Assessing thermophysical properties of parameterized woven composite models using image-based simulations Article Swipe
Collin Foster
,
Lincoln Collins
,
Francesco Panerai
,
Scott Alan Roberts
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.compscitech.2023.110136
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.compscitech.2023.110136
Mesoscale simulations of woven composites using parameterized analytical geometries offer a way to connect constituent material properties and their geometric arrangement to effective composite properties and performance. However, the reality of as-manufactured materials often differs from the ideal, both in terms of tow geometry and manufacturing heterogeneity. As such, resultant composite properties may differ from analytical predictions and exhibit significant local variations within a material. We employ mesoscale finite element method simulations to compare idealized analytical and as-manufactured woven composite materials and study the sensitivity of their effective properties to the mesoscale geometry. Three-dimensional geometries are reconstructed from X-ray computed tomography, image segmentation is performed using deep learning methods, and local fiber orientation is obtained using the structure tensor calculated from image scans. Suitable approximations to composite properties, using analytical unit cell calculations and effective media theory, are assessed. Our findings show that an analytical geometry and sub-unit cell geometry provide reasonable approximations of the full-scale predictions for the effective thermal properties of a multi-layer production composite.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.compscitech.2023.110136
- OA Status
- hybrid
- Cited By
- 16
- References
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4382794477Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.compscitech.2023.110136Digital Object Identifier
- Title
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Assessing thermophysical properties of parameterized woven composite models using image-based simulationsWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-07-01Full publication date if available
- Authors
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Collin Foster, Lincoln Collins, Francesco Panerai, Scott Alan RobertsList of authors in order
- Landing page
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https://doi.org/10.1016/j.compscitech.2023.110136Publisher landing page
- Open access
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.compscitech.2023.110136Direct OA link when available
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Materials science, Parameterized complexity, Mesoscale meteorology, Composite number, Finite element method, Geometry, Composite material, Orientation (vector space), Material properties, Complex geometry, Mathematics, Structural engineering, Algorithm, Physics, Meteorology, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
16Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6, 2024: 10Per-year citation counts (last 5 years)
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46Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2056639583, https://openalex.org/W1801758807, https://openalex.org/W2013489297, https://openalex.org/W37610228, https://openalex.org/W2098386335, https://openalex.org/W2538787141, https://openalex.org/W3116287554, https://openalex.org/W1988569675, https://openalex.org/W2154940804, https://openalex.org/W2604624866, https://openalex.org/W2569214039, https://openalex.org/W2163831916, https://openalex.org/W2794725043, https://openalex.org/W2896073117, https://openalex.org/W2747060304, https://openalex.org/W2540113503, https://openalex.org/W3082256096, https://openalex.org/W3011428238, https://openalex.org/W2178593556, https://openalex.org/W2736753887, https://openalex.org/W1972820300, https://openalex.org/W2887938019, https://openalex.org/W2321035727, https://openalex.org/W6767867023, https://openalex.org/W6639824700, https://openalex.org/W2015159529, https://openalex.org/W2100868869, https://openalex.org/W2886133658, https://openalex.org/W2012496982, https://openalex.org/W4205723518, https://openalex.org/W4224074227, https://openalex.org/W2092001414, https://openalex.org/W2012854819, https://openalex.org/W1995342205, https://openalex.org/W2005682201, https://openalex.org/W2049774453, https://openalex.org/W4376638561, https://openalex.org/W4287198475, https://openalex.org/W1562535252, https://openalex.org/W4212823218, https://openalex.org/W3125132703, https://openalex.org/W1879712795, https://openalex.org/W2107423302, https://openalex.org/W3201195150, https://openalex.org/W2140497924, https://openalex.org/W4236806406 |
