Electric vertical take-off and landing (eVTOL) aircraft are transforming urban air mobility (UAM) by providing efficient, low-emission, and rapid transit in congested cities. However, ensuring safe and stable landings remains a critical challenge, particularly in constrained urban environments with variable wind conditions. This study investigates the landing aerodynamics of a multi-rotor eVTOL using the lattice Boltzmann method (LBM), a computational approach well-suited to complex boundary conditions and parallel processing. This analysis examines the ground effect, descent speed, and crosswind influence on lift distribution and stability. A rooftop landing scenario is also explored, where half of the rotors operate over a rooftop while the rest remain suspended in open air. Results indicate that rooftop landings introduce asymmetric lift distribution due to crosswind and roof-induced flow circulation, significantly increasing rolling moment compared to ground landings. These findings underscore the role of descent speed, crosswinds, and landing surface geometry in eVTOL aerodynamics, particularly the heightened risk of rollover in rooftop scenarios.

Effective integration of human factors and systems engineering has become a technical challenge that constrains the full realization of human performance in unmanned aircraft systems (UAS) for urban air transportation. To address this challenge, breakthroughs are needed in key technologies related to human-system integration (HSI) of UAS. Based on literature review and industry practices, unique HF challenges of UAS are identified, and two research issues, HSI analysis throughout UAS development lifecycle and HSI practice under UAS typical lifecycle stages, are summarized. To address these issues, a model-based human-system integration (MBHSI) design framework is proposed for the UAS development lifecycle, along with an HSI practice framework for UAS under typical human readiness levels. The HSI design and practice framework can provide references for HF design of UAS in urban air transportation.

Nonlinear finite element analysis (FEA) with LS-DYNA3D is used to determine the initial conditions needed for aircraft components to become damaged as observed after impact with the ground. Determining initial conditions of aircraft or components—impact velocity, orientation with respect to the ground before impact, and average soil properties—could reproduce the damage observed at the final state and reveal the condition of the structures immediately prior to collision with the ground. Predictive methods using FEA require detailed reverse engineering modelling of components, highly accurate material model characteristics and multiple numerical simulations for unknown parameters in order to identify average soil properties and initial conditions of components such that the numerically generated final results will agree with the condition observed following impact. A case study is presented in which various scenarios are considered for the impact of the door of a large passenger aircraft with soil at the crash site.

As a common engineering material, wood has been widely used and researched for a long time. The dynamic characteristic of green wood in living trees is very critical in the impact analysis; however, the research on it is limited. This work aims to characterize a material model for green birch wood with strain rate effect, so it can be implemented in the finite-element code LS-DYNA. Several static and impact tests, along with the corresponding numerical simulations, were conducted. The damage mode and the energy absorption ability during the impact process were analyzed and compared. Good agreement is demonstrated between the simulations and the experiments over a wide range of impact velocities with errors of energy absorption less than 8%, indicating good reliability and robustness of the model. This material model can be applied to crashworthiness analysis of aircraft and air crash reconstruction involving birch trees.

The aircraft wing beam serves as the primary load-bearing structure of an aircraft, and the quality of its cross-section design significantly impacts the aircraft’s performance, safety, and economic efficiency. Traditional design methods for wing beams often rely on experience and trial-and-error processes, which are not only time-consuming and labor-intensive but also result in low design efficiency and difficulty in achieving optimal results. Furthermore, conventional optimization algorithms lack transferability, requiring re-optimization for each design iteration. In contrast, reinforcement learning enhances the design process through trial-and-error learning and feedback mechanisms, improving both efficiency and quality. This study proposes a reinforcement learning-based multi-objective optimization design method for aircraft wing beam structures. The method facilitates multi-objective optimization, enables the storage of design knowledge, and allows for the future expansion of the design space.