RAMI methods for DEMO development

Euratom-Tekes 2013 Annual Seminar27-28 May 2013, M/S Silja Serenade

Risto Tuominen & Toni AhonenVTT Technical Research Centre of FinlandRisk and Reliability Management

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RAMI in EFDA/DEMO Work Programmes PPPT WP12-DTM-02: Reliability Growth and Risk Minimisation of In-vessel Components

PPPT WP13-DTM-02: Reliability, Availability, Maintainability & Inspectability (RAMI)

- 1 ppy (PS) + 0.5 ppy (BS)- CCFE (UK)- ENEA (Italy)- CIEMAT (Spain)- VTT/Tekes- LEI (Lithuenia)- IST (Portugal)

- 4/2012 →

- 2 ppy (PS) - CCFE (UK)- ENEA (Italy)- CIEMAT (Spain)- VTT/Tekes- LEI (Lithuenia)

- 4/2013 →

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Motives for the WP Task dedicated to RAMI

DEMO builds the bridge from experimental fusion facilities to first fusionpower plant (FPP)

DEMO is expected to show FPP being an commercially feasible & environmentally justifiable power source

Availability (A) is a primary measure of effectiveness for a FPP and directly affects the cost of generated electricity:

CAC, COM, CF, etc. are the annualized costs of plant acquisition, operation & maintenance, fuel, etc; PE is the net electrical power produced to the grid; A is the plant availability for production

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Motives for the WP Task dedicated to RAMI

(Operational) availability is intrinsically dependant on three qualities of the plant that are driven by design and technology choices: Reliability – impacts tUDT

Maintainability – impacts tSDT & tUDT

Inspectability – impacts tSDT & tUDT

Maintenance support performance – impacts tSDT & tUDT

These qualities can obviously be traded-off against each other, or against other measures of plant effectiveness, for example:

– Less reliability may be acceptable if maintainability is improved– Improved inspectability may allow less maintenance– Lower performance required may provide higher reliability,– etc.

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Motives for the WP Task dedicated to RAMI

Thus RAMI is very much a Systems Engineering issue that cannot be considered in isolation

formal RAMI program with associated processes, tools and methodsthat is strongly integrated in the DEMO development & design process from the start

building on the RAMI knowledge available from previous fusion programs (JET, Tore Supra, ITER, IFMIF, etc.), and the experience from other fields of technology

responding to the challenges that switching from experimental facilities to a power producing plant, technology scale up, and novelty / first-of-kind plant introduce

noticing the uncertainties arising from gaps in knowledge/experience and how they affect the confidence of RAMI predictions

focus on reactor systems and components

safety excluded from the scope (considered in own WP)

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RAMI Management Process for DEMO iterative process over DEMO life; progressing in parallel to development/design

activities

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RAMI requirements analysis & allocation

approach for allocation of initial availability targets to the systems of DEMO consistent with the overall plant operational availability target.

CCFE 2012

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Top-down availability apportionment methodologybased on expert judgment

CCFE 2012

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Tranferring the initial availability targets into R, M & I requirements

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RAMI input data for assessments

many DEMO systems ”first-of-kind”, or represent large scaling in performance demands from existing equipment

need to collect and integrate reliability data from a diverse range of sources of varying quality and relevance to the analysis case

expert judment has also an important role as source of data confidence in expert judgment data requires the use of formal methods

and transparency of the EJ procedure

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RAMI input data for assessments

WP13-T02-1 (VTT): Specification of structured expert judgment methods for producing RAMI input data for DEMO systems consolidate previous work & experience on EJ methods, relevant standards establish preferred methods and guidelines for expert data elicitation, aggregation

and evaluation

T02-2: Specification of agreed/standard approaches for integration of data from different sources evaluation of subjective and objective information fusion methods; special attention for Bayesian methods (with theoretical justifications of use in small

sample cases) in collaboration with ENEA and LEI

LEI 2013

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Fusion Component Failure Rate Database (FCFRDB)

ENEA compiled and maintained database for F4E and ITER organizations

comprises more than 4000 records reporting failure rate (and MDTF) data for components in fusion systems no data records on RH systems are currently available

Available on-line http://fus-se.frascati.enea.it:8080/Enea access is restricted to users operating in fusion field, typically for ITER access requires a granted User account

Merges data from several: plants (Europe - JET, TLK, Tore Supra, Asdex-U; USA - INL, DIII-D,

TFTR&NSTX, TSTA; Japan - TPL) report sources ( ENEA, INEL, EIReDA, IAEA TECDOC, IFMIF Data, OREDA,

WASH1400, etc.)

