Computational Study of Bouncing and Non-bouncing Droplets Impacting on Superhydrophobic Surfaces

We numerically investigate bouncing and non-bouncing of droplets during isothermal impact on superhydrophobic surfaces. An in-house, experimentally-validated, finite-element method based computational model is employed to simulate the droplet impact dynamics and transient fluid flow within the droplet. The liquid-gas interface is tracked accurately in Lagrangian framework with dynamic wetting boundary condition at three-phase contact line. The interplay of kinetic, surface and gravitational energies is investigated via systematic variation of impact velocity and equilibrium contact angle. The numerical simulations demonstrate that the droplet bounces off the surface if the total droplet energy at the instance of maximum recoiling exceeds the initial surface and gravitational energy, otherwise not. The non-bouncing droplet is characterized by the oscillations on the free surface due to competition between the kinetic and surface energy. The droplet dimensions and shapes obtained at different times by the simulations are compared with the respective measurements available in the literature. Comparisons show good agreement of numerical data with measurements and the computational model is able to reconstruct the bouncing and non-bouncing of the droplet as seen in the measurements. The simulated internal flow helps to understand the impact dynamics as well as the interplay of the associated energies during the bouncing and non-bouncing.Comment: Theoretical and Computational Fluid Dynamics, 201

Characterization and modelling of Lithium-ion Batteries for Grid Applications

The demand for energy has been increasing, raising concerns about greenhouse gases and depleting renewable energy sources. This has made the use of renewable energy sources more evident in various applications. These renewable energy sources are recognized for their potential to reduce global warming and climate change. The use of smart grids will help in the reduction of these GHS’s and CO2 emissions. These grid applications necessitate energy storage systems in order to achieve smooth operation. Thus, Energy storage systems have become an essential technology for various applications such as land-based grid applications and Lithium- ion batteries will play a significant role. Lithium-ion batteries have seen considerable development in the last couple of decades to enhance battery characteristics based on its safety, voltage/current capacities, operating temperatures, ageing, etc. Similarly, Lithium titanate oxide (LTO) battery cells have seen significant development as it is regarded to play a vital role in energy storage systems for large scale stationary grid applications. These batteries use LTO as active anode material instead of the traditional graphite. LTO batteries are recognised for various competencies ranging from higher safety margins, high thermal stability, low self-discharge rate and superior cycle performance. LTO batteries are relatively new, and the behaviour and characteristics of this battery are unfamiliar. Thus, the thesis purpose is to study and understand the state-of-art LTO battery cell behaviour under controlled situations. LTO batteries require accurate battery models at different operating conditions due to its non- linear behaviour. Thus, extensive experimental characterization tests were developed to study the behaviour of the LTO battery to determine the dynamic behaviour of the LTO battery cell. The proposed model is suitable for various applications as well as for the development of Energy Management Systems (EMS) and Battery Management Systems (BMS). Hence, hybrid pulse power characterisation (HPPC) tests were developed and applied to a 2.9 Ah LTO battery cell. A second-order equivalent circuit (ECM) model is developed based on the experimental characterisation tests conducted at different SOC’s (0 % - 100% at 10% interval) and operating temperatures (15°C, 25°C, 35°C and 45°C). The results of HPPC tests will be utilised for the parametrisation of the ECM. The ECM variables were incorporated into SOC estimation using the Coulomb counting (CC) method. The simulations model was developed in Simscape Matlab/Simulink software and were compared with experimental measurements for ECM validation

Comparison of As-built FEA Simulations and Experimental Results for Additively Manufactured Dogbone Geometries.

Additive manufacturing (AM) is increasingly used in new product development: from prototyping to functional part testing, tooling and manufacturing. The flexibility of AM results in the ability to develop a geometrically complex part with reduced effort by moderating some manufacturing constraints while imposing other constraints. However, additively manufactured parts entail a certain amount of ambiguity in terms of material properties, microstructures effects and defects. Due to the intensive energy, rapid cooling and phase changes, parts made by Fused Deposition Modelling (FDM “ a branch of AM) and other layer-manufacturing processes may deviate from the designed geometry resulting in inaccuracies such as discontinuities, curling, and delamination, all of which are attributed to the residual stress accumulations during geometry fabrication. Therefore, the FDM part can strongly differ from its design model, in terms of strength and stiffness. In performance critical applications, analyzing and simulating the component is necessary. Identifying appropriate methodologies to simulate and analyze additively manufactured parts accurately, enables better modelling and design of components. The Finite Element Method (FEM) is a widely used analysis tool for various linear and nonlinear engineering problems (structural, vibrational, thermal etc.). Therefore, it is necessary to determine the accuracy of FEA while analyzing the non-continuous, non-linear FDM parts. The goal of this study is to compare Finite Element Analysis (FEA) simulations of the as-built geometry with the experimental tests of actual FDM parts. A dogbone geometry is used as a test specimen for the study, with a set of different infill patterns. A displacement controlled tensile test is conducted using these specimens to obtain the experimental stress-strain results. Further, as built 3D CAD models of these specimens are developed and a displacement controlled tensile test is simulated using different material models in two FEA solvers. The stress-strain results of the analyses are compared and discussed with the experimental results. The metrics of the comparison are the precision and the accuracy of the results. This study found that FEA results are not always an accurate or reliable means of predicting FDM part behaviors, even when advance experimentally derived material models and as-built geometries are incorporated

Cognitive Load Detection For Advanced Driver Assistance Systems

In this thesis, we investigate cognitive load detection and classification based on minimally invasive methods. Cognitive load detection is crucial for many emerging applications such as advanced driver assistance systems (ADAS) and industrial automation. Numerous studies in the past have reported several psychological measures, such as eye-tracking, electrocardiogram (ECG), electroencephalogram (EEG), as indicators of cognitive load. However, existing physiological features are invasive in nature. Consequently, the objective of this study is to determine the feasibility of non-invasive features such as pupil dilation measurements low-cost eye-tracker with minimal constraints on the subject for cognitive load detection. In this study, data from 33 participants were collected while they underwent tasks that are designed to permeate three different cognitive difficulty levels with and without cognitive maskers and the following measurements were recorded: eye-tracking measures (pupil dilation, eye-gaze, and eye-blinks), and the response time from the detection response task (DRT). We also demonstrate the classification of cognitive load experienced by humans under different task conditions with the help of pupil dilation and reaction time. Developing a model that can accurately classify cognitive load can be used in various sectors such as semi-autonomous vehicles and aviation. we have used a data fusion approach by combining pupil dilation and DRT reaction time to determine if the classification accuracy increases. Further, we have compared the classifier with the highest classification accuracy using data fusion against the accuracy of the same classifier with only one feature (pupil dilation; reaction time) at a time

STRUCTURAL ANALYSIS OF INTEGRATION OF A NON-CYLINDRICAL CNG FUEL TANK

The purpose of this study is to computationally model and analyze the vehicle frame which is mounted with a non-conventional, non-cylindrical compressed natural gas (CNG) fuel tank. Integration of this tank in the vehicle underbody will resolve the issue of reduced storage space which is observed in a conventional CNG powered vehicle. This research will ultimately result in making CNG a good alternative to gasoline and reducing the increasing dependency on a single fuel. This tank will be developed in two phases: phase I design of the tank will be a standard rectangular outer box shape with Schwarz P-surface inner structure and phase II will be a complex and conformable shaped tank. This study will only include phase I tanks and the only load case considered is a simple linear static case. Modifications are made to the vehicle frame using a computer aided design (CAD) software in order to accommodate the tank. The results obtained from the finite element analysis of the frame support the design modifications made to the frame and shows the ability of the frame to handle a heavier tank