| referenced_works_count | 46 |
| abstract_inverted_index.a | 10, 63, 163 |
| abstract_inverted_index.As | 47 |
| abstract_inverted_index.We | 65 |
| abstract_inverted_index.an | 143 |
| abstract_inverted_index.in | 39 |
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| abstract_inverted_index.Our | 139 |
| abstract_inverted_index.and | 17, 25, 44, 57, 76, 81, 109, 133, 146 |
| abstract_inverted_index.are | 95, 137 |
| abstract_inverted_index.for | 157 |
| abstract_inverted_index.may | 52 |
| abstract_inverted_index.the | 28, 36, 83, 90, 116, 154, 158 |
| abstract_inverted_index.tow | 42 |
| abstract_inverted_index.way | 11 |
| abstract_inverted_index.both | 38 |
| abstract_inverted_index.cell | 131, 148 |
| abstract_inverted_index.deep | 106 |
| abstract_inverted_index.from | 35, 54, 97, 120 |
| abstract_inverted_index.show | 141 |
| abstract_inverted_index.that | 142 |
| abstract_inverted_index.unit | 130 |
| abstract_inverted_index.X-ray | 98 |
| abstract_inverted_index.fiber | 111 |
| abstract_inverted_index.image | 101, 121 |
| abstract_inverted_index.local | 60, 110 |
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| abstract_inverted_index.terms | 40 |
| abstract_inverted_index.their | 18, 86 |
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| abstract_inverted_index.woven | 3, 78 |
| abstract_inverted_index.differ | 53 |
| abstract_inverted_index.employ | 66 |
| abstract_inverted_index.finite | 68 |
| abstract_inverted_index.ideal, | 37 |
| abstract_inverted_index.method | 70 |
| abstract_inverted_index.scans. | 122 |
| abstract_inverted_index.tensor | 118 |
| abstract_inverted_index.within | 62 |
| abstract_inverted_index.compare | 73 |
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| abstract_inverted_index.exhibit | 58 |
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| abstract_inverted_index.reality | 29 |
| abstract_inverted_index.theory, | 136 |
| abstract_inverted_index.thermal | 160 |
| abstract_inverted_index.However, | 27 |
| abstract_inverted_index.Suitable | 123 |
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| abstract_inverted_index.findings | 140 |
| abstract_inverted_index.geometry | 43, 145, 149 |
| abstract_inverted_index.learning | 107 |
| abstract_inverted_index.material | 15 |
| abstract_inverted_index.methods, | 108 |
| abstract_inverted_index.obtained | 114 |
| abstract_inverted_index.sub-unit | 147 |
| abstract_inverted_index.Mesoscale | 0 |
| abstract_inverted_index.assessed. | 138 |
| abstract_inverted_index.composite | 23, 50, 79, 126 |
| abstract_inverted_index.effective | 22, 87, 134, 159 |
| abstract_inverted_index.geometric | 19 |
| abstract_inverted_index.geometry. | 92 |
| abstract_inverted_index.idealized | 74 |
| abstract_inverted_index.material. | 64 |
| abstract_inverted_index.materials | 32, 80 |
| abstract_inverted_index.mesoscale | 67, 91 |
| abstract_inverted_index.performed | 104 |
| abstract_inverted_index.resultant | 49 |
| abstract_inverted_index.structure | 117 |
| abstract_inverted_index.analytical | 7, 55, 75, 129, 144 |
| abstract_inverted_index.calculated | 119 |
| abstract_inverted_index.composite. | 166 |
| abstract_inverted_index.composites | 4 |
| abstract_inverted_index.full-scale | 155 |
| abstract_inverted_index.geometries | 8, 94 |
| abstract_inverted_index.production | 165 |
| abstract_inverted_index.properties | 16, 24, 51, 88, 161 |
| abstract_inverted_index.reasonable | 151 |
| abstract_inverted_index.variations | 61 |
| abstract_inverted_index.arrangement | 20 |
| abstract_inverted_index.constituent | 14 |
| abstract_inverted_index.multi-layer | 164 |
| abstract_inverted_index.orientation | 112 |
| abstract_inverted_index.predictions | 56, 156 |
| abstract_inverted_index.properties, | 127 |
| abstract_inverted_index.sensitivity | 84 |
| abstract_inverted_index.significant | 59 |
| abstract_inverted_index.simulations | 1, 71 |
| abstract_inverted_index.tomography, | 100 |
| abstract_inverted_index.calculations | 132 |
| abstract_inverted_index.performance. | 26 |
| abstract_inverted_index.segmentation | 102 |
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| abstract_inverted_index.parameterized | 6 |
| abstract_inverted_index.reconstructed | 96 |
| abstract_inverted_index.approximations | 124, 152 |
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| abstract_inverted_index.as-manufactured | 31, 77 |
| abstract_inverted_index.Three-dimensional | 93 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5011538463 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I4210104735 |
| citation_normalized_percentile.value | 0.9222551 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |