{"title":"基于人工神经网络的三体船剩余阻力预测","authors":"Burak Yıldız","doi":"10.21278/brod73107","DOIUrl":null,"url":null,"abstract":"Trimaran hull forms have been popular recently in both commercial and military usage due to reduction in resistance at high speeds, better stability, and greater deck area compared to conventional monohull vessels. Determination of the location of the side hulls is most critical to get higher hydrodynamic performance. Therefore, many studies in the literature are related to defining the location of the side hulls for trimaran vessels. Most of the studies have been carried out experimentally or numerically. In this study, an artificial neural network (ANN) model was used to predict the residual resistance coefficient of a trimaran model. The model uses four parameters which are the transverse and longitudinal positions of the side hulls, the longitudinal centre of buoyancy and the Froude number to predict the residual resistance of the trimaran model. The experimental data of the trimaran model were used to train the neural network model in order to develop a more reliable model. Several neural network models were developed and tested to find the one with minimum error. The study showed that the residual resistance coefficients of the trimaran model were predicted with high accuracy levels compared to the model experimental data. It was also shown that an ANN is a useful alternative method to model tests and numerical simulations. The developed model can be used to reduce the number of model tests or numerical simulations as well as to obtain the optimum location of the side hulls in terms of resistance.","PeriodicalId":55594,"journal":{"name":"Brodogradnja","volume":null,"pages":null},"PeriodicalIF":3.9000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"PREDICTION OF RESIDUAL RESISTANCE OF A TRIMARAN VESSEL BY USING AN ARTIFICIAL NEURAL NETWORK\",\"authors\":\"Burak Yıldız\",\"doi\":\"10.21278/brod73107\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Trimaran hull forms have been popular recently in both commercial and military usage due to reduction in resistance at high speeds, better stability, and greater deck area compared to conventional monohull vessels. Determination of the location of the side hulls is most critical to get higher hydrodynamic performance. Therefore, many studies in the literature are related to defining the location of the side hulls for trimaran vessels. Most of the studies have been carried out experimentally or numerically. In this study, an artificial neural network (ANN) model was used to predict the residual resistance coefficient of a trimaran model. The model uses four parameters which are the transverse and longitudinal positions of the side hulls, the longitudinal centre of buoyancy and the Froude number to predict the residual resistance of the trimaran model. The experimental data of the trimaran model were used to train the neural network model in order to develop a more reliable model. Several neural network models were developed and tested to find the one with minimum error. The study showed that the residual resistance coefficients of the trimaran model were predicted with high accuracy levels compared to the model experimental data. It was also shown that an ANN is a useful alternative method to model tests and numerical simulations. The developed model can be used to reduce the number of model tests or numerical simulations as well as to obtain the optimum location of the side hulls in terms of resistance.\",\"PeriodicalId\":55594,\"journal\":{\"name\":\"Brodogradnja\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Brodogradnja\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.21278/brod73107\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MARINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brodogradnja","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.21278/brod73107","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MARINE","Score":null,"Total":0}
PREDICTION OF RESIDUAL RESISTANCE OF A TRIMARAN VESSEL BY USING AN ARTIFICIAL NEURAL NETWORK
Trimaran hull forms have been popular recently in both commercial and military usage due to reduction in resistance at high speeds, better stability, and greater deck area compared to conventional monohull vessels. Determination of the location of the side hulls is most critical to get higher hydrodynamic performance. Therefore, many studies in the literature are related to defining the location of the side hulls for trimaran vessels. Most of the studies have been carried out experimentally or numerically. In this study, an artificial neural network (ANN) model was used to predict the residual resistance coefficient of a trimaran model. The model uses four parameters which are the transverse and longitudinal positions of the side hulls, the longitudinal centre of buoyancy and the Froude number to predict the residual resistance of the trimaran model. The experimental data of the trimaran model were used to train the neural network model in order to develop a more reliable model. Several neural network models were developed and tested to find the one with minimum error. The study showed that the residual resistance coefficients of the trimaran model were predicted with high accuracy levels compared to the model experimental data. It was also shown that an ANN is a useful alternative method to model tests and numerical simulations. The developed model can be used to reduce the number of model tests or numerical simulations as well as to obtain the optimum location of the side hulls in terms of resistance.
期刊介绍:
The journal is devoted to multidisciplinary researches in the fields of theoretical and experimental naval architecture and oceanology as well as to challenging problems in shipbuilding as well shipping, offshore and related shipbuilding industries worldwide. The aim of the journal is to integrate technical interests in shipbuilding, ocean engineering, sea and ocean shipping, inland navigation and intermodal transportation as well as environmental issues, overall safety, objects for wind, marine and hydrokinetic renewable energy production and sustainable transportation development at seas, oceans and inland waterways in relations to shipbuilding and naval architecture. The journal focuses on hydrodynamics, structures, reliability, materials, construction, design, optimization, production engineering, building and organization of building, project management, repair and maintenance planning, information systems in shipyards, quality assurance as well as outfitting, powering, autonomous marine vehicles, power plants and equipment onboard. Brodogradnja publishes original scientific papers, review papers, preliminary communications and important professional papers relevant in engineering and technology.