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Locally controlled sensing properties of stretchable pressure sensors enabled by micro-patterned piezoresistive device architecture
No full textFor wearable health monitoring systems and soft robotics, stretchable/flexible pressure sensors have continuously drawn attention owing to a wide range of potential applications such as the detection of human physiological and activity signals, and electronic skin (e-skin). Here, we demonstrated a highly stretchable pressure sensor using silver nanowires (AgNWs) and photo-patternable polyurethane acrylate (PUA). In particular, the characteristics of the pressure sensors could be moderately controlled through a micro-patterned hole structure in the PUA spacer and size-designs of the patterned hole area. With the structural-tuning strategies, adequate control of the site-specific sensitivity in the range of 47~83 kPa−1 and in the sensing range from 0.1 to 20 kPa was achieved. Moreover, stacked AgNW/PUA/AgNW (APA) structural designed pressure sensors with mixed hole sizes of 10/200 µm and spacer thickness of 800 µm exhibited high sensitivity (~171.5 kPa−1) in the pressure sensing range of 0~20 kPa, fast response (100~110 ms), and high stretchability (40%). From the results, we envision that the effective structural-tuning strategy capable of controlling the sensing properties of the APA pressure sensor would be employed in a large-area stretchable pressure sensor system, which needs site-specific sensing properties, providing monolithic implementation by simply arranging appropriate micro-patterned hole architectures
Numerical resolution of compressor corner separation problem
No full textCorner separation is a common flow phenomenon within compressors that can significantly affect the compressor performance. The RANS turbulence closures, commonly used in the industrial CFD simulations, often struggle to predict corner separation with reasonable accuracy. In this paper, the results of two RANS-based modelling approaches are presented for the corner separation within a high-loaded Prescribed Velocity Distribution (PVD) compressor cascade. The flow characteristics are studied to facilitate understanding the causes of varying performance of RANS models. It is observed that mixing plays a crucial role in accurately predicting the type, location, and size of flow separation. The source terms that control the turbulence mixing in SA and SST models are identified, based on physical analyses. Both RANS models are modified to better model the mixing process. Based on the modified SST model, an improved RANS-LES blending function has been proposed for a hybrid RANS-LES model. This new blending function ensures reliable shielding of attached boundary layers by the RANS portion of the hybrid model. Finally, to gain further understanding of the endwall flow physics, the turbulence characteristics of the resolved corner separation flow are studied, in terms of the large-scale unsteadiness and loss generation mechanisms
SPACE-CONSTRAINED AUTONOMOUS REVERSING OF ARTICULATED VEHICLES
No full textThis dissertation presents several modern control methods for autonomous reversing of long combination vehicles (LCVs). These approaches not only significantly improve the performance of previous autonomous reversing systems, but also address a major gap in the reversing control literature for LCVs. The methods were validated by implementing them at full-scale on experimental articulated vehicles owned by the ‘Cambridge Vehicle Dynamics Consortium’ (CVDC). Experimental results were in very good agreement with simulation results. Previous path following control methods for autonomous reversing of LCVs have focussed on minimising the tracking error between the rear-end of the combination and a desired path, irrespective of the motion of the rest of the vehicle. A significant disadvantage of these strategies is that the other parts of the vehicle, particularly the tractor unit, can experience large excursions from the reference path, thus making their implementation infeasible for manoeuvres in spatially-limited areas, such as warehouse yards and normal roads. The ‘Minimum Swept Path Control’ (MSPC) method was devised to reduce this problem by relaxing the requirement for very accurate path following, while minimising the maximum excursion of the vehicle. This strategy weights both the path-following error at the rear end of the vehicle and the swept path of the front end of the vehicle. MSPC enables the swept path to be reduced by about 50%, compared with path following control, which gives this method more realistic applications. The ‘Lane-bounded Reversing Control’ (LBRC) method requires a vehicle to satisfy the reversing objectives while constraining the motion to be within a specified ‘lane’. The LBRC controller is ‘intelligent’, which means it can pursue an optimum route without tracking a desired path generated by a path planner and can proactively avoid future potential clashes. Hence, this controller enables the autonomous reversing system to perform a precise, minimum-cost and collision-free manoeuvre to a specified terminal position by planning ahead and making optimal decisions. The controller performance was evaluated in numerous realistic scenarios, both simulated and in field tests at full-scale. The analysis of LBRC provides a solid foundation for the development of more advanced control