A new approach for exploration of tokamak power plant design space Farrokh Najmabadi, Lane Carlson, and the ARIES Team UC San Diego 4 th IAEA Technical Meeting on First Generation of Fusion Power Plants: Design & Technology 8-9 June 2011 IAEA, Vienna

Outline:  The New ARIES Systems Code.  Developed Recently  New Features/models.  The New System Optimization Process  Parameter Space Survey as opposed to single-point optimization.  Data visualization Tools;  Capability of “multi-dimensional” optimization and risk-benefit analysis.  Work in progress, preliminary results.  The Current ARIES Team Project  The New ARIES Systems Code.  Developed Recently  New Features/models.  The New System Optimization Process  Parameter Space Survey as opposed to single-point optimization.  Data visualization Tools;  Capability of “multi-dimensional” optimization and risk-benefit analysis.  Work in progress, preliminary results.  The Current ARIES Team Project

Systems code development

ARIES Systems Code is used for trade-off analysis and NOT as a Design Tool  It is impossible at the present to combine all analysis tools of various components into one package. Systems code, therefore, includes simple and reasonably accurate model of various subsystems.  We have always used the Systems code as an iterative tool during the design process: systems code identifies the area of interest in the parameter space, detailed design of a “preliminary” design point (“strawman”) helps to identify issues and refine systems code models, systems code identifies a new “strawman,” etc.  Typically, we have used COE as the object function for systems analysis while other objectives/constraint were included in the design process.  It is impossible at the present to combine all analysis tools of various components into one package. Systems code, therefore, includes simple and reasonably accurate model of various subsystems.  We have always used the Systems code as an iterative tool during the design process: systems code identifies the area of interest in the parameter space, detailed design of a “preliminary” design point (“strawman”) helps to identify issues and refine systems code models, systems code identifies a new “strawman,” etc.  Typically, we have used COE as the object function for systems analysis while other objectives/constraint were included in the design process.

A New ARIES Systems Code was developed recently.  The original ARIES systems code was developed in late 80s  Outdated programming.  Some models were too crude and had to be refined considerably during the design study. o A more complex model was deemed to be computationally expensive at the time. o A single code was used to examine multiple confinement concept (without using object-oriented programming and tool kits), i.e., models had to fit a large design window.  A new system code was developed recently.  Currently only applies to tokamak concept.  We are using this tool for the first time in our current study.  The original ARIES systems code was developed in late 80s  Outdated programming.  Some models were too crude and had to be refined considerably during the design study. o A more complex model was deemed to be computationally expensive at the time. o A single code was used to examine multiple confinement concept (without using object-oriented programming and tool kits), i.e., models had to fit a large design window.  A new system code was developed recently.  Currently only applies to tokamak concept.  We are using this tool for the first time in our current study.

ASC Flow Chart (medium detail) Systems code consists of many modular building blocks

Major Features of the new ARIES Systems Code  Detailed 2-D axisymmteric geometry based on plasma shape and includes maintenance considerations.  Detailed power flow and blanket/divertor modules.  TF Magnet algorithm bench-marked against finite-element analysis of ARIES magnets.  Distributed PF algorithm which produces accurate description of stored energy, cost, and VS of a free- boundary equilibrium-based PF system.  New costing algorithm  Based on Gen-IV costing rules.  Detailed 2-D axisymmteric geometry based on plasma shape and includes maintenance considerations.  Detailed power flow and blanket/divertor modules.  TF Magnet algorithm bench-marked against finite-element analysis of ARIES magnets.  Distributed PF algorithm which produces accurate description of stored energy, cost, and VS of a free- boundary equilibrium-based PF system.  New costing algorithm  Based on Gen-IV costing rules.

Detailed power flow model is essential for blanket/divertor models

He pumping power in the divertor is a strong function of heat flux capability  Analysis of three options performed, with some amount of optimization vs. heat flux (more design optimization underway).  Results reduced to polynomial fits with a very high fidelity  Analysis of three options performed, with some amount of optimization vs. heat flux (more design optimization underway).  Results reduced to polynomial fits with a very high fidelity

Thermal conversion efficiency models  The entire effect is caused by degradation of bulk outlet temperature due to internal heat transfer: very design-dependent (DCLL) (SiC)

Major Features of the new ARIES Systems Code  Detailed 2-D axisymmteric geometry based on plasma shape and includes maintenance considerations.  Detailed power flow and blanket/divertor modules.  TF Magnet algorithm bench-marked against finite-element analysis of ARIES magnets.  Distributed PF algorithm which produces accurate description of stored energy, cost, and VS of a free- boundary equilibrium-based PF system.  New costing algorithm  Based on Gen-IV costing rules.  Detailed 2-D axisymmteric geometry based on plasma shape and includes maintenance considerations.  Detailed power flow and blanket/divertor modules.  TF Magnet algorithm bench-marked against finite-element analysis of ARIES magnets.  Distributed PF algorithm which produces accurate description of stored energy, cost, and VS of a free- boundary equilibrium-based PF system.  New costing algorithm  Based on Gen-IV costing rules.

