Magnetorheological (MR) dampers have been widely investigated and proposed for vibration mitigation systems because they possess continuous variability of damping coefficient in response to different operating conditions. In the conventional design of MR dampers, a separate controller and power supply are required, causing an increment of complexity and cost, which are not suitable for home appliances like washing machines. To solve these issues and to reuse wasted energy from vibration of washing machines, in this study, a self-powered shear-mode MR damper, which integrates MR damping and energy-harvesting technologies into a single device, is proposed. The MR damper is composed of an inner housing, on which magnetic coils are wound directly, and an outer housing for covering and creating a closed magnetic circuit of the damper. The gap between the inner housing and the moving shaft is filled with MR fluid to produce the damping force. The energy-harvesting part consists of permanent magnets fastened together on the shaft and induction coils wound directly on slots of the housing. The induced power from the induction coils is directly applied to the excitation coils of the damping part to generate a corresponding damping force against the vibration. In order to achieve optimal geometry of the self-powered MR damper, an optimization for both the damping part and the energy harvesting part of the proposed dampers are conducted based on ANSYS finite element analysis. From optimal solutions, a prototype of the proposed damper is designed in detail, manufactured, and experimentally validated.

To analyze hard turning performance characteristics, a new mathematical model was developed for the hard turning process, and cutting force (CF), another important response for cutting machining, was also studied in the present work. The analysis of the mathematical model and experimental results revealed that thrust force (Fy) was the largest, followed by tangential force (Fz) and feed force (Fx). The resultant CF was most influenced by inclination angle (IA) with 25.02, followed by rake angle (RA) (14.26) and cutting edge angle (CEA) (10.04). Increasing CEA changed the position of cutting on the tool-nose radius and increased local negative RA and correspondingly local clearance angle (CA). Meanwhile, increasing negative RA and IA resulted in larger local negative RA and CA. Moreover, local RA and local CA were the main geometric factors affecting surface roughness (SR), tool wear (TW), and CF. Increasing local negative RA resulted in higher SR and CF. In contrast, increasing local CA resulted in lower SR, TW, and CF. Under specific conditions, the positive effects of the local CA overcame the negative effects of the local negative RA, leading to a simultaneous decrease in SR and TW. The proposed novel mathematical model can be further applied to calculate local CF, cutting temperature, and TW for each cutting-edge element, to analyze and optimize the hard turning process.

Carbon nanotube-based nano-electro-mechanical systems (NEMS) have emerged as highly promising platforms for ultrasensitive mass detection and nanoscale signal processing. This paper presents a comprehensive investigation of the resonance behavior, tunability, and instability thresholds of such devices under electrostatic actuation and mass loading. A coupled electro-mechanical finite-element model is developed, wherein the nanotube is represented by shell elements, electrostatic actuation is modeled via reduced-order transducer elements, and adsorbed particles are introduced through lumped mass elements. The proposed model is rigorously validated against beam-theory results and published molecular-dynamics data, yielding mean errors of 1.02%-2.39%. Parametric studies demonstrate strong voltage tuning; for a baseline device, the fundamental frequency rises significantly from 549.28 MHz (0 V) to 2108.1 MHz (60 V). Furthermore, the analysis reveals a geometry-dependent pull-in limit and confirms that mass adsorption causes a commensurate reduction in resonant frequency. Geometrically nonlinear analysis also predicts a higher instability threshold compared to linear models. Ultimately, these findings validate a compact and effective design framework for optimizing CNT geometry and bias conditions in tunable NEMS mass sensors.

In electric vehicle thermal management systems, direct measurement of the internal motor temperature is difficult. Therefore, the coolant pressure drop is an important indicator for estimating the motor thermal state. However, manufacturing and operating uncertainties in water-cooled interior permanent magnet synchronous motors (IPMSMs) can cause variability in cooling performance and pressure drop, requiring a reliability-based design approach. In this study, reliability-based design optimization (RBDO) is performed by considering manufacturing tolerances in the cooling channels and uncertainty in the inlet coolant flow rate. Based on coupled electromagnetic–thermal–fluid analysis and Kriging surrogate models, RBDO is applied to minimize the maximum temperature while satisfying the allowable pressure-drop limit at a target reliability level. The proposed RBDO improves the probability of satisfying the pressure-drop constraint from 54.1% in the baseline design to 99.9%, while increasing the mean maximum temperature by only 0.17 K. These results indicate that RBDO can improve the reliability of the pressure-drop constraint in IPMSM water-cooling systems under practical manufacturing and operating uncertainties, with only a limited change in thermal performance.