Hybridization of multi-objective deterministic particle swarm with derivative-free local searches

The paper presents a multi-objective derivative-free and deterministic global/local hybrid algorithm for the efficient and effective solution of simulation-based design optimization (SBDO) problems. The objective is to show how the hybridization of two multi-objective derivative-free global and local algorithms achieves better performance than the separate use of the two algorithms in solving specific SBDO problems for hull-form design. The proposed method belongs to the class of memetic algorithms, where the global exploration capability of multi-objective deterministic particle swarm optimization is enriched by exploiting the local search accuracy of a derivative-free multi-objective line-search method. To the authors best knowledge, studies are still limited on memetic, multi-objective, deterministic, derivative-free, and evolutionary algorithms for an effective and efficient solution of SBDO for hull-form design. The proposed formulation manages global and local searches based on the hypervolume metric. The hybridization scheme uses two parameters to control the local search activation and the number of function calls used by the local algorithm. The most promising values of these parameters were identified using forty analytical tests representative of the SBDO problem of interest. The resulting hybrid algorithm was finally applied to two SBDO problems for hull-form design. For both analytical tests and SBDO problems, the hybrid method achieves better performance than its global and local counterparts

Hybrid Evolutionary Shape Manipulation for Efficient Hull Form Design Optimisation

‘Eco-friendly shipping’ and fuel efficiency are gaining much attention in the maritime industry due to increasingly stringent environmental regulations and volatile fuel prices. The shape of hull affects the overall performance in efficiency and stability of ships. Despite the advantages of simulation-based design, the application of a formal optimisation process in actual ship design work is limited. A hybrid approach which integrates a morphing technique into a multi-objective genetic algorithm to automate and optimise the hull form design is developed. It is envisioned that the proposed hybrid approach will improve the hydrodynamic performance as well as overall efficiency of the design process

Key Challenges and Opportunities in Hull Form Design Optimisation for Marine and Offshore Applications

New environmental regulations and volatile fuel prices have resulted in an ever-increasing need for reduction in carbon emission and fuel consumption. Designs of marine and offshore vessels are more demanding with complex operating requirements and oil and gas exploration venturing into deeper waters and hasher environments. Combinations of these factors have led to the need to optimise the design of the hull for the marine and offshore industry. The contribution of this paper is threefold. Firstly, the paper provides a comprehensive review of the state-ofthe- art techniques in hull form design. Specifically, it analyses geometry modelling, shape transformation, optimisation and performance evaluation. Strengths and weaknesses of existing solutions are also discussed. Secondly, key challenges of hull form optimisation specific to the design of marine and offshore vessels are identified and analysed. Thirdly, future trends in performing hull form design optimisation are investigated and possible solutions proposed. A case study on the design optimisation of bulbous bow for passenger ferry vessel to reduce wavemaking resistance is presented using NAPA software. Lastly, main issues and challenges are discussed to stimulate further ideas on future developments in this area, including the use of parallel computing and machine intelligence

Modeling and simulation of hydrokinetic composite turbine system

The utilization of kinetic energy from the river is promising as an attractive alternative to other available renewable energy resources. Hydrokinetic turbine systems are advantageous over traditional dam based hydropower systems due to zero-head and mobility. The objective of this study is to design and analyze hydrokinetic composite turbine system in operation. Fatigue study and structural optimization of composite turbine blades were conducted. System level performance of the composite hydrokinetic turbine was evaluated. A fully-coupled blade element momentum-finite element method algorithm has been developed to compute the stress response of the turbine blade subjected to hydrodynamic and buoyancy loadings during operation. Loadings on the blade were validated with commercial software simulation results. Reliability-based fatigue life of the designed composite blade was investigated. A particle swarm based structural optimization model was developed to optimize the weight and structural performance of laminated composite hydrokinetic turbine blades. The online iterative optimization process couples the three-dimensional comprehensive finite element model of the blade with real-time particle swarm optimization (PSO). The composite blade after optimization possesses much less weight and better load-carrying capability. Finally, the model developed has been extended to design and evaluate the performance of a three-blade horizontal axis hydrokinetic composite turbine system. Flow behavior around the blade and power/power efficiency of the system was characterized by simulation. Laboratory water tunnel testing was performed and simulation results were validated by experimental findings. The work performed provides a valuable procedure for the design and analysis of hydrokinetic composite turbine systems --Abstract, page iv

