Characterization and modeling of thermal protective fabrics under Molotov cocktail exposure

dc.contributor.author Mandal, Sumit
dc.contributor.author Song, Guowen
dc.contributor.author Song, Guowen
dc.contributor.author Rossi, Rene
dc.contributor.author Grover, Indu
dc.contributor.department Apparel, Events and Hospitality Management
dc.date 2021-01-13T21:19:45.000
dc.date.accessioned 2021-02-24T18:34:49Z
dc.date.available 2021-02-24T18:34:49Z
dc.date.copyright Fri Jan 01 00:00:00 UTC 2021
dc.date.issued 2021-01-05
dc.description.abstract <p>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.</p>
dc.description.comments <p>This accepted article is published as Mandal S, Song G, Rossi RM, Grover IB. Characterization and modeling of thermal protective fabrics under Molotov cocktail exposure. <em>Journal of Industrial Textiles.</em> January 2021. doi:<a target="_blank">10.1177/1528083720984973</a>. Posted with permission. </p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/aeshm_pubs/144/
dc.identifier.articleid 1144
dc.identifier.contextkey 21073949
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath aeshm_pubs/144
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/93047
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/aeshm_pubs/144/2021_GS_Alert_Pub_manu_SongG_Characterization_and_modeling_of_thermal.pdf|||Fri Jan 14 20:19:47 UTC 2022
dc.source.uri 10.1177/1528083720984973
dc.subject.disciplines Fashion Design
dc.subject.disciplines Fiber, Textile, and Weaving Arts
dc.subject.disciplines Industrial and Product Design
dc.subject.disciplines Other Materials Science and Engineering
dc.subject.keywords Molotov cocktail
dc.subject.keywords protective fabrics
dc.subject.keywords workwear
dc.subject.keywords fabric properties
dc.subject.keywords protective performance
dc.subject.keywords modeling
dc.title Characterization and modeling of thermal protective fabrics under Molotov cocktail exposure
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication bda6a8a4-afd0-418c-8d2d-e7d39993786c
relation.isOrgUnitOfPublication 5960a20b-38e3-465c-a204-b47fdce6f6f2
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