Skip to main content

Robustness of ventilation systems in the control of walking-induced indoor fluctuations: Method development and case study

Abstract

Walking-induced fluctuations have a significant influence on indoor airflow and pollutant dispersion. This study developed a method to quantify the robustness of ventilation systems in the control of walking-induced fluctuation control. Experiments were conducted in a full-scale chamber with four different kinds of ventilation systems: ceiling supply and side return (CS), ceiling supply and ceiling return (CC), side supply and ceiling return (SC), and side supply and side return (SS). The measured temperature, flow and pollutant field data was (1) denoised by FFT filtering or wavelet transform; (2) fitted by a Gaussian function; (3) feature-extracted for the range and time scale disturbance; and then (4) used to calculate the range scale and time scale robustness for different ventilation systems with dimensionless equations developed in this study. The selection processes for FFT filtering and wavelet transform, FFT filter cut-off frequency, wavelet function, and decomposition layers are also discussed, as well as the threshold for wavelet denoising, which can be adjusted accordingly if the walking frequency or sampling frequency differs from that in other studies. The results show that for the flow and pollutant fields, the use of a ventilation system can increase the range scale robustness by 19.7%–39.4% and 10.0%–38.8%, respectively; and the SS system was 7.0%–25.7% more robust than the other three ventilation systems. However, all four kinds of ventilation systems had a very limited effect in controlling the time scale disturbance.

References

  • Agirman A, Cetin YE, Avci M, et al. (2020). Effect of air exhaust location on surgical site particle distribution in an operating room. Building Simulation, 13: 979–988.

    Article  Google Scholar 

  • Bachman G, Narici L, Beckenstein E (2012). Fourier and Wavelet Analysis. New York: Springer.

    MATH  Google Scholar 

  • Brohus H, Balling KD, Jeppesen D (2006). Influence of movements on contaminant transport in an operating room. Indoor Air, 16: 356–372.

    Article  Google Scholar 

  • Cao S-J, Cen D, Zhang W, et al. (2017). Study on the impacts of human walking on indoor particles dispersion using momentum theory method. Building and Environment, 126: 195–206.

    Article  Google Scholar 

  • Chang L, Tu S, Ye W, et al. (2017). Dynamic simulation of contaminant inleakage produced by human walking into control room. International Journal of Heat and Mass Transfer, 113: 1179–1188.

    Article  Google Scholar 

  • Dai H, Zhao B (2020). Association of the infection probability of COVID-19 with ventilation rates in confined spaces. Building Simulation, 13: 1321–1327.

    Article  Google Scholar 

  • Daubechies I (1992). Ten Lectures on Wavelets. Philadelphia, PA, USA: Society for Industrial and Applied Mathematics.

    MATH  Book  Google Scholar 

  • Deng N, Jiang CS (2012). Selection of optimal wavelet basis for signal denoising. In: Proceedings of the 9th International Conference on Fuzzy Systems and Knowledge Discovery, Chongqing, China.

  • Desai PS, Sawant N, Keene A (2021). On COVID-19-safety ranking of seats in intercontinental commercial aircrafts: A preliminary multiphysics computational perspective. Building Simulation, 14: 1585–1596.

    Article  Google Scholar 

  • Dong Y, Zhu L, Li S, et al. (2021). Optimal design of building openings to reduce the risk of indoor respiratory epidemic infections. Building Simulation, https://doi.org/10.1007/s12273-021-0842-3.

  • Gothwal H, Kedawat S, Kumar R (2011). Cardiac arrhythmias detection in an ECG beat signal using fast Fourier transform and artificial neural network. Journal of Biomedical Science and Engineering, 4: 289–296.

    Article  Google Scholar 

  • Guo H (2011). A simple algorithm for fitting a Gaussian function [DSP tips and tricks]. IEEE Signal Processing Magazine, 28: 134–137.

    Article  Google Scholar 

  • Hang J, Li Y, Jin R (2014). The influence of human walking on the flow and airborne transmission in a six-bed isolation room: Tracer gas simulation. Building and Environment, 77: 119–134.

    Article  Google Scholar 

  • He H, Wang Z, Tan Y (2015). Noise reduction of ECG signals through genetic optimized wavelet threshold filtering. In: Proceedings of IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), Shenzhen, China.

  • Ji W, Chen, Zhao B (2021). A comparative study of the effects of ventilation-purification strategies on air quality and energy consumption in Beijing, China. Building Simulation, 14: 813–825.

