SummaryDynamic workflows are scientific workflows to support computational science simulations, typically using dynamic processes based on runtime scientific data analyses. They require the ability of adapting the workflow, at runtime, based on user input and dynamic steering. Supporting data-centric iteration is an important step towards dynamic workflows because user interaction with workflows is iterative. However, current support for iteration in scientific workflows is static and does not allow for changing data at runtime. In this paper, we propose a solution based on algebraic operators and a dynamic execution model to enable workflow adaptation based on user input and dynamic steering. We introduce the concept of iteration lineage that makes provenance data management consistent with dynamic iterative workflow changes. Lineage enables scientists to interact with workflow data and configuration at runtime through an API that triggers steering. We evaluate our approach using a novel and real large-scale workflow for uncertainty quantification on a 640-core cluster. The results show impressive execution time savings from 2.5 to 24 days, compared to noniterative workflow execution. We verify that the maximum overhead introduced by our iterative model is less than 5% of execution time. Also, our proposed steering algorithms are very efficient and run in less than 1 millisecond, in the worst-case scenario.

International audienceSupported by a novel field definition and recent control theory results, a new method to avoid local minima is proposed. It is formally shown that the system has an attracting equilibrium at the target point, repelling equilibriums in the obstacles centers and saddle points on the borders. Those unstable equilibriums are avoided capitalizing on the established Input-to-State Stability (ISS) property of this multistable system. The proposed modification of the PF method is shown to be effective by simulation for a two variables integrator and then applied to an unicycle-like wheeled mobile robots which is subject to additive input disturbances

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.

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SUMMARYIn this work we extend our edge-based stabilized finite element incompressible flow solver to turbulence modeling with the residual-based variational multiscale (RB-VMS) method. Using the advective-form of the convection term of the Navier-Stokes equations, RB-VMS is implemented as a straightforward extension of standard stabilized methods with a modified advective velocity. This requires minimum modification of the existing highly optimized code. Two test cases were solved to assess accuracy and performance of the present implementation. First, the laminar incompressible flow past a circular cylinder at Re = 100 and second, the fully turbulent incompressible flow in a lid-driven cubic cavity at Re=12 000. Comparisons were made with standard stabilized finite element formulations, highly resolved numerical simulations and experimental data. Results have shown that the present implementation is able to achieve reasonable accuracy without performance degradation in different flow regimes.