In this study, 3D printing technique was utilized to fabricate three-dimensional porous electrodes for microbial fuel cells with UV curable resin, followed by copper electroless plating. A maximum voltage of 62.9 ± 2.5 mV and a power density of 6.45 ± 0.5 mWm⁻² were achieved for MFCs with 3D printed porous copper (3D-PPC) anodes, which were 8.3- and 12.3-fold higher than copper mesh electrodes, respectively. This illustrated the great advantage of 3D porous anodes in MFCs compared to flat anode structures. Besides, the biocompatibility of the copper anode with Shewanella oneidensis MR-1 was examined by comparing with carbon cloth, which produced a 3-fold larger maximum voltage and a ~10-fold higher power density vs. 3D-PPC anodes and thus indicated the possible copper corrosion during MFC operation. ICP-MS analysis of MFC solution revealed the high concentration of 732 ± 27.1 μg/L copper ions detected in the MFC effluent. This result, coupled with EDX showing the lower copper content on the 3D-PPC anode surface after >15 days of MFC operation, confirmed the copper dissolving behavior in MFC. MR-1 biofilm formation under copper suppression was finally characterized by SEM and less biofilm was observed on copper anodes, illustrating their poor biocompatibility, even though 3D printing technology and porous structures were quite promising for future scale-up.

Solid propellant is a composite material exhibiting classic nonlinear viscoelastic mechanical characteristic, which is due in a large part to a cumulative damage process caused by the formation and growth of microflaws inside. The standard relaxation tests and uniaxial tension tests under different velocities of hydroxyl-terminated polybutadiene (HTPB) propellant are carried out in this paper, where Digital Image Correlation (DIC) technique is applied to record deformation. The experimental results show that the material mechanical behavior is rate-dependent. It is also observed that the yield stress and failure stress are significantly rate-dependent on the tensile velocity. Based on these experimental results, it can be inferred that the stiffness degradation and damage evolution of HTPB propellant are a rate-dependent processes. Therefore, the damage accumulation of HTPB propellant is considered rate-dependent in this research. In order to describe the mechanical characteristic precisely, a nonlinear viscoelastic constitutive model with rate-dependent cumulative damage is developed. The damage model is developed based on the concept of pseudo strain, in which a Prony series representation of viscoelastic material functions is applied. Besides, a rate-dependent damage variable is introduced into the model through considering the rate-dependent characteristics of cumulative damage process. In addition, a new normalized failure criterion is derived on the basis of the proposed damage model, which is independent of strain-rate after normalization. Finally, it is implemented in commercial finite element software for stress analysis to verify the predictive capacities of the damage model. The accuracy of the constitutive model and failure criterion is validated under uniaxial tensile tests of various strain rates.

Effective traffic signal control (TSC) plays an important role in reducing vehicle emissions and improving the sustainability of the transportation system. Recently, the feasibility of using multi-agent reinforcement learning technology for TSC has been widely verified. However, the process of mapping road network states onto actions has encountered many challenges, due to the limited communication between agents and the partial observability of the traffic environment. To address this problem, this paper proposes a communication-enhancement value decomposition, multi-agent reinforcement learning TSC method (CVDMARL). The model combines two communication methods: implicit and explicit communication, decouples the complex relationships among the multi-signal agents through the centralized-training and decentralized-execution paradigm, and uses a modified deep network to realize the mining and selective transmission of traffic flow features. We compare and analyze CVDMARL with six different baseline methods based on real datasets. The results show that compared to the optimal method MN_Light, among the baseline methods, CVDMARL’s queue length during peak hours was reduced by 9.12%, the waiting time was reduced by 7.67%, and the convergence algebra was reduced by 7.97%. While enriching the information content, it also reduces communication overhead and has better control effects, providing a new idea for solving the collaborative control problem of multi-signalized intersections.

The nozzle of solid rocket motor (SRM) is easily ablated by high temperature, high pressure, high speed, and corrosive particles, which affects the stability of rocket flight. Therefore, the measurement and characterization of the nozzle ablation rate are helpful in providing some guidance for the design of nozzle material and structure. However, due to the high surface roughness of the composite nozzle after ablation, it is difficult to obtain an accurate ablation rate by contact measurement methods. The 3D laser scanning system is a 3D non-contact measurement technology using structured light technology, phase measurement technology, and computer vision technology. It has the advantages of non-contact, large scanning size, flexibility, and portability. In this paper, a 3D reconstruction of the ablation nozzle is carried out based on the 3D laser scanning system. Additionally, the ablation rate of the nozzle is measured without cutting the actual specimen. Furthermore, the pressure, temperature, and surface convective heat transfer coefficient trends are numerically calculated and compared with the ablation rate. Additionally, the empirical formula between ablation rate and pressure, temperature, convective heat transfer coefficient is obtained empirically by the inversion analysis method. The empirical formula can provide theoretical guidance for nozzle size design and optimization. The results show that the non-contact 3D laser scanning system is a valuable method for reconstructing the model of the ablated nozzle, and the empirical formula of ablation rate can accurately predict the ablation rate of the nozzle.

Prioritizing the development of public transport is an effective way to improve the sustainability of the transport system. In recent years, bus passenger flow has been declining in many cities. How to reform the operating mode of the public transportation system is an important issue that needs to be solved. An autonomous modular bus (AMB) is capable of physical coupling and uncoupling to flexibly adjust vehicle capacity as well as provide high-quality service under unbalanced passenger demand conditions. To promote AMB adoption and reduce the operating cost of the bus route, this paper presents a joint optimization method to simultaneously determine the AMB dispatching plan, charging plan, and charging infrastructure configuration scheme. Then, a mixed-integer programming model is formulated to minimize the operating costs of the bus route. A hybrid intelligent algorithm combining enumeration, cloning algorithm, and particle swarm optimization algorithm is designed to resolve the formulated model. Subsequently, an actual bus route is taken as an example to validate the proposed method. Results indicate that the developed method in this paper can reduce the operating costs and operational energy consumption of the route compared with the real route operating plan. Specifically, the reduction ratio of the former is 23.85%, and the reduction ratio of the latter is 5.92%. The results of this study validate the feasibility and advantages of autonomous modular transit service, contributing positively to the sustainable development of the urban public transportation system.

Charging piles in the bus depot provide charging services to multiple electric bus (EB) routes operating in the area. As charging needs may overlap between independently operated routes, EB fleets often have to wait in line for charging. However, affected by the ambient temperature, the length of the waiting time will cause the battery temperature to change at the beginning of each charging, thereby influencing the charging performance and charging time of the battery. To this end, this paper considers the influence of ambient temperature on battery charging performance, and collaboratively optimizes the number of charging piles in the bus depot and the scheduling problem of EB charging. Aiming at minimizing the cost of laying charging piles in bus stations and the charging costs of bus fleets, as well as minimizing the empty time of electric bus fleets and waiting time for charging in queues, a mixed-integer nonlinear programming model is established, and the immune algorithm is used to solve it. At last, an actual bus depot and four EB routes are taken as examples for verification. The results show that by optimizing the charging waiting time of the electric bus at the bus station, the rapid decline in charging performance caused by the sharp drop in battery temperature is avoided. Without increasing the charging cost of the electric bus fleet, the established method reduces the charging pile installation cost, improves the bus depot’s service efficiency, and ensures the punctuality and integrity of the regional bus route operation.