The purpose of the Sandia National Laboratories Advanced Simulation and Computing (ASC) Software Quality Plan is to clearly identify the practices that are the basis for continually improving the quality of ASC software products. The plan defines the ASC program software quality practices and provides mappings of these practices to Sandia Corporate Requirements CPR001.3.2 and CPR001.3.6 and to a Department of Energy document, ''ASCI Software Quality Engineering: Goals, Principles, and Guidelines''. This document also identifies ASC management and software project teams' responsibilities in implementing the software quality practices and in assessing progress towards achieving their software quality goals.
The purpose of the Sandia National Laboratories Advanced Simulation and Computing (ASC) Software Quality Plan is to clearly identify the practices that are the basis for continually improving the quality of ASC software products. The plan defines the ASC program software quality practices and provides mappings of these practices to Sandia Corporate Requirements CPR 1.3.2 and 1.3.6 and to a Department of Energy document, ASCI Software Quality Engineering: Goals, Principles, and Guidelines. This document also identifies ASC management and software project teams responsibilities in implementing the software quality practices and in assessing progress towards achieving their software quality goals.
The purpose of the Sandia National Laboratories Advanced Simulation and Computing (ASC) Software Quality Plan is to clearly identify the practices that are the basis for continually improving the quality of ASC software products. The plan defines the ASC program software quality practices and provides mappings of these practices to Sandia Corporate Requirements CPR 1.3.2 and 1.3.6 and to a Department of Energy document, 'ASCI Software Quality Engineering: Goals, Principles, and Guidelines'. This document also identifies ASC management and software project teams responsibilities in implementing the software quality practices and in assessing progress towards achieving their software quality goals.
The purpose of the Sandia National Laboratories (SNL) Advanced Simulation and Computing (ASC) Software Quality Plan is to clearly identify the practices that are the basis for continually improving the quality of ASC software products. Quality is defined in DOE/AL Quality Criteria (QC-1) as conformance to customer requirements and expectations. This quality plan defines the ASC program software quality practices and provides mappings of these practices to the SNL Corporate Process Requirements (CPR 1.3.2 and CPR 1.3.6) and the Department of Energy (DOE) document, ASCI Software Quality Engineering: Goals, Principles, and Guidelines (GP&G). This quality plan identifies ASC management and software project teams' responsibilities for cost-effective software engineering quality practices. The SNL ASC Software Quality Plan establishes the signatories commitment to improving software products by applying cost-effective software engineering quality practices. This document explains the project teams opportunities for tailoring and implementing the practices; enumerates the practices that compose the development of SNL ASC's software products; and includes a sample assessment checklist that was developed based upon the practices in this document.
ALEGRA is an arbitrary Lagrangian-Eulerian finite element code that emphasizes large distortion and shock propagation. This document describes the user input language for the code.
This paper describes the analysis of the Three Mile Island-2 (TMI-2) standard problem that was performed with MELCOR. The MELCOR computer code is being developed by Sandia National Laboratories for the Nuclear Regulatory Commission for the purpose of analyzing severe accident in nuclear power plants. The primary role of MELCOR is to provide realistic predictions of severe accident phenomena and the radiological source team. The analysis of the TMI-2 standard problem allowed for comparison of the model predictions in MELCOR to plant data and to the results of more mechanistic analyses. This exercise was, therefore valuable for verifying and assessing the models in the code. The major trends in the TMI-2 accident are reasonably well predicted with MELCOR, even with its simplified modeling. Comparison of the calculated and measured results is presented and, based on this comparison, conclusions can be drawn concerning the applicability of MELCOR to severe accident analysis. 5 refs., 10 figs., 3 tabs.