Characterization and modeling of thermal protective fabrics under Molotov cocktail exposure

Abstract

This study aims to characterize and model the thermal protective fabrics usually used in workwear under Molotov cocktail exposure. Physical properties of the fabrics were measured; and, thermal protective performances of the fabrics were evaluated under a fire exposure generated from the laboratory-simulated Molotov cocktail. The performance was calculated in terms of the amount of thermal energy transmitted through the fabrics; additionally, the time required to generate a second-degree burn on wearers’ bodies was predicted from the calculated transmitted thermal energy. For the characterization, the parameters that affected the protective performance were identified and discussed with regards to the theory of heat and mass transfer. The relationships between the properties of the fabric systems and the protective performances were statistically analyzed. The significant fabric properties affecting the performance were further employed in the empirical modeling techniques − Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) for predicting the protective performance. The Coefficient of Determination (R2) and Root Mean Square Error (RMSE) of the developed MLR and ANN models were also compared to identify the best-fit model for predicting the protective performance. This study found that thermal resistance and evaporative resistance are two significant properties (P-Values < 0.05) that negatively affect the transmitted thermal energy through the fabric systems. Also, R2 and RMSE values of ANN model were much higher (R2 = 0.94) and lower (RMSE = 37.42), respectively, than MLR model (R2 = 0.73; RMSE = 191.38); therefore, ANN is the best-fit model to predict the protective performance. In summary, this study could build an in-depth understanding of the parameters that can affect the protective performance of fabrics used in the workwear of high-risk sectors employees and would provide them better occupational health and safety.

Characterization and Modeling of Thermal Protective and Thermo-Physiological Comfort Performance of Polymeric Textile Materials—A Review

“…This is because a fabric with high air permeability could transfer more steam through the porous surface of the fabrics and that could lower the performance of the fabrics. In a recent study, Mandal et al (2021) [ 99 ] investigated the impact of wide range of fabric properties on the performance under steam exposure. It has been found that thickness, air permeability and evaporative resistance of the fabrics are three most significant properties to affect the performance.…”

“…It has been found that thickness, air permeability and evaporative resistance of the fabrics are three most significant properties to affect the performance. By employing these properties, Mandal et al (2021) [ 99 ] also developed the MLR and ANN model to predict the performance. It has been found that ANN modelling approach could effectively represents the relationship between these fabric properties and performance under steam exposure.…”

“…Thermal energy transmitted through the compressed specimen was processed by the sensor for a period of 120 s. From the thermal energy, time required to generate a second-degree skin burn was calculated by the customized HBI software ( Figure 6 ). In the recent study, Mandal et al (2021) [ 99 ] concluded that thickness, air permeability and evaporative resistance are the most important properties to affect the performance of fabrics under hot water immersion and compression exposures of different temperatures and pressures. By employing these properties, it is also possible to predict the performance of the fabrics.…”

Using Artificial Neural Network Modeling to Analyze the Thermal Protective and Thermo-Physiological Comfort Performance of Textile Fabrics Used in Oilfield Workers’ Clothing

“…Secondly, multiple linear regression (MLR) and/or artificial neural network (ANN) models were developed by employing the identified key fabric properties to conveniently predict their performance. For example, a group of researchers [ 21 , 22 , 23 , 24 , 25 ] found that weight, thickness, and thermal and evaporative resistance are the key fabric properties that affect the fabric’s thermal protective performance. On the other hand, these researchers concluded that the thermo-physiological comfort performance is mostly affected by fabric weight, evaporative resistance, and water spreading properties.…”

Aspects of Development of Fireproof Compositions for Structures With Textile Fuel Products

“…Необхідність вимірювання фізичних властивостей тканин; а також теплозахисні характеристики тканин оцінювали під впливом вогню, отриманого в результаті лабораторного моделювання коктейлю Молотова [8]. Ефективність розраховувалась за кількістю теплової енергії, що передається через тканини; крім того, час, необхідний для отримання опіку другого ступеня на тілах носіїв, прогнозували з розрахункової переданої теплової енергії.…”