scholarly journals Rare-Event Chance-Constrained Flight Control Optimization Using Surrogate-Based Subset Simulation

2021 ◽  
Author(s):  
Dalong Shi ◽  
Florian Holzapfel
2021 ◽  
Vol 6 (2) ◽  
pp. 2044-2051
Author(s):  
Danial Sufiyan ◽  
Luke Soe Thura Win ◽  
Shane Kyi Hla Win ◽  
Gim Song Soh ◽  
Shaohui Foong

2020 ◽  
Vol 53 (2) ◽  
pp. 7274-7279
Author(s):  
Dalong Shi ◽  
Xiang Fang ◽  
Florian Holzapfel

2019 ◽  
Author(s):  
Dominic Keidel ◽  
Urban Fasel ◽  
Giulio Molinari ◽  
Paolo Ermanni

Processes ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 185 ◽  
Author(s):  
Patrick Piprek ◽  
Sébastien Gros ◽  
Florian Holzapfel

This study develops a ccoc framework capable of handling rare event probabilities. Therefore, the framework uses the gpc method to calculate the probability of fulfilling rare event constraints under uncertainties. Here, the resulting cc evaluation is based on the efficient sampling provided by the gpc expansion. The subsim method is used to estimate the actual probability of the rare event. Additionally, the discontinuous cc is approximated by a differentiable function that is iteratively sharpened using a homotopy strategy. Furthermore, the subsim problem is also iteratively adapted using another homotopy strategy to improve the convergence of the Newton-type optimization algorithm. The applicability of the framework is shown in case studies regarding battery charging and discharging. The results show that the proposed method is indeed capable of incorporating very general cc within an ocp at a low computational cost to calculate optimal results with rare failure probability cc.


2018 ◽  
Vol 29 (20) ◽  
pp. 3847-3872 ◽  
Author(s):  
Giulio Molinari ◽  
Andres F Arrieta ◽  
Paolo Ermanni

Tailless swept wing airplanes rely on variations of the spanwise lift distribution to achieve controllability in all axes. As every flight condition requires different control moments, the conventional discrete control surfaces will be practically continuously deflected, leading to drag penalties. Shape adaptation base on chordwise morphing can achieve continuous deformations of the wing profile, leading to local lift variations with minimum drag penalties. As the shape is varied continuously along the wingspan, the lift distribution can be tailored to each flight condition. Tailless aircraft appear therefore as prime candidates for morphing, as the attainable benefits are potentially significant. This work presents a methodology to determine the optimal planform, profile shape, and morphing structure for a tailless aircraft. The employed morphing concept is based on a distributed compliance structure, actuated by piezoelectric elements. The multidisciplinary optimization considers the static and dynamic aeroelastic behavior of the structure and aims to maximize the aerodynamic efficiency of the plane while guaranteeing its controllability by means of morphing. The potential of the resulting wing design is fully exploited by means of a second optimization process, which identifies the actuation configuration resulting in the highest aerodynamic efficiency for a wide variety of control moments.


2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Loïc Brevault ◽  
Sylvain Lacaze ◽  
Mathieu Balesdent ◽  
Samy Missoum

The design of complex systems often requires reliability assessments involving a large number of uncertainties and low probability of failure estimations (in the order of 10−4). Estimating such rare event probabilities with crude Monte Carlo (CMC) is computationally intractable. Specific numerical methods to reduce the computational cost and the variance estimate have been developed such as importance sampling or subset simulation. However, these methods assume that the uncertainties are defined within the probability formalism. Regarding epistemic uncertainties, the interval formalism is particularly adapted when only their definition domain is known. In this paper, a method is derived to assess the reliability of a system with uncertainties described by both probability and interval frameworks. It allows one to determine the bounds of the failure probability and involves a sequential approach using subset simulation, kriging, and an optimization process. To reduce the simulation cost, a refinement strategy of the surrogate model is proposed taking into account the presence of both aleatory and epistemic uncertainties. The method is compared to existing approaches on an analytical example as well as on a launch vehicle fallout zone estimation problem.


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