GAN for time series prediction, data assimilation and uncertainty quantification

We propose a new method in which a generative adversarial network (GAN) is used to quantify the uncertainty of forward simulations in the presence of observed data. Previously, a method has been developed which enables GANs to make time series predictions and data assimilation by training a GAN with unconditional simulations of a high-fidelity numerical model. After training, the GAN can be used to predict the evolution of the spatial distribution of the simulation states and observed data is assimilated. In this paper, we describe the process required in order to quantify uncertainty, during which no additional simulations of the high-fidelity numerical model are required. These methods take advantage of the adjoint-like capabilities of generative models and the ability to simulate forwards and backwards in time. Set within a reduced-order model framework for efficiency, we apply these methods to a compartmental model in epidemiology to predict the spread of COVID-19 in an idealised town. The results show that the proposed method can efficiently quantify uncertainty in the presence of measurements using only unconditional simulations of the high-fidelity numerical model.Comment: arXiv admin note: text overlap with arXiv:2105.0772

An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusion

Using an autoencoder for dimensionality reduction, this paper presents a novel projection-based reduced-order model for eigenvalue problems. Reduced-order modelling 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.Comment: 35 pages, 33 figure

Data Assimilation Predictive GAN (DA-PredGAN): applied to determine the spread of COVID-19

We propose the novel use of a generative adversarial network (GAN) (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. To predict the spread of COVID-19 in an idealised town, we apply these methods to a compartmental model in epidemiology that is able to model space and time variations. To do this, the GAN is set within a reduced-order model (ROM), 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

Numerical Modelling of Melt Behaviour in the Lower Vessel Head of a Nuclear Reactor

Acknowledgements The authors would like to thank the EPSRC MEMPHIS multi-phase programme grant, the EPSRC Computational modelling for advanced nuclear power plants project and the EU FP7 projects THINS and GoFastR for helping to fund this work.Peer reviewedPublisher PD

A one-dimensional mechanistic model for tracking unsteady slug flow.

A novel one-dimensional slug tracking mechanistic model for unsteady, upward gas-liquid slug flow in inclined pipes is presented. The model stems from the first principles of mass and momentum conservation applied to a slug unit cell consisting of a slug body of liquid and a region of stratified flow containing an elongated bubble and a liquid film. The slug body front and rear are treated as surfaces of discontinuity where mass and momentum balances or "jump laws"are prescribed. The former is commonly applied in mechanistic models for slug flow, whereas the latter is typically overlooked, thereby leading to the assumption of a continuous pressure profile at these points or to the adoption of a pressure drop due to the fluid acceleration on a heuristic basis. Our analysis shows that this pressure change arises formally from the momentum jump law at the slug body front. The flow is assumed to be isothermal, the gas is compressible, the pressure drop in the elongated bubble region is accounted for, the film thickness is considered uniform, and weight effects in the pressure from the interface level are included. Besides specifying momentum jump laws at both borders of the slug body, another novel feature of the present model is that we avoid adopting the quasi-steady approximation for the elongated bubble-liquid film region, and thus the unsteady terms in the mass and momentum balances are kept. The present model requires empirical correlations for the slug body length and the elongated bubble nose velocity. The non-linear equations are discretized and solved simultaneously for all the slug unit cells filling the pipe. Timespace variation of the slug body and film lengths, liquid holdup and void fraction, and pressures, among other quantities, can be predicted, and model performance is evaluated by comparing with data in the literature

Numerical Modelling of Debris Bed Water Quenching

Acknowledgements The authors would like to thank the EPSRC MEMPHIS multi-phase programme grant, the EPSRC Computational modelling for advanced nuclear power plants project, the EU FP7 projects THINS and GoFastR and ExxonMobil for helping to fund this work.Peer reviewedPublisher PD

Solving the discretised multiphase flow equations with interface capturing on structured grids using machine learning libraries

The authors would like to acknowledge the following EPSRC grants: the PREMIERE programme grant, “AI to enhance manufacturing, energy, and healthcare” (EP/T000414/1); ECO-AI, “Enabling CO capture and storage using AI” (EP/Y005732/1); MUFFINS, “MUltiphase Flow-induced Fluid-flexible structure InteractioN in Subsea” (EP/P033180/1); WavE-Suite, “New Generation Modelling Suite for the Survivability of Wave Energy Convertors in Marine Environments” (EP/V040235/1); INHALE, “Health assessment across biological length scales” (EP/T003189/1); AI-Respire, “AI for personalised respiratory health and pollution” (EP/Y018680/1); RELIANT, “Risk EvaLuatIon fAst iNtelligent Tool for COVID19” (EP/V036777/1); and CO-TRACE, “COvid-19 Transmission Risk Assessment Case Studies — education Establishments” (EP/W001411/1). Also, the authors acknowledge the Innovate UK grant D-XPERT, “AI-Powered Total Building Management System“ (TS/Y020324/1). Support from Imperial-X’s Eric and Wendy Schmidt Centre for AI in Science (a Schmidt Futures program) is gratefully acknowledged. The authors state that, for the purpose of open access, a Creative Commons Attribution (CC BY) license will be applied to any Author Accepted Manuscript version relating to this article.Peer reviewe

Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Models

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

A control volume finite element method for three-dimensional three-phase flows

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