bluuebunny/crossref_metadata_2025_split huggingface dataset:
Electrohydrodynamic Settling Droplet With Weak Inertia Subjected to a Uniform Electric Field Based on the Lattice Boltzmann Method
Authors: Zhang Yimo , Zhang Yu , Luo Kang , Yi Hongliang 
Published: January 2022

Abstract
Electrohydrodynamic settling of droplets is governed by the combined influence of electric field and gravitational field in many practical applications and innovative researches. To simulate the deformation and motion of the droplet under the interaction of gravity and electric stress, a numerically multiphase model based on the lattice Boltzmann method (LBM) is established. A perfectly dielectric (PD) drop suspended in another immiscible medium with perfectly dielectric fluids and a leaky dielectric (LD) drop immersed in a leaky dielectric medium are considered to study the dynamic behavior of an electrified droplet driven by gravity. The predictions of the LBM model demonstrated in this paper are in good agreement with classic analytical solutions and conventional numerical results reported in previous literature, particularly for drops with small deformation. Moreover, the electrohydrodynamics of settling droplets subjected to a uniform electric field which is perpendicular to a weak gravitational field is studied computationally for fluids with various electrical parameters. Numerical simulations for droplets with different permittivity ratios and conductivity ratios are conducted over a range of electric capillary numbers. It is found that, unlike PD settling droplets which always transform into a prolate shape due to the presence of the electric field, whether the LD droplets under weak inertia deform into a prolate or an oblate shape depends on the electrical properties of the dispersed medium and surroundings. This phenomenon is in a similar manner as in the previous work for EHD droplets owing to the imposition of weak inertia. However, with the shape feature unaltered, the electric stress acted at the fluid-fluid surface has a significant influence on the drop deformation and the distribution of charges and consequently results in noticeable changes in transient settling speed.
3D Printed and Nickel-Coated Electrodes for Photocatalytic Electrolysis for Hydrogen Generation
Authors: Ndoye Babacar , Ouedraogo Noufou , Demisse Wondwosen , Grizzle Andrew , Mutunga Eva , Tyagi Pawan 
Published: January 2022

Abstract
The hydrogen-based economy is gaining momentum with the advent of fuel cell electric cars and other systems. Hence hydrogen production becomes critically important to meet a supply demand in the near future. One of the cheapest hydrogen generation sources can be solar energy and seawater or water in the lakes and rivers. One can utilize solar energy to provide electricity or energy for the photocatalytic process to split water into hydrogen and oxygen. Under electrolysis, electricity is supplied to provide the energy required for water splitting converting hydrogen ions into hydrogen gas from the aqueous medium. There is a strong need to create innovative electrodes for hydrogen generation that are economical in production and highly efficient. To address this issue, we focused on designing electrodes for photocatalytic electrolysis for hydrogen generation. We used 3D printing to produce different electrodes with various surface features to provide optimum surface area and used electroless nickel to coat the surface of the 3D printed metal components. It is noteworthy that nickel is a promising metal to produce hydrogen economically. We used the Taguchi Design of Experiment approach to optimize the nickel coating on the 3D printed metal electrodes. We used cyclic voltammetry to quantify the volume of H2 produced by the nickel-coated 3D printed electrodes.
Effect of Coupled Helmholtz Resonators on Sound Control
Authors: Sabat R. , Leveque G. , Pennec Y. , Torrent D. , Djafari-Rouhani B. 
Published: January 2022

Abstract
In our work, we are interested in the effect of coupling between Helmholtz resonators using the finite element model. We show that the coupling between two spheres leads to two peaks of resonance at low frequencies. One of them is symmetric and corresponds to a mono-polar breathing mode. The second one is anti-symmetric and behaves like a dipole. Consequently, the first one produces an isotropic wave in the far pressure field, while the second produces a directional propagation wave. Therefore, by changing the distance and / or the orientation between the resonators, we aim to manipulate and control the direction of the acoustic wave at specific frequencies.
Extending the Life of Classic Cars, the Additive Manufacturing Way
Authors: Luniya Tanmay , Chimata Geetha 
Published: January 2022

