scholarly journals Numerical simulation of groundwater in an unconfined aquifer with a novel hybrid model (case study: Birjand Aquifer, Iran)

2021 ◽  
Author(s):  
Ali Mohtashami ◽  
Seyed Arman Hashemi Monfared ◽  
Gholamreza Azizyan ◽  
Abolfazl Akbarpour
Keyword(s):  
Numerical Modeling ◽  
Data Assimilation ◽  
Hybrid Model ◽  
Numerical Models ◽  
Unconfined Aquifer ◽  
Model Case ◽  
Hydrodynamic Parameters ◽  
East Of Iran ◽  
Assimilation Model

Abstract In recent decades, due to the population growth and low precipitation, the overexploitation of ground water resources has become an important issue. To ensure a sustainable scheme for these resources, understanding the behavior of the aquifers is a key step. This study takes a numerical modeling approach to investigate the behavior of an unconfined aquifer in an arid area located in the east of Iran. A novel hybrid model is proposed that couples the numerical modeling to a data assimilation model to remove the uncertainty in the hydrodynamic parameters of the aquifer including the hydraulic conductivity coefficients and specific yields. The uncertainty that exists in these parameters results in unreliability of the head values acquired from the models. Meshless local Petrov-Galerkin (MLPG) is used as the numerical model, and particle filter (PF) is our data assimilation model. These models are implemented in the MATLAB software. We have calibrated and validated our PF-MLPG model by the observation head data from the piezometers. The RMSE in head values for our model and other commonly used numerical models in the literature including the finite difference method and MPLG are calculated as 0.166, 1.197 and 0.757 m, respectively. This fact shows the necessity of using this method in each aquifer.

Data-driven parametrizations in numerical models using data assimilation and machine learning.

2020 ◽  
Author(s):  
Julien Brajard ◽  
Alberto Carrassi ◽  
Marc Bocquet ◽  
Laurent Bertino
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Data Assimilation ◽  
Physical Model ◽  
Hybrid Model ◽  
Numerical Models ◽  
Coarse Graining ◽  
Model Error ◽  
Data Driven ◽  
Model Combining

<p>Can we build a machine learning parametrization in a numerical model using sparse and noisy observations?</p><p>In recent years, machine learning (ML) has been proposed to devise data-driven parametrizations of unresolved processes in dynamical numerical models. In most of the cases, ML is trained by coarse-graining high-resolution simulations to provide a dense, unnoisy target state (or even the tendency of the model).</p><p>Our goal is to go beyond the use of high-resolution simulations and train ML-based parametrization using direct data. Furthermore, we intentionally place ourselves in the realistic scenario of noisy and sparse observations.</p><p>The algorithm proposed in this work derives from the algorithm presented by the same authors in https://arxiv.org/abs/2001.01520.The principle is to first apply data assimilation (DA) techniques to estimate the full state of the system from a non-parametrized model, referred hereafter as the physical model. The parametrization term to be estimated is viewed as a model error in the DA system. In a second step, ML is used to define the parametrization, e.g., a predictor of the model error given the state of the system. Finally, the ML system is incorporated within the physical model to produce a hybrid model, combining a physical core with a ML-based parametrization.</p><p>The approach is applied to dynamical systems from low to intermediate complexity. The DA component of the proposed approach relies on an ensemble Kalman filter/smoother while the parametrization is represented by a convolutional neural network.  </p><p>We show that the hybrid model yields better performance than the physical model in terms of both short-term (forecast skill) and long-term (power spectrum, Lyapunov exponents) properties. Sensitivity to the noise and density of observation is also assessed.</p>

scholarly journals Multi-scenario numerical modeling applied to groundwater contamination: the Popoli Gorges complex aquifer case study (Central Italy)

2018 ◽  
Author(s):  
Diego Di Curzio ◽  
Sergio Rusi ◽  
Ron Semeraro
Keyword(s):  
Numerical Modeling ◽  
Steady State ◽  
Particle Tracking ◽  
River Flow ◽  
Numerical Models ◽  
Central Italy ◽  
Advective Transport ◽  
Total Budget ◽  
Well Field

