Publications
420 results found
Aristodemou E, Mottet L, Constantinou A, et al., 2020, Turbulent flows and pollution dispersion around tall buildings using adaptive large eddy simulation (LES), Buildings, Vol: 10, Pages: 1-34, ISSN: 2075-5309
The motivation for this work stems from the increased number of high-rise buildings/skyscrapers all over the world, and in London, UK, and hence the necessity to see their effect on the local environment. We concentrate on the mean velocities, Reynolds stresses, turbulent kinetic energies (TKEs) and tracer concentrations. We look at their variations with height at two main locations within the building area, and downstream the buildings. The pollution source is placed at the top of the central building, representing an emission from a Combined Heat and Power (CHP) plant. We see how a tall building may have a positive effect at the lower levels, but a negative one at the higher levels in terms of pollution levels. Mean velocities at the higher levels (over 60 m in real life) are reduced at both locations (within the building area and downstream it), whilst Reynolds stresses and TKEs increase. However, despite the observed enhanced turbulence at the higher levels, mean concentrations increase, indicating that the mean flow has a greater influence on the dispersion. At the lower levels (Z < 60 m), the presence of a tall building enhanced dispersion (hence lower concentrations) for many of the configurations.
Xie Z, Pavlidis D, Salinas P, et al., 2020, A control volume finite element method for three‐dimensional three‐phase flows, International Journal for Numerical Methods in Fluids, Vol: 92, Pages: 765-784, ISSN: 0271-2091
A novel control volume finite element method with adaptive anisotropic unstructured meshes is presented for three‐dimensional three‐phase flows with interfacial tension. The numerical framework consists of a mixed control volume and finite element formulation with a new P1DG‐P2 elements (linear discontinuous velocity between elements and quadratic continuous pressure between elements). A “volume of fluid” type method is used for the interface capturing, which is based on compressive control volume advection and second‐order finite element methods. A force‐balanced continuum surface force model is employed for the interfacial tension on unstructured meshes. The interfacial tension coefficient decomposition method is also used to deal with interfacial tension pairings between different phases. Numerical examples of benchmark tests and the dynamics of three‐dimensional three‐phase rising bubble, and droplet impact are presented. The results are compared with the analytical solutions and previously published experimental data, demonstrating the capability of the present method.
Cheng M, Fang F, Pain CC, et al., 2020, Data -driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network, Computer Methods in Applied Mechanics and Engineering, Vol: 365, Pages: 1-18, ISSN: 0045-7825
Deep learning techniques for fluid flow modelling have gained significant attention in recent years. Advanced deep learning techniques achieve great progress in rapidly predicting fluid flows without prior knowledge of the underlying physical relationships. However, most of existing researches focused mainly on either sequence learning or spatial learning, rarely on both spatial and temporal dynamics of fluid flows (Reichstein et al., 2019). In this work, an Artificial Intelligence (AI) fluid model based on a general deep convolutional generative adversarial network (DCGAN) has been developed for predicting spatio-temporal flow distributions. In deep convolutional networks, the high-dimensional flows can be converted into the low-dimensional “latent” representations. The complex features of flow dynamics can be captured by the adversarial networks. The above DCGAN fluid model enables us to provide reasonable predictive accuracy of flow fields while maintaining a high computational efficiency. The performance of the DCGAN is illustrated for two test cases of Hokkaido tsunami with different incoming waves along the coastal line. It is demonstrated that the results from the DCGAN are comparable with those from the original high fidelity model (Fluidity). The spatio-temporal flow features have been represented as the flow evolves, especially, the wave phases and flow peaks can be captured accurately. In addition, the results illustrate that the online CPU cost is reduced by five orders of magnitude compared to the original high fidelity model simulations. The promising results show that the DCGAN can provide rapid and reliable spatio-temporal prediction for nonlinear fluid flows.
