Document Typedissertation Author NameLantz, Renee Vaillancourt URNetd-0824101-115208 TitleModel Validation in Fire Protection Engineering DegreePhD DepartmentFire Protection Engineering AdvisorsNicholas A. Dembsey, Advisor Jonathan R. Barnett, Co-Advisor John L. de Ris, Co-Advisor KeywordsModel Validity Criterion model uncertainty fire model model evaluation validation Date of Presentation/Defense2001-08-31 Availabilityunrestricted

AbstractIn the prediction of phenomenon behavior there is a presupposition that a similarity exists between model and phenomenon. Success of application is derived from that similarity. An example of this approach is the use of similarity conditions such as Reynolds number in flow problems or Fourier number in heat transfer problems.

The advent of performance-based codes has opened up opportunities for many diverse avenues of fire model implementation. The reliability of models depends upon model correspondence uncertainty. Model correspondence uncertainty is incomplete and distorted information introduced into a simulation by a modeling scheme. It manifests itself as 1) the uncertainty associated with the mathematical relationships hypothesized for a particular model, and 2) the uncertainty of the predictions obtained from the model.

Improving model implementation by providing a method for rank-ordering models is the goal of the Model Validity Criterion (MVC) method. MVC values can be useful as a tool to objectively and quantitatively choose a model for an application or as part of a model improvement program

The MVC method calculates the amount of model correspondence uncertainty introduced by a modeling scheme. Model choice is based upon the strategy of minimizing correspondence uncertainty and therefore provides the model that best corresponds to the phenomenon. The MVC value for a model is quantified as the sum of the length of two files. These files are individual measures of model structure correspondence uncertainty and model behavior correspondence uncertainty. The combination of the two uncertainty components gives an objective and structured evaluation of the relative validity of each model from a set of likely candidate models. The model with the smallest uncertainty files has the lowest MVC value and is the model with the most validity.

Ultimately the value of such a method is only realized from its utility. Example applications of the MVC method are demonstrated. Examples evaluate the rank-ordering of plume physics options used within the computer zone model WPI-Fire when validated against upper layer temperature data from compartment-fire test scenarios. The results show how candidate models of a set may be discriminated against based on validity. These results are powerful in that they allow the user to establish a quantitative measure for level of model performance and/or choose the most valid model for an application.

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