Post on 30-May-2022
Edoardo Tosoni
June 4, 2018
Performance assessment of nuclear waste repositories
2
Table of contents
Part I
• Nuclear waste – sources, categories, management
• Disposal – concepts, options
• Near-surface disposal, Deep geological disposal
• Performance assessment
• Scenario analysis – general structure, pluralistic approach
Part II
• Probabilistic approach – the example of Yucca Mountain
• Pluralistic vs probabilistic approach
• Summary and conclusions
3
Part I
4
Nuclear waste
Particular characteristic ⇒ Radioactive
Radioactive decay ⇒ Waste amount decreases with time
Decay constant
Half life
After ten half lives:
𝑁𝑡 = 𝑁0𝑒−λ𝑡 λ =
1
𝑇
𝑇1 2 = 𝑇
𝑁10𝑇1 2
𝑁0= 𝑒
−λ10 −𝑙𝑛 1 2
λ = 𝑒10𝑙𝑛 1 2 = 𝑒𝑙𝑛 1 210 ≅ 0.001
𝑇1 2 = −𝑙𝑛 1 2
λ
5
Nuclear waste - Sources
Any activity involving radioactive material
Civil nuclear power energy production:
Reactor operations Spent-fuel management Decommissioning
• Ionic exchange resins
• Replaced components
• Individual protection
devices
• Spent fuel
• Vitrified waste from
reprocessing
• Structures
• Equipments
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Nuclear waste - Categories
Two most relevant factors:
• Activity
≻ Relates to the amount of particles
that can decay, and thus emit
radiations
• Half-life
≻ Relates to the time during which the
waste exist, and thus can be
detrimental to health
Classification of radioactive waste – GSG-1, IAEA, 2009
HLW
ILW LLW
7
Nuclear waste - Management
Distinguish between:
• Storage ⇒ Temporary
• Disposal ⇒ Forever
Storage Disposal
Waste
generation
• Allow for decay
o Activity
o Temperature
• Wait for disposal solutions
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Two fundamental disposal philosophies:
• Concentrate and confine
• Dilute and disperse
Disposal - Philosophies
Concentrate
& confine
Dilute &
disperse
Ocean dumping of nuclear
waste was banned
internationally in 1993
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Disposal - Options
Among the studied options:
• Space disposal
• Ice-sheet disposal
• Subseabed disposal
• Transmutation
Geological disposal
• The two philosphies coexist ⇒
• Two main types:
o Near-surface
o Deep geological
• Waste is enclosed in a Russian doll-like
system of barriers
• Due to barrier degradation, some radioactivity
will escape and migrate in groundwater
Concentrate
& confine
Dilute &
disperse
Chapman & McKinley, 1987
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Near-surface disposal
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Near-surface disposal
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Deep geological disposal
posiva.fi
Deposition tunnels
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Deep geological disposal – Multiple barriers: KBS3
Deposition tunnel
Deposition hole
1. Canister
2. Buffer
3. Backfill
4. Host rock
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siv
a.f
i
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Deep geological disposal - Canister
Copper overpack Cast iron insert
Spent fuel bars vtt
.fi
posiva.fi
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Deep geological disposal – Vertical vs. horizontal
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siv
a.f
i
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Deep geological disposal – Projects
Olkiluoto - FIN
SITE94 - SWE
SR SITE - SWECNFWNP - CAN
Yucca Mountain - USA
Kristallin-I - SWI
KRDC – S.KOR
WIPP- USA
ANDRA - FRA
DGR - CAN
H12 – JAP
DryRun3 - UK
One has been constructed
(military waste)
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Deep geological disposal – Conceptual scheme
Recall the two philosphies:
• Concentrate & confine
≻ The waste in disposed of under the
protection of multiple barriers
• Dilute & disperse
≻ Barriers undergo degradation:
o Mechanical strain
– Isostatic load (ice sheet)
– Sheer load (fracture displacement)
o Erosion
o Corrosion
o ...