In this research, a multi-scenario numerical modeling was implemented to assess the effects of changes to abstraction patterns in the Sant’Angelo well-field (central Italy) and their implications on the aquifer hydrodynamic and the advective transport of contaminants. Once implemented and calibrated the steady-state numerical model by means of MODFLOW-2005, the well-field turning off scenario was modelled. In addition, the numerical results were analyzed by means of the post-processors ZONEBUDGET and MODPATH, to assess respectively the contribution of each hydrogeological feature to the total budget and the advective transport of contaminant particles. Comparing the two steady-state numerical models and the relative particle tracking analyses, the well-field turning off, although no longer acting as a hydraulic barrier, increased the residence time of contaminant particles and limited their mobility in the aquifer. Furthermore, the general decrease in groundwater abstractions also caused a higher increase in river flow, favoring contaminants’ dilution in surface water.

scholarly journals Combining data assimilation and machine learning to infer unresolved scale parametrization

2021 ◽  
Vol 379 (2194) ◽  
pp. 20200086
Author(s):  
Julien Brajard ◽  
Alberto Carrassi ◽  
Marc Bocquet ◽  
Laurent Bertino
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Data Assimilation ◽  
Hybrid Model ◽  
Numerical Models ◽  
Model Error ◽  
Climate Modelling ◽  
Combining Data ◽  
Full State ◽  
Direct Data

In recent years, machine learning (ML) has been proposed to devise data-driven parametrizations of unresolved processes in dynamical numerical models. In most cases, the ML training leverages high-resolution simulations to provide a dense, noiseless target state. Our goal is to go beyond the use of high-resolution simulations and train ML-based parametrization using direct data, in the realistic scenario of noisy and sparse observations. The algorithm proposed in this work is a two-step process. First, data assimilation (DA) techniques are applied to estimate the full state of the system from a truncated model. The unresolved part of the truncated model is viewed as a model error in the DA system. In a second step, ML is used to emulate the unresolved part, a predictor of model error given the state of the system. Finally, the ML-based parametrization model is added to the physical core truncated model to produce a hybrid model. The DA component of the proposed method relies on an ensemble Kalman filter while the ML parametrization is represented by a neural network. The approach is applied to the two-scale Lorenz model and to MAOOAM, a reduced-order coupled ocean-atmosphere model. We show that in both cases, the hybrid model yields forecasts with better skill than the truncated model. Moreover, the attractor of the system is significantly better represented by the hybrid model than by the truncated model. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.

Unusual drawdown curves for a pumping test in an unconfined aquifer at Lachenaie, Quebec: field data and numerical modeling

2005 ◽  
Vol 42 (4) ◽  
pp. 1133-1144 ◽  
Author(s):  
Robert P Chapuis ◽  
Djaouida Chenaf ◽  
Nelson Acevedo ◽  
Denis Marcotte ◽  
Michel Chouteau
Keyword(s):  
Numerical Modeling ◽  
Numerical Models ◽  
Field Tests ◽  
Pumping Test ◽  
Unconfined Aquifer ◽  
Specific Yield ◽  
Characteristic Functions ◽  
Constant Flow Rate ◽  
Monitoring Wells ◽  
Highly Nonlinear

An unconfined aquifer was instrumented with monitoring wells over a surface area of about 100 m × 100 m. The aquifer is a sand deposit overlying a thick nonfissured layer of Champlain Sea clay. The paper presents the results of a pumping test at a constant flow rate. None of the curves of drawdown versus time presented the S shape of current theories; however, all drawdowns indicated that the aquifer was homogeneous. The values for the specific yield were too low and varied with distance and time instead of being constant. The theories for steady and unsteady states provided different values for the saturated hydraulic conductivity. To understand the field behavior that differs from theoretical predictions, the pumping test conditions were modeled numerically using a finite element method. The transmissivity was derived from the Dupuit equation, and different curves for capillary retention and unsaturated permeability were examined. The numerical drawdowns agree with the experimental drawdowns. Several numerical models were investigated. All of them solved the inverse problem correctly for steady-state conditions and fairly well for transient conditions with highly nonlinear characteristic functions. The best solution to the transient problem was obtained using trial and error, by considering how the drawdown curves might be modified due to anisotropy and stratification. According to these field tests and the numerical analysis, the S shape is not the rule, and a different shape can be perfectly normal due to the complexity of unsaturated flow.Key words: pumping, unconfined aquifer, permeability, drawdown, numerical modeling.

