Reduced-Order Modelling Applied to the Multigroup Neutron Diffusion Equation Using a Nonlinear Interpolation Method for Control-Rod Movement

Producing high-fidelity real-time simulations of neutron diffusion in a reactor is computationally extremely challenging, due, in part, to multiscale behaviour in energy and space. In many scientific fields, including nuclear modelling, the application of reduced-order modelling can lead to much faster computation times without much loss of accuracy, paving the way for real-time simulation as well as multi-query problems such as uncertainty quantification and data assimilation. This paper compares two reduced-order models that are applied to model the movement of control rods in a fuel assembly for a given temperature profile. The first is a standard approach using proper orthogonal decomposition (POD) to generate global basis functions, and the second, a new method, uses POD but produces global basis functions that are local in the parameter space (associated with the control-rod height). To approximate the eigenvalue problem in reduced space, a novel, nonlinear interpolation is proposed for modelling dependence on the control-rod height. This is seen to improve the accuracy in the predictions of both methods for unseen parameter values by two orders of magnitude for keff and by one order of magnitude for the scalar flux

Solving the discretised neutron diffusion equations using neural networks

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 libraries in this way is that one can exploit their built-in technologies to enable the same code to run on different computer architectures (such as central processing units, graphics processing units or new-generation AI 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 network implementation run on a CPU and a GPU, and a serial Fortran code run on a CPU

The importance of children and young person involvement in scoping the need for a paediatric glucocorticoid-associated patient reported outcome measure.

BackgroundFor many children and young people (CYP) with paediatric rheumatic conditions, glucocorticoid medications and their associated side-effects have a substantial impact on disease experience. Whilst there are physician-rated measures of glucocorticoid toxicity, no parallel patient reported measure has been developed to date for CYP with rheumatic disease. This manuscript describes a series of public patient involvement (PPI) events to inform the development of a future paediatric glucocorticoid-associated patient reported outcome measure (PROM).MethodsOne large group PPI event was advertised to CYP with experience of glucocorticoid medication use and their parents through clinicians, charities and existing PPI groups. This featured education on the team's research into glucocorticoid medication and interactive polls/structured discussion to help participants share their experiences. Further engagement was sought for PPI group work to co-develop future glucocorticoid studies, including development of a glucocorticoid associated PROM. Quantitative and qualitative feedback was collected from online questionnaires. The initiative was held virtually due to the Covid-19 pandemic.ResultsNine families (n = 15) including 6 CYP joined the large group PPI event. Online pre-attendance and post-attendance questionnaires showed improvement in mean self-reported confidence [1 = not at all confident, 5 = very confident] in the following: what steroid medications are (pre = 3.9, post = 4.8), steroid side effects (pre = 3.8, post = 4.6), patient-reported outcome measures (pre = 2.0, post = 4.5), available research on steroids (pre = 2.2, post = 3.5). Five families (n = 7) were involved in a monthly PPI group who worked alongside the research team to identify priorities in glucocorticoid research, produce age-appropriate study materials, identify barriers to study participation (e.g. accessibility & convenience) and recommend appropriate modalities for dissemination. The participants found discussing shared experiences and learning about research to be the most enjoyable aspects of the initiative.ConclusionsThis PPI initiative provided a valuable forum for families, including young children, to share their perspectives. Here, the authors explore the effective use of PPI in a virtual setting and provide a unique case study for the involvement of CYP in PROM development. The monthly PPI group also identified a need for the development of a new PROM related to glucocorticoid medication use and provided unique insights into how such a study could be structured

Data assimilation predictive GAN (DA-PredGAN) applied to a spatio-temporal compartmental model in epidemiology

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

A domain decomposition non-intrusive reduced order model for turbulent flows

In this paper, a new Domain Decomposition Non-Intrusive Reduced Order Model (DDNIROM) is developed for turbulent flows. The method works by partitioning the computational domain into a number of subdomains in such a way that the summation of weights associated with the finite element nodes within each subdomain is approximately equal, and the communication between subdomains is minimised. With suitably chosen weights, it is expected that there will be approximately equal accuracy associated with each subdomain. This accuracy is maximised by allowing the partitioning to occur through areas of the domain that have relatively little flow activity, which, in this case, is characterised by the pointwise maximum Reynolds stresses.A Gaussian Process Regression (GPR) machine learning method is used to construct a set of local approximation functions (hypersurfaces) for each subdomain. Each local hypersurface represents not only the fluid dynamics over the subdomain it belongs to, but also the interactions of the flow dynamics with the surrounding subdomains. Thus, in this way, the surrounding subdomains may be viewed as providing boundary conditions for the current subdomain.We consider a specific example of turbulent air flow within an urban neighbourhood at a test site in London and demonstrate the effectiveness of the proposed DDNIROM

Behçet's syndrome in children and young people in the United Kingdom & Republic of Ireland: a prospective epidemiological study

