Predictive density profile control with discrete pellets, applied to integrated simulations of ITER

Abstract Reliable core density control with pellet fueling will be necessary to achieve required fusion power output in future fusion tokamaks such as ITER. The discrete nature of fuel pellets, however, complicates the density profile control problem significantly. As a solution, we propose a predictive density profile controller that considers fuel pellets as discrete actuators, while ensuring operation within prescribed density limits. The model predictive control (MPC) scheme we deploy combines the offset-free method to correct prediction model inaccuracies and our novel modified penalty term homotopy algorithm for real-time MPC (PTH-MPC). To demonstrate density profile control with discrete pellets, we couple the PTH-MPC density controller with JINTRAC integrated simulations of the ITER 15 MA/5.3 T scenario, using HPI2 to model discrete pellet ablation and deposition. We compare the density controller performance in integrated simulations using the Bohm/gyro-Bohm turbulent transport model against integrated simulations using the TGLF turbulent transport model. We highlight the necessity of treating pellets as discrete events for controller performance and for remaining within density limits. We conclude that PTH-MPC is a promising candidate for density profile control with pellets fueling in ITER and other future tokamaks and recommend further improvements using learning-based and robust MPC. We also note the limitations of quasi-linear turbulent transport models in simulations involving discrete pellets.

Authors

Institutions

Publication Details

Journal
Nuclear Fusion
Published
2025-06-17
DOI
https://doi.org/10.1088/1741-4326/ade54e
Primary Topic
Nuclear reactor physics and engineering
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Predictive density profile control with discrete pellets, applied to integrated simulations of ITER

Dinesh Krishnamoorthy, Lennard Ceelen, T.O.S.J. Bosman, M. van Berkel et al.
Nuclear Fusion
Nuclear reactor physics and engineering
article

Predictive density profile control with discrete pellets, applied to integrated simulations of ITER

Dinesh Krishnamoorthy, Lennard Ceelen, T.O.S.J. Bosman, M. van Berkel, W.P.M.H. Heemels, F. Koechl, C. Orrico
article en

Abstract

Abstract Reliable core density control with pellet fueling will be necessary to achieve required fusion power output in future fusion tokamaks such as ITER. The discrete nature of fuel pellets, however, complicates the density profile control problem significantly. As a solution, we propose a predictive density profile controller that considers fuel pellets as discrete actuators, while ensuring operation within prescribed density limits. The model predictive control (MPC) scheme we deploy combines the offset-free method to correct prediction model inaccuracies and our novel modified penalty term homotopy algorithm for real-time MPC (PTH-MPC). To demonstrate density profile control with discrete pellets, we couple the PTH-MPC density controller with JINTRAC integrated simulations of the ITER 15 MA/5.3 T scenario, using HPI2 to model discrete pellet ablation and deposition. We compare the density controller performance in integrated simulations using the Bohm/gyro-Bohm turbulent transport model against integrated simulations using the TGLF turbulent transport model. We highlight the necessity of treating pellets as discrete events for controller performance and for remaining within density limits. We conclude that PTH-MPC is a promising candidate for density profile control with pellets fueling in ITER and other future tokamaks and recommend further improvements using learning-based and robust MPC. We also note the limitations of quasi-linear turbulent transport models in simulations involving discrete pellets.

Nuclear FusionVol. 65(7)
ITER (FR), Norwegian University of Science and Technology (NO), Dutch Institute for Fundamental Energy Research (NL), Eindhoven University of Technology (NL)
EUROfusion, European Commission, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Euratom Research and Training Programme
Openalex Percentile: Top 82%
Nuclear reactor physics and engineering
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.