Shape optimization program of architectural structure

Abstract

To provide a shape optimization program of an architectural structure capable of executing a computational design by taking virus inactivation into consideration without increasing a calculation load to regard an aerosol infection risk as an objective function.SOLUTION: According to the present invention, a shape optimization program of an architectural structure causes a computer to execute: model creation processing for creating a plurality of simulation models of the architectural structure having different shapes; simulation processing for simulating behaviors of aerosols in the architectural structure by calculating the concentration of viruses in the simulation models created in the model creation processing by solving a virus or aerosol transport equation including a term representing a decrease of virus concentration due to the lapse of time; risk calculation processing for calculating an aerosol infection risk in the architectural structure on the basis of a result of the simulation processing; and optimization processing for determining a shape of the architectural structure in which the aerosol infection risk satisfies a prescribed condition as an optimum shape on the basis of a result of the risk calculation processing.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a shape optimization program for buildings that can be applied to technical fields such as architectural design, air conditioning design, and infection control. [Background technology]

[0002] In recent years, advances in simulation technology have led to the use of computational design, which involves running multiple simulations to obtain designs that maximize or minimize objective functions during the design phase of buildings. In computational design, it is possible to set the risk of aerosol infection as the objective function. Aerosol infection is caused when other people inhale aerosols generated by an infected person's coughing, sneezing, speaking, breathing, etc. In order to suppress aerosol infection indoors, it is important to quickly remove aerosols from the room or to supply clean air that does not contain aerosols to the room. Therefore, the risk of aerosol infection indoors changes depending on the design, such as the location of the air outlet of the air conditioning system and the installation of partitions.

[0003] Here, in order to evaluate the risk of aerosol infection, it is necessary to understand the behavior of aerosols, and it is necessary to take into account airflow, evaporation of particles, gravity and drag acting on particles, etc. In light of this background, a method for evaluating the risk of aerosol infection by executing Computational Fluid Dynamics (CFD) that treats aerosols as particles has been proposed (see Patent Documents 1 and 2). Also, a method for evaluating the risk of aerosol infection by executing CFD that treats aerosols as gases has been proposed (see Non-Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2022-100287 A [Patent Document 2] JP 2023-233 A [Non-patent literature]

[0005] [Non-Patent Document 1] Hiroaki Asanuma, Kazuhide Ito, “Prediction of infection spread by coupled analysis of non-stationary non-uniform concentration distribution and mathematical epidemiological model in hospital space”, Journal of Environmental Engineering, Architectural Institute of Japan, 2013, 78.688:481-487. [Non-Patent Document 2] M. Schuit et al., “Airborne SARS-CoV-2 Is Rapidly Inactivated by Simulated Sunlight”, The Journal of Infectious Diseases, 222(4), p. 564-571. [Non-Patent Document 3] P. Dabisch et al., “The influence of temperature, humidity, and simulated sunlight on the infectivity of SARS-CoV-2 in aerosols”, Aerosol Science and Technology, 55(2), p. 142-153. [Non-Patent Document 4] NVDoremalen et al., “Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1”, In New England Journal of Medicine (Vol. 382, ​​Issue 16, pp. 1564-1567), Massachusetts Medical Society. [Non-Patent Document 5] ACFears et al., “Persistence of Severe Acute Respiratory Syndrome Coronavirus 2 in Aerosol Suspensions”, Volume 26, Number 9, Emerging Infectious Diseases, 26(9). [Non-Patent Document 6] HAAboubakr et al., “Stability of SARS-CoV-2 and other coronaviruses in the environment and on common touch surfaces and the influence of climatic conditions: A review”, Transboundary and Emerging Diseases, 68(2), p. 296-312. Summary of the Invention [Problem to be solved by the invention]

[0006] According to the methods described in Patent Documents 1 and 2, the behavior of aerosols such as precipitation, deposition, evaporation, and virus inactivation can be taken into account, but the computational load on the computer increases when reproducing the behavior of a large number of aerosol particles. For this reason, the methods described in Patent Documents 1 and 2 are not suitable for computational design, which requires a large number of simulations to be tried. In contrast, the method described in Non-Patent Document 1 has a small computational load on the computer, but cannot take into account the behavior of aerosols such as precipitation, deposition, evaporation, and virus inactivation. In order to solve such problems, it is possible to use a drift flux model in the method described in Non-Patent Document 1, but the drift flux model cannot take into account virus inactivation. Against this background, there was a demand for a technology that can perform computational design of buildings with aerosol infection risk as the objective function while taking into account virus inactivation without increasing the computational load.

