Solute diffusion in solid polymers has tremendous applications in packaging, reservoir, and biomedical technologies but remains poorly understood. Diffusion of non-entangled linear solutes with chemically identical patterns (blobs) deviates dramatically in polymers in the solid-state (α_lin > 1, Macromolecules 2013, 46, 874) from their behaviors in the molten state (α_lin = 1, Macromolecules, 2007, 40, 3970). This work uses the scale invariance of the diffusivities, D, of linear probes D(N·M_blob + M_anchor,T,T_g) = N^{−α_lin(T,T_g)}D(M_blob + M_anchor,T,T_g) comprising N identical blobs of mass M_blob and possibly one different terminal pattern (anchor of mass M_anchor) to evaluate the amounts of hole-free volume in seven polymers (aliphatic, semi-aromatic and aromatic) over a broad range of temperatures (−70 K ≤ T − T_g ≤ 160 K). The new parameterization of the concept of hole-free volumes opens the application of the free-volume theory (FVT) developed by Vrentas and Duda to practically any polymer, regardless of the availability of free-volume parameters. The quality of the estimations was tested with various probes including n-alkanes, 1-alcohols, n-alkyl acetates, and n-alkylbenzene. The effects of enthalpic and entropic effects of the blobs and the anchor were analyzed and quantified. Blind validation of the reformulated FVT was tested successfully by predicting from first principles the diffusivities of water and toluene in amorphous polyethylene terephthalate from 4 °C to 180 °C and in various other polymers. The new blob model would open the rational design of additives with controlled diffusivities in thermoplastics.

This paper proposes an adaptive shift schedule design framework based on dynamic programming (DP) algorithm and fuzzy logical control to promote the shift schedule’s adaptability whilst improving the comprehensive performance of the multi-gear automated manual transmission (AMT) electric vehicles in real-time application. First, the DP algorithm is employed to extract an offline optimal gear-shift schedule based on a set of driving conditions, including 11 groups of typical driving cycles. Second, a fuzzy logical controller is formulated considering the variation in the vehicle load and acceleration, where a velocity increment is exported online to adjust the gear-shift velocity of the predesigned DP-based schedule to develop a Fuzzy-DP shift schedule. In addition, multi-objective particle swarm optimization (MOPSO) is utilized to construct a comprehensive shift schedule by simultaneously considering the dynamic and economic performance of the vehicle. Then, the dynamic and economic shift schedules are deployed as the benchmark to examine the performance of the proposed shift schedule. Finally, the effectiveness of the Fuzzy-DP shift schedule is evaluated by comparison with others under various combined driving cycles (including vehicle load and velocity). The comparisons demonstrate the remarkable promotion in the adaptability of the Fuzzy-DP shift schedule in terms of acceleration time, energy-saving potential, and shift frequency. The most significant improvements in the dynamic, economic, and shift frequency can reach 8.86%, 10.12%, and 8.56%, respectively, in contrast to the MOPSO-based shift schedule.

The energy saving of Plug-in hybrid electric bus (PHEB) can be maximized by co-design method (integrated design of topology, component sizing and energy management). In many cases, the components have been manufactured, so it is a shortcut to realize the co-design with existing components. Motivated by this, a Taguchi robust design (TRD) method is proposed for the robust co-design of the PHEB. The main innovation is that the noises of driving cycles and stochastic vehicle mass are considered in the TRD, and the TRD is formulated as a smaller-the-better (STB) problem. Moreover, the signal-to-noise ratio (SNR) is taken as the analysis index, where the fuel economy together with its robustness can be simultaneously enhanced. In specific, the dynamic programming (DP) is firstly taking as the fuel consumption calculation module of the TRD. Then, a series of historical driving cycles and the stochastic vehicle mass are designed as noise factors; the components are designed as control factors. Finally, a receding horizon control (RHC) strategy is deployed to realize adaptive energy management control using the robust component match and the same DP. The TRD results demonstrate that the proposed co-design method is applicable and reasonable; the simulation results show that the RHC strategy can realize adaptive control, and averagely improve the fuel economy by 10.85.

Usually there is a great degree of parameter variation of electro-hydraulic proportional control system which is a typical nonlinear time-varying system. If the conventional Proportional Integral Derivative(PID) controllers are applied to pressure and velocity control, the dynamic performance of system are hardly to meet the requirements. In order to improve the dynamic performance of the system and make parameters' changes and non-linear be adaptable and robust, the strategy of electro-hydraulic proportional control is gradually moving to adaptive control. The paper adopts Lyapnov theory and ensures optimum system performance by designing adaptive control loop of electro-hydraulic proportional control system.

Use ADAMS software to optimize the suspension kinematic parameters to make a certain double wishbone suspension have superior kinematic performance. Firstly, establish a suspension simulation model in ADAMS/Car and make wheel jump simulations for the model. Upon completion of the simulation, ADAMS/PostProcessor delivers the change range of the 4 main kinematic parameters of double wishbone suspension: Toe Angle, Camber Angle, Kingpin Caster Angle, and Kingpin Inclination Angle. Secondly, choose the variable points coordinates. ADAMS/Car and ADAMS/Insight jointly optimized the coordinates. Thirdly, the two wheel jump simulations of the coordinate-optimized suspension are carried out again, and after the simulations, the optimization data of the 4 kinematic parameters are obtained. Finally, analyze the optimization data and evaluate the kinematic performance of the optimized double wishbone suspension. After the optimization, four suspension kinematic parameters change range are basically within the ideal range. This optimization has achieved the excellent results, and at the same time, it provides ideas and reference significance for the optimization of other types of suspensions.

Symmetric corrugated hierarchical honeycombs (SCHHs) are critical lightweight load-bearing structures, featuring distinctive topological architectures and excellent mechanical performance. However, they are prone to local buckling under out-of-plane compression and shear loading, which degrades their overall load-bearing capacity. To address this limitation, this work proposes an innovative dual-optimization strategy integrating cylindrical support structure introduction and nano-silica (SiO₂) matrix modification to synergistically enhance the compressive and tribological properties of SCHHs. 3D-printed SCHHs and their reinforced variant (SCHH-AC) with embedded cylindrical supports were fabricated, and the effects of nano-SiO₂ modification (0–9 wt.%) on the compressive performance and tribological behavior of the photopolymer resin matrix were systematically investigated. Experimental results demonstrate that the SCHH-AC-7% SiO₂ configuration achieves optimal compressive performance. A critical SiO₂ concentration threshold was identified: agglomeration at 9 wt.% induces severe mechanical degradation. Tribological tests confirm that SiO₂ incorporation effectively reduces the resin matrix’s friction coefficient and wear rate, with the 7 wt.% concentration yielding the lowest wear rate. Additionally, geometric parametric analysis reveals that increasing the corrugation period number and amplitude further enhances SCHH’s compressive strength and energy absorption. This study establishes a theoretical and experimental foundation for the structural design and material modification of lightweight honeycombs, advancing their practical application in high-performance engineering fields demanding lightweight load-bearing and wear resistance.