bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Mono-Fractal Characteristics of Dynamic Pressure at the Outlet of Centrifugal Compressor Based on EWT
Authors: Liu Yan , Li Yuchen , Geng Jincheng
Published: February 2023

In this paper, the outlet dynamic pressure of 800kw centrifugal compressor is taken as the research object, and the nonlinear characteristics of the system under different working conditions are analyzed by using empirical wavelet transform combined with mono-fractal. Firstly, the characteristics of the dynamic pressure signal when the system enters the surge state from the steady state through the transition process are analyzed, and the spectrum analysis of the dynamic pressure signal is carried out. Secondly, the modal components of the signal are extracted by empirical wavelet transform and the signal is reconstructed according to the correlation coefficient. Finally, the autocorrelation function of the reconstructed signal in different states and its Hurst parameters are studied. The research results show that when the system enters the surge state from the steady state, the spectrum of the dynamic pressure signal gradually concentrates to the low frequency band. And, the autocorrelation function and Hurst parameter of the reconstructed signal can effectively reflect the working state of the system.
Reliability Test of Mobile Embedded Accelerometers in Measuring Postural Stability for People With Parkinson’s Disease
Authors: Thelen Matthew , Mazumder Fardeen , Zhu Linda , Tang Charlotte , Miller Nathaniel S.
Published: February 2023

Parkinson’s Disease (PD) is the second most common neurodegenerative disease in the United States, affecting at least one million people. The cardinal symptoms of PD are tremor, rigidity, slowed movement, and impaired balance. While some symptoms of PD are responsive to anti-PD medications, other symptoms, are less medication responsive, especially walking and balance. Moreover, daily activities, such as writing, using tools, and walking, affect the quality of life (QoL) of people with PD (PwPD). Monitoring PD symptoms is essential for clinical evaluations and adjusting medication to help maintain QoL for PwPD. we are developing a mobile app to conduct at-home PD symptom monitoring with the goal of providing more timely and frequent measurements of PD symptoms for both patients and clinicians. While the tremor and finger tapping results collected in the mobile app have been discussed in previous publications, this paper focuses on the design and testing of postural stability (balance) tests in the app and the validation of the reliability of the mobile embedded accelerometers. During the test, a dual-purpose shaker was employed to provide vibration in amplitude and frequency range similar to human postural stability signals. A head expander was attached to the shaker and the smartphone holder is screwed to it. The tilt and yaw angles of the smartphone holder are adjustable, therefore the smartphone could be tested in an angled position relative to the shaker. Various types of input signals were tested, including sweep and multiple real postural stability data previously collected from a volunteer. Two models of smartphones were used to measure the signal through multiple trials and the results were compared to the input benchmark signal to verify the accuracy of the smartphone measurements. Besides the evaluation of the time domain raw data, we have also employed several signal processing algorithms to extract postural stability factors, such as the root mean square (RMS) value, the derivative of acceleration, frequency factors, etc., with the goal of identifying the patterns of motion signals which could be used as a summary measures of balance for PD. These signal processing algorithms were used to process raw measurement data from multiple trials, on different input signals, and on different devices. The results were compared, and the consistency of these factors through multiple trials with different smartphone models is tested and summarized. These results help us to find the most reliable measure to be used in the smartphone application. Both the results in raw acceleration signals and calculated factors will be discussed to further the current understanding of the reliability of smartphone measurements with embedded accelerometers.
Using Machine Learning Methods Towards Identifying College Campus Load Profiles and Energy Storage Application for Reducing Peak Energy Demand From the Utility Grid
Authors: Sweeny Christopher J. , Smith Jackson R. , Ghanavati Afsaneh , McCusker James R.
Published: February 2023

