Finite element (MARC) solution technologies for viscoplastic analyses

A need for development of realistic constitutive models for structural components operating at high temperatures, accompanied by appropriate solution technologies for stress/life analyses of these components is studied. Viscoplastic models provide a better description of inelastic behavior of materials, but their mathematical structure is very complex. The highly nonlinear and stiff nature of the constitutive equations makes analytical solutions difficult. Therefore, suitable solution, finite element or other numerical, technologies must be developed to make these models adaptable for better and rational designs of components. NASA-Lewis has developed several solution technologies and successfully applied them to the solution of a number of uniaxial and multiaxial problems. Some of these solution technologies are described along with the models and representative results. The solution technologies developed and presented encompass a wide range of models, such as, isotropic, anisotropic, metal matrix composites, and single crystal models

Analysis and Detection of Information Types of Open Source Software Issue Discussions

Most modern Issue Tracking Systems (ITSs) for open source software (OSS) projects allow users to add comments to issues. Over time, these comments accumulate into discussion threads embedded with rich information about the software project, which can potentially satisfy the diverse needs of OSS stakeholders. However, discovering and retrieving relevant information from the discussion threads is a challenging task, especially when the discussions are lengthy and the number of issues in ITSs are vast. In this paper, we address this challenge by identifying the information types presented in OSS issue discussions. Through qualitative content analysis of 15 complex issue threads across three projects hosted on GitHub, we uncovered 16 information types and created a labeled corpus containing 4656 sentences. Our investigation of supervised, automated classification techniques indicated that, when prior knowledge about the issue is available, Random Forest can effectively detect most sentence types using conversational features such as the sentence length and its position. When classifying sentences from new issues, Logistic Regression can yield satisfactory performance using textual features for certain information types, while falling short on others. Our work represents a nontrivial first step towards tools and techniques for identifying and obtaining the rich information recorded in the ITSs to support various software engineering activities and to satisfy the diverse needs of OSS stakeholders.Comment: 41st ACM/IEEE International Conference on Software Engineering (ICSE2019