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Table 1 Examples of shell models assessed

From: On the use of neural networks to evaluate performances of shell models for composites

 

\({\mathbf {DOF}}\)

\({\mathbf {u}}_{\mathbf {x1}}\)

\({\mathbf {u}}_{\mathbf {y1}}\)

\({\mathbf {u}}_{\mathbf {z1}}\)

\({\mathbf {u}}_{\mathbf {x2}}\)

\({\mathbf {u}}_{\mathbf {y2}}\)

\({\mathbf {u}}_{\mathbf {z2}}\)

\({\mathbf {u}}_{\mathbf {x3}}\)

\({\mathbf {u}}_{\mathbf {y3}}\)

\({\mathbf {u}}_{\mathbf {z3}}\)

\({\mathbf {u}}_{\mathbf {x4}}\)

\({\mathbf {u}}_{\mathbf {y4}}\)

\({\mathbf {u}}_{\mathbf {z4}}\)

\({\mathbf {u}}_{\mathbf {x5}}\)

\({\mathbf {u}}_{\mathbf {y5}}\)

\({\mathbf {u}}_{\mathbf {z5}}\)

N = 4

15

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\(\blacktriangle \)

\(\blacktriangle \)

\(\blacktriangle \)

\(\blacktriangle \)

\(\blacktriangle \)

\(\blacktriangle \)

\(\blacktriangle \)

\(\blacktriangle \)

\(\blacktriangle \)

\(\blacktriangle \)

\(\blacktriangle \)

\(\blacktriangle \)

\(\blacktriangle \)

\(\blacktriangle \)

TSDT

9

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\(\blacktriangle \)

\(\blacktriangle \)

\(\blacktriangle \)

\(\blacktriangle \)

\(\vartriangle \)

\(\blacktriangle \)

\(\blacktriangle \)

\(\vartriangle \)

\(\blacktriangle \)

\(\blacktriangle \)

\(\vartriangle \)

\(\vartriangle \)

\(\vartriangle \)

\(\vartriangle \)

PTD

7

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\(\blacktriangle \)

\(\blacktriangle \)

\(\blacktriangle \)

\(\blacktriangle \)

\(\blacktriangle \)

\(\vartriangle \)

\(\vartriangle \)

\(\blacktriangle \)

\(\vartriangle \)

\(\vartriangle \)

\(\vartriangle \)

\(\vartriangle \)

\(\vartriangle \)

\(\vartriangle \)

FSDT

5

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\(\blacktriangle \)

\(\blacktriangle \)

\(\blacktriangle \)

\(\blacktriangle \)

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\(\vartriangle \)

\(\vartriangle \)

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\(\vartriangle \)

\(\vartriangle \)

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\(\vartriangle \)

\(\vartriangle \)