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Using FCFRDB to estimate component FR

ENEA 2013

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Using FCFRDB for estimating component FR

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Reliability and availability growth

Reliability/availability of developmental systems is typically not constant but follows a reliability (maturity) growth curve with initial reliability/availability, some characteristic reliability/availability growth rate, and mature reliability/availability (i.e. the ultimate operating reliability/availability)

For a novel system: What is a realistic initial availability that can be expected? What is a realistic mature availability that can/could be achieved? What is a realistic growth rate?

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Reliability and availability growth

initial reliability is highly dependent on novelty in the design and the associated potential for unexpected failure modes (i.e. “knowledge-based failures”) the growth rate depends on novelty of the system (i.e. initial number and

types of defects) and the nature of tests and operations to reveal the defects and get them successfully resolved. mature reliability is highly dependent on complexity of the design –

simple designs have less opportunity for “process-based failures”

in a mature system the “knowledge-based failures” are practically eliminated and outweighed by “process-based failures”, that is, by random failures due to variations inherent to manufacturing, assembling, maintenance, and operating processes and environment.

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Reliability and availability growth most early fission power plants began their operational lives with low availability values in

the 30% range the yearly on-line time then gradually improved into the 70% range modern fission plants now have availability averages in the 80 – 90% range

Fragola & Morse, 2012JET Neutral Beam Injectors (NBI)(Pinna et al. 2007)

Design defects &random failures Merely random failures

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Defect model for reliability growth

The total uncertainty regarding the system reliability estimate regarding LOM events (at operation cycle N) is a combination of the uncertainty on the initial number of defects and the uncertainty associated with the growth model parameters λ and τ, υ, φ, γ, the latter depending on the specifics of the risk mitigation /management program implemented in the development process.

(Fragola & Morse, 2012)

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Reports and papers:

Tuominen R., Ahonen T.; WP12-DTM02-REL-D04: RAMI assessmentmethodology for DEMO. IDM reference: EFDA_D_2GDFNJ. 64 p.

Ahonen T., Tuominen R.; WP12-DTM02-REL-D05: Evaluation of RAMI tools for DEMO. IDM reference: EFDA_D_2HCYBN. 23 p.

T. Pinna (ENEA), R. Tuominen, M. Siuko; WP11-DAS-RH-08-ENEA/TEKES: Outlines for the definition of RAMI guidelines for DEMO systems. IDM reference: EFDA_D_2KVG3G. 36 p.

Pinna, T. (ENEA), Tuominen, R. (VTT); Outlines of RAMI Guidelinesfor DEMO Systems. Paper presented in PSAM 11 & ESREL 2012 Conference, Helsinki, Finland, 25 - 29 June 2012. 10p.

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VTT creates business from technology

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Reliability/Availability assessment methodology

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Motives for the WP Task dedicated to RAMI

(Operational) availability refers to the “fraction of total time that an item is able to operate as intended”, and can be calculated as:

top is the plant online time (i.e. mean up-time), tsdt is the scheduled down time and tudt is the unscheduled down time

IST 2012

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Motives for the WP Task dedicated to RAMI

DEMO to build the bridge from experimental fusion facilities to first fusionpower plant (FPP)

DEMO is expected to show FPP being an commercially feasible & environmentally justifiable power source

FPP will have to be competitive with other available energy technologies on cost of generating electricity – currently ~2 to 7¢/kWh

socio-economic situationin 2030 ?

Harman, 2012

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In operations and support: i d l f i d f ili i f RAMI bili

During realization and installation: control quality in manufacturing and installation so that the inherent RAMI qualities of

the design are not degraded

During plant development/engineering design phase: optimise RAMI qualities of sub-system designs (trade-off studies) verify design compliance with corresponding RAMI requirements & higher level targets

During pre-conceptual and conceptual design phases: understand the rationale of plant, operation & functions, and RAMI targets deduce initial RAMI requirements and constraints for the different systems/sub-systems identify key areas for R&D, to control progress & status

RAMI Management Process for DEMO

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Reliability and availability growth

initial reliability is highly dependent on novelty in the design and the associated potential for unexpected failure modes (i.e. “knowledge-based failures”) reliability can be improved when the initial deficiencies in the design, the

manufacturing processes and the interactions with the operating environment get revealed by anomalies and failures in testing and/or operation, and are resolved. the growth rate depends on novelty of the system (i.e. initial number and

type of defects) and the nature of tests and operations. in a mature system the “knowledge-based failures” are practically

eliminated and outweighed by “process-based failures”, that is, by random failures due to variations inherent to manufacturing, assembling, maintenance, and operating processes and environment. mature reliability is highly dependent on complexity of the design -

simple designs have less opportunity for “process-based failures”