Papers for

energy systems engineers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Ignite model simulates fusion plasma behavior from experimental data

IGNITE Tokamak World Model Architecture

Abstract: We introduce IGNITE, a generative world foundation model for fusion plasma behavior simulation trained in a self-supervised manner from over a decade of unlabeled experimental data at the DIII-D National Fusion Facility. The core of IGNITE is a dynamics model that can simulate DIII-D discharges from a given set of actuator trajectories. These trajectories can be supplied or generated on-the-fly from a textual prompt or from desired experimental outcomes. The model architecture consists of several spatio-temporal tokenizers that embed the different input modalities, including time-series like spatio-temporal measurement data, image sequences, and high-resolution spectrograms, each of which collected at vastly different time scales. The backbone is composed of an auto-regressive dynamics model that has the capacity to predict entire DIII-D discharges given initial latent plasma states and actuator trajectories over a theoretical infinite horizon. IGNITE paves the way towards efficient AI-driven experimental planning and world modeling for nuclear fusion.

Thu 1 OctArtificial IntelligenceMachine Learning
The gist
Simulating how plasma behaves during fusion experiments is very complex and important for developing fusion energy. The authors created IGNITE, a model trained on over ten years of real fusion reactor data to predict plasma behavior based on control inputs. It takes multiple types of experimental data and can simulate entire fusion experiments from start to finish. This approach could help plan fusion experiments more efficiently using AI.
Open → 2610.02515v1

Offline reinforcement learning improves wind farm power under changing winds

Offline Reinforcement Learning for Wind Farm Control: A Wind Tunnel Study under Dynamic Wind Directions

Abstract: This paper addresses the wind farm power maximization problem in the presence of wind direction changes. Specifically, a model-free Modified Twin Delayed Deep Deterministic Policy Gradient with Behavior Cloning (MTD3-BC) algorithm is proposed to tackle this task through yaw control under varying wind direction conditions. MTD3-BC is an offline reinforcement learning (RL) algorithm that aims to infer good behavior from only a precollected offline dataset. Additionally, to ensure smooth and moderate yaw adjustments, a new action consistency term is introduced into the policy optimization objective. Unlike online RL methods, MTD3-BC does not require extensive interactions with a wind farm simulator during training, significantly reducing computational costs and training time. A wind tunnel experiment is conducted to validate the effectiveness of the algorithm under varying wind directions. The results demonstrate that MTD3-BC successfully mitigates wake effects, delivering farm-level power gains of approximately 10\% over the baseline greedy strategy and performance on par with a data-calibrated model-based wake-steering benchmark, while requiring no wake model and only a small fraction of the training cost of online RL. To our knowledge, this is the first time an offline RL wind farm control policy has been validated and demonstrated experimentally.

Fri 11 SeptMachine Learning
The gist
Wind farms can produce more electricity if turbines face the wind correctly, but wind directions often change. The authors developed a new way to control turbine angles using offline machine learning, which learns from existing data instead of needing lots of trial runs. They tested this method in a wind tunnel experiment and found that it increased power output by about 10% compared to simple controls. This approach also worked as well as state-of-the-art methods that need detailed wind models, while being faster and cheaper to train.
Open → 2609.12905v1