Parameter identification of airfoil systems using an elite-based clustering Jaya algorithm and incremental vibration responses View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2022-07-11

AUTHORS

Zhenghao Ding, Yuxuan Zhang, Zhongrong Lu, Yong Xia

ABSTRACT

Airfoil systems generally exhibit a variety of nonlinearity under different wind speeds. To effectively draft strategies to restrain undesirable nonlinearity, airfoil systems’ parameters must be figured out beforehand. In this article, a novel cluster-based Jaya algorithm is proposed to identify airfoil systems’ dimension parameters, nonlinear parameters, and vibration frequencies. In the proposed algorithm, the improvement is focused on introducing the elite-based clustering framework to balance the algorithm’s exploration and exploitation to enhance the convergence rate. To improve the effectiveness and efficiency of the proposed algorithm, eight benchmark functions are introduced to test and compared with other latest optimizations. The comparison results show that the proposed algorithm has better performance in convergence rate and accuracy. Afterward, the proposed algorithm is applied to identify airfoil systems by minimizing the incremental acceleration response-based objective function. Different wind speeds are considered in the numerical simulation of the airfoil system, which reveals bifurcation, quasi-periodic oscillation, and chaos. In all cases, the proposed method yields accurate and robust results even when noisy data are used. More... »

PAGES

209

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00158-022-03308-8

DOI

http://dx.doi.org/10.1007/s00158-022-03308-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1149412182


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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China", 
          "id": "http://www.grid.ac/institutes/grid.16890.36", 
          "name": [
            "Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ding", 
        "givenName": "Zhenghao", 
        "id": "sg:person.014325310147.82", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014325310147.82"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China", 
          "id": "http://www.grid.ac/institutes/grid.16890.36", 
          "name": [
            "Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhang", 
        "givenName": "Yuxuan", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Mechanics, Sun Yat-Sen University, 510005, Guangzhou, China", 
          "id": "http://www.grid.ac/institutes/grid.12981.33", 
          "name": [
            "Department of Mechanics, Sun Yat-Sen University, 510005, Guangzhou, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lu", 
        "givenName": "Zhongrong", 
        "id": "sg:person.015122670547.14", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015122670547.14"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China", 
          "id": "http://www.grid.ac/institutes/grid.33199.31", 
          "name": [
            "Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China", 
            "School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Xia", 
        "givenName": "Yong", 
        "id": "sg:person.013765154755.85", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013765154755.85"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s00158-020-02492-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1127736112", 
          "https://doi.org/10.1007/s00158-020-02492-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00366-019-00724-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1112519662", 
          "https://doi.org/10.1007/s00366-019-00724-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11071-018-4490-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1105912082", 
          "https://doi.org/10.1007/s11071-018-4490-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11071-017-3442-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084029309", 
          "https://doi.org/10.1007/s11071-017-3442-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-030-74640-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1139252658", 
          "https://doi.org/10.1007/978-3-030-74640-7"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2022-07-11", 
    "datePublishedReg": "2022-07-11", 
    "description": "Airfoil systems generally exhibit a variety of nonlinearity under different wind speeds. To effectively draft strategies to restrain undesirable nonlinearity, airfoil systems\u2019 parameters must be figured out beforehand. In this article, a novel cluster-based Jaya algorithm is proposed to identify airfoil systems\u2019 dimension parameters, nonlinear parameters, and vibration frequencies. In the proposed algorithm, the improvement is focused on introducing the elite-based clustering framework to balance the algorithm\u2019s exploration and exploitation to enhance the convergence rate. To improve the effectiveness and efficiency of the proposed algorithm, eight benchmark functions are introduced to test and compared with other latest optimizations. The comparison results show that the proposed algorithm has better performance in convergence rate and accuracy. Afterward, the proposed algorithm is applied to identify airfoil systems by minimizing the incremental acceleration response-based objective function. Different wind speeds are considered in the numerical simulation of the airfoil system, which reveals bifurcation, quasi-periodic oscillation, and chaos. In all cases, the proposed method yields accurate and robust results even when noisy data are used.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s00158-022-03308-8", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1050630", 
        "issn": [
          "1615-147X", 
          "1615-1488"
        ], 
        "name": "Structural and Multidisciplinary Optimization", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "7", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "65"
      }
    ], 
    "keywords": [
      "different wind speeds", 
      "airfoil system", 
      "wind speed", 
      "Jaya algorithm", 
      "variety of nonlinearities", 
      "vibration response", 
      "undesirable nonlinearity", 
      "vibration frequency", 
      "numerical simulations", 
      "parameter identification", 
      "dimension parameters", 
      "nonlinear parameters", 
      "comparison results", 
      "better performance", 
      "later optimization", 
      "objective function", 
      "speed", 
      "nonlinearity", 
      "convergence rate", 
      "parameters", 
      "method yields", 
      "system", 
      "algorithm exploration", 
      "algorithm", 
      "simulations", 
      "optimization", 
      "efficiency", 
      "performance", 
      "accuracy", 
      "oscillations", 
      "results", 
      "noisy data", 
      "robust results", 
      "frequency", 
      "rate", 
      "exploitation", 
      "effectiveness", 
      "improvement", 
      "bifurcation", 
      "function", 
      "exploration", 
      "yield", 
      "benchmark functions", 
      "strategies", 
      "framework", 
      "quasi-periodic oscillations", 
      "chaos", 
      "data", 
      "cases", 
      "variety", 
      "response", 
      "identification", 
      "article"
    ], 
    "name": "Parameter identification of airfoil systems using an elite-based clustering Jaya algorithm and incremental vibration responses", 
    "pagination": "209", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1149412182"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00158-022-03308-8"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00158-022-03308-8", 
      "https://app.dimensions.ai/details/publication/pub.1149412182"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-11-24T21:08", 
    "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_943.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s00158-022-03308-8"
  }
]
 

