Node Prediction for Thermal Networks (NOPTHER) is a computer program that predicts steady-state temperatures at un- observed nodes of a thermal network, given noisy values of the temperatures measured at observed nodes. The program is based partly on modeling of the heat fluxes among nodes as sums of external heat loads, conduction terms (products of conduction coefficients and differences among nodal temperatures), and radiation terms (products of radiation coefficients and differences among the fourth powers of nodal temperatures). The temperature-prediction problem is formulated as an optimization problem — more specifically as a nonlinear least-squares minimization problem with a single quadratic constraint imposed by the measured temperatures. The problem is solved by the method of Lagrange multipliers. NOPTHER incorporates algorithms that find local minima of a cost functional through Newton iteration. What distinguishes these algorithms from other such algorithms is that they exploit specific characteristics of the temperature-prediction problem that enable the use of a fast and memory-efficient computational method. The algorithms have been shown to be at least an order of magnitude faster than are prior algorithms used for the same purpose.
This program was written by Mark Milman and Miltiadis Papalexandris of Caltech for NASA’s Jet Propulsion Laboratory.
This software is available for commercial licensing. Please contact Don Hart of the California Institute of Technology at (818) 393- 3425. Refer to NPO-30173.
This Brief includes a Technical Support Package (TSP).
Program Predicts Temperatures at Nodes of a Thermal Network
(reference NPO-30173) is currently available for download from the TSP library.
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Overview
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NOPTHER is designed to predict temperatures at unobserved nodes in a thermal network by utilizing data from observed nodes. It employs optimization techniques to efficiently analyze thermal networks, which is crucial for various applications in engineering and space exploration. The software aims to enhance the accuracy and speed of thermal predictions, thereby improving the design and operation of thermal systems.
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