Probabilistic Risk Assessment Tree Data Structure
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Solution Overview
Problem
Existing probabilistic risk assessment (PRA) tools face computational inefficiencies when analyzing large fault and event trees due to exponential complexity, leading to excessive computational demands and memory requirements, especially when dealing with multiple occurring events and dependent events, which hinders accurate and timely risk assessments in industries like nuclear power.
Innovation Solution
The approach represents fault trees using a general tree data structure, converting logic gates into compressed truth tables and employing algorithms to calculate probabilities efficiently, allowing for bottom-up and dependent node analyses, reducing computational complexity and memory needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fault tree analysis methods are used for large-scale systems, then comprehensive risk assessment coverage is achieved, but computational complexity and memory requirements increase exponentially
Solution Approach 1:
The patent segments the large fault tree into multiple smaller sub-fault trees or modules that can be analyzed independently. Each module is processed separately using the proposed algorithm, reducing the computational burden on any single analysis step while maintaining comprehensive coverage of the entire system through hierarchical or modular composition of results.
Solution Approach 2:
The patent transforms the traditional fault tree representation into a different computational dimension by using a novel algorithmic approach that processes the fault tree structure in a non-traditional manner. This dimensional transformation allows the system to handle large-scale fault trees efficiently by changing how the computational problem is structured and solved, rather than simply adding more computational resources.
2Reliability
If traditional fault tree analysis methods are used for large-scale systems, then comprehensive risk assessment coverage is achieved, but memory storage requirements become excessive
Solution Approach 1:
The patent extracts and processes only the essential information needed for risk assessment from the fault tree structure, rather than storing and manipulating the entire fault tree in memory. By extracting key parameters and intermediate results, the method reduces memory requirements while preserving the accuracy needed for comprehensive risk assessment.
Solution Approach 2:
The fault tree is divided into segments that are processed in smaller chunks, with results aggregated progressively. This segmentation allows the analysis to be performed with limited memory resources at any given time, as only the current segment and accumulated results need to be stored, rather than the entire fault tree simultaneously.
3Productivity
If advanced quantification techniques such as Binary decision diagrams or Bayesian networks are used, then computational efficiency is improved, but the methods fail due to exhaustion of available memory/storage space for larger fault trees
Solution Approach 1:
The patent employs a dynamic processing approach where the fault tree analysis adapts to available memory resources. The algorithm can adjust its processing strategy based on the size of the fault tree and available storage, dynamically allocating computational resources and switching between different processing modes to maintain efficiency without exhausting memory capacity.
Solution Approach 2:
The patent introduces a new computational dimension or perspective that avoids the memory exhaustion problem of traditional methods. By fundamentally changing how the fault tree is processed—using a novel algorithmic structure rather than conventional representations—the method achieves both computational efficiency and memory efficiency simultaneously, breaking the trade-off that plagues existing techniques.
Data Source
AI summary
The present disclosure presents control systems and related methods for accurate estimation of a system-level risk. One such method comprises obtaining, via at least one of one or more computing devices, a tree data structure of a system-level failure risk of a system plant, wherein a binary state failure probability of an internal node in the tree data structure is a function of its logic gate connection and its child nodes; where the tree data structure has only independent nodes, computing a binary state failure probability of a failure event represented as a top node of the tree data structure; where the tree data structure has multiple dependent chains having a common dependent node, evaluating, the binary state failure probability of each dependent chain separately; and outputting the binary state failure probability for the failure event represented as the top node of the tree data structure.