Solving generalized lattice Boltzmann model for 3-D cavity flows using CUDA-GPU View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2012-10

AUTHORS

ChengGong Li, Jerome P. -Y. Maa, HaiGui Kang

ABSTRACT

The generalized lattice Boltzmann equation (GLBE), with the addition of the standard Smagorinsky subgrid-stress (SGS) model, has been proved that it is more suitable for simulating high Reynolds number turbulent flows when compared with the lattice BGK Boltzmann equation (LBGK). However, the computing efficiency of lattice Boltzmann method (LBM) is too low to make it for practical applications, unless using a massive parallel computing clusters facility. In this study, the massive parallel computing power from an inexpensive graphic processor unit (GPU) and a typical personal computer has been developed for improving the computing efficiency, more than 100 times. This developed three-dimensional (3-D) GLBE-SGS model, with the D3Q19 scheme for simplifying collision and streaming courses, has been successfully used to study 3-D rectangular cavity flows with Reynolds number up to 10000. More... »

PAGES

1894-1904

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11433-012-4856-9

DOI

http://dx.doi.org/10.1007/s11433-012-4856-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1008335809


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/2008/0915", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Interdisciplinary Engineering", 
        "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Dalian University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.30055.33", 
          "name": [
            "State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, 116023, Dalian, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Li", 
        "givenName": "ChengGong", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "College of William & Mary", 
          "id": "https://www.grid.ac/institutes/grid.264889.9", 
          "name": [
            "Virginia Institute of Marine Science, College of William and Mary, 23062, Gloucester Point, VA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Maa", 
        "givenName": "Jerome P. -Y.", 
        "id": "sg:person.012131541307.57", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012131541307.57"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Dalian University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.30055.33", 
          "name": [
            "State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, 116023, Dalian, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kang", 
        "givenName": "HaiGui", 
        "id": "sg:person.014602654602.04", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014602654602.04"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.compfluid.2005.04.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005069291"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/10618560802238275", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007634160"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcp.2005.03.022", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016713325"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcp.2005.03.022", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016713325"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/(sici)1097-0363(19980315)26:5<557::aid-fld638>3.0.co;2-r", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020116595"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1098/rsta.2001.0955", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022491816"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.camwa.2009.08.052", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022599292"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.79.026703", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023367652"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.79.026703", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023367652"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0021-9991(82)90058-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028336458"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00791-008-0120-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032945815", 
          "https://doi.org/10.1007/s00791-008-0120-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00791-008-0120-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032945815", 
          "https://doi.org/10.1007/s00791-008-0120-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1006/jcph.1995.1103", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034235143"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.2723153", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035139233"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00371-003-0210-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038074976", 
          "https://doi.org/10.1007/s00371-003-0210-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.matcom.2008.07.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053505096"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.857491", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058110696"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.870387", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058122519"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.55.r6333", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060721111"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.55.r6333", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060721111"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.56.6811", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060721720"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.56.6811", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060721720"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.61.6546", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060725271"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.61.6546", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060725271"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.61.2332", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060797898"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.61.2332", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060797898"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1115/1.3243136", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062110960"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1142/s0217979203017059", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062933128"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0493(1963)091<0099:gcewtp>2.3.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063451320"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1209/0295-5075/17/6/001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064228747"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/sc.2004.26", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095167248"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1017/cbo9780511840531", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098697945"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2012-10", 
    "datePublishedReg": "2012-10-01", 
    "description": "The generalized lattice Boltzmann equation (GLBE), with the addition of the standard Smagorinsky subgrid-stress (SGS) model, has been proved that it is more suitable for simulating high Reynolds number turbulent flows when compared with the lattice BGK Boltzmann equation (LBGK). However, the computing efficiency of lattice Boltzmann method (LBM) is too low to make it for practical applications, unless using a massive parallel computing clusters facility. In this study, the massive parallel computing power from an inexpensive graphic processor unit (GPU) and a typical personal computer has been developed for improving the computing efficiency, more than 100 times. This developed three-dimensional (3-D) GLBE-SGS model, with the D3Q19 scheme for simplifying collision and streaming courses, has been successfully used to study 3-D rectangular cavity flows with Reynolds number up to 10000.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s11433-012-4856-9", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1282972", 
        "issn": [
          "1674-7348", 
          "1869-1927"
        ], 
        "name": "Science China Physics, Mechanics & Astronomy", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "10", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "55"
      }
    ], 
    "name": "Solving generalized lattice Boltzmann model for 3-D cavity flows using CUDA-GPU", 
    "pagination": "1894-1904", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "27b67dc8b29664fc80211fdfdb5d6db157fe9b5b07bb079229077c3c603fba53"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11433-012-4856-9"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1008335809"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11433-012-4856-9", 
      "https://app.dimensions.ai/details/publication/pub.1008335809"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T21:38", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8687_00000520.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs11433-012-4856-9"
  }
]
 