[0007] The present invention has been made to solve the above-mentioned problems, and its purpose is to provide a shape optimization program for buildings that can perform computational design of buildings with aerosol infection risk as an objective function, taking into account virus inactivation without increasing the computational load. [Means for solving the problem]

[0008] The building shape optimization program of the present invention causes a computer to execute the following steps: a model creation process that creates multiple simulation models of buildings with different shapes; a simulation process that simulates the behavior of aerosols within the building by solving a virus or aerosol transport equation that includes a term representing the attenuation of virus concentration over time, thereby calculating the virus concentration in the simulation model created in the model creation process; a risk calculation process that calculates the aerosol infection risk within the building based on the results of the simulation process; and an optimization process that determines the building shape whose aerosol infection risk satisfies specified conditions as the optimal shape based on the results of the risk calculation process. Effect of the Invention

[0009] According to the building shape optimization program of the present invention, it is possible to perform computational design of buildings with aerosol infection risk as an objective function, taking into account virus inactivation without increasing the calculation load. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a block diagram showing the configuration of a shape optimization device in which a building shape optimization program according to one embodiment of the present invention is installed. [Diagram 2] FIG. 2 is a flowchart showing the flow of a shape optimization process according to an embodiment of the present invention. [Diagram 3] FIG. 3 is a diagram illustrating an example of a simulation model. [Figure 4] FIG. 4 is a diagram showing an example of a simulation of the behavior of aerosols in an indoor space. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, a description will be given of a building shape optimization program according to one embodiment of the present invention with reference to the drawings.

[0012] 〔composition〕 First, with reference to FIG. 1, a configuration of a shape optimization device in which a shape optimization program for a building is installed according to one embodiment of the present invention will be described.

[0013] FIG. 1 is a block diagram showing the configuration of an information processing device in which a shape optimization program for a building according to one embodiment of the present invention is installed. As shown in FIG. 1, a shape optimization device 1 in which a shape optimization program for a building according to one embodiment of the present invention is installed is a device for executing computational design of a building, and is configured by an information processing device such as a workstation, a personal computer, or a tablet terminal. In this embodiment, the shape optimization device 1 includes an input device 2 and an output device 3. Examples of the input device 2 include an operation input device such as a touch panel, a keyboard, or a mouse pointer, and an imaging device such as a camera. Examples of the output device 3 include a display device such as a liquid crystal display, a printing device such as a printer, and an audio output device such as a speaker.

[0014] The shape optimization device 1 also includes a calculation processing unit 11 and a storage unit 12. The calculation processing unit 11 is configured with a calculation processing device such as a CPU (Central Processing Unit), and executes a shape optimization process described below by reading and executing a shape optimization program 12a stored in the storage unit 12. The storage unit 12 may be a storage medium fixed to a computer or the like, or a storage medium removable from a computer or the like. Examples of storage media fixed to a computer or the like include an EPROM (Erasable Programmable ROM) and a hard disk drive (HDD, Hard Disk Drive).

[0015] Examples of the recording medium removable from a computer or the like include a Universal Serial Bus (USB) memory, a flexible disk, a magneto-optical disk, a Compact Disc-Read Only Memory (CD-ROM), a Compact Disc-Rewritable (CD-RW), a Digital Versatile Disc (DVD), a Blu-ray (registered trademark) Disc (BD), a Digital Audio Tape (DAT), an 8 mm tape, a memory card, and the like. A Solid State Drive (SSD) can be used as a storage medium removable from a computer or the like, or as a storage medium fixed to a computer or the like. The shape optimization program 12a may be configured to be stored on a computer connected to a telecommunication line such as the Internet and provided by downloading the program via the telecommunication line. The shape optimization program 12a may also be configured to be provided or distributed via a telecommunication line.

