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A Computational Methodology for Simulating Thermal Loss Testing of the Advanced Stirling Convertor
Author(s) -
Terry V. Reid,
Scott D. Wilson,
Nicholas A. Schifer
Publication year - 2011
Publication title -
nasa sti repository (national aeronautics and space administration)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.2514/6.2011-5579
Subject(s) - stirling engine , computer science , stirling cycle , thermal , computational science , mechanical engineering , engineering , physics , thermodynamics
The U.S. Department of Energy (DOE) and Lockheed Martin Space Systems Company (LMSSC) have been developing the Advanced Stirling Radioisotope Generator (ASRG) for use as a power system for space science missions. This generator would use two high-efficiency Advanced Stirling Convertors (ASCs), developed by Sunpower Inc. and NASA Glenn Research Center (GRC). The ASCs convert thermal energy from a radioisotope heat source into electricity. As part of ground testing of these ASCs, different operating conditions are used to simulate expected mission conditions. These conditions require achieving a particular operating frequency, hot end and cold end temperatures, and specified electrical power output for a given net heat input. In an effort to improve net heat input predictions, numerous tasks have been performed which provided a more accurate value for net heat input into the ASCs, including the use of multi-dimensional numerical models. Validation test hardware has also been used to provide a direct comparison of numerical results and validate the multi-dimensional numerical models used to predict convertor net heat input and efficiency. These validation tests were designed to simulate the temperature profile of an operating Stirling convertor and resulted in a measured net heat input of 244.4 W. The methodology was applied to the multi-dimensional numerical model which resulted in a net heat input of 240.3 W. The computational methodology resulted in a value of net heat input that was 1.7 percent less than that measured during laboratory testing. The resulting computational methodology and results are discussed.

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