Efforts to reduce peak energy demand on the utility grid have been a challenge due to unique load profiles for individual customers such as college campuses, businesses, and homeowners. This work illustrates the application of machine learning in the form of Bayes Estimation, Principal Component Analysis (PCA), and Fisher’s Linear Discriminant to identify typical power load profiles for the author’s institution campus buildings. These methods of machine learning are applied to data collected from the campus and focuses on identifying trends in power usage as well as identify optimal times for charging and discharging of an energy storage system (ESS). Application of the algorithms is carried out using MATLAB to better understand the load profiles of various academic and residential buildings on campus. Bayes Estimation is used to determine optimal times for charging and discharging of an ESS using training sets from the power consumption data. Results from the study show Bayes Estimation yields a high accuracy in state estimation for various sample sizes given a limited amount of training data. Principal Component Analysis is used to determine key features from the data that effectively differentiate between the academic and residential buildings being observed. Key features that are observed through PCA include timescales such as hours of the day, days of the week, and months of the year, as well as power demand readings from each of the buildings’ respective electrical meters. Fisher’s Linear Discriminant is applied to the dataset for a similar purpose to Bayes Estimation, however the algorithm is used to determine peak vs non-peak recordings from the hourly power consumption data. Results from Fisher’s Linear Discriminant method proved to be unsuccessful in discriminating between classes of data. Analysis of the results will be used to further understand where and when ESS can be most effective to reduce peak energy demand from the campus on the local utility grid network. The paper presents the process of applying methods of machine learning to the data as well as the results from the mentioned methods.
Cellular Core Square Tube Crushing Analysis With Induced Folding Mechanisms
Authors: Jenson Sean , Ali Muhammad
Published: February 2023

Thin-walled axial crushing members are often employed as energy absorbers in crash attenuators and automobile chassis designs. They improve crashworthiness while maintaining low structural weight addition due to their high strength to weight ratios. The addition of core material has also shown to increase energy absorption capacity and increase structural stability during deformation. Specifically, the addition of functionally graded cellular cores maintained high strength to weight ratios and assisted the increase in strength and stability. The present study aimed to investigate the effects of induced folding of the outer tube structure to maximize energy absorbing performance of the axial crush tube member. This study was conducted through a numerical approach utilizing ABAQUS/Explicit dynamic modeling environment. To facilitate the induced folding mechanism, partial cuts were made around the tube periphery at discrete intervals. The depth of the cuts decreased from impact to distal end to force fold initiation in a descending pattern. Analysis criteria included: crushing force, deformation modes and energy-deformation stroke plots. The induced structural collapse due to the peripheral cuts promoted additional fold initiation at the cut sites providing elevated crushing forces and increase energy absorption. This behavior resulted in a 29.2% increase in reaction forces and 11.1% increase in energy absorption.
Research on Characteristics of Load Distribution and Levitation Clearance Response in High-Speed Maglev Train With Electromagnets Overlapping Structure
Authors: Zhang Jimin , Xue Zhao , Ren Qiao , Zhou Hechao
Published: February 2023

For a certain type of high-speed maglev train with electromagnets overlapping structure, the vertical dynamics model of which was established based on multi-body system dynamics theory, electromagnetic field theory and automatic control theory, the distribution characteristics of the train load on levitation electromagnets were theoretically analyzed. The response characteristics of the levitation clearance, together with the influence of electromagnets overlapping structure on them were studied by numerical calculation. The research results show: it is the electromagnets overlapping structure that causes the uneven distribution of the train load on levitation electromagnets, furthermore leading to the uneven distribution of the levitation clearance at different levitation electromagnets; the larger the load borne by the levitation electromagnet, the smaller the steady-state value of its levitation clearance would be, and the fluctuation range of the levitation clearance would be smaller as well when disturbed by the outside. To avoid the non-uniformity of levitation clearance distribution among different levitation electromagnets, a design method for the vertical stiffness of primary springs based on the characteristics of train load distribution was proposed and proved to be effective by numerical calculation. Through above research, the influence law and mechanism of the electromagnets overlapping structure of high-speed maglev vehicle on its levitation clearance response is revealed, which has guiding significance in terms of operation safety evaluation and levitation control strategy design for the high-speed maglev vehicle. The new design method for the primary springs vertical stiffness shows certain reference for the structural optimization design of the maglev vehicle aiming at higher speed in the future.
An OpenMP Based Parallel Solver for MLPG Analysis of Heat Conduction
Authors: Singh Abhishek Kumar , Singh Krishna Mohan
Published: February 2023

In the present work, we focus our attention on the development of an in-house parallel MLPG code for the analysis of the heat conduction problem. The parallel code has been implemented using OpenMP API on the shared memory multicore CPU architecture. A two-dimensional linear steady-state heat conduction problem is taken as a model test problem. Numerical simulations have been performed to find the critical regions of the serial code, and an OpenMP based parallel MLPG code is developed, considering the critical regions of the sequential code. Based on performance parameters such as speed-up and parallel efficiency, the credibility of the parallelization procedure has been established. This work attempts to develop a robust and fast parallel MLPG solver.
Nonlinear Transient Response of Isotropic and Composite Structures With Variable Kinematic Beam and Plate Finite Elements
Authors: Azzara Rodolfo , Filippi Matteo , Pagani Alfonso , Carrera Erasmo
Published: February 2023

