An application to nuclear safety ua sa using an accident consequence assessment code
1 / 33

ccident Consequence Assessment Code - - PowerPoint PPT Presentation

  • Updated On :

An Application to Nuclear Safety - UA/SA Using An Accident Consequence Assessment Code -. T. Homma Japan Atomic Energy Research Institute SAMO2004 Venice, Sept 12 - 17, 2004. Safety Goal for Nuclear Installations . Level 3 PSA for a reference plant due to internal accidents.

Related searches for ccident Consequence Assessment Code -

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about 'ccident Consequence Assessment Code -' - Angelica

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
An application to nuclear safety ua sa using an accident consequence assessment code l.jpg
An Application to Nuclear Safety- UA/SA Using An Accident Consequence Assessment Code -

  • T. Homma

  • Japan Atomic Energy Research Institute

  • SAMO2004 Venice, Sept 12 - 17, 2004

Safety goal for nuclear installations l.jpg
Safety Goal for Nuclear Installations

Level 3 PSA for a reference plant due to internal accidents

  • The NSC of Japan issues the interim report on safety goal (2004)

    • Individual early fatality risk: the expected (average) value for average individual early fatality risk near the site boundary due to nuclear accidents will be less than about 1×10-6 year-1

    • Individual latent cancer fatality risk: the expected (average) value for average individual latent cancer fatality risk in the some region from site boundary due to nuclear accidents will be less than about 1×10-6 year-1

Average individual risk (reactor year -1)

Distance from the release point (km)

Two types of uncertainty l.jpg
Two Types of Uncertainty

  • Stochastic (aleatory) Uncertainty

    • known as randomness or variability of the system under study

    • variability in environmental conditions (e.g. weather condition)

    • physical variability will not decrease

  • Subjective (epistemic) Uncertainty

    • results from the existing state of knowledge

    • modeling uncertainty and input parameter value uncertainty

    • as we gain more knowledge, uncertainty will decrease

Stochastic Uncertainty

Subjective uncertainty

The problem settings l.jpg
The Problem Settings

  • How do we deal with the stochastic uncertainty (weather conditions) in accident consequence assessments and how much is the statistical variability?

  • How much of the overall uncertainty about individual risk is attributable to stochastic uncertainty and how much to parameter uncertainty?

  • What are the main contributors to uncertainty in individual risk of early and latent cancer fatality?

Oscaar code system l.jpg
OSCAAR Code System


  • Off-Site Consequence Analysis of Atmospheric Releases of radionuclides






















Atmospheric dispersion and deposition l.jpg
Atmospheric Dispersion and Deposition

  • Multi-puff Trajectory Model

    • Dry and wet deposition

Dose calculation models l.jpg


Atmospheric release

Atmospheric dispersion


Dose to man





Foodstuff contamination


Dose Calculation Models

  • Total dose for a specific organ from different exposure pathways

Reduction factors (shielding and filtering factors)

Dose coefficients

Time-integrated concentration, contamination, intake

i: organ

j: pathway

Health deterministic effects model l.jpg
Health (deterministic) Effects Model

Early and Continuing effects(Early mortality and morbidity)

  • Hazard function (two-parameter Weibull function) approach

  • Early fatal effects comprise haematopoietic, pulmonary, and gastrointestinal syndrome. Those depend on the level of medical treatment received

  • Effectiveness of a specified dose for induction of early effects depends on dose rates.

Health stochastic effects model l.jpg
Health (stochastic) Effects Model

Late Somatic Effects (Cancer mortality and morbidity)

  • Linear or linear-quadratic dose-response model and DDREF

  • For estimating the life-time risk in the population, the absolute or relative risk projection models are available

  • Data of Hiroshima and Nagasaki

    • Reassessment of the radiation dosimetry

    • Life span study on atomic bomb survivors

Meteorological sampling l.jpg
Meteorological Sampling

  • Aims of Meteorological Sampling

    • Strong dependence of the magnitude of the consequences on the weather after an accident

    • Huge computer resources using a full year of hourly data

    • Select a representative sample of weather sequences which adequately produce the range of consequences

  • Sampling Techniques

    • Random sampling of the specified number of sequences

    • Cyclic sampling (sequences are selected with a set time interval between them)

      • but, these tend to sample the commonly occurring groups frequently, while overlooking more unusual sequences

    • Stratified or bin sampling (sequences are grouped into a number of categories, which give rise to the similar consequences)

General consideration for met sampling l.jpg
General Consideration for Met. Sampling

  • Completeness

    • The consequences calculated would reflect the full spectrum of the consequences related to the postulated accident under investigation.