[0016] In the shape optimization device 1 having such a configuration, the arithmetic processing unit 11 executes the shape optimization process described below, thereby performing computational design of a building with the aerosol infection risk as an objective function, taking into consideration the inactivation of viruses, without increasing the calculation load. The operation of the arithmetic processing unit 11 when executing the shape optimization process will be described below with reference to the flowchart shown in Fig. 2.

[0017] [Shape optimization process] Fig. 2 is a flowchart showing the flow of the shape optimization process according to one embodiment of the present invention. The flowchart shown in Fig. 2 starts when an execution command for the shape optimization process is input to the shape optimization device 1, and the shape optimization process proceeds to step S1. The operation of the calculation processing unit 11 shown below is realized by the calculation processing unit 11 reading and executing the shape optimization program 12a stored in the storage unit 12.

[0018] In the process of step S1, an operator operates the input device 2 to input information on the conditions of the building (space) to be designed, information on the optimization target, and information on the constraint conditions and the objective function. Examples of the information on the conditions of the building (space) include the shape and dimensions (including obstacles such as fixtures) of the space to be designed, the position, air volume, and temperature of the air intake in the space, heat generation conditions in the space (lighting, human body, PC, display, etc.), the position and capacity of aerosol countermeasure equipment such as air purifiers and UV irradiators in the space, thermal boundary conditions of the floor, ceiling, and walls, the position of an infected person, the position of people other than the infected person, the infectiousness of the target infectious disease, etc. Examples of the information on the optimization target include the shape and dimensions of the space (for example, partitions, etc.), the position, air volume, and temperature of the air intake in the space, the position and capacity of aerosol countermeasure equipment in the space, etc. Examples of the information on the objective function include the number of infected people and objective functions other than aerosol infection (energy consumption, thermal comfort, visibility, etc.). The operator may input information by having the calculation processing unit 11 read input information stored in the storage unit 12 or an external storage device. Then, the calculation processing unit 11 stores the input information in a temporary storage device or the storage unit 12 as setting information for the subsequent shape optimization process. This completes the process of step S1, and the shape optimization process proceeds to the process of step S2.

[0019] In the process of step S2, the calculation processing unit 11 creates multiple simulation models of buildings with different shapes and specifications, for example, as shown in Figures 3(a) and (b), using the information on the conditions of the building to be designed that was input in the process of step S1. This completes the process of step S2, and the shape optimization process proceeds to the process of step S3.

[0020] In the process of step S3, the calculation processing unit 11 simulates the behavior of the aerosol for each simulation model created in the process of step S2 using a numerical model that takes into account the inactivation of the virus. The details of the numerical model that takes into account the inactivation of the virus will be described later. FIG. 4 is a diagram showing an example of a simulation of the behavior of the aerosol in an indoor space. In the example shown in FIG. 4, an airflow flows from the left side to the right side of the figure, and aerosols containing viruses are generated from a part of the left end. Since the numerical model takes into account the settling of the aerosol, the aerosols settle downward in the process of flowing from the left side to the right side. Furthermore, since the numerical model also takes into account the evaporation of the aerosol, the settling of the aerosol gradually becomes gentler. Furthermore, since the numerical model also takes into account the inactivation of the virus, the concentration of the virus gradually decreases in the process of flowing from the left side to the right side. This completes the process of step S3, and the shape optimization process proceeds to the process of step S4.

