CN114528776B - Method for judging dressing comfort and method for selecting clothing with dressing comfort - Google Patents
Method for judging dressing comfort and method for selecting clothing with dressing comfort Download PDFInfo
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Abstract
The invention discloses a method for judging dressing comfort and a method for selecting clothes for realizing the dressing comfort, wherein the judging method comprises the following steps: determining the air temperature T of the environment in which a human body is located a And ambient wind speed v a Simultaneously determining the inherent thermal resistance I of the clothes on which the human body is wearing cl Then, the average skin temperature T of the wearer in the predetermined posture is calculated sk，m Judging whether the temperature exceeds the comfortable average skin temperature range of the human body, if not, the human body is comfortable; otherwise, it is uncomfortable; the garment selection method comprises the following steps: determining T of an environment to which a human is going a And v a While setting the target T sk，m Value, calculating the I required to reach the target value cl And selecting a garment accordingly; or the following steps: firstly, the human body posture is determined, and a map is made, and then the human body posture is determined to be at a certain v by inquiring the map a And T a I required to reach the expected average skin temperature of the wearer cl And selecting a garment accordingly. The method is simple, strong in universality and high in efficiency, and can better guide dressing.
Description
Technical Field
The invention belongs to the technical field of evaluation of thermal comfort of clothes, and relates to a method for judging dressing comfort and a method for selecting clothes with the dressing comfort.
Background
In a heat transfer system of 'human body-clothing-environment', whether the clothing can effectively promote or inhibit heat transfer between the human body and the environment directly influences the heat-moisture comfortable feeling and the heat physiological reaction of the human body wearing the clothing, even the life safety of a wearer is concerned.
The method is a key step before the clothes are put into use, and is used for scientifically and effectively evaluating the heat transfer performance of the clothes and judging whether the heat transfer performance can meet the requirement of human body comfort. In the prior art, recruitment of subjects for physical experiments of dressed human bodies and prediction of thermal physiological parameters by using a human body thermal physiological reaction model constructed based on a human body active thermal regulation mechanism are main technical methods for evaluating the comfort of clothes. The latter has stronger operability and repeatability, and is the preferred technical method for evaluating the performance of the clothes. The existing thermophysical response prediction model does not distinguish a standing human body from a sitting human body when calculating the convective heat transfer between the human body and the environment. However, previous human body heat transfer experiments show that the heat exchange mechanism between the human body and the environment is different in standing posture and sitting posture. The prediction of the thermal physiological reaction parameters of the human body posture is not considered, and the comfort of the clothes in the appointed wearing scene is misjudged.
In addition, the thermophysiological response prediction model is based on a large number of partial differential equation expansion calculations that are packaged for use in commercial products, and are not available to every research institution or individual. At present, a popular and easy-to-understand prediction method which is simple and convenient to operate, simple in operation and friendly to users is lacked, so that a common user can be helped to quickly predict the physiological parameters after wearing the clothes, and immediate prediction is provided for judging whether the clothes can meet the requirement of human body comfort.
Disclosure of Invention
The invention aims to solve the problems in the prior art, provides a method for establishing a linear regression model reflecting the relation between the skin temperature of a human body and the environment and clothing, and provides a quick and accurate method for judging the wearing comfort and a method for selecting the clothing with comfortable wearing based on the linear regression model.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a method for establishing a linear regression model comprises the following steps:
s1: performing simulation operation (namely visualized three-dimensional numerical simulation and calculation) on convective heat transfer between the non-wearing human body and the environment based on Computational Fluid Dynamics (CFD);
s2: constructing a regression model of the convection heat transfer coefficient to realize quantitative calculation of the convection heat transfer of the body surface;
performing regression analysis on the data obtained by the simulation operation in the S1, and acquiring regression models of convective heat transfer coefficients of body surface body sections aiming at standing postures and sitting postures by taking the ambient wind speed, the skin temperature difference and the ambient temperature as independent variables and taking the body surface convective heat transfer coefficient as a dependent variable;
s3: dynamically linking the processes of body surface heat transfer and body temperature regulation, and building a thermophysiological reaction database;
combining a regression model of convective heat transfer coefficient and a thermophysiological reaction model to build a thermophysiological reaction prediction platform suitable for standing and sitting postures, carrying out parametric study on three parameters of air temperature, ambient wind speed and inherent thermal resistance of clothes, and obtaining skin temperature of each body section of the body with clothing under different parameter combinations, or further obtaining average skin temperature of the body with clothing from the skin temperature of each body section of the body with clothing, thereby forming a thermophysiological reaction database;
s4: constructing a linear regression model I taking the average skin temperature of the human body with the clothing in the standing posture as a dependent variable and taking the air temperature, the ambient wind speed and the inherent thermal resistance of the clothing as independent variables, or constructing a linear regression model II taking the average skin temperature of the human body with the clothing in the sitting posture as a dependent variable and taking the air temperature, the ambient wind speed and the inherent thermal resistance of the clothing as independent variables, or constructing a linear regression model taking the skin temperature of each body section of the human body with the clothing in the standing posture and the sitting posture as a dependent variable and taking the air temperature, the ambient wind speed and the inherent thermal resistance of the clothing as independent variables;
the expression of the linear regression model I is as follows:
T sk，m，st ＝C 1 ×T a +C 2 ×I cl +C 3 ×v a +C 4 ；
the expression for linear regression model II is as follows:
T sk，m，se ＝C 5 ×T a +C 6 ×I cl +C 7 ×v a +C 8 ；
in the formula, T sk,m,st The average skin temperature of a human body with a garment in a standing posture is measured in units of; t is sk,m,se The average skin temperature of a human body with clothing in a sitting posture is measured in units of; t is a The value range is 12.0-32.0 ℃ for the air temperature; i is cl The value range is 0.09-2.30 clo for the inherent thermal resistance of the clothes; v. of a Taking a value range for the ambient wind speedThe circumference is 0.2 to 2.5m/s; c 1 Is 0.443; c 2 1.892 ℃/clo; c 3 Is-0.769 ℃ s/m; c 4 At 19.720 ℃; c 5 Is 0.428; c 6 1.799 ℃/clo; c 7 Is-0.741 ℃ s/m; c 8 The temperature was 20.340 ℃.
