New Methodology For Simulation Research Articles

Fabrication Process Stochastic Model for Yield Estimation in Microwave Semiconductor Devices Production

This paper presents a new methodology for simulation of production processes in order to determine device parametric yield. The elaborated methodology is focused on capturing stochastic relations between every parameter of the subsequent processes that are impossible to determine directly. The current state-of-the-art together with the gaps in the knowledge regarding yield modelling is presented. A novel approach to the important issue of effective yield modelling that allows the overcoming of current challenges is presented. The methodology’s usefulness is validated with the example of the fabrication process of AlGaN/GaN HEMT (high electron mobility transistor) for application in high-frequency electronics. Fabrication of AlGaN/GaN HEMTs is a complex process due to the large number of technological stages required, most of which are still the subject of ongoing research. Most importantly, the approach presented in this paper could be easily applied to the modelling of any complex production process in every case where it is necessary to examine relations between the final product parameters distribution and the values of the involved process parameters.

A New Methodology for Network Scale Simulation of Emerging Power Line Communication Standards

This paper presents the development of a new methodology for the simulation of power line communication (PLC) within the popular electromagnetic transients programme-alternative transients programme (EMTP-ATP). As a first application, the comparative performance of the emerging orthogonal frequency division multiplexing (OFDM) based Prime and G3-PLC narrowband standards is investigated. Models of both standards have been created within the EMTP-ATP and simulations performed using frequency-dependent line models and realistic transformer models as terminations. It is observed that both standards are severely affected by the highly frequency selective nature of the Medium Voltage (MV) power line channel and a more considered choice of OFDM parameters may be necessary for optimal performance on MV networks.

Performance simulation of parabolic trough solar collector using two fluids (thermic oil and molten salt)

The Parabolic trough solar collector is considered as one of the most proven, mature and commercial concentrating solar systems implemented in arid and semi-arid regions for energy production. It focuses sunlight onto a solar receiver by using mirrors and is finally converted to a useful thermal energy by means of a heat transfer fluid. The aims of this study are (i) to develop a new methodology for simulation and performance evaluation of parabolic trough solar collector, in addition (ii) to compare the efficiencies of this system using two different fluids; thermic oil and molten solar salt. The validation of obtained results using this methodology shows a good agreement with those obtained by the experimental tests. Furthermore, this study favors the using of thermic oil as heat transfer fluid in the receiver instead of molten salt due to the high efficiency of the concentrator based on the first fluid in comparison to the second one. Keywords: parabolic trough collector; thermic oil; molten salt; performance

A New Methodology for Simulation and Optimization of CO2 Sequestration in a Saline Aquifer Using Artificial Neural Network and Model Predictive Control Approach

Carbon dioxide sequestration efforts are the subject of intense research due to growing international concerns over CO2 emissions critically affecting the global climate change. Extensive studies are being done to determine the extent of the CO2 injection so that CO2 storage is secure and environmentally acceptable. In this work, CO2 injection into a saline aquifer is used to simulate an aquifer in Eclipse-100 using a black oil reservoir simulator whose consequent behavior is predicted by an artificial neural network, being trained via the Levenberg-Marquardt algorithm. A generalized predictive control scheme is accommodated in the proposed design methodology to determine the optimal injection rates during the CO2 injection process, leading to an ultimate goal of maximized residual trapping or equivalently minimized structural trapping of carbon dioxide. This will eventually maximize the total volume of the injected CO2, while diminishing the risk of its leakage to the atmosphere due to a fail in the integrity of the formation cap rock. A set of diverse test scenarios has been organized to demonstrate the capabilities of the proposed methodology to tackle the probable practical considerations.

Effects of Ocean Acidification on the Ballast of Surface Aggregates Sinking through the Twilight Zone

The dissolution of CaCO3 is one of the ways ocean acidification can, potentially, greatly affect the ballast of aggregates. A diminution of the ballast could reduce the settling speed of aggregates, resulting in a change in the carbon flux to the deep sea. This would mean lower amounts of more refractory organic matter reaching the ocean floor. This work aimed to determine the effect of ocean acidification on the ballast of sinking surface aggregates. Our hypothesis was that the decrease of pH will increase the dissolution of particulate inorganic carbon ballasting the aggregates, consequently reducing their settling velocity and increasing their residence time in the upper twilight zone. Using a new methodology for simulation of aggregate settling, our results suggest that future pCO2 conditions can significantly change the ballast composition of sinking aggregates. The change in aggregate composition had an effect on the size distribution of the aggregates, with a shift to smaller aggregates. A change also occurred in the settling velocity of the particles, which would lead to a higher residence time in the water column, where they could be continuously degraded. In the environment, such an effect would result in a reduction of the carbon flux to the deep-sea. This reduction would impact those benthic communities, which rely on the vertical flow of carbon as primary source of energy.

