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Results for “control-oriented models”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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40 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Regulation of the central safety factor and normalized beta under low NBI torque in DIII-D

An algorithm has been designed to simultaneously control the central safety factor (q 0 ) and normalized beta (β N ) while ensuring near-zero torque from the neutral beam injection in DIII-D. Feedback control of q 0 and β N in tokamaks can be beneficial due to the close relationship that these variables have with plasma performance and magneto-hydrodynamic stability. In addition, low neutral-beam-torque conditions are of special interest in present devices because future burning-plasma tokamaks such as ITER will most likely operate at very low plasma rotation. The control synthesis of the algorithm presented in this work is based on a linearized, one-dimensional (1D) model of the current-profile dynamics coupled with a zero-dimensional (0D) plasma-energy balance. The actuators considered are neutral beam injection and electron-cyclotron heating and current drive, and discrete logic determines the neutral-beam injection powers that deliver near-zero torque. Here, the algorithm has been tested in nonlinear, 1D simulations using COTSIM (Control-Oriented Transport SIMulator) and in DIII-D experiments, demonstrating satisfactory performance.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Autoregressive long-horizon prediction of plasma edge dynamics *

Accurate modeling of scrape-off layer (SOL) and divertor-edge dynamics is vital for designing plasma-facing components in fusion devices. High-fidelity edge fluid/neutral codes such as SOLPS-ITER capture SOL physics with high accuracy, but their computational cost limits broad parameter scans and long transient studies. We present transformer-based, autoregressive surrogates for efficient prediction of 2D, time-dependent plasma edge state fields. Trained on SOLPS-ITER spatiotemporal data for the KSTAR tokamak, the surrogates forecast electron temperature, electron density, and radiated power over extended horizons. We evaluate model variants trained with increasing autoregressive horizons (1–100 steps) on short- and long-horizon prediction tasks. Longer-horizon training systematically improves rollout stability and mitigates error accumulation, enabling stable predictions over hundreds to thousands of steps and reproducing key dynamical features such as the motion of high-radiation regions. Measured end-to-end wall-clock times show the surrogate is orders of magnitude faster than SOLPS-ITER, enabling rapid parameter exploration. Prediction accuracy degrades when the surrogate enters physical regimes not represented in the training dataset, motivating future work on data enrichment and physics-informed constraints. Overall, this approach provides a fast, accurate surrogate for computationally intensive plasma edge simulations, supporting rapid scenario exploration, control-oriented studies, and progress toward real-time applications in fusion devices.

autoregressive deep learning↗
DOE OSTI2023

Rigid-flexible coupling multi-body dynamics modeling of a semi-submersible floating offshore wind turbine

A 14-degree-of-freedom (14-DOF) flexible multibody dynamics model is developed and verified for a semi-submersible floating offshore wind turbine (FOWT). The model considers the coupled dynamics of the platform, tower, nacelle, blades, and mooring subjected to external wind and wave loads. The platform is simplified as a rigid body connected to the seabed by the mooring system. The tower and blade are modeled as flexible cantilever beams. The energy method is used to derive the governing equations of motion, where the kinetic energy, potential energy, and work done by external wind-wave forces are all deduced in a global coordinate system. The 5-MW baseline semi-submersible FOWT is used to verify the derived model against the results simulated from FAST developed by the US National Renewable Energy Laboratory (NREL) at two scenarios: free decay state and different wind-wave load cases. Results show that the established FOWT model can well reflect the vibration characteristics of FAST model. The application of the model to the control of platform pitch with a tuned mass damper is studied. Furthermore, the simplified model could provide a low-order method for the structural dynamics analysis and advanced vibration control design for the multi-body components of the semi-submersible FOWT in the future.

17 WIND ENERGY↗
DOE OSTI2021

Neural network model of the multi-mode anomalous transport module for accelerated transport simulations

Here, a neural network version of the Multi-Mode Anomalous Transport Module, known as MMMnet, has been developed to calculate plasma turbulent diffusivities in DIII-D with a calculation time suitable for control applications. MMMnet uses a simple artificial neural network structure to predict the ion thermal, electron thermal, and toroidal momentum diffusivities while reproducing Multi-Mode Model (MMM) data with good accuracy and keeping the calculation time as a fraction of that associated with MMM. Model-based control techniques require models with fast calculation times, making many existing physics-oriented predictive codes unsuitable. The control-oriented predictive code COTSIM (Control Oriented Transport SIMulator) calculates the most significant plasma dynamics in response to the different actuators while running at a speed useful for control design. In order to achieve this calculation speed, COTSIM often relies on scaling laws and control-level models. Replacing some of these scaling laws and control-level models with neural network versions of more complex physics-level models has the potential of increasing the range of validity and the level of accuracy of COTSIM without compromising its computational speed. In this work, MMMnet is integrated into COTSIM to improve the turbulent diffusivity predictions, which will in turn improve the prediction accuracy associated with the dynamics of many plasma properties.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