Download or read book Hybrid and Parallel Domain Decomposition Methods Development to Enable Monte Carlo for Reactor Analyses written by and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper describes code and methods development at the Oak Ridge National Laboratory focused on enabling high-fidelity, large-scale reactor analyses with Monte Carlo (MC). Current state-of-the-art tools and methods used to perform ''real'' commercial reactor analyses have several undesirable features, the most significant of which is the non-rigorous spatial decomposition scheme. Monte Carlo methods, which allow detailed and accurate modeling of the full geometry and are considered the ''gold standard'' for radiation transport solutions, are playing an ever-increasing role in correcting and/or verifying the deterministic, multi-level spatial decomposition methodology in current practice. However, the prohibitive computational requirements associated with obtaining fully converged, system-wide solutions restrict the role of MC to benchmarking deterministic results at a limited number of state-points for a limited number of relevant quantities. The goal of this research is to change this paradigm by enabling direct use of MC for full-core reactor analyses. The most significant of the many technical challenges that must be overcome are the slow, non-uniform convergence of system-wide MC estimates and the memory requirements associated with detailed solutions throughout a reactor (problems involving hundreds of millions of different material and tally regions due to fuel irradiation, temperature distributions, and the needs associated with multi-physics code coupling). To address these challenges, our research has focused on the development and implementation of (1) a novel hybrid deterministic/MC method for determining high-precision fluxes throughout the problem space in k-eigenvalue problems and (2) an efficient MC domain-decomposition (DD) algorithm that partitions the problem phase space onto multiple processors for massively parallel systems, with statistical uncertainty estimation. The hybrid method development is based on an extension of the FW-CADIS method, which attempts to achieve uniform statistical uncertainty throughout a designated problem space. The MC DD development is being implemented in conjunction with the Denovo deterministic radiation transport package to have direct access to the 3-D, massively parallel discrete-ordinates solver (to support the hybrid method) and the associated parallel routines and structure. This paper describes the hybrid method, its implementation, and initial testing results for a realistic 2-D quarter core pressurized-water reactor model and also describes the MC DD algorithm and its implementation.

Download or read book Computational Science ICCS 2021 written by Maciej Paszynski and published by Springer Nature. This book was released on 2021-06-10 with total page 815 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six-volume set LNCS 12742, 12743, 12744, 12745, 12746, and 12747 constitutes the proceedings of the 21st International Conference on Computational Science, ICCS 2021, held in Krakow, Poland, in June 2021.* The total of 260 full papers and 57 short papers presented in this book set were carefully reviewed and selected from 635 submissions. 48 full and 14 short papers were accepted to the main track from 156 submissions; 212 full and 43 short papers were accepted to the workshops/ thematic tracks from 479 submissions. The papers were organized in topical sections named: Part I: ICCS Main Track Part II: Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Applications of Computational Methods in Artificial Intelligence and Machine Learning; Artificial Intelligence and High-Performance Computing for Advanced Simulations; Biomedical and Bioinformatics Challenges for Computer Science Part III: Classifier Learning from Difficult Data; Computational Analysis of Complex Social Systems; Computational Collective Intelligence; Computational Health Part IV: Computational Methods for Emerging Problems in (dis-)Information Analysis; Computational Methods in Smart Agriculture; Computational Optimization, Modelling and Simulation; Computational Science in IoT and Smart Systems Part V: Computer Graphics, Image Processing and Artificial Intelligence; Data-Driven Computational Sciences; Machine Learning and Data Assimilation for Dynamical Systems; MeshFree Methods and Radial Basis Functions in Computational Sciences; Multiscale Modelling and Simulation Part VI: Quantum Computing Workshop; Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning; Software Engineering for Computational Science; Solving Problems with Uncertainty; Teaching Computational Science; Uncertainty Quantification for Computational Models *The conference was held virtually. Chapter “Deep Learning Driven Self-adaptive hp Finite Element Method” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Download or read book Domain Decomposition for Monte Carlo Particle Transport Simulations of Nuclear Reactors written by Nicholas Edward Horelik and published by . This book was released on 2015 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: Monte Carlo (MC) neutral particle transport methods have long been considered the gold-standard for nuclear simulations, but high computational cost has limited their use significantly. However, as we move towards higher-fidelity nuclear reactor analyses the method has become competitive with traditional deterministic transport algorithms for the same level of accuracy, especially considering the inherent parallelism of the method and the ever-increasing concurrency of modern high performance computers. Yet before such analysis can be practical, several algorithmic challenges must be addressed, particularly in regards to the memory requirements of the method. In this thesis, a robust domain decomposition algorithm is proposed to alleviate this, along with models and analysis to support its use for full-scale reactor analysis. Algorithms were implemented in the full-physics Monte Carlo code OpenMC, and tested for a highly-detailed PWR benchmark: BEAVRS. The proposed domain decomposition implementation incorporates efficient algorithms for scalable inter-domain particle communication in a manner that is reproducible with any pseudo-random number seed. Algorithms are also proposed to scalably manage material and tally data with on-the-fly allocation during simulation, along with numerous optimizations required for scalability as the domain mesh is refined and divided among thousands of compute processes. The algorithms were tested on two supercomputers, namely the Mira Blue Gene/Q and the Titan XK7, demonstrating good performance with realistic tallies and materials requiring over a terabyte of aggregate memory. Performance models were also developed to more accurately predict the network and load imbalance penalties that arise from communicating particles between distributed compute nodes tracking different spatial domains. These were evaluated using machine properties and tallied particle movement characteristics, and empirically validated with observed timing results from the new implementation. Network penalties were shown to be almost negligible with per-process particle counts as low as 1000, and load imbalance penalties higher than a factor of four were not observed or predicted for finer domain meshes relevant to reactor analysis. Load balancing strategies were also explored, and intra-domain replication was shown to be very effective at improving parallel efficiencies without adding significant complexity to the algorithm or burden to the user. Performance of the strategy was quantified with a performance model, and shown to agree well with observed timings. Imbalances were shown to be almost completely removed for the finest domain meshes. Finally, full-core studies were carried out to demonstrate the efficacy of domain-decomposed Monte Carlo in tackling the full scope of the problem. A detailed mesh required for a robust depletion treatment was used, and good performance was demonstrated for depletion tallies with 206 nuclides. The largest runs scored six reaction rates for each nuclide in 51M regions for a total aggregate memory requirement of 1.4TB, and particle tracking rates were consistent with those observed for smaller non-domain- decomposed runs with equivalent tally complexity. These types of runs were previously not achievable with traditional Monte Carlo methods, and can be accomplished with domain decomposition with between 1.4x and 1.75x overhead with simple load balancing.