Kampitsis AE, Adam A, Salinas P, et al., 2020, Dynamic adaptive mesh optimisation for immiscible viscous fingering, COMPUTATIONAL GEOSCIENCES, Vol: 24, Pages: 1221-1237, ISSN: 1420-0597
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- Citations: 11
Lei Q, Jackson MD, Muggeridge AH, et al., 2020, Modelling the reservoir-to-tubing pressure drop imposed by multiple autonomous inflow control devices installed in a single completion joint in a horizontal well, Journal of Petroleum Science and Engineering, Vol: 189, Pages: 1-16, ISSN: 0920-4105
Autonomous inflow control devices (AICDs) are used to introduce an additional pressure drop between the reservoir and the tubing of a production well that depends on the fluid phase flowing into the device: a larger pressure drop is introduced when unwanted phases such as water or gas enter the AICD. The additional pressure drop is typically represented in reservoir simulation models using empirical relationships fitted to experimental data for a single AICD. This approach may not be correct if each completion joint is equipped with multiple AICDs as the flow at different AICDs may be different. We use high-resolution numerical modelling to determine the total additional pressure drop introduced by two AICDs installed in a single completion joint in a horizontal well. The model captures the multiphase flow of oil and water through the inner annulus into each AICD. We explore a number of relevant oil-water inflow scenarios with different flow rates and water cuts. Our results show that if only one AICD is installed, the additional pressure drop is consistent with the experimentalzly-derived empirical formulation. However, if two AICDs are present, there is a significant discrepancy between the additional pressure drop predicted by the simulator and the empirical relationship. This discrepancy occurs because each AICD has a different total and individual phase flow rate, and the final steady-state flow results from a self-organising mechanism emerging from the system. We report the discrepancy as a water cut-dependent correction to the empirical equation, which can be used in reservoir simulation models to better capture the pressure drop across a single completion containing two AICDs. Our findings highlight the importance of understanding how AICDs modify flow into production wells, and have important consequences for improving the representation of advanced wells in reservoir simulation models.
Dargaville S, Buchan AG, Smedley-Stevenson RP, et al., 2020, A comparison of element agglomeration algorithms for unstructured geometric multigrid, Publisher: arXiv
This paper compares the performance of seven different element agglomerationalgorithms on unstructured triangular/tetrahedral meshes when used as part of ageometric multigrid. Five of these algorithms come from the literature on AMGemultigrid and mesh partitioning methods. The resulting multigrid schemes aretested matrix-free on two problems in 2D and 3D taken from radiation transportapplications; one of which is in the diffusion limit. In two dimensions allcoarsening algorithms result in multigrid methods which perform similarly, butin three dimensions aggressive element agglomeration performed by METISproduces the shortest runtimes and multigrid setup times.
Zheng J, Fang F, Wang Z, et al., 2020, A new anisotropic adaptive mesh photochemical model for ozone formation in power plant plumes, ATMOSPHERIC ENVIRONMENT, Vol: 229, ISSN: 1352-2310
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- Citations: 3
Yang P, Lei Q, Xiang J, et al., 2020, Numerical simulation of blasting in confined fractured rocks using an immersed-body fluid-solid interaction model, Tunnelling and Underground Space Technology, Vol: 98, Pages: 1-14, ISSN: 0886-7798
We model blast-induced fracturing and fragmentation processes in fractured rocks using a fully coupled fluid-solid interaction model. This model links a finite-discrete element solid solver with a control volume-finite element fluid solver through an immersed-body method. The solid simulator can capture the deformation of intact rocks, interaction of matrix blocks, displacement of existing fractures and propagation of new cracks. The fluid simulator can simulate the highly compressible gas flow involved in the blasting and explosion process, which is assumed to follow the John-Wilkins-Lee equation of state. We design numerical experiments as follows. First, we generate a series of 1 m × 1 m discrete fracture networks associated with different fracture density and mean length values to consider various scenarios of distributed pre-existing fractures in rock. We apply isotropic/anisotropic in-situ stresses to the rock such that the system reaches an equilibrium state. Then we release the compressible gas associated with a prescribed high pressure in the borehole to simulate explosion, which engenders stress wave propagation and new crack generation in the system. We observe that the presence of natural fractures has a significant impact on the blast behaviour of fractured rocks such that new cracks tend to be arrested by pre-existing discontinuities which however accommodate wing cracks at their tips linking with other structures. Blast-driven cracks attempt to propagate along the maximum principal stress direction if an anisotropic stress condition is imposed. Our research findings have important implications for the design and assessment of blasting for underground excavation in fractured formations.
