Abstract
The geometry of systems including the marine engineering problems needs to be optimized in the initial design stage. However, the performance analysis using commercial code is generally time-consuming. To solve this problem, many engineers perform the optimization process using the response surface method (RSM) to predict the system performance, but RSM presents some prediction errors for nonlinear systems. The major objective of this research is to establish an optimal design framework. The framework is composed of three parts: definition of geometry, generation of response surface, and optimization process. To reduce the time for performance analysis and minimize the prediction errors, the response surface is generated using the artificial neural network (ANN) which is considered as NRSM. The optimization process is done for the generated response surface by non-dominated sorting genetic algorithm-II (NSGA-II). Through case study of a derrick structure, we have confirmed the proposed framework applicability. In the future, we will try to apply the constructed framework to multi-objective optimization problems.
Highlights
The optimal design of engineering system is considered as multi-objective optimization problem, and the geometry of engineering system severely affects their performance
To reduce the time for performance analysis and minimize the prediction errors, the response surface is generated using the artificial neural network (ANN) which is considered as NRSM
We subsequently propose an optimal design framework comprising two principal phases: 1st Phase: To predict the system performance, we generate the response surface using ANN that is considered as NRSM in the proposed framework
Summary
The optimal design of engineering system is considered as multi-objective optimization problem, and the geometry of engineering system severely affects their performance. For this reason, determining an optimal geometry is one of the challenging problems in the initial design stage. System optimization based on the performance using the commercial code is a method that was employed for engineering design problem [1, 2]. To reduce the performance calculation time, many researchers try to predict the performance using approximation models. These approximation models represent the relationship between inputs and outputs. The RSM is used to predict the system performance [3, 4, 5], but the method produces some errors in highly nonlinear problems [6, 7, 8]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
