Machine learning for thermal conductivity estimation from optical image
Kiuru, R.; Wawita Widanalage Don, D.M. (2025-05-12)
CRC press
12.05.2025
Johansson, F., Ansell, A., Johansson, D., Funehag, J., & Norrman, J. (Eds.). (2025). Tunnelling into a Sustainable Future – Methods and Technologies: Proceedings of the ITA-AITES World Tunnel Congress 2025 (WTC 2025), 9-15 May 2025, Stockholm, Sweden (1st ed.), 1082-1086. CRC Press. https://doi.org/10.1201/9781003559047
https://creativecommons.org/licenses/by-nc-nd/4.0/
© 2025 The Author(s), ISBN 978-1-032-90462-7. Open Access: www.taylorfrancis.com, CC BY-NC-ND 4.0 license.
https://creativecommons.org/licenses/by-nc-nd/4.0/
© 2025 The Author(s), ISBN 978-1-032-90462-7. Open Access: www.taylorfrancis.com, CC BY-NC-ND 4.0 license.
https://creativecommons.org/licenses/by-nc-nd/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202505273960
https://urn.fi/URN:NBN:fi:oulu-202505273960
Tiivistelmä
Abstract
Thermal conductivity is a parameter of interest in a wide variety of applications, such as the geological final disposal of spent nuclear fuel or geothermal energy. Traditional methods for estimating thermal conductivity rely on laboratory testing or time-consuming in-situ measurements that provide at best sparse data. Thus, there exists a demand for methods that can produce reliable and comprehensive thermal conductivity data faster and more cost-effectively. This study continues to explore the possibility of predicting thermal conductivity based on optical image data using machine learning.
Thermal conductivity is a parameter of interest in a wide variety of applications, such as the geological final disposal of spent nuclear fuel or geothermal energy. Traditional methods for estimating thermal conductivity rely on laboratory testing or time-consuming in-situ measurements that provide at best sparse data. Thus, there exists a demand for methods that can produce reliable and comprehensive thermal conductivity data faster and more cost-effectively. This study continues to explore the possibility of predicting thermal conductivity based on optical image data using machine learning.
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Ellei muuten mainita, aineiston lisenssi on https://creativecommons.org/licenses/by-nc-nd/4.0/