As a preferred technical scheme:
in the above method for establishing a linear regression model, the specific process of step S1 is as follows: firstly, constructing a geometric model of a standing or sitting human body and an environment, namely a visual model which completely reflects the three-dimensional size and shape of the standing or sitting human body and the environment, wherein the human body is positioned in the middle of the environment; then dispersing an environment calculation domain where a human body is located, setting three groups of boundary condition parameters including air temperature, environment wind speed and human body skin temperature, solving the calculation domain, and obtaining convection heat transfer coefficients of all body sections (including 16 body sections including head, chest, back, left upper arm, right upper arm, left lower arm, right lower arm, left hand, right hand, pelvis, left thigh, right thigh, left calf, right calf, left foot and right foot) of the standing posture or sitting posture human body surface within the range of 0.2-2.5 m/s of environment wind speed and 1.6-24.0 ℃ of skin and air temperature difference.
In the above method for establishing a linear regression model, the specific process of step S3 is as follows:
s31: constructing a thermal physiological reaction model according to four general temperature regulation mechanisms (namely, trembling muscles, sweating, vasodilatation and vasoconstriction) of a human body, three laws of heat transfer (namely, heat conduction, heat radiation and heat convection) and an evaporation and heat dissipation principle, wherein the heat convection part uses a regression model of a convection heat transfer coefficient generated by S2 to calculate, so that two groups of thermal physiological reaction models for distinguishing standing postures and sitting postures are formed, and a thermal physiological reaction prediction platform is obtained;
s32: respectively designing n groups of working conditions aiming at standing posture and sitting posture, wherein n is more than or equal to 48, the air temperature range is 12.0-32.0 ℃, the ambient wind speed range is 0.2-2.5 m/s, the inherent thermal resistance range of the garment is 0.09-2.30 clo, the basic metabolic rate of the human body is 1met, calculating by using a thermophysiological reaction prediction platform to obtain the skin temperature of each section of the body with the clothing under 2n groups of working conditions to form a thermophysiological reaction database, or further solving the average skin temperature of the body with the clothing under 2n groups of working conditions according to a four-point method, an eight-point method or a fourteen-point method given by ISO 9886 to form the thermophysiological reaction database.
The invention also provides a method for judging the dressing comfortableness, which comprises the steps of determining the air temperature and the ambient wind speed of the environment where the human body is located, simultaneously determining the inherent thermal resistance of the clothing where the human body is located, calculating the average skin temperature of the human body with the dressing in the specified posture, judging whether the average skin temperature exceeds the comfortable average skin temperature range of the human body, and if not, judging that the human body in the specified posture is comfortable to dress; otherwise, the human body in the designated posture is uncomfortable to wear;
designating the posture as a standing posture, and calculating and utilizing a linear regression model I; or, the posture is designated as a sitting posture, and a linear regression model II is calculated and utilized; the linear regression model I and the linear regression model II are obtained based on the above-described method for establishing a linear regression model.
As a preferred technical scheme:
according to the method for judging the wearing comfort, the comfortable average skin temperature range of a human body is 32.0-34.6 ℃; the comfortable average skin temperature range of the human body actually varies from person to person (sex, age), and the comfortable average skin temperature interval of the human body defined by the invention is an interval suitable for most people based on a large number of statistics, wherein the interval is 32.0 ℃,34.6 ℃; in practical application, a user can properly adjust the temperature according to personal requirements, for example, if the user prefers a slightly lower temperature, the lower limit of the interval of the comfortable average skin temperature can be properly reduced by 32.0 ℃; if the user prefers a slightly higher temperature, the upper limit of the comfortable average skin temperature interval may be raised by as much as 34.6 ℃.
The average absolute error between the calculated value of the average skin temperature of the human body with the dress in the designated posture and the true value is lower than 0.9 ℃, the true value is obtained by testing after 30min of the dress (the average skin temperature of the human body can reach a steady state after 30min of the dress usually), the error value is lower than the average standard deviation of human body experiments with the value of 1.0 ℃, and the average absolute error is the average value of the absolute errors between the actual average skin temperature of a plurality of groups of human bodies and the calculated average skin temperature.
The invention also provides a clothing selection method for realizing comfortable clothing, which comprises the steps of determining the air temperature and the ambient wind speed of the environment where a human body is expected to go, simultaneously setting a target value of the average skin temperature of the human body with clothing, calculating the inherent thermal resistance of the clothing required by the average skin temperature of the human body with clothing in a specified posture to reach the target value, and selecting the corresponding clothing according to the inherent thermal resistance;
designating the posture as a standing posture, and calculating and utilizing a linear regression model I; or, the designated posture is a sitting posture, and a linear regression model II is calculated and utilized; the linear regression model I and the linear regression model II are obtained based on the establishment method of the linear regression model;
selecting corresponding clothes according to inherent thermal resistance of the clothes, namely selecting the types and the number of the clothes to be worn specifically according to the inherent thermal resistance of the clothes, wherein the corresponding relation between the inherent thermal resistance of the clothes and the clothes types can refer to a common indoor clothes inherent thermal resistance database given by ISO 9920, such as 0.09clo inherent thermal resistance of a short-sleeved T-shirt, 0.12clo inherent thermal resistance of a long-sleeved T-shirt, 0.28-0.35 clo inherent thermal resistance of a sweater, 0.25-0.4 clo inherent thermal resistance of a jacket, 0.06clo inherent thermal resistance of short pants, 0.2-0.35 clo inherent thermal resistance of long-pants, 0.15-0.25 clo inherent thermal resistance of a skirt and the like; the inherent thermal resistance of a plurality of combined garments can be obtained by referring to an ISO 9920 database or based on weighted calculation of the inherent thermal resistance of a single garment.
As a preferred technical scheme:
after the corresponding clothes are selected for wearing for 30min (usually, the average skin temperature of the human body can reach a steady state after wearing for 30 min), the average absolute error between the target value and the true value of the average skin temperature of the human body is lower than 0.9 ℃, the error value is lower than the human body experiment average standard deviation with the value of 1.0 ℃, and the average absolute error is the average value of the absolute errors between the target value and the true value of the average skin temperature of a plurality of groups of human bodies.
