CN116205150A - Indoor gaseous pollution source space-time information estimation method based on accompanying pulse algorithm - Google Patents
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Abstract
The invention discloses a method for rapidly estimating space-time information of indoor gaseous pollution sources based on an accompanying pulse algorithm, which comprises the following steps: and measuring room boundary conditions, arranging a pollutant sensor at the position of an air outlet in the room to serve as a master sensor, and arranging pollutant sensors at other positions in the room to serve as slave sensors. By solving the accompanying equation by using the accompanying pulse method, the accompanying concentration response of all positions in the corresponding calculation domain of the sensor can be obtained, and then a response characteristic matrix between the sensor and a leakage source can be quickly established; and further utilizing a regularization algorithm, and analyzing the space-time information of all potential pollution sources in the calculation domain through calculation and comparison of residual errors. The invention can quickly establish the response matrix between different sensors and a large number of potential sources, greatly shortens the forward simulation trial calculation time, can quickly and accurately identify the position and release rate of the pollution source, and has important significance for improving the indoor air quality.
Description
Technical Field
The invention belongs to the field of building environment and health, and particularly relates to a rapid estimation method for indoor gaseous pollution source space-time information based on an accompanying pulse algorithm.
Background
Indoor Air Quality (IAQ) is important to residents because most of the time people spend in indoor environments. Indoor pollution is an important factor causing Sick Building Syndrome (SBS), and if people are exposed to polluted environments for a long time, discomfort symptoms such as headache, fatigue, dyspnea and the like can be caused. Recently, biosafety of public buildings has also become of great concern. In particular for some respiratory infections such as SARS and COVID-19, pathogens may be transmitted in the form of bioaerosols. If not found in time, the health of human body is greatly endangered. Thus, a good source term reverse estimation algorithm is of great importance for the source identification of airborne pathogens based on sensor detection data.
The current indoor pollution source positioning method can be divided into a mobile sensor method and a fixed sensor method. The mobile sensor can automatically identify the source of pollutants under the development of robotics and unmanned aerial vehicles. The basic concept is largely inspired by biological behavior, such as foraging and mating. However, this approach is costly and is generally suitable for situations where manual access is inconvenient. For source identification of fixed sensors, three types can be distinguished: backward, forward, and probabilistic methods. The backward method is a direct solution to the forward transport equation with negative time steps, and mainly includes quasi-reversible (QR) and Lagrangian Reversible (LR) methods. The backward method requires an accurate and reliable airflow field. However, in a real scene, it is difficult to accurately acquire airflow information, which limits the application of the method. The forward method requires a great deal of forward simulation on potential pollution sources in advance, and then calculates the matching degree of sensor monitoring data and prediction data by utilizing an objective function. For the positioning of dynamic sources, the sensor response is usually complex, the solving process is accompanied by a pathological matrix, and a regularization method is introduced to ensure the convergence of the solving. While regularization methods can avoid divergence in the source intensity calculations, it is very laborious to establish the response relationship between the sensor and all potential sources in the room space through forward simulation. Accompanying the probability equations requires a priori information of known sources for continuous emission source identification. In practice, however, the emission form of the source of pollution is generally unknown.
The present invention therefore proposes a new model combining the concomitant pulsing and regularization methods to identify the location and release intensity of the point contamination source throughout the room. The method can quickly and accurately identify the release rate and the position of the pollution source, and has important significance for improving the indoor air quality.
