Wave parameter identification problem for ocean test structure data, part 1, continuous formulation View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1984-10

AUTHORS

A. Miele, T. Wang, J. C. Heideman, J. N. Sharma

ABSTRACT

This paper deals with the solution of the wave parameter identification problem for ocean test structure data. A continuous formulation is assumed. An ocean test structure is considered, and wave elevation and velocities are assumed to be measured with a number of sensors. Within the frame of linear wave theory, a Fourier series model is chosen for the wave elevation and velocities. Then, the following problem is posed: Find the amplitudes of the various wave components of specified frequency and direction, so that the assumed model of wave elevation and velocities provides the best fit to the measured data. Here, the term best fit is employed in the least-square sense over a given time interval.At each time instant, the wave representation involves three indexes (frequency, direction, instrument); hence, three-dimensional arrays are required. This formal difficulty can be avoided by switching to an alternative representation involving only two indexes (frequency-direction, instrument); hence, standard vector-matrix notation can be used. Within this frame, optimality conditions are derived for the amplitudes of the assumed wave model.Numerical results are presented. The effect of various system parameters (number of frequencies, number of directions, sampling time, number of sensors, and location of sensors) is investigated in connection with global or strong accuracy, local or weak accuracy, integral accuracy, and condition number of the system matrix.From the numerical experiments, it appears that the identification problem has a unique solution if the number of directions is smaller than or equal to the number of sensors; it has an infinite number of solutions otherwise. In the case where a unique solution exists, the condition number of the system matrix increases as the size of the system increases, and this has a detrimental effect on the accuracy. However, the accuracy can be improved by proper selection of the sampling time and by proper choice of the number and location of the sensors. More... »