Dur TH, Arcucci R, Mottet L, et al., 2020, Weak Constraint Gaussian Processes for optimal sensor placement, JOURNAL OF COMPUTATIONAL SCIENCE, Vol: 42, ISSN: 1877-7503
Dargaville S, Buchan AG, Smedley-Stevenson RP, et al., 2020, Scalable angular adaptivity for Boltzmann transport, Journal of Computational Physics, Vol: 406, Pages: 1-32, ISSN: 0021-9991
scaling in both runtime and memory usage, where n is the number of adapted angles. This adaptivity uses Haar wavelets, which perform structured h-adaptivity built on top of a hierarchical P0 FEM discretisation of a 2D angular domain, allowing different anisotropic angular resolution to be applied across space/energy. These wavelets can be mapped back to their underlying P0 space scalably, allowing traditional DG-sweep algorithms if desired. Instead we build a spatial discretisation on unstructured grids designed to use less memory than competing alternatives in general applications and construct a compatible matrix-free multigrid method which can handle our adapted angular discretisation. Fixed angular refinement, along with regular and goal-based error metrics are shown in three example problems taken from neutronics/radiative transfer applications.
Wu P, Sun J, Chang X, et al., 2020, Data-driven reduced order model with temporal convolutional neural network, COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, Vol: 360, ISSN: 0045-7825
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- Citations: 53
Salinas P, Pain C, Osman H, et al., 2020, Vanishing artifficial diffusion as a mechanism to accelerate convergence for multiphase porous media flow, Computer Methods in Applied Mechanics and Engineering, Vol: 359, Pages: 1-15, ISSN: 0045-7825
Numerical solution of the equations governing multiphase porous media flow is challenging. A common approach to improve the performance of iterative non-linear solvers for these problems is to introduce artificial diffusion. Here, we present a mass conservative artificial diffusion that accelerates the non-linear solver but vanishes when the solution is converged. The vanishing artificial diffusion term is saturation dependent and is larger in regions of the solution domain where there are steep saturation gradients. The non-linear solver converges more slowly in these regions because of the highly non-linear nature of the solution. The new method provides accurate results while significantly reducing the number of iterations required by the non-linear solver. It is particularly valuable in reducing the computational cost of highly challenging numerical simulations, such as those where physical capillary pressure effects are dominant. Moreover, the method allows converged solutions to be obtained for Courant numbers that are at least two orders of magnitude larger than would otherwise be possible.
Silva VLS, Salinas P, Pain CC, et al., 2020, Non-linear solver optimisation for multiphase porous media flow based on machine learning
Numerical simulation of multiphase flow in porous media is of paramount importance to understand, predict and manage subsurface reservoirs with applications to hydrocarbon recovery, geothermal energy resources, CO2 geological sequestration, groundwater sources and magma reservoirs. However, the numerical solution of the governing equations is very challenging due to the non-linear nature of the problem and the strong coupling between the different equations. Newton methods have been traditionally used to solve the non-linear system of equations, although, the Picard iterative method has been gaining ground in recent years. The Picard method is attractive because the multiphysics problem can be subdivided and each subproblem solved separately, which gives wide flexibility and extensibility. Rapid convergence of the non-linear solver is of vital importance as it strongly affects the overall computational time. Therefore, a great deal of effort has been put on obtaining robust and stable convergence rates. At the same time, machine learning (ML) is gaining more and more attention with revolutionary results in areas such as computer vision, self-driving cars and natural language processing. The success of ML in different fields has inspired recent applications in reservoir engineering and geosciences. Here, we present a Picard non-linear solver with convergence parameters dynamically controlled by ML. The ML is trained based on the parameters of the reservoir model scaled to a dimensionless space. In the approach reported here, data for the ML training is generated using simulation results obtained for multiphase flow in a two-layered reservoir model which captures many of the flow features observed in models of natural reservoirs. The presented method significantly reduces the computational effort required by the non-linear solver as it can adjust itself to the complexity/physics of the system. We demonstrate its efficiency under a variety of numerical tests cases, inc
