Search results

The purpose of this paper is to gain an in-depth understanding of the research progress, hotspots and future trends in the field of functional clothing.

The records of 4,153 pieces of literature related to functional clothing were retrieved from Web of Science by using a comprehensive retrieval strategy. A piece of software, CiteSpace was used as a tool to visualize the results of specific terms, such as author, institution and keyword. By analyzing the knowledge maps with several indicators, the intellectual basis and research fronts for the functional clothing domain could then be demonstrated.

The result indicated that functional clothing was a popular research field, with approximately 500 papers published worldwide in 2020. Its main research area was material science and involved public environmental and occupational health, engineering, etc. showing the characteristic of multi-interdisciplinary. Textile Research Journal and International Journal of Clothing Science and Technology were the top two journals in this field. The USA, China, Australia, England and Germany have been active and frequently cooperating with each other. Donghua University, the Hong Kong Polytechnic University and NASA, with the largest number of publications, were identified as the main research drivers. According to the co-citation analysis, thermal stress, nanogenerator and electrospinning were the topics of most cited articles during the past 20 years.

The findings identified smart clothing and protective clothing to be the research frontiers in the field of functional clothing, which deserved further study in the future.

The outcomes offered an overview of the research status and future trends of the functional clothing field. It could not only provide scholars with convenience in identifying research hotspots and building potential cooperation in the follow-up research, but also assist beginners in searching core scholars and literature of great significance.

The flow and heat transfer of a hybrid nanofluid composed of kerosene and ZnO-Al2O3 nanoparticles (NPs) is investigated. The flow occurs over complex surfaces with stretching and shrinking features. The base fluid is electrically conducting, and an external magnetic field is added so that the nanofluid and the electric field are in equilibrium. Irrotational flow with viscous dissipation effects is considered.

The governing equations of the system are formulated, and a similarity transformation is used to convert the system of equations into ordinary differential equations, which are solved numerically. The friction coefficient of the flow and the Nusselt number are calculated for a wide range of parameters, and the results are presented in graphical form. In addition, dual solutions of the problem were noticed to occur for a certain range of the unsteadiness parameter. A stability analysis has been performed and presented to elucidate the behavior of these dual solutions.

For the solution of the upper branch, the velocity and temperature profiles of the nanofluid are enhanced by increasing the magnetic field parameter M, but the same variables decrease in the solution of the lower branch. The same trend is detected for the velocity of the fluid with the suction parameter. The temperature of the nanofluid decreases in both branches of the solution by increasing the Prandtl number. Similarly, they decrease with the suction parameter. The temperature of the nanofluid slightly increases in both branches of the solution by increasing the Eckert number. With the stability analysis the authors performed, it was determined that the solution is stable in the upper branch, but unstable in the lower branch.

The kerosene nanofluid with hybrid Zinc/Aluminum-oxide is presented for the first time in the literature.

The purpose of this paper is to examine the electro-magnetohydrodynamic behavior of a third-grade non-Newtonian fluid, flowing between a pair of parallel plates in the presence of electric and magnetic fields. The flow medium between the plates is porous. The effects of Joule heating and viscous energy dissipation are studied in the present study.

A semi-analytical/numerical method, the differential transform method, is used to obtain solutions for the system of the nonlinear differential governing equations. This solution technique is efficient and may be adapted to solve a variety of nonlinear problems in simple geometries, as it was confirmed by comparisons between the results using this method and those of a fully numerical scheme.

The results of the computations show that the Darcy–Brinkman–Forchheimer parameter and the third-grade fluid model parameter retards, whereas both parameters have an inverse effect on the temperature profile because the viscous dissipation increases. The presence of the magnetic field also enhances the temperature profile between the two plates but retards the velocity profile because it generates the opposing Lorenz force. A graphical comparison with previously published results is also presented as a special case of this study.

The obtained results are new and presented for the first time in the literature.

The purpose of this study is to predict the thermal protective performance (TPP) of flame-retardant fabric more economically using machine learning and analyze the factors affecting the TPP using model visualization.

A total of 13 machine learning models were trained by collecting 414 datasets of typical flame-retardant fabric from current literature. The optimal performance model was used for feature importance ranking and correlation variable analysis through model visualization.

