Quasi-Isometric Mesh Movement and Deformation with Geometrically Adaptive Metric View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2022-08

AUTHORS

V. Garanzha, L. Kudryavtseva

ABSTRACT

We suggest an algorithm which allows for generation of a moving adaptive mesh with a fixed topology according to the time-dependent geometrically adaptive control metric in the computational domain using a quasi-isometric mesh quality functional. For each time step, we use the preconditioned gradient search technique for the mesh quality functional in order to compute large displacements of each mesh vertex. Intermediate meshes using simple linear interpolation between the initial and the displaced states using time as a parameter, are guaranteed to be nonsingular deformations of the initial mesh. Hence for numerical simulations with small time steps one can use single expensive variational mesh deformation algorithm per 5–10 time steps, which greatly improves the efficiency of the remeshing algorithm for moving mesh flow solvers. Control metric provides anisotropic mesh condensation near boundary of the moving body in the normal direction with special law for normal stretches in the transition zones. Algorithm for computation of target tangential stretches is crucial for realizability of control metric. It takes into account curvature of the boundary surface while small-scale features are represented via medial axis transform. Additional data are encoded on background moving mesh. More... »

PAGES

1275-1295

Identifiers

URI

http://scigraph.springernature.com/pub.10.1134/s0965542522080061

DOI

http://dx.doi.org/10.1134/s0965542522080061

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1150918514


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/2008/0102", 
        "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/2008/0105", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Physics", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Dorodnicyn Computing Center FRC CSC RAS, Moscow, Russia", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Dorodnicyn Computing Center FRC CSC RAS, Moscow, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Garanzha", 
        "givenName": "V.", 
        "id": "sg:person.011314573365.15", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011314573365.15"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Keldysh Institute of Applied Mathematics RAS, Moscow, Russia", 
          "id": "http://www.grid.ac/institutes/grid.435669.b", 
          "name": [
            "Dorodnicyn Computing Center FRC CSC RAS, Moscow, Russia", 
            "Keldysh Institute of Applied Mathematics RAS, Moscow, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kudryavtseva", 
        "givenName": "L.", 
        "id": "sg:person.012112153765.45", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012112153765.45"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/978-3-030-76798-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1141394813", 
          "https://doi.org/10.1007/978-3-030-76798-3"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2022-08", 
    "datePublishedReg": "2022-08-01", 
    "description": "We suggest an algorithm which allows for generation of a moving adaptive mesh with a fixed topology according to the time-dependent geometrically adaptive control metric in the computational domain using a quasi-isometric mesh quality functional. For each time step, we use the preconditioned gradient search technique for the mesh quality functional in order to compute large displacements of each mesh vertex. Intermediate meshes using simple linear interpolation between the initial and the displaced states using time as a parameter, are guaranteed to be nonsingular deformations of the initial mesh. Hence for numerical simulations with small time steps one can use single expensive variational mesh deformation algorithm per 5\u201310 time steps, which greatly improves the efficiency of the remeshing algorithm for moving mesh flow solvers. Control metric provides anisotropic mesh condensation near boundary of the moving body in the normal direction with special law for normal stretches in the transition zones. Algorithm for computation of target tangential stretches is crucial for realizability of control metric. It takes into account curvature of the boundary surface while small-scale features are represented via medial axis transform. Additional data are encoded on background moving mesh.", 
    "genre": "article", 
    "id": "sg:pub.10.1134/s0965542522080061", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1136025", 
        "issn": [
          "0965-5425", 
          "1555-6662"
        ], 
        "name": "Computational Mathematics and Mathematical Physics", 
        "publisher": "Pleiades Publishing", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "8", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "62"
      }
    ], 
    "keywords": [
      "time step", 
      "small time steps", 
      "mesh quality", 
      "gradient search technique", 
      "mesh deformation algorithm", 
      "mesh movement", 
      "adaptive mesh", 
      "flow solver", 
      "computational domain", 
      "simple linear interpolation", 
      "small-scale features", 
      "boundary surface", 
      "search technique", 
      "numerical simulations", 
      "linear interpolation", 
      "initial mesh", 
      "medial axis transform", 
      "account curvature", 
      "mesh vertices", 
      "adaptive metrics", 
      "large displacements", 
      "algorithm", 
      "control metrics", 
      "mesh", 
      "normal direction", 
      "intermediate meshes", 
      "solver", 
      "normal stretch", 
      "special law", 
      "metrics", 
      "vertices", 
      "computation", 
      "tangential stretch", 
      "realizability", 
      "interpolation", 
      "topology", 
      "deformation algorithm", 
      "curvature", 
      "simulations", 
      "law", 
      "parameters", 
      "boundaries", 
      "step", 
      "deformation", 
      "displacement", 
      "direction", 
      "technique", 
      "order", 
      "transform", 
      "state", 
      "efficiency", 
      "transition zone", 
      "additional data", 
      "domain", 
      "control", 
      "features", 
      "surface", 
      "generation", 
      "time", 
      "data", 
      "condensation", 
      "zone", 
      "quality", 
      "background", 
      "body", 
      "movement", 
      "stretch"
    ], 
    "name": "Quasi-Isometric Mesh Movement and Deformation with Geometrically Adaptive Metric", 
    "pagination": "1275-1295", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1150918514"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1134/s0965542522080061"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1134/s0965542522080061", 
      "https://app.dimensions.ai/details/publication/pub.1150918514"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-12-01T06:44", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221201/entities/gbq_results/article/article_940.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1134/s0965542522080061"
  }
]
 

