Evaluation device, evaluation method, and program

Abstract

This evaluation device comprises: a setting unit that sets a 3D model of a simulation target, the respiration rate of occupants for each space and time, the human density or the number of non-infected persons for each space and time, and design parameters including an operation condition for an air conditioner; a flow field analysis unit that analyzes a flow field in the simulation target, on the basis of the 3D model and the design parameters; a coefficient calculation unit which analyzes the concentration distribution for each time of an infectious substance by using an analysis result of the flow field, and calculates the infection propagation coefficient for each space and time on the basis of the concentration distribution and the respiration rate for each time; and an evaluation unit for evaluating the risk on the basis of the infection propagation coefficient and the human density or the number of the non-infected persons.

Description

Title of Invention EVALUATION DEVICE, EVALUATION METHOD, AND PROGRAM Technical Field

[0001] The present disclosure relates to an evaluation device, an evaluation method, and a program. Priority is claimed on Japanese Patent Application No. 2023-048044 filed on March 24, 2023, the content of which is incorporated herein by reference. Background Art

[0002] In the facility design of an air conditioner, a designer constructs a simulation model for simulating the effectiveness of a facility on a computer. The designer sets external environments, operation conditions of the facility and moving bodies, and design parameters (temperature, humidity, and flow rate of supply air) in the simulation model, executes the simulation, and evaluates the results. Therefore, the designer can check whether or not the requirements for the facility can be satisfied under certain environmental conditions and can adjust design parameters, such as the layout and operation conditions (temperature, humidity, and flow rate) of the air conditioner, satisfying the requirements before construction. Typical requirements for the air conditioner include comfort and air quality.

[0003] Examples of the requirements for air quality include "carbon dioxide concentration must remain below X ppm" and "air age in a certain area must not exceed Y seconds".

[0004] In recent years, a technique that applies the air quality evaluation to the evaluation of epidemiological safety has attracted attention. An evaluation equation for quantitatively evaluating the risk of an infectious disease is defined, and an infection risk is calculated from each of simulation results obtained by setting different environmental conditions or design parameters, using the evaluation equation. Therefore, it is possible to compare and evaluate a plurality of simulation results having different environmental conditions or design parameters, based on the infection risk.

[0005] The above-described evaluation equation is defined in terms of by how much margin the simulation results satisfy the requirements, which makes it possible to relatively compare the values of the design parameters or combinations of the values.

[0006] As an evaluation equation defined from the above viewpoint, a method is known in which the requirements are expressed as a logical equation and are compared with legal regulations and other design results. For example, when the requirement "carbon dioxide concentration must remain below X ppm" is expressed as a logical equation, it is "carbon dioxide concentration < X", and the evaluation equation is "evaluation value = X - (maximum value of carbon dioxide concentration in simulation space)".

[0007] When the calculated evaluation value is positive, it means that the requirements are satisfied. When the calculated evaluation value is negative, it means that the requirements are not satisfied. In addition, as the evaluation value is larger, the requirements are satisfied with a larger margin. However, from the viewpoint of epidemiological safety evaluation, a high carbon dioxide concentration does not necessarily indicate the presence of an infectious substance. In addition, a space having a high age of (stagnant) air does not necessarily mean a space in which an infectious substance remains.

[0008] Therefore, for example, NPL 1 discloses a method that uses a coupled simulation of advection-diffusion analysis of the concentration of an infectious substance (bacteria, viruses, or the like) using CFD and a mathematical epidemiological model as a method for more directly evaluating the infection risk.

[0009] In addition, NPL 2 discloses a method that analyzes a concentration field of an infectious substance based on results of a flow field simulation and inputs a calculated concentration distribution to an epidemiological model to calculate an infection risk distribution in a space.

[0010] In the advection-diffusion analysis of the infectious substance, a scalar transport equation of a substance concentration O [1 / m3] is solved using the flow field analysis results to calculate an unsteady and non-uniform concentration distribution. A governing equation thereof is represented by the following Equation (1). Here, for the concentration, for convenience, the numerator of the equation is defined as a dimensionless value in order to respond to various infectious substances. [Equation 1] dt + Vpf<Pv = V(rM) • ■ -(1)

[0012] In Equation (1), v—> (v with an arrow over it) is a flow velocity vector and is given. T is a diffusion coefficient [kg / m-s] and is represented by Equation (2).

[0013] [Equation 2] Mr r = Pfnm + ^     ---(2)

[0014] In Equation (2), pf is air density [kg / m3], Dm is a molecular diffusion coefficient [m2 / s], pt is a turbulent viscosity coefficient [kg / m-s], and Sc t is a turbulent Schmidt number [-].

[0015] Meanwhile, in the epidemiological model, an equation obtained by fusing two mathematical models of a Wells-Riley model (hereinafter, also referred to as a WR model) and an SIR model is used. Hereinafter, each model will be described.

[0016] The WR model is a classical epidemiological model that estimates an infection risk in a closed space using a point-mass system. The WR model is a simple model that can predict an infection risk in an early stage of infection with only a limited number of parameters. An infection risk R is calculated by Equation (3), using the initial number of infected persons Io [persons], an inhalation rate p [m3 / person h], the amount of infectious substance generated q [quanta / h], and a ventilation volume Q [m3 / h] as parameters and an exposure time t [h] as a variable.

