SNS Linac Reliability Model (MAX Task 4.2)

1. SNS Linac Reliability Model (MAX Task 4.2) SHLiPP-3 – Louvain-La-Neuve, April 17-18 Adrian Pitigoi, Pedro Fernández Empresarios Agrupados, Spain

2. SNS Linac Modeling SNS Model - INPUT DATA Modeling Methodology SNS Fault Tree Development SNS Systems - Reliability Analysis SNS RS - Model and results evaluation 6. Next Steps (MAX Task 4.4)

3. 1. SNS Linac Modeling WP 4 Task 4.2 Objective - Reliability model of SNS Linac accelerator Feedback on actual SNS reliability performance, in order to develop a reliability modeling toolfor MAX project Task 4.2 Activities: Selection of the accelerator to be used for modeling (SNS) SNS Design & Reliability data collection Development of SNS Linac RS reliability model Performing reliability analysis of SNS Linac systems, Task 4.2 Targets: Evaluate the SNS Linac model (model results vs. SNS operational data) Conclusions and recommendations on optimization, increasing reliability. Layout of the SNS Linac

4. 2. SNS Model - INPUT DATA SNS Design Data SNS Accelerator overall structure (main and auxiliary systems); system interfaces Systems components/interconnection Number of components (by type) Degree of redundancy Data Sources: SNS RAMI Static Model SNS BlockSim model (Reliasoft) SNS Systems and Functions SNS Parameters Systems and components System functions Systems functional interdependencies Data Sources: SNS website (http://neutrons.ornl.gov/facilities/SNS/): ¨How SNS works¨ - http://neutrons.ornl.gov/facilities/SNS/works.shtml; SNS Parameters (doc no. SNS 100000000-PL001R13) (http://neutrons.ornl.gov/media/pubs/pdf/sns_parameters_list_june05.pdf) SNS Design Control Documents (DCD) SNS BlockSim Model

5. 2. SNS Model - INPUT DATA SNS Reliability Data Number of components (by type) Degree of redundancy Failure data: λ=1/MTTF; MTTR (λ– Failure rate; MTTF-Main Time To Failure; MTTR-Main Time To Repair) Data Sources: RAMI Static Model SNS BlockSim detailed model SNS Operating Status Component failures - cause, type of component, time to repair, etc. Availability data (component failures causing accelerator trips: cause, component and system concerned, duration of trip) Data Sources: SNS Operation Data collection (http://status.sns.ornl.gov/beam.jsp) http://status.sns.ornl.gov/beam.jsp

6. 3. Modeling Methodology • General Assumptions • SNS systems/components not modeled – Ring - RTBT, stripper foil, etc. (considered as not relevant for Max project purposes) • Risk Spectrum Type 1– Repairable component reliability model (continuously monitored) – Type 1 reliability model has been considered for modeling all SNS Linac components failing behavior. • ¨Mean Unavailability¨ type of calculation is used to obtain the unavailability values of the basic events; • (the long-term average unavailability Q is calculated for each basic event) • a) The 1,000-foot SNS linear accelerator is made up of three different types of accelerators. • The SNS ring intensifies the high-speed ion beam and shoots it at the mercury target 60 times a second (60 Hz). • Target

7. 4. SNS Reliability Model - Fault Tree Model 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.

8. 4. SNS Reliability Model - Fault Tree Model SNS Fault Tree (complete model)- graphical representation of the SNS systems functional structure describing undesired events ("failures") and their causes. • The Fault tree is built using gates and basic events. A fault tree can be subdivided between several fault tree pages, which are bound together using transfer gates.

9. 4. Modeling the SNS Linac Fault Tree Structure - The main levels of the fault trees for each main part of the SNS accelerator (Ion Source, LEBT, RFQ, MEBT, DTL-CCL-SCL, HEBT, CONV - auxiliary systems) are presented in the Appendix 3.

10. 4. Modeling the SNS Linac Fault Tree Structure

11. 4. Modeling the SNS Linac Fault Tree Structure - The main levels of the fault trees for each main part of the SNS accelerator (Ion Source, LEBT, RFQ, MEBT, DTL-CCL-SCL, HEBT, CONV - auxiliary systems) are presented in the Appendix 3.

