Branko Blagojević

Fast and accurate resistance prediction is critical in early-stage ship design. While Michell’s thin-ship theory provides rapid evaluations, its linear assumptions limit accuracy, particularly as hull forms deviate from ideal slenderness. This paper introduces a physics-preserving neural calibration method that improves Michell’s theory without replacing the underlying solver. We train a two-dimensional convolutional encoder–decoder, conditioned on Froude numbers via global FiLM modulation, to predict a bounded correction to the geometric effective-slope field. Because the solver remains unchanged, the learned correction acts as an interpretable spatial perturbation rather than a black-box resistance map. Evaluated under a strict leave-one-family-out (LOFO) protocol on a fleet of five slender hull families (DTMB, NPL-4A, Wide-Light Canoe, Wigley, and Delft 372), the neural calibration achieves a mean absolute percentage error (MAPE) of 0.0741. This represents a 24% improvement over a reproduced 2020 baseline and a 7.9% improvement over the uncorrected Michell solver. The 2020 baseline is the rigid boundary-layer and phase-deflection correction of an earlier study by the present group, re-evaluated here on the present hulls at their measured attitudes. Ablation studies show that much of this aggregate gain is captured by a bounded global slope offset, indicating that a spatially uniform displacement correction accounts for most of the improvement on slender hulls, while the spatially varying field mainly adds per-family headroom. Finally, we map the physical boundaries of this approach. Dedicated recovery campaigns on fuller forms (KCS and Series 60) show that the model regresses compared to baselines. This confirms that while the correction successfully refines the linear source distribution for slender hulls, it cannot synthesize missing physics, such as stagnation pressure, separated flow, or wave interference, for fuller or unrelated geometries.

This research addresses and resolves current challenges in meshless Lagrangian methods for simulating viscoelastic materials. A split-step scheme, or pressure Poisson reformulation of the Navier–Stokes equations, is introduced for incompressible viscoelastic flows in a Lagrangian context. The Lagrangian differencing dynamics (LDD) method, which is a thoroughly validated Lagrangian method for Newtonian and non-Newtonian incompressible flows, is extended to solve the introduced split-step scheme to simulate viscoelastic flows based on the Oldroyd-B constitutive model. To validate and evaluate the new method’s capabilities, the following benchmarks were used: lid-driven cavity flow, droplet impact response, 4:1 planar sudden contraction, and die swelling. These findings highlight the LDD method’s effectiveness in accurately simulating viscoelastic flows and capturing large deformations and memory effects. Even though the extra stress was directly modeled without any regularization approach, the method produced stable simulations for high Weissenberg numbers. The stability and performance of the the Lagrangian numerics for complex temporal evolution of material properties and stress responses encourage its use for industrial problems dealing with polymers.

In this paper, the strong formulation of the generalised Navier-Stokes momentum equation is investigated. Specifically, the formulation of shear-stress divergence is investigated, due to its effect on the performance and accuracy of computational methods. It is found that the term may be expressed in two different ways. While the first formulation is commonly used, the alternative derivation is found to be potentially more convenient for direct numerical manipulation. The alternative formulation relocates a part of strain information under the variable-coefficient Laplacian operator, thus making future computational schemes potentially simpler with larger time-step sizes.

This paper introduces a novel meshless and Lagrangian approach for simulating non-Newtonian flows, named Lagrangian Differencing Dynamics (LDD). Second-order-consistent spatial operators are used to directly discretize and solve generalized Navier–Stokes equations in a strong formulation. The solution is obtained using a split-step scheme, i.e., by decoupling the solutions of the pressure and velocity. The pressure is obtained by solving a Poisson equation, and the velocity is solved in a semi-implicit formulation. The matrix-free solution to the equations, and Lagrangian advection of mesh-free nodes allowed for a fully parallelized implementation on the CPU and GPU, which ensured an affordable computing time and large time steps. A set of four benchmarks are presented to demonstrate the robustness and accuracy of the proposed formulation. The tested two- and three-dimensional simulations used Power Law, Casson and Bingham models. An Abram slump test and a dam break test were performed using the Bingham model, yielding visual and numerical results in accordance with the experimental data. A square lid-driven cavity was tested using the Casson model, while the Power Law model was used for a skewed lid-driven cavity test. The simulation results of the lid-driven cavity tests are in good agreement with velocity profiles and stream lines of published reports. A fully implicit scheme will be introduced in future work. As the method precisely reproduces the pressure field, non-Newtonian models that strongly depend on the pressure will be validated.

This article discusses numerical prediction of ventilated flows around the surface-piercing hydrofoil. A ventilated cavity is developed on the hydrofoil surface and the growth of the cavity is uncontrolled, which can have harmful effects as it leads to changes in the lift and unexpected loads. The occurrence of ventilation is computed by a Reynolds-Averaged Navier–Stokes solver based on the Finite Volume Method and the Volume of Fluid free surface capturing. The impact of numerical parameters on the ventilation is investigated by testing various momentum, gradient, and interface-capturing schemes, turbulence intesities, acceleration and time stepping scenarios, for 10 Froude numbers and 2 angles of attack. The analysis shows that simulations are consistent and comparable with the experimental data, if proper free surface capturing scheme and very low Courant numbers are used.

The equations discretization errors are often overlooked compared to the spatial discretization errors. This article presents the results of the influence of various equations discretization schemes in computational fluid dynamics on the prediction of the ship resistance and wave elevation on the hull. For the analysis, steady flow around a model of the Wigley hull is numerically predicted by employing a finite-volume method solver based on Reynolds-averaged Navier–Stokes equations and the volume-of-fluid method. Six momentum discretization schemes, three multi-fluid discretization schemes and three gradient schemes, are used in the analysis. The results show that the choice of discretization schemes has a significant influence on the results of wave elevation and resistance force in the case of the flow around the Wigley hull. In conclusion, second-order discretization schemes should be used for the resistance evaluation, in order to properly capture the non-linear effects.

The paper on the first place summarizes the fatigue yield approach as a cause-effect interaction between fatigue damage progression and fatigue endurance. Secondly it investigates the fatigue strength worsening on experimental S-N data and the load variability effects in shipbuilding. Next it applies the Classification Society’s rule-based procedure for fatigue analysis of ship’s structure that uses a simplified fatigue strength assessment method. The example elaborates fatigue yield effect on the seagoing operation of a double hull 47400 tdw tanker. At the end the paper recommends the procedure for assessment of ship lifetime shortening due to the fatigue yielding under constant and variable amplitude block loadings.