bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Improved Response Function Based Stochastic Meshless Method for Bending Analysis of Thin Plates
Authors: Aswathy M. , Arun C. O. 
Published: January 2022

Abstract
An improved response function (IRF) based stochastic element free Galerkin method (SEFGM) is proposed for the stochastic bending analysis of laterally loaded thin plates. Young’s modulus is considered as uncertain and its spatial variation is modelled as a homogeneous normal random field. Shape function method is used for random field discretization. In IRF method, the total displacement response is represented as the sum of a deterministic part and a stochastic part. The stochastic part is called as IRF and it is a function of the discretized set of random variables. Stochastic stiffness matrix is approximated using Taylor series expansion. A simplified expression for IRF in terms of stiffness derivatives and random variables is derived from the equilibrium considerations of system of stochastic equations. Direct simulations on the IRF combined with deterministic part of response can give the probabilistic characteristics of response quantities. The response characteristics of the plates obtained using the proposed method are compared with those obtained using Monte Carlo simulation (MCS). An ad-hoc response function (ARF) and second order perturbation (SOP) based SEFGM are also used for comparing the results. Square thin plates with two different boundary conditions are solved as the numerical examples. Kirchhoff-Love assumptions and plane stress conditions are considered. The deterministic EFGM used for bending analysis of plates calculates the out of plane deformations and rotations at various points. The probabilistic characteristics of these quantities are measured using the proposed IRF based SEFGM. A parametric study is conducted to investigate the capability of the proposed method in response moment calculations at various coefficients of variation (CV) and correlation length parameters of input random field. The response moments determined using the proposed method are found to be in good agreement with those obtained using MCS even at a CV of 30%. It is found that the proposed method can calculate the response moments accurately, regardless of the correlation length parameters of the input random field. These are attributed to the fact that the proposed IRF method considers the complete displacement response without any approximation. The probability density function and the cumulative distribution function of response quantities of plates calculated using the proposed method are observed to be comparable with those obtained using MCS. From a study on normalized computational times, the proposed IRF method is found to be computationally more efficient compared to MCS. In the proposed method, the stiffness derivatives and deterministic part of the displacement response are calculated outside the simulation loop. This along with the use of discretized set of random variables inside the simulation loop, calculates the IRF and hence the response statistics, that makes the method computationally more efficient compared to MCS.
Crack Detection and Evaluation Method for Self-Piercing Riveting Button Images Based on BP Neural Network
Authors: Hu Ke , Jiang Ling , Zhan Zhenfei , Wu Fei 
Published: January 2022

Abstract
Self-piercing rivet (SPR), a method used for joining sheet materials, is of increasing interest in the automobile industry. Cracks on the SPR joint surface might affect the joint strength significantly. This paper proposed a novel crack detection and evaluation method for SPR button images based on machine learning, which will address the issue of time-consuming and subjective caused by manual visual crack inspection. Firstly, sub-images are cropped from the button images and preprocessed into three categories (i.e., cracks, edges, and smooth regions) as training samples. At the sub-images level, 5 neural networks are trained with the input of different extracted features, respectively. Then, to overcome the representation limitation of one single extracted feature, a weighted combination of 5 neural networks is developed. Thirdly, a search algorithm is developed to extend the application of the learned model from sub-images into the original button images. Lastly, an evaluation system based on the characteristics of SPR button images is proposed to compare and analyze these different application results. The preliminary results on non-cracked and cracked button images show that the proposed crack detection method of weighted combination is an effective approach, which gets better crack detection results compared to other crack detection models with a single extracted feature of input.
Hydrogen: Fuel of the Future?
Authors: Baukal Charles , Whelan Matt , Londerville Steve , Haag Michel , Theis Gilles , Johnson Bill 
Published: January 2022

Abstract
Hydrogen is a unique fuel in many ways. It has the potential to eliminate both carbon monoxide and carbon dioxide emissions. However, it could increase NOx emissions compared to more conventional fuels. Hydrogen has many unique properties such as a wider flammability range, higher adiabatic flame temperature, higher flame speed, lower heating value on a volume basis, and lower combustion air requirements. Some potential safety concerns include a higher propensity to flash back on pre-mix burners because of the higher flame speed and to leak because H2 is a very small molecule. Because of its many unique properties, there are some important design considerations when retrofitting a combustion system designed to use a conventional fuel like natural gas to use hydrogen. This paper investigates some of the key differences when using hydrogen as a fuel compared to typical hydrocarbons. It considers the impact on: the fuel and air delivery systems, flame detection, burner design, heat transfer, and pollution emissions. It includes two specific examples of implementation in process heaters and a boiler.
Investigations Into the Linear Coupling Between Symmetric and Anti-Symmetric Modes of V-Shaped MEMS Resonators Under Electrostatic Perturbation
Authors: Alcheikh Nouha , Ouakad Hassen M. , Ben Mbarek Sofiane , Younis Mohammad I. 
Published: January 2022