  • Consistency

    • The parameters selected for classification of weather sequences and the sampling scheme itself should be seamlessly associated with the models, parameters and methods used in the code system.

  • Stratification

    • The sampling scheme could divide the entire set of meteorological sequences in such a way that the members in each single stratum or group would be very similar.

  • Practicability

    • A practicable number of samples should be predetermined according the models used in the consequence assessment code.

  • Optical Allocation

    • A fixed number of samples need to be optically allocated among the groups in order to “maximize” the precision of consequence assessment.

Sensitivities of early fatality to meteorological parameters l.jpg
Sensitivities of Early Fatality to Meteorological Parameters

SPD0 : initial wind speed

STPi : travel time to i km

I.SPDi : Inverse of wind speed to i km

STABi:mean stability to i km

DURi : period of rain to i km

RAINi : total rainfall to i km

Classification of new sampling scheme l.jpg
Classification of New Sampling Scheme

11 Groups x 9 (wind directions) = 99 Groups

144 Weather sequences

Performance of new sampling scheme l.jpg

New stratified sampling scheme

Cyclic sampling scheme

Conditional Probability, ≧C

Conditional Probability, ≧C

Early Fatalities (normalized), C

Early Fatalities (normalized), C

Performance of New Sampling Scheme

1000 sets of 144 sequences

8760 sequences

  • The statistical variability of the probability distribution of the early health effect is not large and the performance of this scheme is better than other conventional schemes.

  • The advantage of the stratified sampling scheme is to give the rare cases of catastrophic health effects when we use the same number of sequences.

Steps in ua sa on input parameters l.jpg
Steps in UA/SA on Input Parameters

  • Identify uncertain model parameters

  • Assign upper and lower bounds, distribution, and correlation

  • 1. PREP

    • Perform parameter value sampling

      • Simple random sampling

      • Latin hypercube sampling

      • Sobo'l quasi-random sampling

  • 2. Run OSCAAR with the Sampled Input Values

  • 3. SPOP

    • Estimate output distribution functions (UA)

    • Examine relationships between input and output variables (SA)

  • Parameter Xk

    Parameter X1

    Parameter X2

    Prediction Y

    Expert judgement elicitation l.jpg
    Expert Judgement Elicitation

    Joint EC/USNRC project 「Uncertainty Analysis of Accident Consequence Models for Nuclear Power Plants 」(1993-1996).

    • Objectives : to develop credible and traceable uncertainty distributions for the respective ACA code input parameters.

    • Two important principles for the application of formal expert judgement elicitations:

      • The elicitation questions would be based on the existing models used in their codes such as COSYMA and MACCS.(A library of information can be of use to other models and codes.)

      • The experts would only be asked to assess physical quantities which could be hypothetically measured in experiments.

    Expert judgement elicitation cont l.jpg

    Uncertainty distributions of

    the code input parameter values

    Information about 5%、50% and 95% quantiles on the uncertainty distribution from expert judgement

    Parameter A

    Expert A

    Single joint distribution


    distributions on

    code input



    the uncertainty


    Expert B

    Parameter B

    Parameter C

    Expert C

    Expert Judgement Elicitation (Cont.)

    • Uncertainty distributions for physically observable quantities were provided by experts at each expert panel formed for the following areas of codes: atmospheric dispersion, deposition, external doses, internal dosimetry, food-chains,early health effects and late health effects.

    • Combine these uncertainty distributions into a single joint distribution and translate distributions over physically observable quantities into distributions on code input parameters.

    Target variables and elicitation variables l.jpg






    Target Variables and Elicitation Variables

    • Case 1: code input parameters correspond to measurable quantities (e.g. deposition velocity)

    • Case 2: some analytical functional dependence (e.g. dispersion parameter )

    • Case 3: some numerical relationship(e.g. retention of material is modelled using a set of first-order differential equations with code input parameters)

    kAB 、kAC :transfer coefficient(target variable)

    Yi、Zi :retention of material in compartments, B and C(elicitation variable)

    Case 2 and 3 need probabilistic inversion

    Example for dose coefficient l.jpg
    Example for Dose Coefficient

    • Metabolic model of Caesium


    • Quantile information from experts








    tissue A


    tissue B





    • In internal dosimetry panel, 8 experts were asked about the retention of

      materials in the human body.