OBJECTIVES: To define the incidence and prevalence of Behçet's syndrome (BS) in children and young people (CYP) up to the age of 16 years in the United Kingdom (UK) and Republic of Ireland (ROI). METHODS: A prospective epidemiological study was undertaken with the support of the British Paediatric Surveillance Unit (BPSU) and the British Society of Paediatric Dermatologists (BSPD). Consultants reported anonymised cases of BS seen. A follow-up study at one year examined progression of disease and treatment. RESULTS: Over a two-year period, 56 cases met International Criteria for Behçet's Disease. For children under 16 years of age, the two-year period prevalence estimate was 4.2 per million (95% CI 3.2-5.4) and the incidence was 0.96 per million person years (95% CI 0.66-1.41). Mucocutaneous disease was the most common phenotype (56/100%), with ocular (10/56; 17.9%), neurological (2/56; 3.6%) and vascular involvement (3/56; 5.4%) being less common. Median age at onset was 6.34 years and at diagnosis was 11.72 years. There were slightly more female than male children reported (32/56; 55.6%). The majority of cases (85.7%) were white Caucasian. Apart from genital ulcers, which were more common in females, there were no significant differences in frequency of manifestations between male or females, nor between ethnicities. Over 83% of cases had three or more non-primary care healthcare professionals involved in their care. CONCLUSION: BS is extremely rare in CYP in the UK and ROI and most have mucocutaneous disease. Healthcare needs are complex, and coordinated care is key

"I feel so stupid because I can't give a proper answer ..." How older adults describe chronic pain: a qualitative study

Background - Over 50% of older adults experience chronic pain. Poorly managed pain threatens independent functioning, limits social activities and detrimentally affects emotional wellbeing. Yet, chronic pain is not fully understood from older adults’ perspectives; subsequently, pain management in later life is not necessarily based on their priorities or needs. This paper reports a qualitative exploration of older adults’ accounts of living with chronic pain, focusing on how they describe pain, with a view to informing approaches to its assessment. Methods - Cognitively intact men and women aged over sixty-five who lived in the community opted into the study through responding to advertisements in the media and via contacts with groups and organisations in North-East Scotland. Interviews were transcribed and thematically analysed using a framework approach. Results - Qualitative individual interviews and one group interview were undertaken with 23 older adults. Following analysis, the following main themes emerged: diversity in conceptualising pain using a simple numerical score; personalising the meaning of pain by way of stories, similes and metaphors; and, contextualising pain in relation to its impact on activities. Conclusions - The importance of attending to individuals’ stories as a meaningful way of describing pain for older adults is highlighted, suggesting that a narrative approach, as recommended and researched in other areas of medicine, may usefully be applied in pain assessment for older adults. Along with the judicious use of numerical tools, this requires innovative methods to elicit verbal accounts, such as using similes and metaphors to help older adults describe and discuss their experience, and contextualising the effects of pain on activities that are important to them

An AI-based non-intrusive reduced-order model for extended domains applied to multiphase flow in pipes

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 fractions shows a good match with the high fidelity model as shown in the results. Statistics of the flows obtained from the CFD simulations are compared to those of the AI-DDNIROM predictions to demonstrate the accuracy of our approach

A reduced order model for turbulent flows in the urban environment using machine learning

To help create a comfortable and healthy indoor and outdoor environment in which to live, there is a need to understand turbulent air flows within the urban environment. To this end, building on a previously reported method [1], we develop a fast-running Non-Intrusive Reduced Order Model (NIROM) for predicting the turbulent air flows found within an urban environment. To resolve larger scale turbulent fluctuations, we employ a Large Eddy Simulation (LES) model and solve the resulting computational model on unstructured meshes. The objective is to construct a rapid-running NIROM from these results that will have ‘similar’ dynamics to the original LES model. Based on Proper Orthogonal Decomposition (POD) and machine learning techniques, this Reduced Order Model (ROM) is six orders of magnitude faster than the high-fidelity LES model and we demonstrate how ‘similar’ it can be to the high-fidelity model by comparing statistical quantities such as the mean flows, Reynolds stresses and probability densities of the velocities. We also include validation of the high-fidelity model against data from wind tunnel experiments.This paper represents a key step towards the use of reduced order modelling for operational purposes with the tantalising possibility of it being used in place of Gaussian plume models, and the potential for greatly improved model fidelity and confidence

Priorities in Chronic nonbacterial osteomyelitis (CNO) - results from an international survey and roundtable discussions

ObjectiveChronic nonbacterial osteomyelitis (CNO) is an autoinflammatory bone disorder that predominantly affects children and young people. The pathophysiology and molecular mechanisms of CNO remain poorly understood, and diagnostic criteria and biomarkers are lacking. As a result, treatment is empiric and follows personal experience, case series and expert consensus plans.MethodsA survey was designed to gain insight on clinician and patient experiences of diagnosing and treating CNO and to collate opinions on research priorities. A version containing 24 questions was circulated among international expert clinicians and clinical academics (27 contacted, 21 responses). An equivalent questionnaire containing 20 questions was shared to explore the experience and priorities of CNO patients and family members (93 responses).ResultsResponses were used to select topics for four moderated roundtable discussions at the "International Conference on CNO and autoinflammatory bone disease" (Liverpool, United Kingdom, May 25-26th, 2022). The group identified deciphering the pathophysiology of CNO to be the highest priority, followed by clinical trials, necessary outcome measures and classification criteria. Surprisingly, mental wellbeing scored behind these items.ConclusionsAgreement exists among clinicians, academics, patients and families that deciphering the pathophysiology of CNO is of highest priority to inform clinical trials that will allow for the approval of medications for the treatment of CNO by regulatory agencies