Al Kubaisy J, Osman H, Salinas P, et al., 2020, Discontinuous control volume finite element method for multiphase flow in porous media on challenging meshes
Control volume finite element methods (CVFEM) are gaining increasing popularity for modeling multi-phase flow in porous media due to their inherited geometric flexibility for modeling complex shapes. Nonetheless, classical CVFEM suffer from two key problems; first, mass conservation is enforced by the use of control volumes that span element boundaries. Consequently, when modeling flow in regions with discontinuous material properties, control volumes that span geologic domain boundaries result in non-physical leakage that degrades the numerical solution accuracy. Another challenge is to provide an accurate solution for distorted elements; elements with high aspect ratio that are part of the discretized heterogeneous domain. In fact, most numerical methods struggle to provide a converged pressure solution for high aspect ratio elements of the domain. Here, we introduce a numerical scheme that removes non-physical leakage across geologic domains and addresses the accuracy of classical control volume finite element method (CVFEM) in high aspect ratio elements. The scheme utilizes the frameworks of double-CVFEM (DCVFEM) where pressure is discretized CV-wise rather than element-wise. In addition, it introduces discontinuous control volumes by allowing pressure to be discontinuous between elements. The resultant finite element pair has an equal-order of velocity and pressure, with discontinuous linear elements for both the pressure and velocity fields P1DG-P1DG. This type of element pair is LBB unstable. The instability issue is circumvented by global enrichment of the finite element velocity interpolation space with an interior bubble function, given by the new element pair P1(BL)DG-P1DG. This element pair resolves both issues addressed earlier. We demonstrate that the developed numerical method is mass conservative, and it accurately preserves sharp saturation changes across different material properties or discontinuous permeability fields as well as improves converge
Salinas P, Jacquemyn C, Heaney C, et al., 2020, Well location optimisation by using surface-based modelling and dynamic mesh optimisation
Predictions of production obtained by numerical simulation often depend on grid resolution as fine resolution is required to resolve key aspects of flow. Moreover, the controls on flow can depend on well location in a model. In some cases, it may be key to capture coning or cusping; in others, it might be the location of specific high permeability thief zones or low permeability flow barriers. Thus, models with a suitable grid resolution for one particular set of well locations may fail to properly capture key aspects of flow if the wells are moved. During well optimisation, it is impossible to predict a-priori which well locations will be tested in a given model. Thus, it is unlikely to know a-priori if the grid resolution is suitable for all possible locations tested during a well optimisation procedure on a single model, and the problem is even more profound if well optimisation is tested over a range of different models. Here, we report an optimisation methodology based on Dynamic Mesh Optimisation (DMO). DMO will produce optimised meshes for a given model, set of well locations, pressure (and other key fields) distribution and timelevel. Grid-free Surface-Based Modelling (SBM) models are automatically generated in which well trajectories are introduced (also not constrained by a mesh), respected by DMO. For the optimization of the well location a Genetic Algorithm (GA) approach is used, more specifically the open-source software package DEAP. DMO ensures that all the models automatically generated and simulated in the optimisation process are modelled with an equivalent mesh resolution without user interaction, in this way, the local pressure drawdown and associated physical effects (such as coning or cusping) can be properly captured if they appear in any of the many scenarios that are studied . We demonstrate that the method has wide application in reservoir-scale models of oil and gas fields, and regional models of groundwater resources.
