Skip to main content

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 \(\blacktriangle \) \(\blacktriangle \) \(\blacktriangle \) \(\blacktriangle \) \(\blacktriangle \) \(\blacktriangle \) \(\blacktriangle \) \(\blacktriangle \) \(\blacktriangle \) \(\blacktriangle \) \(\blacktriangle \) \(\blacktriangle \) \(\blacktriangle \) \(\blacktriangle \) \(\blacktriangle \)
TSDT 9 \(\blacktriangle \) \(\blacktriangle \) \(\blacktriangle \) \(\blacktriangle \) \(\blacktriangle \) \(\vartriangle \) \(\blacktriangle \) \(\blacktriangle \) \(\vartriangle \) \(\blacktriangle \) \(\blacktriangle \) \(\vartriangle \) \(\vartriangle \) \(\vartriangle \) \(\vartriangle \)
PTD 7 \(\blacktriangle \) \(\blacktriangle \) \(\blacktriangle \) \(\blacktriangle \) \(\blacktriangle \) \(\blacktriangle \) \(\vartriangle \) \(\vartriangle \) \(\blacktriangle \) \(\vartriangle \) \(\vartriangle \) \(\vartriangle \) \(\vartriangle \) \(\vartriangle \) \(\vartriangle \)
FSDT 5 \(\blacktriangle \) \(\blacktriangle \) \(\blacktriangle \) \(\blacktriangle \) \(\blacktriangle \) \(\vartriangle \) \(\vartriangle \) \(\vartriangle \) \(\vartriangle \) \(\vartriangle \) \(\vartriangle \) \(\vartriangle \) \(\vartriangle \) \(\vartriangle \) \(\vartriangle \)