Co-rotational Formulation

Category: Structural Analysis | Integrated 2026-04-06
CAE visualization for corotational theory - technical simulation diagram
Co-rotational Formulation

Co-rotational Formulation: Theoretical Foundations

What is Co-rotational Formulation?

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Professor, what is "co-rotational formulation"?


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Co-rotational formulation is an optimal method for large rotation problems of beams and shells. It makes the local coordinate system of each element follow the rotation, allowing the use of small deformation theory within the element.


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Even if the element rotates, the deformation inside can be treated as "small deformation"?


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Yes. Even for large overall rotations, if each element is sufficiently small, the deformation within the element is infinitesimal. Think of it as "large rotation = accumulation of many small rotations".


The Problem of Large Rotations

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Finite rotations in 3D are non-commutative (the result changes depending on the order of rotations). This complicates the formulation for large rotations.


$$ R_x(\alpha) \cdot R_y(\beta) \neq R_y(\beta) \cdot R_x(\alpha) $$

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The order of rotations affects the result... This can be ignored for small rotations, but is important for large rotations.


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Co-rotational formulation manages the rotation matrix $[R]$ for each element, correctly handling the non-commutativity of rotations. OpenSees' nonlinear beam elements and Abaqus' B31 elements are co-rotational based.


Summary

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Key Points:


  • The local coordinate system of each element follows the rotation — Small deformation within the element
  • 3D large rotations are non-commutative — The order of rotations affects the result
  • Optimal for large deformations of beams/shells — Collapse analysis of frame structures
  • OpenSees, Abaqus B31 — Co-rotational based beam elements

Coffee Break Yomoyama Talk

The Invention of Co-rotational Coordinates and Argyris

Co-rotational formulation was independently developed by Argyris (University of Stuttgart) and Wempner (Georgia Tech) in the 1960s-70s. By having each element possess a "local coordinate system that rotates and translates with itself," it can handle large displacements/rotations while keeping local deformations in the linear range. Argyris described it as "slightly deformed rigid body motion," greatly simplifying the geometric nonlinear formulation for large deformation analysis.

Computational Methods for Co-rotational Formulation

Implementation of Co-rotational

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Co-rotational formulation algorithm:


1. Set the initial local coordinate system for each element.

2. Extract the rigid body rotation from the deformed element's nodal coordinates (Polar decomposition).

3. Update the local coordinate system by the rigid body rotation amount.

4. Calculate the small deformation stiffness matrix in the updated local system.

5. Transform to the global system.


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"Extraction of rigid body rotation" is the core, isn't it.


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Use polar decomposition ($[F] = [R][U]$, where $[R]$ is rotation, $[U]$ is stretch) to separate the rigid body rotation.


Summary

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  • Extract rigid body rotation via polar decomposition — $[F] = [R][U]$
  • Make local coordinate system follow rotation — Use small deformation theory within the element
  • Efficient for large rotations of beams/shells — TL/UL is more suitable for large deformations of solids

  • Coffee Break Yomoyama Talk

    Update Procedure of the Co-rotational Method

    In the co-rotational method, each load step proceeds in the order: ① Update the element's local coordinate system in the current configuration, ② Calculate local displacements, ③ Transform the internal force vector to the global coordinate system, ④ Assemble the tangent stiffness matrix. Updating local displacements using spinor algebra instead of vectorial rotation reduces numerical error in large rotations to less than 1/10. Abaqus' beam and shell elements use this method.

    Co-rotational Formulation in Practice

    Co-rotational in Practice

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    Used for seismic collapse analysis of steel frames, large deformations of marine risers, and deformation tracking of flexible robots.


    Practical Checklist

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    • [ ] For beam/shell elements where large rotations are expected, is NLGEOM=YES set?
    • [ ] Is the analysis stable even when rotation angles exceed 90°?
    • [ ] Are the resulting stresses physically reasonable? (Is the material within the elastic range even with large rotations?)

    • Coffee Break Yomoyama Talk

      Springback Analysis of Thin Sheet Metal Pressing

      "Springback" (shape change due to elastic recovery after press forming) is a typical application example of co-rotational large deformation analysis. In pressing high-strength steel (980MPa class), springback of 5-10mm can occur after punch release, directly affecting dimensional accuracy. Toyota, Honda, and Subaru all use co-rotational formulation-based sheet forming analysis (Autoform, PAM-STAMP, etc.) as standard in design for springback prediction.

      Co-rotational Formulation: Software & Solver Comparison

      Co-rotational Tools

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      • OpenSees — Co-rotational beam elements. Standard for seismic collapse.
      • Abaqus B31 — Co-rotational based. Large rotations of 3D beams.
      • Nastran SOL 400 — Supports large rotation beams.

      • Selection Guide

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        • Frame collapse analysis → OpenSees
        • Large deformation of 3D beamsAbaqus B31 (NLGEOM=YES)

        • Coffee Break Yomoyama Talk

          Accuracy of ANSYS Beam188 Large Rotation Analysis

          ANSYS's BEAM188 adopts Timoshenko beam theory + co-rotational formulation, enabling high-precision large deformation/large rotation analysis for beams with slenderness ratio L/D>10. With the full Newton method setting KEYOPT(2)=2, it converges even for problems involving rotations over 90°. It is also used for robot arm endpoint accuracy analysis and design verification of deployable space structures (unfolding of folded solar panels).

          Advanced Co-rotational Formulation: Modern Research & Trends

          Advanced Research in Co-rotational

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          Recent research directions:

          • Machine learning-accelerated convergence prediction for large rotation problems
          • Hybrid co-rotational formulation for structures with local buckling
          • Real-time co-rotational analysis for digital twins of flexible structures

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