Computational Engineering

The “Computational Engineering” section of Computation is dedicated to the integration and advancement of computational techniques and methodologies in addressing complex engineering challenges across various domains, such as aerospace, agricultural, automotive, biomedical, chemical, civil, computer, electrical, environmental, geological, mechanical, nuclear and safety engineering. This section provides a comprehensive forum for research that pushes the boundaries of engineering practice and innovation through computational approaches.

We invite novel and innovative contributions that span a broad range of topics within computational engineering, including, but not limited to:

• Mesh-Based Numerical Methods for Continuum Problems: Finite difference method, finite element method, finite volume method, boundary element method, lattice Bolzmann method and others. Mesh generation and adaption techniques. Spectral methods.
• Meshless Numerical Methods for Continuum Problems: Smoothed particle hydrodynamics, element-free Galerkin methods, finite point methods and others.
• Numerical Methods for Non-Continuum Problems: Molecular dynamics (investigations at molecular scale), discrete element method (granular material), multibody simulation (systems composed of rigid ot flexible bodies), peridynamics, cellular automata and others.
• Further Mathematical and Numerical Methods: Matrix methods, method of moments, Monte Carlo methods, Laplace and Z transforms, semi-analytical models, phenomenological models, solution methods for equations and equation systems, multigrid techniques, domain decomposition methods, convergence acceleration procedures and others.
• Computational Fluid and Solid Mechanics: Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA) methods used to study and predict fluid mechanical and structural engineering problems.
• Multiphysics Problems: Fluid–structure interactions, acoustics and structural vibration, hydraulic fracturing, conjugate heat transfer, pizoelectricity, thermoelectricity, magnetohydrodynamics, battery modelling, multiphase problems, fluid and solid mechanics problems with chemical or nuclear reactions.
• Multiscale Problems: Simulateneous account of interacting phenomea at different scales encountered in many fields, including nanomaterials, composite materials, climate modelling, geophysics, hydrology, turbulence.
• Machine Learning and Artificial Intelligence: Studies exploring the application of machine learning and artificial intelligence in engineering contexts, such as predictive maintenance, system optimization, automation, and enhanced decision-making processes.
• Optimization and Design Algorithms: Contributions that present innovative approaches for solving complex optimization problems related to engineering design and manufacturing processes.
• Structural Health Monitoring: Research on computational techniques for monitoring structural health, including sensor data analysis, damage detection, and real-time monitoring systems.
• Innovations in Computational Tools and Software: Papers discussing advancements in computational tools, software technologies, and visualization techniques for engineering analysis and simulation.
• Quantum Computing and High-Performance Computing: Studies on the application of quantum computing and high-performance computing (HPC) to tackle large-scale and complex engineering problems, including the development of algorithms and software optimized for these platforms.

This section encourages submissions that advance both the theoretical and practical aspects of computational engineering, offering insights that contribute to the development of innovative solutions and technologies in engineering. By fostering an interdisciplinary dialogue, we aim to bridge the gap between computational research and its application in various engineering disciplines.