methods. Adaptive Lane-bounded Reversing Control (ALBRC) and Adaptive Bi-directional Control (ABC) systems were designed to improve the LBRC method. The tuning of the LBRC controller was based on empirical experience and there are many weights to be tuned in the controller configuration. To offset this drawback, the ALBRC algorithm was developed by attaching ‘virtual bumpers’ to the vehicle system states, and allowing the controller weights to adapt to lane boundaries and obstacles. The ALBRC method simplifies the original tuning process significantly. The ALBRC controller performs well in most cases. However, a solution is not guaranteed if the preview and control horizons are not tuned properly or a vehicle reverses from an arbitrary position. Hence, the ABC algorithm incorporates a so-called ‘cusp technology’, which allows a vehicle to move forward and backward to realign its position and orientation between attempts at the reversing manoeuvre. In this case, the preview and control horizons do not need to change in different scenarios. The ABC method can significantly reduce computational time compared with using a long preview horizon
Numerical Study on the Influence of Copper Former on AC Loss Characteristics of Conductor on Round Core cable
No full textA conductor on round core (CORC) cable wound with REBCO tapes is promising for high current density applications. AC loss analysis can help us to understand its electromagnetic performance. This paper built a two-dimensional (2-D) CORC model by finite element method, and the AC losses induced by changing external magnetic field was examined. Individually, the AC loss dependence on the magnitude and frequency of the external magnetic field is examined. The results prove that the copper former will lead to an increase of the CORC cable magnetization losses in a high frequency
On random sampling and fourier transform estimation in sea waves prediction
No full textImproving the safety of a wide range of launch and recovery operations is of great international maritime interest. Deterministic sea wave prediction (DSWP) is a relatively new branch of science that can offer such opportunities by predicting the actual shape of the sea surface and its evolution for short time in the future. Fourier transform technique is the main building block in DSWP, which requires measurements of the sea surface. Nonetheless, uniformly sampled measurements of the sea surface cannot be practically achieved for various reasons. Conventional X-band radars are the most realistic candidate to provide a low-cost convenient source of two-dimensional wave profile information for DSWP purposes. Ship movement and mechanically rotating scanning antennas are among sources of irregularity in sea surface sampling. This in turn introduces errors when traditional Fourier transform based wave prediction methods are used. In this paper we show that by modelling the radar sampling instants as random variables and using the estimator of Tarczynski and Allay to process the samples, a reliable solution for DSWP can be constituted
Revealing the oxygen reduction reaction activity origin of single atoms supported on g-C<inf>3</inf>N<inf>4</inf> monolayers: A first-principles study
No full textHerein, the potential of single transition metal atoms (TM, from Ti to Au) supported on g-C3N4 (TM/g-C3N4) for the oxygen reduction reaction (ORR) was investigated by first-principles calculations. It was demonstrated that the TM atoms can remain stable in the cavity of g-C3N4 and interact with the substrate via charge transfer from the TM atoms to g-C3N4. Among all the TM/g-C3N4 samples, Pd/g-C3N4 stands out with a low overpotential of 0.46 V, showing good performance for ORR; thus, it has great potential to replace the noble Pt catalyst. The ORR activity of TM/g-C3N4 is a function of ΔE∗OH (an energy descriptor). Furthermore, the d-band center and ICOHP (electronic structure descriptors) can quantitatively describe the variation trend of ΔE∗OH in addition to Bader charge analysis (a charge transfer descriptor). Considering the number of d orbital electrons and the electronegativity of TM, φ (an intrinsic descriptor) can be applied to predict and reveal the origin of the ORR activity. A bridge from intrinsic characteristics to electronic structures, to charge transfer, to electronic structures and then to adsorption energy has been established, which is conducive to better reveal the ORR activity origin and provide guidance for designing effective ORR electrocatalysts
Enhanced electrochemical oxygen evolution reaction activity on natural single-atom catalysts transition metal phthalocyanines: The substrate effect
No full textThe oxygen evolution reaction (OER) plays a crucial role in the field of renewable and clean energy such as electric vehicles and fuel cells. Research towards non-noble metals and highly efficient catalysts for the OER has garnered wide attention. Here, we report a series of macrocyclic transition metal phthalocyanines (TMPc) and transition metal phthalocyanine absorbed oxygen or sulfur atoms (TMOPc and TMSPc), natural photocatalytic and electrocatalytic OER single-atom catalysts (SACs). The results demonstrate that they are all semiconductors and extremely stable. Several Pc SACs such as FeOPc and PtPc show excellent high catalytic activity compared with traditional noble metal catalysts and an OER descriptor of the Pc SACs is developed to establish a volcano plot. In particular, FeOPc features a low overpotential of only 0.48 V. Moreover, single-walled nanotubes (SWNTs) are employed as the substrate of TMPc to improve the stability of active sites and prevent aggregation. The result indicates that the TMPc single-atom catalysts deposited on SWNTs feature far superior OER catalytic performance. Further studies suggest that the enhancement originates from the change of interaction between the central TM atom and OER intermediates and the variation of charge transfer caused by the SWNT substrates. Our work opens a new avenue for finding OER catalysts with higher activity and lower cost. This journal i
