Giesekus Nonlinear Viscoelastic Models

This section documents the Giesekus family of models for polymer melts and solutions exhibiting shear-thinning, normal stress differences, and stress overshoot behavior.

Overview

The Giesekus model (1982) extends the Upper-Convected Maxwell (UCM) framework with a quadratic stress term representing anisotropic molecular mobility:

\[\boldsymbol{\tau} + \lambda \overset{\nabla}{\boldsymbol{\tau}} + \frac{\alpha \lambda}{\eta_p} \boldsymbol{\tau} \cdot \boldsymbol{\tau} = 2 \eta_p \mathbf{D}\]

Where:

  • \(\boldsymbol{\tau}\) is the polymer extra stress tensor

  • \(\lambda\) is the relaxation time

  • \(\alpha\) is the mobility factor (\(0 \leq \alpha \leq 0.5\))

  • \(\eta_p\) is the polymer viscosity

  • \(\overset{\nabla}{\boldsymbol{\tau}}\) is the upper-convected derivative

  • \(\mathbf{D}\) is the rate-of-deformation tensor

The mobility factor \(\alpha\) controls shear-thinning:

  • \(\alpha\) = 0: Recovers UCM (no shear-thinning)

  • \(\alpha\) = 0.5: Maximum anisotropy/shear-thinning

Model Variants

Model

Description

GiesekusSingleMode

Single relaxation time with all 6 protocols

GiesekusMultiMode

N parallel modes for broad relaxation spectra

Key Features

Shear-Thinning Viscosity:

\[\eta(\dot{\gamma}) = \eta_s + \eta_p \cdot f(\text{Wi})\]

where f(Wi) decreases with Weissenberg number Wi = \(\lambda\dot{\gamma}\).

Normal Stress Differences:

\[ \begin{align}\begin{aligned}N_1 = \tau_{xx} - \tau_{yy} > 0 \quad \text{(first normal stress)}\\N_2 = \tau_{yy} - \tau_{zz} < 0 \quad \text{(second normal stress)}\end{aligned}\end{align} \]

Diagnostic Ratio:

\[\frac{N_2}{N_1} = -\frac{\alpha}{2}\]

This provides a direct experimental route to determine \(\alpha\).

Multi-Mode Superposition:

For polydisperse systems with broad relaxation spectra, the multi-mode Giesekus model sums N independent mode contributions:

\[G'(\omega) = \sum_{k=1}^{N} \frac{\eta_{p,k} \lambda_k \omega^2}{1 + (\lambda_k \omega)^2}, \qquad G''(\omega) = \eta_s \omega + \sum_{k=1}^{N} \frac{\eta_{p,k} \omega}{1 + (\lambda_k \omega)^2}\]

See the Handbook for multi-mode fitting strategies and protocol-specific equations.

Supported Protocols

Protocol

Method

Notes

FLOW_CURVE

Analytical

Steady shear \(\sigma(\dot{\gamma})\), \(\eta(\dot{\gamma})\)

OSCILLATION

Analytical

SAOS \(G'(\omega)\), \(G''(\omega)\) (\(\alpha\)-independent)

STARTUP

ODE (diffrax)

Stress overshoot at constant rate

RELAXATION

ODE (diffrax)

Faster-than-exponential decay

CREEP

ODE (diffrax)

Strain evolution under constant stress

LAOS

ODE + FFT

Nonlinear harmonics \(I_3\), \(I_5\), …

Quick Start

Basic Usage:

from rheojax.models.giesekus import GiesekusSingleMode
import numpy as np

# Create model
model = GiesekusSingleMode()
model.parameters.set_value("eta_p", 100.0)
model.parameters.set_value("lambda_1", 1.0)
model.parameters.set_value("alpha", 0.3)

# Predict flow curve
gamma_dot = np.logspace(-2, 2, 50)
sigma = model.predict(gamma_dot, test_mode='flow_curve')

# Predict SAOS
omega = np.logspace(-1, 3, 50)
G_prime, G_double_prime = model.predict_saos(omega)

# Simulate startup with overshoot
t = np.linspace(0, 10, 500)
sigma_t = model.simulate_startup(t, gamma_dot=10.0)

Multi-Mode:

from rheojax.models.giesekus import GiesekusMultiMode

# Create 3-mode model
model = GiesekusMultiMode(n_modes=3)

# Set mode parameters
model.set_mode_params(0, eta_p=100.0, lambda_1=10.0, alpha=0.3)
model.set_mode_params(1, eta_p=50.0, lambda_1=1.0, alpha=0.2)
model.set_mode_params(2, eta_p=20.0, lambda_1=0.1, alpha=0.1)

# Predict SAOS
G_prime, G_double_prime = model.predict_saos(omega)

Note

For comprehensive theory including analytical steady-state solutions, dimensionless formulations, protocol-specific ODE systems, LAOS analysis, and multi-mode fitting strategies, see the Giesekus Handbook.

When to Use Giesekus

Use Giesekus when you observe:

  1. Shear-thinning viscosity

  2. Non-zero first and second normal stress differences

  3. Stress overshoot in startup flow

  4. Linear SAOS that can be fit by Maxwell modes

Decision Tree:

Is N_2 measurable (negative)?
├── YES → Giesekus captures N_2/N_1 = -α/2
│
└── NO → Is only shear-thinning needed?
    ├── YES → Consider simpler Carreau/Cross
    └── NO → Consider PTT or FENE-P for extensional

Material-Specific Recommendations:

Material

Typical \(\alpha\)

n_modes

Key Protocol

Polymer melts

0.1-0.3

3-5

Flow curve + SAOS

Polymer solutions

0.2-0.4

1-3

Startup + SAOS

Wormlike micelles

0.3-0.5

1

Startup overshoot

References

  1. Giesekus, H. (1982). “A simple constitutive equation for polymer fluids based on the concept of deformation-dependent tensorial mobility.” J. Non-Newtonian Fluid Mech., 11, 69-109.

  2. Bird, R.B., Armstrong, R.C., & Hassager, O. (1987). Dynamics of Polymeric Liquids, Vol. 1: Fluid Mechanics. 2nd ed. Wiley.

  3. Larson, R.G. (1988). Constitutive Equations for Polymer Melts and Solutions. Butterworths.

Detailed Documentation