The present research deals with the evaluation of nonlinear transient responses of several isotropic and composite structures with variable kinematic one-dimensional (1D) beam and two-dimensional (2D) plate finite elements with different initial deflection configurations. The aim of current investigations is to show the effect of large amplitudes and the need to adopt an accurate model to capture the correct solution. Particular attention is focused on detailed stress state distribution over time and in the thickness direction. The proposed nonlinear approach is formulated in the framework of the well-established Carrera Unified Formulation (CUF). The formalism enables one to consider the three-dimensional (3D) form of displacement-strain relations and constitutive law. In detail, different geometrical nonlinear strains from the full Green-Lagrange (GL) to the classical von Kármán (vK) models are automatically and opportunely obtained by adopting the CUF due to its intrinsic scalable nature. The Hilber-Hughes-Taylor (HHT)-α algorithm and the iterative Newton-Raphson method are employed to solve the geometrical nonlinear equations derived in a total Lagrangian domain. Both Lagrange (LE) and Taylor (TE) expansions are considered for developing the various kinematic models. The solutions are compared with results found in available literature or obtained using the commercial code Abaqus. The results demonstrated the validity of the proposed formulation and the need to adopt a full Green-Lagrange model in order to describe the highly nonlinear dynamic response and an Layerwise (LW) approach to accurately evaluate the stress distribution.
Influences of Groove Geometry of Friction Disk on the Flow in an Engaged Wet Dual Clutch
Authors: Wu Xiqian , Zhang Xiaofang , Ding Weiyun , Ruan Deng-Fang , Hou Yuanjing
Published: February 2023

Based on FLUENT software, the flow field of cooling oil between a friction pair in an engaged wet dual clutch is simulated and the influence of groove structural parameters on the flow filed characteristics of cooling oil is studied. The results show that the structural parameters of radial groove have a great influence on the cooling oil flow field between a friction pair. When the inclination direction of groove is opposite to the rotation direction of friction pair, the groove inclination angle has a great influence on oil flow field between fiction pair, and the influence is relatively small when the groove inclination angle is consistent with the rotation direction of friction pair. In addition, the velocity distribution trend in the inclined radial groove is the same as that of the inclination angle = 0, but the velocity magnitude is larger and the highest velocity appears near the opposite side of the groove to the inclined direction of the grooves either for clockwise or counter-clockwise rotation of the friction pair. On the other hand, when the cross sectional area of the groove is kept constant, the average velocity in the central circumferential section of the groove is decreased rapidly with the increase of the groove aspect ratio (width to depth ratio) until the aspect ratio is equal to 5, and then slowly.
Assembly Automation Using an Industrial Robot
Authors: Dragun Timofey , Mascaro Seth , Blanchard Jonathan , Chauhan Vedang
Published: February 2023

Industrial automation is a prominent process that has been around for many years and is continuing to evolve. An important aspect of automation is industrial robots. This paper focuses on an automated circuit board assembly process. Components of the assembly are described and identified, as well as a completed assembly is shown with 3D printed components. Important camera setup steps are documented and discussed in addition to the iRVision Geometric Pattern Matching (GPM) tool which is used to teach the camera how to find parts. The process was first developed using ROBOGUIDE simulation software, once validated the program was implemented on a physical FANUC LR Mate 200iD/4s robot. Trial runs were completed with both the physical robot and the ROBOGUIDE simulation, with the data being analyzed to determine the system performance. Comparing the physical robot to the ROBOGUIDE simulation has revealed that the physical robot can perform as well as the simulation in terms of part detection, but suffers when it comes to speed. Simulation in the virtual software is a quick and easy way to visualize and analyze the robotic work cell before investing money into implementing it in the real world.
Performance Evaluation of Conveyor-Less Matrix Assembly System Using Simulation and Mathematical Models
Authors: Kim Min-Soo , Oh Seog-Chan , Chang Eun Hyo , Wells James W. , Arinez Jorge , Jang Young Jae
Published: February 2023

