Digitalisation is reshaping shipyard production, yet its methodological foundations remain fragmented across simulation, optimisation, Artificial Intelligence (AI), and Digital Twin (DT) research streams. This paper presents a domain-specific methodological review of shipyard production modelling from 2010 to 2025, synthesising advances in Discrete-Event Simulation (DES), multi-objective optimisation, hybrid simulation–optimisation architectures, Machine Learning (ML), reinforcement learning (RL), and DT-enabled cyber-physical systems. Using an explicit evaluative framework based on integration depth, validation basis, and decision scope, the review differentiates between analytically mature but execution-decoupled DES/optimisation approaches and integration-rich yet variably validated DT and AI-driven systems. The analysis shows that hybrid DES-optimisation frameworks currently represent the most operationally credible class of methods, delivering measurable production improvements under structured conditions, whereas many DT and AI contributions prioritise architectural integration and data synchronisation over longitudinal yard-wide KPI validation. A comparative assessment of simulation platforms, optimisation engines, and manufacturing execution system/enterprise resource planning/product lifecycle management infrastructures highlights the central role of structured product–process–resource data and execution-layer connectivity, while severe confidentiality constraints and the scarcity of openly available industrial datasets continue to limit reproducibility and benchmarking. Overall, shipyard production research is progressing toward increasingly integrated and cyber-physical systems, but sustained yard-scale validation and shared benchmark development remain critical prerequisites for translating architectural sophistication into demonstrable operational impact.

This study investigates the turbulent skin friction behaviour of original coated, slime/algae-covered, and laser-cleaned marine surfaces through a combined experimental and numerical approach under fully developed channel flow conditions. Numerical validation was performed against established smooth-wall experimental datasets over the investigated Reynolds number range using Reynolds-Averaged Navier–Stokes (RANS) and Large-Eddy Simulation (LES) approaches. LES provided the closest agreement with the reference data, while the two-dimensional wall function RANS $k$-$\omega$ SST model was selected for subsequent rough-wall simulations based on its balance between predictive accuracy and computational cost. The selected RANS approach was independently validated against rough-wall experimental data, showing good agreement in predicted skin friction behaviour. Measured surface elevation data were used to estimate equivalent sand grain roughness for the original coated, slime/algae-covered, and post-cleaning surfaces. The post-cleaning surfaces exhibited lower measured roughness and predicted skin friction coefficients than the slime/algae-covered surfaces, while the three investigated laser configurations produced broadly similar roughness characteristics and hydrodynamic responses. However, the original coating was characterised before immersion, whereas the fouled and post-cleaning surfaces had undergone water exposure. Because the coating is hydrolysing/self-polishing, the observed post-cleaning roughness reduction may be partly or entirely associated with immersion-induced coating changes and cannot be attributed uniquely to laser treatment. The results therefore demonstrate an association between the post-cleaning surface condition and reduced roughness and predicted skin friction, but do not establish a causal relationship between laser treatment and these reductions.

The importance of the growth, proliferation and longevity of reef-forming cold-water corals is paramount as they support various complex bio-diverse habitats and provide many essential ecosystem services. These cold-water coral reefs consist of layers of living coral tissue that grow above large masses of coral skeleton. Here, the Goldilocks Principle is used to promote growth in ‘just right’ conditions and model how cold-water corals engineer their habitat to survive and prosper. A computational fluid dynamics model is created based on the Smoothed Particle Hydrodynamics method, a mesh-free Lagrangian numerical method. The SPH solver is written in the C++ programming language and parallelised with OpenMP to improve its efficiency and reduce the execution times. The solver is validated against analytical and numerical solutions.