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

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

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

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

The perceptions of social responsibility for community resilience to flooding: the impact of past experience, age, gender and ethnicity

Community resilience to flooding depends, to a large extent, on the participation of community members to take more responsibility for enhancing their own resilience. The perception of social responsibility (SR) which is argued to be one of the antecedents influencing individual’s willingness to undertake resilient behaviours can significantly contribute to community resilience through individual and collective actions. Understanding of factors influencing the perceptions of SR of individuals within community might help with developing strategies to increase the perceptions of SR. This research explores perceptions of SR in relation to flooding for householders and local businesses and establishes their relationships with experience of flooding and demographic factors of age, gender and ethnicity. The data were obtained via a questionnaire survey of three communities in Birmingham and one community in South East London, UK, three with experience of flooding and one without. A total of 414 responses were received and used in the multiple regression analysis. The analysis identified ‘experience of flooding’, ‘age’ and ‘South Asian’ ethnic group as significant variables, suggesting that older individuals from South Asian ethnic groups with previous experience of flooding are likely to be more socially responsible than others without these attributes

Corporate reputation past and future: a review and integration of existing literature and a framework for future research

The concept of corporate reputation is steadily growing in interest among management researchers and practitioners. In this article, we trace key milestones in the development of reputation literature over the past six decades to suggest important research gaps as well as to provide contextual background for a subsequent integration of approaches and future outlook. In particular we explore the need for better categorised outcomes; a wider range of causes; and a deeper understanding of contingencies and moderators to advance the field beyond its current state while also taking account of developments in the macro business environment. The article concludes by presenting a novel reputation framework that integrates insights from reputation theory and studies, outlines gaps in knowledge and offers directions for future research

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