Download or read book Monte Carlo and Depletion Reactor Analysis for High performance Computing Applications written by Brenden Thomas Mervin and published by . This book was released on 2013 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation discusses the research and development for a coupled neutron trans- port/isotopic depletion capability for use in high-preformance computing applications. Accurate neutronics modeling and simulation for \real" reactor problems has been a long sought after goal in the computational community. A complementary \stretch goal" to this is the ability to perform full-core depletion analysis and spent fuel isotopic characterization. This dissertation thus presents the research and development of a coupled Monte Carlo transport/isotopic depletion implementation with the Exnihilo framework geared for high-performance computing architectures to enable neutronics analysis for full-core reactor problems. An in-depth case study of the current state of Monte Carlo neutron transport with respect to source sampling, source convergence, uncertainty underprediction and biases associated with localized tallies in Monte Carlo eigenvalue calculations was performed using MCNPand KENO. This analysis is utilized in the design and development of the statistical algorithms for Exnihilo's Monte Carlo framework, Shift. To this end, a methodology has been developed in order to perform tally statistics in domain decomposed environments. This methodology has been shown to produce accurate tally uncertainty estimates in domain-decomposed environments without a significant increase in the memory requirements, processor-to-processor communications, or computational biases. With the addition of parallel, domain-decomposed tally uncertainty estimation processes, a depletion package was developed for the Exnihilo code suite to utilize the depletion capabilities of the Oak Ridge Isotope GENeration code. This interface was designed to be transport agnostic, meaning that it can be used by any of the reactor analysis packages within Exnihilo such as Denovo or Shift. Extensive validation and testing of the ORIGEN interface and coupling with the Shift Monte Carlo transport code is performed within this dissertation, and results are presented for the calculated eigenvalues, material powers, and nuclide concentrations for the depleted materials. These results are then compared to ORIGEN and TRITON depletion calculations, and analysis shows that the Exnihilo transport-depletion capability is in good agreement with these codes.

Download or read book Parallel Domain Decomposition Methods in Fluid Models with Monte Carlo Transport written by and published by . This book was released on 1996 with total page 11 pages. Available in PDF, EPUB and Kindle. Book excerpt: To examine the domain decomposition code coupled Monte Carlo-finite element calculation, it is important to use a domain decomposition that is suitable for the individual models. We have developed a code that simulates a Monte Carlo calculation () on a massively parallel processor. This code is used to examine the load balancing behavior of three domain decomposition () for a Monte Carlo calculation. Results are presented.

