scholarly journals Design Optimization of B-series Marine Propeller using NSGA-II, Iterative and Gekko Algorithm

2021 ◽  
pp. 159-163
Author(s):  
SM Munawar Mahtab ◽  
Debasish Roy ◽  
Md. Sanaul Rabbi ◽  
Md. Iftekharul Alam
Keyword(s):  
Dynamic Optimization ◽  
Optimization Approach ◽  
Primary Concern ◽  
Marine Propeller ◽  
Nsga Ii ◽  
Marine Propellers ◽  
Design Factors ◽  
Architecture Optimization ◽  
Python Package ◽  
Single Objective

The design of a propeller plays a significant role in naval architecture. Optimization of various design factors is the primary concern for effective and efficient propulsion. This study investigates the optimization of the B-series marine propellers using three different methods, i.e. (i) a non-linear constrained single-objective optimization approach using the Non-Dominated Sorting Genetic Algorithm (NSGA-II), (ii) a python package for dynamic optimization based optimization software ‘Gekko’, (iii) an iterative approach and results were compared with each other. Efficiency is considered as the single objective function whereas three constraints are imposed: cavitation, thrust and strength. Analogous characteristics have been found in the comparison of results from all three methods. Comparing the various factors, this study suggests that, Gekko can be used as the optimization algorithm.

Dynamic optimization approach to coordinate industrial production and cogeneration operation under electricity price fluctuations

2021 ◽  
Vol 149 ◽  
pp. 107292
Author(s):  
Cristian Pablos ◽  
Alejandro Merino ◽  
Luis Felipe Acebes ◽  
José Luis Pitarch ◽  
Lorenz T. Biegler
Keyword(s):  
Dynamic Optimization ◽  
Industrial Production ◽  
Electricity Price ◽  
Optimization Approach ◽  
Price Fluctuations

Dynamic Optimization of Steel Catenary Risers Based on Reliability Using Metamodel

2010 ◽  
Author(s):  
Wenqing Zheng ◽  
Hezhen Yang
Keyword(s):  
Dynamic Optimization ◽  
Computational Cost ◽  
Minimum Cost ◽  
Probabilistic Constraints ◽  
Optimization Approach ◽  
Element Analysis ◽  
Steel Catenary Riser ◽  
Reliability Based Design ◽  
Minimum Cost Design ◽  
Steel Catenary Risers

Reliability based design optimization (RBDO) of a steel catenary riser (SCR) using metamodel is investigated. The purpose of the optimization is to find the minimum-cost design subjecting to probabilistic constraints. To reduce the computational cost of the traditional double-loop RBDO, a single-loop RBDO approach is employed. The performance function is approximated by using metamodel to avoid time consuming finite element analysis during the dynamic optimization. The metamodel is constructed though design of experiments (DOE) sampling. In addition, the reliability assessment is carried out by Monte Carlo simulations. The result shows that the RBDO of SCR is a more rational optimization approach compared with traditional deterministic optimization, and using metamodel technique during the dynamic optimization process can significantly decrease the computational expense without sacrificing accuracy.

scholarly journals A two-stage evolutionary optimization approach for an irrigation system design

2016 ◽  
Vol 19 (1) ◽  
pp. 115-122 ◽  
Author(s):  
Milan Cisty ◽  
Zbynek Bajtek ◽  
Lubomir Celar
Keyword(s):  
Water Distribution ◽  
Irrigation System ◽  
Optimal Solution ◽  
Water Distribution Network ◽  
Search Space ◽  
Optimization Method ◽  
Global Optimum ◽  
Optimization Approach ◽  
Nsga Ii ◽  
Irrigation Network

In this work, an optimal design of a water distribution network is proposed for large irrigation networks. The proposed approach is built upon an existing optimization method (NSGA-II), but the authors are proposing its effective application in a new two-step optimization process. The aim of the paper is to demonstrate that not only is the choice of method important for obtaining good optimization results, but also how that method is applied. The proposed methodology utilizes as its most important feature the ensemble approach, in which more optimization runs cooperate and are used together. The authors assume that the main problem in finding the optimal solution for a water distribution optimization problem is the very large size of the search space in which the optimal solution should be found. In the proposed method, a reduction of the search space is suggested, so the final solution is thus easier to find and offers greater guarantees of accuracy (closeness to the global optimum). The method has been successfully tested on a large benchmark irrigation network.

