Abstract

Uncertainties are a daily issue to deal with in aerospace engineering and applications. Robust optimization methods commonly use a random generation of the inputs and take advantage of multi-point criteria to look for robust solutions accounting with uncertainty definition. From the computational point of view, the application to coupled problems, like fluid-dynamics (CFD) or fluid-structure interaction (FSI), can be extremely expensive. This work presents a coupling between stochastic analysis techniques and evolutionary optimization algorithms for the definition of a stochastic robust optimization procedure. At first, a stochastic procedure is proposed to be applied into optimization problems. The proposed method has been applied to both CFD and FSI problems for the reduction of drag and flutter, respectively.

Abstract

International audience We introduce a continuous time stochastic broadcast calculus for mobile and wireless networks. The mobility between nodes in a network is modeled by a stochastic mobility function which allows to change part of a network topology depending on an exponentially distributed delay and a network topology constraint. We allow continuous time stochastic behavior of processes running at network nodes, e.g. in order to be able to model randomized protocols. The introduction of group broadcast and an operator to help avoid flooding allows us to define a novel notion of broadcast abstraction. Finally, we define a weak bisimulation congruence and apply our theory on a leader election protocol. Document type: Part of book or chapter of book