Simulation of historical temperatures using a multi-site, multivariate block resampling algorithm with perturbation

Stochastic weather generators have evolved as tools for creating long time series of synthetic meteorological data at a site for risk assessments in hydrologic and agricultural applications. Recently, their use has been extended as downscaling tools for climate change impact assessments. Non-parametric weather generators, which typically use a K-nearest neighbour (K-NN) resampling approach, require no statistical assumptions about probability distributions of variables and can be easily applied for multi-site use. Two characteristics of traditional K-NN models result from resampling daily values: (1) temporal correlation structure of daily temperatures may be lost, and (2) no values less than or exceeding historical observations can be simulated. Temporal correlation in simulated temperature data is important for hydrologic applications. Temperature is a major driver of many processes within the hydrologic cycle (for example, evaporation, snow melt, etc.) that may affect flood levels. As such, a new methodology for simulation of climate data using the K-NN approach is presented (named KnnCAD Version 4). A block resampling scheme is introduced along with perturbation of the reshuffled daily temperature data to create 675 years of synthetic historical daily temperatures for the Upper Thames River basin in Ontario, Canada. The updated KnnCAD model is shown to adequately reproduce observed monthly temperature characteristics as well as temporal and spatial correlations while simulating reasonable values which can exceed the range of observations. Copyright © 2012 John Wiley & Sons, Ltd.

Simulation of Copper Precipitation in Fe-Cu Alloys

During ageing of Fe-Cu alloys, for standard ageing conditions, peak hardness and strength is often observed after several hours. The significant strengthening is attributed to a dense distribution of very small bcc-Cu precipitates of 2-3 nm size. Using conventional numerical precipitation kinetics models for diffusion-controlled transformations, the kinetics of strengthening cannot be consistently described. One of the issues in this aspect is the fact that, after reaching peak hardness, a strong decrease in number density is observed experimentally, which cannot be explained by classical Ostwald ripening theory. In the present study, a new methodology for simulation of the copper precipitation kinetics in the early stage is suggested. The basic idea of this approach is to take into account the composition variation of the Cu-precipitates with respect to the Fe content during the precipitation reaction. The simulation results are compared to experimental data reported in literature. Consistent agreement between experiment and simulation can be achieved with the new methodology.

Soft tissue modelling through autowaves for surgery simulation

Modelling of soft tissue deformation is of great importance to virtual reality based surgery simulation. This paper presents a new methodology for simulation of soft tissue deformation by drawing an analogy between autowaves and soft tissue deformation. The potential energy stored in a soft tissue as a result of a deformation caused by an external force is propagated among mass points of the soft tissue by non-linear autowaves. The novelty of the methodology is that (i) autowave techniques are established to describe the potential energy distribution of a deformation for extrapolating internal forces, and (ii) non-linear materials are modelled with non-linear autowaves other than geometric non-linearity. Integration with a haptic device has been achieved to simulate soft tissue deformation with force feedback. The proposed methodology not only deals with large-range deformations, but also accommodates isotropic, anisotropic and inhomogeneous materials by simply changing diffusion coefficients.

Material removal simulation of peripheral milling of thin wall low-rigidity structures using FEA

The paper reports on a new methodology for simulation of the material removal process that takes into account the deflection of the part during machining. A finite element part model is used as an input to the simulation. The algorithm aims to iteratively identify the intersection between the tool volume and the deflected part taking into account the applied cutting forces. A new voxel-transformation model and algorithm have been developed to allow accurate representation of cutting through the part voxels intersected by the tool volume. A look-up table describing all possible voxel-cutting schemes has been developed and validated to facilitate the material removal simulation. The methodology has been implemented as a software package that allows integration with mainstream commercial finite element analysis and CAD packages.

New Methodology for Simulation of Soft Errors in Digital Processors

Development and implementation are presented of a new fault injection technique for error rate prediction of processor-based digital architectures operating under radiation. Bit e ips are injected in potentially sensitive memory locations concurrently with the execution of a program. The error rate derived from those fault injection experiments and the underlying cross section of the studied processor will allow the cross section estimation of this circuit running a given program. A comparative analysis of experimental results issued from both soft error injection and ground testing under radiation performed on a board built around a microprocessor demonstrates the efe ciency of this new technique to predict the error rate of an application.

Towards deflection prediction and compensation in machining of low-rigidity parts

Producing the right profile in machining low-rigidity complex parts increasingly depends on specialized computer aided manufacture (CAM) packages for defining appropriate cutting strategies and tool paths. Despite the significant developments in numerically controlled (NC) simulation and verification there are still gaps between the theoretically predicted surfaces and the measured surfaces owing to a relatively small percentage of machining error types that could be detected and interpreted by the existing software systems. This paper reports on a new methodology for simulation and prediction of the deflection of thin wall parts during machining. It aims to increase the understanding of the causes of poor geometric accuracy by considering the impact of the machining forces on the deflection of thin-walled structures. The proposed approach is based on identifying and modelling key processing characteristics that influence part deflection, modelling the material removal process using ‘voxels’ and predicting and correcting the deflection of the parts. The reported work is part of ongoing research on developing an adaptive machining planning environment for modelling, prediction and selection of process and tool path parameters for rapid machining of complex low-rigidity high-accuracy parts.