗)𝜕𝑡
𝐶 (𝑥𝑖 ,𝑡 𝑗+1 )−𝐶 (𝑥 𝑖 ,𝑡 𝑗)∆ 𝑡
𝜕2𝐶𝜕𝑥2
𝐶 (𝑥 𝑖+1, 𝑡 𝑗 )−2𝐶 (𝑥 𝑖 ,𝑡 𝑗 )+𝐶 (𝑥𝑖 −1 ,𝑡 𝑗)
∆ 𝑥2
𝜕𝐶𝜕 𝑥
𝐶 (𝑥 𝑖+1 , 𝑡 𝑗 )−𝐶 (𝑥 𝑖− 1, 𝑡 𝑗)2∆ 𝑥
𝑡 𝑗+1=𝑡 𝑗+∆𝑡 ,𝑥 𝑖+1=𝑥𝑖+∆ 𝑥
Polynomial Chaos Expansion
.
.
(.
Advantages Fast and efficient.
Different probability distributions can be assigned to input parameters.
Simplifying implementation using spectral representation & orthogonal bases.
Reduced computational costs.
Easy post processing statistics, including moments and the probability density function--zero-index term contains the solution mean.
Sensitivity to underlying probability distribution, propagating uncertainty & variability through the simulation.
Polynomial Chaos Expansion
Disadvantages Non-normal random input distributions must be treated with care.
Convergence domains must be studied with care for both smooth and non-smooth outputs.
PC does not quantify the approximation error as a component of uncertainty.
Changes to the input distribution may require output values, the convergence (of the approximation) and truncation parameters to be recomputed.
Polynomial Chaos Expansion
References A O'Hagan, Polynomial Chaos: A Tutorial and Critique from a Statistician's Perspective, 2013
D Datta & S Kushwaha, Uncertainty Quantification Using Stochastic Response Surface Method Case Study-Transport of Chemical Contaminants through Groundwater, International Journal of Energy, Information and Communication, 2011, 2(3), 49-58
DL Parkhurst & CAJ Appelo, User's Guide to PHREEQC (Version 2)--A Computer Program for Speciation, Batch-Reaction, One-Dimensional Transport, and Inverse Geochemical Calculations, accessed at http://wwwbrr.cr.usgs.gov/projects/GWC_coupled/phreeqc/html/final.html on 15/9/2015
K Burrage, PM Burrage, D Donovan, T McCourt & HB Thompson, Estimates on the coverage of parameter space using populations of model, Modelling and Simulation, IASTED, ACTA Press, 2014
K Burrage, PM Burrage, D Donovan & HB Thompson, Populations of Models, Experimental Designs and Coverage of Parameter Space by Latin Hypercube and Orthogonal Sampling, Procedia Computer Science, 2015, 51, 1762-1771
S Tyson, D Donovan, B Thompson, S Lynch & M Tas, Uncertainty Modelling with Polynomial Chaos, Report to the Centre for Coal Seam Gas, University of Queensland, August 2015.
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