Identifying decaying contaminant source location in building HVAC system using the adjoint probability method

Zhai, Zhiqiang; Qi, Jin

doi:10.1007/s12273-018-0453-9

Cited by 14 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Neupauer et al [ 49 , 58 , 59 ] applied the adjoint method to identify the location of groundwater pollutant sources and release times. Zhai et al [ 60 , 61 ] used the adjoint method to achieve rapid identification of indoor pollution sources. Xue et al [ 62 ] further improved the adjoint method for identifying multiple pollutant sources in open spaces.…”

Section: Introductionmentioning

confidence: 99%

Locating leakage in pipelines based on the adjoint equation of inversion modeling

Chang

Lin

et al. 2023

Heliyon

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Locating leakage in pipelines based on the adjoint equation of inversion modeling

Chang

Lin

et al. 2023

Heliyon

View full text Add to dashboard Cite

“…Liu et al applied the adjoint method to indoor environments, illustrating the method of inverse modeling using the example of air pollution in office spaces and aircraft cabins [8,9].Subsequently, adjoint probabilistic methods have been extensively validated in various contexts. As summarized by Zhai et al [10], an adjoint probabilistic approach is used to efficiently identify the location of attenuation sources in a building HVAC system. Zhou [11] et al Use CFD simulations combined with adjoint methods to identify the location of leak sources in an underground tunnel.…”

Section: Introductionmentioning

confidence: 99%

Gaseous Pollutent Source Term Estimation Based on Adjoint Probability and Regularization Method

Jing

et al. 2022

E3S Web Conf.

View full text Add to dashboard Cite

Fast and accurate identification of source locations and release rates is particularly important for improving indoor air quality and ensuring the safety and health of people. Existing methods based on adjoint probability are difficult to distinguish the release rate of dynamic sources, and optimization algorithms based on regularization are limited to analysing only a small amount of potential pollutant source information. Therefore, this study proposed an algorithm combining adjoint equations and regularization models to identify the location and release intensity of pollutant sources in the entire computational domain of a room. Based on the validated indoor CFD computational model, we first obtained a series of response matrices corresponding to the sensor position by solving the adjoint equation, and then used the regularization method and Bayesian inference to extrapolate the release rate and location of dynamic pollutant source in the room. The results shown that the proposed algorithm is convenient and feasible to identify the location and intensity of the indoor pollutant source. Compared with the real source intensity, the identification of constant source intensity is lower than the error threshold (10%) in 97.4% of the time nodes, and the identification of periodic source is lower than the error threshold (10%) in 95.4% of the time nodes. This research provides a new method and perspective for the estimation of indoor pollutant source information.

show abstract

“…In previous studies, Liu and Zhai 15 proposed the probability method to study pollutant source-finding problems with multi-zone model. On the basis of the probability method, Zhai and Jin 16 used the multi-zone model software CONTAM-based inverse method and effectively located the pollutant source in the building HVAC duct system. For the joint simulation method, the multi-zone model obviously cannot achieve the details of the velocity field.…”

Section: Introductionmentioning

confidence: 99%

Prompt location of indoor instantaneous air contaminant source through multi-zone model-based probability method by utilizing airflow data from coarse-grid CFD model

Chen

Wang

Dai

et al. 2021

Indoor and Built Environment

View full text Add to dashboard Cite

In order to ensure indoor air quality safety, locating the airborne contaminant sources accurately and quickly is extremely important so that timely measures can be taken to undermine the spread of pollutant and even eliminate the negative effects. Previous studies have shown that the multi-zone model can greatly reduce the inverse calculation time. However, the multi-zone model cannot describe the details of the indoor velocity field, which limits its application in complex multi-zone or large space buildings. On the premise of the accuracy and computational speed, based on the joint probability method, this study adopted the coarse-grid CFD method to speed up the process of acquiring the indoor airflow field, together with the multi-zone model method, to perform the inverse calculation of indoor airborne contaminant source location. In the backward calculation process, we conducted the ‘transpose' of the velocity field to obtain adjoint matrix, instead of computing ‘negative' of the velocity vector to save the calculation time. A two-dimensional ventilation model was utilized to validate the method, which proved the accuracy and time-saving potential of it. This study provides theoretical and practical prospect for the real-time inverse calculation of locating the indoor airborne contaminant sources.

show abstract

Identifying decaying contaminant source location in building HVAC system using the adjoint probability method

Cited by 14 publications

References 23 publications

Locating leakage in pipelines based on the adjoint equation of inversion modeling

Locating leakage in pipelines based on the adjoint equation of inversion modeling

Gaseous Pollutent Source Term Estimation Based on Adjoint Probability and Regularization Method

Prompt location of indoor instantaneous air contaminant source through multi-zone model-based probability method by utilizing airflow data from coarse-grid CFD model

Contact Info

Product

Resources

About