scholarly journals A Holistic Multi-Objective Design Optimization Approach for Arctic Offshore Supply Vessels

2021 ◽  
Vol 13 (10) ◽  
pp. 5550
Author(s):  
Aleksander A. Kondratenko ◽  
Martin Bergström ◽  
Aleksander Reutskii ◽  
Pentti Kujala
Keyword(s):  
Design Optimization ◽  
Case Studies ◽  
Parametric Design ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Abc Algorithm ◽  
Multi Objective ◽  
Bee Colony ◽  
Conceptual Design Phase ◽  
Wide Range

This article presents a new holistic multi-objective design approach for the optimization of Arctic Offshore Supply Vessels (OSVs) for cost- and eco-efficiency. The approach is intended to be used in the conceptual design phase of an Arctic OSV. It includes (a) a parametric design model of an Arctic OSV, (b) performance assessment models for independently operating and icebreaker-assisted Arctic OSVs, and (c) a novel adaptation of the Artificial Bee Colony (ABC) algorithm for multi-objective optimization of Arctic OSVs. To demonstrate the feasibility and viability of the proposed optimization approach, a series of case studies covering a wide range of operating scenarios are carried out. The results of the case studies indicate that the consideration of icebreaker assistance significantly extends the feasible design space of Arctic OSVs, enabling solutions with improved energy- and cost-efficiency. The results further indicate that the optimal amount of icebreaking assistance and optimal vessel speed differs for different vessels, highlighting the motivation for holistic design optimization. The applied adaptation of the ABC algorithm proved to be well suited and efficient for the multi-objective optimization problem considered.

Integrated Design and Multi-Objective Optimization of a Single Stage Heat-Pump Turbocompressor

2013 ◽  
Author(s):  
J. Schiffmann
Keyword(s):  
Design Optimization ◽  
High Efficiency ◽  
Integrated Design ◽  
Heat Pumps ◽  
Small Scale ◽  
Multi Objective Optimization ◽  
Design Constraints ◽  
Multi Objective ◽  
Wide Range ◽  
Standard Design

Small scale turbomachines in domestic heat pumps reach high efficiency and provide oil-free solutions which improve heat-exchanger performance and offer major advantages in the design of advanced thermodynamic cycles. An appropriate turbocompressor for domestic air based heat pumps requires the ability to operate on a wide range of inlet pressure, pressure ratios and mass flows, confronting the designer with the necessity to compromise between range and efficiency. Further the design of small-scale direct driven turbomachines is a complex and interdisciplinary task. Textbook design procedures propose to split such systems into subcomponents and to design and optimize each element individually. This common procedure, however, tends to neglect the interactions between the different components leading to suboptimal solutions. The authors propose an approach based on the integrated philosophy for designing and optimizing gas bearing supported, direct driven turbocompressors for applications with challenging requirements with regards to operation range and efficiency. Using previously validated reduced order models for the different components an integrated model of the compressor is implemented and the optimum system found via multi-objective optimization. It is shown that compared to standard design procedure the integrated approach yields an increase of the seasonal compressor efficiency of more than 12 points. Further a design optimization based sensitivity analysis allows to investigate the influence of design constraints determined prior to optimization such as impeller surface roughness, rotor material and impeller force. A relaxation of these constrains yields additional room for improvement. Reduced impeller force improves efficiency due to a smaller thrust bearing mainly, whereas a lighter rotor material improves rotordynamic performance. A hydraulically smoother impeller surface improves the overall efficiency considerably by reducing aerodynamic losses. A combination of the relaxation of the 3 design constraints yields an additional improvement of 6 points compared to the original optimization process. The integrated design and optimization procedure implemented in the case of a complex design problem thus clearly shows its advantages compared to traditional design methods by allowing a truly exhaustive search for optimum solutions throughout the complete design space. It can be used for both design optimization and for design analysis.

scholarly journals Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

2021 ◽  
Vol 9 (5) ◽  
pp. 478
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui ◽  
Ping Yang ◽  
Linke Chen
Keyword(s):  
Design Optimization ◽  
Optimization Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Robotic Fish ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Cfd Method ◽  
The Individual

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

Use Multi-Objective Optimization for Designing Cloaks With Acoustic and Elastic Materials

2012 ◽  
Author(s):  
Chunyan Bao ◽  
Liang-Wu Cai
Keyword(s):  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Frequency Range ◽  
Elastic Materials ◽  
Optimization Scheme ◽  
Multi Objective ◽  
Wide Range ◽  
Optimization Schemes ◽  
Elastic Layers

