6 MOMENTUM • VIRGINIA TECH MECHANICAL ENGINEERING
Machine learning reduces hazards in nuclear power plants
Nuclear researcher Juliana Pacheco Duarte is part of a team using nearly $ 800,000 in funding from the U . S . Department of Energy to build new prediction models for hazards that could impact nuclear power plants .
According to Duarte , nuclear energy has the potential to rapidly expand the world ’ s energy market , but advances in technology must come with new methods for risk management . The phrase “ risk management in a nuclear power plant ” may immediately call to mind the avoidance of a nuclear explosion or a radiation leak such as Chernobyl , but this risk management begins with a plantwide assessment to ensure that existing local hazards do not result in an unsafe shutdown of a plant . Duarte ’ s group will create a general risk reduction methodology to more accurately predict local hazards that , if not managed , could lead to significant events .
Though they don ’ t usually spell environmental disaster , local hazards can still equal big costs . Aside from the radioactive elements , there are many combustible sources within a power plant . A control room , pump room , or turbine room are composed of complex networks of mechanics and electronics that demand their own safety standards . The loss of any of those peripherals could spell a major repair cost in addition to the loss of energy production .
Duarte ’ s team includes Virginia Tech Professor Brian Lattimer , University of
Wisconsin-Madison faculty Jun Wang and Michael Corradini , and Convergent Science co-owner and vice president Kelly Senecal . This grouping will combine expertise in the mechanics of fire , engineering physics , and custom machine learning to build predictive models for probabilistic risk assessments .
The Department of Energy made the move to improve hazard prediction models following a period of progress in understanding the properties of fire . The proposal indicates that , while new data and measurement methods have been steadily advancing the understanding of fire , many of the applied risk management models in place for nuclear power plants are based on data collected before these improvements . Duarte ’ s team will identify what new data are needed and provide new tools to set the stage for decreased uncertainty .
To make those connections , the group will use simulation Monte Carlo analysis , statistical analysis , and machine learning to reduce the uncertainty in describing a hazard event . This will be achieved by running a series of simulations that provide variations on hazard event conditions , conducting statistics on existing data and simulation results to identify key parameters that most significantly affect the hazard level , and using deep learning models and statistics to identify how to lower the hazard uncertainty . This will give a clearer picture of any gaps in the current data and inform a more complete approach for risk reduction .