Abstract
There has been a steadily increasing global market for Additively Manufactured (AM) products, with a growth forecast of USD 23.75 billion by 2027. Of the various industrial sectors applying AM, the automotive/motor vehicles market takes up approximately 18% share. Saying AM is being widely used in the automotive sector with rapidly growing application avenues is not an overstatement. One such section of the automotive industry is the classic cars. Classic cars are 20 years or more older cars no longer in regular production, preserved and restored for their historical value. Classic cars face a huge problem of spare parts. The non-availability of the spare part leads to the break-down of the car, leaving them as display pieces or eventual scrapping. It is not economically viable to manufacture the spare parts in small volume due to challenges such as high cost of tooling, and indefinite storage time.
Additive manufacturing offers attractive solutions to problems precisely such as these as it requires no additional tooling and can produce functional parts in small batches on-demand, provided accurate three-dimensional model data is available. This 3D model data is converted to one of the AM compatible file formats such as STL, AMF, 3MF etc. and then is processed using a Slicer Software. The slicer software converts three-dimensional (3-D) model data to two-dimensional (2-D) layer information that will be printed by the AM machine. Obtaining drawings or 3-D model information for classic car parts is a daunting challenge in itself, often deemed impossible. However, with the advances in imaging and scanning combined with computer aided design technologies, it is shown to be possible to generate the 3-D model data from even partial or broken parts. Now, producing spare parts using AM is not just feasible but has been successfully applied. Few notable examples include restoration of Elvis Presley’s BMW 507, originally released in 1957, which took two years to complete, Jaguar’s XK120 SE restored in 2017, 2019 restorations of Volkswagens iconic 1962 minivan, Bentley’s 1929 Blowers and Bugatti’s 1926 Bugatti Baby. Not just car manufacturers, but hobbyist collectors also found success in producing spare parts for their classic cars. This paper discusses various types of additive manufacturing technologies used to manufacture classic car parts and the strategic impact after implementing them using the examples of famous restored classic cars. The discussion further includes commercialization of these technologies, challenges, material selection and availability. Additionally, the economic implications and, the future are explored.
Deep Learning-Based Error Mitigation for Assistive Exoskeleton With Computational-Resource-Limited Platform and Edge Tensor Processing Unit
Authors: Fabarisov Tagir , Morozov Andrey , Mamaev Ilshat , Janschek Klaus 
Published: January 2022

Abstract
Recently we introduced a new model-based fault injection method implemented as a highly customizable Simulink block called FIBlock. It supports the injection of typical faults of essential heterogeneous components of Cyber-Physical Systems (CPS), such as sensors, computing hardware, and network. The FIBlock allows to tune a fault type and configure multiple parameters to tune error magnitude, fault activation time, and fault exposure duration.
The FIBlock is able to generate various types of highly adjustable CPS faults. We demonstrated the performance of the FIBlock on a Simulink case study representing a lower-limb EXOLEGS exoskeleton, an assistive device for the elderly in everyday life. In particular, we discovered the spatial and temporal thresholds for different fault types. Upon exceeding said thresholds, the Dynamic Movement Primitives-based control system could no longer adequately compensate errors.
In this paper, we proposed a new Deep Learning-based approach for system failure prevention. We employed the Long Short-Term Memory (LSTM) network for error detection and mitigation. Error detection is achieved using the prediction approach. The LSTM models are mitigating the detected errors with computed predictions only when they were subject to the imminent failure (i.e., exceeded the aforementioned thresholds). To compare our approach with previous findings, we trained two LSTM models on angular position and angular velocity signals. For evaluation, we performed fault injection experiments with varying fault effect parameters. The ‘Sensor freeze’ fault was injected into the angular position sensor, and the ‘Stuck-at 0’ fault was injected into angular velocity sensor. The presented Deep Learning-based approach prevented system failure even when the injected faults were substantially exceeding thresholds. In addition, reasoning for data access point choice has been evaluated. We compared two options: (i) the input data for LSTM is provided from the sensor output and (ii) from the controller output. In the paper, the pros and cons for both options are presented.
We deployed the trained LSTM models on an Edge Tensor Processing Unit. For that, the models have been quantized, i.e. all the 32-bit floating-point numbers (such as weights and activation outputs) were converted to the nearest 8-bit fixed-point numbers and converted to the TensorFlow Lite models. The Coral USB Accelerator was coupled with a Raspberry Pi 4B for signal processing. The result proves the feasibility of the proposed method. Because the LSTM models were converted to the 8bit integer TensorFlow Lite models, it allowed firm real-time error mitigation. Furthermore, the light weight of the system and minimal power consumption allows its integration into wearable robotic systems.
Active Vibration Control of Aerospace Structural Systems for Specified Damping
Authors: Hanagud Sathya 
Published: January 2022

