bachelorThesis
Guía para la Implementación de análisis en paralelo de modelos dinámicos y estáticos No lineales mediante OpenSees
Fecha
2022-11-16Autor
Coello Chica, Esteban Nicolás
Vintimilla Salinas, Xavier Santiago
Institución
Resumen
Structural engineering research seeks to predict the behavior of structures, usually similar
analyses are performed one after the other, called sequential analysis. However, it is more efficient
to perform parallel analysis, so the computer performs several simultaneous processes, reducing
both resolution times and energy costs. In spite of this, there are no documents that synthesize the
most common methodologies through practical applications of this type of analysis. For this
reason, a guide was developed for the implementation of parallel analysis for nonlinear dynamic
and static models through OpenSees, using both personal computers and clusters.
In this work, incremental dynamic (IDA) and static (Pushover) analyses were performed with a
NIST prototype of the ATC-76-1 project, and the durations, speedups, and efficiencies of various
methodologies were analyzed in order to identify the advantages and disadvantages of each one.
Parallelization (Tcl) and multiprocessing (Python) are the simplest methodologies to implement,
therefore the most recommended. However, OpenSeesMPI (Tcl) or mpi4py (Python) are useful
to achieve greater process control. The most recommended cluster is CEDIA for its computing
power, however, the management of other clusters such as Google Cloud or AWS is similar, so
the guide can be used for the management of other servers to which the user has access.
Furthermore, the commands used in the analyses, the installation and execution processes of each
methodology were detailed, and video tutorials were developed for the implementation of each
one of them, in this sense, it is expected that in future works these tools will be used to simplify
the research work, and it is proposed to investigate more powerful tools than those proposed such
as graphics cards for multiprocessing tasks with OpenSees