The sweating fabric manikin (sweating warm manikin) is a laboratory device that simulates the heat and moisture exchange process in the human body and is primarily used to test the thermal comfort of clothing. By simulating human sweating and temperature regulation mechanisms, combined with high-precision sensors and control systems, it provides stable and repeatable experimental data for evaluating clothing properties such as moisture permeability and thermal resistance in different environments.
The skin's core temperature can be controlled using a fuzzy PID control method. Through epidermal temperature feedback and a sweat microcirculation system consisting of a sweat source and a micro-plunger pump, the bionic skin can be controlled to produce light or heavy sweating. Epidermal temperature is regulated through sweat evaporation and cooling. Experiments were conducted on core and epidermal temperature control. The results showed that the core temperature was controlled at 36.8±0.1°C, while the epidermal temperature stabilized at 33.5±0.3°C. The bionic skin was able to sweat and regulate the elevated epidermal temperature back to 33.5±0.3°C. The bionic skin developed can be used to test the infrared management properties of textiles, simulating the microcirculation system formed by skin, textiles, and the external environment.
Sensor layout affects data accuracy and comprehensiveness. A reasonable layout ensures comprehensive and accurate collection of data such as the manikin's surface temperature and humidity. For example, increasing the number of sensors in key areas such as the chest, back, and joints can more accurately reflect thermal and humidity changes in these areas. Improper sensor placement can lead to localized data distortion, affecting the overall assessment of the garment's thermal and humidity performance.
Sweating thermal manikins can simulate extreme conditions such as high temperature and humidity (e.g., 40°C, 90% RH) or low temperature and high wind speed (e.g., -20°C, 10 m/s). Their materials and design can withstand a certain range of environmental variations, but long-term repeated testing may affect performance due to material aging or sensor drift, necessitating regular calibration and maintenance. For example, when testing firefighter suits in simulated high temperature and humidity environments, the thermal protection performance of the suits can be accurately assessed in extreme fire scenes.
Some sweating thermal manikins have joint mobility, allowing them to simulate movements such as walking and running, and can simulate the effects of dynamic movement on sweating and thermal and humidity transfer. Exercise changes the air flow between the body and clothing, affecting the efficiency of heat and moisture transfer. Perspiration also varies with exercise intensity. By simulating dynamic motion, we can more realistically assess the thermal and moisture comfort of sportswear during actual exercise.
When testing multi-layer clothing systems, the challenge lies in isolating the heat and moisture transfer contributions of each material layer and preventing interlayer air pockets from interfering with the data. Multi-layer clothing layers are made of different materials, resulting in varying heat and moisture transfer properties, and interlayer air pockets can affect heat and moisture transfer. For example, when testing winter thermal garments, it is necessary to accurately distinguish the heat and moisture transfer of base, midlayer, and outer layers, as well as the impact of interlayer air pockets on overall thermal performance.
Standards such as ASTM F1291 and ISO 15831 have different requirements for sweating manikins testing. For example, the EN 13501-2 standard divides smoke toxicity testing into three phases, focusing on data from the critical first 10 minutes of the escape period. Compared to the US ASTM E1678 standard, EN 13501-2 includes five additional gas analysis indicators and requires dynamic combustion testing rather than fixed-temperature testing. This results in approximately 15% of building materials meeting US standards but failing European standards.
Test data reliability can be assessed based on equipment accuracy, calibration cycles, and test environment control. High-precision equipment and regular calibration ensure data accuracy. Strict control of the test environment, such as temperature, humidity, and wind speed, can reduce external interference. For example, regular manikin calibration as required by the standard ensures that environmental parameters remain within specified ranges during testing, thereby improving data reliability.
Limited simulation capabilities: Manikins cannot fully simulate all physiological characteristics of the human body, such as metabolism, heat conduction, and evaporative heat dissipation.
Inadequate control modes: Current manikins' control modes (such as constant surface temperature mode and constant heat flux mode) may not accurately reflect the human body's physiological responses under different environmental conditions.
Non-uniform sweating: Although some new manikins have non-uniform sweating capabilities, they still cannot fully replace the body's natural sweating mechanism.
Influence of the experimental environment: During experiments, manikins are affected by various factors, such as ambient temperature and humidity, which may lead to inaccurate experimental results.
These limitations limit the effectiveness of manikins in practical applications and require further research and improvement.
To improve the long-term testing stability of a sweating manikin, we must focus on five key aspects: equipment maintenance, environmental control, operational standards, data collection, and sweat management. These aspects are as follows:
Key Component Inspection
Regularly inspect the heating system, temperature control system, and data transmission lines, and promptly replace aging or damaged components (such as heating films and sensors). For example, the soft heating film must be in close contact with the manikin surface to prevent temperature fluctuations caused by poor contact.
System Calibration
Regularly calibrate the manikin according to international standards (such as ISO 7730-2005) to ensure that the thermal resistance measurement error is within ±0.03clo and the coefficient of variation between replicate tests does not exceed 3%. Calibration covers temperature sensor accuracy and sweat control accuracy.
Constant Temperature and Humidity Environment
During testing, maintain chamber temperature fluctuations ≤±0.5°C and humidity fluctuations ≤±5%RH. For example, a frequency converter is used to adjust the fan speed, and combined with a fresh air system, a stable environment is maintained to prevent sudden temperature fluctuations from affecting the dummy's surface thermal equilibrium.
Dust Prevention and Cleanliness
Maintain a clean and tidy test environment to prevent dust and other impurities from adhering to the dummy's surface or sensors, thereby preventing data interference.
Appropriate Parameter Settings
Set the dummy's surface temperature, humidity, and sweat flow parameters based on actual wearing conditions. For example, when simulating strenuous exercise, sweat flow must be controlled between a critical value (Go) and a maximum value (Gmx) to prevent data distortion caused by excessive surface moisture or dryness.
Dynamic Response Management
Before the dummy enters the thermal equilibrium phase, the PID temperature control system adjusts the heating rate to ensure a stable state within 40 minutes. During dynamic testing, the skin temperature fluctuation range (±0.1°C) must be monitored, and the water supply rate must be adjusted promptly to maintain moisture equilibrium.
High-Precision Sensors
Use high-precision temperature and humidity sensors, combined with D/A conversion technology, to output real-time data and reduce random errors. For example, evaporation is accurately measured using an electronic weighing balance and combined with body scale data to calculate the actual moisture permeability index.
Error Compensation Mechanism
An error compensation algorithm is incorporated into the software design to correct for systematic and random errors, improving test accuracy.
Dynamic Adjustment Strategy
By detecting the weight gain of the dummy system, the sweat rate at each sweat pore is calculated and the peristaltic pump flow rate is adjusted through D/A conversion. For example, in the initial stage, the skin is rapidly moistened (approaching the set level within 30 minutes). Subsequently, the evaporation rate is compared with the water supply rate to maintain a stable moisture content (within a range of ±2g).
Anti-Drip Design
The sweat pore layout and the hygroscopic properties of the simulated skin material are optimized to prevent excessive sweat from dripping or running, preventing partial moisture in the tested garment from affecting the moisture permeability index calculation.
