Supercomputing Simulations and Machine Learning Help Improve Power Plants


High-performance computing resources and data-driven machine learning help University of Stuttgart researchers model how coal, nuclear, and geothermal power plants could be retrofitted for cleaner, safer, and more efficient and flexible operation.

In conventional steam power plants, residual water must be separated from power-generating steam. This process limits efficiency, and in early generation power plants, could be volatile, leading to explosions.

In the 1920s, Mark Benson realized that the risk could be reduced and power plants could be more efficient if water and steam could cohabitate. This cohabitation could be achieved by bringing water to a supercritical state, or when a fluid exists as both a liquid and gas at the same time.

While the costs associated with generating the temperature and pressure conditions necessary to achieve supercriticality prevented Benson’s patented Benson Boiler from being widely adopted at power plants, his concepts offered the world its first glimpse at supercritical power generation.

Continue reading at Gauss Centre for Supercomputing

Image via University of Stuttgart