Particle‐Scale Investigation of Heat Transfer in Radiation‐Driven Char Gasification

A model of the steam gasification of a single char particle driven by high‐intensity radiation was developed and experimentally verified with available measurements in the literature. This was used to explore the sensitivity of the particle surface temperature and heat‐transfer mechanisms to variations in particle diameters, radiative heat flux, and the concentration of the gasification agent H₂O under typical conditions for solar gasification reactors. The results highlight the importance of the particle diameter in influencing solar‐to‐chemical energy conversion efficiency and assist in the selection of appropriate feedstock particles to match the conditions in specific solar gasification reactors.     

Effect of polyethylenimine on electrophoretic deposition of TiO2 nanoparticles in alternating current electric field

In this work, AC electric field was applied to deposit TiO₂ nanoparticles dispersed in Acetone on coplanar electrodes. The experiments were performed in presence and absence of an additive, polyethylenimine (PEI), at frequencies of 1 Hz and 10 kHz. It was revealed that deposition pattern changed dramatically by addition of PEI which makes particles to fill the inter electrode gap at both frequencies. When PEI is added, particles show different behavior. While they tend to fill the gap randomly at 1 Hz, they form chainlike pattern at 10 kHz. Chain formation of particles in the gap indicates presence of dielectrophoretic (DEP) forces. The ability of particles to polarize in both suspensions at 10 kHz are calculated by a multi-shell model in order to find DEP force. According to this model, the polarizability for particles in the suspension with PEI is more than the other, so DEP forces applied more strongly on them and promotes chain formation.     

Chemical vapor deposition and electrical characterization of sub-10 nm diameter InSb nanowires and field-effect transistors

Synthesis of InSb nanowires using a chemical vapor deposition technique, as a function of growth temperature and time, was investigated. High aspect ratio InSb nanowires, having a diameter of about 5–10 nm, were grown at 400 °C for 1 h on InSb (1 1 1) substrate onto which 60 nm Au particle was used as a metal catalyst. The synthesized InSb nanowires had zinc blend single crystal structure without any stacking faults, and they were covered with a thin (∼1 nm thick) amorphous layer. Electrical characterization of InSb nanowires was conducted utilizing a back-gated SNWFET. Device characterization demonstrated that NWs were n-type and exhibited a high I_on/I_off ratio of 10⁶ and device resistance of 250 kΩ.     

Eigenanalysis and reduced-order modelling of unsteady partial cavity flows using the boundary element method

This paper presents an efficient reduced-order modelling approach to predict unsteady behaviour of partial cavity flows (PCROM). The boundary element method (BEM) along with the potential flow is used to analyze unsteady partial cavity flows. Partial cavity flow is modelled based on a new non-iterative approach and the PCROM is based on fluid eigenmodes. To construct fluid eigenmodes the spatial iterative scheme to find cavity extent is removed. The eigenvalue problem of the unsteady flows is defined based on the unknown wake singularities. Eigenanalysis and reduced-order modelling of unsteady flows over a NACA 16-006 section are performed using the PCROM. Numerical examples are presented to demonstrate the accuracy of the proposed method. Comparison between the obtained results of the proposed method and those of other and conventional method indicates that the present algorithm works well with sufficient accuracy. Moreover, it is shown that the PCROM is computationally more efficient than the conventional one for unsteady sheet cavitations analysis on hydrofoils.     

Electron and hole quantization and their impact on deep submicronsilicon p- and n-MOSFET characteristics

A first-principles approach to inversion layer quantization, valid for arbitrarily complex band structures, has been developed. This has allowed, for the first time, hole quantization and its effects on p-MOSFET device characteristics to be studied. In addition, electron quantization effects are revisited, improving on previous, simpler approaches. In particular, the impact of quantization on the threshold voltages and “effective” gate oxide thicknesses of p- and n-MOSFETs is investigated. A simple compact model is provided to quantitatively describe the threshold voltage shifts at 300 K as a function of the doping concentration and the oxide thickness. The significance of hole quantization for buried channel p-MOS structures is also studied. The results can be used to both identify and model these effects using popular device simulators     

An energy-dependent two-dimensional substrate current model for thesimulation of submicrometer MOSFET's

A multicurrent contour, average-energy-based, substrate current model for silicon submicrometer NMOSFETs is presented as a significant improvement to the local-field model that is commonly used in modern drift-diffusion device simulators. The model is implemented as a post-processor by applying a one-dimensional energy conservation equation to many current contours in order to generate a two-dimensional representation of average energy and impact ionization rate which is integrated to calculate the substrate current. Comparisons of simulations and experimental I-V curves for both simple and LDD MOSFETs are presented. Outstanding agreement has been obtained over a wide range of bias conditions and channel lengths     

Global minimization of multi-funnel functions using particle swarm optimization

In this paper, we consider the problem of finding the global minimum of multi-funnel-shaped functions with many local minima, which is a well-known and interesting problem in computational biology. First, the particle swarm optimization algorithms are briefly reviewed. Then, we have applied a variant of it with linear decreasing inertia weight to solve the underlying global optimization problem. Our computational experiments on several known test problems show the efficiency of the particle swarm optimization algorithm in comparison with global convex quadratic underestimator algorithms that are widely used in the literature.     

Generalization performance of support vector machines and neural networks in runoff modeling

Effective one-day lead runoff prediction is one of the significant aspects of successful water resources management in arid region. For instance, reservoir and hydropower systems call for real-time or on-line site-specific forecasting of the runoff. In this research, we present a new data-driven model called support vector machines (SVMs) based on structural risk minimization principle, which minimizes a bound on a generalized risk (error), as opposed to the empirical risk minimization principle exploited by conventional regression techniques (e.g. ANNs). Thus, this stat-of-the-art methodology for prediction combines excellent generalization property and sparse representation that lead SVMs to be a very promising forecasting method. Further, SVM makes use of a convex quadratic optimization problem; hence, the solution is always unique and globally optimal. To demonstrate the aforementioned forecasting capability of SVM, one-day lead stream flow of Bakhtiyari River in Iran was predicted using the local climate and rainfall data. Moreover, the results were compared with those of ANN and ANN integrated with genetic algorithms (ANN-GA) models. The improvements in root mean squared error (RMSE) and squared correlation coefficient (R²) by SVM over both ANN models indicate that the prediction accuracy of SVM is at least as good as that of those models, yet in some cases actually better, as well as forecasting of high-value discharges.     

Power reduction in HPC data centers: a joint server placement and chassis consolidation approach

Size and number of high-performance data centers are rapidly growing all around the world in recent years. The growth in the leakage power consumption of servers along with its exponential dependence on the ever increasing process variation in nanometer technologies has made it inevitable to move toward variation-aware power reduction strategies in data centers. In this paper, we address the problem of joint server placement and chassis consolidation to minimize power consumption of high-performance computing data centers under process variation. To this end, we introduce two variation-aware server placement heuristics as well as an integer linear programming (ILP)-based server placement method to find the best location of each server in the data center based on its power consumption and the data center heat recirculation model. We then incorporate a novel ILP-based variation-aware chassis consolidation technique to find the optimum task assignment solution under the obtained server placement approach to minimize total power consumption. Experimental results show that by applying the proposed joint variation-aware server placement and chassis consolidation techniques, up to 14.6 % improvement can be obtained at common data center utilization rates compared to state-of-the-art variation-unaware approaches.