In this paper, two design optimisation methods are evaluated using gradient-based optimisation for electric vehicle traction applications. A driving cycle-based approach is used to evaluate specific operational points for the design optimisation proc...
This work presents a strategy to build reduced-order models suitable for aerodynamic shape optimisation, resulting in a multifidelity optimisation framework. A reduced-order model (ROM) based on a discrete empirical interpolation (DEIM) method is emp...
Precipitation hardening is the primary mechanism for strengthening 6xxx series aluminium alloys. The characteristics of the precipitates play a crucial role in determining the mechanical properties. In particular, predicting yield strength (YS) based...
The structural design of aeronautical composite components requires numerical models which capture multilayer behaviour while keeping computational costs manageable. High-fidelity three-dimensional (3D) finite element models are often too expensive f...
This paper proposes JADEGBO, a hybrid gradient-based metaheuristic for solving complex single- and multi-constraint engineering design problems as well as cost-sensitive security optimisation tasks. The method combines Adaptive Differential Evolution...
Diffraction significantly deteriorates the quality of the laser image, causing severe degradation that undermines the theoretical performance parameters of the autofocus system. In this paper, we conduct a comprehensive analysis of the non-uniform fe...
Polarisation encoding is widely used in fibre-based Quantum Key Distribution (QKD), but random birefringence in optical fibres causes the transmitted states to drift, requiring active compensation at the receiver. Electronic Polarisation Controllers...
This study presents a gradient-based topology optimisation framework for heat sinks embedded with phase-change material (PCM) that targets the mitigation of temperature oscillations under cyclic thermal loads. The approach couples transient thermal d...
The optimisation of complex engineering design problems is highly challenging due to the consideration of various design variables. To obtain acceptable near-optimal solutions within reasonable computation time, metaheuristics can be employed for suc...
A physics-based optimisation framework is developed to investigate the potential advantages of novel technologies on the energy efficiency of a midrange passenger aircraft. In particular, the coupled-adjoint aerostructural analysis and optimisation t...
Particle swarm optimisation (PSO) is a swarm intelligence algorithm that finds candidate solutions by iteratively updating the positions of particles in a swarm. The decentralised optimisation methodology of PSO is ideally suited to problems with mul...
The objectives of this work are: (a) to present a new system for building heating which is based on underground energy storage, (b) to develop a mathematical model of the system, and (c) to optimise the energy performance of the system. The system in...
Characterisation and quantification of nonlinearities in the engineering structures include selecting and fitting a good mathematical model to a set of experimental vibration data with significant nonlinear features. These tasks involve solving an op...
Malliavin weight sampling (MWS) is a stochastic calculus technique for computing the derivatives of averaged system properties with respect to parameters in stochastic simulations, without perturbing the system’s dynamics. It applies to systems in or...
In order to decrease the emitted airframe noise by a two-dimensional high-lift configuration during take-off and landing performance, a morphing airfoil has been designed through a shape design optimisation procedure starting from a baseline airfoil...
The adjoint method is a very promising gradient-based optimisation framework for computational fluid dynamics (CFD), because of its independence of the computation cost from the number of design variables. With the aim of improving the robustness of...
The increasing global demand for air travel over the past three decades has led to heightened congestion, environmental concerns, and operational inefficiencies. This study explores the potential of commercial aircraft formation flight, inspired by t...
This article addresses the problem of increasing the energy efficiency of electromechanical systems driven by asynchronous electric drives. In this context, one of the promising areas is the application of a predictive control strategy that allows fo...
Predicting buckling in large-scale composite structures is hindered by the need for highly detailed Finite Element (FE) models, which are computationally expensive and impractical for early-stage design iterations. This study introduces a macromodell...
Topology optimisation is a powerful methodology for identifying efficient material distributions within prescribed design domains. However, conventional approaches rely heavily on gradient-based optimisation and repeated numerical simulations, which...
The extraction of tidal energy from head differences represents a predictable and flexible option for generating electricity. Here, we investigate the generation potential of prospective tidal power plants in the UK. Originally conceived as separate...
Coverage Path Planning (CPP) for fixed-wing aerial robots remains challenging in dynamic and partially unknown environments due to the need to simultaneously satisfy coverage completeness, kinematic feasibility, and real-time adaptability. Convention...
Considering the ambitious climate goals defined by the European Union, the significant share of energy demand represented by buildings, the slow process of their renovation due to challenges such as a need for majority consent from residents and limi...
Diabetes mellitus is a major public health concern, creating a need for reliable computational approaches for diabetes status classification and risk-oriented clinical assessment. Conventional machine learning models can capture predictive relationsh...
Accurate modelling of hydrodynamic damping remains a critical challenge in the dynamic analysis of floating offshore wind turbines (FOWTs), particularly when motion coupling between degrees of freedom is significant. This study addresses the limitati...