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.10.1134/s0965542522080061'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1134/s0965542522080061'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1134/s0965542522080061'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1134/s0965542522080061'


 

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

147 TRIPLES      21 PREDICATES      95 URIs      84 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1134/s0965542522080061 schema:about anzsrc-for:01
2 anzsrc-for:0102
3 anzsrc-for:0103
4 anzsrc-for:0105
5 schema:author N334e9a97ce6c45ad92ce86150ea6f6ef
6 schema:citation sg:pub.10.1007/978-3-030-76798-3
7 schema:datePublished 2022-08
8 schema:datePublishedReg 2022-08-01
9 schema:description We suggest an algorithm which allows for generation of a moving adaptive mesh with a fixed topology according to the time-dependent geometrically adaptive control metric in the computational domain using a quasi-isometric mesh quality functional. For each time step, we use the preconditioned gradient search technique for the mesh quality functional in order to compute large displacements of each mesh vertex. Intermediate meshes using simple linear interpolation between the initial and the displaced states using time as a parameter, are guaranteed to be nonsingular deformations of the initial mesh. Hence for numerical simulations with small time steps one can use single expensive variational mesh deformation algorithm per 5–10 time steps, which greatly improves the efficiency of the remeshing algorithm for moving mesh flow solvers. Control metric provides anisotropic mesh condensation near boundary of the moving body in the normal direction with special law for normal stretches in the transition zones. Algorithm for computation of target tangential stretches is crucial for realizability of control metric. It takes into account curvature of the boundary surface while small-scale features are represented via medial axis transform. Additional data are encoded on background moving mesh.
10 schema:genre article
11 schema:isAccessibleForFree false
12 schema:isPartOf N62ce87f525a54e0c81b96c804bc6f1b7
13 N88577e3c38c14e7699b88e1c607d5b76
14 sg:journal.1136025
15 schema:keywords account curvature
16 adaptive mesh
17 adaptive metrics
18 additional data
19 algorithm
20 background
21 body
22 boundaries
23 boundary surface
24 computation
25 computational domain
26 condensation
27 control
28 control metrics
29 curvature
30 data
31 deformation
32 deformation algorithm
33 direction
34 displacement
35 domain
36 efficiency
37 features
38 flow solver
39 generation
40 gradient search technique
41 initial mesh
42 intermediate meshes
43 interpolation
44 large displacements
45 law
46 linear interpolation
47 medial axis transform
48 mesh
49 mesh deformation algorithm
50 mesh movement
51 mesh quality
52 mesh vertices
53 metrics
54 movement
55 normal direction
56 normal stretch
57 numerical simulations
58 order
59 parameters
60 quality
61 realizability
62 search technique
63 simple linear interpolation
64 simulations
65 small time steps
66 small-scale features
67 solver
68 special law
69 state
70 step
71 stretch
72 surface
73 tangential stretch
74 technique
75 time
76 time step
77 topology
78 transform
79 transition zone
80 vertices
81 zone