Multiobjective Group Search Optimization Approach for Community Detection in Networks

2016 ◽  
Vol 7 (3) ◽  
pp. 50-70 ◽  
Author(s):  
Nidhi Arora ◽  
Hema Banati
Keyword(s):  
Community Detection ◽  
Optimization Techniques ◽  
Optimization Approach ◽  
Multiple Features ◽  
Detection Algorithms ◽  
Search Optimization ◽  
Art Community ◽  
Group Search ◽  
Single Objective ◽  
Connected Communities

Various evolving approaches have been extensively applied to evolve densely connected communities in complex networks. However these techniques have been primarily single objective optimization techniques, which optimize only a specific feature of the network missing on other important features. Multiobjective optimization techniques can overcome this drawback by simultaneously optimizing multiple features of a network. This paper proposes MGSO, a multiobjective variant of Group Search Optimization (GSO) algorithm to globally search and evolve densely connected communities. It uses inherent animal food searching behavior of GSO to simultaneously optimize two negatively correlated objective functions and overcomes the drawbacks of single objective based CD algorithms. The algorithm reduces random initializations which results in fast convergence. It was applied on 6 real world and 33 synthetic network datasets and results were compared with varied state of the art community detection algorithms. The results established show the efficacy of MGSO to find accurate community structures.

FSI Analysis the Dynamic Performance of Composite Propeller

2018 ◽  
Author(s):  
Fanchen Zhang ◽  
Jianjun Ma
Keyword(s):  
Composite Materials ◽  
Dynamic Performance ◽  
High Yield ◽  
Maintenance Cost ◽  
Aluminum Bronze ◽  
Nickel Aluminum ◽  
Marine Propeller ◽  
Marine Propellers ◽  
Superior Corrosion Resistance ◽  
Nickel Aluminum Bronze

The marine propeller is regarded as critical component with regard to the performance of the ships and torpedoes. Traditionally marine propellers are made of manganese-nickel-aluminum-bronze (MAB) or nickel-aluminum-bronze (NAB) for superior corrosion resistance, high-yield strength, reliability, and affordability. Since the composite materials can offer the potential benefits of reduced corrosion and cavitation damage, improved fatigue performance, lower noise, improved material damping properties, and reduced lifetime maintenance cost, Many researches on the application of the composite materials for marine propeller had been conducted. In this work, the INSEAN 1619 large screw 7 bladed propeller is analyzed, to explore the hydrodynamic and structural performance of composite materials effect on propeller’s performances, The commercial software ANSYS Workbench was used in this research. The coupled FSI method was used to analysis the dynamic performance of INSEAN 1619 large screw 7 bladed propeller made of different materials. The simulation results show that the effect of fluid–structure interaction in the analysis of flexible composite propellers should be considered.

Surrogate-Assisted Modeling and Robust Optimization of a Micro Gas Turbine Plant With Carbon Capture

2019 ◽  
Author(s):  
Simone Giorgetti ◽  
Diederik Coppitters ◽  
Francesco Contino ◽  
Ward De Paepe ◽  
Laurent Bricteux ◽  
...  
Keyword(s):  
Robust Optimization ◽  
Gas Turbines ◽  
Carbon Capture ◽  
Computational Cost ◽  
Gaussian Process Regression ◽  
Aspen Plus ◽  
Optimization Approach ◽  
Co2 Absorption ◽  
Exhaust Gas ◽  
Nsga Ii

Abstract The growing share of wind and solar power in the total energy mix has caused severe problems in balancing the electrical power production. Consequently, in the future, all fossil fuel-based electricity generation will need to be run on a completely flexible basis. Micro Gas Turbines (mGTs) constitutes a mature technology which can offer such flexibility. Even though their greenhouse gas emissions are already very low, stringent carbon reduction targets will require them to be completely carbon neutral: this constraint implies the adoption of post-combustion Carbon Capture (CC) on these energy systems. To reduce the CC energy penalty, Exhaust Gas Recirculation (EGR) can be applied to the mGTs increasing the CO2 content in the exhaust gas and reducing the mass flow rate of flue gas to be treated. As a result, a lower investment and operational cost of the CC unit can be achieved. In spite of this attractive solution, an in-depth study along with a robust optimization of this system has not yet been carried out. Hence, in this paper, a typical mGT with EGR has been coupled with an amine-based CC plant and simulated using the software Aspen Plus®. A rigorous rate-based simulation of the CO2 absorption and desorption in the CC unit offers an accurate prediction; however, its time complexity and convergence difficulty are severe limitations for a stochastic optimization. Therefore, a surrogate-based optimization approach has been used, which makes use of a Gaussian Process Regression (GPR) model, trained using the Aspen Plus® data, to quickly find operating points of the plant at a very low computational cost. Using the validated surrogate model, a robust optimization using a Non-dominated Sorting Genetic Algorithm II (NSGA II) has been carried out, assessing the influence of each input uncertainty and varying several design variables. As a general result, the analysed power plant proves to be intrinsically very robust, even when the input variables are affected by strong uncertainties.

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