Structural FEM Analyses of a Landing Gear Testing Machine

Giannella, Venanzio; Baglivo, Giovanni; Giordano, Rosario Francesco; Sepe, Raffaele; Citarella, Roberto

doi:10.3390/met12060937

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…In 2022, De Martin developed an innovative "iron bird" for executing tests including the simulation of complete landing procedures on an innovative landing gear of a small aircraft [15]. Giannella, V. validated the design of this test machine by structural analyses [16]. With the development of technology, the simulation analysis of the landing gear is more detailed in recent years considering more and more factors, and scholars actively test and verify the landing gear on the small pillar landing gear.…”

Section: Introductionmentioning

confidence: 99%

Landing Performance Study for Four Wheels Twin Tandem Landing Gear Based on Drop Test

2022

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The drop tests of a twin tandem landing gear with different filling parameters are carried out in two different landing attitudes (level and tail-down). The overload coefficient and power absorption efficiency are obtained. Curves of dynamic oscillation and pressure change for the pitch damper are obtained. The results show that twin tandem landing gear has a good ability to absorb the work of landing impact. Under landing weight, the vertical overload coefficients during level landing and tail-down landing are 1.14 and 1.07, respectively, when the corresponding efficiencies of its buffer system are 80.9% and 83.3%. During tail-down landing, the work absorbed by the pitch damper accounts for only 14.9% of the maximum pitching kinetic energy of the trolley. When the orifice diameter of the pitch damper keeps the same, its peak axial load does not change significantly with the change of its initial pressure. When the initial pressure of the pitch damper keeps the same, the decrease of its orifice diameter is beneficial to the vibration attenuation of the trolley. The smaller recoil channel may lead to a significant increase in the peak pressure of the pitch damper, which should be a consideration in its design.

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Section: Introductionmentioning

confidence: 99%

Landing Performance Study for Four Wheels Twin Tandem Landing Gear Based on Drop Test

2022

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FEM simulation of the welding process of a butt-welded joint and comparison with experimental data

Giannella

Califano

Bacco

et al. 2023

Procedia Structural Integrity

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Mechanical Stress Prediction of an Aircraft Torque Tube Based on the Neural Network Application

et al. 2023

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The use of a predictive approach in the aviation industry is an important factor in aircraft maintenance. The main goal of this study was to create a new method for stress prediction during the operation of parts and to apply it on an aircraft torque tube (ATT). The method operates in real time during taxiing, takeoff, and landing using a neural network (NN). The stress calculated by the proposed method can be used in the future to calculate fatigue life and to save maintenance costs related to ATTs. This can play an important role in the evaluation of tests, such as unobserved crack failure. The main contribution of the presented methodology is in the fourth part of this study, where a new method of mechanical-stress prediction using a NN is described. The method essentially replaces finite element methods (FEMs), which require large amounts of time. The new method is much faster than commonly available methods, as the NN predicts the mechanical ATT stress in 0.00046 s, whereas the solution time using FEM is 1716 s for the same load step. In total, 36 regimes were calculated by FEMs in 17 h, 9 min and 36 s, whereas the novel method calculated the ATT stress for 36 regimes in 0.0166 s. The accuracy was also high, with R above 0.99. The main innovation presented in this study is the development of a method that can predict ATT stress in a very short time with a high percentage of accuracy and that can be used for stress and life prediction during the operation of parts. The partial results from the experimental tensile tests are also presented, and they are used for FEM calculations. The FEM results are used as inputs for the stress prediction by the NN.

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Structural FEM Analyses of a Landing Gear Testing Machine

Cited by 3 publications

References 27 publications

Landing Performance Study for Four Wheels Twin Tandem Landing Gear Based on Drop Test

Landing Performance Study for Four Wheels Twin Tandem Landing Gear Based on Drop Test

FEM simulation of the welding process of a butt-welded joint and comparison with experimental data

Mechanical Stress Prediction of an Aircraft Torque Tube Based on the Neural Network Application

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