MULTI-OBJECTIVE OPTIMISATION AND ITS APPLICATION TO CONCEPT DEVELOPMENT IN FLOATING OFFSHORE SYSTEMS

2021 ◽  
Vol 162 (A4) ◽  
Author(s):  
Dr P Gallagher
Keyword(s):  
Economic Performance ◽  
Concept Development ◽  
Oil Field ◽  
Rational Approach ◽  
Nsga Ii ◽  
Investment Performance ◽  
Field Development ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Floating System

This paper addresses the need for a rapid, multi-disciplined and rational approach to floating system concept development and selection during the very earliest stages of project definition. It describes the implementation of a modified multi- objective Genetic Algorithm for this purpose. A formulation of the NSGA-II algorithm is combined with additional Target Functions to reduce otherwise large multi-disciplined problems to more tractable solution using tools commonly available in the design office. It also provides a rational basis for the comparison of different design solutions each of which are Pareto Optimal with respect to the technical and economic performance of each underlying concept. A specific example of marginal field development using a novel FPSO concept is presented. Starting with just the oil field location and reserves estimate, the algorithm provides the means to define preliminary hull form and production facility capacities, match performance to payload, and give preliminary indicators of likely investment performance. The method may also be applied more generally in preliminary ship design, particularly where it is possible to model economic performance alongside efficiency, safety and key technical factors in hydrodynamics and structures.

Multi-Objective Optimisation and its Application to Concept Development in Floating Offshore Systems

2020 ◽  
Vol 162 (A4) ◽  
Author(s):  
P Gallagher
Keyword(s):  
Economic Performance ◽  
Concept Development ◽  
Oil Field ◽  
Rational Approach ◽  
Nsga Ii ◽  
Investment Performance ◽  
Field Development ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Floating System

This paper addresses the need for a rapid, multi-disciplined and rational approach to floating system concept development and selection during the very earliest stages of project definition. It describes the implementation of a modified multi- objective Genetic Algorithm for this purpose. A formulation of the NSGA-II algorithm is combined with additional Target Functions to reduce otherwise large multi-disciplined problems to more tractable solution using tools commonly available in the design office. It also provides a rational basis for the comparison of different design solutions each of which are Pareto Optimal with respect to the technical and economic performance of each underlying concept. A specific example of marginal field development using a novel FPSO concept is presented. Starting with just the oil field location and reserves estimate, the algorithm provides the means to define preliminary hull form and production facility capacities, match performance to payload, and give preliminary indicators of likely investment performance. The method may also be applied more generally in preliminary ship design, particularly where it is possible to model economic performance alongside efficiency, safety and key technical factors in hydrodynamics and structures.

Robust Design of Power Distribution Systems Using an Enhanced Multi-Objective Genetic Algorithm

2012 ◽  
pp. 179-200 ◽  
Author(s):  
Cristiane G. Taroco ◽  
Eduardo G. Carrano ◽  
Oriane M. Neto
Keyword(s):  
Power Distribution ◽  
Distribution Systems ◽  
Search Algorithm ◽  
Efficient Solutions ◽  
Optimization Techniques ◽  
Nsga Ii ◽  
Robust Solutions ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Reliability And Robustness

The growing importance of electric distribution systems justifies new investments in their expansion and evolution. It is well known in the literature that optimization techniques can provide better allocation of the financial resources available for such a task, reducing total installation costs and power losses. In this work, the NSGA-II algorithm is used for obtaining a set of efficient solutions with regard to three objective functions, that is cost, reliability, and robustness. Initially, a most likely load scenario is considered for simulation. Next, the performances of the solutions achieved by the NSGA-II are evaluated under different load scenarios, which are generated by means of Monte Carlo Simulations. A Multi-objective Sensitivity Analysis is performed for selecting the most robust solutions. Finally, those solutions are submitted to a local search algorithm to estimate a Pareto set composed of just robust solutions only.

