taking the right decisions with uncertain models
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Transcript taking the right decisions with uncertain models
Nuclear emergency management:
taking the right decisions with
uncertain models
Catrinel Turcanu, Johan Camps & Benny Carlé
[email protected] / [email protected]
Society and Policy Support
Institute Environment, Health and Safety
Belgian Nuclear Research Centre
1
Workshop “All models are wrong…”, Groningen, 14-16/03/2011
Outline
Context
Nuclear emergency
model uncertainties
Conclusions
Model for evaluation of nuclear emergencies
for the Doel NPP site
2
Nuclear emergencies & models
Use of
models
for the
protection
of people
in
emergency
situations
&
preparedness
phase:
Atmospheric transport and dispersion models
(concentrations, deposition)
Dose models (dose adults, children, thyroid, .)
Food models (concentrations, dose)
…
3
The modelling problem:
Inversion layer
Height-dependent
wind velocity
Atmospheric
turbulence
rain
Dry
deposition
Wash-out
Irradiation
Inhalation
Ingestion
4
Irradiation
shielding
How are decisions taken?
Legislation
Reference band of dose values – calculated based
on model predictions (or measurements or both)
Action levels on specific actions (Belgian levels)
14/03/2011
Action
General sheltering
(up to 24h)
Indicative ranges for intervention
(dose – mSv)
5-15 (effective dose)
10-50 (thyroid equivalent dose)
Stable iodine prophylaxis
General evacuation
50-150 (effective dose integrated 1
week)
New recommendations: 20-100 mSv/y, all pathways
Range ≠ uncertainty !
5
Uncertainties
Modelling assumptions
Simplifications of reality
Parameter uncertainty
Calibration of model parameters
Input data
Meteorology
Source term
6
Uncertainties from modelling
assumptions
Average plume
simple
Meandering plume
Conservative
calculation
Best
estimate
Fluctuating plume
complex
Source models:
Torben Mikkelsen, Risø
7
Model intercomparison
Standard conditions
Experimentally validated: factor 2-3 within experiments
8
Model intercomparison
Very specific conditions
Scenario 4
9
10
8
-3
TICair (Bq s m )
10
7
10
6
10
SCK•CEN_A
SCK•CEN_B
HOTSPOT
RODOS (Atstep)
ARGOS (Rimpuff)
Rimpuff (Ismode=1)
NPKPuff4
5
10
4
10
0
5
10
15
Distance (km)
9
20
25
Model intercomparison
realistic scenario
Noodplan Doel
JRODOS
Rimpuff (ARGOS)
TIC [Bq s/m3]
same color scale
10
Potential problems related to the
resolution of the calculation grid
For the same scenario:
Inner grid cell: 1 km
no sheltering
Inner grid cell: 100m
sheltering
>1km
11
11
Parameter uncertainty:
Cs-137 in milk
1000
Messungen
Bq/kg
100
Modell-Vorhersage
10
1
0,1
0
days after
200
400deposition
600
800
1000
Zeit in Tagen nach Deposition (April 30, 1986)
12
1200
Source: Florian Gering
Model uncertainties in radiological
assessments
Malcolm Crick, IAEA
13
Source: Malcolm Crick
Uncertainties in input data (1):
meteorology
Source: Marc de Cort
14
Uncertainties in the input data (2)
with on-site single rain
gauge data
with multiple rain
gauge data
With rain-radar data
Example: Input data precipitation
15
Effect of conservative approach for
treatment of rain
10
Scenario 2
10
9
-2
Ground deposition (Bq m )
10
8
10
7
10
SCK•CEN_A
SCK•CEN_B (depletion off)
SCK•CEN_B (depletion on)
HOTSPOT
RODOS (Atstep)
RODOS (Rimpuff)
ARGOS (Rimpuff)
NPKPuff4
6
10
5
10
0
10
20
16
30
Distance (km)
40
50
Example: conservative approach
Tihange, core melt,
rupture primary circuit
Standard weather conditions
17
Example: conservative approach
Tihange, partial core melt,
rupture primary circuit
Standard weather conditions
18
Example: conservative approach
Tihange, core melt,
rupture primary circuit
unstable weather conditions
19
Conclusions
Complex problem
Often models extended beyond validated
range
Difficult to obtain realistic uncertainties on
calculations
Even more difficult to communicate these
uncertainties to decision-makers
Best estimate often replaced by conservative
approach
But … conservative estimates may lead to
unfeasible countermeasures
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