The invention also provides a clothing selection method for realizing comfortable dressing, which comprises the steps of firstly determining the human body posture to make clothing inherent thermal resistance-average skin temperature map, then determining the clothing inherent thermal resistance required for reaching the expected average skin temperature of the dressed human body under a certain ambient wind speed and a certain air temperature through inquiring the map, and selecting the corresponding clothing according to the clothing inherent thermal resistance;
the clothing inherent thermal resistance-average skin temperature map is a plurality of straight lines distributed on a coordinate system of which the abscissa is clothing inherent thermal resistance and the ordinate is average skin temperature of a human body with clothing, or a plurality of straight lines distributed on a coordinate system of which the abscissa is average skin temperature of the human body with clothing and the ordinate is clothing inherent thermal resistance;
each straight line corresponds to the same ambient wind speed and different air temperatures, or each straight line corresponds to different ambient wind speeds and the same air temperature;
the human body posture is a standing posture, and each straight line is obtained by utilizing a linear regression model I; or the human body posture is a sitting posture, and each straight line is obtained by utilizing a linear regression model II; the linear regression model I and the linear regression model II are obtained based on the establishment method of the linear regression model;
the certain ambient wind speed and the certain air temperature are the ambient wind speed and the air temperature corresponding to one straight line;
selecting corresponding clothes according to inherent thermal resistance of the clothes, namely selecting the types and the number of the clothes to be worn specifically according to the inherent thermal resistance of the clothes, wherein the corresponding relation between the inherent thermal resistance of the clothes and the clothes types can refer to a common indoor clothes inherent thermal resistance database given by ISO 9920, such as 0.09clo inherent thermal resistance of a short-sleeved T-shirt, 0.12clo inherent thermal resistance of a long-sleeved T-shirt, 0.28-0.35 clo inherent thermal resistance of a sweater, 0.25-0.4 clo inherent thermal resistance of a jacket, 0.06clo inherent thermal resistance of short pants, 0.2-0.35 clo inherent thermal resistance of long-pants, 0.15-0.25 clo inherent thermal resistance of a skirt and the like; the inherent thermal resistance of the combined multiple garments can be obtained by referring to an ISO 9920 database or based on the weighted calculation of the inherent thermal resistance of a single garment;
the generation of the inherent thermal resistance-average skin temperature map of the clothes can provide more intuitive dressing opinions for ordinary users so as to achieve the expected comfort effect.
As a preferred technical scheme:
after the average skin temperature of the human body can reach a steady state after the corresponding garment is selected to be worn for 30min, usually after the garment is worn for 30 min), the average absolute error between the expected value and the actual value of the average skin temperature of the human body is lower than 0.9 ℃, the error value is lower than the human body experiment average standard deviation with the value of 1.0 ℃, and the average absolute error is the average value of the absolute errors between the expected value and the actual value of the average skin temperatures of a plurality of groups of human bodies.
Has the beneficial effects that:
(1) The method fully considers the wearing state of the common human body, and the finally proposed calculation method of the average skin temperature of the human body with the clothing in the specified posture can be applied to the human body in the standing posture and the sitting posture respectively, so that the evaluation of the heat transfer performance and the comfort of the clothes is more fit for the actual wearing condition, and the evaluation result is more scientific and reliable;
(2) The calculation formula of the average skin temperature of the human body with clothing in the specified posture is a linear equation set, the application is simple and quick, and compared with the conventional thermophysiological response prediction model, the calculation formula has higher universality and is very user-friendly, and the problem of poor availability of the method in the prior art is effectively solved;
(3) The method of the invention guides the ordinary people to scientifically dress based on the rapid solving application of a linear regression model (namely a calculation formula of the average skin temperature of a human body with clothing in a specified posture), and can match proper clothing according to the environmental characteristics to obtain the comfort level expected by the individual.
Drawings
FIG. 1 is a graph of convective heat transfer coefficients for different modeled right thigh torso section surfaces;
FIG. 2 is a matrix diagram of the average skin temperature (abbreviated as "average skin temperature") of a person in a standing posture, the air temperature, the ambient wind speed and the inherent thermal resistance dispersion point of the garment;
FIG. 3 is a matrix diagram of average skin temperature (abbreviated as "average skin temperature"), air temperature, ambient wind speed, and inherent thermal resistance dispersion point of clothing in sitting posture;
FIG. 4 is an application example of a clothing inherent thermal resistance-average skin temperature map.
Detailed Description
The invention will be further illustrated with reference to specific embodiments. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.