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/s00158-022-03308-8'

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/s00158-022-03308-8'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00158-022-03308-8'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00158-022-03308-8'


 

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

157 TRIPLES      21 PREDICATES      81 URIs      68 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00158-022-03308-8 schema:about anzsrc-for:01
2 anzsrc-for:09
3 schema:author N3f9dd74d156f41dab15f67b995cd127e
4 schema:citation sg:pub.10.1007/978-3-030-74640-7
5 sg:pub.10.1007/s00158-020-02492-9
6 sg:pub.10.1007/s00366-019-00724-1
7 sg:pub.10.1007/s11071-017-3442-0
8 sg:pub.10.1007/s11071-018-4490-9
9 schema:datePublished 2022-07-11
10 schema:datePublishedReg 2022-07-11
11 schema:description Airfoil systems generally exhibit a variety of nonlinearity under different wind speeds. To effectively draft strategies to restrain undesirable nonlinearity, airfoil systems’ parameters must be figured out beforehand. In this article, a novel cluster-based Jaya algorithm is proposed to identify airfoil systems’ dimension parameters, nonlinear parameters, and vibration frequencies. In the proposed algorithm, the improvement is focused on introducing the elite-based clustering framework to balance the algorithm’s exploration and exploitation to enhance the convergence rate. To improve the effectiveness and efficiency of the proposed algorithm, eight benchmark functions are introduced to test and compared with other latest optimizations. The comparison results show that the proposed algorithm has better performance in convergence rate and accuracy. Afterward, the proposed algorithm is applied to identify airfoil systems by minimizing the incremental acceleration response-based objective function. Different wind speeds are considered in the numerical simulation of the airfoil system, which reveals bifurcation, quasi-periodic oscillation, and chaos. In all cases, the proposed method yields accurate and robust results even when noisy data are used.
12 schema:genre article
13 schema:isAccessibleForFree false
14 schema:isPartOf N0ac7cba8790048ac97f8552bfaa3833a
15 Nd6d4a1c2476f49e288752963d19846b3
16 sg:journal.1050630
17 schema:keywords Jaya algorithm
18 accuracy
19 airfoil system
20 algorithm
21 algorithm exploration
22 article
23 benchmark functions
24 better performance
25 bifurcation
26 cases
27 chaos
28 comparison results
29 convergence rate
30 data
31 different wind speeds
32 dimension parameters
33 effectiveness
34 efficiency
35 exploitation
36 exploration
37 framework
38 frequency
39 function
40 identification
41 improvement
42 later optimization
43 method yields
44 noisy data
45 nonlinear parameters
46 nonlinearity
47 numerical simulations
48 objective function
49 optimization
50 oscillations
51 parameter identification
52 parameters
53 performance
54 quasi-periodic oscillations
55 rate
56 response
57 results
58 robust results
59 simulations
60 speed
61 strategies
62 system
63 undesirable nonlinearity
64 variety
65 variety of nonlinearities
66 vibration frequency
67 vibration response
68 wind speed
69 yield
70 schema:name Parameter identification of airfoil systems using an elite-based clustering Jaya algorithm and incremental vibration responses
71 schema:pagination 209
72 schema:productId N7c7a966963c34648a1eb683517f668f9
73 Nc3c4fa18f07942b48e85127eecb1ea17
74 schema:sameAs https://app.dimensions.ai/details/publication/pub.1149412182
75 https://doi.org/10.1007/s00158-022-03308-8
76 schema:sdDatePublished 2022-11-24T21:08
77 schema:sdLicense https://scigraph.springernature.com/explorer/license/
78 schema:sdPublisher N96ff96af14e64d8bbabd5fac8596d273
79 schema:url https://doi.org/10.1007/s00158-022-03308-8
80 sgo:license sg:explorer/license/
81 sgo:sdDataset articles
82 rdf:type schema:ScholarlyArticle
83 N0ac7cba8790048ac97f8552bfaa3833a schema:volumeNumber 65