[0021] In the process of step S4, the calculation processing unit 11 calculates the value of the objective function in each simulation model created in the process of step S2 based on the simulation result in the process of step S3. Specifically, the calculation processing unit 11 calculates the aerosol infection risk for each simulation model based on the simulation result in the process of step S3. For example, the calculation processing unit 11 calculates the number of infected people as the aerosol infection risk by calculating the infection probability P_j (=exp(-M_j)) of the jth occupant from the amount of virus M_j inhaled by the jth occupant obtained by the simulation. In this case, the number of infected people is obtained as an expected value (0.5 people if there are 5 people with an infection probability of 10%). However, the calculation processing unit 11 may calculate the number of infected people assuming that the occupant becomes infected (infection probability P_j=1) only when a predetermined amount or more of virus is inhaled. In this case, the number of infected people is obtained as a natural number. Examples of methods for calculating the amount of virus M_j inhaled by an occupant include a method of directly finding the amount by reproducing the breathing of the occupant in a simulation, and a method of calculating the amount M_j as the product of the virus concentration V_j in the area (respiratory area) where the occupant is expected to inhale and the air volume Q of the occupant's breathing. This completes the process of step S4, and the shape optimization process proceeds to step S5.

[0022] In the process of step S5, the calculation processing unit 11 determines the optimal shape of the building based on the value of the objective function calculated in the process of step S5, and outputs information about the determined optimal shape to the output device 3. For example, when the optimal shape is the shape of the building with the smallest aerosol infection risk, the calculation processing unit 11 outputs information about the shape of the building corresponding to the simulation model with the smallest value of the aerosol infection risk calculated in the process of step S4 (for example, the smallest number of infected people) to the output device 3. This completes the process of step S5, and the series of shape optimization processes ends.

[0023] [Numerical model] In this embodiment, in order to evaluate the risk of aerosol infection taking into account the inactivation of viruses, the behavior of aerosols in an indoor space is simulated using the numerical model shown below. The numerical model taking into account the inactivation of viruses used in simulating the behavior of aerosols will be described in detail below.

[0024] Assuming that the virus titer contained in an aerosol is proportional to the aerosol mass, the virus concentration (virus titer) V is expressed by the following formula (1).

[0025]

number

[0026] In equation (1), t is time, x is coordinate, i is the number of dimensions (=1 to 3 (corresponding to the x, y, and z directions)), u is the wind speed of the flow field, and v eff is the dynamic viscosity coefficient, S v denotes the aerosol generation rate (source term).

[0027] However, the virus titer contained in the aerosol decays over time. M. Schuit et al. have experimentally demonstrated that sunlight accelerates the decay of the virus titer of SARS-CoV-2 (see Non-Patent Document 2). In addition, P. Dabisch et al. have organized the rate of decrease in the virus titer of SARS-CoV-2 based on the experimental results using a regression equation of temperature, humidity, and UV-B intensity (see Non-Patent Document 3). That is, there are many other studies that have measured the rate of decrease in the virus titer of SARS-CoV-2 in aerosol (see Non-Patent Documents 4 and 5), but all of the results show that the virus titer decreases exponentially. This is similar to other coronaviruses such as SARS-CoV-1, MERS-CoV, and HCoV-229E (see Non-Patent Document 6). Therefore, the virus concentration V at a certain time t is expressed by the following formula (2) or formula (3).

[0028]

number

[0029]

number

[0030] In equations (2) and (3), V0 is the initial concentration of the virus, t half represents the half-life of the virus. Furthermore, k represents the inactivation rate of the virus, and is expressed by the following mathematical formula (4) according to the regression equation described in Non-Patent Document 3. However, k in mathematical formula (4) is only an example, and k may be replaced with any other mathematical formula or value.

[0031]

number

[0032] Here, in formula (4), T is temperature, RH is relative humidity, and S is UV-B intensity.

[0033] In addition, the half-life of the virus, t half and the viral inactivation rate k are related by the following equation (5):

[0034]

number

[0035] As shown in formula (2) or formula (3), the virus concentration V is expressed as a function of time t. For this reason, for example, when simulating the virus concentration distribution one hour after the start of aerosol generation, it is necessary to perform unsteady calculations for one hour. However, this work is not suitable for computational design, which requires a large number of simulations to be tried, because the calculation cost is very high.