The core of the invention is to obtain a linear regression model which takes the average skin temperature of a human body with a dress in a standing posture and a sitting posture as a dependent variable and takes the air temperature, the ambient wind speed and the inherent thermal resistance of the dress as independent variables, and then a dress comfort judgment method and a dress comfortable dress selection method are obtained based on the linear regression model; the linear regression model is obtained by the following steps:
s1: performing simulation operation (namely visualized three-dimensional numerical simulation and calculation) on convective heat transfer between the non-wearing human body and the environment based on Computational Fluid Dynamics (CFD);
firstly, constructing a geometric model of a standing posture human body and an environment, namely a visual model which completely reflects the three-dimensional size and shape of the standing posture human body and the environment, wherein the human body is positioned in the middle of the environment;
then, dispersing an environment calculation domain where a human body is located, setting three groups of boundary condition parameters of air temperature, environment wind speed and human body skin temperature, solving the calculation domain, and obtaining convection heat transfer coefficients of all body sections (including 16 body sections of the head, the chest, the back, the left upper arm, the right upper arm, the left lower arm, the right lower arm, the left hand, the right hand, the pelvis, the left thigh, the right thigh, the left calf, the right calf, the left foot and the right foot) of the standing human body surface within the ranges of 0.2-2.5 m/s of the environment wind speed and 1.6-24.0 ℃ of the skin-air temperature difference;
then, constructing a geometric model of a sitting posture human body and an environment, namely a visual model which completely reflects the three-dimensional size and shape of the sitting posture human body and the environment, wherein the human body is positioned in the middle of the environment;
finally, dispersing an environment calculation domain where a human body is located, setting three groups of boundary condition parameters including air temperature, environment wind speed and human body skin temperature, solving the calculation domain, and obtaining convection heat transfer coefficients of all body sections (including 16 body sections including a head, a chest, a back, a left upper arm, a right upper arm, a left lower arm, a right lower arm, a left hand, a right hand, a pelvis, a left thigh, a right thigh, a left calf, a right calf, a left foot and a right foot) of the sitting posture human body surface within the range of 0.2-2.5 m/s of environment wind speed and 1.6-24.0 ℃ of skin and air temperature difference;
s2: constructing a regression model of the convection heat transfer coefficient to realize quantitative calculation of the convection heat transfer of the body surface;
performing regression analysis on the data obtained by the simulation operation in the S1, and acquiring regression models of convective heat transfer coefficients of body surface body sections aiming at standing postures and sitting postures by taking the ambient wind speed, the skin temperature difference and the ambient temperature as independent variables and taking the body surface convective heat transfer coefficient as a dependent variable;
taking a right thigh body section as an example, when the temperature difference between the skin and the environment is 12 ℃, a relationship curve of convective heat transfer coefficient-wind speed obtained by using a regression model of the convective heat transfer coefficient of each body section of the body surface of a standing posture and a sitting posture respectively is shown in fig. 1, and a relationship curve of convective heat transfer coefficient-wind speed obtained by using a thermophysical reaction model (taking a Fiala model as an example, which does not distinguish the standing posture and the sitting posture) in the prior art is shown in fig. 1, and as can be known from fig. 1, the convective heat transfer coefficient of the standing posture obtained by the invention is obviously higher than that of the sitting posture, and the Fiala model has the condition of highly estimating the convective heat transfer coefficient;
s3: dynamically linking the processes of body surface heat transfer and body temperature regulation, and building a thermophysiological reaction database;
the method comprises the following steps of combining a regression model of convection heat transfer coefficients and a thermophysical reaction model to build a thermophysical reaction prediction platform suitable for standing and sitting postures, carrying out parametric study on three parameters of air temperature, ambient wind speed and inherent thermal resistance of clothes to obtain skin temperature of each body section of a body with clothing under different parameter combinations, and obtaining average skin temperature of the body with clothing according to the skin temperature of each body section of the body with clothing, so as to form a thermophysical reaction database, wherein the thermophysical reaction database specifically comprises the following steps:
s31: constructing a thermal physiological reaction model according to the guidance of four general temperature regulation mechanisms (namely, trembling muscles, sweating, vasodilatation and vasoconstriction) and three laws of heat transfer (namely, heat conduction, heat radiation and heat convection) of a human body and an evaporation and heat dissipation principle, replacing an original convection heat transfer coefficient calculation model in a Fiala model with a regression model of convection heat transfer coefficients generated by S2 to form a thermal physiological reaction prediction platform for distinguishing standing postures and sitting postures, and verifying the thermal physiological reaction prediction platform by comparing a prediction result with an actual human body experiment measurement result;
s32: respectively designing 48 groups of working conditions (96 groups in total) aiming at standing posture and sitting posture by investigating a conventional indoor environment and according to the inherent thermal resistance range of the clothes given by ISO 9920, wherein the air temperature range is 12.0-32.0 ℃, the ambient wind speed range is 0.2-2.5 m/s, the inherent thermal resistance range of the clothes is 0.09-2.30 clo, the basic metabolic rate of a human body is 1met, calculating by using a thermophysiological reaction prediction platform to obtain the skin temperature of each body section of the human body with the clothes under 96 groups of working conditions, and then solving the average skin temperature under 96 groups of working conditions according to an eight-point method given by ISO 9886 to form a thermophysiological reaction database;
s4: constructing a linear regression model taking the average skin temperature of a human body with clothes in standing and sitting postures as a dependent variable and taking the air temperature, the ambient wind speed and the inherent thermal resistance of the clothes as independent variables;
performing regression analysis on the database obtained by calculation in the step S3, and preliminarily determining that the average skin temperature, the wind speed, the air temperature and the inherent thermal resistance of the garment have multiple linear relations by analyzing a data scatter diagram (shown in figures 2 to 3); performing linear fitting on the data to respectively generate a linear regression model I taking the average skin temperature of the human body with the clothing in the standing posture as a dependent variable and taking the air temperature, the ambient wind speed and the inherent thermal resistance of the clothing as independent variables, and a linear regression model II taking the average skin temperature of the human body with the clothing in the sitting posture as a dependent variable and taking the air temperature, the ambient wind speed and the inherent thermal resistance of the clothing as independent variables, wherein the goodness of fit is more than 0.9;
the expression of the linear regression model I is as follows:
T sk，m，st ＝C 1 ×T a +C 2 ×I cl +C 3 ×v a +C 4 ；
the expression for linear regression model II is as follows:
T sk，m，se ＝C 5 ×T a +C 6 ×I cl +C 7 ×v a +C 8 ；
in the formula, T sk,m,st The average skin temperature of a human body with a garment in a standing posture is measured in units of; t is sk,m,se The average skin temperature of a human body with clothing in a sitting posture is measured in units of; t is a unit of a The value range is 12.0-32.0 ℃ for the air temperature; i is cl The value range is 0.09-2.30 clo for the inherent thermal resistance of the clothes; v. of a The value range is 0.2-2.5 m/s for the ambient wind speed; c 1 Is 0.443; c 2 1.892 ℃/clo; c 3 Is-0.769 ℃ s/m; c 4 Is 19.720 ℃; c 5 Is 0.428; c 6 At 1.799 ℃/clo; c 7 Is-0.741 ℃ s/m; c 8 The temperature was 20.340 ℃.
The significance of the regression coefficients is analyzed by using a T test, the result shows that the significance probability p values are all less than 0.001, namely the regression coefficients of the regression model are significant, the multiple collinearity analysis shows that the tolerance value is 1 (more than 0.2), the collinearity possibility among independent variables is eliminated, and in addition, the regression residual basically conforms to normal distribution;
therefore, the linear regression model provided by the invention is effective.
