Investigation of the Viscoelastic Effect on Optical- Fiber Sensing and Its Solution for 3D-Printed Sensor Packages

Viscoelasticity is an effect seen in a wide range of materials and it affects the reliability of static measurements made using Fiber Bragg Grating-based sensors, because either the target structure, the adhesive used, or the fiber itself could be viscoelastic. The effect of viscoelasticity on FBG-based sensing has been comprehensively researched through theoretical analysis and simulation using a finite-element approach and a further data processing method to reconstruct the graphical data has been developed. An integrated sensor package comprising of an FBG-based sensor in a polymer host and manufactured by using three-dimensional printing was investigated and examined through tensile testing to validate the approach. The application of the 3D-printed FBG-based sensor package, coupled to the data process method has been explored to monitor the height of a railway pantograph, a critical measurement requirement to monitor elongation, employing a method that can be used in the presence of electromagnetic interference. The results show that the effect of viscoelasticity can be effectively eliminated, and the graphical system response allows results that are sufficiently precise for field use to be generated

Ferromagnetism in Al1−xCrxN thin films by density functional calculations

We report the results of a theoretical study of magnetic coupling between Cr atoms doped in bulk AlN as well as AlN (112¯0) thin films having wurtzite structure. The calculations are based on density fuctional theory with the generalized gradient approximation to the exchange and correlation potential. In the thin film, modeled by a slab of finite thickness, Cr atoms are found to cluster around N on the surface layer and couple ferromagnetically. The results for the Cr-doped AlN crystal are similar, namely, Cr atoms cluster around N and couple ferromagnetically. In the thin film, the preference of Cr to occupy surface sites over the bulk sites is shown to be due to reduced coordination of the surface atoms. As the distance between the Cr atoms increases, both the ferro- and antiferromagnetic states become energetically degenerate and this degeneracy may account for the observed low magnetic moment per Cr atom

Thermodynamic analysis of methane-fueled solid oxide fuel cells considering co electrochemical oxidation

2014-2015 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Indirect exchange of magnetic impurities in zigzag graphene ribbon

We use quantum Monte Carlo method to study the indirect coupling between two magnetic impurities on the zigzag edge of graphene ribbon, with respect to the chemical potential $\mu$. We find that the spin-spin correlation between two adatoms located on the nearest sites in the zigzag edge are drastically suppressed around the zero-energy. As we switch the system away from half-filling, the antiferromagnetic correlation is first enhanced and then decreased. If the two adatoms are adsorbed on the sites belonging to the same sublattice, we find similar behavior of spin-spin correlation except for a crossover from ferromagnetic to antiferromagentic correlation in the vicinity of zero-energy. We also calculated the weight of different components of d-electron wave function and local magnet moment for various values of parameters, and all the results are consistent with those of spin-spin correlation between two magnetic impurities.Comment: 3 pages, 4 figures, conference proceedin

Nitrogen-induced magnetic transition in small chromium clusters

Using density functional theory with generalized gradient approximation for exchange and correlation, we show that otherwise antiferromagnetically coupled chromium atoms in very small chromium clusters couple ferromagnetically when doped with a nitrogen atom, thus leading to giant magnetic moments. For example, the magnetic moment of Cr2N is found to be 9μBwhile that of Cr2 is 0μB. Strong bonding between Cr and N atoms brings about this magnetic transition. The Cr atoms nearest neighbor to N couple ferromagnetically with each other and antiferromagnetically with nitrogen. The significance of these results in understanding the ferromagnetic order in Cr-doped GaN is discussed

Numerical study of a sphere descending along an inclined slope in a liquid

The descending process of a sphere rolling and/or sliding along an inclined slope in a liquid involves interactions between the hydrodynamic forces on the sphere and the contact forces between the sphere and the plane. In this study, the descending process of sphere in a liquid was examined using coupled LBM-DEM technique. The effects of slope angle, viscosity and friction coefficient on the movement of a sphere were investigated. Two distinct descending patterns were observed: (a) a stable rolling/sliding movement along the slope, and (b) a fluctuating pattern along the slope. Five dimensionless coefficients (Reynolds number (Re), drag coefficient, lift coefficient, moment coefficient and rolling coefficient) were used to analyze the observed processes. The vortex structure in the wake of the sphere gives a lift force to the sphere, which in turn controls the different descending patterns. It is found that the generation of a vortex is not only governed by Re, but also by particle rotation. Relationships between the forces/moments and the dimensionless coefficients are established

AutoColor: learned light power control for multi-color holograms

Multi-color holograms rely on simultaneous illumination from multiple light sources. These multi-color holograms could utilize light sources better than conventional single-color holograms and can improve the dynamic range of holographic displays. In this letter, we introduce AutoColor, the first learned method for estimating the optimal light source powers required for illuminating multi-color holograms. For this purpose, we establish the first multi-color hologram dataset using synthetic images and their depth information. We generate these synthetic images using a trending pipeline combining generative, large language, and monocular depth estimation models. Finally, we train our learned model using our dataset and experimentally demonstrate that AutoColor significantly decreases the number of steps required to optimize multi-color holograms from > 1000 to 70 iteration steps without compromising image quality