384 results found
Silva VLS, Salinas P, Jackson MD, et al., 2021, Machine learning acceleration for nonlinear solvers applied to multiphase porous media flow, COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, Vol: 384, ISSN: 0045-7825
Obeysekara A, Salinas P, Heaney CE, et al., 2021, Prediction of multiphase flows with sharp interfaces using anisotropic mesh optimisation, Advances in Engineering Software, Vol: 160, Pages: 1-16, ISSN: 0965-9978
We propose an integrated, parallelised modelling approach to solve complex multiphase flow problems with sharp interfaces. This approach is based on a finite-element, double control-volume methodology, and employs highly-anisotropic mesh optimisation within a framework of high-order numerical methods and algorithms, which include adaptive time-stepping, metric advection, flux limiting, compressive advection of interfaces, multi-grid solvers and preconditioners. Each method is integral to increasing the fidelity of representing the underlying physics while maximising computational efficiency, and, only in combination, do these methods result in the accurate, reliable, and efficient simulation of complex multiphase flows and associated regime transitions. These methods are applied simultaneously for the first time in this paper, although some of the individual methods have been presented previously. We validate our numerical predictions against standard benchmark results from the literature and demonstrate capabilities of our modelling framework through the simulation of laminar and turbulent two-phase pipe flows. These complex interfacial flows involve the creation of bubbles and slugs, which involve multi-scale physics and arise due to a delicate interplay amongst inertia, viscous, gravitational, and capillary forces. We also comment on the potential use of our integrated approach to simulate large, industrial-scale multiphase pipe flow problems that feature complex topological transitions.
Phillips TRF, Heaney CE, Smith PN, et al., 2021, An autoencoder‐based reduced‐order model for eigenvalue problems with application to neutron diffusion, International Journal for Numerical Methods in Engineering, Vol: 122, Pages: 3780-3811, ISSN: 0029-5981
Using an autoencoder for dimensionality reduction, this article presents a novel projection‐based reduced‐order model for eigenvalue problems. Reduced‐order modeling relies on finding suitable basis functions which define a low‐dimensional space in which a high‐dimensional system is approximated. Proper orthogonal decomposition (POD) and singular value decomposition (SVD) are often used for this purpose and yield an optimal linear subspace. Autoencoders provide a nonlinear alternative to POD/SVD, that may capture, more efficiently, features or patterns in the high‐fidelity model results. Reduced‐order models based on an autoencoder and a novel hybrid SVD‐autoencoder are developed. These methods are compared with the standard POD‐Galerkin approach and are applied to two test cases taken from the field of nuclear reactor physics.
Salinas P, Regnier G, Jacquemyn C, et al., 2021, Dynamic mesh optimisation for geothermal reservoir modelling, GEOTHERMICS, Vol: 94, ISSN: 0375-6505
Cheng M, Fang F, Navon IM, et al., 2021, A real-time flow forecasting with deep convolutional generative adversarial network: Application to flooding event in Denmark, PHYSICS OF FLUIDS, Vol: 33, ISSN: 1070-6631
Zheng J, Wu X, Fang F, et al., 2021, Numerical study of COVID-19 spatial-temporal spreading in London, PHYSICS OF FLUIDS, Vol: 33, ISSN: 1070-6631
Tajnafoi G, Arcucci R, Mottet L, et al., 2021, Variational Gaussian process for optimal sensor placement, Applications of Mathematics, Vol: 66, Pages: 287-317, ISSN: 0373-6725
Sensor placement is an optimisation problem that has recently gained great relevance. In order to achieve accurate online updates of a predictive model, sensors are used to provide observations. When sensor location is optimally selected, the predictive model can greatly reduce its internal errors. A greedy-selection algorithm is used for locating these optimal spatial locations from a numerical embedded space. A novel architecture for solving this big data problem is proposed, relying on a variational Gaussian process. The generalisation of the model is further improved via the preconditioning of its inputs: Masked Autoregressive Flows are implemented to learn nonlinear, invertible transformations of the conditionally modelled spatial features. Finally, a global optimisation strategy extending the Mutual Information-based optimisation and fine-tuning of the selected optimal location is proposed. The methodology is parallelised to speed up the computational time, making these tools very fast despite the high complexity associated with both spatial modelling and placement tasks. The model is applied to a real three-dimensional test case considering a room within the Clarence Centre building located in Elephant and Castle, London, UK.
