On the modeling of viscoelastic droplet impact dynamics

This paper was presented at the 4th Micro and Nano Flows Conference (MNF2014), which was held at University College, London, UK. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute, ASME Press, LCN London Centre for Nanotechnology, UCL University College London, UCL Engineering, the International NanoScience Community, www.nanopaprika.eu.In this paper, a numerical modeling of the impact, spreading, and eventually rebound of a viscoelastic droplet is reported. The numerical model is based on the volume of fluid (VOF) method coupled with the FENE-CR constitutive equations, and accounts for both the surface tension and the substrate wettability. The FENE-CR constitutive equations are used to model the polymer solution, while taking advantage of its rheological characterization. The comparison is performed between droplets of Newtonian solvent and a monodisperse polymer solution. The droplet impact on both hydrophilic and superhydrophobic substrate is analyzed through a detailed analysis of the spreading diameter evolution. It is found that while the droplet kinematic phase seems independent of the substrate and fluids properties, the recoiling phase is highly related to all of them. In addition the model infers a critical polymer concentration above which the droplet rebound from a superhydrophobic substrate is suppressed. The simulation is of particular interest to ink-jet processing, and demonstrates the capability of the model to handle complex non-Newtonian droplet dynamics

The Devil's Advocate: Shattering the Illusion of Unexploitable Data using Diffusion Models

Protecting personal data against exploitation of machine learning models is crucial. Recently, availability attacks have shown great promise to provide an extra layer of protection against the unauthorized use of data to train neural networks. These methods aim to add imperceptible noise to clean data so that the neural networks cannot extract meaningful patterns from the protected data, claiming that they can make personal data "unexploitable." This paper provides a strong countermeasure against such approaches, showing that unexploitable data might only be an illusion. In particular, we leverage the power of diffusion models and show that a carefully designed denoising process can counteract the effectiveness of the data-protecting perturbations. We rigorously analyze our algorithm, and theoretically prove that the amount of required denoising is directly related to the magnitude of the data-protecting perturbations. Our approach, called AVATAR, delivers state-of-the-art performance against a suite of recent availability attacks in various scenarios, outperforming adversarial training even under distribution mismatch between the diffusion model and the protected data. Our findings call for more research into making personal data unexploitable, showing that this goal is far from over. Our implementation is available at this repository: https://github.com/hmdolatabadi/AVATAR.Comment: Accepted to the 2024 IEEE Conference on Secure and Trustworthy Machine Learning (SatML

Adversarial Coreset Selection for Efficient Robust Training

Neural networks are vulnerable to adversarial attacks: adding well-crafted, imperceptible perturbations to their input can modify their output. Adversarial training is one of the most effective approaches to training robust models against such attacks. Unfortunately, this method is much slower than vanilla training of neural networks since it needs to construct adversarial examples for the entire training data at every iteration. By leveraging the theory of coreset selection, we show how selecting a small subset of training data provides a principled approach to reducing the time complexity of robust training. To this end, we first provide convergence guarantees for adversarial coreset selection. In particular, we show that the convergence bound is directly related to how well our coresets can approximate the gradient computed over the entire training data. Motivated by our theoretical analysis, we propose using this gradient approximation error as our adversarial coreset selection objective to reduce the training set size effectively. Once built, we run adversarial training over this subset of the training data. Unlike existing methods, our approach can be adapted to a wide variety of training objectives, including TRADES, $\ell_p$-PGD, and Perceptual Adversarial Training. We conduct extensive experiments to demonstrate that our approach speeds up adversarial training by 2-3 times while experiencing a slight degradation in the clean and robust accuracy.Comment: Accepted to the International Journal of Computer Vision (IJCV). Extended version of the ECCV2022 paper: arXiv:2112.00378. arXiv admin note: substantial text overlap with arXiv:2112.0037

New Inequalities for Gamma and Digamma Functions

By using the mean value theorem and logarithmic convexity, we obtain some new inequalities for gamma and digamma functions

Influence of advanced cylinder coatings on vehicular fuel economy and emissions in piston compression ring conjunction