EFFICIENCY INCREASE OF MULTI-OBJECTIVE GENETIC ALGORITHMS USING THE CLUSTERIZATION OF A POPULATION IN THE VARIABLE SPACE

2019 ◽  
pp. 13-20
Author(s):  
P. V. Kazakov
Keyword(s):  
Genetic Algorithm ◽  
Benchmark Problems ◽  
Additional Parameter ◽  
Nsga Ii ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Fitness Value ◽  
Efficiency Increase ◽  
Variable Space ◽  
Special Rule

The paper introduces a new manner for improving of obtained by MOGA (Multi-Objective Genetic Algorithm) solutions. It is based on the concept of dividing the population into set of clusters according to solutions similarity. In different of most MOGA the clusterization of population is implemented in the variable space, enables to enhance diversity of population and to increase the number of non-dominated solutions. The special procedures for the clustering of current population and copying the clusters in the next population were developed. The dominance principal by fitness-value is used for clustering. The number of clusters depends on additional parameter the radius of cluster’s hypersphere that is determined experimentally. By the special rule the individuals corresponded to centroids of clusters are copied in the new population. The clusters are recalculated for every population. The influence of the radius cluster to the number of non-dominated solutions variation was studied. The cluster modification should be integrated into any multi-objective genetic algorithm. By the analytical evaluation has been studied, this MOGA modification has additional computationally complexity from linear to quadratic. In experiments it was tested with the evolutionary algorithms SPEA2, NSGA-II on the special benchmark problems (DTLZ) with a various number of criteria using the set of performance indices. The used clustering in the variable space algorithms were achieved a better distribution and convergence to the true Paretofront in some cases.

Applications of Multi-Objective Genetic Algorithm in Optimizing Steering Performances for Submersible

2013 ◽  
Vol 756-759 ◽  
pp. 3136-3140
Author(s):  
Zhuo Yi Yang ◽  
Yong Jie Pang ◽  
Shao Lian Ma
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Approximation Model ◽  
Horizontal Movement ◽  
Nsga Ii ◽  
Hydrodynamic Coefficient ◽  
Pareto Solution ◽  
Multi Objective ◽  
Minimum Drag ◽  
Multi Objective Genetic Algorithm

Multi-objective arithmetic NSGA-II based on Pareto solution is investigated to deal with integrated optimal design of speedability and manoeuvre performances for submersible. Approximation model of resistance for serial revolving shape is constructed by hydrodynamic numerical calculations. The appraisement criterions of stability and mobility are calculated from linear equation of horizontal movement by estimating hydrodynamic coefficient of submersible. After optimization, the scattered Pareto solution of drag and turning diameter are gained, and from the solutions designer can select the reasonable one based on the actual requirement. The Pareto solution can ensure the minimum drag in this manoeuvre performance or the best manoeuvre performance in this drag value.

Optimization of Force and Moment Balance of a Four-Bar Linkage Using Multi-Objective Genetic Algorithm

2009 ◽  
Author(s):  
Mohammad Reza Farmani ◽  
A. Jaamiolahmadi
Keyword(s):  
Genetic Algorithm ◽  
Objective Functions ◽  
Fuzzy Decision ◽  
Nsga Ii ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Feasible Solutions ◽  
Four Bar Linkage ◽  
External Loads ◽  
Vibration Noise

In this study, force and moment balance of a four-bar linkage is implemented by using a Multi-Objective Genetic Algorithm (MOGA). During the time that an unbalanced linkage moves, it transmits shaking forces and moments to its surroundings. These transmitted forces and moments may cause some serious and undesirable problems such as vibration, noise, wear, and fatigue. In the current problem, the concepts of inertia counterweights and physical pendulum are utilized to complete balance of all mass effects (both linear and rotary, but excluding external loads), independent of input angular velocity. In this paper, Non-Dominated Genetic Algorithm (NSGA-II) is applied to minimize two objective functions subject to some different design constraints. The applied algorithm produced a set of feasible solutions called Pareto optimal solutions for the design problem. Finally, a fuzzy decision maker is applied to select the best solution among the obtained Pareto solutions based on design criteria. The results show that obtained solutions minimize the weights of applied counterweights and eliminate both shaking forces and moments transmitted to the ground, simultaneously.

