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A novel distributed acoustic sensing technique is proposed that exploits both phase and amplitude of the Rayleigh backscattered light to quantify the environmental variation. The system employs a wavelength-scanning laser and an imbalanced Mach-Zehnder interferometer to acquire the reflection spectra and the phase of the detected light, respectively. Fading-free and low-frequency measurements are realized via the crosscorrelation of the reflection spectra. The discrete crosscorrelation is used to circumvent the nonlinear frequency sweeping of the laser. Based on the phase of the backscattered light, it is possible to quantify fast environmental variations. The whole system requires no hardware modification of the existing system and its functionality is experimentally validated. The proposed system has the potential to monitor ground motion/movement at very low frequency band like subsidence around mining areas and at high frequency band like earthquakes and vibrations induced by avalanches.
Machine learning predictions of surface migration barriers in nucleation and non-equilibrium growth
(2021)
Machine learning is playing an increasing role in the discovery of new materials and may also facilitate the search for optimum growth conditions for crystals and thin films. Here, we perform kinetic Monte-Carlo simulations of sub-monolayer growth. We consider a generic homoepitaxial growth scenario that covers a wide range of conditions with different diffusion barriers (0.4–0.55 eV) and lateral binding energies (0.1–0.4 eV). These simulations are used as a training data set for a convolutional neural network that can predict diffusion barriers and binding energies. Specifically, a single Monte-Carlo image of the morphology is sufficient to determine the energy barriers with an accuracy of approximately 10 meV and the neural network is tolerant to images with noise and lower than atomic-scale resolution. We believe this new machine learning method will be useful for fundamental studies of growth kinetics and growth optimization through better knowledge of microscopic parameters.
For the purpose of increasing payload and reduce freight cost, lightweight composite tank containers used for Transportation have been progressively developed during the last years. Compared to conventionally produced cylindrical steel tanks, the fiber-reinforced solutions allow greater flexibility in the tank design. Despite a number of further material-related benefits of fiber-reinforced composites as non-conductive and non-magnetic behavior as well as corrosion resistance and high strength, the optimization of their thermal degradation properties during combustion is still a challenge. To improve the fire performance of lightweight composite containers, special intumescent fire protection coatings can be applied onto the outside tank surface. This paper presents fire tests on glass-fiber-reinforced plastic transport tanks with complex geometries sheltered with different surface-applied fire protection systems. To evaluate the fire resistance of the tank structures, a fiber optic monitoring system was developed. This system is based on distributed temperature measurements using high-Resolution optical backscatter reflectometry and pointwise reference measurements using fiber Bragg gratings. Thereby, all the fiber optic sensors were directly integrated in the composite layer structure of the tanks. The focus of the presented work is on the demonstration of capability of fiber optic monitoring system in such high-temperature application. Moreover, the fiber optic measurements provide new insights into the efficiency of intumescent coating applied for fire protection of fiber-reinforced plastic transport tanks.