State of the Art in the Optimisation of Wind Turbine Performance Using CFD

Wind energy has received increasing attention in recent years due to its sustainability and geographically wide availability. The efficiency of wind energy utilisation highly depends on the performance of wind turbines, which convert the kinetic energy in wind into electrical energy. In order to optimise wind turbine performance and reduce the cost of next-generation wind turbines, it is crucial to have a view of the state of the art in the key aspects on the performance optimisation of wind turbines using Computational Fluid Dynamics (CFD), which has attracted enormous interest in the development of next-generation wind turbines in recent years. This paper presents a comprehensive review of the state-of-the-art progress on optimisation of wind turbine performance using CFD, reviewing the objective functions to judge the performance of wind turbine, CFD approaches applied in the simulation of wind turbines and optimisation algorithms for wind turbine performance. This paper has been written for both researchers new to this research area by summarising underlying theory whilst presenting a comprehensive review on the up-to-date studies, and experts in the field of study by collecting a comprehensive list of related references where the details of computational methods that have been employed lately can be obtained

Mission-based hull-form and propeller optimization of a transom stern destroyer for best performance in the sea environment

An overview is presented of the activities conducted within the NATO STO Task Group AVT-204 to “Assess the Ability to Optimize Hull Forms of Sea Vehicles for the Best Per- formance in a Sea Environment.” The objective is the development of a greater understanding of the potential and limitations of the hydrodynamic optimization tools. These include low- and high-fidelity solvers, automatic shape modification methods, and multi-objective optimiza- tion algorithms, and are limited here to a deterministic application. The approach includes simulation-based design optimization methods from different research teams. Analysis tools include potential flow and Reynolds-averaged Navier-Stokes equation solvers. Design modifica- tion tools include global modification functions, control point based methods, and parametric modelling by hull sections and basic curves. Optimization algorithms include particle swarm optimization, sequential quadratic programming, genetic and evolutionary algorithms. The ap- plication is the hull-form and propeller optimization of the DTMB 5415 model for significant conditions, based on actual missions at sea

Multi-objective optimization of semi-submersible platforms using particle swam optimization algorithm based on surrogate model

An Innovative Semi-submersible platform Optimization Program (ISOP) has been developed to solve the multi-objective optimization problem for semi-submersible platforms (SEMI). Three types of SEMIs, including semi-submersible floating production unit (SEMI FPU), heave and vortex induced motion (VIM) suppressed semi-submersible (HVS) and semi-submersible floating drilling unit (SEMI FDU) are selected for case studies. The hydrodynamic performances of three types of semi-submersible platforms are analyzed by using panel method and Morison's equation. In order to improve the computing efficiency, the hydrodynamic performances for different hull forms during optimization process are estimated by the surrogate models, which are built by artificial neural network prediction method and Inverse Multi-Quadric (IMQ) radial basis function (RBF). The accuracy of surrogate models is ensured by performing leave-one-out cross validation (LOOCV). The most probable maximum (MPM) heave motion and total weight, representing the safety and economy, respectively, are chosen as the two objectives for optimization. The transverse metacentric height, the MPM surge motion, and the most probable minimum (MPMin) airgap are selected as constraints. Based on surrogate models, multi-objective particle swarm optimization (MOPSO) is employed to search for the Pareto-optimal solutions. A Computational Fluid Dynamics (CFD) tool is adopted to validate the proposed model for the prediction of the motion responses. By comparing the obtained Pareto-optimal solutions with the initial design using simple panel method plus Morison's equation, it is confirmed that the MPM heave motions for SEMI FPU, HVS and SEMI FDU can be suppressed by up to 12.68%, 11.92%, and 14.96%, respectively, and the total weights can be reduced by up to 12.16%, 13.00%, and 24.91%, respectively. Through the detailed analyses of optimization results, the most efficient design strategies for semi-submersible platforms are discussed and proposed

Multi-Objective Optimization of Planetary Gearbox with Adaptive Hybrid Particle Swarm Differential Evolution Algorithm

This paper considers the problem of constrained multi-objective non-linear optimization of planetary gearbox based on hybrid metaheuristic algorithm. Optimal design of planetary gear trains requires simultaneous minimization of multiple conflicting objectives, such as gearbox volume, center distance, contact ratio, power loss, etc. In this regard, the theoretical formulation and numerical procedure for the calculation of the planetary gearbox power efficiency has been developed. To successfully solve the stated constrained multi-objective optimization problem, in this paper a hybrid algorithm between particle swarm optimization and differential evolution algorithms has been proposed and applied to considered problem. Here, the mutation operators from the differential evolution algorithm have been incorporated into the velocity update equation of the particle swarm optimization algorithm, with the adaptive population spacing parameter employed to select the appropriate mutation operator for the current optimization condition. It has been shown that the proposed algorithm successfully obtains the solutions of the non-convex Pareto set, and reveals key insights in reducing the weight, improving efficiency and preventing premature failure of gears. Compared to other well-known algorithms, the numerical simulation results indicate that the proposed algorithm shows improved optimization performance in terms of the quality of the obtained Pareto solutions