The FPC Model {1,2,3] was originally developed to make fundamental physiological concepts and models available to engineers and researchers working in other disciplines. Being continually further-developed and extended over the past 20 years, the model has found multiple applications in different disciplines and in national and international research projects [4].
The FPC Model is a numerical framework of submodels which – linked together – predict human overall and local temperature, calorimetric, regulatory, and perceptual responses to steady-state and transient environmental and personal conditions. The model contains two interacting systems of thermoregulation: the controlling active system and the controlled passive system. Included furthermore a dynamic thermal comfort model which predicts human perceptual responses from physiological body states.
The FPC Model accepts different types of boundary conditions as model input to perform thermophysiological simulations [6]. Body (core) temperatures, regulatory and perceptual responses can thus be predicted using non-invasive body sensors measuring e.g. skin temperatures or surface heat fluxes, and heart rate. Acceptable model inputs are also other types of boundary conditions such as surface heat transfer coefficients with info on environmental conditions predicted e.g. by coupled CFD simulations.
The model has been subject to multiple general as well as application-specific national and international validation studies and is currently one of the best validated models of human thermoregulation [2, 4, 8, 10].
The FPC passive system model simulates the physical human body and the heat and mass transfer processes which occur within tissues and at the body surface [1, 5, 6].
The reference passive system represents a 50%-tile, average-population person according to large-scale anthropometric field studies [6]. The computer humanoid is a composite of multi-layered compartments simulating individual body elements built of multiple tissue layers and discretized as tissue nodes in the numerical model. Body elements are subdivided further laterally into sectors to enable realistic simulation of asymmetric boundary conditions.
The dynamic heat and mass transport within tissues is modelled using a composite continuum approach to an extended formulation of the bioheat transfer equation considering thermally significant blood vessels.
Heat losses to the environment are modelled by establishing local heat and mass balances at the surface sectors of each body element. Air exposure simulations consider the components of free and forced convection, long- and short-wave radiation, and evaporation and accumulation of sweating. Water immersion scenarios simulate the governing heat transport by surface convection in the fluid.
The FPC passive system model contains a scalable human anthropometry and morphology model [6] which allows individual adult male and female as well as adolescent and infant body characteristics to be simulated.
The local body dimensions of the simulated individual are specified using so-called anthropometry files. The model furthermore contains the FPC Body Builder model – a user-friendly body generator for automated body scaling based on the four main, easily accessible personal parameters as model input, i.e. body height, weight, age, and sex.
The FPC Body Builder employs a reference passive system model and uses the four basic personal characteristics to calculate the required local anthropometric dimensions and body composition data for the simulated person based on results of anthropometric and morphology field surveys [6].
The above scaling process indirectly changes important body properties including e.g. the overall skin surface area and body fat content. The resultant figures are compared here with experimentally acquired data for various combinations of the body height and body weight [6].
Relative weights of different body compartments provided as percentages of the total body weight. The FPC Body Builder results are compared with measured data [6] originally obtained from human cadaver trials by segmentation and direct weighting.
Lengths of the main body sections as predicted by the FPC Body Builder and compared with measurement results of anthropometric surveys [11] obtained for male subjects grouped in ten stature categories ranging from 155 to 202 cm average body height [6].
The FPC active system [2, 5, 6] is a cybernetic model of the human thermoregulatory system predicting responses of the central nervous system and local autonomic thermoregulation. Predicted responses of the central nervous system include: production of sweat moisture, increase of metabolic heat generation in muscle tissues by shivering, and changes in cutaneous blood flows due to peripheral vasodilatation and -constriction.
The statistically founded reference active system was developed by means of meta-regression analysis using published experimental data to simulate responses of an ‘average’ person [2]. The non-linear, set-point temperature based control system employs the head core temperature and the (mean) skin temperature as the main driving impulses for human thermoregulatory action. A further signal, i.e. the rate of change of the (mean) skin temperature, weighted by a punitive signal associated with the skin temperature, is the driving impulse that governs the dynamics of regulatory responses during sudden exposures to cold.
Human thermoregulatory responses are affected by multiple personal factors the key four of which include: aerobic fitness, status of acclimatization, and anthropometric and morphological properties of the body. Other personal factors lose their influence when ‘corrected’ for the effect of the maximum aerobic power and body fat content [7].
The scalable FPC passive system implicitly accounts for response variations due to changes in the personal anthropometric and body composition characteristics [6]. In addition, the individualized response model [8] implemented in the FPC Model specifically accounts for personal variations in the thermoregulatory responses of sweating and peripheral vasodilatation due to shifts of the setpoint of the head core temperature associated with personal variations of the maximum aerobic power and the status of acclimatization [8].
Thermophysiological responses to a wide range of steady ambient temperatures predicted by the FPC Model after one hour of exposure for two levels of body fat content and using an adaptive outdoor clothing model.
The FPC Model underwent numerous validation tests including exposures to transient conditions. Here, predicted and measured body temperature and regulatory responses are compared for step changes from hot to cold and back again [2].
Thermophysiological responses to a wide range of steady ambient temperatures predicted by the FPC Model after one hour of exposure for two levels of maximum oxygen uptake and using an adaptive outdoor clothing model.
The physiologically based FPC thermal comfort model predicts human perceptual responses in terms of the so-called Dynamic Thermal Sensation, DTS, using the 7-point ASHRAE sensation scale [3].
DTS was developed by systematic simulations of prominent, large-scale thermal comfort experiments involving sedentary and exercising subjects and correlating the observed overall sensation votes with the thermophysiological states of the simulated subjects [3]. The first principles comfort model predicts the human overall thermal sensation for different activity levels under steady-state and transient conditions using temperature error signals from the skin and the head core and the rate of the change of skin temperature as the governing afferent signals.
The FPC comfort model furthermore includes additional models for predicting
thermal acceptability, dissatisfaction, overall and local thermal sensation and comfort responses under homogeneous and asymmetric boundary conditions.
Thermal sensation responses observed for sedentary subjects undergoing step changes in ambient temperature. The sudden changes toward the cold and neutral environment are characterized by a transient cold sensation ‘overshoot’ and a fast adaptation to a neutral sensation, respectively. The dynamic nature of the response is reproduced by DTS using time rates of change of skin temperature as additional input signals [3].
DTS is a composite of afferent signals from different body sites. In non-exercising subjects DTS is mainly governed by static signals from the body periphery, i.e. skin temperature. During exercise concurrent punitive signals from the body core, i.e. the hypothalamus temperature, affect the thermal sensation as a further static component of DTS. Negative or positive rates of change of skin temperature dominate the dynamics of DTS under fast changing ambient conditions.
Periodic changes in environmental conditions, for example, due to poorly controlled HVAC systems, may results in complex thermophysiological conditions of the human body. In such situations several static and dynamic processes may overlap. DTS predictions reproduce observations under such circumstances taking into account both the static and dynamic sensation components [3].
References
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