Creep curve measurement to support wear and adhesion modelling, using a continuously variable creep twin disc machine

Predictive modelling of wear and adhesion at rolling-sliding contacts such as a railway rail and wheel depends on understanding the relationship between slip and shear force at the contact surface, i.e. the creep verses force curve. This paper describes a new approach to creep curve measurement using a twin disc machine running with a continuous programmed variation of creep, enabling an entire creep curve to be defined in a single experiment. The work focuses on very low levels of creep, ranging from zero to 1%, and shows clear correlation between the creep curve gradient and the full slip friction coefficient for dry and lubricated contacts. Comparison of data generated using the new approach with that generated using multiple tests each at a single creep level shows good agreement. Comparison is also made between the twin disc data and results for full size three dimensional rail-wheel contacts to examine how two and three dimensional contact adhesion data are related. The data generated has application in wear and rolling contact fatigue modelling, but the original motivation for the research was generation of creep curves to support prediction of low adhesion conditions at the rail-wheel interface based upon monitored running conditions prior to brake application. The range of contact conditions investigated includes those experienced in service and during driver training, with the correlation found between creep curve gradient (measurable prior to braking) and full slip friction coefficient (not measurable until brakes are applied) representing a key finding

Analysis and Synthesis of Metadata Goals for Scientific Data

The proliferation of discipline-specific metadata schemes contributes to artificial barriers that can impede interdisciplinary and transdisciplinary research. The authors considered this problem by examining the domains, objectives, and architectures of nine metadata schemes used to document scientific data in the physical, life, and social sciences. They used a mixed-methods content analysis and Greenberg’s (2005) metadata objectives, principles, domains, and architectural layout (MODAL) framework, and derived 22 metadata-related goals from textual content describing each metadata scheme. Relationships are identified between the domains (e.g., scientific discipline and type of data) and the categories of scheme objectives. For each strong correlation (\u3e0.6), a Fisher’s exact test for nonparametric data was used to determine significance (p \u3c .05). Significant relationships were found between the domains and objectives of the schemes. Schemes describing observational data are more likely to have “scheme harmonization” (compatibility and interoperability with related schemes) as an objective; schemes with the objective “abstraction” (a conceptual model exists separate from the technical implementation) also have the objective “sufficiency” (the scheme defines a minimal amount of information to meet the needs of the community); and schemes with the objective “data publication” do not have the objective “element refinement.” The analysis indicates that many metadata-driven goals expressed by communities are independent of scientific discipline or the type of data, although they are constrained by historical community practices and workflows as well as the technological environment at the time of scheme creation. The analysis reveals 11 fundamental metadata goals for metadata documenting scientific data in support of sharing research data across disciplines and domains. The authors report these results and highlight the need for more metadata-related research, particularly in the context of recent funding agency policy changes

Enabling quantitative data analysis through e-infrastructures

This paper discusses how quantitative data analysis in the social sciences can engage with and exploit an e-Infrastructure. We highlight how a number of activities which are central to quantitative data analysis, referred to as ‘data management’, can benefit from e-infrastructure support. We conclude by discussing how these issues are relevant to the DAMES (Data Management through e-Social Science) research Node, an ongoing project that aims to develop e-Infrastructural resources for quantitative data analysis in the social sciences

Cognitive networks: brains, internet, and civilizations

In this short essay, we discuss some basic features of cognitive activity at several different space-time scales: from neural networks in the brain to civilizations. One motivation for such comparative study is its heuristic value. Attempts to better understand the functioning of "wetware" involved in cognitive activities of central nervous system by comparing it with a computing device have a long tradition. We suggest that comparison with Internet might be more adequate. We briefly touch upon such subjects as encoding, compression, and Saussurean trichotomy langue/langage/parole in various environments.Comment: 16 page

Olivia Greer: Weil Gotshal and Privacy Practice and Insights

https://larc.cardozo.yu.edu/flyers-2023-2024/1130/thumbnail.jp