Uncertainty Quantification in the Henry Problem Using Multilevel Monte Carlo Method

Core Concepts

Applying the multilevel Monte Carlo method reduces computational costs in uncertainty quantification for the Henry problem.

Abstract

The content discusses the application of the multilevel Monte Carlo (MLMC) method to address uncertainty in density-driven flow problems, focusing on salinisation of coastal aquifers. It explores modeling uncertain parameters like porosity, permeability, and recharge using random fields. The MLMC method is shown to reduce computational and storage costs by solving scenarios on multi-level spatial and temporal meshes. Various numerical experiments are conducted to analyze mean values, variances, and uncertainties in mass fractions at different points and subdomains. Abstract: Investigating MLMC method for density-driven flow problems. Modeling uncertain parameters with random fields. Reducing computational costs through multi-level spatial and temporal meshes. Introduction: Applicability of MLMC to density-driven flow problems. Challenges with input uncertainties propagation. Modeling: Use of random fields for uncertain porosity, permeability, and recharge. Implementation details for modeling hydrogeological properties. Numerical Methods: Discretization techniques for deterministic problem solution. Utilizing implicit Euler method with time discretization. Multilevel Monte Carlo: Telescoping sum approach for estimating expected values efficiently. Optimization of sample sizes across different levels. Numerical Experiments: Test A1: Mean value and variance visualization of mass fraction. Test A2: Analysis of mean and variance changes at specific points.

Stats

Unknown porosity = 0.35 [-] Permeability = 18.8571 x 10^-6 [m^2 · s^-1] Density of pure water = 1000 [kg · m^-3] Density of brine = 1024.99 [kg · m^-3] Viscosity = 10^-3 [kg · m^-1 · s^-1]

Quotes

"The standard methods such as the Monte Carlo or surrogate-based methods is a good choice." "We demonstrate that by solving the Henry problem on multi-level spatial and temporal meshes, the MLMC method reduces the overall computational and storage costs."

Key Insights Distilled From

Uncertainty quantification in the Henry problem using the multilevel Monte Carlo method

by Dmitry Logas... at arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17018.pdf

Deeper Inquiries

How does uncertainty affect decision-making in hydrogeological applications

Uncertainty in hydrogeological applications can significantly impact decision-making processes. In the context of salinisation of coastal aquifers, uncertainties in parameters such as porosity, permeability, and recharge can lead to variations in predictions of saltwater intrusion. These uncertainties make it challenging to accurately assess the potential risks associated with saltwater intrusion and develop effective strategies for managing groundwater resources. Decision-makers rely on models that incorporate these uncertainties to make informed choices regarding water resource management, land use planning, and environmental protection measures. The presence of uncertainty highlights the need for robust risk assessment techniques that consider a range of possible outcomes based on different scenarios. By quantifying uncertainty, decision-makers can better understand the potential consequences of their actions and implement adaptive management strategies to mitigate risks effectively.

What are the limitations of using surrogate models in uncertainty quantification

Surrogate models have limitations when used in uncertainty quantification due to several factors: Smoothness Assumption: Surrogate models often assume a certain level of smoothness in the quantity of interest (QoI). If the QoI is highly nonlinear or discontinuous, surrogate models may not provide accurate approximations. Dimensionality: Surrogate models may struggle with high-dimensional problems where there are many uncertain input parameters. As the dimensionality increases, constructing an accurate surrogate model becomes more challenging. Limited Accuracy: Surrogate models are only as good as their training data and assumptions about underlying relationships between inputs and outputs. They may not capture all nuances present in complex systems accurately. Computational Cost: Constructing a surrogate model requires running multiple simulations to train it properly. This process can be computationally expensive for large-scale problems. Given these limitations, it is essential to carefully evaluate when and how surrogate models are applied in uncertainty quantification studies to ensure reliable results.

How can the results from this study be applied to real-world scenarios beyond coastal aquifers

The results from this study on uncertainty quantification using multilevel Monte Carlo (MLMC) methods in density-driven flow problems like salinisation of coastal aquifers have broader implications beyond academic research: Environmental Management: The findings can be applied by environmental agencies responsible for managing coastal aquifers affected by saltwater intrusion. Understanding how uncertainties propagate through hydrogeological systems helps improve monitoring efforts and decision-making processes related to water resource management. 2Risk Assessment: Industries involved in groundwater extraction or construction near coastal areas could benefit from incorporating stochastic modeling techniques into their risk assessments for potential saltwater intrusion events. 3Climate Change Adaptation: With changing climate patterns impacting sea levels and precipitation rates, utilizing advanced uncertainty quantification methods like MLMC can enhance predictive capabilities for future scenarios involving saline flow dynamics within aquifers. These applications demonstrate how insights gained from this study can inform practical solutions addressing real-world challenges related to groundwater management under uncertain conditions."