418 results found
Phillips T, Heaney C, Chen B, et al., 2023, Solving the discretised neutron diffusion equations using neural networks, International Journal for Numerical Methods in Engineering, Vol: 124, Pages: 4659-4686, ISSN: 0029-5981
This paper presents a new approach which uses the tools within Artificial Intelligence (AI) software libraries as an alternative way of solving partial differential equations (PDEs) that have been discretised using standard numerical methods. In particular, we describe how to represent numerical discretisations arising from the finite volume and finite element methods by pre-determining the weights of convolutional layers within a neural network. As the weights are defined by the discretisation scheme, no training of the network is required and the solutions obtained are identical (accounting for solver tolerances) to those obtained with standard codes often written in Fortran or C++. We also explain how to implement the Jacobi method and a multigrid solver using the functions available in AI libraries. For the latter, we use a U-Net architecture which is able to represent a sawtooth multigrid method. A benefit of using AI librariesin this way is that one can exploit their built-in technologies to enable the same code to run on differentcomputer architectures (such as Central Processing Units, Graphics Processing Units or new-generationAI processors) without any modification. In this article, we apply the proposed approach to eigenvalue problems in reactor physics where neutron transport is described by diffusion theory. For a fuel assembly benchmark, we demonstrate that the solution obtained from our new approach is the same (accounting for solver tolerances) as that obtained from the same discretisation coded in a standard way using Fortran. We then proceed to solve a reactor core benchmark using the new approach. For both benchmarks we give timings for the neural networkimplementation run on a CPU and a GPU, and a serial Fortran code run on a CPU.
Wu X, Gan P, Li J, et al., 2023, A long short-term memory neural network-based error estimator for three-dimensional dynamically adaptive mesh generation, Physics of Fluids, Vol: 35, ISSN: 1070-6631
Adaptive meshes are pivotal in numerical modeling and simulation, offering a means to efficiently, precisely, and flexibly represent intricate physical phenomena, particularly when grappling with their intricacies and varying scales. However, the transition from two dimensions (2D) to three dimensions (3D) poses a substantial challenge, as the computational demands of dynamically adaptive mesh techniques increase exponentially. Addressing this challenge effectively, we turn to the cutting-edge realm of artificial intelligence and neural networks. In our study, we harness the innovative power of a long short-term memory (LSTM) neural network as an error estimator for adapting unstructured meshes in both 2D and 3D scenarios. This LSTM network predicts the evolution of the adaptive grid based on specified variables, presenting itself as an artificial intelligence-driven architecture to optimize the adaptive criterion for the target variable. This is achieved by establishing a direct correspondence between the Riemann metric and these variables. To demonstrate the practical applicability of our approach, we seamlessly integrate the LSTM error estimator into the 3D adaptive atmospheric model Fluidity-Atmosphere (Fluidity-Atmos), thereby enabling real-time mesh adaptation during numerical simulations. We assess the effectiveness of this method in terms of simulation precision and computational efficiency through a series of experiments in both 2D and 3D settings. Our results not only reveal that the mesh patterns generated by the LSTM error estimator within Fluidity-Atmos closely resemble those produced by traditional error estimators but also underscore its superior performance in enhancing simulation accuracy. Notably, as the number of nodes increases, the LSTM mesh generator substantially reduces CPU time requirements by up to 50% in 3D cases compared to the conventional mesh generator within Fluidity-Atmos, highlighting its remarkable computational efficiency.