By referring to the steps, a linear regression model which takes the skin temperature of each body section of a human body with dresses in standing and sitting postures as a dependent variable and takes the air temperature, the ambient wind speed and the inherent thermal resistance of the clothes as independent variables can also be obtained, the specific process is basically the same as the above, and the difference is only that: 1) Step S3, obtaining the skin temperature of each body section of the human body with the clothing under different parameter combinations, namely forming a thermal physiological response database, and obtaining the average skin temperature of the human body with the clothing without the skin temperature of each body section of the human body with the clothing, wherein the step S32 omits 'solving the average skin temperature under 96 working conditions according to an eight-point method given by ISO 9886'; 2) In step S4, the skin temperature of each body part of the human body with the clothing in the standing position and the sitting position is taken as the dependent variable, and the average skin temperature of the human body with the clothing in the standing position and the sitting position is not taken as the dependent variable.
Step S1 of the invention carries out subject crossing, gives full play to the advantages of numerical simulation technology, is applied to the research of heat transfer between human body and environment, obtains a brand-new human body local convection heat transfer coefficient database, and is used for a thermophysiological reaction model, compared with the widely popular thermophysiological reaction model such as the convection heat transfer data used by Fiala model and obtained based on the experiment of a warming dummy in 1990, the invention has higher accuracy and traceability and is more persuasive for students who are applied in later period;
besides the finally formed linear regression model, the intermediate products of the steps also have strong application value, for example, the numerical model of the human body and the environment constructed in the step S1 has adjustability of various environmental parameters and positions of the human body, and can provide tools for researching other related parameters, such as the influence of wind direction and turbulence intensity on heat transfer; the regression model of the convection heat transfer coefficient generated in the step S2 can carry out the continuity prediction of the convection heat transfer coefficient under various environments and provide input parameter data for other related heat transfer mathematical models.
The above linear regression model I and linear regression model II can be used in various ways, and are exemplified as follows:
(1) The user uses the linear regression model I and the linear regression model II forward, the air temperature, the ambient wind speed and the inherent thermal resistance of the worn clothes (the thermal resistance value can be obtained by inquiring a clothes inherent thermal resistance database) are input, the average skin temperature in the state can be solved and predicted, and whether the comfort of the human body can be guaranteed by the clothes can be known according to the comparison between the predicted average skin temperature and the average skin temperature (32.0-34.6 ℃) of the human body in a comfortable state;
the specific process is as follows: determining the air temperature and the ambient wind speed of the environment where the human body is located, simultaneously determining the inherent thermal resistance of the clothing where the human body is located, calculating the average skin temperature of the human body with clothing in the specified posture, and then judging whether the average skin temperature exceeds the comfortable average skin temperature range (32.0-34.6 ℃) of the human body, if not, the human body in the specified posture is comfortable to wear; otherwise, the human body in the designated posture is uncomfortable to wear;
designating the posture as a standing posture, and calculating and utilizing a linear regression model I; or, the designated posture is a sitting posture, and a linear regression model II is calculated and utilized;
the method for checking whether the linear regression model is effective includes the following test analysis methods in addition to the T test described above:
calculating the average absolute error between the calculated value of the average skin temperature of the human body with the dress in the appointed posture and the true value (the true value is usually the actual average skin temperature at which the human body can reach a steady state after 30min of the dress), and comparing the error with the average standard deviation of the human body experiment; the result shows that the average absolute error between the calculated value and the true value of the average skin temperature of the human body with the clothing in the specified posture is lower than 0.9 ℃, the error value is lower than the average standard deviation of the human body experiment with the value of 1.0 ℃, and the linear regression model provided by the invention is effective.
(2) The user reversely uses the linear regression model I and the linear regression model II, and inputs target values of air temperature, ambient wind speed and average skin temperature of the human body with the clothing, so that the user can predict how many pieces of clothing with the clo value need to be worn under the environment to reach the target values;
the specific process is as follows: determining the air temperature and the ambient wind speed of the environment where the human body is going, setting a target value of the average skin temperature of the dressed human body, calculating the inherent thermal resistance of the clothing required by the average skin temperature of the dressed human body reaching the target value in the specified posture, and selecting the corresponding clothing according to the inherent thermal resistance;
designating the posture as a standing posture, and calculating and utilizing a linear regression model I; or, the designated posture is a sitting posture, and a linear regression model II is calculated and utilized;
the method for checking whether the linear regression model is effective includes the following test analysis methods in addition to the T test described above:
after selecting corresponding clothes to dress for 30min, calculating the average absolute error between the target value and the true value of the average skin temperature of the human body, and comparing the error value with the average standard deviation of the human body experiment; the result shows that after the corresponding clothes are selected for wearing for 30min, the average absolute error between the target value and the true value of the average skin temperature of the human body is lower than 0.9 ℃, the error value is lower than the average standard deviation of the human body experiment with the value of 1.0 ℃, and the linear regression model provided by the invention is effective.
(3) The user reversely uses the linear regression model I and the linear regression model II to generate a clothing inherent thermal resistance-average skin temperature map, then the clothing inherent thermal resistance required for reaching the expected average skin temperature of the body with the clothing in the set environment is determined through the query map, and the corresponding clothing is selected;
the specific process is as follows: determining the human body posture to make a clothing inherent thermal resistance-average skin temperature map, determining the clothing inherent thermal resistance required for reaching the expected average skin temperature of the body with clothing at a certain ambient wind speed and a certain air temperature through a query map, and selecting the corresponding clothing according to the clothing inherent thermal resistance;
the clothes inherent thermal resistance-average skin temperature map is a plurality of straight lines distributed on a coordinate system with the abscissa as the clothes inherent thermal resistance and the ordinate as the average skin temperature of a human body with clothing, or a plurality of straight lines distributed on a coordinate system with the abscissa as the average skin temperature of the human body with clothing and the ordinate as the clothes inherent thermal resistance;
each straight line corresponds to the same ambient wind speed and different air temperatures, or each straight line corresponds to different ambient wind speeds and the same air temperature;
the human body posture is a standing posture, and each straight line is obtained by utilizing a linear regression model I; or the human body posture is a sitting posture, and each straight line is obtained by utilizing a linear regression model II;
the certain ambient wind speed and the certain air temperature are the ambient wind speed and the air temperature corresponding to one straight line;
the method for checking whether the linear regression model is effective includes the following test analysis methods in addition to the T test described above:
after selecting corresponding clothes to dress for 30min, calculating the average absolute error between the expected value and the true value of the average skin temperature of the human body, and comparing the error value with the average standard deviation of the human body experiment; the result shows that after the corresponding clothes are selected for wearing for 30min, the average absolute error between the expected value and the actual value of the average skin temperature of the human body is lower than 0.9 ℃, the error value is lower than the average standard deviation of the human body experiment with the value of 1.0 ℃, and the linear regression model provided by the invention is effective.
