Developments in Ceramic Materials Research

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M. A. Sheik
exceeds 1 Million in the HITCO Unit Cell model. Moreover, the real simulation for HITCO
samples may definitely involve the analysis of a full laminate such as the one formed at the
end in Figure 27. This would mean the multiplication of element count for a single RVE Unit
Cell with the total number of Unit Cells used to form the laminate, e.g., 18 from the example
shown in Figure 27. It is emphasized here that even in the presence of a much more powerful
PC - 3 GHz, single processor, 1GB physical memory - a thermal analysis with just two Unit
Cells joined together has not been successful, clearly exhibiting the limitation of a single
processor PC when ABAQUS/CAE [18] is conducting a simple steady-state analysis on such
a model.
The next option used is an SGI Onyx 300 machine with 32 SGI R14000 MIPS processors
running at 600 Megahertz. Each such processor having a peak speed of 1.2 Gigaflops makes
the total peak speed of the machine of approx. 38 Gigaflops. But the system is a shared
memory machine with 16 Gigabytes of physical memory, permitting both shared memory and
distributed memory programming models to all users simultaneously. Although the analyses
conducted on this machine have already yielded better memory management scenarios
compared to single processor Windows PC, but still certain ABAQUS code limitations
regarding thermal analysis mentioned in the software reference documentation has made the
scope for speedup gain rather narrow. Proof of this behavior has been seen through initial
runs conducted using increasing number of parallel processors. Some trends for ABAQUS
and its solver performance are clearly seen in Table 10. Here the comparison is made with
tests that were run on the same Unit Cell with a monotonic tensile loading. Increased speed
up hints at the advantage gained by the use of multiprocessor platform for the mechanical
analysis of HITCO Unit Cell model and larger models are considered to be solved faster here
but only in the regime of mechanical loadings. For thermal application simulation, this study
suggests that in order to benefit from parallelization which is inevitable therefore crucial for
larger models, further investigation are necessary in improving solver performance. This has
also led the present study towards another domain of parallelization specially coded for finite
element modelling and it is being pursued simultaneously now.
Table 10. Speed-up observed for, a comparison (percentage) between 1-D steady-state
heat transfer analyses for thermal conductivity measurement and 1-D monotonic tensile
loading for determining composite stiffness employing parallel processing
Processors Thermal Analysis (%) Mechanical Analysis (%)
2 7 12
4 9 20
8 10 24
5.1. Parallel Processing
Results are now presented of thermal analysis of a benchmark heat flow problem, as
defined in Figure 34, in a multi-processor environment. Figure 35 shows the analysis speedup
against the number of processors with an increasing mesh size.