Dargaville S, Smedley-Stevenson RP, Smith PN, et al., 2023, Angular Adaptivity in P<SUP>0</SUP> Space and Reduced Tolerance Solves for Boltzmann Transport, NUCLEAR SCIENCE AND ENGINEERING, ISSN: 0029-5639
Woodward H, Schroeder A, de Nazelle A, et al., 2023, Do we need high temporal resolution modelling of exposure in urban areas? A test case, Science of the Total Environment, Vol: 885, Pages: 163711-163711, ISSN: 0048-9697
Roadside concentrations of harmful pollutants such as NOx are highly variable in both space and time. This is rarely considered when assessing pedestrian and cyclist exposures. We aim to fully describe the spatio-temporal variability of exposures of pedestrians and cyclists travelling along a road at high resolution. We evaluate the value added of high spatio-temporal resolution compared to high spatial resolution only. We also compare high resolution vehicle emissions modelling to using a constant volume source. We highlight conditions of peak exposures, and discuss implications for health impact assessments. Using the large eddy simulation code Fluidity we simulate NOx concentrations at a resolution of 2 m and 1 s along a 350 m road segment in a complex real-world street geometry including an intersection and bus stops. We then simulate pedestrian and cyclist journeys for different routes and departure times. For the high spatio-temporal method, the standard deviation in 1 s concentration experienced by pedestrians (50.9 μg.m-3) is nearly three times greater than that predicted by the high-spatial only (17.5 μg.m-3) or constant volume source (17.6 μg.m-3) methods. This exposure is characterised by low concentrations punctuated by short duration, peak exposures which elevate the mean exposure and are not captured by the other two methods. We also find that the mean exposure of cyclists on the road (31.8 μg.m-3) is significantly greater than that of cyclists on a roadside path (25.6 μg.m-3) and that of pedestrians on a sidewalk (17.6 μg.m-3). We conclude that ignoring high resolution temporal air pollution variability experienced at the breathing time scale can lead to a mischaracterization of pedestrian and cyclist exposures, and therefore also potentially the harm caused. High resolution methods reveal that peaks, and hence mean exposures, can be meaningfully reduced by avoiding hyper-local hotspots such as bus stops and junctions.
Cheng M, Fang F, Navon IM, et al., 2023, Assessing uncertainty and heterogeneity in machine learning-based spatiotemporal ozone prediction in Beijing-Tianjin- Hebei region in China, Science of the Total Environment, Vol: 881, Pages: 1-13, ISSN: 0048-9697
Accurate prediction of spatiotemporal ozone concentration is of great significance to effectively establish advanced early warning systems and regulate air pollution control. However, the comprehensive assessment of uncertainty and heterogeneity in spatiotemporal ozone prediction remains unknown. Here, we systematically analyze the hourly and daily spatiotemporal predictive performances using convolutional long short term memory (ConvLSTM) and deep convolutional generative adversarial network (DCGAN) models over the Beijing-Tianjin-Hebei region in China from 2013 to 2018. In extensive scenarios, our results show that the machine learning-based (ML-based) models achieve better spatiotemporal ozone concentration prediction performance with multiple meteorological conditions. A further comparison to the air pollution model-Nested Air Quality Prediction Modelling System (NAQPMS) and monitoring observations, the ConvLSTM model demonstrates the practical feasibility of identifying high ozone concentration distribution and capturing spatiotemporal ozone variation patterns at a high spatial resolution (here 15 km × 15 km).
Abubakkar-Waziri H, Kalaiarasan G, Wawman R, et al., 2023, SARS-CoV2 in public spaces in West London UK during COVID-19 pandemic, BMJ Open Respiratory Research, Vol: 10, ISSN: 2052-4439
Background: Spread of SARS-CoV2 by aerosol is considered an important mode of transmission over distances >2 m, particularly indoors.Objectives: We determined whether SARS-CoV2 could be detected in the air of enclosed/semi-enclosed public spaces.Methods and analysis: Between March 2021 and December 2021 during the easing of COVID-19 pandemic restrictions after a period of lockdown, we used total suspended and size-segregated particulate matter (PM) samplers for the detection of SARS-CoV2 in hospitals wards and waiting areas, on public transport, in a university campus and in a primary school in West London.Results: We collected 207 samples, of which 20 (9.7%) were positive for SARS-CoV2 using quantitative PCR. Positive samples were collected from hospital patient waiting areas, from hospital wards treating patients with COVID-19 using stationary samplers and from train carriages in London underground using personal samplers. Mean virus concentrations varied between 429 500 copies/m3 in the hospital emergency waiting area and the more frequent 164 000 copies/m3 found in other areas. There were more frequent positive samples from PM samplers in the PM2.5 fractions compared with PM10 and PM1. Culture on Vero cells of all collected samples gave negative results.Conclusion: During a period of partial opening during the COVID-19 pandemic in London, we detected SARS-CoV2 RNA in the air of hospital waiting areas and wards and of London Underground train carriage. More research is needed to determine the transmission potential of SARS-CoV2 detected in the air.
