TY - GEN
T1 - A Model Generation Method and Optimisation Algorithm to Modify Fire Protection Thickness
AU - Li, Yang
PY - 2023
Y1 - 2023
N2 - With fire temperatures rising above 1000 °C within 5min, hydrocarbon fire causes rapid strength degradation of structural steel members. When designing passive fire protection thickness with simulation, it is time consuming to optimize due to repetitive modelling work and lacking recalculation principle. This method is developed to generate steel beam model and provide an effective algorithm to optimize coating thickness considering the temperature of specific region. The main function of the method includes: Providing section dimensions, initial insulation thickness, target temperature and heating time, temperature allowance and mesh size as variables. Automatically generating Abaqus steel beam model under 3-side heating conditions. Effective iteration algorithm to modify fire protection thickness: test containing 16 random sections with a 5 °C allowance below target shows that 50% were completed within five iterations and 87.5% were completed within ten iterations.
AB - With fire temperatures rising above 1000 °C within 5min, hydrocarbon fire causes rapid strength degradation of structural steel members. When designing passive fire protection thickness with simulation, it is time consuming to optimize due to repetitive modelling work and lacking recalculation principle. This method is developed to generate steel beam model and provide an effective algorithm to optimize coating thickness considering the temperature of specific region. The main function of the method includes: Providing section dimensions, initial insulation thickness, target temperature and heating time, temperature allowance and mesh size as variables. Automatically generating Abaqus steel beam model under 3-side heating conditions. Effective iteration algorithm to modify fire protection thickness: test containing 16 random sections with a 5 °C allowance below target shows that 50% were completed within five iterations and 87.5% were completed within ten iterations.
UR - http://dx.doi.org/10.2139/ssrn.4446612
U2 - 10.2139/ssrn.4446612
DO - 10.2139/ssrn.4446612
M3 - Other contribution
ER -