Robust optimization and uncertainty quantification in the nonlinear mechanics of an elevator brake system
<|reference_start|>Robust optimization and uncertainty quantification in the nonlinear mechanics of an elevator brake system: 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.<|reference_end|>
@article{wolszczak2024robust,
    title={Robust optimization and uncertainty quantification in the nonlinear
  mechanics of an elevator brake system},
    author={Piotr Wolszczak, Pawel Lonkwic, Americo Cunha Jr, Grzegorz Litak,
  Szymon Molski},
    journal={Meccanica, vol. 54, pp. 1057-1069, 2019},
    year={2024},
    doi={10.1007/s11012-019-00992-7},
    archivePrefix={arXiv},
    eprint={2409.18139},
    primaryClass={cs.CE math.OC physics.class-ph stat.AP}
}