    • Estimate the distributions of the biological half life TBlood,TBodyA and TBodyB

      from the distributions of the retention of Cs-137 in Body tissue from a unit

      intake by using probabilistic inversion technique.

    Result of probabilistic inversion l.jpg

    5% 50% 95%



    Biological half life TBlood (d)

    5% 50% 95%

    5% 50% 95%





    Biological half life TBodyA (d)

    Biological half life TBodyB (d)

    Result of Probabilistic Inversion

    • Distributions of the target variables obtained from probabilistic inversion

    • Comparison of distributions of elicitation variables

    Uncertainty distribution of dose coefficients l.jpg

    Rank correlation coefficients extracted from the distribution among target variables





    5% 50% 95%

    Effective dose coefficient (Sv/Bq)

    Uncertainty Distribution of Dose Coefficients

    Uncertainty distributions of the biological half lives


    Calculate inhalation and ingestion

    dose coefficients.

    ICRP metabolic models


    Dosimetry data

    • Uncertainty on effective dose coefficient for Cs-137 from ingestion

    Oscaar calculations l.jpg
    OSCAAR Calculations

    • Site Data

      • A model plant is assumed to be located at a coastal site facing the Pacific Ocean.

      • Population and agricultural production data from the 1990 census

    • Source Term

    Oscaar calculations cont l.jpg


    Release start


    Time before release

    Sheltering zone (>10 mSv/w)

    of release

    3 h

    Warning time

    2 h

    30 km


    Time for



    24 h

    1 h

    10 km

    Sheltering in concrete building


    Time for



    Time for

    Time for




    Evacuation zone (>50 mSv/w)

    2 h

    2 h

    1 h

    1 h


    h = 7 d

    Relocation zone (>140 mSv/y)

    OSCAAR Calculations (cont.)

    Countermeasures Strategy

    Countermeasures Timing

    Uncertainty analysis procedure l.jpg

    M weather sequences

    K parameters

    N runs

    Uncertainty Analysis Procedure

    Subjective Uncertainty

    Stochastic Uncertainty

    Average Individual Risk

    • Individual risk as a function of distance : risk at x km, j th sector : population at x km, j th sector : probability of i th weather sequence

    Example of ccdfs for individual risk l.jpg
    Example of CCDFs for Individual Risk

    Cumulative distribution

    Probability of exceeding X

    99th percentile

    Average individual risk of early fatality at 1 km, X

    Average individual risk of early fatality at 1 km

    Slide27 l.jpg

    Uncertainty of Average Individual Risk

    (Expected Values due to weather variability)

    Conditional probability of cancer fatality

    Conditional probability of early fatality

    Distance from the site (km)

    Distance from the site (km)

    Ratio of 95% to mean value

    Slide28 l.jpg

    Contribution of Stochastic Uncertainty(weather scenario variance)

    • Overall variance





    • Early fatality

    • Latent cancer fatality

    Sensitivity of early fatality l.jpg
    Sensitivity of Early Fatality

    Number of early fatality

    Average individual risk of early fatality




    Distance from the site (km)

    Sensitivity of latent cancer fatality l.jpg
    Sensitivity of Latent Cancer Fatality

    Number of cancer fatality

    Average individual risk of cancer fatality




    Distance from the site (km)

    Sobol sensitivity indices l.jpg

    Total :

    First-order :

    Sobol’ Sensitivity Indices

    • A model output can be decomposed into summands of different dimensions:

    • the variance of can be decomposed as:

    • Sensitivity measures can be introduced:

    Sobol sensitivity indices for a specific weather sequence l.jpg
    Sobol’ Sensitivity Indicesfor a Specific Weather Sequence

    Dry weather sequence

    Wet weather sequence

    Summary l.jpg

    • The uncertainty factors (a ratio of 95% to mean )for the expected values is less than about four for both average individual risks of early and latent cancer fatality near the site boundary.

    • The contribution of stochastic uncertainty to the overall uncertainty for average individual risk of fatality is only dominant close to the site boundary at about 20%, and that for average individual risk of cancer fatality is quite stable about less than 6% at all distances.

    • When considering the computational costs, the correlation/regression measures are useful for understanding the sensitivity of the expectation value and some percentile of the CCDFs to the input parameters.

    • For specific weather conditions, the Sobol’ method with total effect indices is effective in identifying the important input parameters.