# Risk Assessment of Hydrogen Cyanide for Available Safe Egress Time in Fire Simulation
## 2 Description of Fire Simulation
### Computational Domain and Fire Source for Fire Simulation
However, the purpose and scope of this study are to evaluate the risks associated with HCN based on the type of combustibles and input parameters. Both HCN yield and concentration can vary depending on the type of combustible as well as the HRR. In other words, by keeping the HRR constant, this study focuses on examining the impact of changes in combustible type on HCN. Table 2 summarizes the combustion properties of combustibles applied in fire simulations [23]. First, GM21 is a type of flexible polyurethane foam known for its elasticity and commonly used in household furniture such as mattresses, sofas, and chairs [24]. PIR (polyisocyanurate) exhibits excellent insulation performance and is utilized as insulation material in refrigerators, as well as walls, roofs, and floors of residential buildings [25]. PAN (polyacrylonitrile) is a polymer material that is widely used in various industrial sectors  
Figure 2: Fire growth curve for residential building fire scenario. and known for its durability and excellent thermal stability. It is used in fiber products such as clothing and textiles or processed into carbon fiber for use in household appliances through carbonization processes [26]. Wool is a natural fiber used in materials for indoor items such as clothing and carpets. These four combustibles commonly encountered in residential spaces share the common characteristic of potentially generating HCN [27]. The combustion properties of each combustible listed in Table 2 are measured under over-ventilation fire (OVF) conditions [23]. Generally, the yields of CO, soot, and HCN can vary significantly depending on the ventilation conditions [28; 29; 30]. Specifically, for PAN, the measured HCN yield (\(y_{HCN}\)) under OVF conditions is 0.0080 g/g, whereas under under-ventilation fire (UVF) conditions, \(y_{HCN}\) increases approximately nine times to 0.0720 g/g. Similarly, for PIR, \(y_{HCN}\) under OVF conditions is approximately 0.0030 g/g, while under UVF conditions, it increases by approximately seven times to 0.0200 g/g. Furthermore, unlike CO yield (\(y_{CO}\)) or soot yield (\(y_{s}\)), there is relatively limited information available regarding \(y_{HCN}\). The utilization of \(y_{HCN}\) values obtained from specific fire experiments may result in limited fire simulation outcomes. To ensure the validity of the assessment regarding the potential impact of HCN on fire hazard assessment, various fire simulation conditions were considered. Specifically, different types of combustibles, variations in HRR, and sensitivity analyses of \(y_{HCN}\) were conducted. Regarding HRR, variations were based on the fire growth curve presented in Figure 2. The considered fire growth curves were derived through statistical approaches based on PBD reports; generalizing the results obtained under these conditions is challenging. Therefore, fire simulations were conducted by gradually changing the fire growth rate (FGR) from 'Slow' (\(\alpha\) = 0.00293 kW/s2) to 'Ultra-fast' (\(\alpha\) = 0.1876 kW/s2) to analyze the impact of HCN. \(y_{HCN}\) is not only limited in available data but can also vary sensitively with temperature and ventilation conditions. Thus, uncertainty regarding the input information for predicting HCN concentration in fire simulations can be significant. Therefore, a sensitivity analysis was additionally considered by applying \(\pm\)50% variations based on the \(y_{HCN}\) values presented in Table 3.