Reliability Modeling of an ADS Accelerator SNS-ORNL/ Myrrha Linac (MAX project)

1. Reliability Modeling of an ADS Accelerator SNS-ORNL/Myrrha Linac (MAX project) EuCARD2, GENEVA (20-21 March 2014 ) CERN

2. Step 1. SNS Linac modeling (MAX Task 4.2) – Input Data – Methodology – SNS Fault Tree – Reliability Analysis – Modeling results evaluation; SNS Logbook Data – Conclusions Step 2. Myrrha Linac modeling (MAX Task 4.4) – Model Assumptions – Fault tree; Quantification data – Control System; Fault tolerance

3. MAX Task 4.2 - Existing acceleratorreliability modeling (methodology currently applied for NPPs – modeling with Risk Spectrum) • The Spallation Neutron Source (SNS – ORNL) Linac was selected 1. SNS Linac Modeling (MAX Task 4.2) • SNS Linac reliability analysis • - feedbackon SNS Linac reliability performance • - modeling tool for Myrrha Linac(Task 4.4). • Draft preliminary conclusions and recommendations: • - Maximize the reliability/availabilityand the safety of the MYRRHA accelerator • - Guidance for designing MYRRHA accelerator.

4. SNS Linac Modeling – Input Data

5. SNS Design (Systems and Functions) • System functions and interfaces • Components No. (by type) • Degree of redundancy • Data Source: • SNS public info; SNS Design Control Documents (DCDs) • Reliability Data (Quantifying model ) • Failure - MTTF and repair times – MTTR • Data Source: • SNS Operation team(SNS BlockSim model – George Dodson, John Galambos) SNS Linac Modeling – Input Data SNS BlockSim Model http://status.sns.ornl.gov/beam.jsp • SNS Operating Data • Component failures modes - cause, type of component, time to repair, etc • Failures causing acc. trips: cause, component and system concerned, duration of trip (Availability data) • Data Source: • SNS Operation Data collection (http://status.sns.ornl.gov/beam.jsp)

6. General Assumptions • Not modeled – SNS Ring - RTBT, stripper foil, etc. (not relevant for Max project purposes) • Risk Spectrum Type 1 reliability model – Repairable(continuously monitored) – for all SNS Linac components • Failure/Repair processes – exponential distributions; failure/repair rates ct. • It is assumed q=0 • ¨Mean Unavailability¨ type of calculation (unavailability values for the basic events): • Q=λ/(λ+µ • (Long-term average unavailability Q is calculated for each basic event) SNS Linac Modeling – Methodology • The Results from modeling- evaluated with respect to the SNS Logbook operational data- accelerator trip failures and overall availability - recorded during the period October 2011 – June 2012.

7. SNS Linac Modeling – Model development • SNS Module 1- first modeling step: RFQ + MEBT + DTL • Gradual development of the SNS Linac model • In-depth understanding of the SNS design and functioning for an accurate model. • SNS Fault Tree (complete model) - graphical representation of the SNS systems functional structure describing undesired events (“ system failures") and their causes.

8. SNS Linac Modeling – Fault Tree • SNS Linac Fault Tree – main level

9. SNS Linac Modeling – Fault Tree • DTL RF Fault Tree Structure

10. SNS Linac Modeling – Reliability Analysis • Minimal cut set (MCS) analysis- generate minimal cut sets of the fault tree and perform a point-estimate quantification of the top event. • Analysis Case – Results • Q = 2.60E-01 = 0.26; Q = 26 % • A = 1 - Q = 73 % (Mean Availability) 1. SNS Linac Modeling

11. SNS Linac Modeling – Analysis Results

12. SNS Linac Modeling – Results evaluation; SNS Logbook data (Accelerator trip failures) • In line with the conclusions from the SNS RS Model runs: • RF system and electrical system failures - most frequent • Electrical systems failures - most contributingto accelerator downtime Accelerator downtime contribution (by system) Accelerator trip failures frequency (by system)

13. SNS Linac Modeling – Results evaluation; SNS Logbook data (Accelerator trip failures) RF System failures (no. & duration-hours) • In accordance with the SCL RS analysis:Most affected subsystems of the SNS Linac (by failures leading to accelerator trips): • SCL-HPRF (Superconducting Linac - High Power Radiofrequency) - short failures frequency • HVCM (High Voltage Converter Modulator - duration of trips

14. SNS Linac Modeling – Conclusions • The reliability resultsshow that the most affected SNS Linac parts/systemsare: • SCL, Front-End systems (IS, LEBT, MEBT), Diagnostics & Controls • RF systems (especially the SCL RF system) • Power Supplies and PS Controllers • These results are in line with the recordsin the SNS Logbook • Reliability issuethat most needs to be enforced in the linac design is theredundancy of systems, subsystems and components most affected by failures • Need for intelligent fail-over redundancyimplementation in controllers, for compensation purposes • Enough diagnostics have to be implemented to allow reliable functioning of the redundant solutions and to ensure thecompensation function.

15. 2. MyrrhaLinac Modeling (MAX Task 4.4) 1. SNS Linac Modeling • Activities • Design & reliability data base (Sources: SNS, Max team, suppliers, conservative assumptions / reliability targets) • MyrrhaLinac model - based on the SNS RS Model; considering the SNS reliability analysis results and conclusions. • Iterative process – Myrrha Linac Model updating during design work • Myrrha Linac Risk Spectrum fault tree– 95% completed; preliminary results in line with previous • Reliability analysis to be performed, with due consideration of reliability challenges • Special attention - design of advanced Diagnostics and Control systems • Overall approach • Fault Tree, based on SNS model + Max design • Basic Events: Component / Function failures • Undeveloped Events/Systems: Reliability targets • Reliability model: Availability / Failure frequency (Linac shutdown) • Reliability Analysis: Design Optimization • Myrrhalinac - Reliability challenges: • InjectorSwitch • Fault tolerance/compensation function • SSAs (Solid State Amplifiers) reliability

16. MyrrhaLinac Modeling – General Assumptions 1. SNS Linac Modeling Modeling Assumptions RF System:considered  SNS (except Klystrons, modulators, & related)  SSAs (spec. RFQ: Myrrha 4-rod (176MHz) vs. SNS RFQ (4-vane)) AUX syst SNS, modified for Myrrha (current design) Missing Reliability data  Assumptions (Equipment overall Reliability data from manufacturer available? (IS ECR, RFQ, SSAs)  Targets (to be further considered) /// (detailed design developing the fault trees (rel. data?)

17. MyrrhaLinac Modeling (Fault tree; quantification data) Missing Data: - No significant impactexpected - (Comps/Assemblies level of details) - Undeveloped Events - Relevant impact (INJ switch-magnet, Fault tolerance/Comp. syst., Control syst) – Assumptions/Targets 1. SNS Linac Modeling

18. MyrrhaLinac Modeling (Control Syst.; Fault tolerance) 1. SNS Linac Modeling • CTRL System modeling • - Fault treedevelopment (Myrrha control philosophy) • - Rel. Targetsto be assigned for: Diagnostics, Data Acquisition & Processing, C-C signals transm., local Control modules, etc.) • - Defined Diagnostics are currently being included in the general CTRL syst. fault tree

19. 1. SNS Linac Modeling ACKNOWLEDGMENT We would like to thank G. Dodson and J. Galambos (SNS) for their help in completing the SNS Reliability Study. Thank you A.E. PITIGOI – EA (aph@empre.es) P. FERNANDEZ RAMOS – EA (pfr@empre.es)