[0017] [Equation 3] / ? = C /                     ---(3)

[0018] Here, C indicates the number of new infected persons, and So indicates the initial number of non-infected persons. Wells defined a "quantum" as the amount of infectious substance infecting "l-(l / e)" (63.2%) of "non-"infected persons in a closed space. The quantum is defined by probability and statistics, and it is considered that the number of quanta can be physically measured by the amount and pathogenicity of the infectious substance present in the air in the closed space.

[0019] The SIR model is represented by ordinary differential equations (4) to (6) for three variables of a non-infected person (Susceptible), an infected person (Infected), and a recovered person (Recovery). The SIR model is an epidemiological model that can express infection transmission from the infected persons to the non-infected persons over a long time scale (days or months), and the number of persons corresponding to each of S, I, and R is calculated using an infection transmission coefficient p and a recovery rate y as parameters.

[0020] [Equation 4] dS dt -0SI + V(DsVS) • • -(4) [Equation 5] di dt dR = -yl + 0SI +            ---(5)

[0022] [Equation 6] = yl +                 • • -(6)

[0023] Here, when application in an initial stage of infection is assumed, y = 0 and R = 0 are established, and the number of infected persons I can be fixed to Io. In addition, when the diffusion terms Ds, Di, and Dr associated with the movement of the person are omitted, changes in S, I, and R over time are represented by Equations (7), (8), and (9), respectively.

[0024] [Equation 7] dS — —~PSI — ~PS ---(7)

[0025] [Equation 8] dl — = +psi -yl = +p S • • -(8)

[0026] [Equation 9] dR — = +yl = 0     ---(9) dt

[0027] In addition, when only infection transmission P' due to aerosol / air infection excluding contact infection is considered, it is considered that P' correlates only with the infectious substance concentration, and "P' = f(c)" (c: infectious substance concentration [quanta / m3]) is established. From the above, when the time integration of Equations (7) and (8) of the SIR model is compared with Equation (3) of the WR model, Equation (10) can be assumed as the simplest functional form of P'. In Equation (10), Io is the initial number of infected persons [persons], p is the inhalation rate of non-infected persons [m3 / persomh], a is the infectivity [quanta], and O is the infectious substance concentration [1 / m3]. In addition, the infectivity a is a fixed value that is preset for each infectious substance.

[0028] [Equation 10]

[0029] In addition, a coupled simulation of the SIR model and the CFD through the infectious substance concentration O is performed as follows. The infectious substance generated by the breathing of an initial infected person (Io = 1) is advected and diffused by a steady flow field calculated by CFD analysis. The infection transmission coefficient P' is calculated from the concentration O in each cell and each time step, and the population density S of the non-infected persons and the population density I of the infected persons in each cell are calculated from the SIR model using the value of the infection transmission coefficient P'. Further, a local infection risk Riocai is defined as a ratio I / S of the population density (I) of the infected persons to the population density (S) of the non-infected persons by Equation (11). Therefore, it is possible to calculate and visualize infection transmission and an infection risk distribution in time and space. Furthermore, the spatial integration of S and I is performed to calculate the number of non-infected persons Ns and the number of infected persons Ni in the entire space, and an infection risk Rgiobai of the entire space excluding one initial infected person is calculated by Equation (12).

[0030] [Equation 11 ] ->0 d0

[0031] [Equation 12] _ Nj - 1 Rglobatft') —                    • • • (1 2)

[0032] As described above, in the technique disclosed in NPL 2, the infection risk can be directly evaluated by combining the infectious substance concentration and the epidemiological model. Citation List Non Patent Literatures

[0033] [NPL 1] Hiroaki Asanuma, Kazuhide Ito, "Prediction of Infection Transmission by Coupled Analysis of Unsteady Nonuniform Concentration Distribution and Mathematical Epidemiological Model Targeting Hospital Space", Architectural Institute of Japan, Journal of Environmental Engineering Vol. 78 No. 688, June 2013, pp. 481-487 [NPL 2] Yu Nakayama, Tatsuomi Ishiguro, and Takashi Sano, "Infection Risk Evaluation Analysis of Large Space by Coupled Analysis of CFD and Epidemiological Model", "Proceedings of the 99th Annual Meeting of the Japan Society of Mechanical Engineers, Fluid Engineering Division", Japan Society of Mechanical Engineers, November 2021 Summary of Invention Technical Problem

[0034] When the amount of activity of a visitor changes over time and space, the inhalation rate of the visitor also changes according to the change in the amount of activity even though the infectious substance concentration is the same. For example, in a facility including a rest area and an exercise area, the inhalation rate of visitors in the rest area is less than the inhalation rate of visitors in the exercise area. However, in the related art, the infection risk is evaluated on the assumption that the inhalation rate is constant in both time and space. In this case, for example, when the inhalation rate of the visitors changes depending on the space or time in the facility, there is a possibility that the accuracy of estimating the infection risk will be reduced.

