Validation of fire dynamics simulator predictions incorporating measured smoke characteristics – Part 2: Smoke concentration and detector characteristics in fires

Accurate prediction of smoke behavior is crucial in performance-based fire safety engineering, especially for assessing visibility reduction and smoke detection performance. This study investigates the predictive accuracy of the Fire Dynamics Simulator (FDS) in modeling smoke concentration and light attenuation in a mechanically ventilated full-scale compartment fire. Experiments were conducted using acrylonitrile butadiene styrene (ABS)—a thermoplastic known for its high smoke yield—and the results were compared with FDS simulations that incorporated different mass-specific extinction coefficients ( K m = 4.5, 7.7, and 8.7 m 2 /g). The simulation with K m = 7.7 m 2 /g, representing an experimentally validated value for ABS, showed the best agreement with measured smoke concentration and visibility. Comparisons revealed that to reach the same visibility threshold of 3.7 m, the simulation using K m = 4.5 m 2 /g required approximately 67% higher smoke concentration than the simulation using K m = 7.7 m 2 /g. These findings highlight the critical importance of selecting accurate, fuel-specific optical properties for smoke modeling. In particular, the appropriate specification of K m is essential to ensure reliable smoke prediction, tenability assessment, and smoke detector response in performance-based fire protection design, especially in complex, large-scale building environments.

Authors

Institutions

Publication Details

Journal
Case Studies in Thermal Engineering
Published
2025-05-08
DOI
https://doi.org/10.1016/j.csite.2025.106292
Citations
1
Primary Topic
Fire dynamics and safety research
Type
article
Field-Weighted Citation Impact
1.30

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Validation of fire dynamics simulator predictions incorporating measured smoke characteristics – Part 2: Smoke concentration and detector characteristics in fires

Ter-Ki Hong, Seul‐Hyun Park
1 citations
Case Studies in Thermal Engineering
Fire dynamics and safety research
1.30
article

Validation of fire dynamics simulator predictions incorporating measured smoke characteristics – Part 2: Smoke concentration and detector characteristics in fires

Ter-Ki Hong, Seul‐Hyun Park
article en
1 citations

Abstract

Accurate prediction of smoke behavior is crucial in performance-based fire safety engineering, especially for assessing visibility reduction and smoke detection performance. This study investigates the predictive accuracy of the Fire Dynamics Simulator (FDS) in modeling smoke concentration and light attenuation in a mechanically ventilated full-scale compartment fire. Experiments were conducted using acrylonitrile butadiene styrene (ABS)—a thermoplastic known for its high smoke yield—and the results were compared with FDS simulations that incorporated different mass-specific extinction coefficients ( K m = 4.5, 7.7, and 8.7 m 2 /g). The simulation with K m = 7.7 m 2 /g, representing an experimentally validated value for ABS, showed the best agreement with measured smoke concentration and visibility. Comparisons revealed that to reach the same visibility threshold of 3.7 m, the simulation using K m = 4.5 m 2 /g required approximately 67% higher smoke concentration than the simulation using K m = 7.7 m 2 /g. These findings highlight the critical importance of selecting accurate, fuel-specific optical properties for smoke modeling. In particular, the appropriate specification of K m is essential to ensure reliable smoke prediction, tenability assessment, and smoke detector response in performance-based fire protection design, especially in complex, large-scale building environments.

Case Studies in Thermal EngineeringVol. 72
Chosun University (KR)
Ministry of Trade, Industry and Energy, Korea Institute of Energy Technology Evaluation and Planning
Climate action
Openalex Percentile: Top 22%
Fire dynamics and safety research
1.30
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.