Systems Optimization

Issues with “Point-Optimization”  Experience indicates that the power plant parameter space includes many local minima and the optimum region is quite “shallow.”  The systems code indentifies the “mathematical” optimum. There is a large “optimum” region when the accuracy of the systems code is taken into account, requiring “human judgment” to choose the operating point.  Previously, we used the systems code in the optimizer mode and used “human judgment” to select a few major parameters (e.g., aspect ratio, wall loading).  Experience indicates that the power plant parameter space includes many local minima and the optimum region is quite “shallow.”  The systems code indentifies the “mathematical” optimum. There is a large “optimum” region when the accuracy of the systems code is taken into account, requiring “human judgment” to choose the operating point.  Previously, we used the systems code in the optimizer mode and used “human judgment” to select a few major parameters (e.g., aspect ratio, wall loading). “Mathematical” minimum Systems code accuracy Optimum region  Better Approach: Indentify “optimum region ” as opposed to the optimum point.

Identifying “optimum region ” as opposed to the optimum point  New “optimization” approach:  Indentify “optimum” parameters space as opposed to the optimum point.  In addition, experience indicates that “optimum” design points are usually driven by the constraints. In some cases, a large design window is available when the constraint is “slightly” relaxed, allowing a more “robust” and credible design.  Developed a new approach to Systems Analysis based on surveying the design space instead of finding only an optimum design point (e.g., lowest COE).  This approach requires generation of a very large data base of self-consistent physics/engineering points.  Modern visualization and data mining techniques should be used to explore design space.  New “optimization” approach:  Indentify “optimum” parameters space as opposed to the optimum point.  In addition, experience indicates that “optimum” design points are usually driven by the constraints. In some cases, a large design window is available when the constraint is “slightly” relaxed, allowing a more “robust” and credible design.  Developed a new approach to Systems Analysis based on surveying the design space instead of finding only an optimum design point (e.g., lowest COE).  This approach requires generation of a very large data base of self-consistent physics/engineering points.  Modern visualization and data mining techniques should be used to explore design space.

Now to visualize those points…  It is a challenge to visualize large data sets: “Lots of numbers don’t make sense to ‘low- bandwidth’ humans, but visualization can decode large amounts of data to gain insight.” - San Diego Super Computer Center  It is a challenge to visualize large data sets: “Lots of numbers don’t make sense to ‘low- bandwidth’ humans, but visualization can decode large amounts of data to gain insight.” - San Diego Super Computer Center

How do others visualize large data sets?  Visualizing large datasets is a difficult task, almost an art.  Too much information can be overwhelming/deceiving.  10 6 points exceed computer monitor real estate.  Visualizing large datasets is a difficult task, almost an art.  Too much information can be overwhelming/deceiving.  10 6 points exceed computer monitor real estate. NOAA (National Oceanic and Atmospheric Admin.) data-logging buoys SDSC Earthquake Simulation

We have developed a visualization tool to utilize the scanning capability of the new systems code  VASST - Visual ARIES Systems Scanning Tool  Desire to visualize the broad parameter space and to extract meaningful relationships  Graphical user interface (GUI) permits 2D plots of any parameter  VASST - Visual ARIES Systems Scanning Tool  Desire to visualize the broad parameter space and to extract meaningful relationships  Graphical user interface (GUI) permits 2D plots of any parameter Purpose:  give the user visual interaction and explorative power  extract meaningful relationships  understand design tradeoffs

18 Pull-down menus for common parameters Blanket used Number of points in database Constraint parameter can restrict/filter database on-the-fly Auto-labeling Correlation coefficient Save plot Color bar scale Edit plot properties Populate table with click Turn on ARIES-AT point design for reference VASST - Visual ARIES Systems Scanning Tool All self-consistent design points

Snap-shot of COE vs electric output for all data points (SiC blanket) SiC blanket

Example: COE vs Inboard Divertor Heat Flux 20 SiC blanket, 1 GWe constraint: B T < 8.5 T

