OpenPRA Documentation

Data Analysis Technical Element Module ​

Overview ​

The Data Analysis technical element is a critical component of the OpenPRA framework that provides structured types and interfaces for performing and documenting data analysis in probabilistic risk assessments (PRAs). This element enables the estimation, documentation, and management of parameters used throughout the PRA model.

Purpose ​

The primary purposes of the Data Analysis technical element are:

  1. Define parameter types and estimation methods for basic event probabilities
  2. Provide interfaces for documenting data collection and analysis processes
  3. Support uncertainty quantification and propagation
  4. Ensure regulatory compliance through comprehensive documentation
  5. Enable component grouping and outlier handling
  6. Facilitate integration with other technical elements

Structure ​

The Data Analysis element is organized into several logical groupings:

┌─────────────────────────────────────────────────────────────────────────────┐
│                             Data Analysis                                    │
│                                                                             │
│  ┌─────────────────────┐  ┌─────────────────────┐  ┌────────────────────┐  │
│  │  Parameter Definition│  │ Data Collection     │  │ Documentation      │  │
│  │                     │  │                     │  │                    │  │
│  │ - ParameterType     │  │ - DataSource        │  │ - DataAnalysisDoc  │  │
│  │ - DistributionType  │  │ - ExternalDataSource│  │ - ModelUncertainty │  │
│  │ - DataAnalysisParam │  │ - ComponentGrouping │  │ - PreOperational   │  │
│  │ - ProbabilityModel  │  │ - OutlierComponent  │  │ - PeerReviewDoc    │  │
│  └─────────────────────┘  └─────────────────────┘  └────────────────────┘  │
│                                                                             │
│  ┌─────────────────────┐  ┌─────────────────────┐  ┌────────────────────┐  │
│  │ Uncertainty Analysis│  │ Validation & Export │  │ Service Layer       │  │
│  │                     │  │                     │  │                    │  │
│  │ - Uncertainty       │  │ - DataConsistencyChk│  │ - DataAnalysisSvc  │  │
│  │ - BayesianUpdate    │  │ - ValidationRules   │  │ - FailureRateEst   │  │
│  │ - SensitivityStudy  │  │ - ExportImport      │  │ - OperationalData  │  │
│  └─────────────────────┘  └─────────────────────┘  └────────────────────┘  │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

Key Features ​

  • Parameter Definition: Structured interfaces for defining different types of parameters (frequency, probability, unavailability)
  • Component Grouping: Support for creating homogeneous component groups with outlier handling
  • Data Source Management: Interfaces for documenting generic and plant-specific data sources
  • Uncertainty Quantification: Comprehensive uncertainty modeling with support for various distribution types
  • Bayesian Analysis: Interfaces for documenting Bayesian updates of parameter estimates
  • Regulatory Documentation: Complete support for HLR-DA-E documentation requirements
  • Validation Rules: Parameter validation rules to ensure data consistency and quality

Core Components ​

  • DataAnalysisParameter: Defines the fundamental data analysis parameter with value, uncertainty, and documentation
  • ComponentBoundary: Defines system and component boundaries used in data analysis
  • DataSource: Structures for representing data sources with context, applicability, and time period
  • ComponentGrouping: Grouping of similar components for parameter estimation
  • ProbabilityModel: Interfaces for probability distribution models
  • Uncertainty: Comprehensive uncertainty characterization with risk implications
  • BayesianUpdate: Documentation of Bayesian update process and results

Integration with Other Technical Elements ​

The Data Analysis technical element serves as a fundamental building block in the PRA framework, with connections to:

  • Core Module: Uses base interfaces like Unique and Named

  • Systems Analysis:

    • References system definitions and components from Systems Analysis
    • Provides parameter estimates that are used by Systems Analysis models
    • Bidirectional relationship: Data Analysis parameters are associated with Systems Analysis components, while Systems Analysis models use values and distributions from Data Analysis

  • Plant Operating States: Parameters can be specific to plant operating states

  • Initiating Event Analysis: Provides frequency parameters for initiating events

  • Event Sequence Quantification: Supplies probability parameters and uncertainty information

For a visual representation of these relationships, see the Technical Elements Dependencies section in the detailed documentation.

Best Practices ​

When working with the Data Analysis technical element, consider the following best practices to ensure maintainability, scalability, and regulatory compliance:

Parameter Definition ​

  1. Use Descriptive IDs: Create clear, descriptive parameter IDs that indicate the system, component, and failure mode they represent (e.g., PARAM-EDG-FR-001 for EDG failure to run).

  2. Document Parameter Basis: Always provide proper documentation of parameter basis, including data sources, assumptions, and calculations.

  3. Link to System Definitions: Ensure parameters reference the correct system and component definitions from the Systems Analysis technical element.

  4. Validate Parameters: Use the validation rules to check parameter consistency and completeness.

Component Grouping ​

  1. Group Similar Components: Create homogeneous groups based on design, environmental, and service conditions.

  2. Document Outlier Handling: When excluding components as outliers, provide thorough justification for the exclusion.

  3. Review Grouping Periodically: Regularly review component grouping decisions as new data becomes available.

Uncertainty Analysis ​

  1. Select Appropriate Distributions: Choose probability distributions that best represent the underlying physical process.

  2. Document Distribution Selection: Provide rationale for selecting particular distribution types.

  3. Propagate Uncertainty: Ensure uncertainty is properly propagated to downstream analyses.

  4. Consider Correlations: Account for parameter correlations when they significantly impact results.

Documentation ​

  1. Maintain Traceability: Ensure all parameter estimates can be traced back to their source data and calculation methods.

  2. Document Assumptions: Clearly document all assumptions made during data analysis.

  3. Version Control: Maintain version control of parameter estimates and their documentation.

  4. Peer Review: Conduct and document peer reviews of data analysis processes and results.

Integration with Other Elements ​

  1. Coordinate Parameter Updates: When updating parameters, coordinate with other technical elements that use those parameters.

  2. Maintain Consistent References: Use consistent reference patterns (e.g., PlantOperatingStateReference) when linking to other elements.

  3. Document Dependencies: Clearly document dependencies between technical elements.

Regulatory Compliance ​

This technical element is designed to comply with regulatory requirements for data analysis documentation. The implementation ensures complete traceability of the data analysis process, as mandated by regulatory standards.

For a detailed mapping of regulatory requirements to schema implementation, see the Requirements Coverage Matrix in the detailed documentation.

Usage ​

The Data Analysis technical element is used to:

  1. Define data analysis parameters that serve as inputs to other elements
  2. Document the basis for parameter estimates
  3. Track uncertainties associated with parameters
  4. Enable sensitivity studies to understand parameter impacts
  5. Manage component grouping and outlier handling

Additional Resources ​