Disclosure of Invention
Embodiments of the present invention propose a method to identify the location and release intensity of point sources of contamination throughout space in combination with a new model of the concomitant pulsing and regularization method, which can be used to identify sources of respiratory infectious disease pathogens in public buildings in combination with respiratory infectious disease specific biomarker sensors.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical scheme:
the embodiment of the invention provides an indoor gaseous pollution source space-time information estimation method based on an accompanying pulse algorithm, which comprises the following steps:
firstly, measuring room boundary conditions including air supply port speed, wall surface temperature and internal heat source power, and simulating a flow field in numerical simulation software. The flow field simulation adopts a standard k-epsilon model, uses a SIMPLE algorithm to couple pressure and momentum equations, and uses a first-order windward format for all other variables;
a second step of arranging a pollutant sensor at the position of an air outlet in the room as a master sensor, and arranging a pollutant sensor at the position downstream of a main air flow in the room as a slave sensor;
thirdly, reversing the flow field in a numerical model, respectively releasing trace gas at the main sensor position and the auxiliary sensor position in the form of rectangular pulses by taking qs as intensity, and obtaining a sequence corresponding to the position of a potential leakage source along with the change of the concentration along with time through numerical simulation. Respectively releasing trace gas at the position of a master sensor and the position of a slave sensor in the form of rectangular pulses, wherein the duration of the matrix pulses is the measured time resolution step length;
fourth, the concentration of contaminants at the sensor locations in the forward flow field is proportional to the concomitant concentration at the source locations in the reverse flow field, which is mathematically described as:
q s C(x m )＝q m C * (x s ) (1)
Wherein: q m For pollution source release intensity, x m For the source release position, C is the concentration value in the forward flow field, C * Is the accompanying concentration in the reverse flow field, x s Is the coordinates of the sensor, q s Is the intensity of the trace gas released at the sensor location. The response factors of the sensors corresponding to all potential source positions in the whole room space can be obtained through the accompanying concentration obtained in the third step, and the corresponding response vectors are as follows:
wherein:
is a response factor describing the sensor at different moments, which represents the sensitivity of the sensor to the release source,/for>
Is the accompanying concentration at different moments;
fifth, establishing response matrix M (A) of all potential source corresponding sensors,
m (A) is the Toeplitz matrix of A. The relationship between the source intensity q and the monitored concentration C is: c=m (a) q, where
Is a time-varying sequence of pollutant concentration monitored by the sensor at different moments, and q= [ q ] 0 ，...，q i ，...，q n ] T Is a time-varying sequence of the intensity of release of a source of pollution;
sixth, according to the steps, the least square problem is solved through a regularization algorithm by using the pollutant concentration C and the response matrix M (A) monitored by the sensor, and q is obtained through back calculation as shown in the following formula;
seventh, bringing all potential source intensities q calculated in the sixth step into (formula 3) again to obtain residual errors
And comparing residual errors of all potential sources, wherein the minimum residual error corresponds to the position of the leakage source, and q is the leakage source intensity.
Compared with the prior art, the invention has the advantages that: the adjoint impulse method can obtain adjoint concentration responses of all positions in the corresponding calculation domain of the sensor by solving adjoint equations, and then a response characteristic matrix between the sensor and a leakage source is quickly established. And the regularization algorithm is further utilized, analysis of space-time information of all potential pollution sources in the calculation domain is realized through calculation and comparison of residual errors, and the speed and the recognition coverage range of the pollution sources in the building are greatly improved.
Drawings
FIG. 1 is a flow chart of a method for implementing fast estimation of pollution sources according to the present invention;
FIG. 2 is a schematic diagram of the locations of the indoor potential leakage source, the real leakage source and the sensors in the present invention (s 1-3, s1-5, s1-7, s1-9, s2-4 and s2-11 respectively represent different potential leakage sources, s1-4 represents the real leakage source, sensor No. 4 is a master sensor, and the other sensors are slave sensors);
FIG. 3 is a schematic diagram of the constant source strong reverse identification result in the present invention;
Detailed Description
The invention is described below in conjunction with the drawings in the specification, it being understood that the preferred embodiments described herein are provided to illustrate and explain the invention and are not intended to limit the invention.
When pollution source release occurs in a building based on an accompanying method and a pollution source identification algorithm, the position and the release strength of the pollution source are rapidly identified. The implementation flow of the method is shown in the attached figure 1, and the specific implementation comprises the following steps.