SEISMIC RISK AND RESILIENCE ASSESSMENT OF INDUSTRIAL FACILITIES: CASE STUDY ON A BLACK CARBON PLANT

2020 ◽  
Author(s):  
George Karagiannakis
Keyword(s):  
Seismic Risk ◽  
Numerical Models ◽  
Fragility Curves ◽  
Human Life ◽  
Mitigation Strategies ◽  
Economic Losses ◽  
Risk And Resilience ◽  
Industrial Plants ◽  
Plant Equipment

This paper deals with state of the art risk and resilience calculations for industrial plants. Resilience is a top priority issue on the agenda of societies due to climate change and the all-time demand for human life safety and financial robustness. Industrial plants are highly complex systems containing a considerable number of equipment such as steel storage tanks, pipe rack-piping systems, and other installations. Loss Of Containment (LOC) scenarios triggered by past earthquakes due to failure on critical components were followed by severe repercussions on the community, long recovery times and great economic losses. Hence, facility planners and emergency managers should be aware of possible seismic damages and should have already established recovery plans to maximize the resilience and minimize the losses. Seismic risk assessment is the first step of resilience calculations, as it establishes possible damage scenarios. In order to have an accurate risk analysis, the plant equipment vulnerability must be assessed; this is made feasible either from fragility databases in the literature that refer to customized equipment or through numerical calculations. Two different approaches to fragility assessment will be discussed in this paper: (i) code-based Fragility Curves (FCs); and (ii) fragility curves based on numerical models. A carbon black process plant is used as a case study in order to display the influence of various fragility curve realizations taking their effects on risk and resilience calculations into account. Additionally, a new way of representing the total resilience of industrial installations is proposed. More precisely, all possible scenarios will be endowed with their weighted recovery curves (according to their probability of occurrence) and summed together. The result is a concise graph that can help stakeholders to identify critical plant equipment and make decisions on seismic mitigation strategies for plant safety and efficiency. Finally, possible mitigation strategies, like structural health monitoring and metamaterial-based seismic shields are addressed, in order to show how future developments may enhance plant resilience. The work presented hereafter represents a highly condensed application of the research done during the XP-RESILIENCE project, while more detailed information is available on the project website https://r.unitn.it/en/dicam/xp-resilience.

scholarly journals Two-Dimensional Numerical Modeling of Flow in Physical Models of Rock Vane and Bendway Weir Configurations

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 458
Author(s):  
Drew C. Baird ◽  
Benjamin Abban ◽  
S. Michael Scurlock ◽  
Steven B. Abt ◽  
Christopher I. Thornton
Keyword(s):  
Numerical Modeling ◽  
Physical Model ◽  
Numerical Models ◽  
Hydraulic Performance ◽  
Physical Models ◽  
Protection Measures ◽  
Design Engineers ◽  
Model Study ◽  
Study Results ◽  
Wide Range

While there are a wide range of design recommendations for using rock vanes and bendway weirs as streambank protection measures, no comprehensive, standard approach is currently available for design engineers to evaluate their hydraulic performance before construction. This study investigates using 2D numerical modeling as an option for predicting the hydraulic performance of rock vane and bendway weir structure designs for streambank protection. We used the Sedimentation and River Hydraulics (SRH)-2D depth-averaged numerical model to simulate flows around rock vane and bendway weir installations that were previously examined as part of a physical model study and that had water surface elevation and velocity observations. Overall, SRH-2D predicted the same general flow patterns as the physical model, but over- and underpredicted the flow velocity in some areas. These over- and underpredictions could be primarily attributed to the assumption of negligible vertical velocities. Nonetheless, the point differences between the predicted and observed velocities generally ranged from 15 to 25%, with some exceptions. The results showed that 2D numerical models could provide adequate insight into the hydraulic performance of rock vanes and bendway weirs. Accordingly, design guidance and implications of the study results are presented for design engineers.

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