Lyu Z, Lei Q, Yang L, et al., 2021, A novel approach to optimising well trajectory in heterogeneous reservoirs based on the fast-marching method, Journal of Natural Gas Science and Engineering, Vol: 88, Pages: 1-12, ISSN: 1875-5100
To achieve efficient recovery of subsurface energy resources, a suitable trajectory needs to be identified for the production well. In this study, a new approach is presented for automated identification of optimum well trajectories in heterogeneous oil/gas reservoirs. The optimisation procedures are as follows. First, a productivity potential map is generated based on the site characterisation data of a reservoir (when available). Second, based on the fast-marching method, well paths are generated from a number of entrance positions to a number of exit points at opposite sides of the reservoir. The well trajectory is also locally constrained by a prescribed maximum curvature to ensure that the well trajectory is drillable. Finally, the optimum well trajectory is selected from all the candidate paths based on the calculation of a benefit-to-cost ratio. If required, a straight directional well path, may also be derived through a linear approximation to the optimised non-linear trajectory by least squares analysis. Model performance has been demonstrated in both 2D and 3D. In the 2D example, the benefit-to-cost ratio of the optimised well is much higher than that of a straight well; in the 3D example, laterals of various curvatures are generated. The applicability of the method is tested by exploring different reservoir heterogeneities and curvature constraints. This approach can be applied to determine the entrance/exit positions and the well path for subsurface energy system development, which is useful for field applications.
Burridge HC, Bhagat RK, Stettler MEJ, et al., 2021, The ventilation of buildings and other mitigating measures for COVID-19: a focus on wintertime, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol: 477, Pages: 1-31, ISSN: 1364-5021
The year 2020 has seen the emergence of a global pandemic as a result of the disease COVID-19. This report reviews knowledge of the transmission of COVID-19 indoors, examines the evidence for mitigating measures, and considers the implications for wintertime with a focus on ventilation.
Phillips T, Heaney C, Tollit B, et al., 2021, Reduced-order modelling with domain decomposition applied to multi-group neutron transport, Energies, Vol: 14, ISSN: 1996-1073
Solving the neutron transport equations is a demanding computational challenge. This paper combines reduced-order modelling with domain decomposition to develop an approach that can tackle such problems. The idea is to decompose the domain of a reactor, form basis functions locally in each sub-domain and construct a reduced-order model from this. Several different ways of constructing the basis functions for local sub-domains are proposed, and a comparison is given with a reduced-order model that is formed globally. A relatively simple one-dimensional slab reactor provides a test case with which to investigate the capabilities of the proposed methods. The results show that domain decomposition reduced-order model methods perform comparably with the global reduced-order model when the total number of reduced variables in the system is the same with the potential for the offline computational cost to be significantly less expensive.
Heaney CE, Buchan AG, Pain CC, et al., 2021, Reduced-order modelling applied to the multigroup neutron diffusion equation using a nonlinear interpolation method for control-rod movement, Energies, ISSN: 1996-1073
Kumar P, Kalaiarasan G, Porter AE, et al., 2021, An overview of methods of fine and ultrafine particle collection for physicochemical characterisation and toxicity assessments., Science of the Total Environment, Vol: 756, Pages: 1-22, ISSN: 0048-9697
Particulate matter (PM) is a crucial health risk factor for respiratory and cardiovascular diseases. The smaller size fractions, ≤2.5 μm (PM2.5; fine particles) and ≤0.1 μm (PM0.1; ultrafine particles), show the highest bioactivity but acquiring sufficient mass for in vitro and in vivo toxicological studies is challenging. We review the suitability of available instrumentation to collect the PM mass required for these assessments. Five different microenvironments representing the diverse exposure conditions in urban environments are considered in order to establish the typical PM concentrations present. The highest concentrations of PM2.5 and PM0.1 were found near traffic (i.e. roadsides and traffic intersections), followed by indoor environments, parks and behind roadside vegetation. We identify key factors to consider when selecting sampling instrumentation. These include PM concentration on-site (low concentrations increase sampling time), nature of sampling sites (e.g. indoors; noise and space will be an issue), equipment handling and power supply. Physicochemical characterisation requires micro- to milli-gram quantities of PM and it may increase according to the processing methods (e.g. digestion or sonication). Toxicological assessments of PM involve numerous mechanisms (e.g. inflammatory processes and oxidative stress) requiring significant amounts of PM to obtain accurate results. Optimising air sampling techniques are therefore important for the appropriate collection medium/filter which have innate physical properties and the potential to interact with samples. An evaluation of methods and instrumentation used for airborne virus collection concludes that samplers operating cyclone sampling techniques (using centrifugal forces) are effective in collecting airborne viruses. We highlight that predictive modelling can help to identify pollution hotspots in an urban environment for the efficient collection of PM mass. This review provides
Amendola M, Arcucci R, Mottet L, et al., 2021, Data Assimilation in the Latent Space of a Convolutional Autoencoder, Pages: 373-386, ISSN: 0302-9743
Data Assimilation (DA) is a Bayesian inference that combines the state of a dynamical system with real data collected by instruments at a given time. The goal of DA is to improve the accuracy of the dynamic system making its result as real as possible. One of the most popular technique for DA is the Kalman Filter (KF). When the dynamic system refers to a real world application, the representation of the state of a physical system usually leads to a big data problem. For these problems, KF results computationally too expensive and mandates to use of reduced order modeling techniques. In this paper we proposed a new methodology we called Latent Assimilation (LA). It consists in performing the KF in the latent space obtained by an Autoencoder with non-linear encoder functions and non-linear decoder functions. In the latent space, the dynamic system is represented by a surrogate model built by a Recurrent Neural Network. In particular, an Long Short Term Memory (LSTM) network is used to train a function which emulates the dynamic system in the latent space. The data from the dynamic model and the real data coming from the instruments are both processed through the Autoencoder. We apply the methodology to a real test case and we show that the LA has a good performance both in accuracy and in efficiency.
