Numerical investigation of a source extraction technique based on an acoustic correction method

An aerodynamic sound source extraction from a general flow field is applied to a number of model problems and to a problem of engineering interest. The extraction technique is based on a variable decomposition, which results to an acoustic correction method, of each of the flow variables into a dominant flow component and a perturbation component. The dominant flow component is obtained with a general-purpose Computational Fluid Dynamics (CFD) code which uses a cell-centred finite volume method to solve the Reynolds-averaged Navier–Stokes equations. The perturbations are calculated from a set of acoustic perturbation equations with source terms extracted from unsteady CFD solutions at each time step via the use of a staggered dispersion-relation-preserving (DRP) finite-difference scheme. Numerical experiments include (1) propagation of a 1-D acoustic pulse without mean flow, (2) propagation of a 2-D acoustic pulse with/without mean flow, (3) reflection of an acoustic pulse from a flat plate with mean flow, and (4) flow-induced noise generated by the an unsteady laminar flow past a 2-D cavity. The computational results demonstrate the accuracy for model problems and illustrate the feasibility for more complex aeroacoustic problems of the source extraction technique.

HOS-Based Semi-Blind Spatial Equalization for MIMO Rayleigh Fading Channels

In this paper we concentrate on the direct semi-blind spatial equalizer design for MIMO systems with Rayleigh fading channels. Our aim is to develop an algorithm which can outperform the classical training based method with the same training information used, and avoid the problems of low convergence speed and local minima due to pure blind methods. A general semi-blind cost function is first constructed which incorporates both the training information from the known data and some kind of higher order statistics (HOS) from the unknown sequence. Then, based on the developed cost function, we propose two semi-blind iterative and adaptive algorithms to find the desired spatial equalizer. To further improve the performance and convergence speed of the proposed adaptive method, we propose a technique to find the optimal choice of step size. Simulation results demonstrate the performance of the proposed algorithms and comparable schemes.

Investigation into mercury bound to biothiols:structural identification using ESI-ion-trap MS and introduction of a method for their HPLC separation with simultaneous detection by ICP-MS and ESI-MS

Mercury in plants or animal tissue is supposed to occur in the form of complexes formed with biologically relevant thiols (biothiols), rather than as free cation. We describe a technique for the separation and molecular identification of mercury and methylmercury complexes derived from their reactions with cysteine (Cys) and glutathione (GS): Hg(Cys)(2), Hg(GS)(2), MeHgCys, MeHgGS. Complexes were characterised by electrospray mass spectrometry (MS) equipped with an ion trap and the fragmentation pattern of MeHgCys was explained by using MP2 and B3LYP calculations, showing the importance of mercury-amine interactions in the gas phase. Chromatographic baseline separation was performed within 10 min with formic acid as the mobile phase on a reversed-phase column. Detection was done by online simultaneous coupling of ES-MS and inductively coupled plasma MS. When the mercury complexes were spiked in real samples (plant extracts), no perturbation of the separation and detection conditions was observed, suggesting that this method is capable of detecting mercury biothiol complexes in plants.

Adaptive backstepping droop controller design for multi-terminal high-voltage direct current systems

Wind power is one of the most developed renewable energy resources worldwide. To integrate offshore wind farms to onshore grids, the high-voltage direct current (HVDC) transmission cables interfaced with voltage source converters (VSCs) are considered to be a better solution than conventional approaches. Proper DC voltage indicates successive power transfer. To connect more than one onshore grid, the DC voltage droop control is one of the most popular methods to share the control burden between different terminals. However, the challenges are that small droop gains will cause voltage deviations, while higher droop gain settings will cause large oscillations. This study aims to enhance the performance of the traditional droop controller by considering the DC cable dynamics. Based on the backstepping control concept, DC cables are modelled with a series of capacitors and inductors. The final droop control law is deduced step-by-step from the original remote side. At each step the control error from the previous step is considered. Simulation results show that both the voltage deviations and oscillations can be effectively reduced using the proposed method. Further, power sharing between different terminals can be effectively simplified such that it correlates linearly with the droop gains, thus enabling simple yet accurate system operation and control.

Development of a sustainable method for the extraction of lycopene and β-carotene from food wastes

