Editorial UKACM 2022: advances in computational mechanics (original) (raw)

Computational mechanics has been at the forefront of scientific advances in engineering as well as mathematical and applied sciences in the past 4 decades. It continues to play a substantial role in technological advances and is one of the key areas that drives the ongoing revolution, expanding beyond the traditional areas and addressing the next-generation societal and data-driven challenges within the United Kingdom and internationally. From the beginning of the era of computational mechanics in the early 1980s, the UK research community has been pioneering original research by expanding methodologies, developing algorithms, embracing mechanics, mathematics, and numerical methods, and shaping the field today and in the future.

The UK Association of Computational Mechanics (UKACM) was founded in March 1992 to promote research in computational mechanics within the UK and to formally liaise with relevant organisations in Europe and worldwide.

This Special Issue is dedicated to the 2022 conference of the UKACM, a well-established conference series promoted by the UKACM, and the premier national conference on computational mechanics. It focuses on cutting-edge research in all areas associated with computational mechanics, promotes cross-disciplinary collaboration with mathematics, and includes high-calibre lectures on advanced computational methods. The UKACM 2022 conference marked the start of the 30th Anniversary of UKACM. The long and rich history of UKACM is comprehensively presented by the current UKACM president in the opening paper of this Special Issue Sevilla [[22](/article/10.1007/s00366-023-01919-3#ref-CR22 "Sevilla R (2023) The 30th anniversary of the UK association for computational mechanics. Eng Comput. https://doi.org/10.1007/s00366-023-01804-z

            ")\].

Other papers contained in this Special Issue are from contributors of the conference and arranged into the following general topics:

• Advances in computational methods Computational methods form the backbone of computational mechanics, and this Special Issue discusses several new advances. Wang and Papanicolopulos [26] present new integration rules on the tetrahedron, and their intricate analysis. Quaine and Gimperlein [19] discuss a generalized finite-element method for elastodynamics enriched by plane waves. Liu et al. [[13](/article/10.1007/s00366-023-01919-3#ref-CR13 "Liu B, Wang Q, Feng Y, Zang Z, Qu T (2023) A combined boundary element method and discrete element method for particle stress field and breakage evaluation of granular systems with similar particle shapes. Eng Comput

")] propose a combined boundary element and discrete element method for computing the breakage of granular brittle materials. Ricketts et al. [[20](/article/10.1007/s00366-023-01919-3#ref-CR20 "Ricketts EJ, Cleall PJ, Jefferson T, Kerfriden P, Lyons P (2023) Near-boundary error reduction with an optimized weighted dirichlet-neumann boundary condition for stochastic pde-based gaussian random field generators. Eng Comput.
https://doi.org/10.1007/s00366-023-01819-6

            ")\] present a new technique for generating random fields to reduce boundary effects, which is useful for, e.g., generating material heterogeneity. Finally, Akbari and Khazaeinejad \[[2](/article/10.1007/s00366-023-01919-3#ref-CR2 "Akbari S, Khazaeinejad P (2023) Geometrical and mechanical analysis of polylactic acid and polyvinylidine fluoride scaffolds for bone tissue engineering. Eng Comput.  
              https://doi.org/10.1007/s00366-023-01902-y  
                
            ")\] explore the effect of porosity on bone–tissue scaffolds using a finite-element method validated via experiments.

• Coupled problems Specific challenges arise in simulating multi-physics problems with possibly non-linear and strong coupling between equations. One notable example is electrochemical phenomena, which are tackled in Cui et al. [9], studying electrochemical deposition. Barnett et al. [5], instead, present an open-source finite-volume framework for ionic transport in complex porous electrolytes and solid electrodes. With a similar focus on porous structure, the contribution of Fu et al. [11] studies complex micro-structures and proposes a combination of simulation and data-driven approach to predict their permeability. The combination of machine learning techniques and traditional computational methods is particularly promising, in fact, for complex geometries in geological and civil engineering applications, as shown in the work of Makauskas et al. [15], which uses deep neural networks and in the work of Asr et al. [3] which instead employs an evolutionary regression model. The multiscale structure of geological porous media, however, is rarely accessible and, therefore, limited data are available, and properties of sediments and rocks are often unknown. In this regard, Icardi et al. [12] present an open-source computational framework to randomly generate permeability multiscale structure and then solve coupled flow and transport problems.
• Computational methods in solids Elgy and Ledger [10] present various reduced-order modelling approaches for the efficient computation of a key quantity of interest—the magnetic polarizability tensor—of conducting magnetic objects. Olawale et al. [18] present statistics of simulations of the Euler–Bernoulli beam subject to a stochastic external loading.
• Computational methods in fluids Computational Fluid Dynamics (CFD) has seen tremendous improvements in the last decades thanks to the availability of efficient numerical methods, codes, and computing hardware. This special issue includes recent advancements in the aerodynamics of smart blades [17] and wind turbines [25]. Coupling with solid mechanics is instead presented in [1] to study flow-induced vibrations in a duct, while the coupling with heat transfer problems in complex biological structures is solved in [24] through an immersed boundary approach.
• Failure and damage The phase-field method has been proven robust and computationally efficient in modelling the propagation of fractures and damages. Au-Yeung et al. [4] investigate the effect of moisture content upon the degradation behaviour of composite materials. A coupled phase-field framework is developed considering moisture diffusion, hygroscopic expansion, and fracture behaviour. Sarmadi et al. [21] dedicated to developing a methodology for finding an appropriate length-scale parameter to model the fracturing process to match the physical character of failure in materials.
• Material modelling Chen and Izzuddin [8] introduce a finite strain elastoplastic model proposed within a total Lagrangian framework based on the multiplicative decomposition of the deformation gradient, with several simplifications aimed at facilitating more concise code implementation and enhancing computational efficiency.
• Optimisation and machine learning for engineering design Bui et al. [6] present a surrogate modelling approach for tunnel track design, where synthetic data are generated using a cut finite-element method-based multi-phase multi-physics simulation model. Sferza et al. [23] present a novel global–local multidisciplinary optimisation approach for preliminary sizing of aircraft using Airbus’ in-house FE code Lagrange. Cabrera et al. [7] propose two strategies for data and model fusion of experimental and synthetic data-based experimenting with several machine learning algorithms. Miah et al. [16] propose a reduced-order modelling method based on neural networks for predictive modelling of 3D-magneto-mechanical problems. Lock et al. [14] develop a novel neural networks-based meshing algorithm for improving the efficiency of computational modelling. Finally, Yan et al. [27] describe a real-time penalization (SIMP) topology optimization method for solid isotropic materials using deep convolutional neural networks.

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Authors and Affiliations

1. School of Engineering, University of Birmingham, Birmingham, UK
   Jelena Ninic
2. School of Mathematical Sciences, University of Nottingham, Nottingham, UK
   Kristoffer G. van der Zee & Matteo Icardi
3. Department of Civil Engineering, University of Nottingham, Nottingham, UK
   Fangying Wang

Authors

1. Jelena Ninic
2. Kristoffer G. van der Zee
3. Matteo Icardi
4. Fangying Wang

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Correspondence toJelena Ninic.

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Ninic, J., Zee, K.G.v.d., Icardi, M. et al. Editorial UKACM 2022: advances in computational mechanics.Engineering with Computers 39, 3739–3741 (2023). https://doi.org/10.1007/s00366-023-01919-3

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• Published: 13 November 2023
• Version of record: 13 November 2023
• Issue date: December 2023
• DOI: https://doi.org/10.1007/s00366-023-01919-3