Step 1: an office CFD model was built, as shown in fig. 2, with room dimensions of 5.16m x 3.65m x 2.43m. The right side of the room is provided with an air supply opening, the upper part of the room is provided with an air outlet, and two persons, two computers, two tables, two boxes and six lamps are arranged in the room. The model was divided into 269,914 grid cells. The temperature of the air flow at the inlet was 17℃and the air flow rate was 0.09m/s. The air outlet is set as a pressure outlet, and the gauge pressure is 0Pa. The flow field simulation adopts a standard k-epsilon model, a SIMPLE algorithm is adopted to couple the pressure and momentum equations, and the first-order windward format is used for all other variables in the study. When the energy residual is less than 10 -7 The sum of normalized residuals of all cells of other variables is less than 10 -4 These solutions are considered convergent.
Step 2: the positions of the potential sources and the positions of the sensors were determined, as shown in fig. 2, with 30 potential sources evenly distributed at the heights z=0.8 m and z=1.6 m, covering the breathing zone where a person sits and stands. SF (sulfur hexafluoride) 6 Used as a tracer gas, was released at the s1-4 potential source locations in fig. 2 by a porous sphere with a radius of 0.1 m. We studied the results of the calculations in the release mode of the constant source. The 5 sensors are respectively fixed below and around the exhaust port, and the height is 2m. These sensors are used to monitor the concentration of contaminants in the forward flow field and release the accompanying pulses at the sensor locations in the reverse flow field.
Step 3: simulating contaminant release. Selecting one of the potential sources s1-4 to release SF constantly 6 The gas, sensor records contaminant concentration in 2s time steps, and concentration data within 2500s is used to infer contaminant source information. The 2500s monitoring time exceeds the room ventilation time constantTo ensure that complete concentration changes are recorded, the first sensor No. 4 is a master sensor, source intensity is obtained through a regularization method, the other sensors are slave sensors, and source positions are determined through compound Bayesian reasoning.
Step 4: a response factor is obtained. Reversing the flow field using the accompanying principle, the release strength at each sensor location was 0.01kg/m 3 In this way, the response matrix of all potential sources to the sensor can be obtained quickly, with a unit rectangular satellite pulse of/s, and measuring the satellite concentration at all potential source locations. The mathematical description is as follows:
q s C(x m )＝q m C * (x s ) (4)
Wherein: q m For pollution source release intensity, x m For the source release position, C is the concentration value in the forward flow field, C * Is the accompanying concentration in the reverse flow field, x s Is the coordinates of the sensor, q s Is the intensity of the tracer released at the sensor location, the response factor
Can be calculated by the following formula:
step 5: a response matrix M (a) of all potential source-corresponding sensors is established,
m (A) is the Toeplitz matrix of A. The relationship between the source intensity q and the monitored concentration C is: c=m (a) q, where
Is a time-varying sequence of pollutant concentration monitored by the sensor at different moments, and q= [ q ] 0 ，...，q i ，...，q n ] T Is a time variation sequence of the release intensity of the pollution source, according to the steps, the pollutant concentration C and the response matrix M (A) monitored by the sensor are utilized, the regularization algorithm is utilized to solve the least square problem, and the inverse calculation is utilized to obtain q:
the regularization method selects a Ji Hong Nuofu regularization algorithm, and the regularization parameters are selected by using a GCV method. The result of the back calculation of the No. 4 main sensor is shown in fig. 3.
