Aerogel fibers utilized in thermal protective apparel exhibit exceptional heat insulation capabilities; however, concerns arise regarding potential degradation due to the detachment of aerogel particles during repeated washing. Accurate prediction of aerogel fiber thermal resistance is critical for assessing hydrothermal aging in aerogel textiles, yet the precision of such predictions is significantly hindered by limited sample data and numerous uncertainties constrained by testing time and expenses. The present study endeavors to ascertain the optimal parameters dictating aerogel fabric thermal resistance post-washing and establish a prediction model based on these variables using small-sample data. Four aerogel fabric candidates were selected and subjected to multiple washing cycles (0, 1, 5, 10, 15, and 20 cycles). Gray relational analysis (GRA) was initially employed to prioritize the primary thermal resistance parameters, thereby identifying the interrelations among various factors and circumventing the unreasonable equal treatment of samples in conventional gray predictions. Subsequently, a discrete gray linear regression (DGLR) algorithm was proposed and validated to estimate thermal resistance using input from four principal fabric parameters: the fabric weight, thickness, air permeability, and surface temperature distribution coefficient. The findings revealed that the GRA-DGLR model achieved relatively high accuracy, closely aligning with the experimental results. Following repeated washing, aerogel fabric thermal resistance diminished, with the air permeability, weight, thickness, and surface temperature distribution coefficient ranked in descending order of significance. This investigation highlights the considerable impact of repeated washing on aerogel fabric thermal resistance and the efficacy of the GRA-DGLR model in estimating this parameter.
WuJZhangMSuM, et al.
Robust and flexible multimaterial aerogel fabric toward outdoor passive heating. Adv Fiber Mater2022;
4: 1545–1555.
2.
DuYKimH-E.Research trends of the application of aerogel materials in clothing. Fash Text2022;
9: 23.
3.
NayakRKanesalingamSHoushyarS, et al.
Effect of repeated washing and Dry-cleaning on the thermophysiological comfort properties of aramid fabrics. Fibers Polym2016;
17: 954–962.
4.
VassilenkoEWatkinsMChastainS, et al.
Domestic laundry and microfiber pollution: exploring fiber shedding from consumer apparel textiles. PLOS ONE2021;
16: e0250346.
5.
ShaidABhuiyanMARWangL.Aerogel incorporated flexible nonwoven fabric for thermal protective clothing. Fire Saf J2021;
125: 103444.
6.
BhuiyanMARWangLShaidA, et al.
Silica aerogel-integrated nonwoven protective fabrics for chemical and thermal protection and thermophysiological wear comfort. J Mater Sci2020;
55: 2405–2418.
7.
WangY-YZhangFLiN, et al.
Carbon-based aerogels and foams for electromagnetic interference shielding: a review. Carbon2023;
205: 10–26.
8.
AnYXuGShenH.Factors influencing thermal resistance of a sleeping bag. Int J Clothing Sci Technol2021;
33: 966–979.
9.
YangRWuZQianX, et al.
Development of thermal resistance prediction model and measurement of thermal resistance of clothing under fully wet conditions. Text Res J.2023;
93(3–4): 911–924.
10.
AtalieDRotichGK.The influence of yarn parameters on thermophysiological comfort of cotton woven fabrics. J Therm Anal Calorimetr2021;
146: 2035–2047.
11.
AfzalAHussainTMohsinM, et al.
Statistical models for predicting the thermal resistance of polyester/cotton blended interlock knitted fabrics. Int J Therm Sci2014;
85: 40–46.
12.
WangFLaiDShiW, et al.
Effects of fabric thickness and material on apparent ‘wet' conductive thermal resistance of knitted fabric ‘skin' on sweating manikins. J Therm Biol2017;
70: 69–76.
13.
WilsonCANivenBELaingRM.Estimating thermal resistance of the bedding assembly from thickness of materials. Int J Clothing Sci Technol1999;
11: 262–276.
14.
MansoorTHesLBajzikV, et al.
Novel method on thermal resistance prediction and thermophysiological comfort of socks in a wet state. Text Res J2020;
90: 1987–2006.
15.
MitraAMajumdarAMajumdarPK, et al.
Predicting thermal resistance of cotton fabrics by artificial neural network model. Exp Therm Fluid Sci2013;
50: 172–177.
16.
KanatZEÖzdilN.Application of artificial neural network (ANN) for the prediction of thermal resistance of knitted fabrics at different moisture content. J Text Inst2018;
109: 1247–1253.
17.
BhattacharjeeDKothariVK.A neural network system for prediction of thermal resistance of textile fabrics. Text Res J2007;
77: 4–12.
18.
MidhaVK.Ranking fiber and process parameters affecting thermal resistance of needle-punched blankets using neural network model. J Text Inst2011;
102: 668–674.
19.
DiswatJHesLBalK.Thermal resistance of cut pile hand tufted carpet and its prediction. Text Res J2016;
86: 1759–1767.
20.
WilsonCALaingRM.Estimating thermal resistance of dry infant bedding — Part 1: mathematical model. Int J Clothing Sci Technol2002;
14: 25–40.
21.
MatusiakM.Modeling the thermal resistance of woven fabrics. J Text Inst2013;
104: 426–437.
22.
Indu ShekarRKotreshTMSubbulakshmiMS, et al.
Experimental measurement and prediction of thermal resistance in paratrooper clothing. J Ind Text2010;
40: 213–228.
23.