Abstract
Active vibration control is essential in several aerospace structural systems, such as large space structures, twin tailed high performance aircraft operating at high angles of attack and helicopters. Goh and Caughey did show that vibration controller designs can significantly benefit from the use of second order systems, in the controller design. In this paper, the subject of the design of active vibration control of buffet induced vibrations in high performance twin tailed aircraft is discussed using a combination of acceleration feedback and positive position feedback in second order systems. The goal is to design controllers to increase the closed loop damping ratio of the structural dynamic system to a specified level that would reduce the vibrations to the required level within a time frame of interest. The approach of Fanson and Caughey needed an iterative approach to design the controller parameters. They had more unknowns and fewer equations. In this paper, the problem is now formulated differently to obtain closed form solutions. This is accomplished by developing a procedure to design second order controllers to obtain the desired closed loop damping while maintaining the stability of the closed loop structural dynamic system. The design procedure starts with an objective to design control parameters that result in coincident closed loop frequencies for each mode. This is followed by a perturbation procedure to seek optimum solutions. The design procedure also yields the needed control authority that should be provided by actuators on the structure. An option to obtain the needed control authority by using offset piezoceramic stack actuator assemblies are discussed. The designed controllers are applied to control buffet induced vibrations of a high-performance aircraft at high angles of attack. This example also illustrates the control authority of the offset piezoceramic stack structural assembly.
Thermal Transport in Carbon Nanocomposites Under Mechanical Strain
Authors: Park Jungkyu , Kinports Nick , Kudsy Jihad 
Published: January 2022

Abstract
In the present research study, we employ molecular dynamics simulations to investigate strain effect on the thermal conductivity of the polymer composites of carbon nanotubes (CNTs). The results show that the composite exhibits higher thermal conductivity than polymers. The thermal conductivity of polyethylene is only 0.45 W/m-K while the thermal conductivity of the composite reaches 0.57 W/m-K by being combined with a (10,0) single-walled CNT (SWCNT). It is observed that the thermal conductivity of the composite increases up to 1.01 W/m-K with an increase in strain until C-C bonds in CNTs start to break. Strain rate is found to be related to the thermal conductivity of the composite; lower strain rate improves the thermal conductivity of the composite more efficiently by straightening the polymer chains better. By stretching polymer chains while leaving CNTs unstretched, however, we found that phonon scattering between polymer chains and CNTs is increased because of the increased surface contact between the polymer chains and the CNTs, impacting the thermal conductivity of the composite negatively. The results obtained in the present research study will be used to tailer the polymer composites of carbon nanostructures to have a better thermal performance under mechanical deformation, accelerating the development of futuristic flexible electronics and wearable devices.
Design, Modeling, Simulation, and Testing of Flexible Joint
Authors: Munguia Maria , Shibata Gabriella , Castro Osvaldo , Miesner Spencer , Asadi Sufi , De Leon Anthony , Hernandez Allan , Molina Christopher , Bell Jered , Lytle Madison , Wolk Kieran , Roberts Scott , Furst Benjamin , Sunada Eric , Bellardo John , Daimaru Takuro , Kuo Jim 
Published: January 2022

Abstract
Metal materials are used widely in industry for piping, as they provide desirable material properties for use in harsh environments. In this work, we explore different flexible pipe joint designs that enable fluid transport in flexible heat transfer devices. Flexible joints would allow heat transfer devices to fit in variable cooling areas. The proposed flexible joints will be 3D printed in metal with the goal of maximizing their bending angles, while minimizing volume. The objective of this research is to develop flexible pipes to connect parts involving fluid flow, and we propose three types of flexible joint designs: 1) helical coil spring, 2) torsional coil spring, and 3) serpentine structure. Then, we introduce a low-cost design process to improve these joint designs.
Metal 3D printing offers increased design flexibility in comparison to traditional metal manufacturing methods. However, metal 3D printing can be labor intensive and costly, thus the number of design iterations using metal printing should be minimized during the design process. To reduce the frequency of metal prototyping, we used plastic 3D prints for rapid prototyping and evaluation.
The flexible joint design considered the effects of pipe shape, pipe size, pipe diameter, free length, and total pipe length. Although mathematical models exist for the three types of flexible designs, they tend to be complex, difficult to implement, and specific to certain types of boundary conditions. In our experience, it is faster and more meaningful to perform experimental parametric studies using plastic prints due to accessibility and cost-effectiveness of plastic 3D printers. However, analytical models do offer significant insights during the design process. Thus, we developed simple analytical models for each of the three types of flexible joint designs which can be used in the initial design phase.
Tests were performed to characterize the designs. Then, finite element simulations were performed for plastic prints and these simulations were validated against experimental data. We show that the results from analytical models, finite element simulations, and testing results for plastic prints are consistent, and these tools can be used to predict performance. In this paper, we discuss the following findings: 1) development of simple analytical models, including successes, limitations, and challenges; 2) the role of finite element simulations in the design process; 3) testing results from the three types of joint design; 4) quantification, interpretation, and discussion of testing results.
A Deep Convolutional Neural Network-Based Method to Predict Accurate Fracture Strength of Poly-Crystalline Graphene
Authors: Shishir Md. Imrul Reza , Elapolu Mohan Surya Raja , Tabarraei Alireza 
Published: January 2022

