A three-dimensional tunnel model for calculation of train-induced ground vibration

The frequency range of interest for ground vibration from underground urban railways is approximately 20 to 100 Hz. For typical soils, the wavelengths of ground vibration in this frequency range are of the order of the spacing of train axles, the tunnel diameter and the distance from the tunnel to nearby building foundations. For accurate modelling, the interactions between these entities therefore have to be taken into account. This paper describes an analytical three-dimensional model for the dynamics of a deep underground railway tunnel of circular cross-section. The tunnel is conceptualised as an infinitely long, thin cylindrical shell surrounded by soil of infinite radial extent. The soil is modelled by means of the wave equations for an elastic continuum. The coupled problem is solved in the frequency domain by Fourier decomposition into ring modes circumferentially and a Fourier transform into the wavenumber domain longitudinally. Numerical results for the tunnel and soil responses due to a normal point load applied to the tunnel invert are presented. The tunnel model is suitable for use in combination with track models to calculate the ground vibration due to excitation by running trains and to evaluate different track configurations. © 2006 Elsevier Ltd. All rights reserved.

Data-based robust multiobjective optimization of interconnected processes: energy efficiency case study in papermaking.

Reducing energy consumption is a major challenge for "energy-intensive" industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of "optimized" operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method.

Thermal energy minimisation in papermaking via MD control approach

This paper reports the application of Advanced Process Control (APC) techniques for improving the thermal energy efficiency of a paperboard-making process by regulating the Machine Direction (MD) profile of the basis weight and moisture content of the paper-board. A Model Predictive Controller (MPC) is designed so that the sheet moisture and basis weight tracking errors along with variations of the sheet moisture and basis weight are reduced. Also, the drainage is maximised through improved wet-end stability which can facilitate driving the sheet moisture set-point closer to its upper specification limit over time. It is shown that the proposed strategy can result in reducing steam usage by 8-10%. A simulation study based on a UK board machine is presented to show the effectiveness of the proposed technique. © 2011 Intl Journal of Adv Mechatr.

Data-based robust multiobjective optimization of interconnected processes: Energy efficiency case study in papermaking

Reducing energy consumption is a major challenge for energy-intensive industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of optimized operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method. © 2006 IEEE.

The effect of addition of ZrO2 to Fe2O3 for hydrogen production by chemical looping

In this paper, a synthetic mixture of ZrO2 and Fe 2O3 was prepared by coprecipitation for use in chemical looping and hydrogen production. Cycling experiments in a fluidized bed showed that a material composed of 30 mol % ZrO2 and 70 mol % Fe 2O3 was capable of producing hydrogen with a consistent yield of 90 mol % of the stoichiometric amount over 20 cycles of reduction and oxidation at 1123 K. Here, the iron oxide was subjected to cycles consisting of nearly 100% reduction to Fe followed by reoxidation (with steam or CO 2 and then air) to Fe2O3. There was no contamination by CO of the hydrogen produced, at a lower detection limit of 500 ppm, when the conversion of Fe3O4 to Fe was kept below 90 mol %. A preliminary investigation of the reaction kinetics confirmed that the ZrO2 support does not inhibit rates of reaction compared with those observed with iron oxide alone. © 2012 American Chemical Society.

Long range exhaustion-a mathematical model for the axisymmetric air flow of a local exhaust ventilation hood assisted by a turbulent radial jet

Abstract-Mathematical modelling techniques are used to predict the axisymmetric air flow pattern developed by a state-of-the-art Banged exhaust hood which is reinforced by a turbulent radial jet flow. The high Reynolds number modelling techniques adopted allow the complexity of determining the hood's air Bow to be reduced and provide a means of identifying and assessing the various parameters that control the air Bow. The mathematical model is formulated in terms of the Stokes steam function, ψ, and the governing equations of fluid motion are solved using finite-difference techniques. The injection flow of the exhaust hood is modelled as a turbulent radial jet and the entrained Bow is assumed to be an inviscid potential flow. Comparisons made between contours of constant air speed and centre-line air speeds deduced from the model and all the available experimental data show good agreement over a wide range of typical operating conditions. | Mathematical modelling techniques are used to predict the axisymmetric air flow pattern developed by a state-of-the-art flanged exhaust hood which is reinforced by a turbulent radial jet flow. The high Reynolds number modelling techniques adopted allow the complexity of determining the hood's air flow to be reduced and provide a means of identifying and assessing the various parameters that control the air flow. The mathematical model is formulated in terms of the Stokes steam function, Ψ, and the governing equations of fluid motion are solved using finite-difference techniques. The injection flow of the exhaust hood is modelled as a turbulent radial jet and the entrained flow is assumed to be an inviscid potential flow. Comparisons made between contours of constant air speed and centre-line air speeds deduced from the model and all the available experimental data show good agreement over a wide range of typical operating conditions.

