Different Stress Measures
In large deformation analysis, there are multiple ways to define stress. Different FEA software packages output different measures by default. Understanding these measures is essential for correct result interpretation.
The Three Main Stress Measures
1. Cauchy Stress $\boldsymbol{\sigma}$ (True Stress)
Force per unit current area. This is the "true" stress that acts on material in its deformed state.
$$\mathbf{t} = \boldsymbol{\sigma} \cdot \mathbf{n}$$
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where $\mathbf{n}$ is the current normal vector.
Used by : LS-DYNA (default), ANSYS, most commercial codes for results visualization.
2. First Piola-Kirchhoff Stress $\mathbf{P}$
Force per unit reference area. This is the nominal or engineering stress.
$$\mathbf{P} = J \boldsymbol{\sigma} \cdot \mathbf{F}^{-T}$$
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Used by : Some formulations, connects to traction on reference surfaces.
3. Second Piola-Kirchhoff Stress $\mathbf{S}$
Completely in reference configuration. Work-conjugate to Green-Lagrange strain.
$$\mathbf{S} = \mathbf{F}^{-1} \cdot \mathbf{P} = J \mathbf{F}^{-1} \cdot \boldsymbol{\sigma} \cdot \mathbf{F}^{-T}$$
Used by : Material models in Abaqus (UMAT), theoretical derivations.
Physical Interpretation
Consider a rubber specimen being stretched:
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Quantity Cauchy σ 1st P-K P 2nd P-K S Area used Current (smaller) Reference (original) Reference Force direction Current Current Reference (pulled back) Symmetric? Yes No Yes Use case Results visualization Mixed formulations Constitutive laws
Toggle between stress measures to see how traction vectors differ.
Conversion Formulas
If you have one stress measure, you can compute the others:
From Cauchy to 1st P-K:
$$\mathbf{P} = J \boldsymbol{\sigma} \cdot \mathbf{F}^{-T}$$
From Cauchy to 2nd P-K:
$$\mathbf{S} = J \mathbf{F}^{-1} \cdot \boldsymbol{\sigma} \cdot \mathbf{F}^{-T}$$
From 2nd P-K to Cauchy:
$$\boldsymbol{\sigma} = \frac{1}{J} \mathbf{F} \cdot \mathbf{S} \cdot \mathbf{F}^T$$
Python Code
import numpy as np
def convert_cauchy_to_pk1(sigma, F):
"""P = J * sigma * F^{-T}"""
J = np.linalg.det(F)
F_inv_T = np.linalg.inv(F).T
P = J * sigma @ F_inv_T
return P
def convert_cauchy_to_pk2(sigma, F):
"""S = J * F^{-1} * sigma * F^{-T}"""
J = np.linalg.det(F)
F_inv = np.linalg.inv(F)
S = J * F_inv @ sigma @ F_inv.T
return S
# Example
F = np.array([[1.5, 0.2], [0.1, 0.8]]) # Deformation gradient
sigma = np.array([[100, 20], [20, 50]]) # Cauchy stress (MPa)
P = convert_cauchy_to_pk1(sigma, F)
S = convert_cauchy_to_pk2(sigma, F)
print(f"Cauchy σ:\n{sigma}")
print(f"\n1st P-K P:\n{P}")
print(f"\n2nd P-K S:\n{S}")
When to Use Each Measure
Situation Best Choice Why Visualizing results Cauchy σ Physical stress in current state Material modeling 2nd P-K S Symmetric, objective Boundary tractions 1st P-K P Relates to reference areas Small strains All equivalent Differences are negligible
Automotive Application: Engine Mount Rubber
An engine mount under preload experiences large deformation. The FEA software reports stress, but which measure?
LS-DYNA d3plot : Cauchy stress by default
Abaqus UMAT : Typically works with 2nd P-K internally
Engineering reports : Often convert to "engineering stress" (1st P-K divided by reference area)
Understanding these differences prevents factor-of-2 errors in fatigue life predictions!
Key Takeaways
Cauchy σ : True stress, current configuration, symmetric
1st P-K P : Nominal stress, force/reference area, NOT symmetric
2nd P-K S : Reference configuration, symmetric, for material laws
For small strains, all three are essentially equal
Know your FEA software's default output!
What's Next
In the next lesson, we'll review FEM & Shape Functions — how continuous fields are approximated with nodal values and interpolation.