arXiv subjects

Fidel Grandia

Publications and source records attributed to Fidel Grandia.

5 recordsLinked to original sources

arXiv2023

Role of the clay lenses within sandy aquifers in the migration pathway of infiltrating DNAPL plume: A numerical investigation

The use of numerical based multi-phase fluid flow simulation can significantly aid in the development of an effective remediation strategy for groundwater systems contaminated with Dense Non Aqueous Phase Liquid (DNAPL). Incorporating the lithological heterogeneities of the aquifer into the model domain is a crucial aspect in the development of robust numerical simulators. Previous research studies have attempted to incorporate lithological heterogeneities into the domain; however, most of these numerical simulators are based on Finite Volume Method (FVM) and Finite Difference Method (FDM) which have limited applicability in the field-scale aquifers. Finite Element Method (FEM) can be highly useful in developing the field-scale simulation of DNAPL infiltration due to its consistent accuracy on irregular study domain, and the availability of higher orders of basis functions. In this research work, FEM based model has been developed to simulate the DNAPL infiltration in a hypothetical field-scale aquifer. The model results demonstrate the effect of meso-scale heterogeneities, specifically clay lenses, on the migration and accumulation of Dense Non Aqueous Phase Liquid (DNAPL) within the aquifer. Furthermore, this research provides valuable insights for the development of an appropriate remediation strategy for a general contaminated aquifer.

math.NA↗

arXiv2023

Bayesian Physics-Informed Neural Network for the Forward and Inverse Simulation of Engineered Nano-particles Mobility in a Contaminated Aquifer

Globally, there are many polluted groundwater sites that need an active remediation plan for the restoration of local ecosystem and environment. Engineered nanoparticles (ENPs) have proven to be an effective reactive agent for the in-situ degradation of pollutants in groundwater. While the performance of these ENPs has been highly promising on the laboratory scale, their application in real field case conditions is still limited. The complex transport and retention mechanisms of ENPs hinder the development of an efficient remediation strategy. Therefore, a predictive tool to comprehend the transport and retention behavior of ENPs is highly required. The existing tools in the literature are dominated with numerical simulators, which have limited flexibility and accuracy in the presence of sparse datasets and the aquifer heterogeneity. This work uses a Bayesian Physics-Informed Neural Network (B-PINN) framework to model the nano-particles mobility within an aquifer. The result from the forward model demonstrates the effective capability of B-PINN in accurately predicting the ENPs mobility and quantifying the uncertainty. The inverse model output is then used to predict the governing parameters for the ENPs mobility in a small-scale aquifer. The research demonstrates the capability of the tool to provide predictive insights for developing an efficient groundwater remediation strategy.

cs.LG↗

arXiv2023

End-to-End Integrated Simulation for Predicting the Fate of Contaminant and Remediating Nano-Particles in a Polluted Aquifer

Groundwater contamination caused by Dense Non-Aqueous Phase Liquid (DNAPL) has an adverse impact on human health and environment. Remediation techniques, such as the in-situ injection of nano Zero Valent Iron (nZVI) particles, are widely used in mitigating DNAPL-induced groundwater contamination. However, an effective remediation strategy requires predictive insights and understanding of the physiochemical interaction of nZVI and contamination along with the porous media properties. While several stand-alone models are widely used for predictive modeling, the integration of these models for better scalability and accuracy is still rarely utilized. This study presents an end-to-end integrated modeling framework for the remediation of DNAPL-contaminated aquifers using nZVI. The framework simulates the migration pathway of DNAPL and subsequently its dissolution in groundwater resulting in an aqueous contaminant plume. Additionally, the framework includes simulations of nZVI mobility, transport, and reactive behavior, allowing for the prediction of the radius of influence and efficiency of nZVI for contaminant degradation. The framework has been applied to a hypothetical 2-dimensional and heterogeneous silty sand aquifer, considering trichloroethylene (TCE) as the DNAPL contaminant and carboxymethyl cellulose (CMC) coated nZVI for remediation. The results demonstrate the framework's capability to provide comprehensive insights into the contaminant's behavior and the effectiveness of the remediation strategy. The proposed modeling framework serves as a reference for future studies and can be expanded to incorporate real field data and complex geometries for upscaled applications.

math.NA↗

arXiv2023

Efficient Training of Physics-Informed Neural Networks with Direct Grid Refinement Algorithm

This research presents the development of an innovative algorithm tailored for the adaptive sampling of residual points within the framework of Physics-Informed Neural Networks (PINNs). By addressing the limitations inherent in existing adaptive sampling techniques, our proposed methodology introduces a direct mesh refinement approach that effectively ensures both computational efficiency and adaptive point placement. Verification studies were conducted to evaluate the performance of our algorithm, showcasing reasonable agreement between the model based on our novel approach and benchmark model results. Comparative analyses with established adaptive resampling techniques demonstrated the superior performance of our approach, particularly when implemented with higher refinement factor. Overall, our findings highlight the enhancement of simulation accuracy achievable through the application of our adaptive sampling algorithm for Physics-Informed Neural Networks.

cs.LG↗

arXiv2022

Dynamic weights enabled Physics-Informed Neural Network for simulating the mobility of Engineered Nano-particles in a contaminated aquifer

Numerous polluted groundwater sites across the globe require an active remediation strategy to restore natural environmental conditions and local ecosystem. The Engineered Nano-particles (ENPs) have emerged as an efficient reactive agent for the in-situ degradation of groundwater contaminants. While the performance of these ENPs has been highly promising on the laboratory scale, their application in real field case conditions is still limited. The complex transport and retention mechanisms of ENPs hinder the development of an efficient remediation strategy. Therefore, a predictive tool for understanding the transport and retention behavior of ENPs is highly required. The existing tools in the literature are dominated with numerical simulators, which have limited flexibility and accuracy in the presence of sparse datasets. This work uses a dynamic, weight-enabled Physics-Informed Neural Network (dw-PINN) framework to model the nano-particle behavior within an aquifer. The result from the forward model demonstrates the effective capability of dw-PINN in accurately predicting the ENPs mobility. The model verification step shows that the relative mean square error (MSE) of the predicted ENPs concentration using dw-PINN converges to a minimum value of $1.3{e^{-5}}$. In the subsequent step, the result from the inverse model estimates the governing parameters of ENPs mobility with reasonable accuracy. The research demonstrates the tool's capability to provide predictive insights for developing an efficient groundwater remediation strategy.

cs.LG↗