Application of Artificial Neural Networks to Reliable Nuclear Data for Nonproliferation Modeling and Simulation

2016 ◽  
Vol 4 (4) ◽  
pp. 54-64 ◽  
Author(s):  
Pola Lydia Lagari ◽  
Vladimir Sobes ◽  
Miltiadis Alamaniotis ◽  
Lefteri H. Tsoukalas
Keyword(s):  
Modeling And Simulation ◽  
Cross Sections ◽  
Experimental Testing ◽  
Detection Limits ◽  
Nuclear Data ◽  
Nuclear Materials ◽  
Simulation Techniques ◽  
Detection And Identification ◽  
The Us ◽  
Nuclear Systems

Detection and identification of special nuclear materials (SNMs) are an essential part of the US nonproliferation effort. Modern cutting-edge SNM detection methodologies rely more and more on modeling and simulation techniques. Experiments with radiological samples in realistic configurations, is the ultimate tool that establishes the minimum detection limits of SNMs in a host of different geometries. Modern modeling and simulation approaches have the potential to significantly reduce the number of experiments with radioactive sources needed to determine these detection limits and reduce the financial barrier to SNM detection. Unreliable nuclear data is one of the principal causes of uncertainty in modeling and simulating nuclear systems. In particular, nuclear cross sections introduce a significant uncertainty in the nuclear data. The goal of this research is to develop a methodology that will autonomously extract the correct nuclear resonance characteristics of experimental data in a reliable way, a task previously left to expert judgement. Accurate nuclear data will in turn allow contemporary modeling and simulation to become far more reliable, de-escalating the extent of experimental testing. Consequently, modeling and simulation techniques reduce the use and distribution of radiological sources, while at the same time increase the reliability of the currently used methods for the detection and identification of SNMs.

Application of Artificial Neural Networks to Reliable Nuclear Data for Nonproliferation Modeling and Simulation

2020 ◽  
pp. 833-844
Author(s):  
Pola Lydia Lagari ◽  
Vladimir Sobes ◽  
Miltiadis Alamaniotis ◽  
Lefteri H. Tsoukalas
Keyword(s):  
Modeling And Simulation ◽  
Cross Sections ◽  
Experimental Testing ◽  
Detection Limits ◽  
Nuclear Data ◽  
Simulation Techniques ◽  
Detection And Identification ◽  
The Us ◽  
Nuclear Systems ◽  
Minimum Detection Limits

Detection and identification of special nuclear materials (SNMs) are an essential part of the US nonproliferation effort. Modern cutting-edge SNM detection methodologies rely more and more on modeling and simulation techniques. Experiments with radiological samples in realistic configurations, is the ultimate tool that establishes the minimum detection limits of SNMs in a host of different geometries. Modern modeling and simulation approaches have the potential to significantly reduce the number of experiments with radioactive sources needed to determine these detection limits and reduce the financial barrier to SNM detection. Unreliable nuclear data is one of the principal causes of uncertainty in modeling and simulating nuclear systems. In particular, nuclear cross sections introduce a significant uncertainty in the nuclear data. The goal of this research is to develop a methodology that will autonomously extract the correct nuclear resonance characteristics of experimental data in a reliable way, a task previously left to expert judgement. Accurate nuclear data will in turn allow contemporary modeling and simulation to become far more reliable, de-escalating the extent of experimental testing. Consequently, modeling and simulation techniques reduce the use and distribution of radiological sources, while at the same time increase the reliability of the currently used methods for the detection and identification of SNMs.

Comparison of the ENDF/B-VII.0, ENDF/B-VII.1, ENDF/B-VIII.0 and JEFF-3.3 Libraries for the Nuclear Design Calculations of the NPP Krško with the CORD-2 System

2021 ◽  
Author(s):  
Marjan Kromar ◽  
Bojan Kurincic
Keyword(s):  
Cross Sections ◽  
Fission Products ◽  
Nuclear Data ◽  
Isothermal Temperature ◽  
Nuclear Systems ◽  
Full Power ◽  
Design Calculations ◽  
Data Libraries ◽  
Control Rods ◽  
Operating Cycles

Abstract Recently, two new nuclear reaction data evaluations have been released: ENDF/B-VIII.0 and JEFF-3.3. Since the neutron nuclear data profoundly influence predictions of the nuclear systems behavior, many researchers have been investigating new data striving for more accurate predictions. The purpose of this study is to examine the effects of the neutron data libraries on the nuclear design calculations of the NPP Krško core. ENDF/B-VII.0, ENDF/B-VII.1, ENDF/B-VIII.0 and JEFF-3.3 libraries are considered. In the first part of the paper the effect on the depletion of the typical NPP Krško fuel assembly in infinite geometry is investigated. In the second part, analysis of all 30 completed NPP Krško operating cycles is performed. Performed analysis has indicated differences of a few hundred pcm in multiplication factor for a fresh fuel due to differences in 235U cross sections. For a burned fuel assemblies, differences are even higher due to different rate of fission products formation, 235U burnout and Pu production. Observed differences in libraries resulted in differences of several tens of ppm in critical Boron concentration on the core level. Differences in control rods worth and Boron coefficients were inside 1 %. Some differences in isothermal temperature coefficient were observed, however they only marginally affect core power defect going from zero to full power.

