Prioritization of Code Development Efforts in Partitioned Analysis

Abstract: In partitioned analysis of systems that are driven by the interaction of functionally distinct but strongly coupled constituents, the predictive accuracy of the simulation hinges on the accuracy of individual constituent models. Potential improvement in the predictive accuracy of the simulation that can be gained through improving a constituent model depends not only on the relative importance, but also on the inherent uncertainty and inaccuracy of that particular constituent. A need exists for prioritization of code development efforts to cost-effectively allocate available resources to the constituents that require improvement the most. This article proposes a novel and quantitative code prioritization index to accomplish such a task and demonstrates its application on a case study of a steel frame with semirigid connections. Findings show that as high-fidelity constituent models are integrated, the predictive ability of model-based simulation is improved; however, the rate of improvement is dependent upon the sequence in which the constituents are improved.