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Multi-Domain Airflow Modeling and Ventilation Characterization Using Mobile Robots, Stationary Sensors and Machine Learning

Bennetts, Victor Hernandez and Kamarudin, Kamarulzaman and Wiedemann, Thomas and Kucner, Tomasz Piotr and Somisetty, Sai Lokesh and Lilienthal, Achim J. (2019) Multi-Domain Airflow Modeling and Ventilation Characterization Using Mobile Robots, Stationary Sensors and Machine Learning. Sensors. Multidisciplinary Digital Publishing Institute (MDPI). DOI: 10.3390/s19051119 ISSN 1424-8220

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Ventilation systems are critically important components of many public buildings and workspaces. Proper ventilation is often crucial for preventing accidents, such as explosions in mines and avoiding health issues, for example, through long-term exposure to harmful respirable matter. Validation and maintenance of ventilation systems is thus of key interest for plant operators and authorities. However, methods for ventilation characterization, which allow us to monitor whether the ventilation system in place works as desired, hardly exist. This article addresses the critical challenge of ventilation characterization - measuring and modelling air flow at micro-scales - that is, creating a high-resolution model of wind speed and direction from airflow measurements. Models of the near-surface micro-scale flow fields are not only useful for ventilation characterization, but they also provide critical information for planning energy-efficient paths for aerial robots and many applications in mobile robot olfaction. In this article we propose a heterogeneous measurement system composed of static, continuously sampling sensing nodes, complemented by localized measurements, collected during occasional sensing missions with a mobile robot. We introduce a novel, data-driven, multi-domain airflow modelling algorithm that estimates (1) fields of posterior distributions over wind direction and speed (ventilation maps, spatial domain); (2) sets of ventilation calendars that capture the evolution of important airflow characteristics at measurement positions (temporal domain); and (3) a frequency domain analysis that can reveal periodic changes of airflow in the environment. The ventilation map and the ventilation calendars make use of an improved estimation pipeline that incorporates a wind sensor model and a transition model to better filter out sporadic, noisy airflow changes. These sudden changes may originate from turbulence or irregular activity in the surveyed environment and can, therefore, disturb modelling of the relevant airflow patterns. We tested the proposed multi-domain airflow modelling approach with simulated data and with experiments in a semi-controlled environment and present results that verify the accuracy of our approach and its sensitivity to different turbulence levels and other disturbances. Finally, we deployed the proposed system in two different real-world industrial environments (foundry halls) with different ventilation regimes for three weeks during full operation. Since airflow ground truth cannot be obtained, we present a qualitative discussion of the generated airflow models with plant operators, who concluded that the computed models accurately depicted the expected airflow patterns and are useful to understand how pollutants spread in the work environment. This analysis may then provide the basis for decisions about corrective actions to avoid long-term exposure of workers to harmful respirable matter.

Item URL in elib:https://elib.dlr.de/127195/
Document Type:Article
Title:Multi-Domain Airflow Modeling and Ventilation Characterization Using Mobile Robots, Stationary Sensors and Machine Learning
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Bennetts, Victor HernandezMobile Robotics and Olfaction Lab, Örebro Universityhttps://orcid.org/0000-0001-5061-5474
Kamarudin, KamarulzamanCenter of Excellence for Advanced Sensor Technology, School of Mechatronics Engineering, Universiti Malaysia Perlishttps://orcid.org/0000-0001-7764-0821
Wiedemann, ThomasThomas.Wiedemann (at) dlr.dehttps://orcid.org/0000-0002-1740-8841
Kucner, Tomasz PiotrMobile Robotics and Olfaction Lab, Örebro UniversityUNSPECIFIED
Somisetty, Sai LokeshCenter of Excellence for Advanced Sensor Technology, School of Mechatronics Engineering, Universiti Malaysia PerlisUNSPECIFIED
Lilienthal, Achim J.Mobile Robotics and Olfaction Lab, Örebro Universityhttps://orcid.org/0000-0003-0217-9326
Date:5 March 2019
Journal or Publication Title:Sensors
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
DOI :10.3390/s19051119
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Keywords:airflow modeling; ventilation; mobile robotics; static sensor networks; environmental monitoring; machine learning
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Communication and Navigation
DLR - Research area:Raumfahrt
DLR - Program:R KN - Kommunikation und Navigation
DLR - Research theme (Project):R - Vorhaben GNSS2/Neue Dienste und Produkte
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Communication and Navigation
Institute of Communication and Navigation > Communications Systems
Deposited By: Wiedemann, Thomas
Deposited On:21 May 2019 19:30
Last Modified:14 Apr 2020 14:57

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