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Title: User input validation and test driven development in NJOY21


No abstract provided.

  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
Report Number(s):
DOE Contract Number:
Resource Type:
Technical Report
Country of Publication:
United States

Citation Formats

Trainer, Amelia Jo. User input validation and test driven development in NJOY21. United States: N. p., 2017. Web. doi:10.2172/1375898.
Trainer, Amelia Jo. User input validation and test driven development in NJOY21. United States. doi:10.2172/1375898.
Trainer, Amelia Jo. Tue . "User input validation and test driven development in NJOY21". United States. doi:10.2172/1375898.
title = {User input validation and test driven development in NJOY21},
author = {Trainer, Amelia Jo},
abstractNote = {No abstract provided.},
doi = {10.2172/1375898},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Aug 22 00:00:00 EDT 2017},
month = {Tue Aug 22 00:00:00 EDT 2017}

Technical Report:

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  • Before physically-meaningful data can be used in nuclear simulation codes, the data must be interpreted and manipulated by a nuclear data processing code so as to extract the relevant quantities (e.g. cross sections and angular distributions). Perhaps the most popular and widely-trusted of these processing codes is NJOY, which has been developed and improved over the course of 10 major releases since its creation at Los Alamos National Laboratory in the mid-1970’s. The current phase of NJOY development is the creation of NJOY21, which will be a vast improvement from its predecessor, NJOY2016. Designed to be fast, intuitive, accessible, andmore » capable of handling both established and modern formats of nuclear data, NJOY21 will address many issues that many NJOY users face, while remaining functional for those who prefer the existing format. Although early in its development, NJOY21 is quickly providing input validation to check user input. By providing rapid and helpful responses to users while writing input files, NJOY21 will prove to be more intuitive and easy to use than any of its predecessors. Furthermore, during its development, NJOY21 is subject to regular testing, such that its test coverage must strictly increase with the addition of any production code. This thorough testing will allow developers and NJOY users to establish confidence in NJOY21 as it gains functionality. This document serves as a discussion regarding the current state input checking and testing practices of NJOY21.« less
  • The thermodynamic behavior of hydrogen fluoride when diluted with air, particularly moist air, is very different from that of a simple ideal gas. The gas-air mixture can, depending on conditions, be denser than ambient air or substantially less dense than air. This behavior of an HF cloud would have a major influence on the dispersion behavior of HF in the atmosphere if it were released accidentally. For gases such as LNG thermodynamic effects must be included in dispersion models in order to accurately simulate such releases. Because of the unique thermodynamic properties of HF, it was felt that those propertiesmore » would be important in accurately simulating an HF release. This program identified three major areas in which substantial uncertainties existed in previous models: (1) the modeling of the complex thermodynamics of HF/H{sub 2}O/Air mixtures (including aerosol effects on cloud density); (2) the treatment of a wide range of surface roughness conditions (including possible multiple surface roughness conditions) and (3) jet flow and air entrainment for pressurized releases of HF, followed by transition to ground-based dense gas dispersion. Major objectives of this study were to develop and validate computer-based models to calculate the release properties in addition to simulating jet and plume behavior downwind of an accidental release of HF. This report is the HGSYSTEM Program User`s Manual.« less
  • On-line monitoring and tracking of nuclear plant system and component degradation is being investigated as a method for improving the safety, reliability, and maintainability of aging nuclear power plants. Accurate prediction of the current degradation state of system components and structures is important for accurate estimates of their remaining useful life (RUL). The correct quantification and propagation of both the measurement uncertainty and model uncertainty is necessary for quantifying the uncertainty of the RUL prediction. This research project developed and validated methods to perform RUL estimation throughout the lifecycle of plant components. Prognostic methods should seamlessly operate from beginning ofmore » component life (BOL) to end of component life (EOL). We term this "Lifecycle Prognostics." When a component is put into use, the only information available may be past failure times of similar components used in similar conditions, and the predicted failure distribution can be estimated with reliability methods such as Weibull Analysis (Type I Prognostics). As the component operates, it begins to degrade and consume its available life. This life consumption may be a function of system stresses, and the failure distribution should be updated to account for the system operational stress levels (Type II Prognostics). When degradation becomes apparent, this information can be used to again improve the RUL estimate (Type III Prognostics). This research focused on developing prognostics algorithms for the three types of prognostics, developing uncertainty quantification methods for each of the algorithms, and, most importantly, developing a framework using Bayesian methods to transition between prognostic model types and update failure distribution estimates as new information becomes available. The developed methods were then validated on a range of accelerated degradation test beds. The ultimate goal of prognostics is to provide an accurate assessment for RUL predictions, with as little uncertainty as possible. From a reliability and maintenance standpoint, there would be improved safety by avoiding all failures. Calculated risk would decrease, saving money by avoiding unnecessary maintenance. One major bottleneck for data-driven prognostics is the availability of run-to-failure degradation data. Without enough degradation data leading to failure, prognostic models can yield RUL distributions with large uncertainty or mathematically unsound predictions. To address these issues a "Lifecycle Prognostics" method was developed to create RUL distributions from Beginning of Life (BOL) to End of Life (EOL). This employs established Type I, II, and III prognostic methods, and Bayesian transitioning between each Type. Bayesian methods, as opposed to classical frequency statistics, show how an expected value, a priori, changes with new data to form a posterior distribution. For example, when you purchase a component you have a prior belief, or estimation, of how long it will operate before failing. As you operate it, you may collect information related to its condition that will allow you to update your estimated failure time. Bayesian methods are best used when limited data are available. The use of a prior also means that information is conserved when new data are available. The weightings of the prior belief and information contained in the sampled data are dependent on the variance (uncertainty) of the prior, the variance (uncertainty) of the data, and the amount of measured data (number of samples). If the variance of the prior is small compared to the uncertainty of the data, the prior will be weighed more heavily. However, as more data are collected, the data will be weighted more heavily and will eventually swamp out the prior in calculating the posterior distribution of model parameters. Fundamentally Bayesian analysis updates a prior belief with new data to get a posterior belief. The general approach to applying the Bayesian method to lifecycle prognostics consisted of identifying the prior, which is the RUL estimate and uncertainty from the previous prognostics type, and combining it with observational data related to the newer prognostics type. The resulting lifecycle prognostics algorithm uses all available information throughout the component lifecycle.« less
  • Passive, buoyancy-driven ventilation is one approach to limiting hydrogen concentration. We explored the relationship between leak rate, ventilation design, and hydrogen concentrations.