Pharmacokinetic Modeling in Nano-formulations: Concept, Implementation and Challenges

2019 ◽  
Vol 24 (43) ◽  
pp. 5175-5180 ◽  
Author(s):  
Jatinder Kaur Mukker ◽  
Ravi Shankar Prasad Singh
Keyword(s):  
Drug Delivery ◽  
Modeling And Simulation ◽  
Small Molecules ◽  
Pharmacokinetic Modeling ◽  
Simulation Techniques ◽  
Modeling Techniques

The properties of nanoparticles can be exploited to overcome challenges in drug delivery. By virtue of its design and size, the pharmacokinetics of nanoparticles are different than other small molecules. Modeling and simulation techniques have great potential to be used in nanoformulation development; however, their use in optimization of nanoformulation is very limited. This review highlights the differences in absorption, distribution, metabolism and excretion (ADME) characteristics of nanoparticles, use of modeling and simulation techniques in nanoformulation development and challenges in the implementation of modeling techniques.

A Perspective on Modeling Materials in Extreme Environments: Oxidation of Ultrahigh-Temperature Ceramics

MRS Bulletin ◽  
2006 ◽  
Vol 31 (5) ◽  
pp. 410-418 ◽  
Author(s):  
Angelo Bongiorno ◽  
Clemens J. Först ◽  
Rajiv K. Kalia ◽  
Ju Li ◽  
Jochen Marschall ◽  
...  
Keyword(s):  
Modeling And Simulation ◽  
Atomistic Simulation ◽  
Extreme Environments ◽  
Protective Layer ◽  
Ceramic Composites ◽  
Atomistic Modeling ◽  
Temperature Oxidation ◽  
Simulation Techniques ◽  
Oxidation Resistant ◽  
Ultrahigh Temperature

AbstractThe broader context of this discussion, based on a workshop where materials technologists and computational scientists engaged in a dialogue, is an awareness that modeling and simulation techniques and computational capabilities may have matured sufficiently to provide heretofore unavailable insights into the complex microstructural evolution of materials in extreme environments.As an example, this article examines the study of ultrahigh-temperature oxidation-resistant ceramics, through the combination of atomistic simulation and selected experiments.We describe a strategy to investigate oxygen transport through a multi-oxide scale—the protective layer of ultrahigh-temperature ceramic composites ZrB2-SiC and HfB2-SiC—by combining first-principles and atomistic modeling and simulation with selected experiments.

scholarly journals A Comprehensive Review of Cholinesterase Modeling and Simulation

Biomolecules ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 580
Author(s):  
Danna De Boer ◽  
Nguyet Nguyen ◽  
Jia Mao ◽  
Jessica Moore ◽  
Eric J. Sorin
Keyword(s):  
Modeling And Simulation ◽  
Binding Site ◽  
Catalytic Efficiency ◽  
Site Location ◽  
Simulation Techniques ◽  
Therapeutic Value ◽  
Docking Calculations ◽  
Computer Based ◽  
Dynamics Simulations ◽  
And Function

The present article reviews published efforts to study acetylcholinesterase and butyrylcholinesterase structure and function using computer-based modeling and simulation techniques. Structures and models of both enzymes from various organisms, including rays, mice, and humans, are discussed to highlight key structural similarities in the active site gorges of the two enzymes, such as flexibility, binding site location, and function, as well as differences, such as gorge volume and binding site residue composition. Catalytic studies are also described, with an emphasis on the mechanism of acetylcholine hydrolysis by each enzyme and novel mutants that increase catalytic efficiency. The inhibitory activities of myriad compounds have been computationally assessed, primarily through Monte Carlo-based docking calculations and molecular dynamics simulations. Pharmaceutical compounds examined herein include FDA-approved therapeutics and their derivatives, as well as several other prescription drug derivatives. Cholinesterase interactions with both narcotics and organophosphate compounds are discussed, with the latter focusing primarily on molecular recognition studies of potential therapeutic value and on improving our understanding of the reactivation of cholinesterases that are bound to toxins. This review also explores the inhibitory properties of several other organic and biological moieties, as well as advancements in virtual screening methodologies with respect to these enzymes.

Granular Flow Modeling and Simulation Using a Dynamic Finite Element Method

2007 ◽  
Author(s):  
M. A. Kabir ◽  
C. Fred Higgs ◽  
Michael R. Lovell
Keyword(s):  
Finite Element ◽  
Modeling And Simulation ◽  
Granular Flow ◽  
Flow Behavior ◽  
Food Service ◽  
Complex Nature ◽  
Complex Flow ◽  
Dual Nature ◽  
Simulation Techniques ◽  
Flow Phenomena

Granular flow behavior is of fundamental interest to the engineering and scientific community because of the prevalence of these flows in the pharmaceutical, agricultural, food service, and powder manufacturing industries. Granular materials exhibit very complex behavior, oftentimes acting as solids and at other times as fluids. This dual nature leads to very complex and rich behavior, which is not yet well understood. Therefore, the present investigation introduces a new technique that can potentially be used to unveil the mystery of granular flow phenomena. To this end, advanced finite element modeling and simulation techniques have been applied to the study of the complex nature of granular flow. More specifically, the explicit dynamic code LS-DYNA has been utilized to gain an understanding of the complex flow nature and collision stresses of granules in a shear cell.

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