Solutions to mitigate the impact of measurement noise on the air pollution source strength estimation in a multi-zone building

Li, Fēi; Liu, Xiaoran; Liu, Jinxiang; Cai, Hao; Wang, Haidong; Zhang, Kai; Dai, Chunxiang

doi:10.1007/s12273-020-0635-0

Cited by 11 publications

(3 citation statements)

References 28 publications

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“…The computations were considered to have converged when the scaled velocity and continuity residuals approached 10 −3 and the energy residuals reached 10 −6 . The governing equations were solved using the SIMPLE algorithm in the commercial CFD software ANSYS FLUENT 2021 R1 34–37 …”

Section: Methodsmentioning

confidence: 99%

A model to evaluate ozone distribution and reaction byproducts in aircraft cabin environments

Yuan

Pei

et al. 2022

Indoor Air

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Commercial aircraft cabins have a unique closed environment. Cabin air quality is a significant factor that affects the comfort and health of passengers and crew members. 1-3 A vital issue for improving cabin air quality is controlling atmospheric pollutants, such as ozone, entering the cabin. The air in the upper atmosphere contains relatively high levels of ozone, exceeding hundreds of ppb, 4 which can enter the cabin through the supply air and cause health hazards to passengers and crew members, 5 stimulate respiratory responses, and aggravate asthma symptoms. [6][7][8] In the 1980s, in response to health concerns, the Federal Aviation Administration (FAA) established standards (FAR 25.832) limiting levels of ozone in aircraft cabins, which have remained effective until now. 9,10 The FAA states that cabin ozone concentrations during flight must not exceed (1) 0.25 parts per million (ppm) by volume, sea level equivalent, at any time above flight level 320 (industry term, equivalent to 9750 m); and(2) 0.1 ppm by volume, sea level equivalent, time-weighted average during any 3-hour interval above flight level 270 (equivalent to 8230 m). 10 Furthermore, as a strong oxidant, ozone can react with unsaturated organics in cabin air, human skin, and cabin materials to generate oxidation products, [11][12][13] which can be inhaled or diffuse through the skin, and then enter the bloodstream, causing adverse health effects. 14,15

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Section: Methodsmentioning

confidence: 99%

A model to evaluate ozone distribution and reaction byproducts in aircraft cabin environments

Yuan

Pei

et al. 2022

Indoor Air

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“…Compared to Eulerian and Lagrangian methods, Markov chain model has been proved to be able to provide a much higher computing speed (Chen et al 2015). Other investigations have further accelerated the calculation process (Mei et al 2017;Liu et al 2019a) and enabled the capacity of both predicting gravity-induced particle deposition (Mei and Gong 2018) and estimating air pollution source strength (Li et al 2020). In the above-mentioned Markov chain-related investigations, there is one important assumption: Markov chain model is a first-order model.…”

Section: Introductionmentioning

confidence: 99%

Predicting indoor particle dispersion under dynamic ventilation modes with high-order Markov chain model

2021

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Mechanical and natural ventilations are effective measures to remove indoor airborne contaminants, thereby creating improved indoor air quality (IAQ). Among various simulation techniques, Markov chain model is a relatively new and efficient method in predicting indoor airborne pollutants. The existing Markov chain model (for indoor airborne pollutants) is basically assumed as first-order, which however is difficult to deal with airborne particles with non-negligible inertial. In this study, a novel weight-factor-based high-order (second-order and third-order) Markov chain model is developed to simulate particle dispersion and deposition indoors under fixed and dynamic ventilation modes. Flow fields under various ventilation modes are solved by computational fluid dynamics (CFD) tools in advance, and then the basic first-order Markov chain model is implemented and validated by both simulation results and experimental data from literature. Furthermore, different groups of weight factors are tested to estimate appropriate weight factors for both second-order and third-order Markov chain models. Finally, the calculation process is properly designed and controlled, so that the proposed high-order (second-order) Markov chain model can be used to perform particle-phase simulation under consecutively changed ventilation modes. Results indicate that the proposed second-order model does well in predicting particle dispersion and deposition under fixed ventilation mode as well as consecutively changed ventilation modes. Compared with traditional first-order Markov chain model, the proposed high-order model performs with more reasonable accuracy but without significant computing cost increment. The most suitable weight factors of the simulation case in this study are found to be (λ1 = 0.7, λ2 = 0.3, λ3 = 0) for second-order Markov chain model, and (λ1 = 0.8, λ2 = 0.1, λ3 = 0.1) for third-order Markov chain model in terms of reducing errors in particle deposition and escape prediction. With the improvements of the efficiency of state transfer matrix construction and flow field data acquisition/processing, the proposed high-order Markov chain model is expected to provide an alternative choice for fast prediction of indoor airborne particulate (as well as gaseous) pollutants under transient flows.

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“…Pang et al (2014) and Wang and You (2015) considered the influence of sensor measurement noise while studying a single sensor to identify a constant source and found that the sampling time interval and sensor measurement error affected the source strength inverse results. Li et al (2020b) considered the influence of measurement noise and explored methods to reduce measurement noise impact during source strength estimation. They found that properly increasing the time step could reduce the effects of noise, and the performances of filters were different under different time steps.…”

Section: Introductionmentioning

confidence: 99%

Estimation of pollutant sources in multi-zone buildings through different deconvolution algorithms

Jing

et al. 2021

Build. Simul.

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Effective identification of pollution sources is particularly important for indoor air quality. Accurate estimation of source strength is the basis for source effective identification. This paper proposes an optimization method for the deconvolution process in the source strength inverse calculation. In the scheme, the concept of time resolution was defined, and combined with different filtering positions and filtering algorithms. The measures to reduce effects of measurement noise were quantitatively analyzed. Additionally, the performances of nine deconvolution inverse algorithms under experimental and simulated conditions were evaluated and scored. The hybrid algorithms were proposed and compared with single algorithms including Tikhonov regularization and iterative methods. Results showed that for the filtering position and algorithm, Butterworth filtering performed better, and different filtering positions had little effect on the inverse calculation. For the calculation time step, the optimal Tr (time resolution) was 0.667% and 1.33% in the simulation and experiment, respectively. The hybrid algorithms were found to not perform better than the single algorithms, and the SART (simultaneous algebraic reconstruction technique) algorithm from CAT (computer assisted tomography) yielded better performances in the accuracy and stability of source strength identification. The relative errors of the inverse calculation for source strength were typically below 25% using the optimization scheme.

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Solutions to mitigate the impact of measurement noise on the air pollution source strength estimation in a multi-zone building

Cited by 11 publications

References 28 publications

A model to evaluate ozone distribution and reaction byproducts in aircraft cabin environments

A model to evaluate ozone distribution and reaction byproducts in aircraft cabin environments

Predicting indoor particle dispersion under dynamic ventilation modes with high-order Markov chain model

Estimation of pollutant sources in multi-zone buildings through different deconvolution algorithms

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