≻ Radionuclides are released
Radionuclide release
Dose to humans
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Performance assessment – Radiological impact
The repository will lead to some radiological impact
• Radionuclide release
• Dose to humans
In view of licensing, it is required to ascertain that such impact will be acceptably low
The level of acceptance is determined by regulation
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Performance assessment – Regulation
«…reasonable expectation that the
reasonably maximally exposed
individual receives no more than the
following annual dose from releases
from the undisturbed Yucca Mountain
disposal system: (1) 0.15 mSv for
10,000 years following disposal; and
(2) 1.0 mSv after 10,000 years, but
within the period of geologic
stability »
« The disposal of nuclear waste shall be so
designed that the radiation impacts arising
as a consequence of expected evolution: a.
the annual dose to the most exposed
individuals remains below the value of 0.1
mSv; and b. the average annual doses to other
individuals remain insignificantly low »
«…constraints for radioactive releases to the
living environment (average release of
radioactive substances per annum) referred to
in requirement 312 are as follows: a. 0.03
GBq/a for long-lived, alpha-emitting radium,
thorium, protactinium, plutonium…»
U.S. Code of Federal Regulation, Title 10, §63.311 Finland. Guide YVL D.5
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Performance assessment – Uncertainty
Safety must be assessed over long time horizons
• Near surface ⇒ 300 y – k1,000 y
• Deep geological ⇒ 10,000 y – 1,000,000 y
Challenges:
• Site management ⇒ Heritage and communication
• Performance assessment ⇒ Uncertainty about disposal system* evolution
This aleatory uncertainty is typically tackled by Scenario analysis
Pyramids are 4,000 – 5,000 years old
* Repository + Surrounding environment = Disposal system
21
Scenario analysis – General structure
Scenario development (today’s focus)
≻ Stepwise process:
• Identification of Features, Events & Processes (FEPs)
• Construction of the system model
• Scenario generation
Consequence analysis
≻ Scenarios are analyzed quantitatively
(computer code simulations) to assess the
radiological consequences
22
Scenario analysis – Identification of the FEPs
The factors that influence the disposal system:
• Climate
• Groundwater flows
• Chemical concentrations
• Mechanical strains, etc.
List of FEPs are compiled by expert judgment:
• Existing lists or databases
• Ad hoc workshops
23
Scenario analysis – FEP lists
FEP lists vary considerably across different
Performance Assessments
• Case-to-case specificity
• Different levels of abstractions, for instance
o Advection, Heat transfer
o Water flow rate, Temperature
Recent research is trying to promote the
interpretation of the FEPs as physical quantities
Posiva, 2012a
128 FEPs
Representation of the disposal system as a set of FEPs and their
interactions (causal dependences)
Among the first attempts:
• Fault trees
• Event trees
Not very suitable:
• failure is not necessarily defined at the FEP level!
• occur-not occur modeling of FEPs is hard
24
Scenario analysis – System model
On the grounds of classical reliability analysis, FEPs
were treated as the system components, whose failure
could lead to the failure of the repository
(radionuclide release, dose to humans)
Example of chloride
concentration:
o Low values promote buffer
erosion
o High values promote canister
corrosion
o Both can lead to radionuclide
release
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D’Alessandro & Bonne, 1981
Scenario analysis – Fault trees, event trees
Andresson et al., 1989
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FE
Pi
FEPj
Scenario analysis – Interaction matrices, Bayesian networks
Po
siv
a, 2
01
2a
More recently:
• Interaction matrices
• Bayesian networks
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Scenario analysis – Scenario generation
The system model serves as a driver for scenario generation
≻ Scenarios can be seen as combinations of values of the FEPs
≻ Informally speaking, FEPs are bricks to build scenarios
Approaches to scenario generation:
• Pluralistic
• Probabilistic (Part II)
28
Scenario generation – Pluralistic approach
A set of scenarios is formulated by expert judgment
Each scenario is an assumption about a combination of
values of the FEPs
The scenarios capture relevant mechanisms that cause
radionuclide release from the repository
The set of analyzed scenarios is intended to be
illustrative/representative of the possible evolutions of
the disposal system
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Pluralistic approach - Examples
Posiva, 2013
Ondra
f-N
iras, 2
01
2
Belgium, Safety Case for Dessel
Finland, Safety Case for Olkiluoto
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Pluralistic approach – Rationale to safety
Each scenario is checked against the regulatory limit
Po
siv
a, 2
01
2b
Time
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Pluralistic approach – Poll on safety
Would you think the
repository is safe?