Code-specific policy gradient rules for spiking neurons

Although it is widely believed that reinforcement learning is a suitable tool for describing behavioral learning, the mechanisms by which it can be implemented in networks of spiking neurons are not fully understood. Here, we show that different learning rules emerge from a policy gradient approach depending on which features of the spike trains are assumed to influence the reward signals, i.e., depending on which neural code is in effect. We use the framework of Williams (1992) to derive learning rules for arbitrary neural codes. For illustration, we present policy-gradient rules for three different example codes - a spike count code, a spike timing code and the most general "full spike train" code - and test them on simple model problems. In addition to classical synaptic learning, we derive learning rules for intrinsic parameters that control the excitability of the neuron. The spike count learning rule has structural similarities with established Bienenstock-Cooper-Munro rules. If the distribution of the relevant spike train features belongs to the natural exponential family, the learning rules have a characteristic shape that raises interesting prediction problems.

Preliminary evaluation of a nuclear scenario involving innovative gas cooled reactors

In order to guarantee a sustainable supply of future energy demand without compromising the environment, some actions for a substantial reduction of CO 2 emissions are nowadays deeply analysed. One of them is the improvement of the nuclear energy use. In this framework, innovative gas-cooled reactors (both thermal and fast) seem to be very attractive from the electricity production point of view and for the potential industrial use along the high temperature processes (e.g., H 2 production by steam reforming or I-S process). This work focuses on a preliminary (and conservative) evaluation of possible advantages that a symbiotic cycle (EPR-PBMR-GCFR) could entail, with special regard to the reduction of the HLW inventory and the optimization of the exploitation of the fuel resources. The comparison between the symbiotic cycle chosen and the reference one (once-through scenario, i.e., EPR-SNF directly disposed) shows a reduction of the time needed to reach a fixed reference level from ∼170000 years to ∼1550 years (comparable with typical human times and for this reason more acceptable by the public opinion). In addition, this cycle enables to have a more efficient use of resources involved: the total electric energy produced becomes equal to ∼630 TWh/year (instead of only ∼530 TWh/year using only EPR) without consuming additional raw materials. © 2009 Barbara Vezzoni et al.

Passive and hybrid mode-locking from a monolithic InGaN/GaN laser diode

We report 4ps and 8ps pulse generation from a two-section monolithic InGaN/GaN laser by hybrid and passive mode-locking, respectively. Pulse trains at a repetition rate of 28.6GHz and an emission wavelength of 422nm are generated. © 2013 The Optical Society.

The dynamic effect of a piled-foundation building on an incident vibration field from an underground railway tunnel

This paper presents a three-dimensional comprehensive model for the calculation of vibration in a building based on pile-foundation due to moving trains in a nearby underground tunnel. The model calculates the Power Spectral Density (PSD) of the building's responses due to trains moving on floating-slab tracks with random roughness. The tunnel and its surrounding soil are modelled as a cylindrical shell embedded in half-space using the well-known PiP model. The building and its piles are modelled as a 2D frame using the dynamic stiffness matrix. Coupling between the foundation and the ground is performed using the theory of joining subsystems in the frequency domain. The latter requires calculations of transfer functions of a half-space model. A convenient choice based on the thin-layer method is selected in this work for the calculations of responses in a half-space due to circular strip loadings. The coupling considers the influence of the building's dynamics on the incident wave field from the tunnel, but ignores any reflections of building's waves from the tunnel. The derivation made in the paper shows that the incident vibration field at the building's foundation gets modified by a term reflecting the coupling and the dynamics of the building and its foundation. The comparisons presented in the paper show that the dynamics of the building and its foundation significantly change the incident vibration field from the tunnel and they can lead to loss of accuracy of predictions if not considered in the calculation.

Physicochemical characteristics and droplet impact dynamics of superhydrophobic carbon nanotube arrays.

The physicochemical and droplet impact dynamics of superhydrophobic carbon nanotube arrays are investigated. These superhydrophobic arrays are fabricated simply by exposing the as-grown carbon nanotube arrays to a vacuum annealing treatment at a moderate temperature. This treatment, which allows a significant removal of oxygen adsorbates, leads to a dramatic change in wettability of the arrays, from mildly hydrophobic to superhydrophobic. Such change in wettability is also accompanied by a substantial change in surface charge and electrochemical properties. Here, the droplet impact dynamics are characterized in terms of critical Weber number, coefficient of restitution, spreading factor, and contact time. Based on these characteristics, it is found that superhydrophobic carbon nanotube arrays are among the best water-repellent surfaces ever reported. The results presented herein may pave a way for the utilization of superhydrophobic carbon nanotube arrays in numerous industrial and practical applications, including inkjet printing, direct injection engines, steam turbines, and microelectronic fabrication.