Engineering topics
Publications and source records attributed to Sampath, Ramprasad.
This document is a presentation for the SMiRT conference. It is based on the paper INL/CON-19-53841 I submitted earlier this summer.
In recent years there has been an increase in flooding concern to nuclear power plants; thus, tools to reduce the cost of mitigation efforts are valuable to industry. Simulating flooding events is achieved through different methods and at varied levels of detail. However, these simulations must be able to match application conditions, which requires code validation as well as knowledge of which parameters affect the accuracy of the simulation. To validate and determine the significant parameters for these tools, a sloshing experiment was conducted at the George Washington University and then simulated with the smoothed particle hydrodynamic code Neutrino. The simulation setup was constructed to match the experiment as closely as possible, and the end-wall pressure results were analysed. The 90% and 50% end-wall pressure bounds were compared along with the pressure impulse. The simulation bounds fell mostly within the experimental bounds, with exceptions in insignificant low-pressure areas, such as at the beginning (~40% difference) and the end of the cycle (~40% difference). Another exception was seen at a point in the peak pressure (~75% difference). The simulated pressure impulse matched the experiments well and was within 10%. Additionally, seven parameters were qualitatively assessed and then investigated to determine their significance. The parameters’ significance was determined by sampling five values across the parameters’ range using the Risk Analysis Virtual Environment, then comparing the average pressure for each run. All seven parameters were identified as having some significance. However, the particle size, interaction-radius to particle-size ratio, and fluid settling seem to have a greater effect due to the larger fluctuation of the results. Now that the significant parameters are identified, further research is being done to quantify the significance of each parameter.
Modeling and implementing fire safety for nuclear power plants is a costly activity. Because of the complexity of fire phenomenon and multiple operational procedures, it is difficult to computationally provide assurance that the mitigation methods are adequate for critical areas using current analysis methods. An economical method to provide more accurate modeling and optimize mitigation methods is needed to improve nuclear power viability. This report describes the background of current fire model strategy; the first stage of a tool to help reduce resources needed in fire analysis and the plan for the next step in advancing fire. The report lays out the path decided upon by Idaho National Laboratory and Industry representatives to best help reduce costs and improve realism in Fire Probabilistic Risk Analysis.
Modeling and implementing fire safety for nuclear power plants is a costly activity. Because of the complexity of fire phenomenon and multiple operational procedures, it is difficult to computationally provide assurance that the mitigation methods are adequate for critical areas using current analysis methods. An economical method to provide more accurate modeling and optimize mitigation methods is needed to improve nuclear power viability. This report describes the initial investigation into modeling and simulation tools for application of fire as part of the Risk-Informed Systems Analysis (RISA), formerly Risk-Informed Safety Margin Characterization (RISMC). The report provides a framework of how 3D modeling and simulation techniques could be combined with a dynamic Probabilistic Risk Analysis (PRA) to reduce compounding conservatism present in current Fire PRA methods. Electrical Power Research Institute has analyzed current Fire PRA practices and identified the most significant contributors to risk and areas for improving analysis. This framework describes how to apply dynamic PRA and simulation methods for key contributors/scenarios, credit missing factors of manual fire suppression, and applying conditional probabilities.