Rapid Calculation of Safe Acceleration Values for Aircraft Structures Under Flight Test

Dosman, Stephen; Gorman, J T

doi:10.1007/978-3-030-21503-3_18

Cited by 1 publication

(1 citation statement)

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“…The deep learning-based two-phase prediction method addressed most challenges in aircraft load prediction. We make best use of the advantages of practical methods (flight parameter-load model) and classical methods (strain-load equation) [24][25][26][27][28][29][30] , and propose a two-phase process for load prediction as shown in Fig. 1c: Phase I: We establish the strain-load equation F 0 = kE 0 + b for the reference measured-load aircraft 0 through the ground test (solid aircraft icon), and build flight parameter-strain model E a = f(X a ) for other aircrafts a through flight tests.…”

Section: Resultsmentioning

confidence: 99%

A multi-model architecture based on deep learning for aircraft load prediction

Sun

Dui

et al. 2023

Commun Eng

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Monitoring aircraft structural health with changing loads is critical in aviation and aerospace engineering. However, the load equation needs to be calibrated by ground testing which is costly, and inefficient. Here, we report a general deep learning-based aircraft load model for strain prediction and load model calibration through a two-phase process. First, we identified the causality between key flight parameters and strains. The prediction equation was then integrated into the monitoring process to build a more general load model for load coefficients calibration. This model achieves a 97.16% prediction accuracy and 99.49% goodness-of-fit for a prototype system with 2 million collected flight recording data. This model reduces the effort of ground tests and provides more accurate load prediction with adapted aircraft parameters.

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Section: Resultsmentioning

confidence: 99%