TY - GEN
T1 - An advanced high performance replacement for (SINDA) systems improved numerical differencing analyzer
AU - Vander Veer, Joseph R.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/7
Y1 - 2017/6/7
N2 - Space applications often utilize the NASA developed Systems Improved Numerical Differencing Analyzer (SINDA) for thermal systems analysis. SINDA is primarily meant to be a computer programming language designed to assist in the solution of finite difference problems. However, due to a substantial desire to maintain backwards compatibility, SINDAs development environment and performance have lagged behind modern capabilities and techniques. Some such techniques are object oriented programming, integrated debugger, and parallel processing. In the development of the enhanced Multi-Mission Radioisotope Thermoelectric Generator (eMMRTG), a substantial need for the development of a modern SINDA replacement was needed. This need prompted the creation of the Centralized Math Engine or CME. The theory, design, development and a few implementation examples are discussed in detail. The thermal models require substantially less time to develop, requiring approximately 80 vs 280 man-hours. A specific thermal model showed a speed up of 18.75x versus the SINDA equivalent due to the use of more sophisticated linear solvers.
AB - Space applications often utilize the NASA developed Systems Improved Numerical Differencing Analyzer (SINDA) for thermal systems analysis. SINDA is primarily meant to be a computer programming language designed to assist in the solution of finite difference problems. However, due to a substantial desire to maintain backwards compatibility, SINDAs development environment and performance have lagged behind modern capabilities and techniques. Some such techniques are object oriented programming, integrated debugger, and parallel processing. In the development of the enhanced Multi-Mission Radioisotope Thermoelectric Generator (eMMRTG), a substantial need for the development of a modern SINDA replacement was needed. This need prompted the creation of the Centralized Math Engine or CME. The theory, design, development and a few implementation examples are discussed in detail. The thermal models require substantially less time to develop, requiring approximately 80 vs 280 man-hours. A specific thermal model showed a speed up of 18.75x versus the SINDA equivalent due to the use of more sophisticated linear solvers.
UR - https://www.scopus.com/pages/publications/85021194870
UR - https://www.scopus.com/pages/publications/85021194870#tab=citedBy
U2 - 10.1109/AERO.2017.7943890
DO - 10.1109/AERO.2017.7943890
M3 - Conference contribution
AN - SCOPUS:85021194870
T3 - IEEE Aerospace Conference Proceedings
BT - 2017 IEEE Aerospace Conference
PB - IEEE Computer Society
T2 - 2017 IEEE Aerospace Conference, AERO 2017
Y2 - 4 March 2017 through 11 March 2017
ER -