ViaEstrem L, Salinas P, Xie Z, et al., 2020, Robust control volume finite element methods for numerical wave tanks using extreme adaptive anisotropic meshes, International Journal for Numerical Methods in Fluids, Vol: 92, Pages: 1707-1722, ISSN: 0271-2091
Multiphase inertia‐dominated flow simulations, and free surface flow models in particular, continue to this day to present many challenges in terms of accuracy and computational cost to industry and research communities. Numerical wave tanks and their use for studying wave‐structure interactions are a good example. Finite element method (FEM) with anisotropic meshes combined with dynamic mesh algorithms has already shown the potential to significantly reduce the number of elements and simulation time with no accuracy loss. However, mesh anisotropy can lead to mesh quality‐related instabilities. This article presents a very robust FEM approach based on a control volume discretization of the pressure field for inertia dominated flows, which can overcome the typically encountered mesh quality limitations associated with extremely anisotropic elements. Highly compressive methods for the water‐air interface are used here. The combination of these methods is validated with multiphase free surface flow benchmark cases, showing very good agreement with experiments even for extremely anisotropic meshes, reducing by up to two orders of magnitude the required number of elements to obtain accurate solutions.
Cheng M, Fang F, Pain CC, et al., 2020, An advanced hybrid deep adversarial autoencoder for parameterized nonlinear fluid flow modelling, Computer Methods in Applied Mechanics and Engineering, Vol: 372, Pages: 1-19, ISSN: 0045-7825
Considering the high computation cost required in conventional computation fluid dynamic simulations, machine learning methods have been introduced to flow dynamic simulations in years, aiming on reducing CPU time. In this work, we propose a hybrid deep adversarial autoencoder (VAE-GAN) to integrate generative adversarial network (GAN) and variational autoencoder (VAE) for predicting parameterized nonlinear fluid flows in spatial and temporal dimensions. High-dimensional inputs are compressed into the low-dimensional representations by nonlinear functions in a convolutional encoder. In this way, the predictive fluid flows reconstructed in a convolutional decoder contain the dynamic fluid flow physics of high nonlinearity and chaotic nature. In addition, the low-dimensional representations are applied to the adversarial network for model training and parameter optimization, which enables fast computation process. The capability of the hybrid VAE-GAN is illustrated by varying inputs on a flow past a cylinder test case as well as a second case of water column collapse. Numerical results show that this hybrid VAE-GAN has successfully captured the spatio-temporal flow features with CPU speed-up of three orders of magnitude. These promising results suggest that the hybrid VAE-GAN can play a critical role in efficiently and accurately predicting complex flows in future research efforts.
Yekta A, Salinas P, Hajirezaie S, et al., 2020, Reactive transport modeling in heterogeneous porous media with dynamic mesh optimization, Computational Geosciences: modeling, simulation and data analysis, Vol: 25, Pages: 357-372, ISSN: 1420-0597
This paper presents a numerical simulator for solving compositional multiphase flow and reactive transport. The simulator was developed by effectively linking IC-FERST (Imperial College Finite Element Reservoir SimulaTor) with PHREEQCRM. IC-FERST is a next-generation three-dimensional reservoir simulator based on the double control volume finite element method and dynamic unstructured mesh optimization and is developed by the Imperial College London. PHREEQCRM is a state-of-the-art geochemical reaction package and is developed by the United States Geological Survey. We present a step-by-step framework on how the coupling is performed. The coupled code is called IC-FERST-REACT and is capable of simulating complex hydrogeological, biological, chemical, and mechanical processes occurring including processes occur during CO2 geological sequestration, CO2 enhanced oil recovery, and geothermal systems among others. In this paper, we present our preliminary work as well as examples related to CO2 geological sequestration. We performed the model coupling through developing an efficient application programming interface (API). IC-FERST-REACT inherits high-order methods and unstructured meshes with dynamic mesh optimization from IC-FERST. This reduces the computational cost by placing the mesh resolution where and when necessary and it can better capture flow instabilities if they occur. This can have a strong impact on reactive transport simulations which usually suffer from computational cost. From PHREEQCRM the code inherits the ability to efficiently model geochemical reactions. Benchmark examples are used to show the capability of IC-FERST-REACT in solving multiphase flow and reactive transport.
