Fire Modeling Scenario Definition
Defining a fire modeling scenario means selecting an initial ignition source targets that could be damaged or catch fire, the geometry of the room (or rooms) and details of relationships between ignition sources, targets and other obstacles; the geometry of flow paths; the configuration of doors and vents that may vary with time; and the influence of engineered systems such as forced flows and fire protection.
Fire Modeling Software Selection Challenge
Three basic levels of modeling software are used in practice. The essence of the challenge is that no single method is perfect, so that in practice all three approaches are usually followed in order of complexity for a given application.
- Scoping models which are simple equations usually rendered in a spreadsheet.
- Zone models which consider “tank and tube” geometry with a “smoky layer” of combustion products and soot that overlies a “lower layer of air.
- CFD (computational fluid dynamics) models, which solve for detailed distributions of temperature and composition in multiple dimensions.
Fire Modeling Scenario Model and Inputs
A “scenario model and inputs” means definition of details of the heat release rate (HRR) history from the combustible load, appropriate peak HRR and growth and decay characteristics, definition of how the geometries of the room, fire, targets and other obstacles are rendered, definition of pyrolysis characteristics that influence the potential for propagation and definition of performance of engineered systems.
Fire Modeling Interpretation of Results
The key challenge in drawing conclusions from fire modeling results is simply how do you know if the results are correct, conservative or non conservative? This challenge is met through the experience of the analyst and familiarity with experimental data that are used to validate the fire models. The analyst must understand the similarities and differences between experimental configurations and scenarios and those plant configurations and scenarios that are being modeled.
Fire Modeling Expertise & Solutions
Fauske & Associates, LLC (FAI) provides a full range of services related to fire modeling. Levels of attack include modeling to support a full fire PRA, modeling to support safe shutdown analysis; a “triage plan” to determine where fire modeling would certainly resolve issues, could potentially resolve issues, or cannot realistically provide benefit; or a management plan to determine realistic cost and schedule associated with such undertakings given a plant –specific state of information.
Cable Selection and Circuit Analysis
From a U.S. perspective, fire PRA methodology is outlined in a key document created jointly by EPRI and NRC, EPRI-1011989, NUREG/CR-6850. Challenges in applying the cable selection and circuit analysis methodology are discussed here following the order of work flow in practical application.
Interface with PRA / Risk Model
The PRA/Risk model quantifies the logical dependencies between components and whether or not failures propagate. One challenge is to ensure collectively exhaustive, finest-grain, one-to-one correspondence between components used in the risk model and components for circuit analysis. A risk model originally created for a safe shutdown analysis may need to be revised for a PRA, for example to include more components and to consider multiple paths to success. When multiple components are considered in an “OR” gate in the risk model, circuit analysis and cable routing need to be capable of revealing any common mode failures / dependencies, for example, cases when two components share a power supply or cables share routing. The interface challenge is also related to the boundary problem for circuit analysis.
The primary challenge is to identify all the required safety functions for a given piece of equipment. For example, both failure to start and failure to run (continued operation) are typical failure modes for pumps and diesels. The ability to both open and close a valve may be required. The risk model should either explicitly consider supporting equipment such as room chillers in the equipment list, or there need to be documented work-arounds for each piece of supporting equipment.
The primary challenge in cable selection is the quality and form of prerequisite information. The format of cable data can vary from the ideal prerequisite, a qualified electronic database that relates equipment, cables and routing at the room level, to the “nightmare” scenario of no electronic data whatsoever. Even when the database exists, we have seen cases where cables are only associated with equipment when they terminate at the equipment, so that queries on equipment yield an incomplete cable list. Similarly, we have seen cases where cable numbers are not provided on equipment circuit drawings, requiring separate look-up in documentation. The bottom line is that exceptions to the ideal prerequisite electronic data form are very costly to remedy.
Boundaries of Circuit Analysis
The “analysis boundary” challenge is associated with circuit analysis strategy and state of documentation. An example is how multiple paths to success are quantified, and choosing to allow for automatic actions, operator actions in the control room, and/or manual local operations. Another example is that the power distribution system must be well-understood, so that boundaries are clear for component analyses.
Circuit Analysis Issues
Analysis issues challenges are typically related to the analysis strategy and state of documentation. An example is how multiple paths to success are quantified, and choosing to allow for automatic actions, operator actions in the control room, and/or manual local operations. Another example is that the power distribution system must be well-understood, so that boundaries are clear for component analyses.
Cable Selection Expertise / Solutions
FAI has the experience and expertise to provide a full range of services related to cable selection and study to determine the quality of prerequisite information in order to create a management plan and schedule to generate qualified data for such studies.