PAGES

269-302

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf00935439

DOI

http://dx.doi.org/10.1007/bf00935439

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1004748070


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/09", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Engineering", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0102", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Applied Mathematics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0103", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Numerical and Computational Mathematics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0906", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Electrical and Electronic Engineering", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Aero-Astronautics Group, Rice University, Houston, Texas", 
          "id": "http://www.grid.ac/institutes/grid.21940.3e", 
          "name": [
            "Aero-Astronautics Group, Rice University, Houston, Texas"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Miele", 
        "givenName": "A.", 
        "id": "sg:person.015552732657.49", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015552732657.49"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Aero-Astronautics Group, Rice University, Houston, Texas", 
          "id": "http://www.grid.ac/institutes/grid.21940.3e", 
          "name": [
            "Aero-Astronautics Group, Rice University, Houston, Texas"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wang", 
        "givenName": "T.", 
        "id": "sg:person.014414570607.44", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014414570607.44"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Offshore Structures Division, Exxon Production Research Company, Houston, Texas", 
          "id": "http://www.grid.ac/institutes/grid.421234.2", 
          "name": [
            "Offshore Structures Division, Exxon Production Research Company, Houston, Texas"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Heideman", 
        "givenName": "J. C.", 
        "id": "sg:person.015020376163.09", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015020376163.09"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Offshore Structures Division, Exxon Production Research Company, Houston, Texas", 
          "id": "http://www.grid.ac/institutes/grid.421234.2", 
          "name": [
            "Offshore Structures Division, Exxon Production Research Company, Houston, Texas"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sharma", 
        "givenName": "J. N.", 
        "id": "sg:person.010554651121.02", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010554651121.02"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/978-1-4684-3399-9_12", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028088265", 
          "https://doi.org/10.1007/978-1-4684-3399-9_12"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-85567-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050022027", 
          "https://doi.org/10.1007/978-3-642-85567-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00935462", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025276495", 
          "https://doi.org/10.1007/bf00935462"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1984-10", 
    "datePublishedReg": "1984-10-01", 
    "description": "This paper deals with the solution of the wave parameter identification problem for ocean test structure data. A continuous formulation is assumed. An ocean test structure is considered, and wave elevation and velocities are assumed to be measured with a number of sensors. Within the frame of linear wave theory, a Fourier series model is chosen for the wave elevation and velocities. Then, the following problem is posed: Find the amplitudes of the various wave components of specified frequency and direction, so that the assumed model of wave elevation and velocities provides the best fit to the measured data. Here, the term best fit is employed in the least-square sense over a given time interval.At each time instant, the wave representation involves three indexes (frequency, direction, instrument); hence, three-dimensional arrays are required. This formal difficulty can be avoided by switching to an alternative representation involving only two indexes (frequency-direction, instrument); hence, standard vector-matrix notation can be used. Within this frame, optimality conditions are derived for the amplitudes of the assumed wave model.Numerical results are presented. The effect of various system parameters (number of frequencies, number of directions, sampling time, number of sensors, and location of sensors) is investigated in connection with global or strong accuracy, local or weak accuracy, integral accuracy, and condition number of the system matrix.From the numerical experiments, it appears that the identification problem has a unique solution if the number of directions is smaller than or equal to the number of sensors; it has an infinite number of solutions otherwise. In the case where a unique solution exists, the condition number of the system matrix increases as the size of the system increases, and this has a detrimental effect on the accuracy. However, the accuracy can be improved by proper selection of the sampling time and by proper choice of the number and location of the sensors.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/bf00935439", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1044187", 
        "issn": [
          "0022-3239", 
          "1573-2878"
        ], 
        "name": "Journal of Optimization Theory and Applications", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "44"
      }
    ], 
    "keywords": [
      "parameter identification problem", 
      "identification problem", 
      "system matrix", 
      "condition number", 
      "continuous formulation", 
      "number of sensors", 
      "unique solution exists", 
      "wave elevation", 
      "test structure data", 
      "least-squares sense", 
      "linear wave theory", 
      "vector-matrix notation", 
      "optimality conditions", 
      "infinite number", 
      "wave representation", 
      "numerical experiments", 
      "Fourier series model", 
      "unique solution", 
      "Ocean Test Structure", 
      "wave theory", 
      "solution exists", 
      "integral accuracy", 
      "system parameters", 
      "wave model", 
      "numerical results", 
      "best fit", 
      "number of directions", 
      "formal difficulties", 
      "series models", 
      "wave components", 
      "structure data", 
      "alternative representation", 
      "proper choice", 
      "weak accuracy", 
      "sampling time", 
      "three-dimensional array", 
      "system increases", 
      "problem", 
      "velocity", 
      "solution", 
      "formulation", 
      "accuracy", 
      "matrix", 
      "model", 
      "representation", 
      "fit", 
      "amplitude", 
      "test structures", 
      "time interval", 
      "theory", 
      "number", 
      "proper selection", 
      "direction", 
      "Part 1", 
      "parameters", 
      "notation", 
      "sensors", 
      "frame", 
      "sense", 
      "connection", 
      "array", 
      "data", 
      "structure", 
      "strong accuracy", 
      "time", 
      "cases", 
      "frequency", 
      "conditions", 
      "experiments", 
      "results", 
      "choice", 
      "exists", 
      "interval", 
      "selection", 
      "size", 
      "difficulties", 
      "effect", 
      "components", 
      "location", 
      "index", 
      "detrimental effects", 
      "increase", 
      "elevation", 
      "paper"
    ], 
    "name": "Wave parameter identification problem for ocean test structure data, part 1, continuous formulation", 
    "pagination": "269-302", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1004748070"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/bf00935439"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/bf00935439", 
      "https://app.dimensions.ai/details/publication/pub.1004748070"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-11-24T20:45", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221124/entities/gbq_results/article/article_152.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/bf00935439"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/bf00935439'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/bf00935439'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf00935439'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf00935439'


 

This table displays all metadata directly associated to this object as RDF triples.

