An optimal design approach for groundwater remediation is developed through incorporating numerical simulation, health risk assessment, uncertainty analysis and nonlinear optimization within a general framework.
To significantly increase the contribution of numerical computational fluid dynamics (CFD) simulation for risk assessment and decision making, it is important to quantitatively measure the impact of uncertainties to assess the reliability and robustness of the results.
Using a systems engineering methodology the developed model will be the one that guarantees the consistency and derives the different applications needed in every stage of the lifecycle, from simulation, to risk assessment or even documentation maintenance.
The whole flow is divided into three steps: random sampling, transient simulation, and risk value calculation.
Monte Carlo simulation conducts risk analysis based on the outputs with replacing the values of uncertain variables.
Monte Carlo simulation performs risk analysis by building models of possible results by substituting a range of values a probability distribution for any factor that has inherent uncertainty.
The project involved, amongst others, assessing the potential and cost-effectiveness of combined use of new technologies and innovative HMI concepts, developing new simulation models, risk analysis tools, harmonising signing and personalising information, and issuing priority implementation scenarios.
In this regression problem, which is a realistic simulation of risk prediction in genetic data, RR outperforms the competing penalised regression methods, EN and HL.
In particular, the model was designed to permit, for the first time, 3D simulations of risk at receptors located in the wider community (i.e., kilometre scale) surrounding a contaminated site.
