Multi-Objective Optimization Technique Applied to Preliminary Design of a Tanker

2008 ◽  
Author(s):  
Marcelo R. Martins ◽  
Diego F. S. Burgos
Keyword(s):  
Genetic Algorithm ◽  
Early Stage ◽  
Optimization Technique ◽  
Pareto Frontier ◽  
Design Parameters ◽  
Preliminary Design ◽  
Multi Objective Optimization ◽  
Total Cost ◽  
Rational Process

This paper shows one rational process of selecting the optimal dimensions and coefficients of form of tankers using the technique of genetic algorithm in the early stage of design. Two objective attributes are used to evaluate each design: Total Cost and Mean Oil Outflow. It is proposed a procedure to balance the designs in weight and useful space and assesses their feasibility. A genetic algorithm is implemented to search optimal design parameters and identify the non-dominated Pareto frontier. A real Suezmax vessel is used as case study.

Multi-Objective Optimization Design of Tanker Ships via a Genetic Algorithm

2011 ◽  
Vol 133 (4) ◽  
Author(s):  
Marcelo Ramos Martins ◽  
Diego F. Sarzosa Burgos
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Optimization Problem ◽  
Pareto Frontier ◽  
Ship Design ◽  
Design Parameters ◽  
Multi Objective Optimization ◽  
Total Cost ◽  
Multi Objective ◽  
The Cost

The cost of a new ship design heavily depends on the principal dimensions of the ship; however, dimensions minimization often conflicts with the minimum oil outflow (in the event of an accidental spill). This study demonstrates one rational methodology for selecting the optimal dimensions and coefficients of form of tankers via the use of a genetic algorithm. Therein, a multi-objective optimization problem was formulated by using two objective attributes in the evaluation of each design, specifically, total cost and mean oil outflow. In addition, a procedure that can be used to balance the designs in terms of weight and useful space is proposed. A genetic algorithm was implemented to search for optimal design parameters and to identify the nondominated Pareto frontier. At the end of this study, three real ships are used as case studies.

Using Genetic Algorithms to Set Target Values for Engineering Characteristics in the House of Quality

1999 ◽  
Author(s):  
Patricia Brackin ◽  
Jonathan Colton
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Penalty Functions ◽  
Preliminary Design ◽  
Test Results ◽  
House Of Quality ◽  
Design Specifications ◽  
Engineering Characteristics ◽  
Target Values

Abstract As part of a strategy for obtaining preliminary design specifications from the House of Quality, genetic algorithms were used to generate and optimize preliminary design specifications for an automotive case study. This paper describes the House of Quality for the automotive case study. In addition, the genetic algorithm chosen, the genetic coding, the methods used for mutation and reproduction, and the fitness and penalty functions are descrobed. Methods for determining convergence are examined. Finally, test results show that the genetic algorithm produces reasonable preliminary design specifications.

Multi-objective optimization of hydro-viscous flexible drive for dynamic characteristics using genetic algorithm

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jianzhong Cui ◽  
Hu Li ◽  
Dong Zhang ◽  
Yawen Xu ◽  
Fangwei Xie
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Characteristics ◽  
Transmission Efficiency ◽  
Outer Radius ◽  
Transmission Model ◽  
Design Parameters ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Dynamic Transmission Model

Purpose The purpose of this study is to investigate the flexible dynamic characteristics about hydro-viscous drive providing meaningful insights into the credible speed-regulating behavior during the soft-start. Design/methodology/approach A comprehensive dynamic transmission model is proposed to investigate the effects of key parameters on the dynamic characteristics. To achieve a trade-off between the transmission efficiency and time proportion of hydrodynamic and mixed lubrication, a multi-objective optimization of friction pair system by genetic algorithm is presented to obtain the optimal combination of design parameters. Findings Decreasing the engagement pressure or the ratio of inner and outer radius, increasing the lubricating oil viscosity or the outer radius will result in the increase of time proportion of hydrodynamic and mixed lubrication, as well as the transmission efficiency and its maximum value. After optimization, main dynamic parameters including the oil film thickness, angular velocity of the driven disk, viscous torque and total torque show remarkable flexible transmission characteristics. Originality/value Both the dynamic transmission model and multi-objective optimization model are established to analyze the effects of main design parameters on the dynamic characteristics of hydro-viscous flexible drive.