Silva VLS, Heaney CE, Li Y, et al., 2023, Data Assimilation Predictive GAN (DA-PredGAN) Applied to a Spatio-Temporal Compartmental Model in Epidemiology (vol 94, 25, 2023), JOURNAL OF SCIENTIFIC COMPUTING, Vol: 95, ISSN: 0885-7474
Cheng M, Fang F, Navon IM, et al., 2023, Ensemble Kalman filter for GAN-ConvLSTM based long lead-time forecasting, Journal of Computational Science, Vol: 69, Pages: 1-16, ISSN: 1877-7503
Data-driven machine learning techniques have been increasingly utilized for accelerating nonlinear dynamic system prediction. However, machine learning-based models for long lead-time forecasts remain a significant challenge due to the accumulation of uncertainty along the time dimension in online deployment. To tackle this issue, the ensemble Kalman filter (EnKF) has been introduced to machine learning-based long-term forecast models to reduce the uncertainty of long lead-time forecasts of chaotic dynamic systems. Both the deep convolutional generative adversarial network (DCGAN) and convolutional long short term memory (ConvLSTM) are used for learning the complex nonlinear relationships between the past and future states of dynamic systems. Using an iterative Multi-Input Multi-Output (MIMO) algorithm, the two-hybrid forecast models (DCGAN-EnKF and ConvLSTM-EnKF) are able to yield long lead-time forecasts of dynamic states. The performance of the hybrid models has been demonstrated by one-level and two-level Lorenz 96 models. Our results show that the use of EnKF in ConvLSTM and DCGAN models successfully corrects online model errors and significantly improves the real-time forecasting of dynamic systems for a long lead-time.
Arcucci R, Xiao D, Fang F, et al., 2023, A reduced order with data assimilation model: Theory and practice, Computers and Fluids, Vol: 257, Pages: 1-12, ISSN: 0045-7930
Numerical simulations are extensively used as a predictive tool to better understand complex air flows and pollution transport on the scale of individual buildings, city blocks and entire cities. Fast-running Non-Intrusive Reduced Order Model (NIROM) for predicting the turbulent air flows has been proved to be an efficient method to provide numerical forecasting results. However, due to the reduced space on which the model operates, the solution includes uncertainties. Additionally, any computational methodology contributes to uncertainty due to finite precision and the consequent accumulation and amplification of round-off errors. Taking into account these uncertainties is essential for the acceptance of any numerical simulation. In this paper we combine the NIROM method with Data Assimilation (DA), the main question is how to incorporate data (e.g. from physical measurements) in models in a suitable way, in order to improve model predictions and quantify prediction uncertainty. Here, the focus is on the prediction of nonlinear dynamical systems (the classical application example being weather forecasting). DA is an uncertainty quantification technique used to incorporate observed data into a prediction model in order to improve numerical forecasted results. The Reduced Order Data Assimilation (RODA) model we propose in this paper achieves both efficiency and accuracy including Variational DA into NIROM. The model we present is applied to the pollutant dispersion within an urban environment.
Cheng S, Pain CC, Guo Y-K, et al., 2023, Real-time updating of dynamic social networks for COVID-19 vaccination strategies., J Ambient Intell Humaniz Comput, Pages: 1-14, ISSN: 1868-5137
Vaccination strategy is crucial in fighting the COVID-19 pandemic. Since the supply is still limited in many countries, contact network-based interventions can be most powerful to set an efficient strategy by identifying high-risk individuals or communities. However, due to the high dimension, only partial and noisy network information can be available in practice, especially for dynamic systems where contact networks are highly time-variant. Furthermore, the numerous mutations of SARS-CoV-2 have a significant impact on the infectious probability, requiring real-time network updating algorithms. In this study, we propose a sequential network updating approach based on data assimilation techniques to combine different sources of temporal information. We then prioritise the individuals with high-degree or high-centrality, obtained from assimilated networks, for vaccination. The assimilation-based approach is compared with the standard method (based on partially observed networks) and a random selection strategy in terms of vaccination effectiveness in a SIR model. The numerical comparison is first carried out using real-world face-to-face dynamic networks collected in a high school, followed by sequential multi-layer networks generated relying on the Barabasi-Albert model emulating large-scale social networks with several communities.