BhattacharjeeDKothariVK.Prediction of thermal resistance of woven fabrics. Part II: heat transfer in natural and forced convective environments. J Text Inst2008;
99: 433–449.
24.
BhatiaDSinhaSK.Geometrical modeling of herringbone twill fabric for prediction of thermal resistance using finite element method. Fibers Polym2021;
22: 2885–2891.
25.
DuruSCCandanC.Effect of repeated washing on wicking and drying properties of fabrics of seamless garments. Text Res J2012;
83: 591–605.
26.
GholamrezaFSuYLiR, et al.
Modeling and prediction of thermophysiological comfort properties of a single layer fabric system using single sector sweating torso. Materials2022;
15: 5786.
27.
ZhangHSongGGuY, et al.
Effect of moisture content on thermal protective performance of fabric assemblies by a stored energy approach under flash exposure. Text Res J2017;
88: 1847–1861.
28.
WangLHeJLuY, et al.
Interaction effects of washing and abrasion on thermal protective performance of flame-retardant fabrics. Int J Occup Saf Ergon2021;
27: 86–94.
29.
WardiningsihWTroynikovO.Force attenuation capacity and thermophysiological wear comfort of vertically lapped nonwoven fabric. J Text Inst2018;
109: 1035–1043.
30.
AbbasiSAMarmaralıAErtekinG.Thermal comfort properties of weft knitted quilted fabrics. Int J Clothing Sci Technol2020;
32: 837–847.
31.
LauLFanJSiuT, et al.
Effects of repeated washing on the performance of garments with wrinkle-free Treatment. Text Res J2002;
72: 931–937.
32.
GunasekaranGPrakashCPeriyasamyS.Preparation, characterization of bamboo charcoal particles and the effect of their application on thermophysiological comfort properties of woven fabrics. J Text Inst2020;
111: 318–325.
33.
LiRYangJXiangC, et al.
Assessment of thermal comfort of nanosilver-treated functional sportswear fabrics using a dynamic thermal model with human/clothing/environmental factors. Text Res J2016;
88: 413–425.
34.
KesimciMODemirelHÖzdemirÖ, et al.
Influence of flock coating on the thermophysiological comfort properties of woven cotton fabric. J Text Inst2023;
114(3): 398–411.
35.
IslamMRKabirGNgKTW, et al.
Yard waste prediction from estimated municipal solid waste using the gray theory to achieve a zero-waste strategy. Environ Sci Pollut Res2022;
29: 46859–46874.
36.
YaoTLiuSXieN.On the properties of small sample of GM(1,1) model. Appl Math Model2009;
33: 1894–1903.
LiuSYangY.Explanation of terms of gray forecasting models. Gray Syst Theor Appl2017;
7: 123–128.
39.
MaidinNAMohd SapuanSTahaMM, et al.
Material selection of natural fiber using a gray relational analysis (GRA) approach. BioResources2022;
17: 109.
40.
DengJ-L.Control problems of gray systems. Syst Contr Lett1982;
1: 288–294.
41.
XieN-mLiuS-f.Discrete gray forecasting model and its optimization. Appl Math Model2009;
33: 1173–1186.
42.
LiBYangWLiX.Application of combined model with DGM(1,1) and linear regression in grain yield prediction. Gray Syst Theor Appl2018;
8: 25–34.
43.
GaoDGuoLWangF, et al.
Study on the spontaneous combustion tendency of coal based on gray relational and multiple regression analysis. ACS Omega2021;
6: 6736–6746.
WangZ-XLiD-DZhengH-H.Model comparison of GM(1,1) and DGM(1,1) based on Monte-Carlo simulation. Physica A2020;
542: 123341.
46.
DasAMajumderADasPK.Detection of apposite PSO parameters using Taguchi based gray relational analysis: optimization and implementation aspects on manufacturing related problem. Procedia Mater Sci2014;
6: 597–604.
47.
RosopaPJKimB.Robustness of statistical inferences using linear models with meta-analytic correlation matrices. Hum Resour Manag Rev2017;
27: 216–236.
48.
HornGPKerberSAndrewsJ, et al.
Impact of repeated exposure and cleaning on protective properties of structural firefighting turnout gear. Fire Technol2021;
57: 791–813.
49.
WonAYYunC.The effects of washing on the protective performance of firefighter clothing. Fibers Polym2021;
22: 3232–3239.
50.
ZhuLWangXBlanchonetteI, et al.
Thermal comfort properties of bifacial fabrics. Text Res J2017;
89: 43–51.
51.
GerickeAMilitkyJVenkataramanM, et al.
Investigation of thermal comfort properties of fabrics containing mohair. J Text Inst2022;
113: 616–627.
52.
PrakashCThanikai VimalJJebastin RajwinA, et al.
Effect of weave parameters on thermal properties of woven fabrics. J Nat Fibers2021;
18: 1375–1383.
53.
WeiZWangWBradfieldJ, et al.
Large sample size, wide variant spectrum, and advanced machine-learning technique boost risk prediction for inflammatory bowel disease. Am J Hum Gene2013;
92: 1008–1012.
54.
ByrdRHChinGMNocedalJ, et al.
Sample size selection in optimization methods for machine learning. Math Prog2012;
134: 127–155.
55.
XiongXHuXGuoH.A hybrid optimized gray seasonal variation index model improved by whale optimization algorithm for forecasting the residential electricity consumption. Energy2021;
234: 121127.
