Coupling Experiment and Simulation in Electromagnetic Forming Using Photon Doppler Velocimetry

Modeling electromagnetic forming processes is in many ways simpler than modeling traditional metal forming processes. In electromagnetic forming the problem is often dominated by inertial acceleration by a magnetic field. This is a much better posed problem than the more traditional ones that are often dominated by complex three dimensional constitutive behavior and frictional effects. However, important aspects of the problem are dominated by the constitutive properties of the material, and often electromagnetic forming is performed in a regime where there is little reliable material strength data. Strain rates are often high (102 to 104 s-1 is the typical range for electromagnetic forming). Also, heat is generated both by ohmic heating as well as by plastic deformation, and peak temperatures can be quite high. Also, while hightemperature, high-strain-rate data is scarce, there is little or no data in cases where temperature rises significantly over very short times (tens of micro-seconds) as happens in electromagnetic metal forming. This rapid temperature rise is very important to the material response because the short time scales largely preclude the material from recovery and recrystallization processes and precipitates cannot dissolve as they normally would in an age-hardening alloy in these time scales. This presentation will show how advanced instrumentation, particularly the Photon Doppler Velocimeter (PDV) can be coupled with electromagnetic forming and provide both avenues to characterize material as well as to provide very critical tests of numerical models of the process

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed