Kjetil Johannessen

Dynamics of fluidized-bed reactors. Development and application of a new multi-fiber optical probe

Abstract There seems to be no method available to measure all parameters of interests in multiphase flow systems with high solids concentration and with reasonable accuracy. Parameters include, local particle velocity vectors, solids volume fractions and bubble rise velocity, size, frequency and volume fraction. In this study a novel method based on a fiber optical technique and tracer particles has been developed for simultaneous measurements of the mentioned local flow properties in highly concentrated multiphase flow systems such as gas–solid fluidized bed reactors. A particle present in the measuring volume in front of the probe is marked with a fluorescent dye. A light source illuminates the particles and the detecting fibres receive reflected light from uncoated particles and fluorescent light from the tracer particle. Using optical filters, the fluorescent light can be distinguished and together with a small fraction of background light from uncoated particles can be used for the determination of local flow properties. The method gives the possibility for simultaneous measurement of the local movement of a single tracer particle, local bubble properties and the local solids volume fractions in different positions in the bed.

Fiber Optic Condition Monitoring during a Full Scale Destructive Bridge Test

Fiber optic sensors were used to monitor structural parameters during a destructive bridge test. A system composed of four different types of fiber optic sensors was used to demonstrate versatility and possible completeness for these type of measurements using fiber optic sensors. The fiber optic sensors were monitored continuously during the test using a single control unit. Local strain measurements on the reinforcing bars were conducted using fiber optic Bragg grating sensors. A new technique employing both current and temperature tuning of a DFB (Distributed Feedback) laser to interrogate gratings was developed, showing good correspondence with resistance strain gauges. This sensor conducted the only strain measurements during and after the failure of the bridge, since all expensive control electronics could be operated from a safe distance. A Bragg grating laser sensor system was also used, with a laser cavity of 40 m using standard connectors. The system was well behaved and the measurements corresponded well with resistance strain gauges. A fiber optic polarimetric sensor measured displacements over 2.5 m at the bridge surface. The measurements were comparable to conventional extensiometer measurements at the same location. Cracking of the bridge surface was monitored by studying reflection and transmission characteristics of optical fibers glued to the surface. Although less cracking occurred than was expected, both transmission loss and OTDR (Optical Time Domain Reflectometry) reflection measurements successfully detected cracking of the bridge surface. The most severe practical problem was unintentional fiber breaks caused by personnel not accustomed to using fiber optics.

Measurements of frequency responses of three-section distributed Bragg reflector lasers and application to modulation spectroscopy

Abstract The feasibility for wavelength modulation spectroscopy using a three-section distributed Bragg reflector (DBR) laser modulated at the phase section has been investigated. The FM and AM responses have been measured versus frequency from dc to 3 GHz at various positions on the tuning curve. The modulation efficiency as a function of the tuning currents combined with modulation frequency has been studied. In particular the behaviour at the positions of laser mode jumps has been emphasised as well as consistency of the recorded spectra across the entire tuning range. These tests were performed with a tuneable Fabry–Perot filter. The operation conditions were adapted for high and sustained FM response for modulation spectroscopy over a wide wavelength range. A conventional Bragg grating is used as an example of practical application.

Smart structures for sea, land, and space

The term smart structure has traditionally been used for structures with sensing capabilities, and preferably also actuators and local processing capability of sensor signal to close the feed back loop. To a large extent the terminology has been extended to cover structures with more limited capabilities, but where at least a sensor is closely integrated in the structure. This presentation will give examples from areas where SINTEF have investigated fiber optic sensors and discuss smart structures on this basis.

Practical fiber optic sensor for measuring large mechanical deformations

A fiber-optic sensor for measuring large mechanical deformations based on a digital use of fringes in a polarimeter was developed and tested in the laboratory. Two sensors were mounted on an air cushion catamaran and used for measuring strain in the fiberglass laminates and thereby also the relative movement of the two hulls. The data were logged together with other ship movement data and provided useful information on the craft performance.

Experiments with Fiberoptic Smart Skin Sensors Applied to Marine Vehicles

Advanced composite materials are currently finding an increased use in marine vehicle construction. The introduction of new materials reduces hull weight, and is especially attractive for fast vehicles. By the introduction of new materials a need arises to design a cost effective construction which can withstand expected environmental stresses under operation with an optimum use of material for reinforcement. Furthermore, it is desired to monitor the long term integrity of the new materials when subjected to sea loads under operation. Data collected on these composite materials are still rather limited.

Discovering Thermoelectric Materials Using Machine Learning: Insights and Challenges

This work involves the use of combined forces of data-driven machine learning models and high fidelity density functional theory for the identification of new potential thermoelectric materials. The traditional method of thermoelectric material discovery from an almost limitless search space of chemical compounds involves expensive and time consuming experiments. In the current work, the density functional theory (DFT) simulations are used to compute the descriptors (features) and thermoelectric characteristics (labels) of a set of compounds. The DFT simulations are computationally very expensive and hence the database is not very exhaustive. With an anticipation that the important features can be learned by machine learning (ML) from the limited database and the knowledge could be used to predict the behavior of any new compound, the current work adds knowledge related to (a) understanding the impact of selection of influence of training/test data, (b) influence of complexity of ML algorithms, and (c) computational efficiency of combined DFT-ML methodology.