Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 175 tok/s
Gemini 2.5 Pro 54 tok/s Pro
GPT-5 Medium 38 tok/s Pro
GPT-5 High 37 tok/s Pro
GPT-4o 108 tok/s Pro
Kimi K2 180 tok/s Pro
GPT OSS 120B 447 tok/s Pro
Claude Sonnet 4.5 36 tok/s Pro
2000 character limit reached

Information-Theoretical Approach to Relaxation Time Distribution in Rheology: Log-Normal Relaxation Spectrum Model (2509.02059v1)

Published 2 Sep 2025 in cond-mat.soft

Abstract: The relaxation modulus of a viscoelastic fluid can be decomposed into multiple Maxwell models and characterized by the relaxation spectrum for the relaxation time. It is empirically known that the logarithmic relaxation time is useful to express the relaxation spectrum. We use information geometry to analyze the relaxation modulus and shown that the logarithmic relaxation time is the most natural variable for the relaxation spectrum. Then we use information theory to estimate the most probable functional form for the relaxation spectrum. We show that the log-normal distribution is the information-theoretically most probable relaxation spectrum. We analyze the properties of the log-normal relaxation spectrum model and compare it with the fractional Maxwell model. The fractional Maxwell model with a small power-law exponent can be approximated as the log-normal relaxation spectrum model with a large standard deviation. We also compare the log-normal relaxation spectrum model with experimental linear viscoelasticity data for a high-density polyethylene, both at melt and solid states.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Authors (1)

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.