[0035] An object of the present disclosure is to provide an evaluation device, an evaluation method, and a program that can accurately evaluate an infection risk even when an activity state of a visitor changes over time and space. Solution to Problem

[0036] According to an aspect of the present disclosure, there is provided an evaluation device including: a setting unit that sets a 3D model including a layout of an air conditioner in a simulation target, an inhalation rate of visitors for each space and time in the simulation target, a population density of non-infected persons or the number of non-infected persons for each space and time in the simulation target, and design parameters including an operation condition of the air conditioner; a flow field analysis unit that analyzes a flow field of the simulation target based on the 3D model and the design parameters; a coefficient calculation unit that analyzes a concentration distribution of an infectious substance for each time in the simulation target, using an analysis result of the flow field, and calculates an infection transmission coefficient for each space and time in the simulation target, based on the concentration distribution and the inhalation rate for each time; and an evaluation unit that evaluates an infection risk of the simulation target based on the infection transmission coefficient and the population density of the non-infected persons or the number of non-infected persons.

[0037] According to another aspect of the present disclosure, there is provided an evaluation method including: a step of setting a 3D model including a layout of an air conditioner in a simulation target, an inhalation rate of visitors for each space and time in the simulation target, a population density of non-infected persons or the number of non-infected persons for each space and time in the simulation target, and design parameters including an operation condition of the air conditioner; a step of analyzing a flow field of the simulation target based on the 3D model and the design parameters; a step of analyzing a concentration distribution of an infectious substance for each time in the simulation target, using an analysis result of the flow field, and calculating an infection transmission coefficient for each space and time in the simulation target, based on the concentration distribution and the inhalation rate for each time; and a step of evaluating an infection risk of the simulation target based on the infection transmission coefficient and the population density of the non-infected persons or the number of non-infected persons.

[0038] According to still another aspect of the present disclosure, there is provided a program causing an evaluation device to execute: a step of setting a 3D model including a layout of an air conditioner in a simulation target, an inhalation rate of visitors for each space and time in the simulation target, a population density of noninfected persons or the number of non-infected persons for each space and time in the simulation target, and design parameters including an operation condition of the air conditioner; a step of analyzing a flow field of the simulation target based on the 3D model and the design parameters; a step of analyzing a concentration distribution of an infectious substance for each time in the simulation target, using an analysis result of the flow field, and calculating an infection transmission coefficient for each space and time in the simulation target, based on the concentration distribution and the inhalation rate for each time; and a step of evaluating an infection risk of the simulation target based on the infection transmission coefficient and the population density of the noninfected persons or the number of non-infected persons. Advantageous Effects of Invention

[0039] According to the above-described aspects, even when an activity state of visitors changes over time and space, it is possible to accurately evaluate an infection risk. Brief Description of Drawings

[0040] Fig. 1 is a block diagram showing a functional configuration of an evaluation device according to a first embodiment. Fig. 2 is a flowchart showing an example of a process of the evaluation device according to the first embodiment. Fig. 3 is a diagram showing functions of the evaluation device according to the first embodiment. Fig. 4 is a diagram showing functions of an evaluation device according to a second embodiment. Fig. 5 is a flowchart showing an example of a process of the evaluation device according to the second embodiment. Fig. 6 is a flowchart showing an example of a process of an evaluation device according to a third embodiment. Fig. 7 is a block diagram showing a functional configuration of an evaluation device according to a fourth embodiment. Fig. 8 is a flowchart showing an example of a process of the evaluation device according to the fourth embodiment. Description of Embodiments

[0041] <First Embodiment Hereinafter, a first embodiment will be described with reference to Figs. 1 to 3.

[0042] (Functional Configuration of Evaluation Device) Fig. 1 is a block diagram showing a functional configuration of an evaluation device according to the first embodiment. An evaluation device 10 is a device for evaluating an infection risk of an infectious substance in a simulation target. The simulation target is, for example, a facility that accommodates people therein, such as an office, a hospital, and a shopping mall. As shown in Fig. 1, the evaluation device 10 includes a processor 11, a memory 12, a storage 13, and an interface 14.

[0043] The processor 11 operates according to a predetermined program to cause the evaluation device 10 to exhibit various functions. The functions of the processor 11 will be described below.

[0044] The memory 12 has a memory region necessary for the operation of the processor 11.

[0045] The storage 13 is a so-called auxiliary storage device and is, for example, a hard disk drive (HDD), a solid state drive (SSD), or the like. The storage 13 stores data that is acquired, generated, and referred to by each unit of the processor 11 during processes.

[0046] The interface 14 is a connection interface for communicating with devices such as an input device 15 and a display device 16. The input device 15 is a device, such as a mouse or a keyboard, for receiving an input operation of an operator of the evaluation device 10. The display device 16 is a monitor such as a liquid crystal display. The input device 15 and the display device 16 may be integrated into, for example, a touch panel.

[0047] Next, the functions of the processor 11 will be described. The processor 11 operates according to a program prepared in advance to function as a setting unit 111, a flow field analysis unit 112, a coefficient calculation unit 113, and an evaluation unit 114.

[0048] The setting unit 111 sets setting values of the 3D model, environmental conditions, design parameters, and the like of the simulation target. The 3D model is data capable of specifying the shape, dimensions, area, and volume of the simulation target, and the number and layout of air conditioners. The environmental conditions include a population density S of non-infected persons for each space and time in the simulation target and the inhalation rate of visitors for each time in each cell. For example, the simulation target is divided into cells of unit area, and the setting values of the population density and the inhalation rate are set for each time in each cell. The design parameters include operation conditions of the air conditioner (the temperature, humidity and flow rate of supply air of the air conditioner) in the simulation target. The operator of the evaluation device 10 designates these setting values through the input device 15.