Example: COE vs Inboard Divertor Heat Flux 21 SiC blanket, 1 GWe constraint: B T < 7.5 T

Example: COE vs Inboard Divertor Heat Flux SiC blanket, 1 GWe constraint: B T < 6 T

Example: COE vs Inboard Divertor Heat Flux SiC blanket, 1 GWe constraint: B T < 5 T

Example: Impact of field strength  For a given maximum  N :  Increasing B t typically increase the size of parameter space (e.g., relax many parameters:  N, n G /n, H 98, …) but does not reduce the minimum cost.  B t can be viewed as a compensation/insurance knob against R&D failure to achieve certain plasma parameters  For a given maximum  N :  Increasing B t typically increase the size of parameter space (e.g., relax many parameters:  N, n G /n, H 98, …) but does not reduce the minimum cost.  B t can be viewed as a compensation/insurance knob against R&D failure to achieve certain plasma parameters B t < 5 T COE (mills/kWh) Heat flux on in board divertor (MW/m 2 ) B t < 7 T COE (mills/kWh) Heat flux on in board divertor (MW/m 2 ) SiC Blanket

Summary  Exploring the “optimum region” as opposed to the “optimum point” has many desirable features:  Prevents the design point to be pushed into a “constraint corner” and allows for a more robust design point.  Clearly identifies the trade-offs and the “weak” and “strong” constraints, helping the R&D prioritization.  Identifies compensation/insurance knobs against failures of R&D to achieve certain parameters.  The extensive data base can also be used for risk/benefits analyzes by defining different optimization parameters.  Exploring the “optimum region” as opposed to the “optimum point” has many desirable features:  Prevents the design point to be pushed into a “constraint corner” and allows for a more robust design point.  Clearly identifies the trade-offs and the “weak” and “strong” constraints, helping the R&D prioritization.  Identifies compensation/insurance knobs against failures of R&D to achieve certain parameters.  The extensive data base can also be used for risk/benefits analyzes by defining different optimization parameters.

Project goals and plans

Goals of Current ARIES Study  Revisit ARIES Designs with a focus on detailed analysis edge plasma physics and plasma- material interaction, high heat flux components and off-normal events in a fusion power plant.  What would ARIES designs look like if we use current predictions on heat/particles fluxes?  What would be the maximum fluxes that can be handled by in-vessel components in a power plant?  What level of off-normal events are acceptable in a commercial power plant?  Can the current physics predictions (ITER rules, others) be accommodated and/or new solutions have to be found?  Revisit ARIES Designs with a focus on detailed analysis edge plasma physics and plasma- material interaction, high heat flux components and off-normal events in a fusion power plant.  What would ARIES designs look like if we use current predictions on heat/particles fluxes?  What would be the maximum fluxes that can be handled by in-vessel components in a power plant?  What level of off-normal events are acceptable in a commercial power plant?  Can the current physics predictions (ITER rules, others) be accommodated and/or new solutions have to be found?

Frame the “parameter space for attractive power plants” by considering the “four corners” of parameter space Reversed-shear ( β N = ) DCLL blanket Reversed-shear ( β N = ) DCLL blanket Reversed-shear ( β N = ) SiC blanket Reversed-shear ( β N = ) SiC blanket 1 st Stability ( no-wall limit) DCLL blanket 1 st Stability ( no-wall limit) DCLL blanket 1 st Stability ( no-wall limit) SiC blanket 1 st Stability ( no-wall limit) SiC blanket ARIES-RS/AT SSTR-2 EU Model-D ARIES-1 SSTR Lower thermal efficiency Higher Fusion/plasma power Higher P/R Metallic first wall/blanket Higher thermal efficiency Lower fusion/plasma power Lower P/R Composite first wall/blanket Higher power density Higher density Lower current-drive power Lower power density Lower density Higher CD power Decreasing P/R Physics Extrapolation

Study is divided into Two Phases:  Phase 1:  Examination of power-plant parameter space with the systems code to understand trade-offs.  Examination of capabilities of in-vessel components (heat and particle fluxes) in both steady-state and off- normal events, e.g., ELM, disruption (thermal).  Phase 2:  Detailed design o To verify systems code predications o To develop an integrated design o To document trade-offs and R&D needs.  Phase 1:  Examination of power-plant parameter space with the systems code to understand trade-offs.  Examination of capabilities of in-vessel components (heat and particle fluxes) in both steady-state and off- normal events, e.g., ELM, disruption (thermal).  Phase 2:  Detailed design o To verify systems code predications o To develop an integrated design o To document trade-offs and R&D needs.  Project began in Jan