84 rdf:type schema:PublicationVolume
85 N2bc13f560b3149e8a65dec46d9a67c83 rdf:first N4779c498fa48463e90312e4d40cb182c
86 rdf:rest N40f6e637de6f46fa88904146bc6f0ad8
87 N3f9dd74d156f41dab15f67b995cd127e rdf:first sg:person.014325310147.82
88 rdf:rest N2bc13f560b3149e8a65dec46d9a67c83
89 N40f6e637de6f46fa88904146bc6f0ad8 rdf:first sg:person.015122670547.14
90 rdf:rest N764677cfc72d4de38e25d4eb7f33d128
91 N4779c498fa48463e90312e4d40cb182c schema:affiliation grid-institutes:grid.16890.36
92 schema:familyName Zhang
93 schema:givenName Yuxuan
94 rdf:type schema:Person
95 N764677cfc72d4de38e25d4eb7f33d128 rdf:first sg:person.013765154755.85
96 rdf:rest rdf:nil
97 N7c7a966963c34648a1eb683517f668f9 schema:name doi
98 schema:value 10.1007/s00158-022-03308-8
99 rdf:type schema:PropertyValue
100 N96ff96af14e64d8bbabd5fac8596d273 schema:name Springer Nature - SN SciGraph project
101 rdf:type schema:Organization
102 Nc3c4fa18f07942b48e85127eecb1ea17 schema:name dimensions_id
103 schema:value pub.1149412182
104 rdf:type schema:PropertyValue
105 Nd6d4a1c2476f49e288752963d19846b3 schema:issueNumber 7
106 rdf:type schema:PublicationIssue
107 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
108 schema:name Mathematical Sciences
109 rdf:type schema:DefinedTerm
110 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
111 schema:name Engineering
112 rdf:type schema:DefinedTerm
113 sg:journal.1050630 schema:issn 1615-147X
114 1615-1488
115 schema:name Structural and Multidisciplinary Optimization
116 schema:publisher Springer Nature
117 rdf:type schema:Periodical
118 sg:person.013765154755.85 schema:affiliation grid-institutes:grid.33199.31
119 schema:familyName Xia
120 schema:givenName Yong
121 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013765154755.85
122 rdf:type schema:Person
123 sg:person.014325310147.82 schema:affiliation grid-institutes:grid.16890.36
124 schema:familyName Ding
125 schema:givenName Zhenghao
126 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014325310147.82
127 rdf:type schema:Person
128 sg:person.015122670547.14 schema:affiliation grid-institutes:grid.12981.33
129 schema:familyName Lu
130 schema:givenName Zhongrong
131 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015122670547.14
132 rdf:type schema:Person
133 sg:pub.10.1007/978-3-030-74640-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1139252658
134 https://doi.org/10.1007/978-3-030-74640-7
135 rdf:type schema:CreativeWork
136 sg:pub.10.1007/s00158-020-02492-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1127736112
137 https://doi.org/10.1007/s00158-020-02492-9
138 rdf:type schema:CreativeWork
139 sg:pub.10.1007/s00366-019-00724-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112519662
140 https://doi.org/10.1007/s00366-019-00724-1
141 rdf:type schema:CreativeWork
142 sg:pub.10.1007/s11071-017-3442-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084029309
143 https://doi.org/10.1007/s11071-017-3442-0
144 rdf:type schema:CreativeWork
145 sg:pub.10.1007/s11071-018-4490-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105912082
146 https://doi.org/10.1007/s11071-018-4490-9
147 rdf:type schema:CreativeWork
148 grid-institutes:grid.12981.33 schema:alternateName Department of Mechanics, Sun Yat-Sen University, 510005, Guangzhou, China
149 schema:name Department of Mechanics, Sun Yat-Sen University, 510005, Guangzhou, China
150 rdf:type schema:Organization
151 grid-institutes:grid.16890.36 schema:alternateName Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
152 schema:name Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
153 rdf:type schema:Organization
154 grid-institutes:grid.33199.31 schema:alternateName School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
155 schema:name Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
156 School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
157 rdf:type schema:Organization
 




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


...