[0036] Therefore, in the present invention, attention is paid to the fact that the virus concentration distribution reaches a steady state when a sufficient amount of time has passed since the start of aerosol generation. In other words, when the virus concentration distribution reaches a steady state, the "increase in viruses due to outbreak", the "decrease in viruses due to inactivation", the "increase in viruses due to inflow", and the "decrease in viruses due to outflow" are balanced (balanced) at each point. Of these, the "increase in viruses due to outbreak" can be taken into account as the source term on the right side of formula (1), and the "increase in viruses due to inflow" and the "decrease in viruses due to outflow" can be taken into account as the second term on the left side of formula (1). For this reason, in the present invention, the following method has been devised as a method for taking into account the "decrease in viruses due to inactivation".

[0037] Now, the change in virus concentration V over time can be expressed as shown below in equation (6) by differentiating equation (2).

[0038]

number

[0039] If we add the decay of the virus concentration V over time as a sink term to equation (1), the virus transport equation can be expressed as equation (7) below.

[0040]

number

[0041] This sink term (second term on the right-hand side) is set to the entire space. Therefore, by solving formula (7) as the governing equation for the virus concentration V, a simulation can be made that takes into account the inactivation of the virus while keeping the calculation cost low. If aerosol settling is also taken into account, the following formula (8) can be used instead of formula (1).

[0042]

number

[0043] In addition, when taking into account the evaporation of water in the aerosol, it is advisable to solve the following equation (9) as the transport equation for the aerosol mass concentration, solve the following equation (10) as the transport equation for the aerosol number concentration, and then calculate the virus concentration V using the following equation (11).

[0044]

number

[0045]

number

[0046]

number

[0047] In equations (8) to (11), C is the mass concentration of the aerosol, v is the settling velocity of the aerosol, and S c is the mass concentration generation rate of aerosol, m is the mass per aerosol particle, N is the number concentration of aerosol, S N is the aerosol number concentration generation rate, c V,C indicates the virus concentration in the aerosol (based on the mass of the aerosol particles). If it is considered that the virus concentration V in the aerosol is proportional to the number of aerosol particles, it is better to calculate the virus concentration V from the following formula (12) instead of formula (11).

[0048]

number

[0049] Here, in formula (12), c V,N indicates the virus concentration in the aerosol (based on the number of aerosol particles).

[0050] As is clear from the above description, in the shape optimization process according to one embodiment of the present invention, the calculation processing unit 11 creates multiple simulation models of buildings with different shapes, and calculates the virus concentration in the simulation model by solving a transport equation for viruses or aerosols including a term representing the attenuation of the virus concentration over time, thereby simulating the behavior of aerosols in the building, and calculates the aerosol infection risk in the building based on the simulation results. Based on the calculation results, the shape of the building in which the aerosol infection risk satisfies a predetermined condition is determined as the optimal shape. This makes it possible to perform computational design of a building with the aerosol infection risk as the objective function, taking into account the inactivation of the virus without increasing the calculation load. In addition to the inactivation of the virus, gravity acting on the aerosol, evaporation of water in the aerosol, adhesion of the aerosol to the wall surface, etc. can also be considered.

[0051] Although the embodiments of the present invention have been described above, the present invention is not limited by the descriptions and drawings that form part of the disclosure of the present invention according to the present embodiments. In other words, other embodiments, examples, and operation techniques, etc., made by those skilled in the art based on the present embodiments are all included in the scope of the present invention. [Explanation of symbols]

[0052] 1. Shape optimization device 2 Input Devices 3 Output Device 11. Processing unit 12 Storage section 12a Shape optimization program

Claims

[Claim 1] A model creation process for creating multiple simulation models of buildings with different shapes; A simulation process for simulating the behavior of aerosols in the building by calculating the concentration of viruses in the simulation model created in the model creation process by solving a transport equation for viruses or aerosols, the transport equation including a term representing the attenuation of the concentration of the viruses over time; A risk calculation process for calculating an aerosol infection risk in the building based on the result of the simulation process; An optimization process for determining an optimal shape of a building that satisfies a predetermined condition for the aerosol infection risk based on the result of the risk calculation process; A building shape optimization program that runs on a computer.

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