Chen S, Tai Z, Liu J, 2023, Barriers, Facilitators, and Sustainers in Tai Ji Quan Practice: A Mixed-Methods RE-AIM Assessment of College Students Versus the General Population, JOURNAL OF PHYSICAL ACTIVITY & HEALTH, Vol: 20, Pages: 239-249, ISSN: 1543-3080
Kumar P, Zavala-Reyes JC, Kalaiarasan G, et al., 2023, Characteristics of fine and ultrafine aerosols in the London underground., Science of the Total Environment, Vol: 858, ISSN: 0048-9697
Underground railway systems are recognised spaces of increased personal pollution exposure. We studied the number-size distribution and physico-chemical characteristics of ultrafine (PM0.1), fine (PM0.1-2.5) and coarse (PM2.5-10) particles collected on a London underground platform. Particle number concentrations gradually increased throughout the day, with a maximum concentration between 18:00 h and 21:00 h (local time). There was a maximum decrease in mass for the PM2.5, PM2.5-10 and black carbon of 3.9, 4.5 and ~ 21-times, respectively, between operable (OpHrs) and non-operable (N-OpHrs) hours. Average PM10 (52 μg m-3) and PM2.5 (34 μg m-3) concentrations over the full data showed levels above the World Health Organization Air Quality Guidelines. Respiratory deposition doses of particle number and mass concentrations were calculated and found to be two- and four-times higher during OpHrs compared with N-OpHrs, reflecting events such as train arrival/departure during OpHrs. Organic compounds were composed of aromatic hydrocarbons and polycyclic aromatic hydrocarbons (PAHs) which are known to be harmful to health. Specific ratios of PAHs were identified for underground transport that may reflect an interaction between PAHs and fine particles. Scanning transmission electron microscopy (STEM) chemical maps of fine and ultrafine fractions show they are composed of Fe and O in the form of magnetite and nanosized mixtures of metals including Cr, Al, Ni and Mn. These findings, and the low air change rate (0.17 to 0.46 h-1), highlight the need to improve the ventilation conditions.
Wu X, Abubakar-Waziri H, Fang F, et al., 2023, Modeling for understanding of coronavirus disease-2019 (COVID-19) spread and design of an isolation room in a hospital, Physics of Fluids, Vol: 35, Pages: 1-9, ISSN: 1070-6631
We have modeled the transmission of coronavirus 2019 in the isolation room of a patient suffering from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the Royal Brompton Hospital in London. An adaptive mesh computational fluid dynamics model was used for simulation of three-dimensional spatial distribution of SARS-CoV-2 in the room. The modeling set-up is based on data collected in the room during the patient stay. Many numerical experiments have been carried out to provide an optimal design layout of the overall isolation room. Our focus has been on (1) the location of the air extractor and filtration rates, (2) the bed location of the patient, and (3) consideration of the health and safety of the staff working in the area.
Venkateshwaran A, Kumar M, Kumar MBS, et al., 2023, Numerical study of the effect of geometry on the behaviour of internally heated melt pools for in-vessel melt retention, PROGRESS IN NUCLEAR ENERGY, Vol: 156, ISSN: 0149-1970
Cheng S, Chen J, Anastasiou C, et al., 2023, Generalised latent assimilation in heterogeneous reduced spaces with machine learning surrogate models, Journal of Scientific Computing, Vol: 94, Pages: 1-37, ISSN: 0885-7474
Reduced-order modelling and low-dimensional surrogate models generated using machine learning algorithms have been widely applied in high-dimensional dynamical systems to improve the algorithmic efficiency. In this paper, we develop a system which combines reduced-order surrogate models with a novel data assimilation (DA) technique used to incorporate real-time observations from different physical spaces. We make use of local smooth surrogate functions which link the space of encoded system variables and the one of current observations to perform variational DA with a low computational cost. The new system, named generalised latent assimilation can benefit both the efficiency provided by the reduced-order modelling and the accuracy of data assimilation. A theoretical analysis of the difference between surrogate and original assimilation cost function is also provided in this paper where an upper bound, depending on the size of the local training set, is given. The new approach is tested on a high-dimensional (CFD) application of a two-phase liquid flow with non-linear observation operators that current Latent Assimilation methods can not handle. Numerical results demonstrate that the proposed assimilation approach can significantly improve the reconstruction and prediction accuracy of the deep learning surrogate model which is nearly 1000 times faster than the CFD simulation.