Abstract
A data-driven deep convolution neural network model is used to predict the fracture properties of polycrystalline graphene from the atomic resolution image. A large dataset is prepared using molecular dynamic simulations and atomic resolution image of polycrystalline graphene. The results show that our model after training can predict the fracture properties of polycrystalline graphene with high accuracy.
Morphological Markers and Determinants of Left Ventricular Systolic Dysfunction in Repaired Tetralogy of Fallot
Authors: Govil Sachin , Forsch Nickolas , Salehyar Sara , Gilbert Kathleen , Suinesiaputra Avan , Hegde Sanjeet , Perry James C. , Young Alistair A. , Omens Jeffrey H. , McCulloch Andrew D. 
Published: January 2022

Abstract
Patients with repaired tetralogy of Fallot (rTOF) are at risk of long-term left ventricular (LV) dysfunction associated with poor outcomes. In this study, we examined the association of LV end-diastolic (ED) shape with components of systolic wall motion (SWM) that contribute to global systolic dysfunction in an rTOF patient cohort. Features of LV shape associated with conicity and septal wall curvature correlated with components of SWM. The effect of ED shape perturbations on SWM were examined in a finite element analysis of systolic ventricular mechanics. Variations in the combination of ED shape and myocardial contractility were able to match predicted measures of SWM. From these results, we hypothesize that greater LV conicity and a flatter septal wall are markers of reduced global myocardial contractility and should be examined further for clinical prognostic utility to improve patient outcomes.
Experimental Investigation of the Combustion Behavior of Jet-A/Water Emulsified Fuel and Ethanol-Blended Jet-A/Water Emulsified Fuel Droplets
Authors: Parveg A. S. M. Sazzad , Hentges Nicholas , Ratner Albert 
Published: January 2022

Abstract
The demanding emission requirements must be satisfied by the modern aero-engines which led the researchers into exploration and development of environment-friendly, and sustainable aviation fuels. Water emulsification in fuels is a proven mechanism to reduce nitrogen oxide (NOX) and pollutant emissions. Furthermore, water addition in liquid fuel can improve the combustion performance and fuel economy by micro-explosions and puffing phenomenon due to the volatility difference between liquid fuels and water. These micro-explosions and puffing result in the breaking of fuel droplets into smaller droplets which promotes secondary atomization of the liquid fuels. The secondary atomization contributes to a higher combustion rate, improves fuel-air mixing, and thus improves fuel economy. Also, with the depletion of fossil-fuel sources, the scientific community is exploring renewable sources which can be both reliable and better substitutes for conventional fossil fuels. Blending biofuel with fossil fuel is an interesting way to a sustainable solution for green energy production and carbon footprint reduction of conventional aviation fuels. In these contexts, the present study reports an experimental investigation on the combustion characteristics of water emulsified Jet-A and water emulsified ethanol-blended Jet-A fuels. The Ultrasonication method is used to prepare homogenized fuel emulsions with three different water concentrations (2.5%, 5% & 10% by weight) for both Jet-A and ethanol-blended Jet-A fuels. Blended Jet-A fuels are prepared for different ethanol concentrations (2.5%, 5% & 10% by weight). Combustion characteristics such as ignition delay, combustion rate, and droplet burning time data are generated for each emulsified fuel. The results are compared for both Jet-A, and ethanol-blended Jet-A fuel emulsions. It is found that there is a general decrease in combustion rate and droplet burning time for all the emulsions. This decrease is higher with higher content of water and ethanol. There is also a general increase in ignition delay. Again, this increase is higher for emulsions with higher water concentration. Hopefully, the outcomes of the present study will increase further interest in combustion characteristics modification of conventional aviation fuels by emulsification. In addition, the findings of the present study will provide experimental data which can be applied for future computational modeling of the combustion process of emulsified fuel.
Tool Remaining Useful Life Prediction in Robotic Machining of Composite Materials Based on Mechanical Vibrations
Authors: Savazzi José Otávio , Shiki Sidney Bruce , Barbosa Gustavo Franco , Guerra-Zubiaga David 
Published: January 2022

Abstract
The development of materials and methods used in the aircraft manufacturing industry has been advancing in order to provide a reliable and light aircraft. The use of composite materials becomes indispensable, meanwhile, the processing of this kind of material must be studied to obtain the higher manufacturing efficiency and the best quality of the final product. Industry 4.0 concepts as internet of things, cloud computing and others can be used to fulfil these demands. In this sense, this study aims to create a remaining useful life prediction model for the tools used on the machining of composite materials with robotic manipulators. This task is performed by monitoring and analyzing the mechanical vibrations of the motor assembly and the cutting tool, then reducing the consumption of this material and ensuring the quality and surface integrity of the finished parts. The self-awareness of the process is improved by combining signal processing algorithms and statistical techniques to assist the constant monitoring of the tool wear. In this sense, a digital model is constantly updated aiming the optimization of the cutting process. In the conclusions of the paper, the advantages and drawbacks of the proposed methodology are presented.