Step 6: bringing all potential source intensities q calculated in the step 5 into (formula 6) again to obtain residual errors
And comparing residual errors of all potential sources, wherein the minimum residual error corresponds to the position of the leakage source, and q is the leakage source intensity. />
Claims (4)
1. The method for estimating the space-time information of the indoor gaseous pollution source based on the accompanying pulse algorithm is characterized by comprising the following steps:
firstly, measuring room boundary conditions including air supply port speed, wall surface temperature and internal heat source power, and simulating a flow field in numerical simulation software;
a second step of arranging a pollutant sensor at the position of an air outlet in the room as a master sensor, and arranging a pollutant sensor at the position downstream of a main air flow in the room as a slave sensor;
third, the flow field is inverted in the numerical model, and q is respectively applied to the position of the master sensor and the position of the slave sensor in the form of pulses s Obtaining a sequence of corresponding accompanying concentration changes with time at the potential leakage source position through numerical simulation for releasing the trace gas for intensity;
fourth, the concentration of contaminants at the sensor locations in the forward flow field is proportional to the concomitant concentration at the source locations in the reverse flow field, which is mathematically described as:
q s C(x m )＝q m C * (x s ) (1)
Wherein: q m For pollution source release intensity, x m For the source release position, C is the concentration value in the forward flow field, C * Is the accompanying concentration in the reverse flow field, x s Is the coordinates of the sensor, q s Is the intensity of the trace gas released at the sensor location. And obtaining response factors of all potential source positions corresponding to the sensors in the whole room space through the accompanying concentration obtained in the third step, wherein the corresponding response vectors are as follows:
wherein:
is a response factor describing the sensor at different moments, which represents the sensitivity of the sensor to the release source,/for>
Is the accompanying concentration at different moments;
fifth, establishing response matrix M (A) of all potential source corresponding sensors,
m (A) is the Toeplitz matrix of A. The relationship between the source intensity q and the monitored concentration C is: c=m (a) q, where
Is a time-varying sequence of pollutant concentration monitored by the sensor at different moments, and q= [ q ] 0 ，...，q i ，...，q n ] T Is a time-varying sequence of the intensity of release of a source of pollution;
sixth, according to the steps, the least square problem is solved by using the pollutant concentration C monitored by the sensor and a regularization algorithm, and q is obtained by back calculation:
seventh, bringing all potential source intensities q calculated in the sixth step into (formula 3) again to obtain residual errors
And comparing residual errors of all potential sources, wherein the minimum residual error corresponds to the position of the leakage source, and q is the leakage source intensity.
2. The method for estimating the space-time information of the indoor gaseous pollution source based on the accompanying pulse algorithm according to claim 1 is characterized in that:
in said step 2, the slave sensor is arranged at a location downstream of the room master air flow.
3. The method for estimating the space-time information of the indoor gaseous pollution source based on the accompanying pulse algorithm according to claim 1 is characterized in that:
in the step 3, trace gas is released at the position of the master sensor and the position of the slave sensor respectively in the form of rectangular pulses, and the duration of the matrix pulses is the measured time resolution step length.
4. The method for estimating the space-time information of the indoor gaseous pollution source based on the accompanying pulse algorithm according to claim 1 is characterized in that:
in the step 3, the sensor records the concentration of the pollutant in 2s time steps, and the concentration data in 2500s is used for deducing the information of the source of the pollutant.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116644689A (en) * | 2023-07-24 | 2023-08-25 | 北京工业大学 | Method and system for strong and rapid back calculation of atmospheric pollution source of local scale under complex underlying surface |
| CN116975505A (en) * | 2023-09-25 | 2023-10-31 | 北京科技大学 | Method for calculating distribution of pollutants in room during pollution release of ventilation air conditioning system with return air |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116644689A (en) * | 2023-07-24 | 2023-08-25 | 北京工业大学 | Method and system for strong and rapid back calculation of atmospheric pollution source of local scale under complex underlying surface |
| CN116644689B (en) * | 2023-07-24 | 2023-11-03 | 北京工业大学 | Method and system for strong and rapid back calculation of atmospheric pollution source of local scale under complex underlying surface |
| CN116975505A (en) * | 2023-09-25 | 2023-10-31 | 北京科技大学 | Method for calculating distribution of pollutants in room during pollution release of ventilation air conditioning system with return air |
| CN116975505B (en) * | 2023-09-25 | 2024-01-02 | 北京科技大学 | Method for calculating distribution of pollutants in room during pollution release of ventilation air conditioning system with return air |
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