Silva VLS, Heaney CE, Li Y, et al., 2022, Data assimilation predictive GAN (DA-PredGAN) applied to a spatio-temporal compartmental model in epidemiology, Journal of Scientific Computing, Vol: 94, ISSN: 0885-7474
We propose a novel use of generative adversarial networks (GANs) (i) to make predictions in time (PredGAN) and (ii) to assimilate measurements (DA-PredGAN). In the latter case, we take advantage of the natural adjoint-like properties of generative models and the ability to simulate forwards and backwards in time. GANs have received much attention recently, after achieving excellent results for their generation of realistic-looking images. We wish to explore how this property translates to new applications in computational modelling and to exploit the adjoint-like properties for efficient data assimilation. We apply these methods to a compartmental model in epidemiology that is able to model space and time variations, and that mimics the spread of COVID-19 in an idealised town. To do this, the GAN is set within a reduced-order model, which uses a low-dimensional space for the spatial distribution of the simulation states. Then the GAN learns the evolution of the low-dimensional states over time. The results show that the proposed methods can accurately predict the evolution of the high-fidelity numerical simulation, and can efficiently assimilate observed data and determine the corresponding model parameters.
Kumar P, Kalaiarasan G, Bhagat RK, et al., 2022, Active air monitoring for understanding the ventilation and infection risks of SARS-CoV-2 transmission in public indoor spaces, Atmosphere, Vol: 13, Pages: 1-24, ISSN: 2073-4433
Indoor, airborne, transmission of SARS-CoV-2 is a key infection route. We monitored fourteen different indoor spaces in order to assess the risk of SARS-CoV-2 transmission. PM2.5 and CO2 concentrations were simultaneously monitored in order to understand aerosol exposure and ventilation conditions. Average PM2.5 concentrations were highest in the underground station (261 ± 62.8 μgm−3), followed by outpatient and emergency rooms in hospitals located near major arterial roads (38.6 ± 20.4 μgm−3), the respiratory wards, medical day units and intensive care units recorded concentrations in the range of 5.9 to 1.1 μgm−3. Mean CO2 levels across all sites did not exceed 1000 ppm, the respiratory ward (788 ± 61 ppm) and the pub (bar) (744 ± 136 ppm) due to high occupancy. The estimated air change rates implied that there is sufficient ventilation in these spaces to manage increased levels of occupancy. The infection probability in the medical day unit of hospital 3, was 1.6-times and 2.2-times higher than the emergency and outpatient waiting rooms in hospitals 4 and 5, respectively. The temperature and relative humidity recorded at most sites was below 27 °C, and 40% and, in sites with high footfall and limited air exchange, such as the hospital medical day unit, indicate a high risk of airborne SARS-CoV-2 transmission.
Kadeethum T, O'Malley D, Ballarin F, et al., 2022, Enhancing high-fidelity nonlinear solver with reduced order model, SCIENTIFIC REPORTS, Vol: 12, ISSN: 2045-2322
Wu P, Qiu F, Feng W, et al., 2022, A non-intrusive reduced order model with transformer neural network and its application, PHYSICS OF FLUIDS, Vol: 34, ISSN: 1070-6631
Xiang J, Chen B, Latham J-P, et al., 2022, Numerical simulation of rock erosion performance of a high-speed water jet using an immersed-body method, INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES, Vol: 158, ISSN: 1365-1609
Phillips T, Heaney CE, Benmoufok E, et al., 2022, Multi-Output Regression with Generative Adversarial Networks (MOR-GANs), Applied Sciences, Vol: 12, Pages: 1-24, ISSN: 2076-3417
Regression modelling has always been a key process in unlocking the relationships betweenindependent and dependent variables that are held within data. In recent years, machine learninghas uncovered new insights in many fields, providing predictions to previously unsolved problems.Generative Adversarial Networks (GANs) have been widely applied to image processing producinggood results, however, these methods have not often been applied to non-image data. Seeing thepowerful generative capabilities of the GANs, we explore their use, here, as a regression method. Inparticular, we explore the use of the Wasserstein GAN (WGAN) as a multi-output regression method.The resulting method we call Multi-Output Regression GANs (MOR-GANs) and its performanceis compared to a Gaussian Process Regression method (GPR) - a commonly used non-parametricregression method that has been well tested on small datasets with noisy responses. The WGANregression model performs well for all types of datasets and exhibits substantial improvements overthe performance of the GPR for certain types of datasets, demonstrating the flexibility of the GAN asa model for regression.
