1. Confidence and robustness in fuel cycle simulations
3rd Technical Workshop on Fuel Cycle Simulation
CEA – SPRC | Guillaume Krivtchik
Inspired by the uncertainty breakout session talks from TW FCS 2 – Columbia
3rd Technical Workshop on Fuel Cycle Simulation | 9-11 JULY 2018

2. Confidence in nuclear fuel cycle simulation
Sociology insight: scenarios disconnected from the political world [1]
Originates from a lack of trust?
Scenarios appear as written in stone while even short-term future is unpredictable
Instrumentalized to defend positions motivated by other parameters?
Up to the scenario community to step up and provide better decision-making material
First steps
Evaluate confidence in scenario results, including uncertainty
Evaluate and expose flexibility and adaptability of scenarios
Provide scenarios especially designed around their capability to deal with uncertainty
[1] S. Tillement, “Between heterogeneity and cooperation, the (electronuclear) scenario as a “boundary object” for decision-making”, 2nd annual Technical Workshop on Fuel Cycle Simulation, Columbia, USA, 2017.
3rd Technical Workshop on Fuel Cycle Simulation | 9-11 JULY 2018

3. Uncertainty / Bias / Variability sources – 1/2
The uncertainty associated with existing things
Model Bias
Simulation backbone: time model (steps vs event), flow vs batch
Physical models: depletion, cooling, fresh fuel equivalence
Facilities models: formalism of plants and associated mass flows
“Explicit hypotheses”: transfer functions
“Implicit hypotheses”: push vs pull, transitions between campaigns
Benchmarks (micro / macro) and validation give the magnitude of bias
Nuclear data (cross-sections, fission yields, etc.)
Well defined framework coming from safety (covariance matrices etc.)
Not so easy to take into account adequately (collapsed data + simplified models)
Usually relatively low impact (at least on global, integrated outputs)
Convenient to study for physicists – possibly too much emphasis on nuclear data?
Past and current industrial data (current burnup uncertainty, yields, rates, etc.)
Difficult to obtain data
Can be tricky to take into account (may require more detailed models or data framework than usually present in scenario codes)
Impact may be moderate to strong, but there is possibility of validation
3rd Technical Workshop on Fuel Cycle Simulation | 9-11 JULY 2018

4. Uncertainty / Bias / Variability sources – 2/2
The uncertainty associated with non-existing things
Prospective industrial data (prospective burnup, yields, rates, etc.)
Hypotheses on prospective industrial parameters
No measure, no certainty
Impact may be strong
Scenario hypotheses ~ strategy (reactor fleet, fuel recycling strategy, etc.)
Hypotheses on prospective industrial strategies
Choices made to provide a scenario meeting economic & policy goals
Cannot be predicted because the future context is unknown
Impact is strong
Policy changes and future economy (installed power, scenario objectives, etc.)
Wide range of expected future economic trends
Extremely limited knowledge of possible policy changes – even at short-term
Impact is game-changing (e.g. French Act on Energy Transition)
3rd Technical Workshop on Fuel Cycle Simulation | 9-11 JULY 2018

5. Uncertainty in the scenario as a physical system
The scenario is a physical system
It is a trajectory between a beginning and end
A scenario is a physical system, with its input and its output
The input bears uncertainty ⇒ so does the output
Uncertainty propagation methods
Deterministic (mostly for simplified models)
Sensitivity (derivatives or deltas) + sandwich formula, problem with thresholds
Monte-Carlo sampling / parameters space exploration
Brute-force (+surrogate models / sub-models)
Global sensitivity, variance decomposition and ranking
Morris, Sobol etc.
Which uncertainty sources?
Nuclear data
Industrial data?
Scenario hypotheses and policy changes??
The scenario was defined to reach a given goal
It is possible for a scenario to become obsolete after a preference change
Does it mean that scenarios are irrelevant because of “deep”[2] uncertainty ?
[2] W.E. Walker et. al., “Deep Uncertainty”, Encyclopedia of Operations Research and Management Science
3rd Technical Workshop on Fuel Cycle Simulation | 9-11 JULY 2018

6. Scenario simulation and scenario search
Scenario simulation (linear)
Scenario search (iterative)
scenario code
scenario input
scenario output 1 2
(fixed) goal 1
While objective output ≠ goal
scenario code
adjustable scenario input
objective scenario output 2 3
fixed scenario input
non-objective scenario output
|||
scenario search
(fixed) goal
adjustable scenario input 1 2
fixed scenario input
non-objective scenario output
3rd Technical Workshop on Fuel Cycle Simulation | 9-11 JULY 2018