Five models with better performance were screened, all of which showed R2 greater than 0.96 and root mean squared error less than 3.0. Heat map results revealed that the TPP of fabrics differed significantly under different types of thermal exposure. The effect of fabric weight was more apparent in the flame or low thermal radiation environment. The increase in fabric weight, fabric thickness, air gap width and relative humidity of the air gap improved the TPP of the fabric.

The findings suggested that the visual analysis method of machine learning can intuitively understand the change trend and range of second-degree burn time under the influence of multiple variables. The established models can be used to predict the TPP of fabrics, providing a reference for researchers to carry out relevant research.

The findings of this study contribute directional insights for optimizing the structure of thermal protective clothing, and introduce innovative perspectives and methodologies for advancing heat transfer modeling in thermal protective clothing.

The purpose of this paper is to present an efficient, interactive foreground/background image segmentation method using mean shift (MS) and graph cuts, in order to improve the segmentation performance with little user interaction.

By incorporating the advantages of the mean shift method and the graph cut algorithm, the proposed approach ensures the accuracy of segmentation results. First, the user marks certain pixels as foreground or background. Then the graph is constructed and the cost function composed of the boundary properties and the region properties is defined. To obtain the hidden information of user interaction, the foreground and background marks are clustered separately by the mean shift method. The region properties are determined by the minimum distances from the unmarked pixels to the foreground and background clusters. The boundary properties are determined by the relationship between the unmarked pixels and its neighbor pixels. Finally, using the graph cuts method solves the energy minimization problem to get the foreground which is of interest.

The paper presents experimental results and compares the results to other methods. It can be seen from the comparison that this method can obtain a better segmentation performance in many cases.

The paper incorporates the advantages of the mean shift method and the graph cut algorithm to obtain better segmentation results, even though the scene is complex.

The purpose of this study is to determine the effect of ventilation openings and fire intensity on heat transfer and fluid flow within the microclimate between 3D human body and clothing.

On account of interaction effects of fire and ventilation openings on heat transfer process, a 3D transient computational fluid dynamics model considering the real shape of human body and clothing was developed. The model was validated by comparing heat flux history and distribution with experimental results. Heat transfer modes and fluid flow were investigated under three levels of fire intensity for the microclimate with ventilation openings and closures.

Temperature distribution on skin surface with open microclimate was heavily depended on the heat transfer through ventilation openings. Higher temperature for the clothing with confined microclimate was affected by the position and direction of flames injection. The presence of openings contributed to the greater velocity at forearms, shanks and around neck, which enhanced the convective heat transfer within microclimate. Thermal radiation was the dominant heat transfer mode within the microclimate for garment with closures. On the contrary, convective heat transfer within microclimate for clothing with openings cannot be neglected.

The findings provided fundamental supports for the ease and pattern design of the improved thermal protective systems, so as to realize the optimal thermal insulation of the microclimate on the garment level in the future.

The outcomes broaden the insights of results obtained from the mesoscale models. Different high skin temperature distribution and heat transfer modes caused by thermal environment and clothing structure provide basis for advanced thermal protective clothing design.

The present study provides a comprehensive review of the advancements in five active heating modes for cold-proof clothing as of 2021. It aims to evaluate the current state of research for each heating mode and identify their limitations. Further, the study provides insights into the optimization of intelligent temperature control algorithms and design considerations for intelligent cold-proof clothing.

This article presents a classification of active heating systems based on five different heating principles: electric heating system, solar heating system, phase-change material (PCM) heating system, chemical heating system and fluid/air heating system. The systems are analyzed and evaluated in terms of heating principle, research advancement, scientific challenges and application potential in the field of cold-proof clothing.

The rational utilization of active heating modes enhances the thermal efficiency of cold-proof clothing, resulting in enhanced cold-resistance and reduced volume and weight. Despite progress in the development of the five prevalent heating modes, particularly with regard to the improvement and advancement of heating materials, the current integration of heating systems with cold-proof clothing is limited to the torso and limbs, lacking consideration of the thermal physiological requirements of the human body. Additionally, the heating modes of each system tend to be uniform and lack differentiation to meet the varying cold protection needs of various body parts.

The effective application of multiple heating modes helps the human body to maintain a constant body temperature and thermal equilibrium in a cold environment. The research of heating mode is the basis for realizing the temperature control of cold-proof clothing and provides an effective guarantee for the future development of the intelligent algorithms for temperature control of non-uniform heating of body segments.

The integration of multiple heating modes ensures the maintenance of a constant body temperature and thermal balance for the wearer in cold environments. The research of heating modes forms the foundation for the temperature regulation of cold-proof clothing and lays the groundwork for the development of intelligent algorithms for non-uniform heating control of different body segments.

