Dynamic, risk informed decision support systems and methods
View Patent ↗The present disclosure is directed to a decision support system or tool based on a Bayesian Network (BN) framework. The diagnostic support tool is created by using advanced Probabilistic Risk Assessment (PRA) method(s) to construct Bayesian Networks (BNs) that form a Bayesian Decision Support Process (BDSP) to provide science-based decision support for understanding and managing events in complex systems. In an embodiment, the PRA method(s) may include Discrete Dynamic Event Trees (DDETs) and simulations.
1. A method for responding to a system condition of a system, comprising:
performing a probabilistic assessment by executing instructions on a computer system for a system analysis code to estimate observed parameters comprising incident source terms, incident source term sensitivities and incident source term uncertainties for a variety of scenarios further executed by a simulation tool;
outputting results from the probabilistic assessment into a Bayesian network decision support system being executed on the computer system; and
generating on the computer system a probabilistic map of relationships between the variety of scenarios and observed parameters for the system;
the user using the probabilistic map to provide real-time decision support for a system condition diagnosis and a response plan for the system condition, wherein the real-time decision support includes one or more decision guidelines; and
executing a control action by the user within one of the one or more decision guidelines;
wherein the probabilistic risk assessment methods is selected from a group consisting of a discrete dynamic event tree and a computer model of a progression simulation.
2. The method of claim 1 , further comprising:
adding a human simulation model to the one or more probabilistic assessment methods.
3. The method of claim 1 , wherein the progression simulations comprise progression simulations of accidents.
4. The method of claim 1 , further comprising:
using the probabilistic map to provide real-time decision support for system condition diagnosis and response planning by the user.
5. The method of claim 4 , wherein the user is a plant operating crew, technical support center, and other party interested in the progression of accidents.
6. The method of claim 1 , wherein the probabilistic map is used to generate starter procedures to be used in safety analyses.
7. The method of claim 1 , further comprising:
using the probabilistic map to enable users to perform probabilistic queries selected from a group consisting of dynamically diagnosing specific faults, identifying key indicators, and predicting future plant parameters.
8. The method of claim 1 , further comprising:
determining critical operations parameters and diagnosing unknown accidents for an extensive range of possible accident sequences.
9. A method for constructing risk-informed decision guidelines for a system condition of a system, comprising:
performing a probabilistic assessment by executing instructions on a computer system for a system analysis code to estimate observed parameters comprising incident source terms, incident source term sensitivities and incident source term uncertainties for a variety of scenarios further executed by a simulation tool;
outputting the results of the probabilistic risk assessment into a Bayesian network decision support system on a computer;
generating from the computer a probabilistic map of relationships between the system condition and observed system parameters for the; and
performing probabilistic queries using the probabilistic map, the probabilistic queries selected from a group consisting of dynamically diagnosing specific faults, identifying key indicators, and predicting future system parameters;
wherein the probabilistic map is used to provide real-time decision support for system condition diagnosis and a response plan by a user for the system condition, wherein the real-time decision support includes one or more decision guidelines; and
wherein a control action is executed by the user within one of the one or more decision guidelines; and
wherein the system analysis code comprises a discrete dynamic event tree and the simulation tool is a computer model of a progression simulations.
10. The method of claim 9 , wherein the risk-informed decision guidelines are selected from a group including Smart Severe Accident Management Guidelines, Emergency Operating Procedures and other types of operating procedures.
11. The method of claim 10 , wherein the progression of accidents are in nuclear reactors.
12. The method of claim 9 , further comprising:
determining critical operations parameters and diagnosing unknown accidents for an extensive range of possible accident sequences.
13. The method of claim 9 , further comprising:
adding a human simulation model to the one or more probabilistic risk assessment methods.
14. The method of claim 9 , further comprising:
using the probabilistic map to provide real-time decision support for system condition diagnosis and response planning by a user.
15. The method of claim 14 , wherein the user is a plant operating crew, technical support center, and other parties interested in the progression of accidents.
16. The method of claim 9 , wherein the probabilistic map is used to generate starter procedures to be used in safety analyses.
17. A computer program product stored on a non-transitory computer readable medium, wherein executed by a process on a computer, the computer program product configured to:
a) output results of a probabilistic assessment into a Bayesian network decision support system; and
b) generate a probabilistic map of relationships between a system condition and observed parameters;
wherein the probabilistic risk assessment methods comprise a discrete dynamic event tree and a computer model of a progression simulations; and
wherein the probabilistic map generates operational procedures for addressing the system condition; and
wherein the probabilistic map is used to provide real-time decision support for system condition diagnosis and a response plan by a user for the system condition, wherein the real-time decision support includes one or more decision guidelines; and
wherein a control action is executed by the user within one of the one or more decision guidelines; and
wherein the probabilistic risk assessment methods comprise a discrete dynamic event tree and a computer model of a progression simulations.
18. The computer program product of claim 17 , further comprising:
performing probabilistic queries selected from a group consisting of dynamically diagnosing specific faults, identifying key indicators, and predicting future plant parameters.