Download or read book Forward Weighted CADIS Method for Variance Reduction of Monte Carlo Reactor Analyses written by and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Current state-of-the-art tools and methods used to perform 'real' commercial reactor analyses use high-fidelity transport codes to produce few-group parameters at the assembly level for use in low-order methods applied at the core level. Monte Carlo (MC) methods, which allow detailed and accurate modeling of the full geometry and energy details and are considered the 'gold standard' for radiation transport solutions, are playing an ever-increasing role in correcting and/or verifying the several-decade-old methodology used in current practice. However, the prohibitive computational requirements associated with obtaining fully converged system-wide solutions restrict the role of MC to benchmarking deterministic results at a limited number of state-points for a limited number of relevant quantities. A goal of current research at Oak Ridge National Laboratory (ORNL) is to change this paradigm by enabling the direct use of MC for full-core reactor analyses. The most significant of the many technical challenges that must be overcome is the slow non-uniform convergence of system-wide MC estimates and the memory requirements associated with detailed solutions throughout a reactor (problems involving hundreds of millions of different material and tally regions due to fuel irradiation, temperature distributions, and the needs associated with multi-physics code coupling). To address these challenges, research has focused on development in the following two areas: (1) a hybrid deterministic/MC method for determining high-precision fluxes throughout the problem space in k-eigenvalue problems and (2) an efficient MC domain-decomposition algorithm that partitions the problem phase space onto multiple processors for massively parallel systems, with statistical uncertainty estimation. The focus of this paper is limited to the first area mentioned above. It describes the FW-CADIS method applied to variance reduction of MC reactor analyses and provides initial results for calculating group-wise fluxes throughout a generic 2-D pressurized water reactor (PWR) quarter core model.

Download or read book Development of Subspace based Hybrid Monte Carlo Deterministric Algorithms for Reactor Physics Calculations written by and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The development of hybrid Monte-Carlo-Deterministic (MC-DT) approaches, taking place over the past few decades, have primarily focused on shielding and detection applications where the analysis requires a small number of responses, i.e. at the detector locations(s). This work further develops a recently introduced global variance reduction approach, denoted by the SUBSPACE approach is designed to allow the use of MC simulation, currently limited to benchmarking calculations, for routine engineering calulations. By way of demonstration, the SUBSPACE approach is applied to assembly level calculations used to generate the few-group homogenized cross-sections. These models are typically expensive and need to be executed in the order of 10-10 times to properly characterize the few-group cross-sections for deownstream core-wide calculations. Applicability to k-eigenvalue core-wide models is also demonstrated in this work. Given the faborable results obtained in this work, we believe the applicability of the MC method for reactor analysis calculations could be realized in the near future.

Download or read book Fifth World Congress Quebec Canada 1954 written by and published by . This book was released on 1955 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Domain Decomposition Methods for Parallel Laser tissue Models with Monte Carlo Transport written by and published by . This book was released on 1998 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Achieving parallelism in simulations that use Monte Carlo transport methods presents interesting challenges. For problems that require domain decomposition, load balance can be harder to achieve. The Monte Carlo transport package may have to operate with other packages that have different optimal domain decompositions for a given problem. To examine some of these issues, we have developed a code that simulates the interaction of a laser with biological tissue; it uses a Monte Carlo method to simulate the laser and a finite element model to simulate the conduction of the temperature field in the tissue. We will present speedup and load balance results obtained for a suite of problems decomposed using a few domain decomposition algorithms we have developed.

Download or read book Sensitivity Analysis in Practice written by Andrea Saltelli and published by John Wiley & Sons. This book was released on 2004-07-16 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sensitivity analysis should be considered a pre-requisite for statistical model building in any scientific discipline where modelling takes place. For a non-expert, choosing the method of analysis for their model is complex, and depends on a number of factors. This book guides the non-expert through their problem in order to enable them to choose and apply the most appropriate method. It offers a review of the state-of-the-art in sensitivity analysis, and is suitable for a wide range of practitioners. It is focussed on the use of SIMLAB – a widely distributed freely-available sensitivity analysis software package developed by the authors – for solving problems in sensitivity analysis of statistical models. Other key features: Provides an accessible overview of the current most widely used methods for sensitivity analysis. Opens with a detailed worked example to explain the motivation behind the book. Includes a range of examples to help illustrate the concepts discussed. Focuses on implementation of the methods in the software SIMLAB - a freely-available sensitivity analysis software package developed by the authors. Contains a large number of references to sources for further reading. Authored by the leading authorities on sensitivity analysis.

