Coupling a Borehole Thermal Model and<scp>MT3DMS</scp>to Simulate Dynamic Ground Source Heat Pump Efficiency

Zong, Yifei; Valocchi, Albert J.; Lin, Yu-Feng

doi:10.1111/gwat.13159

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…Heat transport can be simulated using the MT3DMS-USGS software (Bedekar et al, 2016), which is a numerical model for simulating advective and dispersive transport of conservative and non-conservative solutes in three-dimensional saturated subsurface flow systems. Hecht-Méndez et al (2010) demonstrated that MT3DMS is a suitable tool for simulating heat transport in the shallow subsurface due to the similarities between thermal and solute transport (Zheng, 2009;Zong et al, 2021).…”

Section: D Case Parameterizationmentioning

confidence: 99%

Machine Learning for Bayesian Experimental Design in the Subsurface

Thibaut¹

2023

Preprint

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Section: D Case Parameterizationmentioning

confidence: 99%

Machine Learning for Bayesian Experimental Design in the Subsurface

Thibaut¹

2023

Preprint

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“…The MODFLOW groundwater model was applied for groundwater level simulation, and the MT3DMS model, in the form of an integrated model with MODFLOW, was used for aquifer-wide simulation of spatial distribution and concentration variation of total dissolved solids (TDS). A proper cell grid must be created following the conditions of the aquifer to simulate using the MODFLOW and MT3DMS models (Zong et al 2021). Various packages, including boundary conditions, quantitative and qualitative observation wells, hydrodynamic parameters (hydraulic conductivity, specific yield, longitudinal dispersivity, and effective porosity), recharge, and discharge, must be prepared to simulate groundwater's quantitative and qualitative behavior.…”

Section: The Numerical Modelsmentioning

confidence: 99%

Aquifer-wide estimation of longitudinal dispersivity by the combination of empirical equations, inverse solution, and aquifer zoning methods

et al. 2022

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Longitudinal dispersivity is a crucial parameter for the numerical simulation of groundwater quality, which is highly variable. The use of empirical equations and the inverse solution are two main methods of estimating longitudinal dispersivity. This study investigates the estimation of value and aquifer-wide spatial distribution of longitudinal dispersivity parameters using a combination of the empirical equation, the inverse solution method, and the aquifer zoning technique. The combined approach is applied to Bandar-e-Gaz aquifer in northern Iran, and Willmott’s index of agreement was used to assess the simulation precision of total dissolved solids in this aquifer. The values of this criterion were 0.9985–0.9999 and 0.9756–0.9992 in calibration and validation periods showing the developed combined approach obtained high precision for both calibration and validation periods, and the simulation shows remarkable consistency. Also, the one-way sensitivity analysis indicates that the longitudinal dispersivity is more sensitive than the effective porosity in this simulation. The investigation of the spatial distribution of the estimated longitudinal dispersivity by the combined approach indicates that the value of the parameter has a decreasing trend from the south to the north (50–8 m) in the aquifer environment, which is consistent with the changes in the characteristics of porous media in this study area. Therefore, it concludes that the combined approach provides a reliable and appropriate estimation of the spatial distribution of longitudinal dispersivity.

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Modelling Seasonal Variability in Parameters Defining Volumetric Water Content in a Low Permeability Soil in Central Illinois: An Application of MODFLOW‐6 and the Unsaturated Zone Flow Package

Krasowski,

Gulsen,

Jones

et al. 2024

Hydrological Processes

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Increasing interest in solute transport phenomena in agricultural systems on a sub‐annual basis necessitates a better understanding of seasonal changes in natural systems and how these changes can be incorporated into modelling. A better understanding of the seasonal timing of nutrient loading in tile drained agricultural systems in particular is essential for efforts trying to replicate or predict the occurrence of harmful algal blooms. Literature exists showing there are seasonal dynamics (freeze–thaw, plant‐root processes, land management practices, etc.) that may cause changes in the hydraulic properties of the soil zone including hydraulic conductivity and porosity. To test whether these changes are important in an agricultural system, a MODFLOW‐6 model using the unsaturated zone flow package was constructed. The simulation was comprised of separate, seasonal models to be run sequentially with each year being broken into a winter and summer seasons. As part of this architecture, model parameters representing soil hydraulic properties were allowed to vary by season. The model was calibrated against soil moisture observations at multiple depths using a genetic algorithm machine learning technique. The parameters of the sub‐models were compared for the winter and summer seasons. Brook‐Corey epsilon, saturated vertical conductivity, saturated volumetric water content and residual volumetric water content were found to be consistently different between the modelled summer and winter periods. A more traditional model which did not allow hydraulic properties to vary seasonally was also run and compared to the seasonal architecture and the seasonal architecture was found to improve simulation results. The hydrologic dynamics of the unsaturated zone—particularly in tile drained agricultural systems—control the residence time for water and solutes, which is critical for in‐field chemical processes such as denitrification. This work has important implications for seasonal transport phenomena in agricultural systems and improving the simulation and prediction of harmful algal blooms.

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Coupling a Borehole Thermal Model andMT3DMSto Simulate Dynamic Ground Source Heat Pump Efficiency

Cited by 7 publications

References 29 publications

Machine Learning for Bayesian Experimental Design in the Subsurface

Machine Learning for Bayesian Experimental Design in the Subsurface

Aquifer-wide estimation of longitudinal dispersivity by the combination of empirical equations, inverse solution, and aquifer zoning methods

Modelling Seasonal Variability in Parameters Defining Volumetric Water Content in a Low Permeability Soil in Central Illinois: An Application of MODFLOW‐6 and the Unsaturated Zone Flow Package

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