Emilio F. Campana

A level set technique applied to unsteady free surface flows

An unsteady Navier–Stokes solver for incompressible fluid is coupled with a level set approach to describe free surface motions. The two-phase flow of air and water is approximated by the flow of a single fluid whose properties, such as density and viscosity, change across the interface. The free surface location is captured as the zero level of a distance function convected by the flow field. To validate the numerical procedure, two classical two-dimensional free surface problems in hydrodynamics, namely the oscillating flow in a tank and the waves generated by the flow over a bottom bump, are studied in non-breaking conditions, and the results are compared with those obtained with other numerical approaches. To check the capability of the method in dealing with complex free surface configurations, the breaking regime produced by the flow over a high bump is analyzed. The analysis covers the successive stages of the breaking phenomenon: the steep wave evolution, the falling jet, the splash-up and the air entrainment. In all phases, numerical results qualitatively agree with the experimental observations. Finally, to investigate a flow in which viscous effects are relevant, the numerical scheme is applied to study the wavy flow past a submerged hydrofoil. Copyright

Particle Swarm Optimization: efficient globally convergent modifications

In this paper we consider the Particle Swarm Optimization (PSO) algorithm [1], [2], in the class of Evolutionary Algorithms, for the solution of global optimization problems. We analyze a couple of issues aiming at improving both the effectiveness and the efficiency of PSO. In particular, first we recognize that in accordance with the results in [3], the initial points configuration required by the method, may be a crucial issue for the efficiency of PSO iteration. Therefore, a promising strategy to generate initial points is provided in the paper.

Dynamic analysis for the selection of parameters and initial population, in particle swarm optimization

In this paper we consider the evolutionary Particle Swarm Optimization (PSO) algorithm, for the minimization of a computationally costly nonlinear function, in global optimization frameworks. We study a reformulation of the standard iteration of PSO (Clerc and Kennedy in IEEE Trans Evol Comput 6(1) 2002), (Kennedy and Eberhart in IEEE Service Center, Piscataway, IV: 1942–1948, 1995) into a linear dynamic system. We carry out our analysis on a generalized PSO iteration, which includes the standard one proposed in the literature. We analyze three issues for the resulting generalized PSO: first, for any particle we give both theoretical and numerical evidence on an efficient choice of the starting point. Then, we study the cases in which either deterministic and uniformly randomly distributed coefficients are considered in the scheme. Finally, some convergence analysis is also provided, along with some necessary conditions to avoid diverging trajectories. The results proved in the paper can be immediately applied to the standard PSO iteration.

High-fidelity global optimization of shape design by dimensionality reduction, metamodels and deterministic particle swarm

Advances in high-fidelity shape optimization for industrial problems are presented, based on geometric variability assessment and design-space dimensionality reduction by Karhunen–Loève expansion, metamodels and deterministic particle swarm optimization (PSO). Hull-form optimization is performed for resistance reduction of the high-speed Delft catamaran, advancing in calm water at a given speed, and free to sink and trim. Two feasible sets (A and B) are assessed, using different geometric constraints. Dimensionality reduction for 95% confidence is applied to high-dimensional free-form deformation. Metamodels are trained by design of experiments with URANS; multiple deterministic PSOs achieve a resistance reduction of 9.63% for A and 6.89% for B. Deterministic PSO is found to be effective and efficient, as shown by comparison with stochastic PSO. The optimum for A has the best overall performance over a wide range of speed. Compared with earlier optimization, the present studies provide an additional resistance reduction of 6.6% at 1/10 of the computational cost.

Parameter selection in synchronous and asynchronous deterministic particle swarm optimization for ship hydrodynamics problems

Graphical abstractDisplay Omitted HighlightsParametric study of deterministic PSO setting under limited computational resources.Comparison of synchronous and asynchronous implementations.Identification of most significant parameter based on more than 40k optimizations.Identification of most promising and robust setup for simulation-based problems.Hydrodynamic hull-form optimization of a high speed catamaran. Deterministic optimization algorithms are very attractive when the objective function is computationally expensive and therefore the statistical analysis of the optimization outcomes becomes too expensive. Among deterministic methods, deterministic particle swarm optimization (DPSO) has several attractive characteristics such as the simplicity of the heuristics, the ease of implementation, and its often fairly remarkable effectiveness. The performances of DPSO depend on four main setting parameters: the number of swarm particles, their initialization, the set of coefficients defining the swarm behavior, and (for box-constrained optimization) the method to handle the box constraints. Here, a parametric study of DPSO is presented, with application to simulation-based design in ship hydrodynamics. The objective is the identification of the most promising setup for both synchronous and asynchronous implementations of DPSO. The analysis is performed under the assumption of limited computational resources and large computational burden of the objective function evaluation. The analysis is conducted using 100 analytical test functions (with dimensionality from two to fifty) and three performance criteria, varying the swarm size, initialization, coefficients, and the method for the box constraints, resulting in more than 40,000 optimizations. The most promising setup is applied to the hull-form optimization of a high speed catamaran, for resistance reduction in calm water and at fixed speed, using a potential-flow solver.

