ARIES- Pathways, October 25-26, 2010 Bethesda, MD Page 1 L. Waganer Consultant for The Boeing Company ARIES-Pathways Project Meeting 25-26 October 2010.

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Presentation transcript:

ARIES- Pathways, October 25-26, 2010 Bethesda, MD Page 1 L. Waganer Consultant for The Boeing Company ARIES-Pathways Project Meeting October 2010 Bethesda, MD The work contained in this presentation was sponsored by PPPL Pilot Plant Study and the ARIES Project Methodology for Scaling Plant Availability to First-of-a-kind and One-of-a-kind Plants

ARIES- Pathways, October 25-26, 2010 Bethesda, MD Page 2 Importance of Plant Availability Plant Availability is one of the most important factors to be demonstrated for commercial plant viability – it is extremely important to power producers One of the necessary decisions to be made is the level of availability to be demonstrated at each facility step toward the first-of-a-kind fusion power plant ● Pilot Plant (Q eng <1) ● Demo (power producing) ● First-of-a-kind Power Plant

ARIES- Pathways, October 25-26, 2010 Bethesda, MD Page 3 Verifying Plant Availability Achieving a verifiable Plant Availability requires a vast database of reliable components and subsystems that have been rigorously tested and validated in the relevant system configuration and environment That is, It must look like and act like the final product. ● Pilot Plant (Q eng <1) ● Demo (power producing) ● First-of-a-kind Power Plant

ARIES- Pathways, October 25-26, 2010 Bethesda, MD Page 4 Plant Availability Background All the conceptual designs of fusion power plants were based on the assumption of 10 th -of-a-kind technology (PNL document “Fusion Reactor Design Studies – Standard Accounts for Cost Estimates”, May 1978.) Early plant studies relied on general assumptions and hypotheses: UWMAK-1 (80%), Starfire (75%) and early ARIES (76%) Later ARIES studies have done “semi-detailed” studies - ARIES-RS, Analysis 91%, Study baseline 76% Some detail, some scoping - ARIES-AT, Analysis 87.6%, Study baseline 85% More detailed analysis - ARIES-CS, Analysis 84.6%, Study baseline 85% Considerably detailed analysis

ARIES- Pathways, October 25-26, 2010 Bethesda, MD Page 5 Correct Method to Determine Plant Availability Need to develop a complete set of detailed and verifiable reliability and lifetime data plus maintenance procedures and timelines for all components, subsystems and systems This data is not existing at present and will not be available for decades Best current approach is to use failure and maintenance scoping studies to determine reasonable requirements and determine plant maintenance and availability goals Data from other existing power technologies can be applied to 10 th -of-a-kind cases. Then scale these back to first-of-a- kind and one-of-a-kind.

ARIES- Pathways, October 25-26, 2010 Bethesda, MD Page 6 Plant Availability Definition Fusion power plants will be capital intensive with low fuel and operational costs, therefore they will be base-loaded plants, operating at full power except for scheduled and unscheduled maintenance periods Plant availability is defined as being directly proportional to the time the plant is available for power production divided by the total time, which is the sum of the operational time plus the scheduled and unscheduled maintenance periods Availability = FPY/(FPY + Maintenance Days/FPY) = /( Maintenance Days/FPY)]

ARIES- Pathways, October 25-26, 2010 Bethesda, MD Page 7 Major Plant Maintenance Actions Scheduled power core, major Scheduled power core, minor Unscheduled power core Power core equipment, scheduled and unscheduled Balance of plant, scheduled and unscheduled Regular replacement of blankets, divertors Regular replacement of H/CD, PS, etc Dependent on power core failure rates Heat transport, fuel handling, I&C, maintenance, rad waste, etc Turbine plant, electric plant, facilities These actions must result in the plant operation ceasing to be considered in the Availability calculation