The increasing demand for electric vehicles and the emergence of autonomous transport technologies within production like autonomous mobile robot or self-driving rolling chassis have raised the possibility of developing conveyor-less matrix assembly system. The conveyor-less matrix assembly system is characterized by high adaptability to demand changes with low-cost investments and asynchronous operations allowing high routing and process flexibilities where the sequence of assembly operations is only limited by the product-specific precedence orders. With high flexibility potentials, however, the system needs to face high complexity issues in modeling and control. The high complexity may reduce the system performance and thus, the performance evaluation is a key component in modeling and control of conveyor-less matrix systems. To study the evaluation methods for the conveyor-less matrix assembly, this paper introduces two models, a mixed-integer linear programming model as exact method and a simulation model as heuristic method, and then illustrates how two models are different but can work collaboratively in a hypothetical example drawn from automotive assembly trim area. Finally, this paper intends to provide insights on the business value calculation of conveyor-less matrix assembly system, for practitioners who are interested in building conveyor-less matrix assembly system in the future.
Experimental Investigation on Severe Plastic Deformation of AA7075 Alloy at Elevated Temperature
Authors: Kolla Lakshman Rao , Gupta Amit Kumar , Mathur Devanshi , Bhardwaj Akhil
Published: February 2023

Lightweight alloys play a significant role in different manufacturing applications in the aerospace, automobile, and medical industries. Among other light alloys, 7075 aluminum alloy (AA7075) is widely utilized due to its excellent mechanical strength, low weight to strength ratio, and good corrosion resistance. One of the prominent ways to improve the alloy’s properties is grain refinement, which is easily accomplished by severe plastic deformation (SPD) techniques. Among different SPD techniques, constrained groove pressing (CGP) is one of the prominent SPD techniques, particularly for the sheet/plate material. In this work, AA7075 alloy was subjected to the CGP process at 300°C with various heat treatments. The repetitive shear. Grain refinement of CGPed samples was observed through an optical microscope to measure the average grain size of the specimen. SEM and XRD were used to investigate the precipitation and crystallography of the processed alloy. Results indicate that the heat treatment significantly affects grain growth as precipitates are segregated along grain boundaries.
Physical and Virtual Robotic Cells in Industry 4.0 Towards Industry 5.0: An XR-Based Conceptual Framework
Authors: Kuts Vladimir , Singh Maulshree , Alsamhi Saeed Hamood , Devine Declan , Murray Niall
Published: February 2023

The Digital Twin (DT) in the manufacturing domain is already the everyday tool for visualizing the various industrial systems, equipment, and produced products. When designing a new manufacturing unit or enlarging an existing factory, it is important to do so without affecting the manufacturing process flow itself. There are opportunities through simulation and digital manufacturing to plan and optimize this design process. Within usage of the actual physical machinery data gathered from the Industrial Internet of Things (IIoT) sensors and feeding to the DT, optimizing the layout can be done more precisely and effectively. However, there is no way to test the potential equipment simultaneously with the physical one in real-time. This paper aims to propose a Mixed Reality (MR) based system framework and toolkit, which will enable physical industrial robots to interact with virtual equipment and other virtual robots. This way, via Virtual Reality (VR), it will be possible to design a system layout. Furthermore, via the Augmented Reality (AR) view, it will be possible to simulate the interaction between multiple robots by enhancing the possibilities of the physical environment and using the new precise scale real-time design method.
Multi-Objective Optimization of Composite Square Tube for Minimizing Peak Crushing Force and Maximizing Specific Energy Absorption Using Artificial Neural Network and Genetic Algorithm
Authors: Zende Pradnya , Dalir Hamid
Published: February 2023

Optimization of composite materials is essential considering their high costs and complex manufacturing processes. This paper includes optimization of composite material square tube for crashworthiness requirements using Artificial Neural Networks and Genetic Algorithms. Objectives of optimization were peak crushing force and maximizing specific energy absorbed by the composite square tube. Training data for the artificial neural network was obtained by crash analysis of a square tube against a rigid wall for various samples using LS Dyna. Latin Hypercube sampling technique was used to generate the required sampling plan. Neural Network Training was performed to obtain the highest regression value and minimum mean square error. The trained network was further combined with Multi-Objective Genetic Algorithms (MOGA) to find optimized values of the variables to fulfill the crashworthiness requirements. The fitness function used in MOGA was obtained by ANN with a 5% allowable error in the objective values. Optimized solutions obtained were presented by the Pareto frontier curve. The methodology was developed for square tubes with four and eight plies.
Asset Management in the Mining Sector Based on Reliable and Sustainable Supplier Selection: A South African Case Scenario
Authors: Gidiagba Joachim , Okwu Modestus , Tartibu Lagouge
Published: February 2023

