Variable selection with LASSO regression for complex survey data

Variable selection is an important step to end up with good prediction models. LASSO regression models are one of the most commonly used methods for this purpose, for which cross-validation is the most widely applied validation technique to choose the tuning parameter (λ). Validation techniques in a complex survey framework are closely related to “replicate weights”. However, to our knowledge, they have never been used in a LASSO regression context. Applying LASSO regression models to complex survey data could be challenging. The goal of this paper is two-fold. On the one hand, we analyze the performance of replicate weights methods to select the tuning parameter for fitting LASSO regression models to complex survey data. On the other hand, we propose new replicate weights methods for the same purpose. In particular, we propose a new design-based cross-validation method as a combination of the traditional cross-validation and replicate weights. The performance of all these methods has been analyzed and compared by means of an extensive simulation study to the traditional cross-validation technique to select the tuning parameter for LASSO regression models. The results suggest a considerable improvement when the new proposal design-based cross-validation is used instead of the traditional crossvalidation.IT1456-22 PIF18/21

An application of tomographic PIV to investigate the spray-induced turbulence in a direct-injection engine

Fuel sprays produce high-velocity, jet-like flows that impart turbulence onto the ambient flow field. The spray-induced turbulence augments fuel-air mixing, which has a primary role in controlling pollutant formation and cyclic variability in engines. This paper presents tomographic particle image velocimetry (TPIV) measurements to analyse the 3D spray-induced turbulence during the intake stroke of a direct-injection engine. The spray produces a strong spray-induced jet in the far field, which travels through the cylinder and imparts turbulence onto the surrounding flow. Planar high-speed PIV measurements at 4.8 kHz are combined with TPIV at 3.3 Hz to evaluate spray particle distributions and validate TPIV measurements in the particle-laden flow. An uncertainty analysis is performed to assess the uncertainty associated with vorticity and strain rate components. TPIV analyses quantify the spatial domain of the turbulence in relation to the SIJ and describe how turbulent flow features such as turbulent kinetic energy, strain rate and vorticity evolve into the surrounding flow field. Access to the full tensors facilitate the evaluation of turbulence for individual spray events. TPIV images reveal the presence of strong shear layers (visualized by high S magnitudes) and pockets of elevated vorticity along the immediate boundary of the SIJ. Values are extracted from spatial domains extending in 1mm increments from the SIJ. Turbulence levels are greatest within the 0-1mm region from the SIJ boarder and dissipate with radial distance. Individual strain rate and vorticity components are analyzed in detail to describe the relationship between local strain rates and 3D vortical structures produced within strong shear layers of the SIJ. Analyses are intended to understand the flow features responsible for rapid fuel-air mixing and provide valuable data for the development of numerical models