Including Computational Validation as Part of a Model Validation Process: Using Legacy Type Models_Crimson Publishers

 The advances made in computational algorithms, computing power and visualization tools are motivating researchers to develop new numerical models or enhance the predictive abilities of existing numerical models. These advances include a combination of enhanced flow equations, improved discretization techniques that reduce the flow equations to algebraic equations, grid methodology, a better understanding of the flow phenomena, highperformance computers, optimization tools, interfaces to enter and process the data input, post-processor visualization and analysis tools. These next-generation computational models are facilitating in capturing the flow physics at various spatial and temporal scales that were not possible until two decades back, primarily due to limitations in computing power. As this limitation eases, numerical modeling will continue to be at the forefront for advancing the frontiers of knowledge across all modeling disciplines.

The reliability of these enhanced models is measured by validating their results with standard benchmark tests. Experiments play an important role, and their data vastly improves the understanding of physical flow. Using the measured physical data as a benchmark for testing various numerical models has been a standard practice across all disciplines. To this end, the physical experiments and the associated measurements need to be done in sufficient detail for a range of flow scenarios to arrive at an acceptable dataset for calibrating, verifying and validating the models. Model validation with experimental benchmark data, although highly recommended, is not feasible for all flow cases due to the costs, time, and limitations in the equipment to measure chosen flow variables in the flow domain. Because the numerical model can simulate complex flow phenomena across different scales, it might not be feasible to obtain the corresponding experimental data. In its absence, using the output from other numerical models as the benchmark data is the second approach to validate the models of interest.

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