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Fast modeling of turbulent transport in fusion plasmas using neural networks
Related concepts (33)
Magnetic confinement fusion
Magnetic confinement fusion is an approach to generate thermonuclear fusion power that uses magnetic fields to confine fusion fuel in the form of a plasma. Magnetic confinement is one of two major branches of fusion energy research, along with inertial confinement fusion. The magnetic approach began in the 1940s and absorbed the majority of subsequent development. Fusion reactions combine light atomic nuclei such as hydrogen to form heavier ones such as helium, producing energy.
Plasma-facing material
In nuclear fusion power research, the plasma-facing material (or materials) (PFM) is any material used to construct the plasma-facing components (PFC), those components exposed to the plasma within which nuclear fusion occurs, and particularly the material used for the lining the first wall or divertor region of the reactor vessel. Plasma-facing materials for fusion reactor designs must support the overall steps for energy generation, these include: Generating heat through fusion, Capturing heat in the first wall, Transferring heat at a faster rate than capturing heat.
Fusion power
Fusion power is a proposed form of power generation that would generate electricity by using heat from nuclear fusion reactions. In a fusion process, two lighter atomic nuclei combine to form a heavier nucleus, while releasing energy. Devices designed to harness this energy are known as fusion reactors. Research into fusion reactors began in the 1940s, but as of 2023, no device has reached net power. Fusion processes require fuel and a confined environment with sufficient temperature, pressure, and confinement time to create a plasma in which fusion can occur.
Recurrent neural network
A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.
Dense plasma focus
A dense plasma focus (DPF) is a type of plasma generating system originally developed as a fusion power device starting in the early 1960s. The system demonstrated scaling laws that suggested it would not be useful in the commercial power role, and since the 1980s it has been used primarily as a fusion teaching system, and as a source of neutrons and X-rays. The original concept was developed in 1954 by N.V. Filippov, who noticed the effect while working on early pinch machines in the USSR.
Neural network
A neural network can refer to a neural circuit of biological neurons (sometimes also called a biological neural network), a network of artificial neurons or nodes in the case of an artificial neural network. Artificial neural networks are used for solving artificial intelligence (AI) problems; they model connections of biological neurons as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed.
Stellarator
A stellarator is a plasma device that relies primarily on external magnets to confine a plasma. Scientists researching magnetic confinement fusion aim to use stellarator devices as a vessel for nuclear fusion reactions. The name refers to the possibility of harnessing the power source of the stars, such as the Sun. It is one of the earliest fusion power devices, along with the z-pinch and magnetic mirror.
Artificial neural network
Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.
Aneutronic fusion
Aneutronic fusion is any form of fusion power in which very little of the energy released is carried by neutrons. While the lowest-threshold nuclear fusion reactions release up to 80% of their energy in the form of neutrons, aneutronic reactions release energy in the form of charged particles, typically protons or alpha particles. Successful aneutronic fusion would greatly reduce problems associated with neutron radiation such as damaging ionizing radiation, neutron activation, reactor maintenance, and requirements for biological shielding, remote handling and safety.
Control theory
Control theory is a field of control engineering and applied mathematics that deals with the control of dynamical systems in engineered processes and machines. The objective is to develop a model or algorithm governing the application of system inputs to drive the system to a desired state, while minimizing any delay, overshoot, or steady-state error and ensuring a level of control stability; often with the aim to achieve a degree of optimality. To do this, a controller with the requisite corrective behavior is required.