189 TRIPLES      21 PREDICATES      115 URIs      101 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/bf00935439 schema:about anzsrc-for:01
2 anzsrc-for:0102
3 anzsrc-for:0103
4 anzsrc-for:09
5 anzsrc-for:0906
6 schema:author Nf1f01cbed3c44828b77e4ebbfeb9fc5a
7 schema:citation sg:pub.10.1007/978-1-4684-3399-9_12
8 sg:pub.10.1007/978-3-642-85567-2
9 sg:pub.10.1007/bf00935462
10 schema:datePublished 1984-10
11 schema:datePublishedReg 1984-10-01
12 schema:description This paper deals with the solution of the wave parameter identification problem for ocean test structure data. A continuous formulation is assumed. An ocean test structure is considered, and wave elevation and velocities are assumed to be measured with a number of sensors. Within the frame of linear wave theory, a Fourier series model is chosen for the wave elevation and velocities. Then, the following problem is posed: Find the amplitudes of the various wave components of specified frequency and direction, so that the assumed model of wave elevation and velocities provides the best fit to the measured data. Here, the term best fit is employed in the least-square sense over a given time interval.At each time instant, the wave representation involves three indexes (frequency, direction, instrument); hence, three-dimensional arrays are required. This formal difficulty can be avoided by switching to an alternative representation involving only two indexes (frequency-direction, instrument); hence, standard vector-matrix notation can be used. Within this frame, optimality conditions are derived for the amplitudes of the assumed wave model.Numerical results are presented. The effect of various system parameters (number of frequencies, number of directions, sampling time, number of sensors, and location of sensors) is investigated in connection with global or strong accuracy, local or weak accuracy, integral accuracy, and condition number of the system matrix.From the numerical experiments, it appears that the identification problem has a unique solution if the number of directions is smaller than or equal to the number of sensors; it has an infinite number of solutions otherwise. In the case where a unique solution exists, the condition number of the system matrix increases as the size of the system increases, and this has a detrimental effect on the accuracy. However, the accuracy can be improved by proper selection of the sampling time and by proper choice of the number and location of the sensors.
13 schema:genre article
14 schema:isAccessibleForFree false
15 schema:isPartOf N84904e48a36940e8a73e137ff9dc02d8
16 Nfcafd050bd954028b93562bdb862b175
17 sg:journal.1044187
18 schema:keywords Fourier series model
19 Ocean Test Structure
20 Part 1
21 accuracy
22 alternative representation
23 amplitude
24 array
25 best fit
26 cases
27 choice
28 components
29 condition number
30 conditions
31 connection
32 continuous formulation
33 data
34 detrimental effects
35 difficulties
36 direction
37 effect
38 elevation
39 exists
40 experiments
41 fit
42 formal difficulties
43 formulation
44 frame
45 frequency
46 identification problem
47 increase
48 index
49 infinite number
50 integral accuracy
51 interval
52 least-squares sense
53 linear wave theory
54 location
55 matrix
56 model
57 notation
58 number
59 number of directions
60 number of sensors
61 numerical experiments
62 numerical results
63 optimality conditions
64 paper
65 parameter identification problem
66 parameters
67 problem
68 proper choice
69 proper selection
70 representation
71 results
72 sampling time
73 selection
74 sense
75 sensors
76 series models
77 size
78 solution
79 solution exists
80 strong accuracy
81 structure
82 structure data
83 system increases
84 system matrix
85 system parameters
86 test structure data
87 test structures
88 theory
89 three-dimensional array
90 time
91 time interval
92 unique solution
93 unique solution exists
94 vector-matrix notation
95 velocity
96 wave components
97 wave elevation
98 wave model
99 wave representation
100 wave theory
101 weak accuracy
102 schema:name Wave parameter identification problem for ocean test structure data, part 1, continuous formulation
103 schema:pagination 269-302
104 schema:productId N0d99300e3c7d4edfa67ead05cbbce8f4
105 N11049fd2ac484c7da96db1e25852c9ac