[0049] The flow field analysis unit 112 analyzes a flow field of the simulation target based on the design parameters.

[0050] The coefficient calculation unit 113 analyzes a concentration distribution of the infectious substance in the simulation target, using the analysis result of the flow field, and calculates an infection transmission coefficient for each space (cell) and time in the simulation target based on the concentration distribution and the inhalation rate for each time.

[0051] The evaluation unit 114 evaluates the infection risk of the simulation target based on the infection transmission coefficient and the population density of the noninfected persons.

[0052] (Flowchart of Risk Evaluation Device) Fig. 2 is a flowchart showing an example of a process of the evaluation device according to the first embodiment. Here, the process of evaluating the infection risk of the simulation target by the evaluation device 10 will be described in detail with reference to Fig. 2.

[0053] First, the setting unit 111 receives an input operation from the operator and sets a simulation target (Step SI01). Specifically, the setting unit 111 sets a 3D model (CAD model) that defines the shape and dimensions of a facility as the simulation target, the number and layout of air conditioners, and the like.

[0054] Then, the setting unit 111 receives an input operation from the operator and sets the environmental conditions of the simulation target (Step S102).

[0055] For example, the setting unit 111 sets the inhalation rate per unit time in each area as one of the environmental conditions.

[0056] Fig. 3 is a diagram showing the functions of the evaluation device according to the first embodiment. For example, as shown in (a) of Fig. 3, in the simulation target, such as a shopping mall, including a plurality of areas 1 to 4 (a rest facility, an exercise facility, an eating and drinking facility, a passage, and the like), the activity state (sitting, exercising, walking, and the like) of visitors may differ depending on the area. The operator inputs activity amount data per unit time for each area. In addition, the 3D model is associated with definitions, such as the position and range (corresponding cell) of each area, in advance. Further, a standard respiratory volume corresponding to the amount of activity is defined in advance. The setting unit 111 creates respiratory volume data in which the inhalation rate per unit time [m3 / persomh] is set for each cell in the simulation target, based on the activity amount data input by the operator.

[0057] Further, the setting unit 111 sets the population density S of the non-infected persons and a population density I of infected persons for each time in each area. For example, the operator inputs the population densities S and I per unit time in each area shown in Fig. 3. Then, the setting unit 111 creates population density data in which the population densities S and I per unit time in each area are set. In addition, the population density may be, for example, the number of persons per unit area "persons / m2" or the number of persons per unit volume [persons / m3]. Further, in another embodiment, the number of non-infected persons and the number of infected persons [persons] in the entire simulation target may be used instead of the population density.

[0058] In addition, the setting unit 111 receives an input operation from the operator and sets the design parameters of the simulation target (Step S103). The design parameters include the temperature, humidity, flow rate, and the like of air supplied by the air conditioner installed in the simulation target.

[0059] Then, the flow field analysis unit 112 executes a flow field simulation using the known CFD technique, based on the 3D model and design parameters of the simulation target (Step S104).

[0060] In addition, the coefficient calculation unit 113 executes a concentration simulation of the infectious substance using the analysis result of the flow field and analyzes the concentration distribution of the simulation target for each time. Specifically, the concentration distribution is calculated by the above-described Equations (1) and (2). Then, the coefficient calculation unit 113 calculates an infection transmission coefficient P' for each space and time, based on the concentration of the infectious substance and the inhalation rate IR for each space (cell) and time in the simulation target (Step SI05).

[0061] Specifically, the coefficient calculation unit 113 calculates the infection transmission coefficient P' using the following Equation (13), instead of Equation (10). The IR is the inhalation rate per unit time for each space set by the setting unit 111 in Step S102. n is the concentration of the infectious substance concentration per unit time for each area based on the concentration distribution analyzed by the coefficient calculation unit 113 and corresponds to aO in the above-described Equation (10). That is, the infection transmission coefficient P' in the present embodiment indicates the amount of infectious substance inhaled by the non-infected persons per unit time (the amount of exposure to the infectious substance).

[0062] [Equation 13] p'(r, t) = IR(r, t) x n(r, t)

[0063] Then, the evaluation unit 114 evaluates the infection risk in the simulation target based on the infection transmission coefficient P' (Step S106).

[0064] In the present embodiment, Equation (7) indicating a temporal change in the number of non-infected persons in the related art is written as Equation (14) in consideration of a spatiotemporal change in the amount (infection transmission coefficient P') of infectious substance inhaled by the non-infected persons.

[0065] [Equation 14]

[0066] Further, Equation (14) is solved as Equations (15) to (17) to obtain Equation (18).

[0067] [Equation 15]

[0068] [Equation 16]

[0069] [Equation 17] InSCr, t) = j — t)dt ---(17)

[0070] [Equation 18] S(r,f) = So(fo,toyS’---(18)

[0071] In addition, an equation indicating the temporal change in the number of infected persons is the following Equation (19).

[0072] [Equation 19] I(r,t)=S0—S      ---(19)

[0073] From Equation (18), an evaluation equation for the infection risk R is Equation (20).