Scars and Vortices Induced by Ship Bow and Shoulder Wave Breaking

Experimental data are provided for physical understanding and computational fluid dynamics (CFD) validation for the surface combatant David–Taylor model basin Model 5415 bow and shoulder wave breaking. A photographic study was conducted using 5.72m replica and 3.05m geosim models of Model 5415 over a range of Froude numbers (Fr) to identify Fr and scale effects on wave breaking and choose the best Fr for the local flow measurements, which include near- and far-field means and rms wave elevation and mean velocity under the breaking waves. The larger model and Fr=0.35 were selected due to the large extents of quasisteady plunging bow and spilling shoulder wave breaking. A direct correlation is shown between regions of wave slope larger than 17deg and regions of large rms in wave height variation. Scars characterized by sudden changes in the mean wave height and vortices induced by wave breaking were identified. Complementary CFD solutions fill the gaps in the relatively sparse measurements enabling a more complete description of the bow and shoulder wave breaking and induced vortices and scars. The combined results have important implications regarding the modeling of the bubbly flow around surface ships, especially for bubble sources and entrainment.

Simulation based design optimization of waterjet propelled Delft catamaran

The present work focuses on the application of simulation-based design for the resistance optimization of waterjet propelled Delft catamaran, using integrated computational and experimental fluid dynamics. A variable physics/variable fidelity approach was implemented wherein the objective function was evaluated using both low fidelity potential flow solvers with a simplified CFD waterjet model and high fidelity RANS solvers with discretized duct flow calculations. Both solvers were verified and validated with data for the original hull. The particle swarm optimizer was used for single speed optimization at Fr = 0.5, and genetic algorithms were used for multi speed optimization at Fr = 0.3, 0.5 and 0.7. The variable physics/variable fidelity approach was compared with high fidelity approach for the bare-hull shape optimization and it showed an overall CPU time reduction of 54% and converged to the same optimal design at Fr = 0.5. The multi-speed optimization showed design improvement at Fr = 0.5 and 0.7, but not at Fr = 0.3 since the design variables were obtained based on sensitivity analysis at Fr = 0.5. High fidelity simulation results for the optimized barehull geometry indicated 4% reduction in resistance and the optimized waterjet equipped geometry indicated 11% reduction in effective pump power required at self-propulsion. Verification was performed for the optimized hull form and its reduction in powering will be validated in forthcoming experimental campaign.

Global Optimization Algorithms in Naval Hydrodynamics

Abstract Several global optimization algorithms are discussed with results for a real ship design application. The focus is on the development of deterministic methods, which are preferred for their good theoretical properties and for the reduced number of objective function evaluations required.

Formulation and parameter selection of multi-objective deterministic particle swarm for simulation-based optimization

Abstract Global derivative-free deterministic algorithms are particularly suitable for simulation-based optimization, where often the existence of multiple local optima cannot be excluded a priori, the derivatives of the objective functions are not available, and the evaluation of the objectives is computationally expensive, thus a statistical analysis of the optimization outcomes is not practicable. Among these algorithms, particle swarm optimization (PSO) is advantageous for the ease of implementation and the capability of providing good approximate solutions to the optimization problem at a reasonable computational cost. PSO has been introduced for single-objective problems and several extension to multi-objective optimization are available in the literature. The objective of the present work is the systematic assessment and selection of the most promising formulation and setup parameters of multi-objective deterministic particle swarm optimization (MODPSO) for simulation-based problems. A comparative study of six formulations (varying the definition of cognitive and social attractors) and three setting parameters (number of particles, initialization method, and coefficient set) is performed using 66 analytical test problems. The number of objective functions range from two to three and the number of variables from two to eight, as often encountered in simulation-based engineering problems. The desired Pareto fronts are convex, concave, continuous, and discontinuous. A full-factorial combination of formulations and parameters is investigated, leading to more than 60,000 optimization runs, and assessed by three performance metrics. The most promising MODPSO formulation/parameter is identified and applied to the hull-form optimization of a high-speed catamaran in realistic ocean conditions. Its performance is finally compared with four stochastic algorithms, namely three versions of multi-objective PSO and the genetic algorithm NSGA-II.

Globally Convergent Hybridization of Particle Swarm Optimization Using Line Search-Based Derivative-Free Techniques

The hybrid use of exact and heuristic derivative-free methods for global unconstrained optimization problems is presented. Many real-world problems are modeled by computationally expensive functions, such as problems in simulation-based design of complex engineering systems. Objective-function values are often provided by systems of partial differential equations, solved by computationally expensive black-box tools. The objective-function is likely noisy and its derivatives are often not available. On the one hand, the use of exact optimization methods might be computationally too expensive, especially if asymptotic convergence properties are sought. On the other hand, heuristic methods do not guarantee the stationarity of their final solutions. Nevertheless, heuristic methods are usually able to provide an approximate solution at a reasonable computational cost, and have been widely applied to real-world simulation-based design optimization problems. Herein, an overall hybrid algorithm combining the appealing properties of both exact and heuristic methods is discussed, with focus on Particle Swarm Optimization (PSO) and line search-based derivative-free algorithms. The theoretical properties of the hybrid algorithm are detailed, in terms of limit points stationarity. Numerical results are presented for a specific test function and for two real-world optimization problems in ship hydrodynamics.