The various optimization schemes have been recently shown to be a capable tool for designing acoustic cloaks based on Cummer-Schurig prescription without requiring material singularity. However, when using an optimization scheme to optimize a cloak at one specified frequency, sometimes it is observed that the cloaking effect deteriorate at other frequencies. This paper explores the use of multi-objective optimization approach such that the cloaking performance is maintained over a wide range of frequencies. Two examples are presented. The first cloak is comprised of all aocustic layers, and the second cloak is comprised of a mixture of acoustic and elastic layers. The results show the effectiveness of the multi-objective optimization approach on maintaining cloaking performance over a specified frequency range.

scholarly journals Multi-objective optimization of traffic signal timing using non-dominated sorting artificial bee colony algorithm for unsaturated intersections

2018 ◽  
Vol 46 (2) ◽  
pp. 85-97 ◽  
Author(s):  
Hongxing Zhao ◽  
Ruichun He ◽  
Jiangsheng Su
Keyword(s):  
Optimization Model ◽  
Artificial Bee Colony ◽  
Optimization Method ◽  
Signal Timing ◽  
Multi Objective Optimization ◽  
Abc Algorithm ◽  
Multi Objective ◽  
Bee Colony ◽  
Vehicle Delay ◽  
Single Objective

Vehicle delay and stops at intersections are considered targets for optimizing signal timing for an isolated intersection to overcome the limitations of the linear combination and single objective optimization method. A multi-objective optimization model of a fixed-time signal control parameter of unsaturated intersections is proposed under the constraint of the saturation level of approach and signal time range. The signal cycle and green time length of each phase were considered decision variables, and a non-dominated sorting artificial bee colony (ABC) algorithm was used to solve the multi-objective optimization model. A typical intersection in Lanzhou City was used for the case study. Experimental results showed that a single-objective optimization method degrades other objectives when the optimized objective reaches an optimal value. Moreover, a reasonable balance of vehicle delay and stops must be achieved to flexibly adjust the signal cycle in a reasonable range. The convergence is better in the non-dominated sorting ABC algorithm than in non-dominated sorting genetic algorithm II, Webster timing, and weighted combination methods. The proposed algorithm can solve the Pareto front of a multi-objective problem, thereby improving the vehicle delay and stops simultaneously.

Simultaneous Requirement and Design Optimization of an Industrial Robot Family Using Multi-Objective Optimization

2011 ◽  
Author(s):  
Daniel Wa¨ppling ◽  
Xiaolong Feng ◽  
Hans Andersson ◽  
Marcus Pettersson ◽  
Bjo¨rn Lunden ◽  
...  
Keyword(s):  
Design Optimization ◽  
Product Design ◽  
Industrial Robot ◽  
Simultaneous Optimization ◽  
Engineering Practice ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Research Experience ◽  
Requirement Specification ◽  
Multi Objective

Simultaneous development of an industrial robot family, consisting typically of 2–10 robots, has been an engineering practice in robotics industry. In this process, significant scenario studies on defining product requirement specifications and associated design change are conducted. This implies that understanding the relation between product requirements and design of the robot family is of critical importance. However, in the current engineering practice, any change in requirement specification results in tremendous efforts in the re-design of the robot family. This discloses the need for efficient methodology and tools for simultaneously optimizing product requirements and design of an industrial robot family. In this work, methodology and tools have been successfully developed for simultaneously optimizing product requirements and design of an industrial robot family in a fully automated way. This problem is formulated to a multi-objective optimization problem and solved using multi-objective genetic algorithm (MOGA). Results of this work have demonstrated clearly the efficiency of this approach and the insight obtained on the relation between product requirement and product design. The developed methodology and results of simultaneous requirement specification and design optimization will be detailed in this paper. In addition, research experience and future work will also be discussed. To our best knowledge, the simultaneous optimization of product requirement and product design has not been widely investigated and explored in academia. The trade-off information explored by such approach is crucial in product development in industrial practice. Such approach will further increase the complexity of traditional design optimization approach where product requirement is normally pre-defined and used as constraint. It is certain that discussions of the addressed problem and developed methodology will contribute to promoting the significance of efforts in the research society of multi-objective design optimization, multi-objective design optimization of product families, and design automation.

scholarly journals Solving Security of Cyber-Physical Systems with Multi-Objective Optimization Algorithms