[0074] [Equation 20] •••(20)

[0075] In the present embodiment, since the amount (infection transmission coefficient P') of infectious substance inhaled by the non-infected persons changes spatially and temporally, the exponent of e in Equations (18) and (20) is integrated, which makes it possible to evaluate the cumulative amount of infectious substance inhaled. Therefore, it is possible to more strictly evaluate the infection risk according to the cumulative amount of infectious substance inhaled.

[0076] (Operations and Effects) As described above, the evaluation device 10 according to the present embodiment includes: the setting unit 111 that sets the 3D model of the simulation target, the inhalation rate IR of visitors for each space and time in the simulation target, the population density S of the non-infected persons for each space and time in the simulation target, and the design parameters including the operation conditions of the air conditioner; the flow field analysis unit 112 that analyzes the flow field of the simulation target based on the 3D model and the design parameters; the coefficient calculation unit 113 that analyzes the concentration distribution of the infectious substance for each time in the simulation target, using the analysis result of the flow field, and calculates the infection transmission coefficient P' for each space (unit area) and time in the simulation target based on the concentration distribution and the inhalation rate IR for each time; and the evaluation unit 114 that evaluates the infection risk R of the simulation target based on the infection transmission coefficient P' and the population density S of the non-infected persons.

[0077] As described above, in the related art, the infection risk is evaluated on the assumption that the inhalation rate is constant in both time and space. In this case, for example, when the inhalation rate of the visitors changes depending on the space and time in the simulation target, there is a possibility that the accuracy of the infection risk will be reduced. In contrast, the evaluation device 10 according to the present embodiment calculates the infection transmission coefficient P' using the spatiotemporal change in the inhalation rate of the visitors in the simulation target as a parameter. Therefore, the evaluation device 10 according to the present embodiment can more strictly evaluate the infection risk R due to aerosol / air.

[0078] In addition, the evaluation unit 114 evaluates the infection risk R based on an integral value of the infection transmission coefficient.

[0079] Therefore, the evaluation device 10 can accurately estimate the cumulative amount (exposure amount) of infectious substance inhaled by the non-infected persons and improve the accuracy of estimating the infection risk R.

[0080] <Second Embodiment Next, a second embodiment will be described with reference to Figs. 4 and 5. The same components as those in the above-described embodiment are denoted by the same reference numerals, and a detailed description thereof will be omitted.

[0081] In the above-described embodiment, the population densities S and I of the non-infected persons and the infected persons in the simulation target are set as the population density per unit area [persons / m2]. In contrast, in the present embodiment, the population densities S and I of the non-infected persons and the infected persons are set as the population density per unit volume [persons / m3].

[0082] Fig. 4 is a diagram showing the functions of an evaluation device according to the second embodiment. Visitors (for example, adults and children) having different heights are mixed in the simulation target. The height of the mouth or the nose through which the infectious substance is inhaled is different between the adults and the children. In this case, there is a possibility that the amounts of infectious substance inhaled by the adults and the children will vary depending on the concentration distribution of the infectious substance.

[0083] Therefore, in the present embodiment, for example, two spaces of a space V1 in which the mouth and nose of the adult are located and a space V2 in which the mouth and nose of the child are located are set. The space VI is a three-dimensional space of a certain range LI (for example, 20 cm) having, as its center, a height Hl (for example, 1.5 m from a floor) at which the mouth and nose of the adult are assumed to be located. The space V2 is a three-dimensional space of a certain range L2 (for example, 20 cm) having, as its center, a height H2 (for example, 1 m from the floor) at which the mouth and nose of the child are assumed to be located.

[0084] For example, when the number of adult non-infected persons is 50, the number of children is 30, the population density S per unit volume [persons / m3] is set assuming that 50 persons are present in the space VI and 30 persons are present in the space V2.

[0085] Fig. 5 is a flowchart showing an example of a process of the evaluation device according to the second embodiment. Here, the process of evaluating the infection risk of the simulation target by the evaluation device 10 will be described in detail with reference to Fig. 5.

[0086] First, the setting unit 111 sets a simulation target (Step S201). This process is the same as that in the first embodiment (Step SI01 in Fig. 2).

[0087] Then, the setting unit 111 sets environmental conditions (Step S202). The population density S of non-infected persons and the population density of infected persons set here are the population density per unit volume [persons / m3] as described above. In addition, a population density Ns of non-infected persons and a population density Ni of infected persons in the entire simulation target are obtained by integrating the population density of each space as shown in Fig. 5.

[0088] The setting of the inhalation rate is the same as that in the first embodiment (Step SI02 in Fig. 2). In addition, the processes in Steps S203 to S206 are the same as the processes in the first embodiment (Steps SI03 to SI06 in Fig. 2).

[0089] As described above, the evaluation device 10 according to the present embodiment specifies the population densities S and I of the non-infected persons and the infected persons as the population density per unit volume.

[0090] Therefore, it is possible to evaluate the infection risk while distinguishing between the non-infected persons (adults and children) having different heights when the concentration distribution of the infectious substance differs depending on the height. In addition, even when the population density differs depending on the height (for example, there is a bias in the number of adults and children), it is possible to more accurately evaluate the infection risk.

