•Introduce sediment-related tidal asymmetry proxy based on skewness method.•Oceanic tides control peak current asymmetry and inter-tidal flats determine slack water asymmetry.•SLR reduces both peak ...current asymmetry and slack water asymmetry, suggesting a negative feedback mechanism.
Tidal asymmetry in estuaries and lagoons (tidal basins) controls residual sediment transport, and quantifying tidal asymmetry is important for understanding the factors contributing to long-term morphological changes. Asymmetry in peak flood and ebb currents (Peak Current Asymmetry – PCA) controls residual transport of coarse sediment, and asymmetry in slack water duration preceding flood and ebb currents (Slack Water Asymmetry – SWA) controls residual transport of fine sediment. PCA and SWA are analyzed herein for Newport Bay, a tidal embayment in southern California, based on the skewness of tidal currents predicted for several stations by a hydrodynamic model. Use of skewness for tidal asymmetry is relatively new and offers several advantages over a traditional harmonic method including the ability to resolve variability over a wide range of time scales. Newport Bay is externally forced by mixed oceanic tides that are shown to be ebb dominant because of shorter falling tide than rising tide. Both PCA and SWA indicate ebb dominance that favors export of coarse and fine sediments, respectively, to the coastal ocean. However, we show that the ebb dominance of SWA is derived from the basin geometry and not the external forcing, while ebb dominance of PCA is linked to the external forcing and the basin geometry. We also show that tidal flats in Newport Bay play an important role in maintaining ebb dominated transport of both coarse and fine sediments. Loss of tidal flats could weaken PCA and reverse SWA to become flood dominant. Specifically, we show that sea level rise > 0.8 m that inundates tidal flats will begin to weaken ebb dominant PCA and SWA and that sea level rise > 1.0 m will reverse SWA to become flood dominant. This feedback mechanism is likely to be important for predicting long-term evolution of tidal basins under accelerating sea level rise.
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IJS, IMTLJ, KILJ, KISLJ, NUK, SBCE, SBJE, UL, UM, UPCLJ, UPUK
•We study the alternate bars formation in an engineered mountainous reach.•Aerial photos, cross-sections, and satellite images are used to perform the study.•We link field observations with ...theoretical models of alternate bars formation.•A conceptual model is suggested for the evolution of the system toward a confined wandering system.
Formation and development of alternate bars in an engineered mountainous reach of the Arc River, France, is studied using photo analysis, 1D modelling and by applying theoretical and empirical models for alternate bar systems. Alternate bars already existed in the 80s in the form of a stable confined wandering system. In 1994, the river bed was flattened after engineering works. However, aerial photographs and cross-sectional profiles show that bars rapidly recovered within a few years. The alternate bar system evolved rapidly with a reduction of the number of bars and so an increasing bar length. The width-to-depth ratio, the slope change, the bend upstream of the reach, and the sediment supplies are the main controls of bar formation and evolution. The system appears to lead to force bars due to the bend but also due to a bridge in the downstream part of the reach. Nevertheless some free mobile bars are still observed in the middle of the reach. A discussion on the alternate bar formation is provided using empirical and analytical models. Finally, impacts of low flows and vegetation seem to be significant in the stabilization of the system toward a confined wandering system as observed before the engineering works.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
•Semi-Implicit and Compound-fast MRT methods are proposed based on Implicit–Explicit approach.•Semi-Implicit MRT gives higher speed-up but lower accuracy compared to Compound-fast MRT.•Stability of ...Semi-Implicit MRT depends on the relative activity of the system-components.•Stability of Compound-fast MRT is relatively independent of the activity of the system-components.•Significant speed-up can be expected for complex 3D settings.
We present an extrapolation-based semi-implicit multi-rate time stepping (MRT) scheme and a compound-fast MRT scheme for a naturally partitioned, multi-time-scale hydro-geomechanical hydrate reservoir model. We evaluate the performance of the two MRT methods compared to an iteratively coupled solution scheme and discuss their advantages and disadvantages. The performance of the two MRT methods is evaluated in terms of speed-up and accuracy by comparison to an iteratively coupled solution scheme. We observe that the extrapolation-based semi-implicit method gives a higher speed-up but is strongly dependent on the relative time scales of the latent (slow) and active (fast) components. On the other hand, the compound-fast method is more robust and less sensitive to the relative time scales, but gives lower speed up as compared to the semi-implicit method, especially when the relative time scales of the active and latent components are comparable.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
•Minimum requirements for predictive pore-network modeling of transport determined.•Novel pore-network model and network extraction methods developed.•Compared to direct numerical simulation for a ...range of micromodel heterogeneities.•Impact of common network modeling practices on predictions assessed.