The applicability of adjoint-based gradient computation is investigated in the context of interfacial flows. Emphasis is set on the approximation of the transport of a characteristic function in a potential flow by means of an algebraic volume-of-flu...
Optimising the learning rate is essential for efficient neural network training, but static methods can cause overshooting or undershooting, while adaptive techniques like ADAM often struggle to balance exploration and exploitation. We introduce the...
The accurate prediction of significant wave height presents a complex yet vital challenge in the fields of ocean engineering. This capability is essential for disaster prevention, fostering sustainable development and deepening our understanding of v...
Extracting maximum energy from photovoltaic (PV) systems at varying conditions is crucial. It represents a problem that is being addressed by researchers who are using several techniques to obtain optimal outcomes in real-life scenarios. Among the ma...
One of the biggest challenge in the field of deep learning is the parameter selection and optimization process. In recent years different algorithms have been proposed including bio-inspired solutions to solve this problem, however, there are many ch...
Objectives: This study aims to predict patient-specific Average Glandular Dose (AGD) in mammography using machine learning-based models to support personalised radiation dose optimisation and reduce unnecessary exposure during breast cancer screening...
Digital images play an increasingly central role in journalism, legal investigations, and cybersecurity. However, modern editing tools make image manipulation difficult to detect with traditional forensic methods. This research addresses the challeng...
Given the increasing demand for rapid emergency management decision-making, which must be both timely and reliable, even slight delays can result in substantial human and economic losses. However, current systems and recent state-of-the-art work ofte...
This paper presents a data driven framework for performance optimisation of Narrow-Band IoT user equipment. The proposed framework is an edge micro-service that suggests one-time configurations to user equipment communicating with a base station. Sug...
The simulation of complex engineering components and structures under loads requires the formulation and adequate calibration of appropriate material models. This work introduces an optimisation-based scheme for the calibration of viscoelastic materi...
This paper presents an optimisation framework based on an open-source CAD system and CFD solver. In this work, the high-fidelity flow solutions and surface sensitivities are obtained using the primal and discrete adjoint formulations of
Wireless communications systems are traditionally designed by independently optimising signal processing functions based on a mathematical model. Deep learning-enabled communications have demonstrated end-to-end design by jointly optimising all compo...
Automatic voltage regulation (AVR) systems play an important role in maintaining voltage stability, ensuring efficiency, and enhancing the reliability of synchronous generators. Although conventional PID (proportional-integral-derivative) controllers...
This study aims to develop effective screening tools for cognitive impairment by integrating optimised speech classification features with various machine learning models. A total of 65 patients diagnosed with early-stage Mild Cognitive Impairment (M...
Modern turbofans with high bypass ratios, low blade passage frequencies and short nacelles require continuous development of acoustic linings to achieve the noise reductions expected by the international aviation authorities. Metamaterials and metafl...
One of the key components of the designing procedure of a structure of hard anti-wear coatings deposited via Physical Vapour Deposition (PVD) is the analysis of the stress and strain distributions in the substrate/coating systems, initiated during th...
This paper develops an extended-state-observer (ESO)-enhanced actor–critic reinforcement learning (RL) scheme for the trajectory tracking control of 3-DOF marine vessels subject to uncertain hydrodynamics and environmental disturbances. A coord...
New economic development trends have brought challenges and transformation directions to China’s urban planning process, in which the relationship between supply and demand of urban housing needs urgent optimisation. Using data on multiple type...
In this paper, we propose a secure protocol to compute the quadratic optimisation problem under a three-party outsourcing architecture in the scenario of cyber-physical systems. To enable real-world implementation, we propose an encoding framework th...
The COVID-19 pandemic has challenged all physical interactions. Social distancing, face masks and other rules have reshaped our way of living during the last year. The impact of these measures for indoor establishments, such as education or hostelry...
Evacuated-tube solar collectors charged with hybrid nanofluids can raise thermal output, but their coupled thermal and hydraulic response depends on many interacting variables, making the design an optimisation rather than a prediction problem. This...
This work presents and compares different methodologies for the joint optimisation of the fractional derivative order and the parameters of the right-hand-side neural network in Neural Fractional Differential Equation models. The proposed strategies...
High dropout rates remain a significant challenge in Massive Open Online Courses (MOOCs), making early identification of at-risk students crucial. This study introduces a novel approach called In-Session Stacked Ensemble Learning for Dropout Predicti...
Concrete in cold areas is often subjected to a freeze–thaw cycle period, and a harsh environment will seriously damage the structure of concrete and shorten its life. The frost resistance of concrete is primarily evaluated by relative dynamic e...
This study investigated the application of machine learning to optimise the pumping load shift of a complex dewatering system in a deep-level mine, aiming to reduce energy costs associated with the dewatering process, which consumes an average of 14%...
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