ARIES- Pathways, October 25-26, 2010 Bethesda, MD Page 8 Example of Maintenance and Availability Data (ref ARIES-AT) Table 1. System Maintenance Days/Full Power Year and Availabilities Goal was to achieve an overall plant availability of ~ 90% Power core scheduled maintenance was determined to be 98.9% by analysis (4.23 d/FPY) Scheduled minor power was thought to about 60% longer than scheduled major (6.05 d/FPY) Unscheduled power core was estimated to be four times sum of scheduled minor and major maintenance (20.56 d/FPY) BOP and Power Core Equipment would need to 97.5% to meet goal (9.37 d/FPY d/FPY ) Most knowledge, difficult, fully remote, no redundancy Some redundancy, R. handling Reliability TBD, anomalous maint Large systems with many components, reliability TBD

ARIES- Pathways, October 25-26, 2010 Bethesda, MD Page 9 Logic for Scaling Availability A 10 th -of-a-kind facility will employ refined subsystems, have detailed reliability and lifetime databases, proven failure and end of life prediction methods, sophisticated autonomous maintenance equipment and verified maintenance procedures. Availability ~ 90% The First-of-a-kind power plant will have first generation of many of the technologies and procedures with limited validated experience. Availability ~ 60-70% One-of-a-kind plants will likely have prototypes and test articles of many of the technologies and procedures to gain and establish the reliability and maintainability data. It is desirable that these facilities use the same technologies and configurations of the demo and power plant, but mission requirements may preclude this. Availability ~ 40-50% These availability values are desirable goals to minimize risk for future facilities. These values are for the plant after it has “matured”, solved all early failure problems and finalized on effective procedures.

ARIES- Pathways, October 25-26, 2010 Bethesda, MD Page 10 Relative Maintenance Factors to Achieve Desired Availability Goals To assess the difficulty to achieve the desired availability goals, the maintenance durations for the main plant areas were assessed and factors applied as shown in the table below Relative Maintenance Duration Factors Relating to Plant Maturity Logic for values: Scheduled power core maintenance will have a lot of attention and will exhibit a moderate learning curve Unscheduled power core maintenance (failures and creative maintenance) will represent a significant learning experience Power core equipment (HT, Fuel, I&C, Maint, Rad W) will be steeper learning due to lesser attention BOP will benefit from competitive power sources

ARIES- Pathways, October 25-26, 2010 Bethesda, MD Page 11 Relative Maintenance Factors to Achieve Desired Availability Goals To assess the difficulty to achieve the desired availability goals, the maintenance durations for the main plant areas were assessed and factors applied as shown in the table below Relative Maintenance Duration Factors Relating to Plant Maturity Logic: Scheduled power core maintenance will have a lot of attention and will exhibit a moderate learning curve Unscheduled power core maintenance (failures and creative maintenance) will represent a significant learning experience Power core equipment (HT, Fuel, I&C, Maint, Rad W) will be steeper learning due to lesser attention BOP will benefit from competitive power sources

ARIES- Pathways, October 25-26, 2010 Bethesda, MD Page 12 Combining Scaling Logic with Maintenance Factor Yields Availability The availability scaling logic relates the 10 th -of-a-kind, first-of-a-kind and one-of-a-kind plants. Combining this logic with the respective maintenance duration factors yields the expected availability values. Relative Maintenance Duration Factors and Availability Relating to Plant Maturity These values are reasonably close to the desired “mature” value of the first- of-a-kind plants at 60-70% and one-of-a-kind plants at 40-50%. This also illustrates the relative maintenance durations of the major plant systems.

ARIES- Pathways, October 25-26, 2010 Bethesda, MD Page 13 Next Actions The ability to scale the plant capital and operating costs from the current 10 th -of-a-kind to the first-of-a-kind and one-of-a-kind would be very useful in the development of a fusion roadmap and studies of future fusion facilities. The methodology to accomplish this scaling involves cost learning curves, quantity of identical components or subsystems, level of the subsystem technology and the production method. Each subsystem has to be evaluated for these factors to determine the appropriate learning curve factor. This learning curve methodology was applied by L. Waganer in the economic estimate of the Prometheus Laser and Heavy Ion Power Plants. If this is deemed to be a worthy task, work on it will commence under the auspices of ARIES.