The present article systematically reviews five active heating modes suitable for use in cold-proof clothing and offers guidance for the selection of heating systems in future smart cold-proof clothing. Furthermore, the findings of this research provide a basis for future research on non-uniform heating modes that are aligned with the thermal physiological needs of the human body, thus contributing to the development of cold-proof clothing that is better suited to meet the thermal needs of the human body.

The purpose of this paper is the presentation and research of a novel sensor fusion-based system for obstacle detection and identification, which uses the millimeter-wave radar to detect the position and velocity of the obstacle. Afterwards, the image processing module uses the bounding box regression algorithm in deep learning to precisely locate and identify the obstacles.

Unlike the traditional algorithms that use radar and vision to detect obstacles separately, the purposed method of this paper uses radar to determine the approximate location of obstacles and then uses bounding box regression to achieve accurate positioning and recognition. First, the information of the obstacles can be acquired by the millimeter-wave radar, and the effective target is extracted by filtering the data. Then, use coordinate system conversion and camera parameter calibration to project the effective target to the image plane, and generate the region of interest (ROI). Finally, based on image processing and machine learning techniques, the vehicle targets in the ROI are detected and tracked.

The millimeter wave is used to determine the presence of an obstacle, and the deep learning algorithm of the image is combined to determine the shape and the class of the obstacle. The experimental results indicate that the detection rate of this method is up to 91.6 per cent, which can better implement the perception of the environment in front of the vehicle.

The originality is based on the combination of millimeter-wave sensors and deep learning. Using the bounding box regression algorithm in RCNN, the ROI detected by radar is analyzed to realize real-time obstacle detection and recognition. This method does not require processing the entire image, greatly reducing the amount of data processing and improving the efficiency of the algorithm.

The purpose of this paper is to study heat and steam transfer in a vertical air gap and improve thermal protective performance of protective clothing under thermal radiation and hot steam.

An experiment-based model was introduced to analyze heat and moisture transfer in the vertical air gap between the protective clothing and human body. A developed test apparatus was used to simulate different air gap sizes (3, 6, 9, 12, 15, 18, 21 and 24 mm). The protective clothing with different air gap sizes was subjected to dry and wet heat exposures.

The increase of the air gap size reduced the heat and moisture transfer from the protective clothing to the skin surface under both heat exposures. The minimum air gap size for the initiation of natural convection in the dry heat exposure was between 6 and 9 mm, while the air gap size for the occurrence of natural convection was increased in the wet heat exposure. In addition, the steam mass flux presented a sharp decrease with the rising of the air gap size, followed by a stable state, mainly depending on the molecular diffusion and the convection mass transfer.

This research provides a better understanding of the optimum air gap under the protective clothing, which contributes to the design of optimum air gap size that provided higher thermal protection against dry and wet heat exposures.

This study aims to investigate the heat transfer mechanism of the uniforms used by people working in hot, humid and windy environments. Furthermore, the effectiveness of an opening structure added to the armpit of the uniforms in improving thermal comfort was comparatively examined.

A set of uniforms was tested with the opening at the armpit alternatively zipped or unzipped. Thermal manikin and human tests were performed in a climatic chamber simulating the specific environmental conditions, including wind speeds at four levels (0.15, 0.5, 2, 4 m/s) and relative humidities at two levels (50 and 85%). Static and dynamic thermal insulations of clothing (I_T) were examined by the thermal manikin tests. The human bodies' thermal responses, including heart rates (HR), eardrum temperatures (T_e), skin temperatures (T_sk) and subjective perceptions, were given by the human tests.

Special mechanisms of heat transfer in the specific uniforms used in tropical monsoon climates were revealed. Reductions on I_T were caused by the movement of the human body and the environmental wind, and the empirical equations would underestimate this reduction. The opening at the armpit was able to prompt more heat transfer under dynamic condition, with reducing the I_T by 11.8%, lowering the mean T_sk by 0.92°C, and significantly improving the subjective perceptions (p < 0.05). The heat exhaustion was alleviated with lowering the T_e by 0.32°C.

This study managed to improve the thermal performance of uniforms for workers under unforgiving conditions. The evaluation and design methods introduced by this study provided practical guidance for similar products with strict dress codes and cost control requirements based on the findings from thorough product tests and analysis.