Numerical Simulation of Groundwater Flow with Gradually Increasing Heterogeneity due to Clogging

Well injection replenishes depleting water levels in a well field. Observation well water levels some distance away from the injection well are the indicators of the success of a well injection program. Simulation of the observation well response, located a few tens of meters from the injection well, is likely to be affected by the effects of nonhomogeneous medium, inclined initial water table, and aquifer clogging. Existing algorithms, such as the U.S. Geological Survey groundwater flow software MODFLOW, are capable of handling the first two conditions, whereas time-dependent clogging effects are yet to be introduced in the groundwater flow models. Elsewhere, aquifer clogging is extensively researched in theory of filtration; scope for its application in a well field is a potential research problem. In the present paper, coupling of one such filtration theory to MODFLOW is introduced. Simulation of clogging effects during “Hansol” well recharge in the parts of western India is found to be encouraging.

Simulation blocks for TOSSIM-T2

We develop several hardware and software simulation blocks for the TinyOS-2 (TOSSIM-T2) simulator. The choice of simulated hardware platform is the popular MICA2 mote. While the hardware simulation elements comprise of radio and external flash memory, the software blocks include an environment noise model, packet delivery model and an energy estimator block for the complete system. The hardware radio block uses the software environment noise model to sample the noise floor. The packet delivery model is built by establishing the SNR-PRR curve for the MICA2 system. The energy estimator block models energy consumption by Micro Controller Unit(MCU), Radio, LEDs, and external flash memory. Using the manufacturerpsilas data sheets we provide an estimate of the energy consumed by the hardware during transmission, reception and also track several of the MCUs states with the associated energy consumption. To study the effectiveness of this work, we take a case study of a paper presented in [1]. We obtain three sets of results for energy consumption through mathematical analysis, simulation using the blocks built into PowerTossim-T2 and finally laboratory measurements. Since there is a significant match between these result sets, we propose our blocks for T2 community to effectively test their application energy requirements and node life times.

Geant4 Hadronic Cascade Models and CMS Data Analysis : Computational Challenges in the LHC era

This work belongs to the field of computational high-energy physics (HEP). The key methods used in this thesis work to meet the challenges raised by the Large Hadron Collider (LHC) era experiments are object-orientation with software engineering, Monte Carlo simulation, the computer technology of clusters, and artificial neural networks. The first aspect discussed is the development of hadronic cascade models, used for the accurate simulation of medium-energy hadron-nucleus reactions, up to 10 GeV. These models are typically needed in hadronic calorimeter studies and in the estimation of radiation backgrounds. Various applications outside HEP include the medical field (such as hadron treatment simulations), space science (satellite shielding), and nuclear physics (spallation studies). Validation results are presented for several significant improvements released in Geant4 simulation tool, and the significance of the new models for computing in the Large Hadron Collider era is estimated. In particular, we estimate the ability of the Bertini cascade to simulate Compact Muon Solenoid (CMS) hadron calorimeter HCAL. LHC test beam activity has a tightly coupled cycle of simulation-to-data analysis. Typically, a Geant4 computer experiment is used to understand test beam measurements. Thus an another aspect of this thesis is a description of studies related to developing new CMS H2 test beam data analysis tools and performing data analysis on the basis of CMS Monte Carlo events. These events have been simulated in detail using Geant4 physics models, full CMS detector description, and event reconstruction. Using the ROOT data analysis framework we have developed an offline ANN-based approach to tag b-jets associated with heavy neutral Higgs particles, and we show that this kind of NN methodology can be successfully used to separate the Higgs signal from the background in the CMS experiment.

Computer simulation of H and He effects in fusion reactor materials

Fusion power is an appealing source of clean and abundant energy. The radiation resistance of reactor materials is one of the greatest obstacles on the path towards commercial fusion power. These materials are subject to a harsh radiation environment, and cannot fail mechanically or contaminate the fusion plasma. Moreover, for a power plant to be economically viable, the reactor materials must withstand long operation times, with little maintenance. The fusion reactor materials will contain hydrogen and helium, due to deposition from the plasma and nuclear reactions because of energetic neutron irradiation. The first wall divertor materials, carbon and tungsten in existing and planned test reactors, will be subject to intense bombardment of low energy deuterium and helium, which erodes and modifies the surface. All reactor materials, including the structural steel, will suffer irradiation of high energy neutrons, causing displacement cascade damage. Molecular dynamics simulation is a valuable tool for studying irradiation phenomena, such as surface bombardment and the onset of primary damage due to displacement cascades. The governing mechanisms are on the atomic level, and hence not easily studied experimentally. In order to model materials, interatomic potentials are needed to describe the interaction between the atoms. In this thesis, new interatomic potentials were developed for the tungsten-carbon-hydrogen system and for iron-helium and chromium-helium. Thus, the study of previously inaccessible systems was made possible, in particular the effect of H and He on radiation damage. The potentials were based on experimental and ab initio data from the literature, as well as density-functional theory calculations performed in this work. As a model for ferritic steel, iron-chromium with 10% Cr was studied. The difference between Fe and FeCr was shown to be negligible for threshold displacement energies. The properties of small He and He-vacancy clusters in Fe and FeCr were also investigated. The clusters were found to be more mobile and dissociate more rapidly than previously assumed, and the effect of Cr was small. The primary damage formed by displacement cascades was found to be heavily influenced by the presence of He, both in FeCr and W. Many important issues with fusion reactor materials remain poorly understood, and will require a huge effort by the international community. The development of potential models for new materials and the simulations performed in this thesis reveal many interesting features, but also serve as a platform for further studies.