The main objective of the present work is the study of a profitable process not only in the extraction and selective separation of lycopene and β-carotene, two compounds present in tomato, but also in its potential application to food industry wastes. This is one of the industries that produce larger amounts of wastes, which are rich in high value biomolecules with great economic interest. However, the conventional methods used to extract this kind of compounds are expensive which limits their application at large scale. Lycopene and βcarotene are carotenoids with high commercial value, known for their antioxidant activity and benefits to human health. Their biggest source is tomato, one of the world’s most consumed fruits, reason for which large quantities of waste is produced. This work focuses on the study of diverse solvents with a high potential to extract carotenoids from tomato, as well as the search for more environmentally benign solvents than those currently used to extract lycopene and β-carotene from biomass. Additionally, special attention was paid to the creation of a continuous process that would allow the fractionation of the compounds for further purification. Thus, the present work started with the extraction of both carotenoids using a wide range of solvents, namely, organic solvents, conventional salts, ionic liquids, polymers and surfactants. In this stage, each solvent was evaluated in what regards their capacity of extraction as well as their penetration ability in biomass. The results collected showed that an adequate selection of the solvents may lead to the complete extraction of both carotenoids in one single step, particularly acetone and tetrahydrofuran were the most effective ones. However, the general low penetration capacity of salts, ionic liquids, polymers and surfactants makes these solvents ineffective in the solid-liquid extraction process. As the organic solvents showed the highest capacity to extract lycopene and βcarotene, in particular tetrahydrofuran and acetone, the latter solvent used in the development process of fractionation, using to this by strategic use of solvents. This step was only successfully developed through the manipulation of the solubility of each compound in ethanol and n-hexane. The results confirmed the possibility of fractionating the target compounds using the correct addition order of the solvents. Approximately, 39 % of the β-carotene was dissolved in ethanol and about 64 % of lycopene was dissolved in n-hexane, thus indicating their separation for two different solvents which shows the selective character of the developed process without any prior stage optimization. This study revealed that the use of organic solvents leads to selective extraction of lycopene and β-carotene, allowing diminishing the numerous stages involved in conventional methods. At the end, it was possible to idealize a sustainable and of high industrial relevance integrated process, nevertheless existing the need for additional optimization studies in the future.

A time frequency approach to blind deconvolution in multipath underwater channels

Blind deconvolution is studied in the underwater acoustic channel context, by time-frequency (TF) processing. The acoustic propagation environment is modelled by ray tracing and mathematically described by a multipath propagation channel. Representation of the received signal by means of a signal-dependent TF distribution (radially Gaussian kernel distribution) allowed to visualize the resolved replicas of the emitted signal, while signi cantly attenuating the inherent interferences of classic quadratic TF distributions. The source signal instantaneous frequency estimation was the starting point for both source and channel estimation. Source signature estimation was performed by either TF inversion, based on the Wigner-Ville distribution of the received signal, or a subspace- -based method. The channel estimate was obtained either via a TF formulation of the conventional matched- lter, or via matched- - ltering with the previously obtained source estimate. A shallow water realistic scenario is considered, comprising a 135-m depth water column and an acoustic source located at 90-m depth and 5.6-km range from the receiver. For the corresponding noiseless simulated data, the quality of the best estimates was 0.856 for the source signal, and 0.9664 and 0.9996 for the amplitudes and time-delays of the impulse response, respectively. Application of the proposed deconvolution method to real data of the INTIMATE '96 sea trial conduced to source and channel estimates with the quality of 0.530 and 0.843, respectively. TF processing has proved to remove the typical ill-conditioning of single sensor deterministic deconvolution techniques.

A Method For Producing Airborne Pollen Source Inventories: An Example of Ambrosia (Ragweed) on the Pannonian Plain

We present here a simple methodology for calculating species inventories for allergenic pollen that can be used by atmospheric transport models. Ragweed (Ambrosia) species distribution or infection level on the Pannonian Plain has been used as an example of how the methodology can be used. The Pannonian Plain is one of the three main regions in Europe recognized as being polluted by Ambrosia. The methodology relies on spatial variations in annual Ambrosia pollen counts, knowledge on ragweed ecology and detailed land cover information. The results of this analysis showed that some of the highest mean annual ragweed pollen concentrations were witnessed around Kecskemét in central Hungary and Novi Sad in northern Serbia. These areas are also the areas with the highest density of Ambrosia habitats. The resulting inventory can be entered into atmospheric transport models in combination with other components such as a phenological model and a model for daily pollen release, in order to simulate the movement of ragweed pollen from the Pannonian Plain. The methodology is likely to be generally applicable for creating inventories of species distribution of allergenic plants. The main requirement is availability of: detailed land cover information; pollen indexes; a list of the most important habitats; and a region of interest that is mainly influenced by local sources.

A hybrid method for sag source location in power network

The work presented in this paper belongs to the power quality knowledge area and deals with the voltage sags in power transmission and distribution systems. Propagating throughout the power network, voltage sags can cause plenty of problems for domestic and industrial loads that can financially cost a lot. To impose penalties to responsible party and to improve monitoring and mitigation strategies, sags must be located in the power network. With such a worthwhile objective, this paper comes up with a new method for associating a sag waveform with its origin in transmission and distribution networks. It solves this problem through developing hybrid methods which hire multiway principal component analysis (MPCA) as a dimension reduction tool. MPCA reexpresses sag waveforms in a new subspace just in a few scores. We train some well-known classifiers with these scores and exploit them for classification of future sags. The capabilities of the proposed method for dimension reduction and classification are examined using the real data gathered from three substations in Catalonia, Spain. The obtained classification rates certify the goodness and powerfulness of the developed hybrid methods as brand-new tools for sag classification