2019 ◽  
Vol 7 (2) ◽  
Author(s):  
Seyed Mahmood Hashemi
Keyword(s):  
Imperialist Competitive Algorithm ◽  
Cyber Physical Systems ◽  
The Internet ◽  
Multi Objective Optimization ◽  
Physical Systems ◽  
Multi Objective ◽  
Bee Colony ◽  
Wide Range ◽  
Objective Model ◽  
The Internet Of Things

Cyber-physical systems (CPS), as a significant set of the Internet of Things (IoT), play a key role in our life. CPS has a wide range of applications. Regardless of the benefits of CPS, they need a secure approach to communication. In this paper, an approach to CPS security is proposed. Usability of the proposed approach is the major characteristic of it because it employs a multi-objective model (MOM) of security. In this study, three algorithms were used to solve MOM (Multi-Objective Imperialist Competitive Algorithm, Multi-Objective Automata, Multi-Objective Bee Colony), because the evolutionary structure of the proposed algorithms causes the best adaptation on the network.

Integrated Design and Multi-objective Optimization of a Single Stage Heat-Pump Turbocompressor

2015 ◽  
Vol 137 (7) ◽  
Author(s):  
J. Schiffmann
Keyword(s):  
Design Optimization ◽  
Integrated Design ◽  
Heat Pumps ◽  
Small Scale ◽  
Multi Objective Optimization ◽  
Design Constraints ◽  
Design Procedures ◽  
Multi Objective ◽  
Wide Range ◽  
Standard Design

Small-scale turbomachines in domestic heat pumps reach high efficiency and provide oil-free solutions, which improve heat-exchanger performance and offer major advantages in the design of advanced thermodynamic cycles. An appropriate turbocompressor for domestic air based heat pumps requires the ability to operate on a wide range of inlet pressure, pressure ratios, and mass flows, confronting the designer with the necessity to compromise between range and efficiency. Further, the design of small-scale direct driven turbomachines is a complex and interdisciplinary task. Textbook design procedures propose to split such systems into subcomponents and to design and optimize each element individually. This common procedure, however, tends to neglect the interactions between the different components leading to suboptimal solutions. The author proposes an approach based on the integrated philosophy for designing and optimizing gas bearing supported, direct driven turbocompressors for applications with challenging requirements with regards to operation range and efficiency. Using experimentally validated reduced order models for the different components an integrated model of the compressor is implemented and the optimum system found via multi-objective optimization. It is shown that compared to standard design procedures, the integrated approach yields an increase of the seasonal compressor efficiency of more than 12 points. Further, a design optimization based sensitivity analysis allows to investigate the influence of design constraints determined prior to optimization such as impeller surface roughness, rotor material, and impeller force. A relaxation of these constrains yields additional room for improvement. Reduced impeller force improves efficiency due to a smaller thrust bearing mainly, whereas a lighter rotor material improves rotordynamic performance. A hydraulically smoother impeller surface improves the overall efficiency considerably by reducing aerodynamic losses. A combination of the relaxation of the three design constraints yields an additional improvement of six points compared to the original optimization process. The integrated design and optimization procedure implemented in the case of a complex design problem thus clearly shows its advantages compared to traditional design methods by allowing a truly exhaustive search for optimum solutions throughout the complete design space. It can be used for both design optimization and for design analysis.

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.

Design optimization of rotary regenerator using artificial bee colony algorithm

2011 ◽  
Vol 225 (8) ◽  
pp. 1088-1098 ◽  
Author(s):  
R V Rao ◽  
V Patel
Keyword(s):  
Design Optimization ◽  
Artificial Bee Colony ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Design Variables ◽  
Fitness Value ◽  
Single Objective

This study explores the use of artificial bee colony (ABC) algorithm for the design optimization of rotary regenerator. Maximization of regenerator effectiveness and minimization of regenerator pressure drop are considered as objective functions and are treated individually and then simultaneously for single-objective and multi-objective optimization, respectively. Seven design variables such as regenerator frontal area, matrix rotational speed, matrix rod diameter, matrix thickness, porosity, and split are considered for optimization. A case study is also presented to demonstrate the effectiveness and accuracy of the proposed algorithm. The results of optimization using ABC algorithm are validated by comparing with those obtained using genetic algorithm for the same case study. The effect of variation of ABC algorithm parameters on convergence and fitness value of the objective function has also been presented.

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