106 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004748070
107 https://doi.org/10.1007/bf00935439
108 schema:sdDatePublished 2022-11-24T20:45
109 schema:sdLicense https://scigraph.springernature.com/explorer/license/
110 schema:sdPublisher N7295564ef35a4200b65f9268cba6ef93
111 schema:url https://doi.org/10.1007/bf00935439
112 sgo:license sg:explorer/license/
113 sgo:sdDataset articles
114 rdf:type schema:ScholarlyArticle
115 N0d99300e3c7d4edfa67ead05cbbce8f4 schema:name dimensions_id
116 schema:value pub.1004748070
117 rdf:type schema:PropertyValue
118 N11049fd2ac484c7da96db1e25852c9ac schema:name doi
119 schema:value 10.1007/bf00935439
120 rdf:type schema:PropertyValue
121 N4fd9953a071b47a9a2dcc4b28f62a8c2 rdf:first sg:person.015020376163.09
122 rdf:rest N61a91a1086c741b59bced0f4bbebd6da
123 N61a91a1086c741b59bced0f4bbebd6da rdf:first sg:person.010554651121.02
124 rdf:rest rdf:nil
125 N7295564ef35a4200b65f9268cba6ef93 schema:name Springer Nature - SN SciGraph project
126 rdf:type schema:Organization
127 N84904e48a36940e8a73e137ff9dc02d8 schema:issueNumber 2
128 rdf:type schema:PublicationIssue
129 Nf1f01cbed3c44828b77e4ebbfeb9fc5a rdf:first sg:person.015552732657.49
130 rdf:rest Nf99da9e1a8914c459afadac7bf00bace
131 Nf99da9e1a8914c459afadac7bf00bace rdf:first sg:person.014414570607.44
132 rdf:rest N4fd9953a071b47a9a2dcc4b28f62a8c2
133 Nfcafd050bd954028b93562bdb862b175 schema:volumeNumber 44
134 rdf:type schema:PublicationVolume
135 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
136 schema:name Mathematical Sciences
137 rdf:type schema:DefinedTerm
138 anzsrc-for:0102 schema:inDefinedTermSet anzsrc-for:
139 schema:name Applied Mathematics
140 rdf:type schema:DefinedTerm
141 anzsrc-for:0103 schema:inDefinedTermSet anzsrc-for:
142 schema:name Numerical and Computational Mathematics
143 rdf:type schema:DefinedTerm
144 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
145 schema:name Engineering
146 rdf:type schema:DefinedTerm
147 anzsrc-for:0906 schema:inDefinedTermSet anzsrc-for:
148 schema:name Electrical and Electronic Engineering
149 rdf:type schema:DefinedTerm
150 sg:journal.1044187 schema:issn 0022-3239
151 1573-2878
152 schema:name Journal of Optimization Theory and Applications
153 schema:publisher Springer Nature
154 rdf:type schema:Periodical
155 sg:person.010554651121.02 schema:affiliation grid-institutes:grid.421234.2
156 schema:familyName Sharma
157 schema:givenName J. N.
158 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010554651121.02
159 rdf:type schema:Person
160 sg:person.014414570607.44 schema:affiliation grid-institutes:grid.21940.3e
161 schema:familyName Wang
162 schema:givenName T.
163 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014414570607.44
164 rdf:type schema:Person
165 sg:person.015020376163.09 schema:affiliation grid-institutes:grid.421234.2
166 schema:familyName Heideman
167 schema:givenName J. C.
168 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015020376163.09
169 rdf:type schema:Person
170 sg:person.015552732657.49 schema:affiliation grid-institutes:grid.21940.3e
171 schema:familyName Miele
172 schema:givenName A.
173 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015552732657.49
174 rdf:type schema:Person
175 sg:pub.10.1007/978-1-4684-3399-9_12 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028088265
176 https://doi.org/10.1007/978-1-4684-3399-9_12
177 rdf:type schema:CreativeWork
178 sg:pub.10.1007/978-3-642-85567-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050022027
179 https://doi.org/10.1007/978-3-642-85567-2
180 rdf:type schema:CreativeWork
181 sg:pub.10.1007/bf00935462 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025276495
182 https://doi.org/10.1007/bf00935462
183 rdf:type schema:CreativeWork
184 grid-institutes:grid.21940.3e schema:alternateName Aero-Astronautics Group, Rice University, Houston, Texas
185 schema:name Aero-Astronautics Group, Rice University, Houston, Texas
186 rdf:type schema:Organization
187 grid-institutes:grid.421234.2 schema:alternateName Offshore Structures Division, Exxon Production Research Company, Houston, Texas
188 schema:name Offshore Structures Division, Exxon Production Research Company, Houston, Texas
189 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...