Titus Z, Pain C, Jacquemyn C, et al., 2020, Conditioning surface-based geological models to well data using neural networks
Generating representative reservoir models that accurately describe the spatial distribution of geological heterogeneities is crucial for reliable predictions of historic and future reservoir performance. Surface-based geological models (SBGMs) have been shown to better capture complex reservoir architecture than grid-based methods; however, conditioning such models to well data can be challenging because it is an ill-posed inverse problem with spatially distributed parameters. Here, we propose the use of deep Convolutional Neural Networks (CNNs) to generate geologically plausible SBGMs that honour well data. Deep CNNs have previously demonstrated capability in learning representative features of spatially correlated data for large scale and highly non-linear geophysical systems similar to those encountered in subsurface reservoirs. In the work reported here, a CNN is trained to learn the relationship between parameterised inputs to SBGM, the resulting geometry and heterogeneity distribution, and the mis-match between model surfaces and well data. We show that the trained CNN can generate a range of geologically plausible models that honour well data. The method is demonstrated for a 2D example model, representing a shallow marine reservoir and a 3D extension of the model that captures typical heterogeneities encountered in the subsurface such as parasequences, clinoforms and facies boundaries. These test cases highlight the improvement in reservoir characterisation for realistic geological cases. We present here a method of generating geologically consistent reservoir models that match well data. The developed method will allow the generation of new high-fidelity realizations of subsurface geology conditioned to information at wells, which is the most direct observational data that can be acquired. Technical Contributions - The use of surface-based modelling to describe even complex geological features compared to grid-based modelling significantly decreases the co
Arcucci R, Casas CQ, Xiao D, et al., 2020, A Domain Decomposition Reduced Order Model with Data Assimilation (DD-RODA), Conference on Parallel Computing - Technology Trends (ParCo), Publisher: IOS PRESS, Pages: 189-198, ISSN: 0927-5452
Arcucci R, Mottet L, Casas CAQ, et al., 2020, Adaptive Domain Decomposition for Effective Data Assimilation, 25th International Conference on Parallel and Distributed Computing (Euro-Par), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 583-595, ISSN: 0302-9743
Buchan AG, Dargaville S, Pain CC, 2019, A combined immersed body and adaptive mesh method for simulating neutron transport within complex structures, ANNALS OF NUCLEAR ENERGY, Vol: 134, Pages: 88-100, ISSN: 0306-4549
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- Citations: 3
Dargaville S, Buchan AG, Smedley-Stevenson RP, et al., 2019, Angular adaptivity with spherical harmonics for Boltzmann transport, Journal of Computational Physics, Vol: 397, Pages: 1-19, ISSN: 0021-9991
This paper describes an angular adaptivity algorithm for Boltzmann transport applications which uses Pn and filtered Pn expansions, allowing for different expansion orders across space/energy. Our spatial discretisation is specifically designed to use less memory than competing DG schemes and also gives us direct access to the amount of stabilisation applied at each node. For filtered Pn expansions, we then use our adaptive process in combination with this net amount of stabilisation to compute a spatially dependent filter strength that does not depend on a priori spatial information. This applies heavy filtering only where discontinuities are present, allowing the filtered Pn expansion to retain high-order convergence where possible. Regular and goal-based error metrics are shown and both the adapted Pn and adapted filtered Pn methods show significant reductions in DOFs and runtime. The adapted filtered Pn with our spatially dependent filter shows close to fixed iteration counts and up to high-order is even competitive with P0 discretisations in problems with heavy advection.
Lim EM, Molina Solana M, Pain C, et al., 2019, Hybrid data assimilation: An ensemble-variational approach, Pages: 633-640
Data Assimilation (DA) is a technique used to quantify and manage uncertainty in numerical models by incorporating observations into the model. Variational Data Assimilation (VarDA) accomplishes this by minimising a cost function which weighs the errors in both the numerical results and the observations. However, large-scale domains pose issues with the optimisation and execution of the DA model. In this paper, ensemble methods are explored as a means of sampling the background error at a reduced rank to condition the problem. The impact of ensemble size on the error is evaluated and benchmarked against other preconditioning methods explored in previous work such as using truncated singular value decomposition (TSVD). Localisation is also investigated as a form of reducing the long-range spurious errors in the background error covariance matrix. Both the mean squared error (MSE) and execution time are used as measure of performance. Experimental results for a 3D case for pollutant dispersion within an urban environment are presented with promise for future work using dynamic ensembles and 4D state vectors.
Aristodemou E, Arcucci R, Mottet L, et al., 2019, Enhancing CFD-LES air pollution prediction accuracy using data assimilation, Building and Environment, Vol: 165, ISSN: 0007-3628
It is recognised worldwide that air pollution is the cause of premature deaths daily, thus necessitating the development of more reliable and accurate numerical tools. The present study implements a three dimensional Variational (3DVar) data assimilation (DA) approach to reduce the discrepancy between predicted pollution concentrations based on Computational Fluid Dynamics (CFD) with the ones measured in a wind tunnel experiment. The methodology is implemented on a wind tunnel test case which represents a localised neighbourhood environment. The improved accuracy of the CFD simulation using DA is discussed in terms of absolute error, mean squared error and scatter plots for the pollution concentration. It is shown that the difference between CFD results and wind tunnel data, computed by the mean squared error, can be reduced by up to three order of magnitudes when using DA. This reduction in error is preserved in the CFD results and its benefit can be seen through several time steps after re-running the CFD simulation. Subsequently an optimal sensors positioning is proposed. There is a trade-off between the accuracy and the number of sensors. It was found that the accuracy was improved when placing/considering the sensors which were near the pollution source or in regions where pollution concentrations were high. This demonstrated that only 14% of the wind tunnel data was needed, reducing the mean squared error by one order of magnitude.
