On the density and discrepancy of a 2D point set with applications to thermal analysis of VLSI chips

In this era of giga-scale integration, thermal analysis has become one of the hot topics in VLSI chip design. Active thermal sources may be abstracted as a set of weighted points on a 2D chip-floor. The conventional notion of discrepancy that deals with the congestion properties of a set of scattered points may not be able to capture properly all real-life instances in this context. In this paper, we have introduced a new concept, called the density of a region to study some of the properties of the distribution of these weighted points. We prove several counter-intuitive results concerning the properties of the regions that have maximum or minimum density. We then outline algorithms for recognizing these regions. We also compare the attributes of density with the existing concept of discrepancy.     

Load sharing and power quality enhanced operation of a distributed microgrid

Control methods for proper load sharing between parallel converters connected in a microgrid and supplied by distributed generators (DGs) are described. It is assumed that the microgrid spans a large area and it supplies loads in both in grid-connected and islanded modes. A control strategy is proposed to improve power quality and proper load sharing in both islanded and grid-connected modes. It is assumed that each of the DGs has a local load connected to it which can be unbalanced and/or non-linear. The DGs compensate the effects of unbalance and non-linearity of the local loads. Common loads are also connected to the microgrid, which are supplied by the utility grid under normal conditions. However, during islanding, each of the DGs supplies its local load and shares the common load through droop characteristics. Both impedance and motor loads are considered to verify the system response. The efficacy of the controller has been validated through simulation for various operating conditions using PSCAD. It has been found through simulation that the total harmonic distortion (THD) of the microgrid voltage is ~10% and the negative and zero sequence components are ~20% of the positive sequence component before compensation. After compensation, the THD remains <0.5%, whereas negative and zero sequence components of the voltages remain <0.02% of the positive sequence component.     

Control of internal fungi of sorghum by fumigation

Laboratory methods of fumigation of food commodities and surface sterilization of grains have been described. Out of eight fumigants tested ethylene oxide, methyl bromide, sulphur dioxide, EDB-MB mixture could give a hundred per cent control of internal fungi of sorghum. Ethylene oxide reduced the viability totally, while sulphur dioxide at 64 mg/l maintained 75 per cent viability. There was increase in the germination percentage over the control with the treatments of ethylene dibromide and phosphine.     

Modeling Color Properties of Tiled Displays

The concept of tiled displays can be successful only if such displays are made to look like a single display perceptually. The two issues that need to be solved to achieve this goal are geometric correction and color seamlessness of images spanning across tiles. Geometric correction algorithms borrow pin‐hole camera models to model projector display geometry. In this paper, we introduce an abstract modeling function that describes the color seen by a viewer when displayed by a display device. Though this function can be used to model color displayed by any common display device, in this paper, we use it to model color in multiprojector display systems. We use the model to explain the reasons for different types of color variations in a multiprojector display, to compare different color correction algorithms, and to derive such algorithms directly from the model.     

Successive approximate model based multi-objective optimization for an industrial straight grate iron ore induration process using evolutionary algorithm

Multi-objective optimization of any complex industrial process using first principle computationally expensive models often demands a substantially higher computation time for evolutionary algorithms making it less amenable for real time implementation. A combination of the above-mentioned first principle model and approximate models based on artificial neural network (ANN) successively learnt in due course of optimization using the data obtained from first principle models can be intelligently used for function evaluation and thereby reduce the aforementioned computational burden to a large extent. In this work, a multi-objective optimization task (simultaneous maximization of throughput and Tumble index) of an industrial iron ore induration process has been studied to improve the operation of the process using the above-mentioned metamodeling approach. Different pressure and temperature values at different points of the furnace bed, grate speed and bed height have been used as decision variables whereas the bounds on cold compression strength, abrasion index, maximum pellet temperature and burn-through point temperature have been treated as constraints. A popular evolutionary multi-objective algorithm, NSGA II, amalgamated with the first principle model of the induration process and its successively improving approximation model based on ANN, has been adopted to carry out the task. The optimization results show that as compared to the PO solutions obtained using only the first principle model, (i) similar or better quality PO solutions can be achieved by this metamodeling procedure with a close to 50% savings in function evaluation and thereby computation time and (ii) by keeping the total number of function evaluations same, better quality PO solutions can be obtained.     

