Numerical simulation plays a crucial role in today’s aviation industry. Modern computers with the latest commercial FE codes have fueled the triumph of establishing simulation as a prerequisite of aerostructure part design from micro modeling of materials to large-scale structural analysis. In this current research paper, a Mooney-Rivlin material model of ballistic gelatin with Lagrange code is analyzed as a potential candidate for the computational bird model to simulate bird strike case studies. To investigate the practicability, the model is compared with other established Lagrange and SPH EOS models for both rigid and deformable body impacts along with experimental data found in the literature adopting explicit solver Ansys Autodyn. Despite some discrepancies found during rigid body impacts, deformable plate impacts confirm the robustness of the model with significantly faster computation time. Besides, the biggest criticism of the Lagrange model, mesh distortion problem as fluid during bird strike case studies is efficiently tackled by adopting the node erosion algorithm as an effective technique to solve Lagrange bird models without affecting the outcome of the solutions.

The current research aimed to investigate the critical buckling load of a simply supported aerospace-grade stiffened panel made of additively manufactured cubic lattice unit cell arrays, namely simple cubic, face-centered cubic (FCC) and body-centered cubic (BCC) structures. Ansys Design Modeler was chosen to design and analyze the critical buckling load of the panel, while a popular material, Ti-6Al-4V, was used as the build material. Numerical validation on both the stiffened panel and a lattice beam structure was established from multiple resources from the literature. Finally, the panels were tested against increments of a strut diameter ranging from 0.5 mm to 2 mm, which corresponds to a relative density of 6% to 78%. It was found that considering the relative density and fixed relative density, the simple cubic lattice cell outperformed the buckling results of the FCC and BCC panels. Moreover, the relationship of the parameters was found to be non-linear. Finally, the data samples collected from numerical outcomes were utilized to train four different machine learning models, namely multi-variable linear regression, polynomial regression, the random forest regressor and the K-nearest neighbor regressor. The evaluation metrics suggest that polynomial regression provides the highest accuracy among all the tested models, with the lowest mean squared error (MSE) value of 0.0001 and a perfect ${\mathrm{R}}^2$ score. The current research opens up the discussion of using cubic lattice cells as potential structures for future stiffened panels.

Elastic moduli are important mechanical properties that describe a material’s stiffness and its deformation under elastic loading. In addition to experimental techniques, computational homogenization is commonly used for composite materials to calculate their elastic moduli. This research employs a deep learning algorithm, specifically a Feedforward Neural Network (FNN), to predict the longitudinal and transverse Young’s modulus, shear modulus, and Poisson’s ratio of various unidirectional (UD) composites. The predictions are based on several features, including the names of the composites, Young’s moduli and Poisson’s ratios of the fibers and matrices, and the fiber volume fraction. Initially, 20 different UD composites were selected from the existing literature. ANSYS-19 Material Designer was then utilized to calculate the elastic moduli of these materials while varying the fiber volume fraction from 0.2 to 0.7. This process generated a dataset of 1948 samples, with 80% of the data allocated for training the FNN model and the remaining 20% used to evaluate performance metrics of the test data. These metrics include mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and R² score. The results indicate that, with optimized hyperparameters, the FNN model can accurately predict the elastic moduli, demonstrating its effectiveness as a tool for calculating the elastic properties of UD composites.

Over the past two decades, aircraft crashworthiness has seen major developments, mainly with modern computing systems and commercial finite element (FE) codes. The structure and the material have been designed to absorb more kinetic energy to ensure enough safety during a controlled crash condition. However, the fuselage section with an onboard auxiliary fuel tank requires special arrangements, since the inclined strut system with an efficient energy absorber is difficult to install under the cabin floor due to the space occupied by the fuel tank. To solve this shortcoming, a PVC composite foam along with an aluminum plate is introduced beneath the fuel tank to improve the crashworthiness metrics of the fuselage. Drop tests for both the conventional design and the proposed model are investigated by adopting the nonlinear explicit dynamics code Ansys Autodyn, with an impact velocity of 9.14 m/s. It was found that the kinetic energy absorption of the original fuselage section can be improved by 3.54% by reinforcing the foam and the plate. Moreover, they contribute to 20% of total internal energy dissipation. Numerical outcomes also suggest that the cabin floor surface experiences a 41% lower maximum stress, in addition to the mitigation of the maximum peak acceleration responses of the cabin floor at different measured locations from 6% to 36%.

In this current research, a commercial aircraft metallic leading edge structurally reinforced with a Y-shaped and V-shaped plate system is numerically examined to investigate the effectiveness of such reinforcements against soft impacts, more commonly known as bird strikes in the aviation industry. A non-linear finite element code Ansys Explicit is adopted to run the virtual test cases. The computational bird model is presented with the Lagrange algorithm and Mooney–Rivlin hyperelastic material parameters which are validated against the experimental data found in the literature. A second validation of the leading edge deformation pattern is also carried out to ensure the accuracy of the present work. Numerical outcomes suggest that due to the presence of the reinforcement, the leading edge skin is restrained from being drastically deformed and the bird model tears apart into two pieces requiring the leading edge model to absorb much less kinetic energy. Additionally, it is found that both the reinforcements have similar crashworthiness performance against bird impacts. The novelty of the research lies in founding the structural reinforcement as a primary preference to strengthen the vulnerable wing leading edge during bird impacts.