Robust optimization and uncertainty quantification in the nonlinear mechanics of an elevator brake system

TL;DR

This paper presents a probabilistic approach to model and analyze the nonlinear mechanics of an elevator brake system under uncertainties, using Monte Carlo simulations for detailed statistical response characterization.

Contribution

It introduces a stochastic formalism with maximum entropy-based distributions and integrates uncertainty quantification into the optimal design of the brake system.

Findings

Monte Carlo method effectively propagates uncertainties.

Probabilistic modeling enhances brake system design robustness.

Statistical analysis provides detailed response characterization.

Abstract

This paper deals with nonlinear mechanics of an elevator brake system subjected to uncertainties. A deterministic model that relates the braking force with uncertain parameters is deduced from mechanical equilibrium conditions. In order to take into account parameters variabilities, a parametric probabilistic approach is employed. In this stochastic formalism, the uncertain parameters are modeled as random variables, with distributions specified by the maximum entropy principle. The uncertainties are propagated by the Monte Carlo method, which provides a detailed statistical characterization of the response. This work still considers the optimum design of the brake system, formulating and solving nonlinear optimization problems, with and without the uncertainties effects.