Abstract
In this paper, we investigate experimentally and theoretically the linear coupling between the first two symmetric and anti-symmetric modes of an electrothermally and electrostatically actuated in-plane V-shaped micro-beam. The micro-beam is fabricated from highly doped silicon and is sandwiched between four electrodes to electrostatically activate both modes of vibrations. When tuning the electrothermal voltage, the natural frequencies of the two modes approach each other until they cross. Under electrostatic actuation, it is shown experimentally that the system undergoes a transition between modes crossing to veering. In addition, an analytical study is presented based on a Galerkin-based reduced-order model of a nonlinear Euler–Bernoulli shallow arch beam equation. The analytical results are compared to experimental data showing excellent agreement.
Survey of Existing Literature Data on the Biomass Combustion Behavior in Industrial Grate-Fired Boilers
Authors: Silva João Pedro , Teixeira Senhorinha , Peters Bernhard , Teixeira José Carlos 
Published: January 2022

Abstract
In the last two decades, the interest in the use of solid biomass as a fuel for power generation, together with the continuous tightening of regulations, has motivated the improvement of accurate design techniques which are required to optimize the combustion process, new technologies, and simultaneously control pollutant emissions to the environment. The present review presents an overview of the existing CFD methodologies to model the entire interaction in a grate-fired boiler. This is an important task that delivers a meaningful and trustworthy conclusion that will benefit the community of researchers and engineers.
To date, several studies have been conducted experimentally and numerically. However, although it is difficult to draw a general conclusion about which strategy or models are the most appropriate to the combustion simulation inside a biomass grate-fired boiler, in general, numerical studies have been contributed to a better understanding of the development of this phenomenon, its analysis, and its optimization. Nonetheless, in the last years, a growing trend for integrated bed models inside the CFD models was observed. This approach has become popular due to the rapid advances in computational power and the requirement to obtain more detailed information on the phenomenon. This work is essential to the next task related to the development of a numerical model of biomass combustion grate-fired boiler. It will contribute to the results of industry-academic collaboration applying scientific research to understand and improve the combustion process inside an industrial boiler.
Numerical Simulation of the Flow Inside a Horizontal Closed Refrigerated Display Cabinet
Authors: Silva João , Guedes Vitor , Teixeira Senhorinha , Lobarinhas Pedro , Teixeira José Carlos , Rodrigues Nelson 
Published: January 2022

Abstract
A refrigerated display cabinet is a device often used to preserve the products contained inside while enabling the costumer to have a view to the products stored. The main objective of this work was to investigate, using the ANSYS Fluent software, the airflow in a horizontal closed refrigerated display cabinet to better understand the fluid flow behavior in its interior. The turbulent airflow and non-isothermal heat transfer process were computed in a 2D transient state mathematical model where the basic equations governing the transport phenomena inside of the refrigerated display cabinet were solved. Regarding the turbulence model, this was modeled with the three-equation model since it can address the boundary-layer transition regions within the cabinet. After a complete understanding of the fluid flow behavior inside the cabinet, the influence of the door opening was analyzed.
Results of the CFD simulations allowed to achieve a detailed mapping of the cooling process inside the equipment. Generally, stabilizing the interior temperature for an empty cabinet is rapidly achieved with minimal heat losses. The inclusion of products that are at a higher temperature than the cooling air creates a zone of high thermal inertia and makes the temperature stabilization a longer process. Even though specific equipment is used, the results provide standard information on the phenomena occurring inside the cabinet and contribute to the industry and academic society to understand and improve industrial products and obtain more information that is very reduced in the literature.
Analytical Impact Analysis of the Brain Motion in Low-Velocity Head Impacts Using Concentric Viscoelastic Bodies
Authors: Thapa Pradip , Mansoor Baghaei Shahab , Sadegh Ali M. 
Published: January 2022