Co- and counter-current mass transfer in bubble column

Design and scale-up has gained considerable attention in recent years because of complex hydrodynamics and its influence on bubble column reactor performance. The concentration difference is important variable while characterizing bubble column reactor efficiency and this is a function of hydrodynamics prevailing inside the column. The efficiency of bubble column reactor is a function of physical properties of phases, geometry of column and operating conditions. Literature lacks on the simulation work on variation of concentration of solute for mass transfer rate and mass transfer efficiency in the complex system of bubble column device. In the present work a mechanistic model is formulated to predict the mass transfer efficiency of column and its dependency on various physical parameters, operating condition and column geometry. In this work the mass transfer efficiency has been analyzed based on a mechanistic model in the case of both co-current and counter current operations in bubble column reactor. The concentration variation of the phases obtained by simulation of model may be useful for further understanding the mass transfer phenomena in bubble column reactor.     

Empirical correlations and CFD simulations of vertical two-phase gas–liquid (Newtonian and non-Newtonian) slug flow compared against experimental data of void fraction

Gas-Newtonian liquid two-phase flows (TPFs) are presented in several industrial processes (e.g. oil-gas industry). In spite of the common occurrence of these TPFs, the understanding of them is limited compared to single-phase flows. Various studies on TPF focus on developing empirical correlations based on large sets of experimental data for void fraction, which have proven accurate for specific conditions for which they were developed limiting their applicability. On the other hand, few studies focus on gas-non-Newtonian liquids TPFs, which are very common in chemical processes. The main reason is due to the characterization of the viscosity, which determines the hydraulic regime and flow behaviours of the system.     

Prediction and experimental verification of bubble and processing characteristics in blown‐film extrusion

Blown‐film modeling is useful to the flexible packaging industry for predicting process and bubble characteristics, such as freeze line height (FLH), bubble diameter, and film thickness. The use of a suitable rheological equation to describe material properties is critical in simulating the blown‐film process. In this article, we present an improved rheological constitutive equation, which incorporates more realistic parameters of stress and deformation properties of the materials by combining the Hookean model with the Phan‐Thien Tanner (PTT) model. The proposed PTT–Hookean model is aimed at enhancing the viscoelastic behavior of the melt during biaxial stretching in the blown‐film extrusion. Predictions of the blown‐film bubble characteristics and FLH obtained with the PTT–Hookean model agreed well with the experimental data of this study and previous studies with different materials and different die geometries. The justification for combining the Hookean model with the PTT model in the blown‐film process is also reported here. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2009     

Entropic lattice Boltzmann method for crystallization processes

A lattice Boltzmann method (LBM) is introduced for accurate simulation of crystallization processes modelled using one-dimensional population balance equations (PBEs) with growth and nucleation phenomena. LBM for PBEs with size independent growth is developed by identifying their similarity with the advection equation. To obtain an efficient method for PBEs with size dependent growth, a coordinate transformation scheme is introduced, which can handle processes with size independent and size dependent growth rates in the same framework. The performance of the proposed scheme is verified using benchmark examples drawn from literature, which shows that LBM provides at least the same level of accuracy, while requiring lower computation time than the well-established high resolution finite volume method.     

A connectionist model for category perception: theory andimplementation

A connectionist model for learning and recognizing objects (or object classes) is presented. The learning and recognition system uses confidence values for the presence of a feature. The network can recognize multiple objects simultaneously when the corresponding overlapped feature train is presented at the input. An error function is defined, and it is minimized for obtaining the optimal set of object classes. The model is capable of learning each individual object in the supervised mode. The theory of learning is developed based on some probabilistic measures. Experimental results are presented. The model can be applied for the detection of multiple objects occluding each other.