Simulation driven machine learning methods to optimise design of physical experiments and enhance data analysis for testing of fusion energy heat exchanger components

Plasma facing components (PFCs) must be designed to routinely withstand the harsh environment of a fusion device, where temperatures at the core of the plasma exceed 150,000,000 °C. The heat by induction to verify extremes (HIVE) experimental facility was established to replicate the thermal loads a PFC is subjected to during normal operation of a fusion device.To maximise its impact on the design of PFCs, HIVE must deliver smarter testing and improved component insight. Currently, the experimental parameters required to deliver a certain response to the component are decided at the point of testing through a combination of previous experience, intuition, and trial & error, which is both time-consuming and unreliable. To assess a PFC’s suitability, knowledge of its mechanical performance while operating at high temperatures is desirable, however HIVE only records pointwise temperature measurements on the component’s surface using thermocouples. Currently, HIVE has no method of inferring a component’s mechanical response using the temperature measure-ments.Both the challenges of smarter testing and improved component insight can be achieved through the identification of inverse solutions. A popular approach to solving engineering inverse problems is surrogate assisted optimisation, where a machine learning model is trained using finite element (FE) simulation data. Much of the work in literature use single value surrogate models on quite simplistic problems, however HIVE is a real-world, multi-physics problem which requires full field (FF) surrogate models to solve its multitude of inverse problems.The development of a method which can easily construct FE data driven FF surrogates would be invaluable for a variety of tasks in engineering, as well as solving inverse problems. In this work, it demonstrates that it can provide a much more robust and comprehensive method of characterising a PFC’s strengths and limitations, enabling more informed decisions to be made during its design cycle

Two example optimisation problems from the world of education

This work considers two distinct combinatorial optimisation problems related to education, namely lecture timetabling and school bus scheduling, both of which are known to be NP-hard. Our research into these problems has centred around the design of various high-performance heuristics that are able to produce good quality solutions to these problems in short amounts of time. To do this, we propose that it is necessary to “get to the heart” of these problems by identifying their underlying sub-problems. This, in turn, helps to inform the design of algorithmic operators that are able to exploit these structures and help to produce the solutions we need. In this extended abstract these problems are briefly considered in turn

A comparison of Dijkstra's Algorithm using fibonacci heaps, binary heaps, and self-balancing binary trees

This paper describes the shortest path problem in weighted graphs and examines the differences in efficiency that occur when using Dijkstra's algorithm with a Fibonacci heap, binary heap, and self-balancing binary tree. Using C++ implementations of these algorithm variants, we find that the fastest method is not always the one that has the lowest asymptotic complexity. Reasons for this are discussed and backed with empirical evidence

Multiscale computational study to predict the irradiation-induced change in engineering properties of fusion reactor materials

In this study, we address the impact of irradiation conditions in a tokamak on the engineering properties of materials, leading to potential degradation of in-vessel components over their lifecycle. Our approach involves a predictive model for irradiation-induced damage, employing a multiscale computational framework. This framework integrates various simulation techniques, including Monte Carlo-based neutronics (OpenMC), dislocation dynamics (DD) using MoDELib, and finite element analysis (FEA) with Code_Aster. This integration offers a versatile solver capable of analysing tokamak components exposed to different irradiation doses and temperature conditions. To showcase the utility of this multiscale computational framework, we present a case study focused on tungsten monoblock designs. We assess the failure probabilities of these designs at different stages of their lifecycle. Neutron heating and damage energy values are obtained from OpenMC neutronics simulations. The neutron heating values serve as volumetric heat sources for the FEA thermal simulation. We calculate the displacement per atom (dpa) across the monoblock at various full power days (day 0, day 100, and day 1000) using the damage energy values. The irradiation-induced defect densities, dependent on temperature and dpa, are inputs to DD microstructural simulations performed on the representative volume element (RVE) using MoDELib. This allows us to obtain the yield stress of the material. Subsequently, the thermal fields from the FEA thermal simulation, along with the dpa and temperature-dependent yield stress from the DD simulation, are implemented for FEA mechanical simulations. To evaluate the failure probability of the monoblock designs at different stages of their lifecycle, we conduct an SDC-IC assessment, incorporating a plastic flow localization rule within the current framework. This comprehensive approach provides insights into the thermo-mechanical behaviour of in-vessel components subjected to neutron irradiation, offering a predictive capability for assessing their performance over time

Path planning in payment channel networks with multi-party channels

Payment Channel Networks (PCNs) provide a means to improve the scaling of cryptocurrency payments by allowing peers to make payments between themselves in an efficient manner. To make a payment between two peers, the task of path planning must first be performed to determine a path in the PCN connecting the peers in question before the payment in question is performed using this path. To date, existing research has focused on the problem of performing path planning in PCNs that contain two-party channels. It has been hypothesised that the scaling of PCNs could be further improved by considering the inclusion of multi-party channels that contain more than two peers. However, the problem of performing path planning in PCNs that contain multi-party channels has not yet been considered. In this article, we address this gap in the research literature and propose a novel path planning method for PCNs containing multi-party channels. This method involves modelling the PCN with multi-party channels as a hypergraph, a type of graph where edges can contain two or more vertices, and using this model to solve the path planning problem in question. We prove that the proposed method is correct and computationally efficient. Furthermore, assuming path planning is performed using this method, we also present theoretical and experimental analyses that demonstrate the scaling benefits of using multi-party channels

The "engaged" interaction: important considerations for the HCI design and development of a web application for solving a complex combinatorial optimization problem

As the boundaries of aesthetics are broadening out to take on board and appreciate the experiences of new technologies, so too, the designers and developers of these new technologies are realising the power of aesthetics to create ‘intended’ human computer interaction (HCI) experiences. In this paper, the interest lies in the ‘engaged’ interaction and what actually needs to be harnessed between the aesthetic and the algorithm to ensure the ‘intended’ HCI experience is achieved. The paper will focus on the design and development of an interactive website for solving a real world combinatorial optimization problem. Its main contribution lies in its investigation into the ‘engaged’ interaction whilst highlighting how we really need to understand and appreciate the interface between the aesthetic and the algorithm in order to fully get to the heart of HCI experience

Evaluating the influence of parameter setup on the performance of heuristics for the graph colouring problem

This paper aims to analyse the influence of parameter setup over a set of five heuristic methods applied to the graph colouring problem.rnEach heuristic is applied to a considerable set of problem instances, using a range of different parameter values.rnMultidimensional analysis is applied to extract and express knowledge about the performance of heuristic methods according to problem instance feature values, highlighting the effect of different parameter setups.rnThe dynamic behaviour of the heuristics is also evaluated at different stages of execution (runtime), providing additional knowledge about speed of convergence/stagnation.rnResults demonstrate that it is possible to associate regions of the instance space in which problem instances exhibit particular features with specific parameter values yielding superior performance.rnInformation relating runtime with average rate of solution improvement also suggests that certain instance features can be used to determine for how long the heuristics need to run before they converge or stagnate

Post enrolment based course timetabling: a description of the problem model used for track two of the second International Timetabling Competition

In this paper we give a detailed description of the problem model used in track-two of the second International Timetabling Competition, 2007-2008 www.cs.qub.ac.uk/itc2007/). This model is an extension of that used in the first timetabling competition, and we discuss the rationales behind these extensions. We also describe in detail the criteria that are used for judging solution quality and discuss other issues that are related to this. Finally we go over some of the strengths and limitations of the model. This paper can be regarded as the official documentation for track-two of the timetabling competition