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A Model generation method and iteration algorithm for optimising fire protection thickness.

Yang Li1, Zhuoran Feng1, Simon Thurlbeck1

  • 1Department of Solids and Structures, The University of Manchester, Booth St E, Manchester M13, UK.

Methodsx
|March 25, 2024
PubMed
Summary
This summary is machine-generated.

Hydrocarbon fires rapidly degrade steel strength. This study introduces a method to optimize intumescent coating thickness for steel structures, improving fire protection efficiency and reducing modeling time.

Keywords:
Abaqus kernal scripting methodFire safety designHeat transferIntumescent coatingModel generation method for composite model and Iteration algorithm for optimising fire protection thickness

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Area of Science:

  • Structural Engineering
  • Fire Safety Engineering
  • Materials Science

Background:

  • Hydrocarbon fires cause rapid strength degradation in structural steel, posing significant risks in industries like oil and gas.
  • Intumescent coatings are crucial for passive fire protection of steel structures, preventing material property reduction.
  • Current heat transfer simulations for optimizing fire protection are hindered by repetitive modeling and a lack of recalculation principles.

Purpose of the Study:

  • To develop an automated method for generating steel beam models for fire protection analysis.
  • To create an effective algorithm for optimizing intumescent coating thickness based on regional temperature considerations.
  • To enhance productivity in fire protection optimization by reducing manual modeling efforts.

Main Methods:

  • A novel method is presented to automatically generate Abaqus steel beam models under 3-side heating conditions.
  • Key variables include section dimensions, insulation thickness, target temperature, heating time, temperature allowance, and mesh size.
  • An iterative algorithm refines fire protection thickness, incorporating a temperature allowance for precise optimization.

Main Results:

  • The method successfully generated steel beam models and optimized coating thickness for 38 Universal beam sections.
  • A 5 °C allowance below the target temperature was used in testing.
  • Over 55% of models were optimized within five iterations, and 76.3% within eight iterations.

Conclusions:

  • The developed method significantly improves the efficiency of optimizing intumescent coating thickness for steel structures against hydrocarbon fires.
  • The automated modeling and iterative algorithm reduce the time and effort required for fire protection design.
  • This approach offers a practical solution for enhancing the fire safety of steel structures in high-risk industries.