Dargaville S, Smedley-Stevenson RP, Smith PN, et al., 2020, Goal-based angular adaptivity for Boltzmann transport in the presence of ray-effects, Journal of Computational Physics, Vol: 421, Pages: 1-19, ISSN: 0021-9991
Boltzmann transport problems often involve heavy streaming, where particles propagate long distance due to the dominance of advection over particle interaction. If an insufficiently refined non-rotationally invariant angular discretisation is used, there are areas of the problem where no particles will propagate. These “ray-effects” are problematic for goal-based error metrics with angular adaptivity, as the metrics in the pre-asymptotic region will be zero/incorrect and angular adaptivity will not occur. In this work we use low-order filtered spherical harmonics, which are rotationally invariant and hence not subject to ray-effects, to “bootstrap” our error metric and enable highly refined anisotropic angular adaptivity with a Haar wavelet angular discretisation. We test this on three simple problems with pure streaming in which traditional error metrics fail. We show our method is robust and produces adapted angular discretisations that match results produced by fixed a priori refinement with either reduced runtime or a constant additional cost even with angular refinement.
Cheng M, Fang F, Kinouchi T, et al., 2020, Long lead-time daily and monthly streamflow forecasting using machine learning methods, Journal of Hydrology, Vol: 590, Pages: 1-13, ISSN: 0022-1694
Long lead-time streamflow forecasting is of great significance for water resources planning and management in both the short and long terms. Despite of some studies using machine learning methods in streamflow forecasting, only few studies have been conducted to explore long lead-time forecasting capabilities of these methods, and gain an insight into systematic comparison of model forecasting performance in both the short and long terms. In this work, an artificial neural network (ANN) and a long short term memory (LSTM), a powerful tool for learning long-term temporal dependencies and capturing nonlinear relationship, have been adopted to forecast streamflow at daily and monthly scales for a long lead-time period. For long lead-time streamflow forecasting, a recursive forecasting procedure, which takes the last one-step-ahead forecast as a new input for the next-step-ahead forecast, is used in the ANN and LSTM forecasting systems. Two models are trained and validated for streamflow forecasting using the rainfall and runoff datasets collected from the Nan River Basin and Ping River Basin, Thailand, covering the period 1974 to 2014. To further explore the impact of parameter settings on model performance, two parameters, i.e. the length of time lag and the number of maximum epochs, are examined in the ANN and LSTM models. The main findings are highlighted here. First, with an optimal setting up of model parameters, both the ANN and LSTM model can provide accurate daily forecasting (up to 20 days ahead). Second, in comparison to the ANN model, the LSTM model exhibits better model performance in long lead-time daily forecasting, but less satisfactory in multi-monthly forecasting due to lack of large monthly training dataset. Third, the selection of the length of the time lag and number of maximum epochs used in both ANN and LSTM modelling are the key for long lead-time streamflow forecasting at daily and monthly scales. These findings suggest that the LSTM could be ad
Joulin C, Xiang J, Latham J-P, et al., 2020, Capturing heat transfer for complex-shaped multibody contact problems, a new FDEM approach, Computational Particle Mechanics, Vol: 7, Pages: 919-934, ISSN: 2196-4378
This paper presents a new approach for the modelling of heat transfer in 3D discrete particle systems. Using a combined finite–discrete element (FDEM) method, the surface of contact is numerically computed when two discrete meshes of two solids experience a small overlap. Incoming heat flux and heat conduction inside and between solid bodies are linked. In traditional FEM (finite element method) or DEM (discrete element method) approaches, to model heat transfer across contacting bodies, the surface of contact is not directly reconstructed. The approach adopted here uses the number of surface elements from the penetrating boundary meshes to form a polygon of the intersection, resulting in a significant decrease in the mesh dependency of the method. Moreover, this new method is suitable for any sizes or shapes making up the particle system, and heat distribution across particles is an inherent feature of the model. This FDEM approach is validated against two models: a FEM model and a DEM pipe network model. In addition, a multi-particle heat transfer contact problem of complex-shaped particles is presented.
Kramer S, Wilson C, Davies R, et al., 2020, FluidityProject/fluidity: New test cases "Analytical solutions for mantle flow in cylindrical and spherical shells"
This release adds new test cases described in the GMD paper "Analytical solutions for mantle flow in cylindrical and spherical shells"
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, ISSN: 0045-7825
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
Yang L, Buchan A, Pavlidis D, et al., 2020, A three-phase interpenetrating continua approach for wave and porous structure interaction, ENGINEERING COMPUTATIONS, Vol: 38, Pages: 1157-1169, ISSN: 0264-4401
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.
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