12. SNS Linac Model (resuming): • Basic events - RS Repairable Model • (RS model Type1) • Quantification of BE (λ and MTTR – • R and TR in RS model) • BE - Mean Unavailability type of • calculation • SNS Linac complete Model has been analyzed (SNS ACC DOWN) • RS MCS (Minimal Cut Set) • type of analysis. • MCS generates the minimal cut sets • of the FT and • perform a mean availability point-estimate • quantification of the top event • Analysis Case – Results • Q = 2.60E-01 = 0.26; Q = 26 % • A = 1 - Q = 73 % (the limit Availability • – Mean Availability) 5. SNS Systems - Reliability Analysis

13. Analysis Case – Results • Q = 2.60E-01 = 0.26; Q = 26 % • A = 1 - Q = 73 % (the limit Availability – Mean Availability) 5. SNS Systems - Reliability Analysis Results • MCS Analysis has been performed for the SNS Linac complete model (SNS ACC DOWN), as well as for different parts of the accelerator, with the following conclusions: • The Linac, (DTL-CCL-SCL) represents the most concerned part (Q=1.25E-01; A=87.5%) • The higher values of Unavailability have been found for: • SCL (Q=9.85E-02; A=90%) • DGN&C (Q=7.15E-02; A=93%) • Front-End (Q=6.93E-02; A=93%) • The most concerned part of the SCL is the SCL RF (Radiofrequency system of the Superconducting Linac): Q=6.33E-02; A=94% • The most concerned parts of the Front-End are the LEBT (Q=2.83E-02; A=97%) and MEBT (Q= 2.82E-02; A=97%)

14. SNS Reliability considerations (from past operation experience) • The SNS reliability has increased significantly (beyond 85%) just over the past few years. • The reliability data mix used (RAMI static model, BlockSim model) - compilation from different sources, including internal SNS operational staff records (data from staff Engineers, manufacturers - e.g. Titan, Varian, Maxwel l-, design reviews, etc.) • SNS staff will update the BlockSim model: to be quantified using data statistically processed from the Logbook - more realistic model. • SNS RS Model Limitations • The SNS reliability data (MTTF; MTTR) used for RS model quantification, are from • the SNS previous experience (data mentioned above) • The improved maintenance implemented during the last years based on the operation • experience gained, has not been represented and quantified in the model. • The LEBT and DGN&C modules are relatively developed (lack of information; • reliability of available data) From G.Dodson ´s talk given at the accelerator reliability Workshop in Cape Town, South Africa in April 2011 5. SNS Reliability modeling – Model and results evaluation • However, the availability results obtained separately for the different SNS Linac parts (IS, RFQ, MEBT, DTL, CCL, SCL, HEBT) have shown very good match with the SNS Logbook Availability records, although the global result is A=73%. This is due to the fact that the MTTF and MTTR values used for model quantification are too conservative. • Considering the reliability database used for quantifying, and the fact that the last years reliability improvements have not been included in the model, it can be stated that the overall Availability result (A=73%) given by the SNS RS model, is in line with the SNS Linac availability from the first 4 years of operation.

15. 5. SNS Reliability modeling – Model and results evaluation SNS Reliability graphics (Logbook availability and failure data) SNS Outages (jan-febr. and june 2012)

16. 5. SNS Reliability modeling – Model and results evaluation

17. The reliability results have shown that the most affected SNS Linac parts/systems are: • SCL, Front-End systems (IS, LEBT, MEBT), Diagnostics & Controls • RF systems (especially the SCL RF system) • Power Supplies and PS Controllers • The results are in the same line as the records in the SNS Logbook • The best reliability consideration that needs to be enforced in LINAC design is redundancy, for the most • concerned systems/subsystems/components • Need for Intelligent fail-over redundancy implementation in controllers, for compensation purposes • Need for implementation of enough diagnostics, to allow reliable functioning of the redundant solutions. 5. Reliability Analysis. Conclusions and Recommendations

18. Development of the MAX Linac Reliability model, starting from the SNS RS Model results and • conclusions • Iterative process – the MAX Model should be developed and continuously updated during design work, • assimilating the current design information and providing recommendations for reliability improvements. 6. Next Steps (MAX Task 4.4)

19. 6. MAX Model – Methodology & Input Data Overall approach Fault Tree (based on SNS model) - Max design available info Undeveloped Events/Systems: Reliability targets <= (Del. 1.1 – gen. machine spec.) Fault Tree update (Del. 1.2 /design activities - WP2, WP3, Task 4.5-RF) Reliability model: Availability / Failure (MAX shutdown) frequency Reliability Analysis: Optimization Design & reliability data baseData Source: SNS, Max team, suppliers, conservative assumptions / reliability targets Basis: SNS Model base (SNS–MAX design comp.+ Max design specific) Basic Events: Component / Function Failures Further develop: Parts/Systems of special interest; ¨critical¨ reliability issues Support systems – gen. level

20. Thank you