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/s11433-012-4856-9'

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/s11433-012-4856-9'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11433-012-4856-9'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11433-012-4856-9'


 

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

154 TRIPLES      21 PREDICATES      52 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11433-012-4856-9 schema:about anzsrc-for:09
2 anzsrc-for:0915
3 schema:author N4fcdc1281de8480787f93b856c985a9c
4 schema:citation sg:pub.10.1007/s00371-003-0210-6
5 sg:pub.10.1007/s00791-008-0120-2
6 https://doi.org/10.1002/(sici)1097-0363(19980315)26:5<557::aid-fld638>3.0.co;2-r
7 https://doi.org/10.1006/jcph.1995.1103
8 https://doi.org/10.1016/0021-9991(82)90058-4
9 https://doi.org/10.1016/j.camwa.2009.08.052
10 https://doi.org/10.1016/j.compfluid.2005.04.009
11 https://doi.org/10.1016/j.jcp.2005.03.022
12 https://doi.org/10.1016/j.matcom.2008.07.001
13 https://doi.org/10.1017/cbo9780511840531
14 https://doi.org/10.1063/1.2723153
15 https://doi.org/10.1063/1.857491
16 https://doi.org/10.1063/1.870387
17 https://doi.org/10.1080/10618560802238275
18 https://doi.org/10.1098/rsta.2001.0955
19 https://doi.org/10.1103/physreve.55.r6333
20 https://doi.org/10.1103/physreve.56.6811
21 https://doi.org/10.1103/physreve.61.6546
22 https://doi.org/10.1103/physreve.79.026703
23 https://doi.org/10.1103/physrevlett.61.2332
24 https://doi.org/10.1109/sc.2004.26
25 https://doi.org/10.1115/1.3243136
26 https://doi.org/10.1142/s0217979203017059
27 https://doi.org/10.1175/1520-0493(1963)091<0099:gcewtp>2.3.co;2
28 https://doi.org/10.1209/0295-5075/17/6/001
29 schema:datePublished 2012-10
30 schema:datePublishedReg 2012-10-01
31 schema:description The generalized lattice Boltzmann equation (GLBE), with the addition of the standard Smagorinsky subgrid-stress (SGS) model, has been proved that it is more suitable for simulating high Reynolds number turbulent flows when compared with the lattice BGK Boltzmann equation (LBGK). However, the computing efficiency of lattice Boltzmann method (LBM) is too low to make it for practical applications, unless using a massive parallel computing clusters facility. In this study, the massive parallel computing power from an inexpensive graphic processor unit (GPU) and a typical personal computer has been developed for improving the computing efficiency, more than 100 times. This developed three-dimensional (3-D) GLBE-SGS model, with the D3Q19 scheme for simplifying collision and streaming courses, has been successfully used to study 3-D rectangular cavity flows with Reynolds number up to 10000.
32 schema:genre research_article
33 schema:inLanguage en
34 schema:isAccessibleForFree false
35 schema:isPartOf N50b86345ac8c46aeb35278bc36e90522
36 N68e6144dd50b44ff9720b033f43a1e70
37 sg:journal.1282972
38 schema:name Solving generalized lattice Boltzmann model for 3-D cavity flows using CUDA-GPU
39 schema:pagination 1894-1904
40 schema:productId N5d963ee9812a47789ceabe8c62943e35
41 Nc1d72698122f467198102d2710503c88
42 Nf02ad5c825a844338231d4fdf1262835
43 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008335809
44 https://doi.org/10.1007/s11433-012-4856-9
45 schema:sdDatePublished 2019-04-10T21:38
46 schema:sdLicense https://scigraph.springernature.com/explorer/license/
47 schema:sdPublisher Nf5b30c11068345d3ab6f8d241d8c7e49
48 schema:url http://link.springer.com/10.1007%2Fs11433-012-4856-9
49 sgo:license sg:explorer/license/
50 sgo:sdDataset articles
51 rdf:type schema:ScholarlyArticle
52 N4fcdc1281de8480787f93b856c985a9c rdf:first Ne50100d958ba4c26b70bb9d74e7fdb36
53 rdf:rest Nca46e014803c460eb1b7d09311aa8744
54 N50b86345ac8c46aeb35278bc36e90522 schema:issueNumber 10
55 rdf:type schema:PublicationIssue
56 N5d963ee9812a47789ceabe8c62943e35 schema:name doi
57 schema:value 10.1007/s11433-012-4856-9
58 rdf:type schema:PropertyValue
59 N68e6144dd50b44ff9720b033f43a1e70 schema:volumeNumber 55
60 rdf:type schema:PublicationVolume
61 Nc1d72698122f467198102d2710503c88 schema:name readcube_id
62 schema:value 27b67dc8b29664fc80211fdfdb5d6db157fe9b5b07bb079229077c3c603fba53
63 rdf:type schema:PropertyValue
64 Nca46e014803c460eb1b7d09311aa8744 rdf:first sg:person.012131541307.57
65 rdf:rest Nd4605e3f33f8407ca96378b22b68691d
66 Nd4605e3f33f8407ca96378b22b68691d rdf:first sg:person.014602654602.04
67 rdf:rest rdf:nil
68 Ne50100d958ba4c26b70bb9d74e7fdb36 schema:affiliation https://www.grid.ac/institutes/grid.30055.33
69 schema:familyName Li
70 schema:givenName ChengGong
71 rdf:type schema:Person
72 Nf02ad5c825a844338231d4fdf1262835 schema:name dimensions_id
73 schema:value pub.1008335809
74 rdf:type schema:PropertyValue
75 Nf5b30c11068345d3ab6f8d241d8c7e49 schema:name Springer Nature - SN SciGraph project
76 rdf:type schema:Organization
77 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
78 schema:name Engineering