    Article  Google Scholar 

  • John AM, Khanna K, Prasad RR, et al. (2020). A Review on Application of Fourier Transform in Image Restoration. In: Proceedings of the Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India.

  • Kong X, Guo C, Lin Z, et al. (2021). Experimental study on the control effect of different ventilation systems on fine particles in a simulated hospital ward. Sustainable Cities and Society, 73: 103102.

    Article  Google Scholar 

  • Kumar A, Singh M (2015). Optimal selection of wavelet function and decomposition level for removal of ECG signal artifacts. Journal of Medical Imaging and Health Informatics, 5: 138–146.

    Article  Google Scholar 

  • Liu X, Han G (2018). Information-theoretic extensions of the Shannon-Nyquist sampling theorem. arXiv:1810.08089.

  • Liu Z, Liu H, Rong R, et al. (2020). Effect of a circulating nurse walking on airflow and bacteria-carrying particles in the operating room: An experimental and numerical study. Building and Environment, 186: 107315.

    Article  Google Scholar 

  • Liu T, Guo Y, Hao X, et al. (2021a). Evaluation of an innovative pediatric isolation (PI) bed using fluid dynamics simulation and aerosol isolation efficacy. Building Simulation, 14: 1543–1552.

    Article  Google Scholar 

  • Liu Z, Zhu H, Song Y, et al. (2021b). Quantitative distribution of human exhaled particles in a ventilation room. Building Simulation, https://doi.org/10.1007/s12273-021-0836-1.

  • Liu S, Zhao X, Nichols SR, et al. (2022). Evaluation of airborne particle exposure for riding elevators. Building and Environment, 207: 108543.

    Article  Google Scholar 

  • Lv L, Wu Y, Cao C, et al. (2021). Impact of different human walking patterns on flow and contaminant dispersion in residential kitchens: Dynamic simulation study. Building Simulation, https://doi.org/10.1007/s12273-021-0844-1.

  • Mallat SG (1989). A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11: 674–693.

    MATH  Article  Google Scholar 

  • Massarotti N, Mauro A, Mohamed S, et al. (2021). Fluid dynamic and thermal comfort analysis in an actual operating room with unidirectional airflow system. Building Simulation, 14: 1127–1146.

    Article  Google Scholar 

  • Mazumdar S, Poussou SB, Lin CH, et al. (2011). Impact of scaling and body movement on contaminant transport in airliner cabins. Atmospheric Environment, 45: 6019–6028.

    Article  Google Scholar 

  • McKeen P, Liao Z (2022). The influence of airtightness on contaminant spread in MURBs in cold climates. Building Simulation, 15: 249–264.

    Article  Google Scholar 

  • Mei X, Zeng C, Gong G (2021). Predicting indoor particle dispersion under dynamic ventilation modes with high-order Markov chain model. Building Simulation, https://doi.org/10.1007/s12273-021-0855-y.

  • Ngui WK, Leong MS, Hee LM, et al. (2013). Wavelet analysis: Mother wavelet selection methods. Applied Mechanics and Materials, 393: 953–958.

    Article  Google Scholar 

  • Paskaranandavadivel N, O’Grady G, Du P, et al. (2013). Comparison of filtering methods for extracellular gastric slow wave recordings. Neurogastroenterology and Motility, 25: 79–83.

    Article  Google Scholar 

  • Poussou SB, Mazumdar S, Plesniak MW, et al. (2010). Flow and contaminant transport in an airliner cabin induced by a moving body: Model experiments and CFD predictions. Atmospheric Environment, 44: 2830–2839.

    Article  Google Scholar 

  • Rao KR, Kim DN, Hwang JJ (2010). Fast Fourier Transform: Algorithms and Applications. Dordrecht, Netherlands: Springer.

    MATH  Book  Google Scholar 

  • Ren J, Wade M, Corsi RL, et al. (2020). Particulate matter in mechanically ventilated high school classrooms. Building and Environment, 184: 106986.

    Article  Google Scholar 

  • Ren J, He J, Kong X, et al. (2022a). A field study of CO2 and particulate matter characteristics during the transition season in the subway system in Tianjin, China. Energy and Buildings, 254: 111620.

    Article  Google Scholar 

  • Ren J, Tang M, Novoselac A (2022b). Experimental study to quantify airborne particle deposition onto and resuspension from clothing using a fluorescent-tracking method. Building and Environment, 209: 108580.

    Article  Google Scholar 

  • Resnikoff HL, Wells RO Jr (1998). The mallat algorithm. Wavelet Analysis. New York: Springer.

