Finite Element Modelling and Multi-Objective Optimization of Composite Submarine Pressure Hull Subjected to Hydrostatic Pressure

2019 ◽  
Vol 953 ◽  
pp. 53-58 ◽  
Author(s):  
Elsayed Fathallah
Keyword(s):  
Hydrostatic Pressure ◽  
Parametric Design ◽  
Epoxy Composite ◽  
Load Capacity ◽  
Optimization Process ◽  
Valuable Insight ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimized Model ◽  
Better Than

Excellent mechanical behavior and low density of composite materials make them candidates to replace metals for many underwater applications. This paper presents a comprehensive study about the multi-objective optimization of composite pressure hull subjected to hydrostatic pressure to minimize the weight of the pressure hull and maximize the buckling load capacity according to the design requirements. Two models were constructed, one model constructed from Carbon/Epoxy composite (USN-150), the other model is metallic pressure hull constructed from HY100. The analysis and the optimization process were completely performed using ANSYS Parametric Design Language (APDL). Tsai-Wu failure criterion was incorporated in the optimization process. The results obtained emphasize that, the submarine constructed from Carbon/Epoxy composite (USN-150) is better than the submarine constructed from HY100. Finally, an optimized model with an optimum pattern of fiber orientations was presented. Hopefully, the results may provide a valuable insight for the future of designing composite underwater vehicles.

Multi-Objective Optimization of Composite Elliptical Submersible Pressure Hull for Minimize the Buoyancy Factor and Maximize Buckling Load Capacity

2014 ◽  
Vol 578-579 ◽  
pp. 75-82 ◽  
Author(s):  
Fathallah Elsayed ◽  
Hui Qi ◽  
Li Li Tong ◽  
Mahmoud Helal
Keyword(s):  
Parametric Design ◽  
Load Capacity ◽  
Buckling Load ◽  
Multi Objective Optimization ◽  
Element Analysis ◽  
Multi Objective ◽  
Design Variables ◽  
Wide Range ◽  
Conflicting Objectives ◽  
Sophisticated Analysis

Due to the wide range of variables involved and sophisticated analysis techniques required, optimum structural design of composite submersible pressure hull is known to be a challenge for designers. The major challenge involved in the coupled design problem is to handle multiple conflicting objectives. The problem with its proper consideration through multi-objective optimization is studied in this paper. Minimize the buoyancy factor and maximize buckling load capacity of the submersible pressure hull under hydrostatic pressure is considered as the objective function to reach the operating depth equal to 6000m. Finite element analysis of composite elliptical submersible pressure hull is performed using ANSYS parametric design language (APDL). The constraints based on the failure strength of the hulls are considered. The fiber orientation angles and the thickness in each layer, the radii of the ellipse, the ring beams and the stringers dimensions are taken as design variables. Additionally, a sensitivity analysis is performed to study the influence of the design variables up on objectives and constraints functions. Results of this study provide a valuable reference for designers of composite underwater vehicles.

Multi-Objective Evolutionary Algorithm Based on Two Reference Points Decomposition and Historical Information Prediction

2021 ◽  
Vol 01 ◽  
Author(s):  
Er-chao Li ◽  
Kang-wei Li
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Reference Points ◽  
Multi Objective Optimization ◽  
Test Function ◽  
Historical Information ◽  
Multi Objective ◽  
Nadir Point ◽  
Overall Performance ◽  
Better Than

Aims: The main purpose of this paper is to solve the issues that the poor quality of offspring solutions generated by traditional evolutionary operators, and that the inability of the evolutionary algorithm based on decomposition to better solve the multi-objective optimization problems (MOPs) with complicated Pareto fronts (PFs). Background: For some complicated multi-objective optimization problems, the effect of the multi-objective evolutionary algorithm based on decomposition (MOEA/D) is poor. For specific complicated problems, there is less research on improving the algorithm's performance by setting and adjusting the direction vector in the decomposition-based evolutionary algorithm. And considering that in the existing algorithms, the optimal solutions are selected according to the selection strategy in the selection stage, without considering if it could produce the better solutions in the stage of individual generation to achieve the optimization effect faster. As a result of these, a multi-objective evolutionary algorithm that is based on two reference points decomposition and historical information prediction is proposed. Objective: In order to verify the feasibility of the proposed strategy, the F-series test function with complicated PFs is used as the test function to simulate the proposed strategy. Method: Firstly, the evolutionary operator based on Historical Information Prediction (EHIP) is used to generate better offspring solutions to improve the convergence of the algorithm; secondly, the decomposition strategy based on ideal point and nadir point is used to select solutions to solve the MOPs with complicated PFs, and the decomposition method with augmentation term is used to improve the population diversity when selecting solutions according to the nadir point. Finally, the proposed algorithm is compared to several popular algorithms by the F-series test function, and the comparison is made according to the corresponding performance metrics. Result: The performance of the algorithm is improved obviously compared with the popular algorithms after using the EHIP. When the decomposition method with augmentation term is added, the performance of the proposed algorithm is better than the algorithm with only the EHIP on the whole. However, the overall performance is better than the popular algorithms. Conclusion and Prospect: The experimental results show that the overall performance of the proposed algorithm is superior to the popular algorithms. The EHIP can produce better quality offspring solutions, and the decomposition strategy based on two reference points can well solve the MOPs with complicated PFs. This paper mainly demonstrates the theory without testing the practical problems. The following research mainly focuses on the application of the proposed algorithm to the practical problems such as robot path planning.

