UNCERTAINTY QUANTIFICATION IN COMPUTATIONAL PREDICTIVE MODELS FOR FLUID DYNAMICS USING A WORKFLOW MANAGEMENT ENGINE

Abstract

Computational simulation of complex engineered systems requires intensive computation and a significant amount of data management. Today, this management is often carried out on a case-by-case basis and requires great effort to track it. This is due to the complexity of controlling a large amount of data flowing along a chain of simulations. Moreover, many times there is a need to explore parameter variability for the same set of data. On a case-by-case basis, there is no register of data involved in the simulation, making this process prone to errors. In addition, if the user wants to analyze the behavior of a simulation sample, then he/she must wait until the end of the whole simulation. In this context, techniques and methodologies of scientific workflows can improve the management of simulations. Parameter variability can be put in the general context of uncertainty quantification (UQ), which provides a rational perspective for analysts and decision makers. The objective of this work is to use scientific workflows to provide a systematic approach in: (i) modeling UQ numerical experiments as scientific workflows, (ii) offering query tools to evaluate UQ processes at runtime, (iii) managing the UQ analysis, and (iv) managing UQ in parallel executions. When using scientific workflow engines, one can collect data in a transparent manner, allowing execution steering, the postassessment of results, and providing the information for reexecuting the experiment, thereby ensuring reproducibility, an essential characteristic in a scientific or engineering computational experiment.