    Book  Google Scholar 

  • Ruikar SD, Doye DD (2011). Wavelet based image denoising technique. International Journal of Advanced Computer Science and Applications, 2(3): 49–53.

    Google Scholar 

  • Satheesan MK, Mui KW, Wong LT (2020). A numerical study of ventilation strategies for infection risk mitigation in general inpatient wards. Building Simulation, 13: 887–896.

    Article  Google Scholar 

  • Shemi PM, Shareena EM (2016). Analysis of ECG signal denoising using discrete wavelet transform. In: Proceedings of IEEE International Conference on Engineering and Technology (ICETECH), Coimbatore, India.

  • Sifuzzaman M, Islam M, Ali M (2009). Application of wavelet transform and its advantages compared to Fourier transform.

  • Srivastava S, Zhao X, Manay A, et al. (2021). Effective ventilation and air disinfection system for reducing coronavirus disease 2019 (COVID-19) infection risk in office buildings. Sustainable Cities and Society, 75: 103408.

    Article  Google Scholar 

  • Tao Y, Inthavong K, Tu JY (2017). Dynamic meshing modelling for particle resuspension caused by swinging manikin motion. Building and Environment, 123: 529–542.

    Article  Google Scholar 

  • Veer K, Agarwal R (2015). Wavelet and short-time Fourier transform comparison-based analysis of myoelectric signals. Journal of Applied Statistics, 42: 1591–1601.

    MathSciNet  MATH  Article  Google Scholar 

  • Wang J, Chow TT (2011). Numerical investigation of influence of human walking on dispersion and deposition of expiratory droplets in airborne infection isolation room. Building and Environment, 46: 1993–2002.

    Article  Google Scholar 

  • Wang L, Xu L, Feng S, et al. (2013). Multi-Gaussian fitting for pulse waveform using Weighted Least Squares and multi-criteria decision making method. Computers in Biology and Medicine, 43: 1661–1672.

    Article  Google Scholar 

  • Wang H, Tian C, Wang W, et al. (2019). Temporal cross-correlations between ambient air pollutants and seasonality of tuberculosis: a time-series analysis. International Journal of Environmental Research and Public Health, 16: 1585.

    Article  Google Scholar 

  • Weisberg M (2006). Robustness analysis. Philosophy of Science, 73: 730–742.

    MathSciNet  Article  Google Scholar 

  • Whyte W, Whyte WM, Blake S, et al. (2013). Dispersion of microbes from floors when walking in ventilated rooms. International Journal of Ventilation, 12: 271–284.

    Article  Google Scholar 

  • Wu TX, Hua HX (2014). Mechanical Vibration. Beijing: Tsinghua University Press. (in Chinese)

    Google Scholar 

  • Wu W, Lin Z (2015). Experimental study of the influence of a moving manikin on temperature profile and carbon dioxide distribution under three air distribution methods. Building and Environment, 87: 142–153.

    Article  Google Scholar 

  • Ye J, Qian H, Ma J, et al. (2021). Using air curtains to reduce short-range infection risk in consulting ward: A numerical investigation. Building Simulation, 14: 325–335.

    Article  Google Scholar 

  • Yin Y, He J, Zhao L, et al. (2022). Identification of key volatile organic compounds in aircraft cabins and associated inhalation health risks. Environment International, 158: 106999.

    Article  Google Scholar 

  • Zhang D (2019).Wavelet transform. In Fundamentals of Image Data Mining.

  • Zhao RM, Cui HM (2015). Improved threshold denoising method based on wavelet transform, In: Proceedings of the 7th International Conference on Modelling, Identification and Control (ICMIC), Sousse, Tunisia.

Download references

Acknowledgements

This study was supported by the National Natural Science Foundation of China (No. 52108075), Natural Science Foundation of Hebei Province, China (No. E2020202147), S&T Program of Hebei (No. 216Z4502G), Fundamental Research Funds of Hebei University of Technology (No. JBKYTD2003) and Hebei Province Funding Project for Returned Scholars, China (No. C20190507).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiangfei Kong.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ren, J., He, J., Kong, X. et al. Robustness of ventilation systems in the control of walking-induced indoor fluctuations: Method development and case study. Build. Simul. 15, 1645–1660 (2022). https://doi.org/10.1007/s12273-022-0888-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12273-022-0888-x

Keywords

  • FFT filtering
  • wavelet denoising
  • Gaussian fitting
  • feature extraction
  • robust