Studies on Binary Time Series Models with Applications to Empirical Macroeconomics and Finance

This thesis studies binary time series models and their applications in empirical macroeconomics and finance. In addition to previously suggested models, new dynamic extensions are proposed to the static probit model commonly used in the previous literature. In particular, we are interested in probit models with an autoregressive model structure. In Chapter 2, the main objective is to compare the predictive performance of the static and dynamic probit models in forecasting the U.S. and German business cycle recession periods. Financial variables, such as interest rates and stock market returns, are used as predictive variables. The empirical results suggest that the recession periods are predictable and dynamic probit models, especially models with the autoregressive structure, outperform the static model. Chapter 3 proposes a Lagrange Multiplier (LM) test for the usefulness of the autoregressive structure of the probit model. The finite sample properties of the LM test are considered with simulation experiments. Results indicate that the two alternative LM test statistics have reasonable size and power in large samples. In small samples, a parametric bootstrap method is suggested to obtain approximately correct size. In Chapter 4, the predictive power of dynamic probit models in predicting the direction of stock market returns are examined. The novel idea is to use recession forecast (see Chapter 2) as a predictor of the stock return sign. The evidence suggests that the signs of the U.S. excess stock returns over the risk-free return are predictable both in and out of sample. The new "error correction" probit model yields the best forecasts and it also outperforms other predictive models, such as ARMAX models, in terms of statistical and economic goodness-of-fit measures. Chapter 5 generalizes the analysis of univariate models considered in Chapters 2 4 to the case of a bivariate model. A new bivariate autoregressive probit model is applied to predict the current state of the U.S. business cycle and growth rate cycle periods. Evidence of predictability of both cycle indicators is obtained and the bivariate model is found to outperform the univariate models in terms of predictive power.

Hybrid moving block bootstrap for stochastic simulation of multi-site multi-season streamflows

The Hybrid approach introduced by the authors for at-site modeling of annual and periodic streamflows in earlier works is extended to simulate multi-site multi-season streamflows. It bears significance in integrated river basin planning studies. This hybrid model involves: (i) partial pre-whitening of standardized multi-season streamflows at each site using a parsimonious linear periodic model; (ii) contemporaneous resampling of the resulting residuals with an appropriate block size, using moving block bootstrap (non-parametric, NP) technique; and (iii) post-blackening the bootstrapped innovation series at each site, by adding the corresponding parametric model component for the site, to obtain generated streamflows at each of the sites. It gains significantly by effectively utilizing the merits of both parametric and NP models. It is able to reproduce various statistics, including the dependence relationships at both spatial and temporal levels without using any normalizing transformations and/or adjustment procedures. The potential of the hybrid model in reproducing a wide variety of statistics including the run characteristics, is demonstrated through an application for multi-site streamflow generation in the Upper Cauvery river basin, Southern India. (C) 2004 Elsevier B.V. All rights reserved.

Inference on Cointegration in Vector Autoregressive Models (summary section only)

In the thesis we consider inference for cointegration in vector autoregressive (VAR) models. The thesis consists of an introduction and four papers. The first paper proposes a new test for cointegration in VAR models that is directly based on the eigenvalues of the least squares (LS) estimate of the autoregressive matrix. In the second paper we compare a small sample correction for the likelihood ratio (LR) test of cointegrating rank and the bootstrap. The simulation experiments show that the bootstrap works very well in practice and dominates the correction factor. The tests are applied to international stock prices data, and the .nite sample performance of the tests are investigated by simulating the data. The third paper studies the demand for money in Sweden 1970—2000 using the I(2) model. In the fourth paper we re-examine the evidence of cointegration between international stock prices. The paper shows that some of the previous empirical results can be explained by the small-sample bias and size distortion of Johansen’s LR tests for cointegration. In all papers we work with two data sets. The first data set is a Swedish money demand data set with observations on the money stock, the consumer price index, gross domestic product (GDP), the short-term interest rate and the long-term interest rate. The data are quarterly and the sample period is 1970(1)—2000(1). The second data set consists of month-end stock market index observations for Finland, France, Germany, Sweden, the United Kingdom and the United States from 1980(1) to 1997(2). Both data sets are typical of the sample sizes encountered in economic data, and the applications illustrate the usefulness of the models and tests discussed in the thesis.

Construction and use of linear regression models for processor performance analysis

Processor architects have a challenging task of evaluating a large design space consisting of several interacting parameters and optimizations. In order to assist architects in making crucial design decisions, we build linear regression models that relate Processor performance to micro-architecture parameters, using simulation based experiments. We obtain good approximate models using an iterative process in which Akaike's information criteria is used to extract a good linear model from a small set of simulations, and limited further simulation is guided by the model using D-optimal experimental designs. The iterative process is repeated until desired error bounds are achieved. We used this procedure to establish the relationship of the CPI performance response to 26 key micro-architectural parameters using a detailed cycle-by-cycle superscalar processor simulator The resulting models provide a significance ordering on all micro-architectural parameters and their interactions, and explain the performance variations of micro-architectural techniques.

Reliability models for existing structures based on dynamic state estimation and data based asymptotic extreme value analysis

The problem of time variant reliability analysis of existing structures subjected to stationary random dynamic excitations is considered. The study assumes that samples of dynamic response of the structure, under the action of external excitations, have been measured at a set of sparse points on the structure. The utilization of these measurements m in updating reliability models, postulated prior to making any measurements, is considered. This is achieved by using dynamic state estimation methods which combine results from Markov process theory and Bayes' theorem. The uncertainties present in measurements as well as in the postulated model for the structural behaviour are accounted for. The samples of external excitations are taken to emanate from known stochastic models and allowance is made for ability (or lack of it) to measure the applied excitations. The future reliability of the structure is modeled using expected structural response conditioned on all the measurements made. This expected response is shown to have a time varying mean and a random component that can be treated as being weakly stationary. For linear systems, an approximate analytical solution for the problem of reliability model updating is obtained by combining theories of discrete Kalman filter and level crossing statistics. For the case of nonlinear systems, the problem is tackled by combining particle filtering strategies with data based extreme value analysis. In all these studies, the governing stochastic differential equations are discretized using the strong forms of Ito-Taylor's discretization schemes. The possibility of using conditional simulation strategies, when applied external actions are measured, is also considered. The proposed procedures are exemplifiedmby considering the reliability analysis of a few low-dimensional dynamical systems based on synthetically generated measurement data. The performance of the procedures developed is also assessed based on a limited amount of pertinent Monte Carlo simulations. (C) 2010 Elsevier Ltd. All rights reserved.

Computer simulation of protein—carbohydrate complexes: application to arabinose-binding protein and pea lectin

The CCEM method (Contact Criteria and Energy Minimisation) has been developed and applied to study protein-carbohydrate interactions. The method uses available X-ray data even on the native protein at low resolution (above 2.4 Å) to generate realistic models of a variety of proteins with various ligands.The two examples discussed in this paper are arabinose-binding protein (ABP) and pea lectin. The X-ray crystal structure data reported on ABP-β-l-arabinose complex at 2.8, 2.4 and 1.7 Å resolution differ drastically in predicting the nature of the interactions between the protein and ligand. It is shown that, using the data at 2.4 Å resolution, the CCEM method generates complexes which are as good as the higher (1.7 Å) resolution data. The CCEM method predicts some of the important hydrogen bonds between the ligand and the protein which are missing in the interpretation of the X-ray data at 2.4 Å resolution. The theoretically predicted hydrogen bonds are in good agreement with those reported at 1.7 Å resolution. Pea lectin has been solved only in the native form at 3 Å resolution. Application of the CCEM method also enables us to generate complexes of pea lectin with methyl-α-d-glucopyranoside and methyl-2,3-dimethyl-α-d-glucopyranoside which explain well the available experimental data in solution.

Towards Understanding Powder Behavior Via Simulation

The aim of this study was to investigate powder and tablet behavior at the level of mechanical interactions between single particles. Various aspects of powder packing, mixing, compression, and bond formation were examined with the aid of computer simulations. The packing and mixing simulations were based on spring forces interacting between particles. Packing and breakage simulations included systems in which permanent bonds were formed and broken between particles, based on their interaction strengths. During the process, a new simulation environment based on Newtonian mechanics and elementary interactions between the particles was created, and a new method for evaluating mixing was developed. Powder behavior is a complicated process, and many of its aspects are still unclear. Powders as a whole exhibit some aspects of solids and others of liquids. Therefore, their physics is far from clear. However, using relatively simple models based on particle-particle interaction, many powder properties could be replicated during this work. Simulated packing densities were similar to values reported in the literature. The method developed for describing powder mixing correlated well with previous methods. The new method can be applied to determine mixing in completely homogeneous materials, without dividing them into different components. As such, it can describe the efficiency of the mixing method, regardless of the powder's initial setup. The mixing efficiency at different vibrations was examined, and we found that certain combinations of amplitude, direction, and frequencies resulted in better mixing while using less energy. Simulations using exponential force potentials between particles were able to explain the elementary compression behavior of tablets, and create force distributions that were similar to the pressure distributions reported in the literature. Tablet-breaking simulations resulted in breaking strengths that were similar to measured tablet breaking strengths. In general, many aspects of powder behavior can be explained with mechanical interactions at the particle level, and single particle properties can be reliably linked to powder behavior with accurate simulations.

Optimizing simulation model on forest policy.

XVIII IUFRO World Congress, Ljubljana 1986.

Computer simulation of multi-elemental fusion reactor materials

Thermonuclear fusion is a sustainable energy solution, in which energy is produced using similar processes as in the sun. In this technology hydrogen isotopes are fused to gain energy and consequently to produce electricity. In a fusion reactor hydrogen isotopes are confined by magnetic fields as ionized gas, the plasma. Since the core plasma is millions of degrees hot, there are special needs for the plasma-facing materials. Moreover, in the plasma the fusion of hydrogen isotopes leads to the production of high energetic neutrons which sets demanding abilities for the structural materials of the reactor. This thesis investigates the irradiation response of materials to be used in future fusion reactors. Interactions of the plasma with the reactor wall leads to the removal of surface atoms, migration of them, and formation of co-deposited layers such as tungsten carbide. Sputtering of tungsten carbide and deuterium trapping in tungsten carbide was investigated in this thesis. As the second topic the primary interaction of the neutrons in the structural material steel was examined. As model materials for steel iron chromium and iron nickel were used. This study was performed theoretically by the means of computer simulations on the atomic level. In contrast to previous studies in the field, in which simulations were limited to pure elements, in this work more complex materials were used, i.e. they were multi-elemental including two or more atom species. The results of this thesis are in the microscale. One of the results is a catalogue of atom species, which were removed from tungsten carbide by the plasma. Another result is e.g. the atomic distributions of defects in iron chromium caused by the energetic neutrons. These microscopic results are used in data bases for multiscale modelling of fusion reactor materials, which has the aim to explain the macroscopic degradation in the materials. This thesis is therefore a relevant contribution to investigate the connection of microscopic and macroscopic radiation effects, which is one objective in fusion reactor materials research.

Molecular theory of ion solvation dynamics in water, acetonitrile and methanol: A unified microscopic description of collective dynamics in dipolar liquids

A recently developed microscopic theory of solvation dynamics in real dipolar liquids is used to calculate, for the first time, the solvation time correlation function in liquid acetonitrile, water and methanol. The calculated results are in excellent agreement with known experimental and computer simulation studies.

Solvation dynamics in a Brownian dipolar lattice. Comparison between computer simulation and various molecular theories of solvation dynamics

Several recent theoretical and computer simulation studies have considered solvation dynamics in a Brownian dipolar lattice which provides a simple model solvent for which detailed calculations can be carried out. In this article a fully microscopic calculation of the solvation dynamics of an ion in a Brownian dipolar lattice is presented. The calculation is based on the non‐Markovian molecular hydrodynamic theory developed recently. The main assumption of the present calculation is that the two‐particle orientational correlation functions of the solid can be replaced by those of the liquid state. It is shown that such a calculation provides an excellent agreement with the computer simulation results. More importantly, the present calculations clearly demonstrate that the frequency‐dependent dielectric friction plays an important role in the long time decay of the solvation time correlation function. We also find that the present calculation provides somewhat better agreement than either the dynamic mean spherical approximation (DMSA) or the Fried–Mukamel theory which use the simulated frequency‐dependent dielectric function. It is found that the dissipative kernels used in the molecular hydrodynamic approach and in the Fried–Mukamel theory are vastly different, especially at short times. However, in spite of this disagreement, the two theories still lead to comparable results in good agreement with computer simulation, which suggests that even a semiquantitatively accurate dissipative kernel may be sufficient to obtain a reliable solvation time correlation function. A new wave vector and frequency‐dependent dissipative kernel (or memory function) is proposed which correctly goes over to the appropriate expressions in both the single particle and the collective limits. This form is expected to lead to better results than all the existing descriptions.