Asset management is fast becoming the backbone of modern production as it enables production facilities to operate at a high level of availability, reliability, and operability. One of the major challenges facing the mining sector is the identification of reliable and sustainable suppliers necessary for a matured asset management program (MAMP). This study contributes to that direction. The classical techniques have been used for the selection of sustainable suppliers for optimal operations of mines. However, a survey was conducted among executives and mine operators in the South African mining industry, the results obtained clearly illustrate that the industry employs economic, environmental, and social criteria in the selection of reliable and sustainable suppliers for asset management necessary for the optimal operation of the mining industry. This study further revealed that for the mining industry to operate at an optimal level, it requires a global MAMP, comprising of systems, people, equipment, spare parts, and services. A computerized maintenance management system (CMMS) facilitates the planning, scheduling, workflow, material flow, information flow, purchasing, and inventory management of mining operations. The effectiveness of the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) and Best Worst Method (BWM) was demonstrated in this study to address the challenges encountered by decision-makers in selecting a reliable and sustainable CMMS within the mining space. In conclusion, selecting a reliable and sustainable supplier that satisfies the key performance indicators of the organization is crucial in attaining optimum operational performance and meeting the business needs of the organization.
Effective Behavior of Peridynamic Random Structure Composites Subjected to Body Force With Compact Support
Authors: Buryachenko Valeriy A.
Published: February 2023

We consider a static problem for statistically homogeneous matrix linear bond-based peridynamic composite materials (CMs). Even for locally elastic composites subjected to inhomogeneous loading, the effective deformations are described by a nonlocal (either the differential or integral) operator relating a statistical average of stresses in the point being considered with a statistical average of strains in the vicinity of this point. The basic hypotheses of locally elastic micromechanics are generalized to their peridynamic counterparts (see for details [1]). The method is based on estimation of a perturbator introduced by one inclusion that is, in fact, the solutions of the basic problem for one inclusion inside the infinite peristatic matrix subjected to the body force. The statistical averages of both the displacements and stresses are estimated by summation of these perturbators for all possible locations of inclusions (in so doing, renormalizing procedure is not required). It allows us to estimate not only the effective nonlocal constitutive equation but also to evaluate the statistical field averages (inhomogeneous and nonlocal) inside the phases at the fine scale that is critically important for advanced modelling (e.g. for any nonlinear phenomena). In particular, in the generalized effective field method (EFM) proposed, the effective field is evaluated from self-consistent estimations by the use of closing of a corresponding integral equation in the framework of the quasi-crystalline approximation. In so doing, the classical effective field hypothesis is relaxed, and the hypothesis of the ellipsoidal symmetry of the random structure of CMs is not used. Numerical results for the estimation of effective displacements are obtained for 1D statistically homogeneous CM bar with the prescribed self equilibrated body forces.
An Artificial Neural Network for Parametric Analysis of Metallic Additive Manufacturing Using Discrete Element Method
Authors: Wu Yuxuan , Namilae Sirish
Published: February 2023

Powder bed fusion (PBF) is a widely used metal additive manufacturing method. There is strong evidence that the performance of the final part built using PBF depends on the dispersive quality of the particle bed. Understanding this process through computational modeling and machine learning is an efficient low-cost way for process design. Discrete element method (DEM) is an effective tool for analyzing the particle flow behavior. However, one challenge for parametric modeling of highly multivariate powder spreading process through DEM is the high computational cost, for traversing the large parameter space. We address this problem through innovative use of parallel computing using GNU parallel, and by developing a machine learning algorithm to correlate the process parameters and spread quality. We first perform DEM simulations systematically varying four parameters, the particle size, the coefficient of friction, the spread layer thickness, and the recoating velocity. The dataset containing inputs with spread parameters and target variables that measure the spread quality are fed to a finely-tuned artificial neural network (ANN). We observe that the neural network presents at least 95% accuracy in predicting the test data. Ultimately this approach provides the parameter combinations that produce high quality compaction before sintering.
Modeling the Interplay Between Process Parameters and Part Attributes in Additive Manufacturing Process With Artificial Neural Network
Authors: Deb Jayanta , Ahsan Nazmul , Majumder Sharmin
Published: February 2023

