Computational fluid dynamics analysis and experimental study of artificially ventilated temperature sensors for meteorological observations
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Wei Jin
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Jie Yang
Abstract
To address the critical role of atmospheric temperature in climate change and disaster monitoring, enhancing measurement accuracy to 0.1 °C is essential. Current instruments are susceptible to radiation interference, resulting in errors of approximately 1 °C. This study introduces a novel temperature sensor that improves accuracy by combining natural ventilation with forced ventilation. Silver-coated aluminum plates (95 % reflectivity) and white-coated deflectors (87 % reflectivity) minimize solar radiation errors. A neural network algorithm, along with CFD simulations, further corrects radiation errors under varying weather conditions. Field tests based on the 076B ventilation device demonstrate that this new sensor reduces the average radiation error to 0.02 °C, achieving a RMSE of 0.034 °C and a MAE of 0.028 °C. The correlation coefficient (r) with the reference temperature reached 0.999, demonstrating the sensor’s high precision and providing an effective solution for reducing temperature measurement errors to below 0.1 °C.
Zusammenfassung
Um die entscheidende Rolle der atmosphärischen Temperatur im Klimawandel und bei der Katastrophenüberwachung zu adressieren, ist eine Verbesserung der Messgenauigkeit auf 0,1 °C unerlässlich. Aktuelle Instrumente sind anfällig für Strahlungsstörungen, was zu Fehlern von etwa 1 °C führt. Diese Studie stellt einen neuartigen Temperatursensor vor, der die Genauigkeit durch die Kombination von natürlicher Belüftung und erzwungener Belüftung verbessert. Silberbeschichtete Aluminiumplatten (95 % Reflektivität) und weißbeschichtete Abweiser (87 % Reflektivität) minimieren Strahlungsfehler. Ein neuronales Netzwerk-Algorithmus sowie CFD-Simulationen korrigieren zusätzlich Strahlungsfehler unter variierenden Wetterbedingungen. Feldtests basierend auf dem 076B Belüftungsgerät zeigen, dass dieser neue Sensor den durchschnittlichen Strahlungsfehler auf 0,02 °C reduziert und einen RMSE von 0,034 °C sowie einen MAE von 0,028 °C erreicht. Der Korrelationskoeffizient (r) mit der Referenztemperatur betrug 0,999, was die hohe Präzision des Sensors demonstriert und eine effektive Lösung zur Reduzierung der Temperaturmessfehler auf unter 0,1 °C bietet.
About the authors
Wei Jin received the M.S. degree in electronic and information engineering from the Nanjing University of Information Science and Technology, Nanjing, China, in 2017. From 2017 to 2022, he worked as a software architecture engineer at a technology company in Nanjing. Currently, he is pursuing the Ph.D. degree in information and communication engineering. His research interests include temperature sensor and computational fluid dynamics (CFD) simulation.
Qingquan Liu (Member, IEEE) received the Ph.D. degree in electrical engineering from the University of California at Davis, Davis, CA, USA, in 2006. From 2006 to 2008, he was an Associate Researcher with the Department of Electrical Engineering and Computer Science, Case Western Reserve University, USA. Since 2008, he has been a Full Professor with the Nanjing University of Information Science and Technology. His research interests include MEMS sensors, meteorological observation, and integrated systems.
Keya Yuan received the Ph.D. degree in control theory and control engineering from Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 2009. In 2018, he joined the School of Robotics at Beijing Union University, where he is engaged in teaching in the field of automation. His research interests include embedded technology, small signal processing for sensors, and artificial intelligence.
Jie Yang received the Ph.D. degree in atmospheric physics and atmospheric environment from Nanjing University of Information Science and Technology, Nanjing, China, in 2017. Since 2017, he has been a Research Scientist with the Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology. His research interests include meteorological sensor technology, intelligent information processing circuit, and CFD simulation.
Jie Tang received the B.S. degree in electronic and information engineering from Changzhou Institute of Technology, Changzhou, China, in 2023. He is currently pursuing the M.S. degree in electronic information with the Nanjing University of Information Science and Technology, Nanjing, China. His research interests include embedded systems.
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Research ethics: Not applicable.
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Informed consent: Informed consent was obtained from all individuals included in this study, or their legal guardians or wards.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission. Conceptualization, K.Y. and Q.L.; data curation, W.J.; formal analysis, K.Y. and J.Y.; investigation, Q.L. and W.J.; methodology, J.Y.; software, W.J.; validation, K.Y. and J.T.; writing – original draft preparation, W.J.; writing – review and editing, H.M.A.; visualization, H.M.A.; project administration, J.T.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: This work was supported by the National Natural Science Foundation of China (42275143), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX24_1488), and the Innovation and Entrepreneurship Training Program for College Students Foundation of the Jiangsu Higher Education Institutions of China (202310300007Z).
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Data availability: The raw data can be obtained on request from the corresponding author.
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© 2024 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Editorial
- Themenheft Sensoren und Messsysteme 2024
- Research Articles
- Verbesserte luftakustische Diagnose von Druckluftleckage mithilfe merkmalsbasierter maschineller Lernverfahren
- Computational fluid dynamics analysis and experimental study of artificially ventilated temperature sensors for meteorological observations
- Fortschritte der magnetometrischen Durchflussmessung: Grundsatznachweis der Fließgeschwindigkeitsmessung und Einfluss des Strömungsprofils auf die RF-Pulseffizienz
- Experimentelle Untersuchung und numerische Analyse konvektiver Wärme- und Stofftransportprozesse in Salzschmelzen unter Wirkung von Magnetfeldern und Wärmestrahlung
- Untersuchung der Blasendynamik von Flüssigkeitskavitation im Schmierfilm eines hydrodynamischen Gleitlagers
- Pressure measurement stocking for prevention and therapy support for diabetic foot ulcers
- Uncertainty evaluation using virtual experiments: bridging JCGM 101 and a Bayesian framework
Articles in the same Issue
- Frontmatter
- Editorial
- Themenheft Sensoren und Messsysteme 2024
- Research Articles
- Verbesserte luftakustische Diagnose von Druckluftleckage mithilfe merkmalsbasierter maschineller Lernverfahren
- Computational fluid dynamics analysis and experimental study of artificially ventilated temperature sensors for meteorological observations
- Fortschritte der magnetometrischen Durchflussmessung: Grundsatznachweis der Fließgeschwindigkeitsmessung und Einfluss des Strömungsprofils auf die RF-Pulseffizienz
- Experimentelle Untersuchung und numerische Analyse konvektiver Wärme- und Stofftransportprozesse in Salzschmelzen unter Wirkung von Magnetfeldern und Wärmestrahlung
- Untersuchung der Blasendynamik von Flüssigkeitskavitation im Schmierfilm eines hydrodynamischen Gleitlagers
- Pressure measurement stocking for prevention and therapy support for diabetic foot ulcers
- Uncertainty evaluation using virtual experiments: bridging JCGM 101 and a Bayesian framework
