Accurate estimation of temperature distributions for IR signature monitoring with a dynamic thermal model and data assimilation

B. J. A. Peet

doi:10.1117/12.2532755

17 October 2019 Accurate estimation of temperature distributions for IR signature monitoring with a dynamic thermal model and data assimilation

B. J. A. Peet

Author Affiliations +

Proceedings Volume 11158, Target and Background Signatures V; 111580C (2019) https://doi.org/10.1117/12.2532755
Event: SPIE Security + Defence, 2019, Strasbourg, France

Abstract

For infrared signature monitoring, knowledge on the surface temperature distribution of an object is required. TNO has developed a model to estimate the real-time evolution of the surface temperature distribution of an object that requires only sensor data from a standard weather station as an input. First, heat fluxes from and to surface elements are estimated based on these meteorological data. The dynamic heat balance is then solved numerically with the Finite Element Method. Radiation exchange, convection, heat conduction between surface facets and shadowing are all taken into account in the model. The model results were compared to half a year of thermocouple data of a CUBI test-object. The temperatures of all facets of the CUBI could be estimated with a diurnal root mean squared error smaller than 2 C. Similar accuracy was achieved when facets of the CUBI were cooled down or heated because they were rotated towards or from the sun. The performance of the model is mainly limited by estimates of the solar irradiance on tilted surfaces. Data assimilation techniques were used to further improve the estimates of the real-time CUBI surface temperature distribution. It is demonstrated that estimates of the temperature distribution of the CUBI are significantly improved when the data of a limited number of thermocouples is used to update the model results with a Kalman filter.

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B. J. A. Peet "Accurate estimation of temperature distributions for IR signature monitoring with a dynamic thermal model and data assimilation", Proc. SPIE 11158, Target and Background Signatures V, 111580C (17 October 2019); https://doi.org/10.1117/12.2532755

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KEYWORDS

Data modeling

Infrared signatures

Thermal modeling

Sensors

Convection

Data analysis

Error analysis

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