Abstract
A canyon building houses special nuclear material processing facilities in two canyon like structures, each with approximately a million cubic feet of air space and a hundred thousand hydraulic equivalent feet of ductwork of various cross sections. The canyon ventilation system is a “once through” design with separate supply and exhaust fans, utilizes two large sand filters to remove radionuclide particulate matter, and exhausts through a tall stack. The ventilation equipment is similar to most industrial ventilation systems. However, in a canyon building, nuclear contamination prohibits access to a large portion of the system and therefore limits the kind of plant data possible. The facility investigated is 40 years old and is operating with original or replacement equipment of comparable antiquity. These factors, access and aged equipment, present a challenge in gauging the performance of canyon ventilation, particularly under uncommon operating conditions.
The ability to assess canyon ventilation system performance became critical with time, as the system took on additional exhaust loads and aging equipment approached design maximum. Many “What if?” questions, needed to address modernization/safety issues, are difficult to answer without a dynamic model.
This paper describes the development, the validation and the utilization of a dynamic model to analyze the capacity of this ventilation system, under many unusual but likely conditions.
The development of a ventilation model with volume and hydraulics of this scale is unique. The resultant model resolutions of better than 0.05″wg under normal plant conditions and approximately 0.2″wg under all plant conditions achievable with a desktop computer is a benchmark of the power of micro-computers. The detail planning and the persistent execution of large scale plant experiments under very restrictive conditions not only produced data to validate the model but lent credence to subsequent applications of the model to mission oriented analysis.
Modelling methodology adopted a two parameter space approach, rational parameters and irrational parameters. Rational parameters, such as fan age-factors, idle parameters, infiltration areas and tunnel hydraulic parameters are deduced from plant data based on certain hydraulic models. Due to limited accessibility and therefore partial data availability, the identification of irrational model parameters, such as register positions and unidentifiable infiltrations, required unique treatment of the parameter space. These unique parameters were identified by a numerical search strategy to minimize a set of performance indices. With the large number of parameters, this further attests to our strategy in utilizing the computing power of modern micros. Nine irrational parameters at five levels and 12 sets of plant data, counting up to 540 runs, were completely searched over the time span of a long weekend.
Some key results, in assessing emergency operation, in evaluating modernization options, are presented to illustrate the functions of the dynamic model.
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