{"title":"用单求和海模型预测水槽试验的线性确定性海浪","authors":"M.R. Belmont","doi":"10.1016/j.apor.2025.104714","DOIUrl":null,"url":null,"abstract":"<div><div>This paper deals with the creation of phase resolved sea models from wave data both for creating sea wave generation models and for building wave prediction models. The main application of these considered here is in Deterministic Sea Wave Prediction (DSWP). The focus is on the so-called Single Summation Method (SSM). The SSM was introduced by certain researchers to address artifacts found in experimental tank testing work encountered when using discrete forms of the standard linear oceanographic wave description, i.e., the Double Summation Method (DSM). Unlike DSM the SSM is essentially a one-dimensional method, because only one wave component is used in each propagation direction. This might seem to make the SSM potentially attractive for wider applications than tank testing because it requires less computational resources than DSM which is fully two dimensional. Exploration of the SSM shows that it is not suitable for use with the spatial wave data typically employed for DSM. As developed here SSM based DSWP relies on time series data from two separate locations which considerably reduces the predict ahead time available in DSWP applications as compared to DSM. Furthermore, to avoid multi-valuedness in estimating the wave propagation directions conditions must be imposed that make SSM very sensitive to additive noise.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"162 ","pages":"Article 104714"},"PeriodicalIF":4.4000,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Linear deterministic sea wave prediction for tank testing using single summation sea models\",\"authors\":\"M.R. Belmont\",\"doi\":\"10.1016/j.apor.2025.104714\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper deals with the creation of phase resolved sea models from wave data both for creating sea wave generation models and for building wave prediction models. The main application of these considered here is in Deterministic Sea Wave Prediction (DSWP). The focus is on the so-called Single Summation Method (SSM). The SSM was introduced by certain researchers to address artifacts found in experimental tank testing work encountered when using discrete forms of the standard linear oceanographic wave description, i.e., the Double Summation Method (DSM). Unlike DSM the SSM is essentially a one-dimensional method, because only one wave component is used in each propagation direction. This might seem to make the SSM potentially attractive for wider applications than tank testing because it requires less computational resources than DSM which is fully two dimensional. Exploration of the SSM shows that it is not suitable for use with the spatial wave data typically employed for DSM. As developed here SSM based DSWP relies on time series data from two separate locations which considerably reduces the predict ahead time available in DSWP applications as compared to DSM. Furthermore, to avoid multi-valuedness in estimating the wave propagation directions conditions must be imposed that make SSM very sensitive to additive noise.</div></div>\",\"PeriodicalId\":8261,\"journal\":{\"name\":\"Applied Ocean Research\",\"volume\":\"162 \",\"pages\":\"Article 104714\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2025-08-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Ocean Research\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0141118725003001\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, OCEAN\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Ocean Research","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0141118725003001","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, OCEAN","Score":null,"Total":0}
Linear deterministic sea wave prediction for tank testing using single summation sea models
This paper deals with the creation of phase resolved sea models from wave data both for creating sea wave generation models and for building wave prediction models. The main application of these considered here is in Deterministic Sea Wave Prediction (DSWP). The focus is on the so-called Single Summation Method (SSM). The SSM was introduced by certain researchers to address artifacts found in experimental tank testing work encountered when using discrete forms of the standard linear oceanographic wave description, i.e., the Double Summation Method (DSM). Unlike DSM the SSM is essentially a one-dimensional method, because only one wave component is used in each propagation direction. This might seem to make the SSM potentially attractive for wider applications than tank testing because it requires less computational resources than DSM which is fully two dimensional. Exploration of the SSM shows that it is not suitable for use with the spatial wave data typically employed for DSM. As developed here SSM based DSWP relies on time series data from two separate locations which considerably reduces the predict ahead time available in DSWP applications as compared to DSM. Furthermore, to avoid multi-valuedness in estimating the wave propagation directions conditions must be imposed that make SSM very sensitive to additive noise.
期刊介绍:
The aim of Applied Ocean Research is to encourage the submission of papers that advance the state of knowledge in a range of topics relevant to ocean engineering.