Predicting Whole Ensemble (3D) Thermal and Vapor Resistances from Swatch Textile Measures (2D)

Abstract

This report has been prepared to support the Personalized Protective Biosystem (PPB). The thermal resistances of ensembles are influenced by a number of complex variables that are not fully understood and quantifying each of these variables (textile, the air gap between the skin and ensemble, and the boundary layer air outside the ensemble) is technically challenging. This report outlined a multi-step analysis to estimate total three-dimensional (3D) ensemble thermal and vapor resistances from two-dimensional (2D) textile measurements. Data from this analysis used 30 paired sets of measured values from both sweating guarded hot plate (SGHP) (i.e., 2D) and whole human thermal manikins (i.e., 3D). These paired data were then used to develop estimation methods based on two predictive approaches: 1) generally applied simple machine learning methods and 2) a multiplicative solver function. Calculated bias and errors were compared between methods and usable equations were proposed as initial approaches for functionally estimating 3D values from 2D-obtained data.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Nov 18, 2022
Accession Number
AD1186790

Entities

People

  • Adam W Potter
  • Julio A. Gonzalez

Organizations

  • United States Army Research Institute of Environmental Medicine

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Air Gaps
  • Boundary Layer
  • Data Mining
  • Fabrics
  • Gaussian Processes
  • Heat Transfer
  • Information Science
  • Machine Learning
  • Materials
  • Mathematical Models
  • Measurement
  • Military Research
  • Models
  • Standards
  • Supervised Machine Learning
  • Test Methods
  • Textiles
  • Thermal Resistance
  • Three Dimensional
  • Two Dimensional
  • Vapor Pressure
  • Wind Velocity

Readers

  • Materials Science
  • Regression Analysis.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference