Bow shape modification through multi-objective hydrodynamic optimization: Methodology comparison between CAD-based FreeForm Deformation and Mesh-based Radial Basis Function approach

Marine industrial engineering face crucial challenges because of environmental footprint of vehicles, global recession, construction, and operation cost. Meanwhile, Shape optimization is the key feature to improve ship efficiency and ascertain better design. Accordingly, the present paper proposes an automated optimization framework for ship hullform modification to reduce total resistance at two cruise and sprint speeds. The case study is a bow shape of a wave-piercing bow trimaran hull. To this end, a multi-objective hydrodynamic problem needs to be solved. A combined optimization strategy using CFD hullform optimization is presented using the software tools STAR-CCM+ and SHERPA algorithm as optimizer. Furthermore, a comparison is made between CAD-based and Mesh-based parametrization techniques. Comparison between geometry regeneration methods is performed to present a practical and efficient parametrization tool. Design variables are control points of FreeForm Deformation (FFD) for CAD-based method and Radial Basis Function (RBF) for Mesh-based method. The optimization results show a 4.77% and 2.47% reduction in the total resistance at cruise and sprint speed, respectively.

Feature Data-driven Reinforced Fuzzy Radial Basis Function Neural Network Classifier With the Aid of Preprocessing Techniques and Particle Swarm Optimization

In this study, reinforced fuzzy radial basis function neural networks (FRBFNN) classifier driven by feature extracted data completed with the aid of effectively preprocessing techniques and evolutionary optimization, and its comprehensive design methodology are introduced. An Overall structure of the reinforced FRBFNN comprises the preprocessing part, the premise part and the consequence part of fuzzy rules of the network. In the preprocessing part, four types of preprocessing algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), combination of PCA and LDA (Hybrid PCA) and fuzzy transform (FT) are considered. To extract feature data suitable to characterize signal data, the feature extraction of information data is carried out through the dimensionality reduction done by the preprocessing technique, and then the reduced data are used as the input to the FRBFNN classifier. In the premise part of fuzzy rules of the network, the number of fuzzy rules is determined according to the number of clusters by fuzzy c-means (FCM) clustering. The fitness values of individual fuzzy rules are obtained based on data distribution. In the consequence part of fuzzy rules of the network, the parameters of connection weights located between the hidden layer and the output layer of FRBFNN classifier are estimated by means of the least square estimation (LSE). Particle swarm optimization (PSO) is exploited for structural as well as parametric optimization in the FRBFNN classifier. The parameters to be optimized by PSO are related to six factors such as the determination of whether to use data preprocessing, the type of data preprocessing technique, the number of input variables reduced by the preprocessing technique, fuzzification coefficient (FC) and the number of fuzzy rules used in fuzzy c-means (FCM) clustering, and the type of connection weights. By using diverse benchmark dataset obtained from UCI repository, the classification performance of the reinforced FRBFNN classifier was evaluated. Through a variety of classification algorithms existed in the Weka data mining software (Weka), the classification performance of the reinforced FRBFNN classifier was compared as well. The superiority of the proposed classifier is demonstrated through Friedman test. Furthermore, we assessed the classification performance of the reinforced FRBFNN classifier applied to black plastic wastes spectral data acquired from Raman and Laser induced breakdown spectroscopy (LIBS) equipment for the practical application of the material sorting system of the black plastic wastes.

A Novel Radial Basis Function Neural Network with High Generalization Performance for Nonlinear Process Modelling

A radial basis function neural network (RBFNN), with a strong function approximation ability, was proven to be an effective tool for nonlinear process modeling. However, in many instances, the sample set is limited and the model evaluation error is fixed, which makes it very difficult to construct an optimal network structure to ensure the generalization ability of the established nonlinear process model. To solve this problem, a novel RBFNN with a high generation performance (RBFNN-GP), is proposed in this paper. The proposed RBFNN-GP consists of three contributions. First, a local generalization error bound, introducing the sample mean and variance, is developed to acquire a small error bound to reduce the range of error. Second, the self-organizing structure method, based on a generalization error bound and network sensitivity, is established to obtain a suitable number of neurons to improve the generalization ability. Third, the convergence of this proposed RBFNN-GP is proved theoretically in the case of structure fixation and structure adjustment. Finally, the performance of the proposed RBFNN-GP is compared with some popular algorithms, using two numerical simulations and a practical application. The comparison results verified the effectiveness of RBFNN-GP.