Temperature gauge

Improving Material Design for Real-World Conditions

Aug. 19, 2021
Research aims to use machine learning algorithms to predict how fibrous materials react to temperature and humidity.

Researchers from Rensselaer Polytechnic Institute are using their expertise in fluid and solid mechanics to study the mechanical performance of a fibrous, porous material.

Their research, funded by a National Science Foundation grant, is a collaboration with a team from the University of Utah, aims to determine the degree to temperature changes and humidity affect materials. For example, they will study the impact of body heat and a person’s breath on the effectiveness of face masks.

Fibrous, porous materials are used in emerging technologies in aerospace, bioengineering, energy and electronics. This necessitates an in-depth knowledge about the effects of moisture and temperature on the mechanical performance of the materials.

“As you are wearing a mask, body temperature rises, and as you’re breathing through the mask, the local humidity also rises,” said Lucy Zhang, a professor of mechanical, aerospace and nuclear engineering at Rensselaer, who is leading this research. “We’re looking at the structure, functionality and the effectiveness of porous materials over time, and how they change based on these varying conditions.”

Numerical Framework

Researchers will examine various properties of fibrous materials—down to the microscale. This information will be used to build a computational model that can predict how effective a material will be in blocking various sized particles under different circumstances.

Machine learning algorithms will be employed to process the vast array of parameters and scenarios they will study, such as fiber orientation, porosity, moisture content, temperature levels, and amount of humidity and other failure mechanisms.

NSF lists the following three project objectives:

  1. Uncover new knowledge in microscale phenomena that have not previously been explored in detail involving complex transient multi-physics interactions through rigorous numerical investigations;
  2. Develop a novel approach that combines the physics-based machine-learning algorithms to draw thermo-hygro-mechanical relationships; and
  3. Establish a virtual material testing platform that enables the future design of fibrous porous materials with high mechanical efficiency and performance. 

The research team hopes to develop an accessible approach that labs can use to evaluate and improve materials intended for use in wearables, such as medical-grade masks and other protective equipment used in the aerospace, food or energy industries.

“What’s going to come out of this research is going to fundamentally change how materials are designed,” Zhang said.

The researchers point out that the project will also provide opportunities for STEM participation of women and underrepresented minorities to become the future leaders and innovators of data-enabled engineering technologies.

Editor’s Note: Machine Design's Women in Science and Engineering (WISE) hub compiles our coverage of gender representation issues affecting the engineering field, in addition to contributions from equity seeking groups and subject matter experts within various subdisciplines. Click here for more.

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