74 research outputs found
Recommended from our members
Computation of analytical bounds for cross section self-shielding factors
The shielding factor method (SFM) is a frequently used economical procedure for computing the effective multigroup cross sections needed in reactor analysis. While initially developed and employed in codes used by the fast reactor community, the method has been receiving increased attention in recent years from the electric utility industry, for applications to power reactors. A fundamental problem regarding the method's applicability is to determine the limits of the range of values within which a cross section shielding factor is restricted, and whether these limits are physically meaningful. In a previous paper strict upper and lower bounds for the transport f-factor and for the sum of reaction f-factors were derived and discussed. The purpose of the present work is to present extensions of the methodology used for the upper and lower bounds of the transport f-factor and reaction f-factors
Recommended from our members
On the parallelization of the acoustic wave equation with absorbing boundary conditions
Many practical problems involve wave propagation through atmosphere, oceans, or terrestrial crust. Modeling and analysis of these problems is usually done in (semi)infinite domains, but numerical calculations obviously impose restriction to finite domains. To mimic the actual behavior in the (semi)infinite medium, artificial absorbing boundary conditions are imposed at the boundaries, whereby waves can only exit, but not enter the finite computational domain. Efficient absorbing boundary conditions are difficult to analyze and costly to run. In particular, it is of interest to assess whether the wave equation with (approximate or exact) absorbing boundary conditions admits a suitable diagonalization. This would open the possibility for parallelizing many important numerical codes used in applications. In this paper the authors propose a set of stable, local, absorbing boundary conditions for the discrete acoustic wave equation. They show that the acoustic wave equation with absorbing boundary conditions cannot be exactly diagonalized
Recommended from our members
Global optimization for multisensor fusion in seismic imaging
The accurate imaging of subsurface structures requires the fusion of data collected from large arrays of seismic sensors. The fusion process is formulated as an optimization problem and yields an extremely complex energy surface. Due to the very large number of local minima to be explored and escaped from, the seismic imaging problem has typically been tackled with stochastic optimization methods based on Monte Carlo techniques. Unfortunately, these algorithms are very cumbersome and computationally intensive. Here, the authors present TRUST--a novel deterministic algorithm for global optimization that they apply to seismic imaging. The excellent results demonstrate that TRUST may provide the necessary breakthrough to address major scientific and technological challenges in fields as diverse as seismic modeling, process optimization, and protein engineering
Recommended from our members
DeepNet: An Ultrafast Neural Learning Code for Seismic Imaging
A feed-forward multilayer neural net is trained to learn the correspondence between seismic data and well logs. The introduction of a virtual input layer, connected to the nominal input layer through a special nonlinear transfer function, enables ultrafast (single iteration), near-optimal training of the net using numerical algebraic techniques. A unique computer code, named DeepNet, has been developed, that has achieved, in actual field demonstrations, results unattainable to date with industry standard tools
Constant-time solution to the Global Optimization Problem using Bruschweiler's ensemble search algorithm
A constant-time solution of the continuous Global Optimization Problem (GOP)
is obtained by using an ensemble algorithm. We show that under certain
assumptions, the solution can be guaranteed by mapping the GOP onto a discrete
unsorted search problem, whereupon Bruschweiler's ensemble search algorithm is
applied. For adequate sensitivities of the measurement technique, the query
complexity of the ensemble search algorithm depends linearly on the size of the
function's domain. Advantages and limitations of an eventual NMR implementation
are discussed.Comment: 14 pages, 0 figure
Recommended from our members
Chemical detection using the airborne thermal infrared imaging spectrometer (TIRIS)
A methodology is described for an airborne, downlooking, longwave infrared imaging spectrometer based technique for the detection and tracking of plumes of toxic gases. Plumes can be observed in emission or absorption, depending on the thermal contrast between the vapor and the background terrain. While the sensor is currently undergoing laboratory calibration and characterization, a radiative exchange phenomenology model has been developed to predict sensor response and to facilitate the sensor design. An inverse problem model has also been developed to obtain plume parameters based on sensor measurements. These models, the sensors, and ongoing activities are described
Recommended from our members
Neural network accuracy measures and data transforms applied to the seismic parameter estimation problem
The accuracy of an artificial neural network (ANN) algorithm is a crucial issue in the estimation of an oil field reservoir`s properties from remotely sensed seismic data. This paper demonstrates the use of the k-fold cross validation technique to obtain confidence bounds on an ANN`s accuracy statistic from a finite sample set. In addition, we also show that an ANN`s classification accuracy is dramatically improved by transforming the ANN`s input feature space to a dimensionally smaller, new input space. The new input space represents a feature space that maximizes the linear separation between classes. Thus, the ANN`s convergence time and accuracy are improved because the ANN must merely find nonlinear perturbations to the starting linear decision boundaries. These techniques for estimating ANN accuracy bounds and feature space transformations are demonstrated on the problem of estimating the sand thickness in an oil field reservoir based only on remotely sensed seismic data
Recommended from our members
Oil reservoir properties estimation using neural networks
This paper investigates the applicability as well as the accuracy of artificial neural networks for estimating specific parameters that describe reservoir properties based on seismic data. This approach relies on JPL`s adjoint operators general purpose neural network code to determine the best suited architecture. The authors believe that results presented in this work demonstrate that artificial neural networks produce surprisingly accurate estimates of the reservoir parameters
Recommended from our members
VITAMIN E: a multipurpose ENDF/B-V coupled neutron-gamma cross section library
The US Department of Energy Office of Fusion Energy and the Division of Reactor Research and Technology jointly sponsored the development of a coupled fine-group cross section library (VITAMIN-C). The experience gained in the generation, validation, and utilization of the VITAMIN-C library along with its broad range of applicability has led to the request for updating this data set using ENDF/B-V. Additional support in this regard has been provided by the Defense Nuclear Agency (DNA) and by EPRI in support of weapons analyses and light water reactor shielding and dosimetry problems, respectively. The rationale for developing the multipurpose ENDF/B-V-based VITAMIN-E library is presented, with special emphasis on new models used in the data generation algorithms. The library specifications and testing procedures are also discussed in detail. The distribution of the VITAMIN-E library is currently subject to the same restrictions as the distribution of the ENDF/B-V data. 2 tables
- …