Correlating thermal balance test results with a thermal mathematical model using evolutionary algorithms

The results of a series of thermal balance tests have been correlated with a thermal mathematical model. Three different optimization algorithms have been used for this: Monte Carlo simulation, Genetic Algorithm and Adaptive Particle Swarm Optimization. Based on a correlation criterion that minimizes the temperature difference between tests and model, the correlation can be optimized. APSO proved to be most useful, for its ability to optimize both locally and globally, its ability to search in a continuous search space, and its fast convergence. In this research, an average residual error of only 1.1◦C was found. In general, optimization algorithms are feasible for thermal balance test results correlation. Comparing to manual correlation, optimization algorithms take less time, yield better results since they scan the entire search space, and are more flexible since several uncertain parameters can be varied at the same time. However, optimization techniques tend to find mathematical solutions rather than physical solutions, so boundaries on the parameter space are needed, for example from other tests. Even though this research indicates a good correlation, the set-up was relatively small (only 129 nodes and 24 relevant temperature measurements and comparisons) and comprehensible. For larger (satellite) test programs, the thermal network might be less easily understood and contain more unknowns and uncertainties. In that case a correlation using optimization techniques might be less optimal. Some engineering judgement of the thermal engineer will always be needed. Note: An article explaining the method in more detail is included behind the presentation. 27th European Space Thermal Analysis Workshop 3–4 December 2013 1 Correlating thermal balance test results using optimisation algorithms Correlating thermal balance test results using optimisation algorithms Niek van Zijl, Bruin Benthem, Barry Zandbergen 2 Correlating thermal balance test results using optimisation algorithms Use (evolutionary) optimisation algorithms for TB test correlation! • In industry so far TB test correlation was always done manually • Nowadays, with increase in computer speed optimisation algorithms are possible • This research shows that optimisation algorithms, in particular APSO, are very much suited for this correlation! • This presentation will prove this, based on a series of tests performed at Dutch Space Correlating thermal balance test results with a thermal mathematical model using evolutionary algorithms 91 27th European Space Thermal Analysis Workshop 3–4 December 2013 3 Correlating thermal balance test results using optimisation algorithms A solar panel is stacked before launch Unfolded solar panel Stacked solar panel 4 Correlating thermal balance test results using optimisation algorithms The panel is held together using an HDRM Hold Down and Release Mechanism (HDRM) 92 Correlating thermal balance test results with a thermal mathematical model using evolutionary algorithms 27th European Space Thermal Analysis Workshop 3–4 December 2013 5 Correlating thermal balance test results using optimisation algorithms Several TB tests were performed for TMM validation • Validate the TMM of the HDRM by determining the unknown couplings and optical properties • Reduce endfitting temperature uncertainty Test goals 6 Correlating thermal balance test results using optimisation algorithms In total 4 dedicated tests were performed • In total 4 tests have been performed • 3 dedicated tests on parts of an HDRM to determine 8 thermal parameters • Final test on entire HDRM to validate earlier found values and correlate 8 other parameters • First two tests at Dutch Space (1 week) • Last two tests at ESTEC (2 weeks) Correlating thermal balance test results with a thermal mathematical model using evolutionary algorithms 93 27th European Space Thermal Analysis Workshop 3–4 December 2013 7 Correlating thermal balance test results using optimisation algorithms Thermocouples were placed in accordance with the TMM HDRM model versus measurement locations thermocouples 8 Correlating thermal balance test results using optimisation algorithms 6 test phases have been performed on the total set-up Phase Cold plate [°C] Solar simulator flux White cap D1 T1 60°C 1423 W/m2 no D1 T2 60°C 1322 W/m2 no D1 T3 40°C 1423 W/m2 no D2 T1 60°C 1423 W/m2 yes D2 T2 60°C 1322 W/m2 yes D2 T3 40°C 1423 W/m2 yes 94 Correlating thermal balance test results with a thermal mathematical model using evolutionary algorithms 27th European Space Thermal Analysis Workshop 3–4 December 2013 9 Correlating thermal balance test results using optimisation algorithms Thermal balance test correlation aims to minimise ΔT Thermal balance test correlation: “The process of adaptation of TMM parameters in order to minimise the difference between the temperature measurements and the temperature predictions of the TMM” 10 Correlating thermal balance test results using optimisation algorithms The correlation process: minimising through adaptation predicted temperatures TMM test temperatures