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Development of the New ARIES Tokamak Systems Code

This paper discusses the development of a new systems code for the ARIES Tokamak, integrating physics, engineering, design, and costing to establish a database for studying different metrics. The code enables tradeoff studies, optimization, and cost estimation for Tokamak reactor designs.

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Development of the New ARIES Tokamak Systems Code

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  1. Development of the New ARIES Tokamak Systems Code Zoran Dragojlovic, Rene Raffray, Farrokh Najmabadi, Charles Kessel, Lester Waganer US-Japan Workshop on Power Plant Studies and Advanced Technologies 5-7 March 2008, San Diego CA

  2. Motivation • In order to help identify and quantify the needs for the next step Tokamak reactor, a new systems code is being developed at the UCSD. • The systems code, which integrates physics, engineering, design and costing will establish a database for studying the benefits of different metrics. • The physics basis for the code was originally developed for the burning plasma experiment of the FIRE project (Charles Kessel). • In order to provide accurate estimate of costs, a realistic 3-D Tokamak geometry is implemented. • Engineering algorithms are utilized to define advanced power core features. • Cost basis is being updated from prior Tokamak studies.

  3. Tradeoff Studies • Most systems codes produce a single operating point, which offers a limited amount of information about the operating space and is sometimes difficult to justify. • Instead we are developing an operating space approach, where we observe trends that affect Cost of Electricity based on many operating points. • In this concept, large system scans are initially performed in order to identify the most critical parameters that impact the COE. • Targeted system scans are intended to narrow down the parametric space to reveal the most attractive Tokamaks. • Once the parametric space is sufficiently narrowed down, the code will be run in an optimizer mode in order to identify one or few optimal operating points.

  4. ARIES Systems Code is Composed of Generic Building Blocks Class DesignPoint {data; functions that operate on data;}; • Foundation for the algorithm is a general-purpose systems analysis toolbox. • Consists of ready-to-use generic objects (classes) that serve as building blocks for different systems algorithms with different objectives. • Class DesignPoint holds design-specific data that describe the entire machine, such as plasma parameters, builds, power flow, building volumes, etc. These data are accessed, operated on and displayed by special functions that belong to the same class. • Class Part holds part-specific data such as contours, areas, volumes, etc. • Class CostingAccount holds the costing account structure for the selected machine design. • Simple declaration statement such as “Part Blanket_II;” declares all the variables and associated functions needed to define this particular object. • Systems Code is generated by connecting the elements of the toolbox together. Class Part {data; functions;}; Class CostingAccount {data; Functions; };

  5. Code Layout Geometry and Engineering Algorithms Costing Algorithms COE $ 2 3 Power Core Parts DesignPoint Aries_AT; Aries_AT.get_physics(); • An example configuration that calculates the cost of electricity for ARIES-AT Tokamak. Display Output Aries_AT.show(); Plasma Physics 1 4

  6. Power Core Geometry is Based on ARIES-AT PF Coil Bucking Cylinder • Contours of the power core elements are composed from second order polynomials, based on CAD drawings of the ARIES-AT. • Volumes are used for calculating masses of different elements, which are then multiplied by costs per unit mass in order to obtain total costs. • Surface areas are combined with power flow information in order to calculate quantities such as neutron wall load, for example. Toroidal Cap Divertor Plates First Wall TF Coil Vacuum Vessel HT Shield Plasma Blanket II Blanket I Central Solenoid Inboard Blanket

  7. Engineering Algorithms • TF Coil • Cross section is determined based on maximum magnetic field in the coil and experimental dependence of current density on maximum magnetic field for several advanced superconductors, such as YBCO @ 75K and Nb3Sn @ 4.2K. • Structural support is determined by scaling from finite element analyses for Tokamak designs of similar geometry (ARIES-AT, ARIES-I and ARIES-RS). • Coil shape matches ARIES-AT. • PF Coil • Thickness in the poloidal cross section is determined by the required current in the coil and the maximum current density in the superconductor. • PF coil currents are based on scaling with plasma current, average distance from PF coils to plasma and flux state needed to establish currents in coils. • Bucking Cylinder • Thickness based on simple radial load. • HT Shield • Thickness is estimated based on average neutron wall load.

  8. Power Flow is Based on ARIES-AT 1 GW 1.72GW • The power magnitudes are taken from the systems code output and mapped to a color bar shown on the bottom [W]. The example shown here matches the ARIES-AT. • Efficiency of the Brayton cycle is estimated based on the maximum neutron wall load and maximum surface heat flux. For ARIES-AT, this efficiency is 58.5%.

  9. Validation of the Systems Code • After the algorithms were completed, the systems code was validated against geometry and major physics, technology and economics parameters that define the ARIES-AT. Comparison was made for • Volumes • Power plant parameters • Plasma parameters • First wall and blanket parameters • Costing accounts

  10. Major Power Plant Parameters for ARIES-AT • Outputs of the systems code (red) indicate a close match with the results that were obtained from the previous studies.

  11. New Systems Code Costing Accounts and Economic Parameters We haven’t yet achieved a complete match with previously published data (old systems code) but we are getting close.

  12. Database of Tokamak Operating Points for Tradeoff Studies • After the major algorithms of the systems code were brought to a close match with the ARIES-AT, the code was used for generating a database of 22,952 operating points. The following parameters were scanned: • Plasma aspect ratio (A): 2.5 to 4. • Normalized beta (bn): 3 to 6. • q95: 3.2 to 4.0 • Plasma triangularity (D): 0.6 to 0.8 • Ratio of line averaged plasma density to Greenwald density (n/nGr): 0.4 to 1.0. • Q :25 to 50 • Plasma elongation (k): 1.8 to 2.2 • Plasma major radius ( R ): 4.8 to 7.8  (for A=2, scanned from 2.8 to 7.8) • Argon fraction:  0.1 to 0.3 % • Magnetic field at plasma major ratio (BT)  5.0 to 10.0 ( for A=2, scanned from 1.50 to 5.5)

  13. Data Point Locations All Costs of Electricity Optimal COE Versus 3 Parameters Optimal COE Surface arbitrary parameter varies across COE surface Visualization of Data

  14. Cost of Electricity Versus Plasma Major Radius, Toroidal Field at Plasma Major Radius and Normalized Beta • The results here are not “official” and we still may have not fully debugged the costing accounts, however, we are able to observe the behavior of the COE with the major parameters that impact it. bn

  15. max = 93.6 COE [mill/kWh] min = 65.6 min = 0.5 fGW max = 1.0 min = 0.03 bn max = 0.06 Four Versus Five Parameter Plot Greenwald Density Fraction Across Optimal COE Surface Normalized Beta, Greenwald Fraction and Optimal COE • We are still experimenting with various ways to visualize data. An alternative to a 4 parameter surface plot shown on the left could be to use multiple slices which show variation of different parameters. Figure on the right shows the relative topography of COE, Greenwald fraction and normalized beta. fGW

  16. Conclusions • ARIES Systems Code has approached the level at which it can be used for the intended tradeoff studies. • All the major physics and engineering algorithms are included and their validation is in progress. • Presently, costing is based on previous studies (ARIES-AT) but we are actively working on updating our materials and technology cost base. • Once the code is fully validated, we are planning to explore the data base of operating points and experiment with visualization, data mining and optimization methods before moving on to production runs.

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