[0091] Further, in the present embodiment, the example has been described in which the population density is set assuming two spaces of the space VI in which the adults are present and the space V2 in which the children are present. However, the present invention is not limited thereto. In another embodiment, for example, the present invention may also be applied to a facility having a plurality of floors as the simulation target. For example, in the case of a facility having three floors of the first to third floors, the population density of a space corresponding to the first floor, the population density of a space corresponding to the second floor, and the population density of a space corresponding to the third floor may be set. In addition, when there is an open ceiling in a central portion of the first to third floors, the population density in an open ceiling space on the second to third floors is set to 0. Therefore, it is possible to evaluate the infection risk for each floor even in the simulation target having a plurality of floors.

[0092] <Third Embodiment> Next, a third embodiment will be described with reference to Fig. 6. The same components as those in the above-described embodiment are denoted by the same reference numerals, and a detailed description thereof will be omitted.

[0093] In general, the infectivity of the infectious substance weakens (is inactivated) over time. However, in the related art, inactivation in air was not considered. That is, the infection risk was evaluated on the premise that the generated infectious substance was not eliminated. However, in practice, the infectious substance is inactivated over time depending on the environment (temperature, humidity, and the like) of the simulation target. Therefore, in the present embodiment, in an advection diffusion simulation (flow field simulation) of the infectious substance, an inactivation model in air is introduced.

[0094] Fig. 6 is a flowchart showing an example of a process of an evaluation device according to the third embodiment. Here, the process of evaluating the infection risk of the simulation target by the evaluation device 10 will be described in detail with reference to Fig. 6. In addition, Steps S301 to S304 are the same as those in the first embodiment (Steps S201 to S104 in Fig. 2) or the second embodiment (Steps S201 to S204 in Fig. 5).

[0095] The coefficient calculation unit 113 according to the present embodiment analyzes the concentration distribution of the simulation target for each time, using Equation (21) obtained by introducing the inactivation model in air into a governing equation of the infectious substance concentration represented by the above-described Equation (1) (Step S305).

[0096] [Equation 21 ]

[0097] The inactivation model is represented by the following Equation (22).

[0098] [Equation 22] 1 _ = -~n(r,t)      ---(22) A

[0099] In Equation (22), So indicates the generation or extinction [quanta / s-m3] of the infectious substance, X indicates a time constant [sec] of the inactivation (natural decay) of the infectious substance, and n indicates the infectious substance concentration [quanta / m3].

[0100] In addition, when the infectious substance is a virus, the time constant X of the natural decay generally depends on temperature and humidity. Therefore, the time constant X is approximated by a linear function represented by the following Equation (23) based on the actual measurement results of the temperature (room temperature) T [°C] and relative humidity [%RH] of the simulation target.

[0101] [Equation 23] - aH + PT + 8      ---(2 3)

[0102] As described above, the evaluation device 10 according to the present embodiment analyzes the concentration distribution of the infectious substance, further using the inactivation model of the infectious substance.

[0103] The evaluation device 10 can add an inactivation phenomenon of the infectious substance in air to the simulation in this way to calculate a concentration distribution closer to reality. Therefore, it is possible to more accurately evaluate the infection risk R.

[0104] <Fourth Embodiment> Next, a fourth embodiment will be described with reference to Fig. 7. The same components as those in the above-described embodiment are denoted by the same reference numerals, and a detailed description thereof will be omitted.

[0105] In each of the above-described embodiments, a temporary value is used as the infectivity a of the infectious substance. For example, when the infection risk R is relatively evaluated between a plurality of air conditioning plans in which the disposition of the air conditioner and the design parameters are changed in a certain simulation target, the methods according to the first to third embodiments are effective. In the present embodiment, an analysis and verification process of specifying the infectivity a of the infectious substance based on the actual infection cases is further performed in order to accurately perform the absolute evaluation of the infection risk R.

[0106] Fig. 7 is a block diagram showing a functional configuration of an evaluation device according to the fourth embodiment. As shown in Fig. 7, in the evaluation device 10 according to the present embodiment, the processor 11 further functions as an estimation unit 115. The estimation unit 115 back-calculates an average concentration n of the infectious substance by substituting the known values of the initial (t = 0) number of noninfected persons So, the initial number of infected persons Io, an exposure time t to the infectious substance, the number of non-infected persons S after the exposure time t, and the activity state of the persons (respiratory volume IR) into Equations (10) and (11) based on the infection case in which these values are known. In addition, the infectivity a is estimated from the back-calculated concentration n.

[0107] Fig. 8 is a flowchart showing an example of a process of the evaluation device according to the fourth embodiment. Here, a flow of the process of estimating the infectivity from the infection case by the evaluation device 10 will be described with reference to Fig. 8.

[0108] First, the setting unit 111 sets a facility, in which an infection case has occurred, as the simulation target (Step S401). In addition, the setting unit 111 performs the setting of the environmental conditions (the number of non-infected persons S, the number of infected persons I, and the inhalation rate IR) (Step S402) and the setting of the design conditions (Step S403) based on the infection case. These processes are the same as those in the first embodiment (Steps SI01 to SI03 in Fig- 2).

[0109] In addition, the processes in Steps S404 to S406 are the same as the processes in the first embodiment (Steps SI04 to SI06 in Fig. 2).

