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Review question / Objective: The aim of this systematic review is to investigate the state of the art of digital human models (DHMs) applied in the field of transportation and automotive engineering, to better inform the development of new models for such use cases. To this end, the proposed systematic review will address the following questions: What is the general trend of research in this field? Which specific use cases, methodologies, and human models are being more widely studied or utilized than others? How can we describe such study characteristics in a structured and quantitative manner? Eligibility criteria: Eligible publications included in the review are screened according to the following criteria: (a) The publication must be a full-text article published in an academic journal or in the proceedings of an academic conference, (b) The publication must be final and the article must be in press, (c) The language of the publication must be English, (d) The publication must apply digital human models in a transportation or automotive engineering context, (e) No particular restrictions are placed on the country and/or region of origin of the publication.

There is no universal model for evaluating the clothing comfort sensation under various conditions due to the different effects of the sensory factors for different activities or environments. The current study aimed to develop a prediction method of the clothing comfort sensation for different activities. Two models were built using fuzzy comprehensive evaluation based on the constant weight (FCE-CW) and variable weight (FCE-VW). Firstly, four sensory factors (wetness, stickiness, hotness, roughness) were selected as input data. Subsequently, grey relation analysis was introduced to obtain the constant weight during the complete experimental process and variable weight for different activities. Finally, the FCE-CW and FCE-VW models were built. A psychological wearing evaluation experiment with different activities was conducted to examine the validity of the model. The results showed that the determination coefficient ( R2) of the FCE-VW model was above 85%, which was superior to that of the FCE-CW model, especially in the running and recovery phases. The high accuracy of the FCE-VW model indicated that grey relation analysis was a practical algorithm for determining the weight of sensory factors. The roughness and hotness sensations demonstrated higher weights during the resting and walking phases, respectively, whereas the wetness and stickiness sensations had a high weight during the running and recovery phases. Moreover, the associations between the physiological parameters and weights of the sensory factors were also explored. In the future, a more comprehensive model based on physiological and psychological parameters can be developed using this method.

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.