Parametric design and multi-objective optimization of a general 6-PUS parallel manipulator

2020 ◽  
Vol 152 ◽  
pp. 103913 ◽  
Author(s):  
S. Nader Nabavi ◽  
Morteza Shariatee ◽  
Javad Enferadi ◽  
Alireza Akbarzadeh
Keyword(s):  
Parallel Manipulator ◽  
Parametric Design ◽  
Multi Objective Optimization ◽  
Multi Objective

A Method for Solving Multi-Objective Optimization Problems Containing an Infinite Number of Parameterized Objectives

2020 ◽  
Author(s):  
Eliot Rudnick-Cohen
Keyword(s):  
Decision Making ◽  
Infinite Number ◽  
Optimization Problems ◽  
Pareto Frontier ◽  
Brute Force ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Multi Objective ◽  
Brute Force Approach ◽  
Better Than

Abstract Multi-objective decision making problems can sometimes involve an infinite number of objectives. In this paper, an approach is presented for solving multi-objective optimization problems containing an infinite number of parameterized objectives, termed “infinite objective optimization”. A formulation is given for infinite objective optimization problems and an approach for checking whether a Pareto frontier is a solution to this formulation is detailed. Using this approach, a new sampling based approach is developed for solving infinite objective optimization problems. The new approach is tested on several different example problems and is shown to be faster and perform better than a brute force approach.

scholarly journals On the Hardness of Offline Multi-objective Optimization

2007 ◽  
Vol 15 (4) ◽  
pp. 475-491 ◽  
Author(s):  
Olivier Teytaud
Keyword(s):  
Evolutionary Algorithms ◽  
Convergence Rate ◽  
Random Search ◽  
Computational Cost ◽  
Multiobjective Evolutionary Algorithms ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Insight Into ◽  
Better Than

It has been empirically established that multiobjective evolutionary algorithms do not scale well with the number of conflicting objectives. This paper shows that the convergence rate of all comparison-based multi-objective algorithms, for the Hausdorff distance, is not much better than the convergence rate of the random search under certain conditions. The number of objectives must be very moderate and the framework should hold the following assumptions: the objectives are conflicting and the computational cost is lower bounded by the number of comparisons is a good model. Our conclusions are: (i) the number of conflicting objectives is relevant (ii) the criteria based on comparisons with random-search for multi-objective optimization is also relevant (iii) having more than 3-objectives optimization is very hard. Furthermore, we provide some insight into cross-over operators.

Application of Constraint Multi-Objective Optimization to the Design of Offshore Structure Hulls

2007 ◽  
Author(s):  
Lothar Birk
Keyword(s):  
Computer Aided Design ◽  
Parametric Design ◽  
Pareto Frontier ◽  
Optimization Procedure ◽  
Design System ◽  
Multi Objective Optimization ◽  
Offshore Structure ◽  
Multi Objective ◽  
Heave Motion ◽  
Automated Optimization

The paper reports on the continuous development of an automated optimization procedure for the design of offshore structure hulls. Advanced parametric design algorithms, numerical analysis of wave-body interaction and formal multi-objective optimization are integrated into a computer aided design system that produces hull shapes with superior seakeeping qualities. By allowing multiple objectives in the procedure naval architects may pursue concurrent design objectives, e.g. minimizing heave motion while simultaneously maximizing deck load. The system develops a Pareto frontier of the best design alternatives for the user to choose from. Constraints are directly considered within the optimization algorithm thus eliminating infeasible or unfit designs. The paper summarizes the new developments in the shape generation, illustrates the optimization procedure and presents results of the multi-objective hull shape optimization.

scholarly journals A Decomposition-Based Interactive Method for Multi-Objective Evolutionary Algorithm

2012 ◽  
Author(s):  
Nguye Long ◽  
Bui Thu Lam
Keyword(s):  
Optimization Problems ◽  
Decision Makers ◽  
Optimization Process ◽  
Multi Objective Optimization ◽  
Interactive Method ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Single Solution ◽  
Optimal Set

Multi-objectivity has existed in many real-world optimization problems. In most multi-objective cases, objectives are often conflicting, there is no single solution being optimal with regards to all objectives. These problems are called Multi-objective Optimization Problems (MOPs). To date, there have been al large number of methods for solving MOPs including evolutionary methods (namly Multi-objective Evolutionary Algorithms MOEAs). With the use of a population of solutions for searching. MOEAs are naturally suitable for approximating optimal solutions (called the Pareto Optimal Set (POS) or the efficient set). There has been a popular trend in MOEAs considering the role of Decision Makers (DMs) during the optimization process (known as the human-in-loop) for checking, analyzing the results and giving the preference to guide the optimization process. This is call the interactive method.

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