Download or read book Topological and Statistical Methods for Complex Data written by Janine Bennett and published by Springer. This book was released on 2014-11-19 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains papers presented at the Workshop on the Analysis of Large-scale, High-Dimensional, and Multi-Variate Data Using Topology and Statistics, held in Le Barp, France, June 2013. It features the work of some of the most prominent and recognized leaders in the field who examine challenges as well as detail solutions to the analysis of extreme scale data. The book presents new methods that leverage the mutual strengths of both topological and statistical techniques to support the management, analysis, and visualization of complex data. It covers both theory and application and provides readers with an overview of important key concepts and the latest research trends. Coverage in the book includes multi-variate and/or high-dimensional analysis techniques, feature-based statistical methods, combinatorial algorithms, scalable statistics algorithms, scalar and vector field topology, and multi-scale representations. In addition, the book details algorithms that are broadly applicable and can be used by application scientists to glean insight from a wide range of complex data sets.

Download or read book A Guide to Monte Carlo Simulations in Statistical Physics written by David P. Landau and published by Cambridge University Press. This book was released on 2000-08-17 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes all aspects of Monte Carlo simulation of complex physical systems encountered in condensed-matter physics and statistical mechanics, as well as in related fields, such as polymer science and lattice gauge theory. The authors give a succinct overview of simple sampling methods and develop the importance sampling method. In addition they introduce quantum Monte Carlo methods, aspects of simulations of growth phenomena and other systems far from equilibrium, and the Monte Carlo Renormalization Group approach to critical phenomena. The book includes many applications, examples, and current references, and exercises to help the reader.

Download or read book Global Sensitivity Analysis written by Andrea Saltelli and published by John Wiley & Sons. This book was released on 2008-02-28 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex mathematical and computational models are used in all areas of society and technology and yet model based science is increasingly contested or refuted, especially when models are applied to controversial themes in domains such as health, the environment or the economy. More stringent standards of proofs are demanded from model-based numbers, especially when these numbers represent potential financial losses, threats to human health or the state of the environment. Quantitative sensitivity analysis is generally agreed to be one such standard. Mathematical models are good at mapping assumptions into inferences. A modeller makes assumptions about laws pertaining to the system, about its status and a plethora of other, often arcane, system variables and internal model settings. To what extent can we rely on the model-based inference when most of these assumptions are fraught with uncertainties? Global Sensitivity Analysis offers an accessible treatment of such problems via quantitative sensitivity analysis, beginning with the first principles and guiding the reader through the full range of recommended practices with a rich set of solved exercises. The text explains the motivation for sensitivity analysis, reviews the required statistical concepts, and provides a guide to potential applications. The book: Provides a self-contained treatment of the subject, allowing readers to learn and practice global sensitivity analysis without further materials. Presents ways to frame the analysis, interpret its results, and avoid potential pitfalls. Features numerous exercises and solved problems to help illustrate the applications. Is authored by leading sensitivity analysis practitioners, combining a range of disciplinary backgrounds. Postgraduate students and practitioners in a wide range of subjects, including statistics, mathematics, engineering, physics, chemistry, environmental sciences, biology, toxicology, actuarial sciences, and econometrics will find much of use here. This book will prove equally valuable to engineers working on risk analysis and to financial analysts concerned with pricing and hedging.

Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1994 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book International Aerospace Abstracts written by and published by . This book was released on 1997 with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Parallel Processing for Scientific Computing written by Michael A. Heroux and published by SIAM. This book was released on 2006-01-01 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: Parallel processing has been an enabling technology in scientific computing for more than 20 years. This book is the first in-depth discussion of parallel computing in 10 years; it reflects the mix of topics that mathematicians, computer scientists, and computational scientists focus on to make parallel processing effective for scientific problems. Presently, the impact of parallel processing on scientific computing varies greatly across disciplines, but it plays a vital role in most problem domains and is absolutely essential in many of them. Parallel Processing for Scientific Computing is divided into four parts: The first concerns performance modeling, analysis, and optimization; the second focuses on parallel algorithms and software for an array of problems common to many modeling and simulation applications; the third emphasizes tools and environments that can ease and enhance the process of application development; and the fourth provides a sampling of applications that require parallel computing for scaling to solve larger and realistic models that can advance science and engineering.

Download or read book Bayesian Learning for Neural Networks written by Radford M. Neal and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional training methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. A practical implementation of Bayesian neural network learning using Markov chain Monte Carlo methods is also described, and software for it is freely available over the Internet. Presupposing only basic knowledge of probability and statistics, this book should be of interest to researchers in statistics, engineering, and artificial intelligence.