Study on Multi-objective Optimization of Airbag Landing Attenuation System for Heavy Airdrop

Hong-yan, Wang; Hong, Huangjie; Jian-yang, LI; Qiang, Rui

doi:10.1016/j.dt.2013.12.004

Cited by 7 publications

(4 citation statements)

References 2 publications

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“…Kurowicka and Cooke [22] proved that, using a BN with continuous invertible distribution nodes specifying (conditional) rank correlations (11) through the zero-independence copula, the structure of the BN uniquely determines a joint distribution, and the correlations are algebraically independent.…”

Section: Combination Of Vine and Bnmentioning

confidence: 99%

“…For illustration, we numbered the nodes in Figure 8(b) and assigned a (conditional) correlation to each arc according to Equation (11), as shown in Figure 10. When updating the network, the number also corresponds to the sampling order.…”

Section: Quantification Of Dependency Between Nodesmentioning

confidence: 99%

“…Third, it is computationally demanding for MCS applications, which is prominent in the assessment of the landing process. Many airdrop systems use airbags for landing buffers, and finite element methods are often used to model the airbag buffer process [11][12][13], which can simulate instantaneous deformation with high precision but with less computational efficiency. If the MCS were to be used to analyze the buffering characteristics under different landing conditions, it would face a massive computational effort.…”

Section: Introductionmentioning

confidence: 99%

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Landing Reliability Assessment of Airdrop System Based on Vine-Bayesian Network

Cheng

Yang

2023

International Journal of Aerospace Engineering

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The landing phase of an airdrop process is prone to accidents, and thus, it is important to assess the landing reliability for an airdrop system. However, full field tests to assess the reliability are unacceptable due to their cost and the time required. As such, it is necessary to estimate the reliability in the design stage. To address this problem, a method based on vine-Bayesian Network (vine-BN) is proposed to assess the landing reliability by fusing multisource information. First, the network structure is determined by the relationship between data of simulation or ground tests and failure modes. Then, nodes are defined as random variables on [0, 1] based on the definition of the performance metric. Finally, the dependence between nodes is quantified by expert opinions. To illustrate the effectiveness of the method, a particular ground test or simulation is chosen to establish a network for a typical heavy cargo airdrop system (HCADS). Forward and backward propagation is carried out on the network. The forward analysis predicts the landing reliability in the design stage through multisource information fusion. Beta distribution is applied to fit the fusion result, so Bayesian inference is made to perform field test times decision-making. The backward analysis works to identify the key performance metrics related to landing reliability. The results and analysis manifest that vine-BN is feasible for fusing multisource information. Through the network, the reliability of the current design can be predicted effectively, and the field test times can be remarkably reduced. This method plays a crucial role in airdrop system design and reducing test time and labor.

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Section: Combination Of Vine and Bnmentioning

confidence: 99%

Section: Quantification Of Dependency Between Nodesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Landing Reliability Assessment of Airdrop System Based on Vine-Bayesian Network

Cheng

Yang

2023

International Journal of Aerospace Engineering

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“…He et al [7] utilized the independence of the structure finite element model from the full impact finite element model to develop a hierarchical updating scheme for the FE model of the recovery module and airbag system. Wang et al [8] established a finite element model of a vehicle with its airbag system and conducted experimental verification. Zhou et al [9] proposed a new direct modeling method for cylindrical cushion airbags and verified the correctness of the method via finite element analysis.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization Design of an Origami-Inspired Combined Cushion Airbag

Xu,

Yang,

Huang

et al. 2024

Aerospace

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To improve the cushioning performance of soft-landing systems, a novel origami-inspired combined cushion airbag is proposed. The geometry size, initial pressure, and exhaust vent area of the cushion airbags are designed preliminarily using a theoretical model. The finite element models, including the returnable spacecraft and cushion airbags, are established via the control volume method (CVM) to analyze the impact dynamic behavior and cushioning performance during the landing attenuation process. The cushioning performance of the cushion airbags in complex landing environments are studied to investigate the influence of horizontal velocity, lateral velocity and nonhorizontal landing surfaces. Four design parameters of the cushion airbags, including the initial pressure, venting threshold pressure, exhaust vent area and polygon edge number, are employed to study their influence on the cushioning performance. A multi-objective optimization model of the cushion airbags based on the neural network and multi-objective water cycle algorithm is established to realize the rapid optimization design. The Pareto front of the maximum overload and specific energy absorption is obtained. The analysis results show that the maximum overload of the proposed combined cushion airbags is 7.30 g. The system with the anti-rollover design can avoid rollover and achieve outstanding cushioning performance in complex landing environments. The maximum overload of the returning spacecraft is decreased by 16.4% from 7.30 g to 6.10 g after multi-objective optimizations. This study could provide the technical support for the soft-landing system design of returnable spacecrafts.

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