CFD analyses of fibre transport and fibre deposition at plunging jet conditions

The investigation of insulation debris generation, transport and sedimentation becomes important with regard to reactor safety research for PWR and BWR, when considering the long-term behaviour of emergency core cooling systems during all types of loss of coolant accidents. A joint research project on such questions is being performed in cooperation between the University of Applied Sciences Zittau/Görlitz (HSZG) and the Helmholtz-Zentrum Dresden-Rossendorf (HZDR). The project deals with the experimental investigation of particle transport phenomena in coolant flow and the development of CFD models for its description (see [10-12]). While the experiments are performed at the University at Zittau/Görlitz, the theoretical modelling efforts are concentrated in Rossendorf. In the current paper, the basic concepts for CFD modelling are described and feasibility studies are presented. The model capabilities are demonstrated via complex flow situations, where a plunging jet agitates insulation debris. © Carl Hanser Verlag, München.

Disruption prediction at JET [Joint European Torus]

The sudden loss of the plasma magnetic confinement, known as disruption, is one of the major issue in a nuclear fusion machine as JET (Joint European Torus), Disruptions pose very serious problems to the safety of the machine. The energy stored in the plasma is released to the machine structure in few milliseconds resulting in forces that at JET reach several Mega Newtons. The problem is even more severe in the nuclear fusion power station where the forces are in the order of one hundred Mega Newtons. The events that occur during a disruption are still not well understood even if some mechanisms that can lead to a disruption have been identified and can be used to predict them. Unfortunately it is always a combination of these events that generates a disruption and therefore it is not possible to use simple algorithms to predict it. This thesis analyses the possibility of using neural network algorithms to predict plasma disruptions in real time. This involves the determination of plasma parameters every few milliseconds. A plasma boundary reconstruction algorithm, XLOC, has been developed in collaboration with Dr. D. Ollrien and Dr. J. Ellis capable of determining the plasma wall/distance every 2 milliseconds. The XLOC output has been used to develop a multilayer perceptron network to determine plasma parameters as ?i and q? with which a machine operational space has been experimentally defined. If the limits of this operational space are breached the disruption probability increases considerably. Another approach for prediction disruptions is to use neural network classification methods to define the JET operational space. Two methods have been studied. The first method uses a multilayer perceptron network with softmax activation function for the output layer. This method can be used for classifying the input patterns in various classes. In this case the plasma input patterns have been divided between disrupting and safe patterns, giving the possibility of assigning a disruption probability to every plasma input pattern. The second method determines the novelty of an input pattern by calculating the probability density distribution of successful plasma patterns that have been run at JET. The density distribution is represented as a mixture distribution, and its parameters arc determined using the Expectation-Maximisation method. If the dataset, used to determine the distribution parameters, covers sufficiently well the machine operational space. Then, the patterns flagged as novel can be regarded as patterns belonging to a disrupting plasma. Together with these methods, a network has been designed to predict the vertical forces, that a disruption can cause, in order to avoid that too dangerous plasma configurations are run. This network can be run before the pulse using the pre-programmed plasma configuration or on line becoming a tool that allows to stop dangerous plasma configuration. All these methods have been implemented in real time on a dual Pentium Pro based machine. The Disruption Prediction and Prevention System has shown that internal plasma parameters can be determined on-line with a good accuracy. Also the disruption detection algorithms showed promising results considering the fact that JET is an experimental machine where always new plasma configurations are tested trying to improve its performances.

Water detection in jet fuel using a polymer optical fibre Bragg grating

Water is a common impurity of jet fuel, and can exist in three forms: dissolved in the fuel, as a suspension and as a distinct layer at the bottom of the fuel tank. Water cannot practically be eliminated from fuel but must be kept to a minimum as large quantities can cause engine problems, particularly when frozen, and the interface between water and fuel acts as a breeding ground for biological contaminants. The quantities of dissolved or suspended water are quite small, ranging from about 10 ppm to 150 ppm. This makes the measurement task difficult and there is currently a lack of a convenient, electrically passive system for water-in-fuel monitoring; instead the airlines rely on colorimetric spot tests or simply draining liquid from the bottom of fuel tanks. For all these reason, people have explored different ways to detect water in fuel, however all these approaches have problems, e.g. they may not be electrically passive or they may be sensitive to the refractive index of the fuel. In this paper, we present a simple, direct and sensitive approach involving the use of a polymer optical fibre Bragg grating to detect water in fuel. The principle is that poly(methyl methacrylate) (PMMA) can absorb moisture from its surroundings (up to 2% at 23 °C), leading to both a swelling of the material and an increase in refractive index with a consequent increase in the Bragg wavelength of a grating inscribed in the material.

Viscoelasticity and high buckling stress of dense carbon nanotube brushes

We report on the mechanical behavior of a dense brush of small-diameter (1–3 nm) non-catalytic multiwall (2–4 walls) carbon nanotubes (CNTs), with ~10 times higher density than CNT brushes produced by other methods. Under compression with spherical indenters of different radii, these highly dense CNT brushes exhibit a higher modulus (~17–20 GPa) and orders of magnitude higher resistance to buckling than vapor phase deposited CNT brushes or carbon walls. We also demonstrate the viscoelastic behavior, caused by the increased influence of the van der Waals’ forces in these highly dense CNT brushes, showing their promise for energy-absorbing coatings.

Water detection in jet fuel using a polymer optical fibre Bragg grating

Water is a common impurity of jet fuel, and can exist in three forms: dissolved in the fuel, as a suspension and as a distinct layer at the bottom of the fuel tank. Water cannot practically be eliminated from fuel but must be kept to a minimum as large quantities can cause engine problems, particularly when frozen, and the interface between water and fuel acts as a breeding ground for biological contaminants. The quantities of dissolved or suspended water are quite small, ranging from about 10 ppm to 150 ppm. This makes the measurement task difficult and there is currently a lack of a convenient, electrically passive system for water-in-fuel monitoring; instead the airlines rely on colorimetric spot tests or simply draining liquid from the bottom of fuel tanks. For all these reason, people have explored different ways to detect water in fuel, however all these approaches have problems, e.g. they may not be electrically passive or they may be sensitive to the refractive index of the fuel. In this paper, we present a simple, direct and sensitive approach involving the use of a polymer optical fibre Bragg grating to detect water in fuel. The principle is that poly(methyl methacrylate) (PMMA) can absorb moisture from its surroundings (up to 2% at 23 °C), leading to both a swelling of the material and an increase in refractive index with a consequent increase in the Bragg wavelength of a grating inscribed in the material.

Surrogate-based analysis and optimization for the design of heat sinks with jet impingement

Heat sinks are widely used for cooling electronic devices and systems. Their thermal performance is usually determined by the material, shape, and size of the heat sink. With the assistance of computational fluid dynamics (CFD) and surrogate-based optimization, heat sinks can be designed and optimized to achieve a high level of performance. In this paper, the design and optimization of a plate-fin-type heat sink cooled by impingement jet is presented. The flow and thermal fields are simulated using the CFD simulation; the thermal resistance of the heat sink is then estimated. A Kriging surrogate model is developed to approximate the objective function (thermal resistance) as a function of design variables. Surrogate-based optimization is implemented by adaptively adding infill points based on an integrated strategy of the minimum value, the maximum mean square error approach, and the expected improvement approaches. The results show the influence of design variables on the thermal resistance and give the optimal heat sink with lowest thermal resistance for given jet impingement conditions.