[0110] Then, the estimation unit 115 substitutes the initial number of non-infected persons So, the exposure time t to the infectious substance, the number of non-infected persons S after the exposure time t, the number of infected persons I after the exposure time t, and the inhalation rate IR, which are known from the infection case, into the above-described Equations (10) and (11) to calculate the average concentration n of the infectious substance. In addition, the evaluation unit 114 statistically estimates the infectivity a from the calculated concentration n = aO (Step S407). [OHl] The infectivity a based on the infection case estimated by the estimation unit 115 is used to evaluate the infection risk of the same infectious substance in another simulation target. Specifically, in Step S102 of Fig. 2 (or Step S202 of Fig. 5 or Step S302 of Fig. 6), the setting unit 111 sets the infectivity a as one of the environmental conditions. Therefore, the infectivity a can be closer to a realistic value based on the actual infection case.

[0112] In addition, in Step SI 05 of Fig. 2 (or Step S205 of Fig. 5 or Step S305 of Fig. 6), the coefficient calculation unit 113 substitutes the concentration n = aO obtained by considering the infectivity a estimated by the estimation unit 115 into Equation (13) to calculate the infection transmission coefficient P'. Therefore, it is possible to calculate a more accurate infection transmission coefficient P'.

[0113] As described above, the evaluation device 10 according to the present embodiment further includes the estimation unit 115 that estimates the infectivity of the infectious substance based on the known infection case. In addition, the coefficient calculation unit 113 calculates the infection transmission coefficient P' in another simulation target for the same infectious substance based on the infectivity estimated by the estimation unit 115.

[0114] Therefore, the evaluation device 10 can make the infectivity a closer to a realistic value based on the actual infection case and thus can calculate a more accurate infection transmission coefficient P'. Therefore, the evaluation device 10 can also accurately perform the absolute evaluation of the infection risk R in the simulation target.

[0115] <Other Embodirnents> The embodiments have been described in detail above with reference to the drawings. However, the specific configuration is not limited to the above description, and various design changes and the like can be made. That is, in other embodiments, the procedures of the above-described processes may be changed as appropriate. In addition, some of the processes may be executed in parallel.

[0116] Supplementary Notes> For example, the evaluation device, the evaluation method, and the program according to the above-described embodiments are understood as follows.

[0117] (1) According to a first aspect, there is provided the evaluation device 10 including: the setting unit 111 that sets a 3D model including a layout of an air conditioner in a simulation target, the inhalation rate IR of visitors for each space and time in the simulation target, a population density S of non-infected persons or the number of non-infected persons for each space and time in the simulation target, and design parameters including an operation condition of the air conditioner; the flow field analysis unit 112 that analyzes a flow field of the simulation target based on the 3D model and the design parameters; the coefficient calculation unit 113 that analyzes the concentration distribution n of an infectious substance for each time in the simulation target, using an analysis result of the flow field, and calculates the infection transmission coefficient P' for each space and time in the simulation target, based on the concentration distribution n and the inhalation rate IR for each time; and the evaluation unit 114 that evaluates the infection risk R of the simulation target based on the infection transmission coefficient P' and the population density S of the noninfected persons or the number of non-infected persons.

[0118] Therefore, the evaluation device 10 can calculate the infection transmission coefficient P' in consideration of a spatiotemporal change in the inhalation rate corresponding to the activity state of the visitors. As a result, it is possible to more strictly evaluate the infection risk R due to aerosol / air.

[0119] (2) According to a second aspect, in the evaluation device 10 according to the first aspect, the evaluation unit 114 evaluates the infection risk R based on an integral value of the infection transmission coefficient P'.

[0120] Therefore, the evaluation device 10 can accurately estimate the cumulative amount (exposure amount) of infectious substance inhaled by the non-infected persons and improve the accuracy of estimating the infection risk R.

[0121] (3) According to a third aspect, in the evaluation device 10 according to the first or second aspect, the setting unit 111 sets the population density S of the noninfected persons per unit volume in the simulation target.

[0122] Therefore, it is possible to evaluate the infection risk while distinguishing between the non-infected persons (adults and children) having different heights when the concentration distribution of the infectious substance differs depending on the height. In addition, even when the population density differs depending on the height (for example, there is a bias in the number of adults and children), it is possible to more accurately evaluate the infection risk.

[0123] (4) According to a fourth aspect, in the evaluation device 10 according to any one of the first to third aspects, the coefficient calculation unit 113 analyzes the concentration distribution, further using the inactivation model S$ of the infectious substance.

[0124] The evaluation device 10 can add an inactivation phenomenon of the infectious substance in air to the simulation in this way to calculate a concentration distribution closer to reality. Therefore, it is possible to more accurately evaluate the infection risk R.

[0125] (5) According to a fifth aspect, the evaluation device 10 according to any one of the first to fourth aspects further includes the estimation unit 115 that estimates, based on an infection case in which the initial number of non-infected persons So, the inhalation rate IR, the exposure time t to the infectious substance, and the number of infected persons S after the exposure time t in a first simulation target are known, the infectivity a of the infectious substance, in which the coefficient calculation unit 113 calculates the infection transmission coefficient P' for the same infectious substance as that in the first simulation target for each space and time in a second simulation target different from the first simulation target, further based on the infectivity a estimated by the estimation unit 115.

[0126] Therefore, the evaluation device 10 can make the infectivity a closer to a realistic value based on the actual infection case and thus can calculate a more accurate infection transmission coefficient P'. Therefore, the evaluation device 10 can also accurately perform the absolute evaluation of the infection risk R in the simulation target.

