Delving into the fusion of atoms and the subsequent release of energy has captivated scientists for generations. Now, a convergence of human innovation and artificial intelligence is occurring at the U.S. Department of Energy’s Princeton Plasma Physics Laboratory (PPPL) to tackle one of humanity’s most crucial challenges: generating clean, dependable energy through plasma fusion.
Unlike traditional computer programming, machine learning—a form of artificial intelligence—is not a mere set of instructions. It’s a dynamic software capable of analyzing data, discerning connections between variables, learning from acquired insights, and adapting accordingly. PPPL researchers envision that this adaptability could revolutionize their command over fusion reactions in numerous ways, from refining the design of vessels housing the scorching plasma to optimizing heating techniques and ensuring sustained control over reactions for extended durations.
Already, the laboratory’s foray into artificial intelligence is yielding substantial dividends. In a recent publication in Nature Communications, PPPL researchers elucidated how they harnessed machine learning to circumvent magnetic disruptions, which can destabilize fusion plasma.
“The outcomes are particularly remarkable because we replicated them across two different tokamaks using identical code,” remarked PPPL Staff Research Physicist SangKyeun Kim, the lead author of the study. A tokamak is a torus-shaped apparatus employing magnetic fields to confine plasma.
Egemen Kolemen, an associate professor jointly appointed with the Andlinger Center for Energy and the Environment and the PPPL, emphasized the importance of this achievement, stating, “Instabilities within plasma can inflict severe damage on fusion devices. Such disruptions are untenable in a commercial fusion vessel. Our research represents a significant advancement and underscores the potential of artificial intelligence in managing fusion reactions, averting instabilities while maximizing fusion energy output.”
Every millisecond demands critical decisions to control plasma and sustain fusion reactions. Kolemen’s system executes these decisions at a pace far surpassing human capabilities, automatically adjusting fusion vessel settings to maintain optimal plasma conditions. It can forecast disruptions, identify necessary adjustments, and implement them preemptively, all before instabilities manifest.
Kolemen underscored the significance of the results, noting that in both cases, the plasma was operating in high-confinement mode, known as H-mode. This mode, characterized by a sudden improvement in plasma confinement and the disappearance of turbulence at the plasma’s edge, is the most challenging to stabilize yet indispensable for commercial power generation.
The successful deployment of the system on two tokamaks, DIII-D and KSTAR, both achieving H-mode without instabilities, marks a groundbreaking accomplishment in reactor settings pertinent to the future deployment of fusion power on a commercial scale.
PPPL’s legacy of leveraging artificial intelligence to mitigate instabilities is notable. Principal Research Physicist William Tang and his team pioneered the transfer of this process from one tokamak to another in 2019. Tang emphasized the breakthroughs achieved through the integration of artificial intelligence and machine learning with powerful computing resources, enabling the development of models to address disruptive physics events well in advance.
Beyond tokamaks, PPPL’s Michael Churchill employs machine learning to enhance the design of stellarators, another type of fusion reactor characterized by a more intricate, twisted configuration.
The pursuit of higher-fidelity calculations, essential for optimization, faces a challenge due to the time-intensive nature of sophisticated codes. To address this, Churchill harnesses artificial intelligence to accelerate different codes and the optimization process, facilitating rapid advancements in fusion research.
Similarly, Research Physicist Stefano Munaretto’s team focuses on accelerating HEAT, a code crucial for plasma simulation, by updating it to a 3D model to match the tokamak divertor’s 3D CAD model. By streamlining the code and optimizing it through machine learning, the researchers aim to predict and manage heat fluxes efficiently, crucial for designing future fusion power plants.
Researchers also explore the optimization of ion cyclotron radio frequency heating (ICRF) to heat ions in plasma. Standard simulation codes are often slow, hindering real-time decision-making. Álvaro Sánchez Villar and his team demonstrate accelerated versions of these codes using machine learning, enabling real-time control applications vital for fusion research.
PPPL’s Principal Research Physicist Fatima Ebrahimi leads a project leveraging experimental data, plasma simulations, and artificial intelligence to study plasma behavior at the edge during fusion. The project aims to develop strategies for effective plasma confinement in commercial-scale tokamaks, crucial for the viability of fusion power plants.
In summary, the fusion of human ingenuity with artificial intelligence at PPPL is propelling fusion research into new frontiers, promising cleaner, more sustainable energy solutions for the future.
Source: miragenews.com