Why?
32
Pluralistic approach – Challenges
Major challenge ⇒ Comprehensiveness
≻ We recognize that we cannot analyze all possible scenarios
≻ We cannot cover the spectrum of all possible future evolutions of
the disposal system (residual uncertainty)
≻ Does this limited coverage still give us enough knowledge to support
statements about the safety of the repository?
The pluralistic approach is not fully clear about whether we know enough
• quantifying the extent to which the analyzed scenarios cover the possible futures
• characterizing the residual uncertainty about the future
33
Pluralistic approach – Comprehensiveness
Nuclear safety authorities typically react by asking for additional scenarios
ST
UK
, 2
01
5
OECD-NEA, 2012
34
Scenario generation – Probabilistic approach
The probabilistic approach has the potential to overcome these difficulties by
quantifying the uncertainty about the future, so that it can be possible to understand
whether enough is known about the safety of the repository
35
Part II
36
Probabilistic approach – Yucca Mountain
Proposed repository for HLW
Volcanic tuff, above the water table
⇒ unique!
Project interrupted in 2010 due to
political opposition
Not the only possible formalization of a
probabilistic approach
Yet illustrative
Rechard et al., 2014
The methodology is presented in Helton &
Sallaberry, 2009
37
Yucca Mountain – FEPs & system model
List of FEPs, divided into:
• Disruptive events
• Normal-evolution FEPs
Normal-evolution FEPs are simulated by
a large suite of computer codes
Calculation of the dose
Aleatory uncertainty concentrates in the
disruptive events ⇒ Stochastic modeling
Seismic
events
Volcanic
events
Early
failures
Dose to
humans
Disruptive
events
Normal-evolution FEPs
Computer codes
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Yucca Mountain – Scenario generation
Scenario vector
For simplicity, one type of disruptive event:
Limited to
Probability space
Ideal interpretation
≻ A scenario is a point in A, that is, a realization of a
Number of occurrences
Time of j-th event
Properties of j-th
event (peak ground
velocity, duration...)
Sample space Set of
subsets
of A
Probability
distribution
over A
For instance
o Poisson process for times of occurrence
o Normal distribution for properties
Seismic events Volcanic events Early failures
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Yucca Mountain – Consequence analysis Seismic
events
Volcanic
events
Early
failures
Dose to
humans
Disruptive
events
Normal-evolution FEPs
Computer codes
Given a scenario, the
dose is calculated by
simulation
Computer
codes
40
Yucca Mountain – Monte Carlo simulation
Procedure
1. Sample
2. Calculate
3. Repeat nS times
4. Estimate
• Distribution of
• Expected value
This is a characterization of the aleatory uncertainty
about the future
Probability
of violating
regulatory
limits
• Check against L
• Check against a max acceptable threshold
41
Yucca Mountain – Computational strategies
Straightforward MC computationally ineffective ⇒ Very large sample for adequate
representation of disruptive events
Strategy
1. Subdivide the sample space into regions
2. Calculate expected dose for individual regions
3. Aggregate afterwards
Possible subdivisions
Number of disruptive events
Type of disruptive events
Mathematically neat, but only for the
illustrative case of one type of distruptive
events
What they actually did in the Performance
Assessment, but strong assumptions
42
Yucca Mountain – Subdivision by number of events
n = 0
n = 1
n = 2
n = ...
n = 3
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Yucca Mountain – Subdivision by type of events
Seismic
Events
Volcanic
Events Early
Failures Normal
Evolution
Seismic
Events
Volcanic
Events Early
Failures Normal
Evolution
Assumptions
• Dose from normal evolution always present
• Not included in dose from disruptive events
• No synergies among disruptive events
44
Subdivision by type of events – Dose curves
Normal evolution D
ose
Do
se
Do
se
Do
se
45
Yucca Mountain – Risk triplet
The probabilistic approach is meant to map onto the classical three questions of risk
1. What can happen? ⇒
2. How likely is it to happen? ⇒
3. What are the consequences? ⇒
With an additional question
4. What are the associated uncertainties?
46
Yucca Mountain – Epistemic uncertainty
Thus far, only aleatory uncertainty about disposal system evolution
Nevertheless, a further type of uncertainty ⇒ Epistemic
Gaps of knowledge about tools of the analysis, e.g. parameters of
• Computer codes (densities, porosities, dispersivities...)