Woodward H, Schroeder A, Le Cornec C, et al., 2022, High resolution modelling of traffic emissions using the large eddy simulation code Fluidity, Atmosphere, Vol: 13, ISSN: 2073-4433
The large eddy simulation (LES) code Fluidity was used to simulate the dispersion of NOx traffic emissions along a road in London. The traffic emissions were represented by moving volume sources, one for each vehicle, with time-varying emission rates. Traffic modelling software was used to generate the vehicle movement, while an instantaneous emissions model was used to calculate the NOx emissions at 1 s intervals. The traffic emissions were also modelled as a constant volume source along the length of the road for comparison. A validation of Fluidity against wind tunnel measurements is presented before a qualitative comparison of the LES concentrations with measured roadside concentrations. Fluidity showed an acceptable comparison with the wind tunnel data for velocities and turbulence intensities. The in-canyon tracer concentrations were found to be significantly different between the wind tunnel and Fluidity. This difference was explained by the very high sensitivity of the in-canyon tracer concentrations to the precise release location. Despite this, the comparison showed that Fluidity was able to provide a realistic representation of roadside concentration variations at high temporal resolution, which is not achieved when traffic emissions are modelled as a constant volume source or by Gaussian plume models.
Heaney C, Liu X, Go H, et al., 2022, Extending the capabilities of data-driven reduced-order models to make predictions for unseen scenarios: applied to flow around buildings, Frontiers in Physics, Vol: 10, Pages: 1-16, ISSN: 2296-424X
We present a data-driven or non-intrusive reduced-order model (NIROM) which is capable of making predictions for a significantly larger domain than the one used to generate the snapshots or training data. This development relies on the combination of a novel way of sampling the training data (which frees the NIROM from its dependency on the original problem domain) and a domain decomposition approach (which partitions unseen geometries in a manner consistent with the sub-sampling approach). The method extends current capabilities of reduced-order models to generalise, i.e., to make predictions for unseen scenarios. The method is applied to a 2D test case which simulates the chaotic time-dependent flow of air past buildings at a moderate Reynolds number using a computational fluid dynamics (CFD) code. The procedure for 3D problems is similar, however, a 2D test case is considered sufficient here, as a proof-of-concept. The reduced-order model consists of a sampling technique to obtain the snapshots; a convolutional autoencoder for dimensionality reduction; an adversarial network for prediction; all set within a domain decomposition framework. The autoencoder is chosen for dimensionality reduction as it has been demonstrated in the literature that these networks cancompress information more efficiently than traditional (linear) approaches based on singular value decomposition. In order to keep the predictions realistic, properties of adversarial networks are exploited. To demonstrate its ability to generalise, once trained, the method is applied to a larger domain which has a different arrangement of buildings. Statistical properties of the flows from the reduced order model are compared with those from the CFD model in order to establish how realistic the predictions are.
Wu P, Pan K, Ji L, et al., 2022, Navier-stokes Generative Adversarial Network: a physics-informed deep learning model for fluid flow generation, NEURAL COMPUTING & APPLICATIONS, Vol: 34, Pages: 11539-11552, ISSN: 0941-0643
Buchan AG, Cacuci DG, Dargaville S, et al., 2022, Optimised Adjoint Sensitivity Analysis Using Adjoint Guided Mesh Adaptivity Applied to Neutron Detector Response Calculations, ENERGIES, Vol: 15
Hamzehloo A, Bahlali ML, Salinas P, et al., 2022, Modelling saline intrusion using dynamic mesh optimization with parallel processing, ADVANCES IN WATER RESOURCES, Vol: 164, ISSN: 0309-1708
Heaney CE, Wolffs Z, Tómasson JA, et al., 2022, An AI-based non-intrusive reduced-order model for extended domains applied to multiphase flow in pipes, Physics of Fluids, Vol: 34, Pages: 1-22, ISSN: 1070-6631
The modeling of multiphase flow in a pipe presents a significant challenge for high-resolution computational fluid dynamics (CFD) models due to the high aspect ratio (length over diameter) of the domain. In subsea applications, the pipe length can be several hundreds of meters vs a pipe diameter of just a few inches. Approximating CFD models in a low-dimensional space, reduced-order models have been shown to produce accurate results with a speed-up of orders of magnitude. In this paper, we present a new AI-based non-intrusive reduced-order model within a domain decomposition framework (AI-DDNIROM), which is capable of making predictions for domains significantly larger than the domain used in training. This is achieved by (i) using a domain decomposition approach; (ii) using dimensionality reduction to obtain a low-dimensional space in