Deep learning — a newer and more powerful version of modern machine-learning software — improves predictions of plasma disruptions.
ITER is a large international fusion experiment aimed at demonstrating the scientific and technological feasibility of fusion energy.
ITER (Latin for "the way") will play a critical role advancing the worldwide availability of energy from fusion — the power source of the sun and the stars.
To produce practical amounts of fusion power on earth, heavy forms of hydrogen are joined together at high temperature with an accompanying production of heat energy. The fuel must be held at a temperature of over 100 million degrees Celsius. At these high temperatures, the electrons are detached from the nuclei of the atoms, in a state of matter called plasma.
A major challenge facing the development of fusion energy is maintaining the ultra-hot plasma that fuels fusion reactions in a steady state, or sustainable, form using superconducting magnetic coils to avoid the tremendous power requirement of copper coils. While superconductors can allow a fusion reactor to operate indefinitely, controlling the plasma with superconductors presents a challenge because engineering constraints limit how quickly such magnetic coils can adjust when compared to copper coils that do not have the same constraints.
International collaboration sets leading-edge example for controlling elongated plasma in superconducting devices such as ITER.
The arrival of six truckloads of electrical supplies at a warehouse for the international ITER fusion experiment on Oct. 2 brings to a successful conclusion a massive project that will provide 120 megawatts of power – enough to light up a small city − to the 445-acre ITER site in France.
A major issue facing ITER, the international tokamak under construction in France that will be the first magnetic fusion device to produce net energy, is whether the crucial divertor plates that will exhaust waste heat from the device can withstand the high heat flux, or load, that will strike them. Alarming projections extrapolated from existing tokamaks suggest that the heat flux could be so narrow and concentrated as to damage the tungsten divertor plates in the seven-story, 23,000 ton tokamak and require frequent and costly repairs.
Detailed computer simulation indicates good news for the international tokamak under construction in France.
Physicist Francesca Poli of the U.S. Department of Energy’s Princeton Plasma Physics Laboratory (PPPL) has been appointed an ITER Scientist Fellow. She will join a network of researchers who have achieved international recognition and will work closely with ITER, an international tokamak under construction in France, to develop the scientific program to be carried out during the fusion device’s lifetime.
Machine learning, which lets researchers determine if two processes are causally linked without revealing how, could help stabilize the plasma within doughnut-shaped fusion devices known as tokamaks. Such learning can facilitate the avoidance of disruptions — off-normal events in tokamak plasmas that can lead to very fast loss of the stored thermal and magnetic energies and threaten the integrity of the machine.
Such learning can facilitate the avoidance of disruptions — off-normal events in tokamak plasmas that can lead to very fast loss of the stored thermal and magnetic energies and threaten the integrity of the machine.
Predhiman Kaw, an internationally-known plasma physicist who is considered the father of India’s nuclear fusion program, was remembered fondly by his colleagues at the U.S. Department of Energy’s Princeton Plasma Physics Laboratory (PPPL) last week after they learned of Kaw’s June 19 death. He was 69.
Princeton Plasma Physics Laboratory is a U.S. Department of Energy national laboratory managed by Princeton University.
© 2018 Princeton Plasma Physics Laboratory. All rights reserved.