79 rdf:type schema:DefinedTerm
80 anzsrc-for:0915 schema:inDefinedTermSet anzsrc-for:
81 schema:name Interdisciplinary Engineering
82 rdf:type schema:DefinedTerm
83 sg:journal.1282972 schema:issn 1674-7348
84 1869-1927
85 schema:name Science China Physics, Mechanics & Astronomy
86 rdf:type schema:Periodical
87 sg:person.012131541307.57 schema:affiliation https://www.grid.ac/institutes/grid.264889.9
88 schema:familyName Maa
89 schema:givenName Jerome P. -Y.
90 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012131541307.57
91 rdf:type schema:Person
92 sg:person.014602654602.04 schema:affiliation https://www.grid.ac/institutes/grid.30055.33
93 schema:familyName Kang
94 schema:givenName HaiGui
95 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014602654602.04
96 rdf:type schema:Person
97 sg:pub.10.1007/s00371-003-0210-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038074976
98 https://doi.org/10.1007/s00371-003-0210-6
99 rdf:type schema:CreativeWork
100 sg:pub.10.1007/s00791-008-0120-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032945815
101 https://doi.org/10.1007/s00791-008-0120-2
102 rdf:type schema:CreativeWork
103 https://doi.org/10.1002/(sici)1097-0363(19980315)26:5<557::aid-fld638>3.0.co;2-r schema:sameAs https://app.dimensions.ai/details/publication/pub.1020116595
104 rdf:type schema:CreativeWork
105 https://doi.org/10.1006/jcph.1995.1103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034235143
106 rdf:type schema:CreativeWork
107 https://doi.org/10.1016/0021-9991(82)90058-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028336458
108 rdf:type schema:CreativeWork
109 https://doi.org/10.1016/j.camwa.2009.08.052 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022599292
110 rdf:type schema:CreativeWork
111 https://doi.org/10.1016/j.compfluid.2005.04.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005069291
112 rdf:type schema:CreativeWork
113 https://doi.org/10.1016/j.jcp.2005.03.022 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016713325
114 rdf:type schema:CreativeWork
115 https://doi.org/10.1016/j.matcom.2008.07.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053505096
116 rdf:type schema:CreativeWork
117 https://doi.org/10.1017/cbo9780511840531 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098697945
118 rdf:type schema:CreativeWork
119 https://doi.org/10.1063/1.2723153 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035139233
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1063/1.857491 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058110696
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1063/1.870387 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058122519
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1080/10618560802238275 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007634160
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1098/rsta.2001.0955 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022491816
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1103/physreve.55.r6333 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060721111
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1103/physreve.56.6811 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060721720
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1103/physreve.61.6546 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060725271
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1103/physreve.79.026703 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023367652
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1103/physrevlett.61.2332 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060797898
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1109/sc.2004.26 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095167248
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1115/1.3243136 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062110960
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1142/s0217979203017059 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062933128
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1175/1520-0493(1963)091<0099:gcewtp>2.3.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063451320
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1209/0295-5075/17/6/001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064228747
148 rdf:type schema:CreativeWork
149 https://www.grid.ac/institutes/grid.264889.9 schema:alternateName College of William & Mary
150 schema:name Virginia Institute of Marine Science, College of William and Mary, 23062, Gloucester Point, VA, USA
151 rdf:type schema:Organization
152 https://www.grid.ac/institutes/grid.30055.33 schema:alternateName Dalian University of Technology
153 schema:name State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, 116023, Dalian, China
154 rdf:type schema:Organization
 




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


...