IC engines contribute to global warming through extensive use of fossil fuel energy and emission of combustion by‐products. Innovative technologies such as cylinder de‐activation (CDA), after‐exhaust heat treatment, surface texturing and coatings are proposed to improve fuel economy and reduce emissions of the vehicle fleet. Therefore, study of coating technology through a comprehensive multi‐physics analytical model of engine top compression ring is important to ascertain ways of promoting energy savings. This paper presents a multi‐scale, multi‐physics model of the compression ring‐cylinder bore conjunction, using three alternative bore surfaces. The model comprises ring dynamics, contact tribology, heat transfer and gas blow‐by. Tribological and thermal properties of advanced coatings, such as Nickel Nanocomposite (NNC) and diamond‐like carbon (DLC) are compared with an uncoated steel bore surface as the base line configuration. Such a comprehensive analysis has not hitherto been reported in open literature, particularly with original contributions made through inclusion of salient properties of alternative bore materials for high performance race engines. Power loss and FMEP are evaluated in a dynamometric test, representative of the World‐ wide harmonised Light vehicles Test Cycle (WLTC). The NNC coating shows promising tribological improvements. The DLC coating is detrimental in terms of frictional power loss and FMEP, although it can effectively improve sealing of the combustion chamber. The differences in power loss of nominated bore surfaces are represented as fuel mass and CO emissions, using theoretical and empirical relations. For the first time the paper shows that advanced coatings can potentially mitigate the adverse environmental impacts of spark ignition (SI) engines, with significant repercussions when applied to the global gasoline‐powered vehicle fleet

A transient tribodynamic approach for the calculation of internal combustion engine piston slap noise

An analytical/numerical methodology is presented to calculate the radiated noise due to internal combustion engine piston impacts on the cylinder liner through a film of lubricant. Both quasi-static and transient dynamic analyses coupled with impact elasto-hydrodynamics are reported. The local impact impedance is calculated, as well as the transferred energy onto the cylinder liner. The simulations are verified against experimental results for different engine operating conditions and for noise levels calculated in the vicinity of the engine block. Continuous wavelet signal processing is performed to identify the occurrence of piston slap noise events and their spectral content, showing good conformance between the predictions and experimentally acquired signals

Tribodynamics of hydraulic actuated clutch system for engine-downsizing in heavy duty off-highway vehicles

Engine downsizing is desired for modern heavy-duty vehicles to enhance fuel economy and reduce emissions. However, the smaller engines usually cannot overcome the parasitic loads during engine start-up. A new clutch system is designed to disconnect the downsized engine from the parasitic losses prior to the idling speed. A multi-scale, multi-physics model is developed to study the clutch system. Multi-body dynamics is used to study the combined translational–rotational motions of the clutch components. A micro-scale contact model is incorporated to represent the frictional characteristics of the sliding surfaces. Although the clutch is designed for dry contact operation, leakage of actuating hydraulic fluid can affect the interfacial frictional characteristics. These are integrated into the multi-body dynamic analysis through tribometric studies of partially wetted surfaces using fresh and shear-degraded lubricants. Multi-scale simulations include sensitivity analysis of key operating parameters, such as contact pressure. This multi-physics approach is not hitherto reported in the literature. The study shows the importance of adhesion in dry clutch engagement, enabling full torque capacity. The same is also noted for any leakage of significantly shear-degraded lubricant into the clutch interfaces. However, the ingression of fresh lubricant into the contact is found to reduce the clutch torque capacity

Oil Control Ring Friction And Low Viscosity Lubricants: A Combined Numerical and Experimental Analysis

A common strategy to reduce engine parasitic power losses is to decrease pumping and viscous friction losses through use of a low viscosity engine oil. However, reducing lubricant viscosity can also decrease the contact load carrying capacity, thus exacerbating direct interaction of contacting surfaces. This leads to boundary frictional losses in contacts prone to mixed regime lubrication. As a result, detailed experimental and modelling studies of engine component frictional behaviour is required to ensure the engine level trade-offs. This paper presents a combined experimental and numerical investigation of frictional behaviour of three-piece piston oil control rings. A bespoke tribometer replicates kinematics of the contact between a full oil control ring and the cylinder liner. The three-piece oil control ring is composed of two segments, separated by a waveform type expander. The experimental results indicate the dominance of mixed regime of lubrication throughout the stroke. This is particularly the case when the experiments are conducted at 80 °C; a typical engine sump temperature, when compared with the case of 20 °C (a typical engine start-up temperature in the UK in the Spring). A mixed hydrodynamic numerical model of the oil control ring-cylinder liner tribological interface is employed to apportion frictional contributions with their physical underlying mechanisms. The combined experimental-predictive approach provides key information for engine designers when considering the efficiency trade-offs