Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Evaluating the performance of an Inexact Newton method with a preconditioner for dynamic building system simulation

Published

Author(s)

Zhelun Chen, Jin Wen, Anthony J. Kearsley, Amanda Pertzborn

Abstract

Efficiently, robustly, and accurately solving systems of nonlinear differential algebraic equations (DAE) for dynamic building system simulation is becoming more important due to the increasing demand to simulate large-scale problems, including the integration of multiple buildings. Currently, many of the tools for dynamic building system simulation employ direct methods to solve simulation DAEs. These methods may fail to converge for stiff problems or when iterates are far from a solution. Moreover, as problem size grows, they are less likely to meet the increased memory requirements associated with large-scale problems. Newton-Krylov methods constitute an interesting option to satisfy the computational needs of large-scale dynamic building system simulation. Here, a preconditioned Newton-Krylov method is applied to a series of test problems, the numerical performance of which is presented and compared to the frequently employed Powell's Hybrid Method.
Citation
Journal of Building Performance Simulation
Volume
15
Issue
1

Keywords

Newton-Krylov methodHVAC simulationbuilding systemlarge-scale simulation

Citation

Chen, Z. , Wen, J. , Kearsley, A. and Pertzborn, A. (2021), Evaluating the performance of an Inexact Newton method with a preconditioner for dynamic building system simulation, Journal of Building Performance Simulation, [online], https://doi.org/10.1080/19401493.2021.2007285 (Accessed December 10, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created December 26, 2021, Updated September 28, 2022