Investigation of SiN<inf>x</inf> and AlN passivation for AlGaN/GaN high-electron-mobility transistors: Role of interface traps and polarization charges
No full textIn this work, we studied the mechanisms and switching properties of AlGaN/GaN high-electron-mobility-transistors (HEMTs) passivated by amorphous-SiNx and monocrystal-like AlN. The effects of interface traps and polarization charges on current collapse are investigated by TCAD simulations and experimental characterizations. Surface/interface deep levels can be compensated by both shallow donor-like traps (SiNx passivation) and polarization charges (AlN passivation) at passivation/heterostructure interface, but with different levels of effectiveness under fast switching conditions. SiNx-passivation introduces shallow donor-like trap states with short time constant that favors a fast emission of trapped electrons in the access region and suppressed current collapse, but nevertheless exhibits more severe time-dependent recovery of dynamic on-resistance. For AlN passivation, interface traps are compensated by the fixed positive polarization charges and the off-state depletion region (in the 2DEG channel) is formed predominantly by electric-field effect, leading to an immediate accumulation of high channel electron concentration after switching the HEMT devices back to on-state and instant response of drain current to gate and drain bias. The field plate structure is necessary in SiNx-passivated devices for both current collapse suppression and electric field alleviation. With AlN passivation, the field plate can be solely designed for achieving more uniform electric field distribution for gate reliability concern without the concern of current collapse
Learning with Imbalanced Data in Smart Manufacturing: A Comparative Analysis
No full textThe Internet of Things (IoT) paradigm is revolutionising the world of manufacturing into what is known as Smart Manufacturing or Industry 4.0. The main pillar in smart manufacturing looks at harnessing IoT data and leveraging machine learning (ML) to automate the prediction of faults, thus cutting maintenance time and cost and improving the product quality. However, faults in real industries are overwhelmingly outweighed by instances of good performance (faultless samples); this bias is reflected in the data captured by IoT devices. Imbalanced data limits the success of ML in predicting faults, thus presents a significant hindrance in the progress of smart manufacturing. Although various techniques have been proposed to tackle this challenge in general, this work is the first to present a framework for evaluating the effectiveness of these remedies in the context of manufacturing. We present a comprehensive comparative analysis in which we apply our proposed framework to benchmark the performance of different combinations of algorithm components using a real-world manufacturing dataset. We draw key insights into the effectiveness of each component and inter-relatedness between the dataset, the application context, and the design of the ML algorithm
An Inductive Force Sensor for In-Shoe Plantar Normal and Shear Load Measurement
No full textDiabetic foot ulcers (DFUs) are a severe global public health issue. Plantar normal and shear load are believed to play an important role in the development of foot ulcers and could be a valuable indicator to improve assessment of DFUs. However, despite their promise, plantar load measurements currently have limited clinical application, primarily due to the lack of reliable measurement techniques particularly for shear load measurements. In this paper we report on the design and evaluation of a novel tri-axis force sensor to measure both normal and shear load on the foot's plantar surface simultaneously. The sensor consists of a group of inductive sensing coils above which a conductive target is placed on a hyperelastic elastomer. Movement of the target under load affects the coil inductances which are measured and digitized by an embedded system. Using a computational finite element model, we investigated the influence of sensing coil form and configuration on sensor performance. A sensor configured with four-square coils and maximal turns provided the best performance for plantar load measurements. A prototype was fabricated and calibrated using a neural network to map the non-linear relationship between the sensor output and the applied tri-axis load. Experimental evaluation indicates that the tri-axis sensor can effectively detect shear load of ±16 N and normal load up to 105 N (RMS errors: 1.05 N and 1.73 N respectively) with a high performance. Overall, this sensor provides a promising basis for plantar normal and shear load measurement which are crucial for improved assessment of DFU