Woodward H, Stettler M, Pavlidis D, et al., 2019, A large eddy simulation of the dispersion of traffic emissions by moving vehicles at an intersection, Atmospheric Environment, Vol: 215, Pages: 1-16, ISSN: 1352-2310
Traffic induced flow within urban areas can have a significant effect on pollution dispersion, particularly for traffic emissions. Traffic movement results in increased turbulence within the street and the dispersion of pollutants by vehicles as they move through the street. In order to accurately model urban air quality and perform meaningful exposure analysis at the microscale, these effects cannot be ignored. In this paper we introduce a method to simulate traffic induced dispersion at high resolution. The computational fluid dynamics software, Fluidity, is used to model the moving vehicles through a domain consisting of an idealised intersection. A multi-fluid method is used where vehicles are represented as a second fluid which displaces the air as it moves through the domain. The vehicle model is coupled with an instantaneous emissions model which calculates the emission rate of each vehicle at each time step. A comparison is made with a second Fluidity model which simulates the traffic emissions as a line source and does not include moving vehicles. The method is used to demonstrate how moving vehicles can have a significant effect on street level concentration fields and how large vehicles such as buses can also cause acute high concentration events at the roadside which can contribute significantly to overall exposure.
Li J, Steppeler J, Fang F, et al., 2019, Potential numerical techniques and challenges for atmospheric modeling, Bulletin of the American Meteorological Society, Vol: 125, Pages: ES239-ES242, ISSN: 0003-0007
Xiao D, Fang F, Heaney CE, et al., 2019, A domain decomposition method for the non-intrusive reduced order modelling of fluid flow, Computer Methods in Applied Mechanics and Engineering, Vol: 354, Pages: 307-330, ISSN: 0045-7825
In this paper we present a new domain decomposition non-intrusive reduced order model (DDNIROM) for the Navier–Stokes equations. The computational domain is partitioned into subdomains and a set of local basis functions is constructed in each subdomain using Proper Orthogonal Decomposition (POD). A radial basis function (RBF) method is then used to generate a set of hypersurfaces for each subdomain. Each local hypersurface represents, not only the fluid dynamics over the subdomain to which it belongs, but also the interactions with the surrounding subdomains. This implicit coupling between the subdomains provides the global coupling necessary to enforce incompressibility and is a means of providing boundary conditions for each subdomain.The performance of this DDNIROM is illustrated numerically by three examples: flow past a cylinder, and air flow over 2D and 3D street canyons. The results show that the DDNIROM exhibits good agreement with the high-fidelity full model while the computational cost is reduced by several orders of magnitude. The domain decomposition (DD) method provides the flexibility to choose different numbers of local basis functions for each subdomain depending on the complexity of the flow therein. The fact that the RBF surface representation takes input only from its current subdomain and the surrounding subdomains, means that, crucially, there is a reduction in the dimensionality of the hypersurface when compared with a more traditional, global NIROM. This comes at the cost of having a larger number of hypersurfaces.