Pore-scale models are now an integral part of analyzing fluid dynamics in porous materials (e.g., rocks, soils, fuel cells). Pore network models (PNM) are particularly attractive due to their computational efficiency. However, quantitative predictions with PNM have not always been successful. We focus on single-phase transport of a passive tracer under advection-dominated regimes and compare PNM with high-fidelity direct numerical simulations (DNS) for a range of micromodel heterogeneities. We identify the minimum requirements for predictive PNM of transport. They are: (a) flow-based network extraction, i.e., discretizing the pore space based on the underlying velocity field, (b) a Lagrangian (particle tracking) simulation framework, and (c) accurate transfer of particles from one pore throat to the next. We develop novel network extraction and particle tracking PNM methods that meet these requirements. Moreover, we show that certain established PNM practices in the literature can result in first-order errors in modeling advection-dominated transport. They include: all Eulerian PNMs, networks extracted based on geometric metrics only, and flux-based nodal transfer probabilities. Preliminary results for a 3D sphere pack are also presented. The simulation inputs for this work are made public to serve as a benchmark for the research community.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Optimal perennial groundwater yield pumping strategies were computed for a complex multilayer aquifer with: (i) confined and unconfined flow, and (ii) many flows typically described by ...piecewise-linear (nonsmooth) equations. The latter flows account for over 50% of the aquifer discharge from the test area, the eastern shore of the Great Salt Lake in Utah. Normally utilized response matrix (RM) and embedding (EM) simulation/optimization modelling procedures did not converge to optimal solutions for this area; they diverged or oscillated. However, the newly presented linear RM and EM approaches satisfactorily addressed the nonlinearities posed by over 2000 piecewise-linear constraints for evapotranspiration, discharge from flowing wells, drain discharge, and vertical interlayer flow reduction due to desaturation of a confined aquifer. Both presented modelling approaches converged to the same optimal solution. Superposition was applied to the nonlinear problem by: making a cycle within the RM analogous to an iteration in a simulation model (such as MODFLOW); and using a modified MODFLOW to develop influence coefficients. The EM model contained about 40 000 nonzero elements and 12 000 single equations and variables, demonstrating its suitability for large scale planning.
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IJS, IMTLJ, KILJ, KISLJ, NUK, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Numerical simulation of multiphase flow in porous media is essential for many geoscience applications. Machine learning models trained with numerical simulation data can provide a faster alternative ...to traditional simulators. Here we present U-FNO, a novel neural network architecture for solving multiphase flow problems with superior accuracy, speed, and data efficiency. U-FNO is designed based on the newly proposed Fourier neural operator (FNO), which has shown excellent performance in single-phase flows. We extend the FNO-based architecture to a highly complex CO2-water multiphase problem with wide ranges of permeability and porosity heterogeneity, anisotropy, reservoir conditions, injection configurations, flow rates, and multiphase flow properties. The U-FNO architecture is more accurate in gas saturation and pressure buildup predictions than the original FNO and a state-of-the-art convolutional neural network (CNN) benchmark. Meanwhile, it has superior data utilization efficiency, requiring only a third of the training data to achieve the equivalent accuracy as CNN. U-FNO provides superior performance in highly heterogeneous geological formations and critically important applications such as gas saturation and pressure buildup “fronts” determination. The trained model can serve as a general-purpose alternative to routine numerical simulations of 2D-radial CO2 injection problems with significant speed-ups than traditional simulators.
•U-FNO model for multiphase flow designed based on Fourier neural operator.•Data-efficient multiphase flow predictions for gas saturation and pressure buildup.•Results are significantly faster and more accurate than state-of-the-art CNNs.
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IJS, IMTLJ, KILJ, KISLJ, NUK, SBCE, SBJE, UL, UM, UPCLJ, UPUK
•Standard DNN methods might lead to highly uncertain and inaccurate predictions.•Multiphysics-informed neural network (MPINN) method is proposed.•Multiphisics constraints improve predictive ability ...of DNNs.•MPINN allows assimilating multiphysics data.•MPINN reduces data requirements for parameter estimation.
Data assimilation for parameter and state estimation in subsurface transport problems remains a significant challenge because of the sparsity of measurements, the heterogeneity of porous media, and the high computational cost of forward numerical models. We present a multiphysics-informed deep neural network machine learning method for estimating space-dependent hydraulic conductivity, hydraulic head, and concentration fields from sparse measurements. In this approach, we employ individual deep neural networks (DNNs) to approximate the unknown parameters (e.g., hydraulic conductivity) and states (e.g., hydraulic head and concentration) of a physical system. Next, we jointly train these DNNs by minimizing the loss function that consists of the governing equations residuals in addition to the error with respect to measurement data. We apply this approach to assimilate conductivity, hydraulic head, and concentration measurements for the joint inversion of these parameter and states in a steady-state advection–dispersion problem. We study the accuracy of the proposed data assimilation approach with respect to the data size (i.e., the number of measured variables and the number of measurements of each variable), DNN size, and the complexity of the parameter field. We demonstrate that the physics-informed DNNs are significantly more accurate than the standard data-driven DNNs, especially when the training set consists of sparse data. We also show that the accuracy of parameter estimation increases as more different multiphysics variables are inverted jointly.
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