which to approximate the CFD model; (iii) training a neural network to make predictions for a single subdomain; and (iv) using an iteration-by-subdomain technique to converge the solution over the whole domain. To find the low-dimensional space, we compare Proper Orthogonal Decomposition with several types of autoencoder networks, known for their ability to compress information accurately and compactly. The comparison is assessed with two advection-dominated problems: flow past a cylinder and slug flow in a pipe. To make predictions in time, we exploit an adversarial network, which aims to learn the distribution of the training data, in addition to learning the mapping between particular inputs and outputs. This type of network has shown the potential to produce visually realistic outputs. The whole framework is applied to multiphase slug flow in a horizontal pipe for which an AI-DDNIROM is trained on high-fidelity CFD simulations of a pipe of length 10 m with an aspect ratio of 13:1 and tested by simulating the flow for a pipe of length 98 m with an aspect ratio of almost 130:1. Inspection of the predicted liquid volume
Cheng M, Fang F, Navon IM, et al., 2022, Spatio-temporal hourly and daily ozone forecasting in china using a hybrid machine learning model: autoencoder and generative adversarial networks, Journal of Advances in Modeling Earth Systems, Vol: 14, Pages: 1-26, ISSN: 1942-2466
Efficient and accurate real-time forecasting of national spatial ozone distribution is critical to the provision of effective early warning. Traditional numerical air quality models require a high computational cost associated with running large-scale numerical simulations. In this work, we introduce a hybrid model (VAE-GAN) combining a generative adversarial network (GAN) with a variational autoencoder (VAE) to learn the dynamic ozone distributions in spatial and temporal spaces. The VAE-GAN model can not only decipher the complex nonlinear relationship between the inputs (the past states/ozone and meteorological factors) and outputs (ozone), but also provide ozone forecasts for a long lead-time beyond the training period. The performance of VAE-GAN is demonstrated in hourly and daily spatio-temporal ozone forecasts over China. The training datasets from 2013 to 2017 and validation datasets from 2018 to 2019 are the collection of data from the air quality reanalysis datasets. With the use of VAE, large dataset sizes are decreased by three orders of magnitude, enabling hourly and daily forecasts to be computed in seconds. Results show that the VAE-GAN achieves a reasonable accuracy in the prediction of both the spatial and temporal evolution patterns of hourly and daily ozone fields, as compared to the Nested Air Quality Prediction Modeling System (commonly used in China), the reanalysis data and observations during the validation period. Thus, the VAE-GAN is a cost-effective tool for large data-driven predictions, which can potentially reinforce air pollution prediction efforts in providing risk assessment and management in a timely manner.
Silva V, Regnier G, Salinas P, et al., 2022, Rapid modelling of reactive transport in porous media using machine learning
Reactive transport in porous media can play an important role in a variety of processes in subsurface reservoirs, such as groundwater flow, geothermal heat production, oil recovery and CO2 storage. However, numerical solution of fluid flow in porous media coupled with chemical reaction is very computationally demanding. Simultaneously, the success of machine learning in different fields has opened up new possibilities in reactive transport simulations. In this project, we focus on using machine learning techniques to replace the geochemical kinetic calculations generated by PHREEQC. PHREEQC is an open-source aqueous geochemical code that can be used in stand-alone mode or as a reaction module coupled with a flow and transport simulator. Here, we apply machine learning approaches to produce a fast proxy model of PHREEQC. This enables us to have a coupling between transport and reaction while minimizing the added computational cost. We focus initially on calcite dissolution during CO2 sequestration. Different machine learning techniques are investigated and compared to see which is more appropriate for the calcite dissolution problem. The proposed machine learning approach is designed to deal with different time-step sizes and unstructured elements. It accelerates the numerical simulation and proves to be practical to replace the reaction model presented in PHREEQC. This considerably reduces the computational cost of reactive transport while ensuring excellent simulation accuracy. The rapid modelling of reactive transport in porous media has a broad potential to replace many other phase equilibrium models across a wide range of reactive transport problems.
Arcucci R, Casas CQ, Joshi A, et al., 2022, Merging Real Images with Physics Simulations via Data Assimilation, 27th International European Conference on Parallel and Distributed Computing (Euro-Par), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 255-266, ISSN: 0302-9743
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