Simulation-Supported Early Stage Design Optimisation for a Case Study of Life Cycle Oriented Buildings

2019 ◽  
Vol 887 ◽  
pp. 353-360 ◽  
Author(s):  
Sören Eikemeier ◽  
Ardeshir Mahdavi ◽  
Robert Wimmer
Keyword(s):  
Life Cycle ◽  
Architectural Design ◽  
Early Stage ◽  
Preliminary Design ◽  
Design Phase ◽  
Design Optimisation ◽  
Stage Design ◽  
Simulation Based ◽  
Early Stage Design

To reduce the energy and resource consumption in the building sector this study is focusing on a design optimisation of life cycle oriented buildings. In order to optimise the performance of the buildings and in consequence also to achieve improved results for the mandatory Austrian energy certificate a simulation-based rapid design approach is used for the early stage design phase of the buildings, in particular for the architectural design of the buildings.Methods like the Window to Wall Ratio, at the very beginning of the design process, a parametric simulation with EnergyPlus or a more detailed optimisation approach with GenOpt are integrated in this study applied to example buildings. The results are showing that the method can be used in a circular approach for improving the heating demand of the Austrian energy certificate for this case study by more than 25 % compared to the preliminary design

scholarly journals Thermal-economic multiobjective optimization of heat pipe heat exchanger for energy recovery in HVAC applications using genetic algorithm

2014 ◽  
Vol 18 (suppl.2) ◽  
pp. 375-391 ◽  
Author(s):  
Sepehr Sanaye ◽  
Davood Modarrespoor
Keyword(s):  
Genetic Algorithm ◽  
Heat Exchanger ◽  
Heat Pipe ◽  
Operating Cost ◽  
Design Parameters ◽  
Nsga Ii ◽  
Pipe Length ◽  
Total Cost ◽  
Continuous And Discrete Variables ◽  
Pipe Heat

Cost and effectiveness are two important factors of heat pipe heat exchanger (HPHE) design. The total cost includes the investment cost for buying equipment (heat exchanger surface area) and operating cost for energy expenditures (related to fan power). The HPHE was thermally modeled using e-NTU method to estimate the overall heat transfer coefficient for the bank of finned tubes as well as estimating pressure drop. Fast and elitist non-dominated sorting genetic algorithm (NSGA-II) with continuous and discrete variables was applied to obtain the maximum effectiveness and the minimum total cost as two objective functions. Pipe diameter, pipe length, numbers of pipes per row, number of rows, fin pitch and fin length ratio were considered as six design parameters. The results of optimal designs were a set of multiple optimum solutions, called ?Pareto optimal solutions?. The comparison of the optimum values of total cost and effectiveness, variation of optimum values of design parameters as well as estimating the payback period were also reported for various inlet fresh air volume flow rates.

Application of Extremal Distributions in the Design of Thermal Systems

1980 ◽  
Vol 102 (3) ◽  
pp. 481-489 ◽  
Author(s):  
S. S. Rao ◽  
B. D. Gupta
Keyword(s):  
Optimum Design ◽  
Optimization Technique ◽  
Unconstrained Minimization ◽  
Design Parameters ◽  
Penalty Function Method ◽  
Cooling Load ◽  
Extreme Value Distributions ◽  
Thermal Systems ◽  
Maximum Daily Temperature ◽  
Total Cost

Three types of extreme value distributions are fitted to the maximum daily temperature and solar radiation. It is found that type III distribution for the largest value fits the data most closely. A methodology using the maximum yearly temperature data and extremal distributions is developed for the optimum design of refrigerated warehouses. The use of the concept of return period in the optimum design of thermal systems is also suggested. The interior penalty function method with Davidon-Fletcher-Powella method of unconstrained minimization is used as the optimization technique for solving the problems. A sensitivity analysis is conducted about the optimum design point to find the influence of changes in various design parameters on the cooling load and total cost.

Multi-Objective Optimization of Axial Crush Performance of Square Metal–Composite Hybrid Tubes

2013 ◽  
Author(s):  
Roozbeh Kalhor ◽  
Hossein Akbarshahi ◽  
Scott W. Case
Keyword(s):  
Impact Load ◽  
Pareto Frontier ◽  
Back Propagation ◽  
Design Parameters ◽  
Metal Composite ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Axial Crush ◽  
Composite Layers ◽  
Test Sets

This article deals with the multi objective optimization of square hybrid tubes (metal-composite) under axial impact load. Maximum crushing load and absorbed energy are objective functions and fiber orientation angles of the composite layers are chosen as design parameters while the maximum crush load is limited. Back-propagation artificial neural networks (ANNs) are utilized to construct the mapping between the variables and the objectives. Non-dominated sorting Genetic algorithm–II (NSGAII) is applied to obtain the optimal solutions and the finite element commercial software LS-DYNA is used to generate the training and test sets for the ANNs. Optimum results are presented as a Pareto frontier.

scholarly journals Effects of Robust Convex Optimization on Early-Stage Design Space Exploratory Behavior