82 schema:name Quasi-Isometric Mesh Movement and Deformation with Geometrically Adaptive Metric
83 schema:pagination 1275-1295
84 schema:productId N0fd9ae6816bb43e8a0a679a1710c087b
85 Nbcd5144f196e4d6c9087725c1fb3bd7e
86 schema:sameAs https://app.dimensions.ai/details/publication/pub.1150918514
87 https://doi.org/10.1134/s0965542522080061
88 schema:sdDatePublished 2022-12-01T06:44
89 schema:sdLicense https://scigraph.springernature.com/explorer/license/
90 schema:sdPublisher N7ad8b7c74d454163990b03900ec611c1
91 schema:url https://doi.org/10.1134/s0965542522080061
92 sgo:license sg:explorer/license/
93 sgo:sdDataset articles
94 rdf:type schema:ScholarlyArticle
95 N0fd9ae6816bb43e8a0a679a1710c087b schema:name dimensions_id
96 schema:value pub.1150918514
97 rdf:type schema:PropertyValue
98 N2008e877428f49f686b4682f14e5453a rdf:first sg:person.012112153765.45
99 rdf:rest rdf:nil
100 N334e9a97ce6c45ad92ce86150ea6f6ef rdf:first sg:person.011314573365.15
101 rdf:rest N2008e877428f49f686b4682f14e5453a
102 N62ce87f525a54e0c81b96c804bc6f1b7 schema:issueNumber 8
103 rdf:type schema:PublicationIssue
104 N7ad8b7c74d454163990b03900ec611c1 schema:name Springer Nature - SN SciGraph project
105 rdf:type schema:Organization
106 N88577e3c38c14e7699b88e1c607d5b76 schema:volumeNumber 62
107 rdf:type schema:PublicationVolume
108 Nbcd5144f196e4d6c9087725c1fb3bd7e schema:name doi
109 schema:value 10.1134/s0965542522080061
110 rdf:type schema:PropertyValue
111 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
112 schema:name Mathematical Sciences
113 rdf:type schema:DefinedTerm
114 anzsrc-for:0102 schema:inDefinedTermSet anzsrc-for:
115 schema:name Applied Mathematics
116 rdf:type schema:DefinedTerm
117 anzsrc-for:0103 schema:inDefinedTermSet anzsrc-for:
118 schema:name Numerical and Computational Mathematics
119 rdf:type schema:DefinedTerm
120 anzsrc-for:0105 schema:inDefinedTermSet anzsrc-for:
121 schema:name Mathematical Physics
122 rdf:type schema:DefinedTerm
123 sg:journal.1136025 schema:issn 0965-5425
124 1555-6662
125 schema:name Computational Mathematics and Mathematical Physics
126 schema:publisher Pleiades Publishing
127 rdf:type schema:Periodical
128 sg:person.011314573365.15 schema:affiliation grid-institutes:None
129 schema:familyName Garanzha
130 schema:givenName V.
131 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011314573365.15
132 rdf:type schema:Person
133 sg:person.012112153765.45 schema:affiliation grid-institutes:grid.435669.b
134 schema:familyName Kudryavtseva
135 schema:givenName L.
136 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012112153765.45
137 rdf:type schema:Person
138 sg:pub.10.1007/978-3-030-76798-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1141394813
139 https://doi.org/10.1007/978-3-030-76798-3
140 rdf:type schema:CreativeWork
141 grid-institutes:None schema:alternateName Dorodnicyn Computing Center FRC CSC RAS, Moscow, Russia
142 schema:name Dorodnicyn Computing Center FRC CSC RAS, Moscow, Russia
143 rdf:type schema:Organization
144 grid-institutes:grid.435669.b schema:alternateName Keldysh Institute of Applied Mathematics RAS, Moscow, Russia
145 schema:name Dorodnicyn Computing Center FRC CSC RAS, Moscow, Russia
146 Keldysh Institute of Applied Mathematics RAS, Moscow, Russia
147 rdf:type schema:Organization
 




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


...