7. Uncertainty in the scenario search as a decision-making tool
There are major differences between scenarios and other physical systems
The impact of the “environment” is strong [3]
Unforeseen events
Changing preferences
Actions of other players
The system is not closed between the beginning and the end
Additional data can be extracted during the simulated / real time
Decisions can be made during the simulated / real time
A scenario search does not provide a prediction but an example, usually associated with answering the question “which action available today are likely to best serve the future”
Does not imply that all decisions must be made today
Possible to adapt an ongoing scenario to any changes or bias
Does not imply that goals remain identical over time
Conclusion: important to take adaptability into account
[3] W.E. Walker et. al., “Adaptive policies, policy analysis, and policy-making”, European Journal of Operational Research 128 (2001)
3rd Technical Workshop on Fuel Cycle Simulation | 9-11 JULY 2018

8. Adaptability of strategies under uncertainty
Resistant strategy: performs almost the same in any situation
“If I close my eyes for the next 50 years…” policy
Raw uncertainty propagation checks if scenarios are resistant
Possibly oversized and too expensive, may not exist
Resilient strategy: scenario can be adjusted back into expected behavior
Need to determine levers
Potentially expensive computation (sampling + optimization…)
Robust strategy: finds another way to reach goals, or at least “do well” in any situation
Same philosophy as resilient scenario, but more flexible framework
Possibility to change goals?
Implementation may be difficult in
out
Resistant
in’
out’ ≈ out such that Goal OK in
out
Resilient
in’
out’ ≈ out such that Goal OK
levers in
out
Robust
in’
out’ ≠ out such that Goal’ OK
3rd Technical Workshop on Fuel Cycle Simulation | 9-11 JULY 2018
levers

9. Remarks and conclusions
Scenario studies: from spreadsheets-based trends to roadmaps for deployment
More uncertainty than certainty
Scenarios are not predictions, they are rather used to demonstrate feasibility of a strategy in a context regardless the plausibility of the context
Goal of scenario studies is not to make a predictors, but having confidence in the strategies
Traditional uncertainty propagation methods can be applied to scenarios to assess resistance
Need of innovative methods can be used to test the adaptability of strategies in a range of contexts
3rd Technical Workshop on Fuel Cycle Simulation | 9-11 JULY 2018

10. Confidence and robustness in fuel cycle simulation
Before Lunch
G. Krivtchik et. al., Nuclear Scenarios: an exercise of robustness analysis
A.V. Skarbeli et. al., Strategies of the uncertainty quantification of fuel cycle scenarios
After lunch
A.A. Zakari-Issoufou et. al., Impact of macro reactor approximation on scenario modelization in CLASS
N. Thiollière et. al., Functionality Isolation Test
Discussion
3rd Technical Workshop on Fuel Cycle Simulation | 9-11 JULY 2018

11. Thank you for your attention
One does not predict the future, but prepares for it.
Maurice Blondel
Prediction is very difficult, especially about the future.
Niels Bohr
3rd Technical Workshop on Fuel Cycle Simulation | 9-11 JULY 2018

12. Backup
3rd Technical Workshop on Fuel Cycle Simulation | 9-11 JULY 2018

13. Questions What information is provided by a nuclear scenario?
→ trend / example / roadmap / prediction…
2. What is the question that a scenario is supposed to answer?
3. Is the scenario (vs strategy) the right object for uncertainty propagation?
4. What is a strategy?
→ set of rules / policies to reach a goal?
3rd Technical Workshop on Fuel Cycle Simulation | 9-11 JULY 2018

14. General context of nuclear scenario studies
Why – Provide decision-making elements
Assess the performance of a nuclear system (reactors, reprocessing, etc.)
Identify strengths and weaknesses
Analyze strategies and help choosing a strategy
For who – Decision-makers? It is not always clear…
For the industry, who owns the fleet
For the government, who defends the interest of the civil society
For the scientists, in order to guide research
What – Characterize fleets and fuel cycles
Feasibility and impact of decisions & technologies on facilities
Performance
Cost
How – Assess the material flows and inventories
Dynamic modeling of the nuclear fuel cycle
Physical models for depletion, cooling, equivalence etc.
3rd Technical Workshop on Fuel Cycle Simulation | 9-11 JULY 2018

15. simulated time ≠ CPU time
Appendix – vocabulary
First operation
observable
real time
scenario family
objective
simulated
observable
scenario
initialization
simulated time ≠ CPU time
3rd Technical Workshop on Fuel Cycle Simulation | 9-11 JULY 2018

16. Policy changes, future economy
Scenario hypotheses
(strategy guidelines)
Prospective industrial data
(strategy implementation)
3rd Technical Workshop on Fuel Cycle Simulation | 9-11 JULY 2018