2020 ◽  
Vol 142 (12) ◽  
Author(s):  
Priya P. Pillai ◽  
Edward Burnell ◽  
Xiqing Wang ◽  
Maria C. Yang
Keyword(s):  
Robust Optimization ◽  
Design Space Exploration ◽  
Design Space ◽  
Early Stage ◽  
Pareto Frontier ◽  
Design Parameters ◽  
Safety Factors ◽  
Worst Case ◽  
Robust Convex Optimization ◽  
Early Stage Design

Abstract Engineers design for an inherently uncertain world. In the early stages of design processes, they commonly account for such uncertainty either by manually choosing a specific worst-case and multiplying uncertain parameters with safety factors or by using Monte Carlo simulations to estimate the probabilistic boundaries in which their design is feasible. The safety factors of this first practice are determined by industry and organizational standards, providing a limited account of uncertainty; the second practice is time intensive, requiring the development of separate testing infrastructure. In theory, robust optimization provides an alternative, allowing set-based conceptualizations of uncertainty to be represented during model development as optimizable design parameters. How these theoretical benefits translate to design practice has not previously been studied. In this work, we analyzed the present use of geometric programs as design models in the aerospace industry to determine the current state-of-the-art, then conducted a human-subjects experiment to investigate how various mathematical representations of uncertainty affect design space exploration. We found that robust optimization led to far more efficient explorations of possible designs with only small differences in an experimental participant’s understanding of their model. Specifically, the Pareto frontier of a typical participant using robust optimization left less performance “on the table” across various levels of risk than the very best frontiers of participants using industry-standard practices.

scholarly journals Early-Stage Uncertainty: Effects of Robust Convex Optimization on Design Exploration

2020 ◽  
Author(s):  
Priya P. Pillai ◽  
Edward Burnell ◽  
Xiqing Wang ◽  
Maria C. Yang
Keyword(s):  
Robust Optimization ◽  
Design Space Exploration ◽  
Early Stage ◽  
Human Subjects ◽  
Model Development ◽  
Pareto Frontier ◽  
Design Parameters ◽  
Safety Factors ◽  
Worst Case ◽  
Robust Convex Optimization

Abstract Engineers design for an inherently uncertain world. In the early stages of design processes, they commonly account for such uncertainty either by manually choosing a specific worst-case and multiplying uncertain parameters with safety factors or by using Monte Carlo simulations to estimate the probabilistic boundaries in which their design is feasible. The safety factors of this first practice are determined by industry and organizational standards, providing a limited account of uncertainty; the second practice is time intensive, requiring the development of separate testing infrastructure. In theory, robust optimization provides an alternative, allowing set based conceptualizations of uncertainty to be represented during model development as optimizable design parameters. How these theoretical benefits translate to design practice has not previously been studied. In this work, we analyzed present use of geometric programs as design models in the aerospace industry to determine the current state-of-the-art, then conducted a human-subjects experiment to investigate how various mathematical representations of uncertainty affect design space exploration. We found that robust optimization led to far more efficient explorations of possible designs with only small differences in an experimental participant’s understanding of their model. Specifically, the Pareto frontier of a typical participant using robust optimization left less performance “on the table” across various levels of risk than the very best frontiers of participants using industry-standard practices.

A Genetic Algorithm Based Multi-Objective Optimization of Squealer Tip Geometry in Axial Flow Turbines: A Constant Tip Gap Approach

2019 ◽  
Vol 142 (2) ◽  
Author(s):  
H. Maral ◽  
C. B. Şenel ◽  
K. Deveci ◽  
E. Alpman ◽  
L. Kavurmacıoğlu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Tip Clearance ◽  
Design Parameters ◽  
Leakage Flow ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Tip Leakage Flow ◽  
Tip Leakage ◽  
Multi Objective ◽  
Blade Tip

Abstract Tip clearance is a crucial aspect of turbomachines in terms of aerodynamic and thermal performance. A gap between the blade tip surface and the stationary casing must be maintained to allow the relative motion of the blade. The leakage flow through the tip gap measurably reduces turbine performance and causes high thermal loads near the blade tip region. Several studies focused on the tip leakage flow to clarify the flow-physics in the past. The “squealer” design is one of the most common designs to reduce the adverse effects of tip leakage flow. In this paper, a genetic-algorithm-based optimization approach was applied to the conventional squealer tip design to enhance aerothermal performance. A multi-objective optimization method integrated with a meta-model was utilized to determine the optimum squealer geometry. Squealer height and width represent the design parameters which are aimed to be optimized. The objective functions for the genetic-algorithm-based optimization are the total pressure loss coefficient and Nusselt number calculated over the blade tip surface. The initial database is then enlarged iteratively using a coarse-to-fine approach to improve the prediction capability of the meta-models used. The procedure ends once the prediction errors are smaller than a prescribed level. This study indicates that squealer height and width have complex effects on the aerothermal performance, and optimization study allows to determine the optimum squealer dimensions.

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