Now, the example is described with reference to fig. 4, each straight line corresponds to the same ambient wind speed (0.15 m/s) and different air temperatures, the basic metabolic rate of the human body is 1met, the posture of the human body is a standing posture, and the inherent thermal resistance-average skin temperature map of the garment is shown in fig. 4, a user directly obtains the relationship between the inherent thermal resistance of the garment and the possible average skin temperature by querying the map, and directly obtains the thermal resistance value of the garment to be worn according to the expected average skin temperature of the human body with the garment; in consideration of individual differences of average skin temperature and thermal comfort feeling, the invention provides a recommended expected comfortable average skin temperature range (32.0-34.6 ℃) of a human body with a garment, a user can carry out proper adjustment according to personal preference, and the provided garment inherent thermal resistance-average skin temperature map guides common people to carry out garment matching according to self comfort requirements; the method solves the problem that the evaluation result is not in accordance with the actual situation because the actual dressing posture of the human body cannot be considered in the prior technical scheme; in addition, the method is simple and easy to operate, improves the efficiency of clothes comfort estimation, and has stronger universality.
Claims (7)
1. A method for judging the wearing comfort is characterized in that the air temperature and the ambient wind speed of the environment where a human body is located are determined, after the inherent thermal resistance of clothes where the human body is located is determined, the average skin temperature of the human body with the clothes in a specified posture is calculated, whether the average skin temperature exceeds the comfortable average skin temperature range of the human body is judged, and if not, the human body in the specified posture is comfortable to wear; otherwise, the human body in the designated posture is uncomfortable to wear;
designating the posture as a standing posture, and calculating and utilizing a linear regression model I; or, the posture is designated as a sitting posture, and a linear regression model II is calculated and utilized;
the expression of the linear regression model I is as follows:
T sk,m,st ＝C 1 ×T a +C 2 ×I cl +C 3 ×v a +C 4 ；
the expression for linear regression model II is as follows:
T sk,m,se ＝C 5 ×T a +C 6 ×I cl +C 7 ×v a +C 8 ；
in the formula, T sk,m,st The average skin temperature of a human body with a garment in a standing posture is measured in units of; t is a unit of sk,m,se The average skin temperature of a human body with clothes in a sitting posture is measured in units of; t is a unit of a The value range is 12.0-32.0 ℃ for the air temperature; I.C. A cl The value range is 0.09-2.30 clo for the inherent thermal resistance of the clothing; v. of a The value range is 0.2-2.5 m/s for the ambient wind speed; c 1 Is 0.443; c 2 1.892 ℃/clo; c 3 Is-0.769 ℃ s/m; c 4 At 19.720 ℃; c 5 Is 0.428; c 6 At 1.799 ℃/clo; c 7 Is-0.741 ℃ s/m; c 8 At 20.340 ℃;
the construction steps of the linear regression model I or the linear regression model II are as follows:
s1: performing simulation operation on convective heat transfer between the unworn human body and the environment based on computational fluid dynamics;
firstly, constructing a geometric model of a standing or sitting human body and an environment, namely a visual model which completely reflects the three-dimensional size and shape of the standing or sitting human body and the environment, wherein the human body is positioned in the middle of the environment; dispersing an environment calculation domain where the human body is located, setting three groups of boundary condition parameters including air temperature, ambient wind speed and human body skin temperature, solving the calculation domain, and obtaining the convection heat transfer coefficient of each body surface section of the standing or sitting human body within the ranges of 0.2-2.5 m/s of ambient wind speed and 1.6-24.0 ℃ of skin-air temperature difference;
s2: constructing a regression model of the convection heat transfer coefficient to realize the quantitative calculation of the convection heat transfer of the body surface;
performing regression analysis on the data obtained by the simulation operation in the S1, and acquiring regression models of convective heat transfer coefficients of body surface body sections aiming at standing postures and sitting postures by taking the ambient wind speed, the skin temperature difference and the ambient temperature as independent variables and taking the body surface convective heat transfer coefficient as a dependent variable;
s3: dynamically linking the processes of body surface heat transfer and body temperature regulation, and building a thermophysiological reaction database;
the method comprises the following steps of combining a regression model of convective heat transfer coefficients and a thermophysiological reaction model to build a thermophysiological reaction prediction platform suitable for standing and sitting postures, carrying out parametric study on three parameters of air temperature, ambient wind speed and inherent thermal resistance of clothes, and obtaining skin temperature of each body section of a dressed human body under different parameter combinations or obtaining average skin temperature of the dressed human body from the skin temperature of each body section of the dressed human body, so as to form a thermophysiological reaction database, wherein the method specifically comprises the following steps:
s31: constructing a thermal physiological reaction model according to the guidance of four general temperature regulation mechanisms of a human body, three laws of heat transfer and an evaporation heat dissipation principle, wherein the thermal convection part uses a regression model of convection heat transfer coefficients generated by S2 to calculate to form two groups of thermal physiological reaction models for distinguishing standing postures and sitting postures, and then obtaining a thermal physiological reaction prediction platform;
s32: respectively designing n groups of working conditions aiming at standing postures and sitting postures, wherein n is more than or equal to 48, the air temperature range is 12.0-32.0 ℃, the ambient wind speed range is 0.2-2.5 m/s, the inherent thermal resistance range of the garment is 0.09-2.30 clo, the basic metabolic rate of the human body is 1met, calculating by using a thermal physiological reaction prediction platform to obtain the skin temperature of each section of the human body with the clothing under 2n groups of working conditions to form a thermal physiological reaction database, or solving the average skin temperature of the human body with the clothing under 2n groups of working conditions according to a four-point method, an eight-point method or a fourteen-point method given by ISO 9886 to form the thermal physiological reaction database;
s4: and constructing a linear regression model I taking the average skin temperature of the human body with the clothing in a standing posture as a dependent variable and taking the air temperature, the ambient wind speed and the inherent thermal resistance of the clothing as independent variables, or constructing a linear regression model II taking the average skin temperature of the human body with the clothing in a sitting posture as a dependent variable and taking the air temperature, the ambient wind speed and the inherent thermal resistance of the clothing as independent variables.
