During an airborne infectious disease outbreak, bus passengers can be easily infected by the dispersion of exhaled droplets from an infected passenger. Therefore, measures to control the transport of droplets are necessary, such as a mask or purifier. The current research examined aerosol transport in a bus with air-conditioning. To determine the dispersion path, deposition distribution, and droplet escape time, the computational fluid dynamics were used to predict the flow field and the dispersion of droplets considering the effects of droplet size, location of the infected person, and purifier type. In addition, based on the viability and the number of virus particles in a droplet, the total number of virus particles inhaled by passengers over a 4-h journey was obtained by the superposition method. The Wells–Riley equation was then used to assess the infection risk of the passengers in the bus cabin. The results showed that droplets with a size of 1–20 μm have essentially the same deposition characteristics, and the location of the infected passenger affects the distribution of droplets' transport and the effectiveness of a purifier in removing droplets. A purifier can effectively remove droplets from passengers' coughs and reduce the infection risk of passengers. The performance of the smaller purifiers is not as stable as that of the larger purifiers, and the performance is influenced by the airflow structure where the infected passenger is located.

This study presents a novel optimization framework applying the multi-objective response surface method to enhance the safety of electric micro commercial vehicles (E-MCVs) during side pole impacts. By focusing on seven critical load-bearing components, including the B-pillar and door frame beam, we achieved a 2% reduction in component weight while significantly improving energy absorption by 22.2%. The optimization led to a substantial decrease in intrusion, with B-pillar abdominal intrusions reduced by 22.5% and lower threshold intrusions down by 26.3%. Despite these improvements, challenges remained regarding battery pack deformation. To address this, we proposed two innovative solutions: strengthening the side longitudinal beams and integrating a bionic thin-walled energy-absorbing structure. These approaches effectively reduced side intrusions of the battery pack by 43.5% to 43.8%, with the bionic structure showing superior performance in weight management. However, the manufacturing feasibility and cost implications of the bionic design necessitate further exploration. The innovation in this study lies in the dual application of a response surface optimization method for load-bearing components and the integration of biomimetic design principles, significantly advancing collision safety for E-MCVs while providing new insights into the weight-efficient safety design.

Latent thermal energy storage is regarded as an effective strategy to utilize solar energy and recover automotive waste heat. Based upon an enthalpy-porosity method, the influence characteristics and mechanism of fin location on phase change material melting behavior in vertical rectangular enclosures were explored numerically. The results show that as fin location increases, the melting time decreases before attaining the minimum at the fin location of 0.20 after which it increases and finally surpasses the no fin case, because (1) the influence range of fins for conduction is limited by the bottom surface when putting fins next to this surface, (2) the liquid flow resistance increases with moving fins up, and (3) mounting fins near the top surface accelerates melting at the upper part, facilitating thermal stratification formation and weakening natural convection. Nu is higher than the no fin case, i.e., Nu enhancement factor is a positive value, in the melting process for a lower fin location, while for other fin locations, a transition to a negative value takes place. The higher the fin location is, the earlier the transition that arises. Finally, a strategy of increasing the maximum liquid flow velocity is proposed to reinforce melting for cases with considerable natural convection.

Current topology optimization methods for nonlinear continuum structures often suffer from low computational efficiency and limited applicability to complex nonlinear problems. To address these issues, this paper proposes an improved bi-directional evolutionary structural optimization (BESO) method tailored for maximizing stiffness in nonlinear structures. The optimization program is developed in Python and can be combined with Abaqus software to facilitate finite element analysis (FEA). To accelerate the speed of optimization, a novel adaptive evolutionary ratio (ER) strategy based on the BESO method is introduced, with four distinct adaptive ER functions proposed. The Newton-Raphson method is utilized for iteratively solving nonlinear equilibrium equations, and the sensitivity information for updating design variables is derived using the adjoint method. Additionally, this study extends topology optimization to account for both material nonlinearity and geometric nonlinearity, analyzing the effects of various nonlinearities. A series of comparative studies are conducted using benchmark cases to validate the effectiveness of the proposed method. The results show that the BESO method with adaptive ER significantly improves the optimization efficiency. Compared to the BESO method with a fixed ER, the convergence speed of the four adaptive ER BESO methods is increased by 37.3%, 26.7%, 12% and 18.7%, respectively. Given that Abaqus is a powerful FEA platform, this method has the potential to be extended to large-scale engineering structures and to address more complex optimization problems. This research proposes an improved BESO method with novel adaptive ER, which significantly accelerates the optimization process and enables its application to topology optimization of nonlinear structures.