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ELSEVIER SCIENCE INCPayri, R.; Gimeno, J.; Marti-Aldaravi, P.; Manin, J. (2012). Fuel concentration in isothermal Diesel sprays through structured planar laser imaging measurements. International Journal of Heat and Fluid Flow. 34:98-106. doi:10.1016/j.ijheatfluidflow.2011.12.007 Fuel concentration in isothermal Diesel sprays through structured planar laser imaging measurements AbstractThe mixing of isothermal liquid sprays in engine-like conditions has been investigated by applying the Structured Planar laser Imaging technique to remove multiple light scattering. The intensity of the illumination plane has been recovered by removing multiply scattered light and mapping the spray three-dimensionally via discrete tomography. Based on the extinction of light within the illumination plane, the number density has been extracted. Coupled with 2-D maps of droplet diameters obtained through LIF/Mie ratio, the number density allowed to calculate the fuel concentration in the sprays. The mixture fraction of DI Diesel sprays injected into an inert environment held at room temperature has been evaluated and compared to a validated model based on gas-jet theory. The experimental results showed good agreement with the predictions when assuming a Gaussian radial distribution and tuning the Schmidt number. A Schmidt number below the unity demonstrated to be the best fit to the results. In addition, the Gaussian distribution showed to be a good assumption based on the closeness to the experiments, although some differences in shape have been noticed. For different testing conditions, whilst the influence of ambient density on mixing was expected, the effect of injection pressure brought up additional information concerning the global mixing of liquid sprays.

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Performance of an ultra wideband (UWB) wireless system for real-time neural signal monitoring is evaluated by comparing spiking characteristics between transmitted and received signals for different experimental set-ups. Spike detection quality is selected as the main spiking characteristic of evaluated signals. Results are presented in receiver-operating characteristics and area-under-the-curve (AUC). In order to assess spike detection quality, a set of artificially generated neural signals is constructed from real neural recordings such that the ground truth is known. Data analysis shows how channel signal-to-noise-ratio (SNR) variation affects AUC in different signal SNR cases. Signals with low SNRs get less affected by reduced channel SNRs than those with higher SNR. Increasing bit error rate modifies spiking characteristics such that an under-estimation of the spiking frequency occurs due to spike losses. For practical application of real-time neural signal monitoring, UWB seems to offer best transmission conditions in a near-body environment.