Broadband combustion noise simulation of open non-premixed turbulent jet flames

Numerical broadband combustion noise simulations of open non-premixed turbulent jet flames applying the Random Particle-Mesh for Combustion Noise (RPM-CN) approach are presented. The RPM-CN approach is a hybrid Computational Fluid Dynamics/Computational Aeroacoustics (CFD/CAA) method for the numerical simulation of turbulent combustion noise, based on a stochastic source reconstruction in the time domain. The combustion noise sources are modeled on the basis of statistical turbulence quantities, for example achieved by a Reynolds averaged Navier-Stokes (RANS) simulation, using the Random Particle-Mesh (RPM) method. RPM generates a statistically stationary fluctuating sound source that satisfies prescribed one- and two-point statistics which implicitly specify the acoustic spectrum. Subsequently, the propagation of the combustion noise is computed by the numerical solution of the Linearized Euler Equations (LEE). The numerical approach is applied to the DLR-A, the DLR-B and the H3 flames. The open non-premixed turbulent jet flames differ in the mean jet exit velocity, therefore in their respective Reynolds number, and in the fuel composition. Computed radial profiles of the reacting flow field are compared to experimental data and discussed. Computed sound pressure level spectra of the DLR-A and DLR-B flames and acoustic intensity level spectra of the H3 flame at different microphone locations are presented and compared to measurements. </jats:p

Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty

This study explores how researchers’ analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers’ expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team’s workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers’ results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings

The Crowdsourced Replication Initiative: Investigating Immigration and Social Policy Preferences. Executive Report.

In an era of mass migration, social scientists, populist parties and social movements raise concerns over the future of immigration-destination societies. What impacts does this have on policy and social solidarity? Comparative cross-national research, relying mostly on secondary data, has findings in different directions. There is a threat of selective model reporting and lack of replicability. The heterogeneity of countries obscures attempts to clearly define data-generating models. P-hacking and HARKing lurk among standard research practices in this area.This project employs crowdsourcing to address these issues. It draws on replication, deliberation, meta-analysis and harnessing the power of many minds at once. The Crowdsourced Replication Initiative carries two main goals, (a) to better investigate the linkage between immigration and social policy preferences across countries, and (b) to develop crowdsourcing as a social science method. The Executive Report provides short reviews of the area of social policy preferences and immigration, and the methods and impetus behind crowdsourcing plus a description of the entire project. Three main areas of findings will appear in three papers, that are registered as PAPs or in process