• Probability distributions characterizing aleatory uncertainty
Characterization of epistemic uncertainty by e.g. distributions
and become distributions as well!
≻ Challenging to attain comprehensiveness
47
Pluralistic vs. Probabilistic
Pluralistic Probabilistic
Scenario
generation
Fromulation by expert
judgments
Sampling from probability
spaces
Rationale to
Safety Dose below the limit
Expected dose below the limit
Violation probability below
the threshold
48
Pluralistic vs. Probabilistic - Tensions
Practitioners are often tight to their positions
• Pluralistic ⇒ the probabilistic approach is a black box
• Probabilistic ⇒ the worst-case perspective does not build confidence on safety
Chapman et al., 1995
Goodwin et al., 1994
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Take home
Nuclear activities produce radioactive waste that must be disposed of safely
Nuclear waste can be disposed of in geological repositories (near-surface or deep)
Large aleatory uncertainty about the disposal system evolution ⇒ Scenario analysis
Different approaches to scenario generation
• Pluralistic ⇒ Formulation of scenarios by expert judgments
• Probabilistic ⇒ Sampling scenarios from probability distributions
The key challenge is to attain comprehensiveness (hard by the pluralistic approach),
that is, to support robust statements about the safety of the repository
50
References • IAEA, Classification of Radioactive Waste, General Safety Guide, No. CSG-1. Vienna, Austria: IAEA, 2009
• Chapman NA,McKinley IG. TheGeologicalDisposal of Nuclear Waste. Chichester, UK: Wiley, 1987
• Posiva. Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto—Features, Events and Processes. Eurajoki, Finland:Posiva Oy, 2012a
• D’Alessandro M, Bonne A. Radioactive waste disposal into a plastic clay formation. Brussels and Luxembourg: Harwood academic publishers, 1981
• Andersson J, Carlsson T, Eng T, Kautsky F, S¨oderman E, Wingefors S. The Joint SKI/SKB Scenario Development Project—SKB Report 89-35. Stockholm, Sweden: SKB, 1989
• Posiva. Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto—Formulation of Radionuclide Release Scenarios 2012. Eurajoki, Finland: Posiva Oy, 2013
• ONDRAF/NIRAS. Selection of scenarios for long-term radiological safety assessment - Project near surface disposal of category A waste at Dessel. Brussels, Belgium: ONDRAF/NIRAS, 2012
• Posiva. Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto—Assessment of Radionuclide Release Scenarios for the Repository System 2012. Eurajoki, Finland: Posiva Oy, 2012b
• STUK (Sateilyturvakeskus). STUK’s Review on the Construction License Stage Post Closure Safety Case of the Spent Nuclear Fuel Disposal in Olkiluoto. Helsinki, Finland: STUK, 2015
• OECD/NEA. The Long-term Radiological Safety of a Surface Disposal Facility for Low-level Waste in Belgium. OECD ,2012
• Rechard RP, Freeze GA, Perry FV. Hazards and scenarios examined for the Yucca Mountain disposal system for spent nuclear fuel and high-level radioactive waste. Reliability Engineering
and System Safety, 2014; 122:74–95
• Helton JC, Sallaberry CJ. Conceptual basis for the definition and calculation of expected dose in performance assessments for the proposed high-level radioactive waste repository at Yucca
Mountain, Nevada. Reliability Engineering and System Safety, 2009; 94:677–698
• Chapman NA, Andersson J, Robinson P, Skagius K, Wene CO, Wiborgh M, Wingefors S. Systems Analysis, Scenario Construction and Consequence Analysis Definition for SITE-94—SKI
Report 95-26. Stockholm, Sweden: SKI, 1995
• Goodwin BW, McConnell DB, Andres TH, HajasWC, LeNeveu DM, Melnyk TW, Sherman GR, Stephens ME, Szekely JG, Bera PC, Cosgrove CM, Dougan KD, Keeling SB, Kitson CI, Kummen
BC, Oliver SE, Witzke K, Wojciechowski L, Wikjord AG. The Disposal of Canada’s Nuclear Fuel Waste: Post Closure Assessment of a Reference System. Pinawa, Canada: AECL, 1994