Abstract
Mild traumatic brain injury (mTBI) and concussion could occur in vehicular accidents, contact sports, or other physical traumas when the head is subjected to high linear or angular acceleration. Understanding the physiology and dynamics of such events has attracted many researchers’ attention. Due to the hidden risks in such events, it is very important to understand the cause and effect of the relative motion between the brain and skull and the implications of normal and shear stresses in the meningeal region.
Since the early 70’s to date wide variations of experimental, analytical, and numerical models has been developed to analyze multilayer spherical head impact model to quantify the dynamic response of the human head due to blunt impact and explain the process and likely cause of mild traumatic brain injuries. There are many high-fidelity finite element models and research studies of the various head models, but very limited analytical models to date for parametric studies. Analytical models of head impact play a vital role in predicting relative displacement between skull and brain, transmitted forces, post-impact velocity, and acceleration of the head system. However, to define a reliable mathematical model which can illustrate the mechanisms of motion and deformation of the brain within the skull requires knowledge of dynamics of a multibody system, material properties, boundary conditions at the brain–skull interface, and experimental data or FEM simulation for validation.
In this paper, a mathematical model of the brain and meningeal layers, as two separate viscoelastic materials that are modeled using a Kelvin–Voigt model, have been investigated and the motion of the brain relative to the skull during blunt head impacts have been analyzed. Specifically, the model consists of three concentric spherical mediums including a spherical shell (skull), a thin spherical layer (meningeal layer), and a spherical mass (brain). The interface between these spherical mediums consists of springs and dashpots representing stiffness and viscoelasticity of the skull, meningeal layer, and the brain. This model of the head is initially at rest and subjected to an impulse load. The equations of motion for this multi-body system were obtained, solved, and validated by performing a lumped mechanical model and the multibody dynamics finite element analysis simulation. Multiple parametric studies were performed to determine the maximum amplitude of impact force for which there is a contact between the skull and the brain.
Assessment of Interlaminar Stress Components in Laminated Composites Manufactured by Ply-Drop Technique
Authors: Suresh Babu Sandeep , Mourad Abdel-Hamid I. 
Published: January 2022

Abstract
Tapered composites have found applications in a wide spectrum of areas, mainly owing to its weight savings and ’tailoring’ options possible for its mechanical properties. This calls for a thorough analysis required to predict the response of the tapered laminates, which are formed by dropping some part of the plies at certain fixed locations. The present study is a linear elastic finite element study of a three-dimensional rectangular laminate made of an orthotopic material T300/5208 graphite-epoxy unidirectional tape, and its response when exposed to an axial or extensional load. Delamination is the major source of failure in laminates with ply drop-offs. Therefore, only the major interlaminar stresses affects the behavior of the laminate and all other stresses which do not directly contribute to the interlaminar failure can be neglected. Based on the above observations, a two-dimensional model (2D) of a particular symmetric ply-configuration of the body, subjected to a tensile load is analyzed on a commercial package, ANSYS 16.2 and the results obtained are compared against established results. It is shown that stress concentration is high near the drop-off and becomes comparatively insignificant at further locations. This analysis can provide insight into the parameters affecting the strength and endurance of the structure to external load conditions and obtaining an ideal configuration extending scope of maximum performance.
Effects of Personal Protective Equipment on Spinal Column Loads From Underbody Blast Loading
Authors: Umale Sagar , Humm John R. , Yoganandan Narayan 
Published: January 2022

Abstract
Combat-related spine injuries from improvised explosive devices are attributed to vertical loading transmitted from the seat to the pelvis to the torso and head-neck regions. The presence of personal protective equipment (PPE) adds to the weight of the torso, influencing the load transmission within the vertebral column. In this study, a detailed mid-size male finite element model from the Global Human Body Models Consortium was used to investigate the effect of PPE on spine kinematics, forces, and moments along the vertebral column. The model was positioned on a rigid seat, such that the posture represented an upright seated soldier. Once positioned, the model was updated with PPE. The models, with and without PPE were simulated under two high acceleration vertical loading pulses and the spine accelerations, forces and moments were investigated. The PPE increased the spinal loads, with reduced time to peak. The presence of PPE increased forces in the cervical and thoracic spines up to 14% and 9%, while it decreased the lumbar spine forces up to 7%. PPE increased cervical spine extension moment up to 104%, thoracic spine flexion moment up to 14%, and decreased the lumbar spine flexion moment up to 11%. The increase in thoracic spine compressive forces and flexion moments due to PPE suggest increased risk of injury in compression-flexion, such as anterior or burst fractures of the thoracic vertebrae with or without the distraction of posterior elements/ligaments. Whereas, the PPE may be effective in reducing the injury in lumbar spine, with reduced forces and moments. The pulse variation showed that the seat velocity along with the acceleration influence the spine kinematics and further parametric studies are needed to understand the effectiveness of PPE for varying seat velocities/accelerations. Spinal accelerations peaked earlier with PPE; however, their peak and morphologies were unchanged. This study delineates the kinetics of the spine injury during underbody blast loading and the role of PPE on potential injuries and injury mechanisms based on forces and moments.
The Influence of Cooling Rate and Mold Temperature on Polymers Crystallization Kinetics in Injection Molding
Authors: Alzahrani Faisal J. , Duhduh Alaauldeen A. , Gao Peng , Coulter John P. 
Published: January 2022

