| Summary: | Large thermal-fluid network simulations are usually conducted for fixed input steady-state conditions
without accounting for statistical variations of these variables. This is not realistic and gives no indication
of possible deviations in results. A typical example of a large thermal-fluid network is the Pebble Bed
Modular Reactor (PBMR) power plant. To accurately predict the electricity that is going to be generated,
all the variations of the input variables must be accounted for in the simulation. Therefore a need was
identified to derive a suitable Monte Carlo type algorithm that can be applied to large thermal-fluid
network simulations.
An extensive literature survey was conducted and it was also found that Monte Carlo methods in general
were used extensively to predict or forecast certain events. However, it also revealed that little previous
work has been done specifically on Monte Carlo techniques applied to large thermal-fluid networks.
Other sampling techniques could also been used to conduct the sensitivity analysis, but these are usually
more complicated than the Monte Carlo technique.
This study deals with the derivation, validation and verification of a suitable Monte Carlo type algorithm
as well as an investigation into the extent of uncertainties typically found in thermal-fluid component
simulation. The latter is required as input to the algorithm. The new algorithm was implemented in
different software models and successfully verified and validated using problems with varying degrees of
complexity. Following this, the methodology was implemented into the Flownex software and applied to
simulate a comprehensive network case study namely the Micro Model of the PBMR project.
The results of Flownex along with the results of the sensitivity analysis were used to do certain
comparisons to determine the integrity of the algorithm. The Flownex steady state results were well
within the range obtained from the Monte Carlo analysis. The results of the comparison obtained from
the steady state and Monte Carlo analysis provide more confidence in the actual steady state results
obtained from thermal-fluid network simulations. With the aid of the Monte Carlo analysis, it is now
possible to simulate thermal fluid networks, while considering all the uncertainties involved for each component. === Thesis (M.Ing. (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2004.
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