Erfahrungs- und Forschungsbericht 2012 - Ensi

Erfahrungs- und Forschungsbericht 2012 - Ensi

Figure 5:

Experimental data (red

circles) and corresponding

fit (blue curves)

(a-b) and modeling

re-sults (blue curves)

compared vs. experimental

points (red

circles) (c-d) for selected

PDS tests.

a) PDS-E20 closure

b) PDS-E21 closure

c) PDS-E20 modeling

d) PDS-E21 modeling

data allows us to build the experimental closure

in a form of a map used in the model. The model

is based on semi-empirical approach where the

mass-balance equation for the debris bed is solved

with provided experimental closure of the particle

mass flux (see above). The details on our analytical

approach are reported in [12]. As demonstrated in

Fig. 5, the model of the particulate debris spreading

has been successfully validated against experimental

results obtained in PDS tests.

The modeling of the particulate debris spreading

showed that a good agreement between the simulation

and experimental results is achieved regardless

of the fit method used to interpolate closures

(Fig. 5). In order to apply the model for assessment

of the efficacy of particulate debris spreading in

SA conditions further work is necessary on development

of experimental closures covering wide

range of gas injection rates, physical properties of

particles, their morphologies, size distributions etc.

3.4 Progress in DECOSIM Code Development

and Risk Assessment of Debris Coolability

Focus of the work in this task was on development

of approaches to assessment of uncertainties

and risks related to debris bed coolability [13],

[14], [15]. Coolability of heat-releasing debris bed

is an important issue in the severe accident analysis

and management. Traditionally, theoretical studies

of top or bottom-fed debris bed coolability

have been focused on obtaining a «best estimate»

value for the Dryout Heat Flux (DHF) as a function

of debris bed parameters (mean particle diameter

and porosity). However, an important question for

safety analysis is the quantification of uncertainties

inherent in the problem. A one-dimensional coolability

problem was considered in [13], with the aim

of analyzing the influence of aleatory uncertainties

in input physical parameters and modeling

(epistemic) uncertainties on the prediction of DHF.

Global sensitivity analysis is applied to rank the

aleatory and epistemic parameters according to

their effects on DHF and average pressure drop.

The most influential model parameters are then

calibrated to achieve the best fit to experimental

data available. On the one hand, we demonstrate

that model calibration is instrumental in achieving

considerable improvement of quantitative agreement

between the experimental and simulation

data. On the other hand, experience of model

calibration also suggested that (i) optimization of

model parameters with respect to available experimental

data on DHF is an ill-posed problem, and (ii)

model calibration with respect to one-dimensional

pressure drop experiments does not automatically

improve the prediction of DHF and in some cases

can even worsen it. Based on these insights, one

can speculate that further analytical and experimental

efforts are necessary to establish a better

consistency between model form and experimental

data on pressure drop and DHF.

One-dimensional coolability problem for a flat


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