An experimental approach is presented to evaluate the impact of plasma composition arising from pulse-to-pulse energy and mixture fluctuations on the non-resonant laser-induced ignition of sprays. This allows for spark events to be conditioned on the successful or failed establishment of a flame kernel, a phase dominated by plasma decomposition and recombination reactions and the on-set of combustion reactions, that is, independent of the subsequent flame growth phase controlled by propagation phenomena only, such as fuel availability and turbulent strain. For that, laser-induced breakdown spectroscopy of the spark-generated plasma is carried out, followed by OH
high-speed imaging of the kernel. Exploratory experiments in spatially uniform and polydisperse kerosene droplet distributions in a jet suggest that the hydrogen concentration in the plasma deriving from the fuel dissociated by the spark is closely related to the generated OH
radicals levels and, in turn, with the success of establishing a flame kernel. This suggests that the ignition process is heavily controlled by mixture fluctuations at the spark, inherent of spray flows. The instantaneous mixture at the spark is estimated with a stochastic model, with the probability density function of the equivalence ratio exhibiting values higher than twice the mean value, while the highest probability occurs at lean conditions between the gaseous equivalence ratio and the overall equivalence ratio. The findings corroborate insights on the early-phase ignition obtained from direct numerical simulations, and the framework paves the way for the development of smart online engine-health tools to assess relight capability in future aeroengines.
Outstanding Paper Awards 2013
Measurement Science and Technology is pleased to bring together the best early-career researchers in measurement and metrology and publish their exceptional work in an annual collection dedicated to emerging leaders.
An emerging leader is defined as a top researcher in their field who completed their PhD in 2009 or later (10 years excluding career breaks).
Papers
Xiang Li Xiang Li is currently an Associate Professor in School of Mechanical Engineering at Xi'an Jiaotong University, China. He was a Postdoctoral Fellow at University of Cincinnati, US, from 2019 to 2021, and an Associate/Assistant Professor at Northeastern University, China, prior to that. He obtained his Ph.D. and B.S. degrees in Tianjin University, China, in 2017 and 2012, respectively. From 2015 to 2016, he was a visiting scholar at University of California at Merced, US. His research interests are industrial artificial intelligence, industrial big data, intelligent maintenance and system optimization.
M.A. Mendezi
M. A. Mendez received his PhD in engineering science from "Université Libre de Bruxelles"
in 2018. He is currently an Assistant Professor at the von Karman Institute for Fluid Dynamics, where he teaches courses on modelling and control of fluid flows, measurement techniques, signal processing and machine learning. He has extensively used data-driven methods for post-processing numerical and experimental data. His main research activities include experimental fluid mechanics (particularly image-based velocimetry and image processing), reduced-order modelling, flow control, and machine learning.
Angelo Gaitas Dr. Gaitas earned a PhD in electrical engineering (microsystems) from Delft University of Technology in the Netherlands (2013). He holds a Masters in Mesoscopic Physics from the University of London - Royal Holloway, an M.B.A. from the University of Wisconsin, Madison,a nd a Bachelors is Physics and Mathematics from the University of Wales - Swansea. His main research interests are in micro- and nano- devices for biological, biomedical and chemical applications. He has additional interests in studying effects at the micro- and nano- scales. Examples include: nanoscale temperature measurements of biological systems, improvements on existing techniques using micro- and nano- technologies such as patch clamping, and organ-on-a-chip applications. His group has been funded by NSF and NIH.
Pedro Magalhães de Oliveira Dr Pedro M. de Oliveira received his PhD from the University of Cambridge in 2019. He has a Masters and an Engineering Degree from the Federal University of Santa Catarina and was a visiting researcher at the Technical University of Munich. His research covers turbulent, reacting and multiphase flows, with particular interest in the development of experimental methods and laser diagnostics for sprays and gas-liquid pipe flows. Applications of his work range from cooling to gas-turbine combustor technologies. Pedro is the recipient of ERCOFTAC's da Vinci Medal, the Clean Sky Academy Best PhD Award, the Combustion Institute's Sugden Prize, and the ABCM‐Embraer Prize.