2. The method of claim 1, wherein the average comfortable skin temperature of the human body is in the range of 32.0-34.6 ℃.
3. A dressing comfortability judgment method according to claim 1, wherein the average absolute error between the calculated value and the true value of the average skin temperature of a human body with a dressing in a given posture is less than 0.9 ℃.
4. A clothing selection method for realizing dressing comfort is characterized in that the air temperature and the ambient wind speed of the environment where a human body is going are determined, after a target value of the average skin temperature of the human body with the clothing is set, the inherent thermal resistance of the clothing required by the average skin temperature of the human body with the clothing in a specified posture reaching the target value is calculated, and the corresponding clothing is selected according to the inherent thermal resistance;
designating the posture as a standing posture, and calculating and utilizing a linear regression model I; or, the posture is designated as a sitting posture, and a linear regression model II is calculated and utilized;
the expression of the linear regression model I is as follows:
T sk,m,st ＝C 1 ×T a +C 2 ×I cl +C 3 ×v a +C 4 ；
the expression for linear regression model II is as follows:
T sk,m,se ＝C 5 ×T a +C 6 ×I cl +C 7 ×v a +C 8 ；
in the formula, T sk,m,st Is the average skin of a person wearing the garment in a standing positionTemperature in units of; t is a unit of sk,m,se The average skin temperature of a human body with clothing in a sitting posture is measured in units of; t is a The value range is 12.0-32.0 ℃ for the air temperature; i is cl The value range is 0.09-2.30 clo for the inherent thermal resistance of the clothes; v. of a The value range is 0.2-2.5 m/s for the ambient wind speed; c 1 Is 0.443; c 2 1.892 ℃/clo; c 3 Is-0.769 ℃ s/m; c 4 Is 19.720 ℃; c 5 Is 0.428; c 6 At 1.799 ℃/clo; c 7 Is-0.741 ℃ s/m; c 8 Is 20.340 ℃;
the construction steps of the linear regression model I or the linear regression model II are as follows:
s1: performing simulation operation on convective heat transfer between the unworn human body and the environment based on computational fluid dynamics;
firstly, constructing a geometric model of a standing or sitting human body and an environment, namely a visual model which completely reflects the three-dimensional size and shape of the standing or sitting human body and the environment, wherein the human body is positioned in the middle of the environment; dispersing an environment calculation domain where the human body is located, setting three groups of boundary condition parameters including air temperature, ambient wind speed and human body skin temperature, solving the calculation domain to obtain convection heat transfer coefficients of each body section of the standing or sitting human body surface within the ranges of 0.2-2.5 m/s of ambient wind speed and 1.6-24.0 ℃ of skin-air temperature difference;
s2: constructing a regression model of the convection heat transfer coefficient to realize quantitative calculation of the convection heat transfer of the body surface;
performing regression analysis on the data obtained by the simulation operation in the S1, and acquiring regression models of convective heat transfer coefficients of body surface body sections aiming at standing postures and sitting postures by taking the ambient wind speed, the skin temperature difference and the ambient temperature as independent variables and taking the body surface convective heat transfer coefficient as a dependent variable;
s3: dynamically linking the processes of body surface heat transfer and body temperature regulation, and building a thermophysiological reaction database;
the method comprises the following steps of combining a regression model of convection heat transfer coefficients and a thermophysical reaction model to build a thermophysical reaction prediction platform suitable for standing and sitting postures, carrying out parametric study on three parameters of air temperature, ambient wind speed and inherent thermal resistance of clothes, and obtaining skin temperature of each body section of a dressed human body under different parameter combinations or obtaining average skin temperature of the dressed human body from the skin temperature of each body section of the dressed human body, so as to form a thermophysical reaction database, wherein the thermophysical reaction database specifically comprises the following steps:
s31: guiding and constructing a thermophysiological reaction model according to four general temperature regulation mechanisms of a human body, three laws of heat transfer and an evaporation and heat dissipation principle, wherein the heat convection part calculates by using a regression model of a convection heat transfer coefficient generated by S2 to form two groups of thermophysiological reaction models for distinguishing a standing posture and a sitting posture, namely a thermophysiological reaction prediction platform;
s32: respectively designing n groups of working conditions aiming at standing postures and sitting postures, wherein n is more than or equal to 48, the air temperature range is 12.0-32.0 ℃, the ambient wind speed range is 0.2-2.5 m/s, the inherent thermal resistance range of the garment is 0.09-2.30 clo, the basic metabolic rate of the human body is 1met, calculating by using a thermal physiological reaction prediction platform to obtain the skin temperature of each section of the human body with the clothing under 2n groups of working conditions to form a thermal physiological reaction database, or solving the average skin temperature of the human body with the clothing under 2n groups of working conditions according to a four-point method, an eight-point method or a fourteen-point method given by ISO 9886 to form the thermal physiological reaction database;
s4: and constructing a linear regression model I taking the average skin temperature of the human body with the clothing in a standing posture as a dependent variable and taking the air temperature, the ambient wind speed and the inherent thermal resistance of the clothing as independent variables, or constructing a linear regression model II taking the average skin temperature of the human body with the clothing in a sitting posture as a dependent variable and taking the air temperature, the ambient wind speed and the inherent thermal resistance of the clothing as independent variables.
5. The method of claim 4, wherein the average absolute error between the target value and the actual value of the average skin temperature of the human body is less than 0.9 ℃ after the corresponding garment is selected for wearing for 30 min.
6. A clothing selection method for realizing comfortable dressing is characterized in that the human body posture is determined, a clothing inherent thermal resistance-average skin temperature map is made, then the clothing inherent thermal resistance required for reaching the expected average skin temperature of a dressed human body under a certain ambient wind speed and a certain air temperature is determined through an inquiry map, and the corresponding clothing is selected according to the clothing inherent thermal resistance;
the clothes inherent thermal resistance-average skin temperature map is a plurality of straight lines distributed on a coordinate system with the abscissa as the clothes inherent thermal resistance and the ordinate as the average skin temperature of a human body with clothing, or a plurality of straight lines distributed on a coordinate system with the abscissa as the average skin temperature of the human body with clothing and the ordinate as the clothes inherent thermal resistance;
each straight line corresponds to the same ambient wind speed and different air temperatures, or each straight line corresponds to different ambient wind speeds and the same air temperature;
the human body posture is a standing posture, and each straight line is obtained by using a linear regression model I; or the human body posture is a sitting posture, and each straight line is obtained by utilizing a linear regression model II;
the expression of the linear regression model I is as follows:
T sk,m,st ＝C 1 ×T a +C 2 ×I cl +C 3 ×v a +C 4 ；
the expression for linear regression model II is as follows:
T sk,m,se ＝C 5 ×T a +C 6 ×I cl +C 7 ×v a +C 8 ；
in the formula, T sk,m,st The average skin temperature of a human body with a garment in a standing posture is measured in units of; t is a unit of sk,m,se The average skin temperature of a human body with clothing in a sitting posture is measured in units of; t is a unit of a The value range is 12.0-32.0 ℃ for the air temperature; i is cl The value range is 0.09-2.30 clo for the inherent thermal resistance of the clothing; v. of a The value range is 0.2-2.5 m/s for the ambient wind speed; c 1 Is 0.443; c 2 1.892 ℃/clo; c 3 Is-0.769 ℃ s/m; c 4 Is 19.720 ℃; c 5 Is 0.428; c 6 1.799 ℃/clo; c 7 Is-0.741 ℃ s/m; c 8 Is 20.340 ℃;
the certain ambient wind speed and the certain air temperature are the ambient wind speed and the air temperature corresponding to one straight line;
the construction steps of the linear regression model I or the linear regression model II are as follows:
s1: carrying out simulation operation on convective heat transfer between the body without clothing and the environment based on computational fluid dynamics;
firstly, constructing a geometric model of a standing or sitting human body and an environment, namely a visual model which completely reflects the three-dimensional size and shape of the standing or sitting human body and the environment, wherein the human body is positioned in the middle of the environment; dispersing an environment calculation domain where the human body is located, setting three groups of boundary condition parameters including air temperature, ambient wind speed and human body skin temperature, solving the calculation domain to obtain convection heat transfer coefficients of each body section of the standing or sitting human body surface within the ranges of 0.2-2.5 m/s of ambient wind speed and 1.6-24.0 ℃ of skin-air temperature difference;
s2: constructing a regression model of the convection heat transfer coefficient to realize quantitative calculation of the convection heat transfer of the body surface;
performing regression analysis on the data obtained by the simulation operation in the S1, and acquiring regression models of convective heat transfer coefficients of body surface body sections aiming at standing postures and sitting postures by taking the ambient wind speed, the skin temperature difference and the ambient temperature as independent variables and taking the body surface convective heat transfer coefficient as a dependent variable;
s3: dynamically linking the processes of body surface heat transfer and body temperature regulation, and building a thermophysiological reaction database;
the method comprises the following steps of combining a regression model of convective heat transfer coefficients and a thermophysiological reaction model to build a thermophysiological reaction prediction platform suitable for standing and sitting postures, carrying out parametric study on three parameters of air temperature, ambient wind speed and inherent thermal resistance of clothes, and obtaining skin temperature of each body section of a dressed human body under different parameter combinations or obtaining average skin temperature of the dressed human body from the skin temperature of each body section of the dressed human body, so as to form a thermophysiological reaction database, wherein the method specifically comprises the following steps:
s31: constructing a thermal physiological reaction model according to the guidance of four general temperature regulation mechanisms of a human body, three laws of heat transfer and an evaporation heat dissipation principle, wherein the thermal convection part uses a regression model of convection heat transfer coefficients generated by S2 to calculate to form two groups of thermal physiological reaction models for distinguishing standing postures and sitting postures, and then obtaining a thermal physiological reaction prediction platform;
s32: respectively designing n groups of working conditions aiming at standing postures and sitting postures, wherein n is more than or equal to 48, the air temperature range is 12.0-32.0 ℃, the ambient wind speed range is 0.2-2.5 m/s, the inherent thermal resistance range of the garment is 0.09-2.30 clo, the basic metabolic rate of the human body is 1met, calculating by using a thermal physiological reaction prediction platform to obtain the skin temperature of each section of the human body with the clothing under 2n groups of working conditions to form a thermal physiological reaction database, or solving the average skin temperature of the human body with the clothing under 2n groups of working conditions according to a four-point method, an eight-point method or a fourteen-point method given by ISO 9886 to form the thermal physiological reaction database;
s4: constructing a linear regression model I taking the average skin temperature of a human body with clothing in a standing posture as a dependent variable and taking the air temperature, the ambient wind speed and the inherent thermal resistance of the clothing as independent variables, or constructing a linear regression model II taking the average skin temperature of the human body with clothing in a sitting posture as a dependent variable and taking the air temperature, the ambient wind speed and the inherent thermal resistance of the clothing as independent variables.
7. The method of claim 6, wherein the average absolute error between the expected value and the actual value of the average skin temperature of the human body is less than 0.9 ℃ after the corresponding garment is selected for wearing for 30 min.
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