This work describes the development of a hybrid framework of Runge–Kutta (RK), discontinuous Galerkin (DG), level set (LS) and direct ghost fluid (DGFM) methods for the simulation of near-field and early-time underwater explosions (UNDEX) in early-stage ship design. UNDEX problems provide a series of challenging issues to be solved. The multi-dimensional, multi-phase, compressible and inviscid fluid-governing equations must be solved numerically. The shock front in the solution field must be captured accurately while maintaining the total variation diminishing (TVD) properties. The interface between the explosive gas and water must be tracked without letting the numerical diffusion across the material interface lead to spurious pressure oscillations and thus the failure of the simulation. The non-reflecting boundary condition (NRBC) must effectively absorb the wave and prevent it from reflecting back into the fluid. Furthermore, the CFD solver must have the capability of dealing with fluid–structure interactions (FSI) where both the fluid and structural domains respond with significant deformation. These issues necessitate a hybrid model. In-house CFD solvers (UNDEXVT) are developed to test the applicability of this framework. In this development, code verification and validation are performed. Different methods of implementing non-reflecting boundary conditions (NRBCs) are compared. The simulation results of single and multi-dimensional cases that possess near-field and early-time UNDEX features—such as shock and rarefaction waves in the fluid, the explosion bubble, and the variation of its radius over time—are presented. Continuing research on two-way coupled FSI with large deformation is introduced, and together with a more complete description of the direct ghost fluid method (DGFM) in this framework will be described in subsequent papers.

This work presents a Direct Ghost Fluid Method (DGFM) as part of a two-fluid numerical framework suitable to model explosive gas and water flows resulting from underwater explosion (UNDEX). Due to the presence of explosive gas and water with shock waves in the modeling domain, classic Eulerian methods with inherent diffusion may not be effective. Numerical diffusion occurs due to nonphysical diffused density at material interfaces, which creates spurious pressure oscillations and significantly degrades the quality of the numerical results. To eliminate or minimize numerical diffusion, sharp interface methods having no mixed elements may be used in multifluid flow computations. The Direct Ghost Fluid Method (DGFM) described in this paper uses direct extrapolation of density (vice pressure) and tangential velocity from real to ghost fluid. The spurious pressure oscillations near the material interface are therefore minimized. One-, two-, and three-dimensional computational fluid dynamics (CFD) solvers that have DGFM as an essential part in their framework to model UNDEX interface conditions are developed, explored, and applied to the simulation of a series of benchmark problems. Excellent agreement is obtained among the simulations, the analytical solutions, and the experiments.

Solution of near-field underwater explosion (UNDEX) problems frequently require the modeling of two-way coupled fluid-structure interaction (FSI). This paper describes the addition of an embedded boundary method to an UNDEX modeling framework for multiphase, compressible and inviscid fluid using the combined algorithms of Runge-Kutta, discontinuous-Galerkin, level-set and direct ghost-fluid methods. A computational fluid dynamics (CFD) solver based on these algorithms has been developed as described in previous work. A fluid-structure coupling approach was required to perform FSI simulation interfacing with an external structural mechanics solver. Large structural deformation and possible rupture and cracking characterize the FSI phenomenon in an UNDEX, so the embedded boundary method (EBM) is more appealing for this application in comparison to dynamic mesh methods such as the arbitrary Lagrangian-Eulerian (ALE) method to enable the fluid-structure coupling algorithm in the fluid. Its limitation requiring a closed interface that is fully submerged in the fluid domain is relaxed by an adjustment described in this paper so that its applicability is extended. Two methods of implementing the fluid-structure wall boundary condition are also compared. The first solves a local 1D fluid-structure Riemann problem at each intersecting point between the wetted elements and fluid mesh. In this method, iterations are required when the Tait equation of state is utilized. A second method that does not require the Riemann solution and iterations is also implemented and the results are compared.

With the adoption of more ambitious emission reduction strategies in the shipping industry by the International Maritime Organization and the resulting stricter greenhouse gas emission reduction requirements, it is particularly important for all stakeholders in the global maritime shipping industry to assess the energy efficiency of shipping vessels. Forming predictive capabilities for ship fuel consumption and Carbon Intensity Indicator (CII) annual ratings, for example, are two important works. This article adopted 14 different algorithms in three categories of data-driven approaches, i.e., statistics, machine learning and deep learning, including polynomial regression, ridge regression, adaptive boosting, categorical boosting, elastic net, etc., and built the ship fuel consumption prediction model using ship noon report as the data source. The prediction accuracy and computational efficiency of model training were compared based on metrics of coefficient of determination, mean absolute percentage error and floating-point operations per amount of training data. Cross-validations were performed for all 14 algorithms to analyze their sensitivities to their respective tuned parameters. Comparisons indicated that algorithms of the statistics approach were sensitive to the quality of the data source, compared with the machine learning and the deep learning approaches. The accuracy of the elastic net algorithm was sensitive to the tuned parameters. Two algorithms, light gradient boosting machine and random forest, were selected based on their performances of prediction accuracy and computational efficiency of model training. Then, the selected algorithms were separately combined with long short-term memory as the time-series prediction algorithm to form their respective coupled framework. Both of the coupled frameworks achieved successful prediction of the CII annual discriminant and rating of the studied ships. The prediction accuracy was validated to be sufficient.