[0127] (6) According to a sixth aspect, there is provided an evaluation method including: a step of setting a 3D model including a layout of an air conditioner in a simulation target, the inhalation rate IR of visitors for each space and time in the simulation target, a population density S of non-infected persons or the number of noninfected persons for each space and time in the simulation target, and design parameters including an operation condition of the air conditioner; a step of analyzing a flow field of the simulation target based on the 3D model and the design parameters; a step of analyzing the concentration distribution n of an infectious substance for each time in the simulation target, using an analysis result of the flow field, and calculating the infection transmission coefficient P' for each space and time in the simulation target, based on the concentration distribution n and the inhalation rate IR for each time; and a step of evaluating the infection risk R of the simulation target based on the infection transmission coefficient P' and the population density S of the non-infected persons or the number of non-infected persons.

[0128] (7) According to a seventh aspect, there is provided a program causing the evaluation device 10 to execute: a step of setting a 3D model including a layout of an air conditioner in a simulation target, the inhalation rate IR of visitors for each space and time in the simulation target, a population density S of non-infected persons or the number of non-infected persons for each space and time in the simulation target, and design parameters including an operation condition of the air conditioner; a step of analyzing a flow field of the simulation target based on the 3D model and the design parameters; a step of analyzing the concentration distribution n of an infectious substance for each time in the simulation target, using an analysis result of the flow field, and calculating the infection transmission coefficient P' for each space and time in the simulation target, based on the concentration distribution n and the inhalation rate IR for each time; and a step of evaluating the infection risk R of the simulation target based on the infection transmission coefficient P' and the population density S of the non-infected persons or the number of non-infected persons. Industrial Applicability

[0129] According to the above-described aspects, even when an activity state of visitors changes over time and space, it is possible to accurately evaluate an infection risk. Reference Signs List

[0130] 10 Evaluation device 11 Processor 111 Setting unit 112 Field analysis unit 113 Coefficient calculation unit 114 Evaluation unit 115 Estimation unit 12 Memory 13 Storage 14 Interface 15 Input device 16 Display device

Claims

1. An evaluation device comprising:a setting unit that sets a 3D model including a layout of an air conditioner in a simulation target, an inhalation rate of visitors for each space and time in the simulation target, a population density of non-infected persons or the number of noninfected persons for each space and time in the simulation target, and design parameters including an operation condition of the air conditioner;a flow field analysis unit that analyzes a flow field of the simulation target based on the 3D model and the design parameters;a coefficient calculation unit that analyzes a concentration distribution of an infectious substance for each time in the simulation target, using an analysis result of the flow field, and calculates an infection transmission coefficient for each space and time in the simulation target, based on the concentration distribution and the inhalation rate for each time; andan evaluation unit that evaluates an infection risk of the simulation target based on the infection transmission coefficient and the population density of the noninfected persons or the number of non-infected persons.

2. The evaluation device according to Claim 1,wherein the evaluation unit evaluates the infection risk based on an integral value of the infection transmission coefficient.

3. The evaluation device according to Claim 1,wherein the setting unit sets the population density of the non-infected persons per unit volume in the simulation target.

4. The evaluation device according to Claim 1,wherein the coefficient calculation unit analyzes the concentration distribution, further using an inactivation model of the infectious substance.

5. The evaluation device according to any one of Claims 1 to 4, further comprising:an estimation unit that estimates, based on an infection case in which an initial number of non-infected persons, an inhalation rate, an exposure time to an infectious substance, and the number of infected persons after the exposure time in a first simulation target are known, an infectivity of the infectious substance,wherein the coefficient calculation unit calculates an infection transmission coefficient for the same infectious substance as that in the first simulation target for each space and time in a second simulation target different from the first simulation target, further based on the infectivity estimated by the estimation unit.

6. An evaluation method comprising:a step of setting a 3D model including a layout of an air conditioner in asimulation target, an inhalation rate of visitors for each space and time in the simulation target, a population density of non-infected persons or the number of noninfected persons for each space and time in the simulation target, and design parameters including an operation condition of the air conditioner;a step of analyzing a flow field of the simulation target based on the 3D model and the design parameters;a step of analyzing a concentration distribution of an infectious substance for each time in the simulation target, using an analysis result of the flow field, and calculating an infection transmission coefficient for each space and time in the simulation target, based on the concentration distribution and the inhalation rate for each time; anda step of evaluating an infection risk of the simulation target based on the infection transmission coefficient and the population density of the non-infected persons or the number of non-infected persons.

7. A program causing an evaluation device to execute:a step of setting a 3D model including a layout of an air conditioner in a simulation target, an inhalation rate of visitors for each space and time in the simulation target, a population density of non-infected persons or the number of noninfected persons for each space and time in the simulation target, and design parameters including an operation condition of the air conditioner;a step of analyzing a flow field of the simulation target based on the 3D model and the design parameters;a step of analyzing a concentration distribution of an infectious substance foreach time in the simulation target, using an analysis result of the flow field, and calculating an infection transmission coefficient for each space and time in the simulation target, based on the concentration distribution and the inhalation rate for each time; anda step of evaluating an infection risk of the simulation target based on the infection transmission coefficient and the population density of the non-infected persons or the number of non-infected persons.

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