Rapid identification of multiple constantly-released contaminant sources in indoor environments with unknown release time

Cai, Hao; Li, Xianting; Chen, Zhi Long; Wang, Mingyang

doi:10.1016/j.buildenv.2014.06.006

Cited by 35 publications

(6 citation statements)

References 44 publications

(26 reference statements)

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“…The probability-based CFD modeling which uses the results of CFD calculation for all the potential point sources has the same limitation though the method could identify the source location with very high accuracy by using the limited number of sensor(s) (Liu and Zhai 2008). Assuming that the potential locations of source(s) are limited, rapid identification of the source location(s) can be performed by using the limited number of sensors (Cai et al 2012(Cai et al , 2014. These studies indicate that the accuracy of source estimation can be improved by imposing a restriction about the source information.…”

Section: Introductionmentioning

confidence: 99%

Estimation of indoor contamination source location by using variational continuous assimilation method

et al. 2015

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Information on concentration of contaminants is important for management of indoor air quality. Recently, data assimilation techniques are used in order to accurately estimate location and intensity of contamination source in addition to concentration field. In this study, the variational continuous assimilation (VCA) method, which was originally developed in meteorological simulations, was applied to estimates of indoor air quality. The method modifies the governing equations of computational fluid dynamics (CFD) model by adding a correction term which reduces the error between original CFD calculation and observed data. In the mass conservation equation, the correction term can be assumed to be a pseudo source term. The validity of VCA method was confirmed by numerical experiments for two-dimensional steady-state calculation with the following procedures: (i) "true" concentration field was produced by CFD calculations with "true" concentration source; (ii) "pseudo-observation" data were extracted from "true" concentration field; (iii) the VCA method was applied to "false" CFD calculations without contamination source to produce "corrected" concentration field using "pseudo-observation" data; (iv) "corrected" concentration field and contamination source were compared with "true" dataset. The numerical experiments revealed the following findings: the VCA method can identify the area where the contamination source was located; and the VCA method can also reduce errors between "true" and CFD-calculated concentration field although the peak concentration was not well-estimated. These results suggest that the VCA method is a utilizable method to estimate concentration field, and location and intensity of contamination source.

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Section: Introductionmentioning

confidence: 99%

Estimation of indoor contamination source location by using variational continuous assimilation method

et al. 2015

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“…The fixed-sensor network method can apply "forward" or "backward" models. A forward model stores all potential release scenarios that are pre-simulated as a database, and then a potential indoor source can be identified by matching the pre-simulated and measured concentrations from an efficient search algorithm such as the Bayesian probability algorithm [5][6][7][8][9] or optimization algorithm [10][11][12][13][14]. By https://doi.org/10.1016/j.buildenv.2018.…”

Section: Introductionmentioning

confidence: 99%

An improved particle swarm optimization method for locating time-varying indoor particle sources

Feng

Cai

et al. 2019

Building and Environment

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A B S T R A C TThe indoor transmission of airborne particles can spread disease and have health-related and even life-threatening effects on occupants, thus necessitating effective ways to locate indoor particle sources. The identification of particle sources from concentration distributions is a difficult task because particles are often released at a time-varying rate, and particle transport mechanisms are more complex than those of gas. This study proposes an improved multi-robot olfactory search method for locating two types of time-varying indoor particle sources: 1) periodic sources such as occupants' respiratory activities and 2) decaying sources such as laboratory leaky containers with hazardous chemicals. The method considers both particle concentrations and indoor air velocities by including an upwind term in the standard particle swarm optimization (PSO) algorithm, preventing robots from becoming trapped into a local optimum, which occurs when using other algorithms. We also considered two ventilation types (mixing ventilation and displacement ventilation) when particles are emitted from different source types, comprising four scenarios. For each scenario, particle concentration and air velocity were simulated using computational fluid dynamics (CFD) and then fed to the PSO algorithm for source localization. In addition, we validated the CFD approach for one scenario by comparing experimental data (e.g., velocities and particle concentrations) under laboratory settings. The results showed that the proposed method can locate the two types of particle sources within approximately 55 s, and the success rates of source localization exceeding 96%, which is a much higher level than levels achieved from the standard PSO and wind utilization II algorithms.

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“…However, under some circumstances, it is difficult to obtain the initial conditions based on prediction because of unknown boundary conditions, such as an unknown release number, location and rate of release from the contaminant sources. [24][25][26] In this situation, a limited number of sensors should be arranged in the room to record the initial concentration distribution. Nevertheless, this may provide insufficient information, as compared to the predicted initial concentration, since the concentrations are only determined at the specific sensor locations at a given time.…”

Section: Introductionmentioning

confidence: 99%

Rapid prediction of the transient effect of the initial contaminant condition using a limited number of sensors

Shao

Wang

2018

Indoor and Built Environment

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Rapid prediction of a transient rise in a contaminant concentration attributed to a sudden release of a contaminant is crucial for decision-making in relation to deployment of emergency ventilation. The superposition theorem for a fixed flow field is an alternative approach that has been used to accomplish fast prediction. In this study, the accuracy of a superposition method was investigated based on a novel method to obtain the initial condition using a limited number of sensors. Sensors were placed at the centre of each divided zone to record the initial zonal concentration, and the prediction was performed in accordance to an algebraic expression that uses the measured concentrations and the index of transient accessibility of the sub-initial condition (TASIC) as inputs. The analysis of these numerical cases has led to the following conclusions: (1) the prediction accuracy is improved when the prediction time is longer, and when there are more zones and less nonuniformity for the sub-initial conditions, (2) the relative prediction deviations are larger when the initial condition is measured using sensors rather than those elicited based on the predicted initial conditions, and (3) the use of eight sensors is appropriate for the studied case in accordance to sensor cost and accuracy. The method is helpful for fast decision-making when the initial conditions are unpredictable.

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Rapid identification of multiple constantly-released contaminant sources in indoor environments with unknown release time

Cited by 35 publications

References 44 publications

Estimation of indoor contamination source location by using variational continuous assimilation method

Estimation of indoor contamination source location by using variational continuous assimilation method

An improved particle swarm optimization method for locating time-varying indoor particle sources

Rapid prediction of the transient effect of the initial contaminant condition using a limited number of sensors

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