Abstract
Crystallinity plays a significant role in determining the quality of molded polymeric products. Higher crystallinity yields higher impact strength, while lower crystallinity yields higher water absorption and faster degradation. Therefore, the crystallinity level within a product should be tuned based on the desired outcome. The degree of crystallinity is determined by the crystallization rate, which is influenced by many processing factors such as cooling rate and mold temperature in injection molding. Moreover, there is not much research conducted to numerically investigate crystal size that develops locally in injection molded products and its relation to cooling rate and mold temperature. In this regard, the cooling rate and mold temperature influence in injection molding is investigated numerically to understand their local effect on crystallization rate and crystal size. Cooling rate and mold temperature were found to play a significant role in influencing crystallization kinetics and crystal size. Moreover, crystallinity was found to vary within one product based on the design and the distance between the investigated sample and the gate. The minor crystallinity point was found to be near the gate, and the largest crystal size was found to be at the furthest point away from the gate.
Agile Tasking of Robotic Kitting
Authors: Michaloski John , Aksu Murat , Schlenoff Craig , Cardoso Rafael C. , Fisher Michael 
Published: January 2022

Abstract
Manufacturers are looking for intelligent solutions to increase quality and productivity. Smart manufacturing envisions production empowered by autonomous robots that can complete tasks intelligently, with the focus on adaptability, flexibility, and versatility. In such systems, agile tasking plays an important role, as it is critical for robots to be quickly tasked to perform an operation. However, task agility is not limited to the speed of tasking robots, but also includes other features such as handling task failure, planning for new goals, interchangeability of data and task plans between different robots, and adapting to dynamic environments. Because robot task agility requires sophisticated dynamic and continuous planning and replanning, the Gwendolen agent programming language was chosen to evaluate as the agile robot planner. In this paper, we develop a manufacturing kitting case study and provide a list of kitting performance metrics to evaluate performance. The case study uses Gwendolen, Canonical Robot Command Language (CRCL), Robot Operating System (ROS) and Gazebo software components in combination to simulate and evaluate kitting. We explore the strengths of Gwendolen agile tasking to assess the operation against the kitting performance metrics.
Deep Neural Network Real-Time Control of a Motorized Functional Electrical Stimulation Cycle With an Uncertain Time-Varying Electromechanical Delay
Authors: Sweatland Hannah M. , Allen Brendon C. , Greene Max L. , Dixon Warren E. 
Published: January 2022

Abstract
Closed-loop functional electrical stimulation (FES) control methods are developed to facilitate motor-assisted cycling as a rehabilitative strategy for individuals with neurological disorders. One challenge for this type of control design is accounting for an input delay called the electromechanical delay (EMD) that exists between stimulation and the resultant muscle force. The EMD can cause an otherwise stable system to become unstable. A real-time deep neural network (DNN)-based motor control architecture is used to estimate the nonlinear and uncertain dynamics of each leg of the cycle-rider system. The DNN estimate of the system’s dynamics updates in real-time and is used as a feedforward term in the motor controller allowing the cycle crank to meet position and cadence tracking objectives. The nonsmooth Lyapunov-based stability analysis proves semiglobal asymptotic tracking.
Fault Diagnosis With Deep Learning for Standard and Asymmetric Involute Spur Gears
Authors: Karpat Fatih , Dirik Ahmet Emir , Kalay Onur Can , Yüce Celalettin , Doğan Oğuz , Korcuklu Burak 
Published: January 2022

Abstract
Gears are critical power transmission elements used in various industries. However, varying working speeds and sudden load changes may cause root cracks, pitting, or missing tooth failures. The asymmetric tooth profile offers higher load-carrying capacity, long life, and the ability to lessen vibration than the standard (symmetric) profile spur gears. Gearbox faults that cannot be detected early may lead the entire system to stop or serious damage to the machine. In this regard, Deep Learning (DL) algorithms have started to be utilized for gear early fault diagnosis. This study aims to determine the root crack for both symmetric and asymmetric involute spur gears with a DL-based approach. To this end, single tooth stiffness of the gears was obtained with ANSYS software for healthy and cracked gears (50–100%), and then the time-varying mesh stiffness (TVMS) was calculated. A six-degrees-offreedom dynamic model was developed by deriving the equations of motion of a single-stage spur gear mechanism. The vibration responses were collected for the healthy state, 50% and 100% crack degrees for both symmetric and asymmetric tooth profiles. Furthermore, the white Gaussian noise was added to the vibration data to complicate the early crack diagnosis task. The main contribution of this paper is that it adapts the DL-based approaches used for early fault diagnosis in standard profile involute spur gears to the asymmetric tooth concept for the first time. The proposed method can eliminate the need for large amounts of training data from costly physical experiments. Therefore, maintenance strategies can be improved by early crack detection.