Hu R, Fang F, Pain CC, et al., 2019, Rapid spatio-temporal flood prediction and uncertainty quantification using a deep learning method, Journal of Hydrology, Vol: 575, Pages: 911-920, ISSN: 0022-1694
Recently accrued attention has been given to machine learning approaches for flooding prediction. However, most of these studies focused mainly on time-series flooding prediction at specified sensors, rarely on spatio-temporal prediction of inundations. In this work, an integrated long short-term memory (LSTM) and reduced order model (ROM) framework has been developed. This integrated LSTM-ROM has the capability of representing the spatio-temporal distribution of floods since it takes advantage of both ROM and LSTM. To reduce the dimensional size of large spatial datasets in LSTM, the proper orthogonal decomposition (POD) and singular value decomposition (SVD) approaches are introduced. The LSTM training and prediction processes are carried out over the reduced space. This leads to an improvement of computational efficiency while maintaining the accuracy. The performance of the LSTM-ROM developed here has been evaluated using Okushiri tsunami as test cases. The results obtained from the LSTM-ROM have been compared with those from the full model (Fluidity). In predictive analytics, it is shown that the results from both the full model and LSTM-ROM are in a good agreement whilst the CPU cost using the LSTM-ROM is decreased by three orders of magnitude compared to full model simulations. Additionally, prescriptive analytics has been undertaken to estimate the uncertainty in flood induced conditions. Given the time series of the free surface height at a specified detector, the corresponding induced wave conditions along the coastline have then been provided using the LSTM network. Promising results indicate that the use of LSTM-ROM can provide the flood prediction in seconds, enabling us to provide real-time predictions and inform the public in a timely manner, reducing injuries and fatalities.
Yang L, Yang J, Boek E, et al., 2019, Image based simulations of absolute permeability with pseudo-compressible stabilised finite element., Computational Geosciences.
We apply an accurate parallel stabilised finite element method to solve for Navier-Stokes equations directly on a binarised three-dimensional rock image, obtained by micro-CT imaging. The proposed algorithm has several advantages. First, the linear equal-order finite element space for velocity and pressure is ideal for presenting the pixel images. Second, the algorithm is fully explicit and versatile for describing complex boundary conditions. Third, the fully explicit matrix–free finite element implementation is ideal for parallelism on high-performance computers, similar to lattice Boltzmann. In the last, the memory usage is low compared with lattice Boltzmann or implicit finite volume. We compute the permeability of a range of rock images. The stabilisation parameter may affect the velocity, and an optimal parameter is chosen from the numerical tests. The steady state results are comparable with lattice Boltzmann method and implicit finite volume. The transient behaviour of pseudo-compressible stabilised finite element and lattice Boltzmann method is very similar. Our analysis shows that the stabilised finite element is an accurate and efficient method with low memory cost for the image- based simulations of flow in the pore scale up to 1 billion voxels on 128-GB ram workstation and on distributed clusters.
Osman H, Salinas P, Pain C, et al., 2019, An Enriched Control Volume Finite Element Method for Multi-Phase Flow in Porous Media on Challenging Meshes, EAGE annual
Osman H, Salinas P, Pain C, et al., 2019, An enriched control volume finite element method for multi-phase flow in porous media on challenging meshes
© 81st EAGE Conference and Exhibition 2019. All rights reserved. We introduce a new, efficient control volume finite element method that improves the modelling of multi-phase flow in heterogeneous porous media. The method uses discontinuous piecewise linear functions enriched with bubble functions for velocity and discontinuous piecewise linear functions for pressure evaluated on control volumes (CVs). LBB stability is maintained with a very efficient velocity:pressure degrees of freedom ratio of 1.25 on tetrahedral meshes. Classical CVFE methods on the other hand may reach a ratio of 5. The method does not require CVs to span element boundaries and as a result is able to accurately preserve saturation discontinuities across material boundaries. Finally, the use of control volume representation for pressure yields significant improvements in stability of the method on challenging meshes.
Osman H, Salinas P, Pain C, et al., 2019, An enriched control volume finite element method for multi-phase flow in porous media on challenging meshes
We introduce a new, efficient control volume finite element method that improves the modelling of multi-phase flow in heterogeneous porous media. The method uses discontinuous piecewise linear functions enriched with bubble functions for velocity and discontinuous piecewise linear functions for pressure evaluated on control volumes (CVs). LBB stability is maintained with a very efficient velocity:pressure degrees of freedom ratio of 1.25 on tetrahedral meshes. Classical CVFE methods on the other hand may reach a ratio of 5. The method does not require CVs to span element boundaries and as a result is able to accurately preserve saturation discontinuities across material boundaries. Finally, the use of control volume representation for pressure yields significant improvements in stability of the method on challenging meshes.
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