From a Novel

We identify the physical mechanism through which newly developed quaternary ammonium salt (QAS) deposit control additives (DCAs) affect the rheological properties of cavitating turbulent flows, resulting in an increase in the volumetric efficiency of clean injectors fuelled with diesel or biodiesel fuels. Quaternary ammonium surfactants with appropriate counterions can be very effective in reducing the turbulent drag in aqueous solutions, however, less is known about the effect of such surfactants in oil-based solvents or in cavitating flow conditions. Small-angle neutron scattering (SANS) investigations show that in traditional DCA fuel compositions only reverse spherical micelles form, whereas reverse cylindrical micelles are detected by blending the fuel with the QAS additive. Moreover, experiments utilising X-ray micro computed tomography (micro-CT) in nozzle replicas, quantify that in cavitation regions the liquid fraction is increased in the presence of the QAS additive. Furthermore, high-flux X-ray phase contrast imaging (XPCI) measurements identify a flow stabilization effect in the region of vortex cavitation by the QAS additive. The effect of the formation of cylindrical micelles is reproduced with computational fluid dynamics (CFD) simulations by including viscoelastic characteristics for the flow. It is demonstrated that viscoelasticity can reduce turbulence and suppress cavitation, and subsequently increase the injector’s volumetric efficiency.

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Turbulence and Cavitation Suppression by Quaternary Ammonium Salt Additives.

Entropy Generation Minimization

Cavitation and Bubble Dynamics: Preface

Alternative designs of parabolic trough solar collectors

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;The fuel spray process is of utmost importance to internal combustion engine design as it dominates engine performance and emissions characteristics. While designers rely on computational fluid dynamics (CFD) modeling for understanding of the air-fuel mixing process, there are recognized shortcomings in current CFD spray predictions, particularly under super-critical or flash-boiling conditions. In contrast, time-resolved optical spray experiments have now produced datasets for the three-dimensional liquid distribution for a wide range of operating conditions and fuels. By utilizing such a large amount of detailed experimental data, the machine learning (ML) techniques have opened new pathways for the prediction of fuel sprays under various engine-like conditions. The ML approach for spray prediction is promising because (1) it does not require phenomenological spray models, (2) it can provide time-resolved spray data without time-stepping simulation, and (3) its evaluation has only a tiny fraction of the computational cost of a CFD simulation. In this study, an Artificial Neural Network (ANN) was applied for gasoline spray prediction under realistic engine conditions. Experimental data obtained under seven different fuels and three ambient conditions, totaling 21 different cases, were fed into a training procedure to investigate fuel effects on spray morphology. The quantitative validation results showed that the ANN is capable of predicting spray performance with nine input features, including fuel properties and ambient conditions. The ANN model fully trained on the experimental dataset showed greater accuracy in capturing the details of plume dynamics especially under flash-boiling conditions than the current state-of-the-art CFD model. While the ANN model cannot yet function or replace CFD in a full engine simulation, the ANN can be used now as a convenient design tool incorporating vast physical conditions.&lt;/div&gt;&lt;/div&gt; 

I.K. Karathanassis

Papers

A New Pathway for Prediction of Gasoline Sprays using Machine-Learning Algorithms

実サイズ光ディーゼルインジェクタにおけるキャビテーションと渦構造の結合可視化【jst・京大機械翻訳】

A general predictive methodology for fuel-mixture properties up to supercritical conditions

Assessment of injector-flow characteristics of additised and renewable diesel blends through high-speed imaging

Dilute viscoelastic polymer solutions for dielectric heat transfer applications: A molecular dynamics study