In-house techniques and expertise | Computational Engineering and Design Group

The group is a pioneer in design search and optimisation (DSO), utilizing the OPTIONS and OPTIMAT software suites. Originally developed by Professor Andy Keane, these tools allow for the rapid comparison of evolutionary and classical search methods.
Professor David Toal extends this expertise through multi-fidelity Kriging and machine learning techniques (such as AeroINR and SDF-GAN) to accelerate the design of aerodynamic geometries and engine components.
Professor András Sóbester develops tools that automate geometry-centric design processes, exploiting advances in parametric geometry modelling, generative AI, and optimisation. He also maintains ADRpy (Aircraft Design Recipes in Python), an open-source library that facilitates rapid sizing and performance analysis, enabling geometry-centric optimisation and the digitalization of complex aerospace design processes.

Dr Kamal Djidjeli leads research into Meshless/Particle (MPS) Method codes. These techniques are particularly effective for modeling Fluid–Structure Interaction (FSI) and bioinspired designs, such as reverse spoilers and flapping foils, which are critical for both aerospace efficiency and renewable energy harvesting.
Professor Simon Cox is an expert in developing high performance and novel computing systems, cloud computing, commercial distributed computing technologies, and internet of things. He also specialises in applied computational algorithms, meshless methods, computational electromagnetics, and fast solvers. These techniques are utilized to solve interdisciplinary problems in engineering and science with a focus on delivering faster, cheaper, and/or better results.

Dr Haris Moazam Sheikh specialises in data-efficient surrogate modelling and optimisation for high-cost engineering challenges. He leads the development of Design-by-Morphing (DbM) technique, a data-driven technique that enables precise local and global shape control using minimal design variables; and MixMOBO, a Bayesian optimisation framework designed for complex, mixed-variable, multi-objective black-box problems. His work seeks to deliver significant performance gains for metamaterials and aero-structures, while drastically reducing the need for costly data evaluations.
Professor Ali Elham focuses on the development of multidisciplinary design optimisation techniques for applications in aviation, automotive engineering, thermal systems, and wind energy. He develops and applies specialised in-house computational tools, including FEMWET, a coupled-adjoint nonlinear aero-structural optimisation code for high-fidelity wing design; AeroTop, a topology optimisation tool for internal and external (thermo)fluid problems; and TopSteer, an adjoint-based optimisation code for topology and fibre-path design of tow-steered composite structures. He also develops integrated frameworks for sustainable aircraft design, including electric, hybrid-electric, and hydrogen-powered configurations.

Dr Jie Yuan manages a suite of cutting-edge simulation tools focused on stochastic and nonlinear dynamic analysis. These techniques are applied to large-scale engineering systems to quantify uncertainty and ensure robust structural mechanics under complex loading conditions.
Dr Sifeng Bi is an expert in uncertainty quantification, stochastic model updating, and numerical model verification and validation, with a particular emphasis on complex aerospace engineering dynamics. His work integrates cutting-edge techniques such as advanced Monte Carlo simulation, approximate Bayesian computation, and reliability-based optimization, with a special interest in imprecise and non-probabilistic analysis methods.

Professor Nico Avdelidis is an expert in nondestructive testing and evaluation (NDT&E) of materials and structures, advanced infrared and other non‑invasive imaging techniques. He specialises in developing digital twins to process sensor data (from inspection and/or monitoring) for the health management of aerospace systems. His work also covers digital maintenance, repair, and overhaul (DMRO) to enhance traditional maintenance into proactive data-driven approaches.
Professor Gennaro Scarselli’s research includes structural health monitoring (SHM), non-destructive testing (NDT), damage detection, and machine-learning-enabled monitoring of aerospace structures.
Professor Michele Meo develops nonlinear ultrasonic NDT and SHM techniques, thermal‑wave imaging, and wave‑based damage detection for composite aerospace structures.

Professor Gennaro Scarselli specialises on lightweight aerospace structures and composite materials, with current research spanning morphing structures, structural dynamics, fracture toughness of composites, and adhesion/adhesives.
Professor Michele Meo develops smart, multifunctional composite structures, including aerogels with tunable acoustic and multifunctional properties and studies impact/damage behaviour and vibration/wave phenomena in advanced composites.
Dr Tanmoy Mukhopadhyay develops programmable metamaterials and multifunctional composites by blending solid mechanics, smart materials, multi-scale homogenisation, nonlinear dynamics and topology optimisation with data-efficient machine learning, delivering tailored stiffness, strength, damping, wave control and energy absorption.
Dr Haris Moazam Sheikh specialises in developing data-driven topology optimisation approaches for designing architected mechanical metamaterials and composite structures.

Professor James Scanlan and Dr Bob Entwistle develop and employ the DECODE (Decision Environment for Complex Designs) environment. This platform streamlines the design and manufacture of low-cost civilian UAVs. Their specialized in-house capabilities also include UAV Ground Risk planning and health monitoring systems to ensure the safety and reliability of autonomous flight operations.
Dr Sergio Araujo-Estrada leads research into the dynamics and control of autonomous vehicles, particularly VTOL and rotorcraft platforms. He utilizes custom flight dynamics simulation frameworks integrated with adaptive machine learning controllers to improve robustness against environmental disturbances. His experimental specialization includes multi-DOF free-motion testing platforms with distributed sensing for high-fidelity aerodynamic state estimation.