scholarly journals MULTI-OBJECTIVE CALIBRATION OF IBIS MODEL BY GENETIC ALGORITHM WITH PARAMETRIC SENSITIVITY ANALYSIS

2016 ◽  
Vol 38 ◽  
pp. 90
Author(s):  
Amarísio Da Silva Araújo ◽  
Haroldo De Campos Velho ◽  
Lu Minjiao
Keyword(s):  
Genetic Algorithm ◽  
Good Description ◽  
Synthetic Data ◽  
Search Space ◽  
Adjustment Process ◽  
Nsga Ii ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Integrated Biosphere Simulator ◽  
Ibis Model

Atmospheric circulation models combine different modules for a good description of the atmospheric dynamics. One of these modules is the representation of surface coverage, since the dynamics depends on the interaction between the atmosphere and the surface of the planet. However, these modules depend on a number of parameters that need to be adjusted. The parameter adjustment process is called model calibration. In this study, the IBIS (Integrated Biosphere Simulator) model is calibrated following a multi-objective strategy. The Pareto set, which embraces the non-dominated solutions in the search space of objective functions, is determined by a version of multi-objective genetic algorithm (NSGA-II). The model sensitivity to the parameters is evaluated by the Morris’ method. Synthetic data for calibration were obtained from the Tapajós National Forest (FloNa Tapajós), located near to the 67 km from Santarém-Cuiabá highway (2,51S, 54,58W).

scholarly journals Multi-Objective Optimization and Fluid Selection of Different Cogeneration of Heat and Power Systems Based on Organic Rankine Cycle

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4967
Author(s):  
Shiyang Teng ◽  
Yong-Qiang Feng ◽  
Tzu-Chen Hung ◽  
Huan Xi
Keyword(s):  
Power Systems ◽  
Economic Performance ◽  
Waste Heat ◽  
Organic Rankine Cycle ◽  
Optimal Solution ◽  
Rankine Cycle ◽  
Exergy Efficiency ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Calculation Results

Cogeneration of heat and power systems based on the organic Rankine cycle (ORC-CHP) has been proven to be an effective way to utilize waste heat at medium and low temperatures. In this work, three ORC-CHP (combined heat and power based on organic Rankine cycle) systems are simulated and compared, including the SS (serial system), the CS (the condensation system), and the SS/CS. The multi-objective genetic algorithm (MOGA) is used to optimize the three systems respectively to achieve higher exergy efficiency and profit ratio of investment (PRI). The optimal thermal-economic performance is obtained. Twelve organic fluids are adopted to evaluate their performance as working fluids. The calculation results show that SS has the highest exergy efficiency, while SS/CS has the best economic performance. Compared with the highest exergy efficiency of SS and the best economic performance of SS/CS, CS will be the optimal solution considering these two objective functions. Under the optimal working conditions, SS has the highest thermal efficiency because it has the highest net power output. The components with the largest proportion of exergy destruction are the heat exchangers, which also has the highest cost.

Technical Challenge on VLFS in Japan After Mega-Float Project

2017 ◽  
Author(s):  
Hideyuki Suzuki ◽  
Hidetoshi Harada ◽  
Takumi Natsume ◽  
Katsuya Maeda ◽  
Kazuhiro Iijima ◽  
...  
Keyword(s):  
Research And Development ◽  
Concept Development ◽  
Oil Field ◽  
Research Association ◽  
Shipping Company ◽  
Technical Challenge ◽  
Field Development ◽  
Oil Field Development ◽  
Calm Water ◽  
Oil Gas

Intensive research and development of Mega-float was implemented by Technological Research Association of Mega-Float (TRAM) in late 1990s. Mega-float is a concept of a pontoon type VLFS installed in protected calm water typically large bay, and the concept was promoted by TRAM after relatively long concept development phase in Japan. This paper reviews the research and development of VLFS in Japan. The research and development of Mega-float is briefly reviewed firstly and two research and developments after Mega-float are reviewed. Concerning development of Oil-Gas development, logistic hub concept has been developed by J-DeEP, a consortium comprised of major Japanese heavy industries, shipping company, ClassNK and NMRI, and proposed for presalt oil field development offshore Brazil. Another research is pontoon type coal storage and offloading facility to be installed offshore South Sumatra, Indonesia. The coal storage and offloading facility was investigated by JIP comprised of JMU, ClassNK, Osaka University and University of Tokyo.

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