In this study, we model the interplay between the process parameters and the part attributes with artificial neural networks (ANN) to predict the effect of a set of process parameters on the part attributes in extrusion-based AM process. Five process parameters including build orientation, print speed, extrusion temperature, deposition direction, and layer thickness with three levels are used in this study to fabricate parts following an orthogonal array experimental design. Three attributes including dimensional accuracy, surface roughness, and tensile strength of the fabricated parts are measured and used to train, validate, and test the proposed multilayer artificial neural network models. Four different ANN models are proposed where three of them are for the three individual part attributes and the fourth model is for the combination of all three attributes. The results indicate that the individual part attribute ANN models outperform the model for the combination of three attributes in terms of the RMSE and correlation coefficient. Comparison among the individual part attributes with respect to the process parameters is performed to analyze which parameters have a greater effect on the individual part attributes. The trained ANN models can be utilized to predict and optimize the part attributes in extrusion-based AM processes.
Mitigation of Thrust Deterioration at a High Flapping Frequency of a 2D Airfoil in Forwarding Flight Conditions Using Asymmetric Flapping Strokes
Authors: Sinha Jit , Roy Sohan , Manohar Dash Sunil
Published: February 2023

In this article, the detrimental time-averaged thrust performance of a two-dimensional elliptic symmetrical flapping airfoil at very high flapping frequencies is addressed, and an attempt is made to enhance the thrust performance by implementing two separate flapping frequencies during the downstroke and upstroke of a flapping cycle thus making it asymmetric. Three different pivot locations, three effective angle of attack amplitudes, and a specific range of St are considered for our investigation, keeping the Reynolds number fixed as 5000. In general, the downstroke flapping frequency is always selected with a value higher than the critical Strouhal number, but the upstroke flapping frequency is kept fixed as the critical Strouhal number. It is worth highlighting that the observations are periodic and swapped suitably when the flapping stroke frequency selection is reversed in the flapping cycle. Asymmetric flapping stroke configurations yield more time-averaged thrust and non-zero lift than symmetric flapping stroke at thrust degrading flapping frequency range. Interestingly, the leading edge side pivot point produces positive lift when the implementation of a faster downstroke and slower upstroke configuration. At the same time, it’s the opposite for the center and trailing edge pivot points. To understand these enhanced aerodynamic performances at high flapping frequencies, we have further analyzed the transient thrust and lift force profiles and their associated flow structures for all the pivot locations.
Dynamics of a Viscous Drag Pump for Maxwell Immiscible Fluids Under Interfacial Electroosmotic and Hydrophobic Effects in a Microannulus
Authors: Escandón Juan P. , Valencia Cesar A. , Torres David A. , Hernández Clara G.
Published: February 2023

The present study analyzes the flow dynamics of two immiscible viscoelastic fluids through a cylindrical microannulus with hydrophobic walls. The pumping technique is produced by electroosmotic effects that act on the conducting fluid dragging the non-conducting fluid by viscous forces. The dimensionless mathematical model considers the linearized Poisson Boltzmann equation, the Cauchy momentum equation, and the rheological Maxwell model, which is solved semi-analytically by the Laplace transform method. The results of the transient flow field are presented in terms of velocity profiles, velocity tracking, and flow rate. Due to the elastic properties of fluids, oscillatory behavior of the flow is produced, whose frequency, amplitude, and duration depend on the relaxation times of fluids. Also, other dimensionless parameters are investigated, such as the electrokinetic parameter of the conducting fluid, the viscosity ratios in both fluids, as well as the hydrodynamic slip length in the walls, and the liquid-liquid interface position, showing its influence on the velocity magnitude, flow rate, and time in which the flow reaches the steady-state regime. The present investigation extends the knowledge about the transport methods of non-Newtonian and non-conducting fluids in microfluidic devices.
Designing a Whirling Arm for Water Droplet Erosion Testing Apparatus
Authors: Behlmann Julia , Anderson Keldon , Karimi Soroor
Published: February 2023

Erosion on wind turbine blades due to falling water droplets cannot be quantitatively measured with modern methods of analysis. Initially, erosion caused by water droplets was ignored in wind turbine designs because the rotational speed did not reach the erosion threshold; with larger blades rotating at faster speeds, water droplet erosion generates concern in lowering the wind turbine’s overall efficiency, so the need for quantitative data is prevalent. In order to obtain this data, an experimental facility is being developed at the University of Tulsa. This facility allows for investigation into the phenomenon of water droplet erosion by allowing multiple parameters to be tested. Thus, a model can be developed to predict the erosion resistance of wind turbine blade materials under different conditions. An important part of this facility is the whirling arm apparatus which simulates the movement of wind turbine blades and is capable of reaching tip velocities up to 100 m/s. This paper considers different aspects of designing and developing the whirling arm, including the durability, corrosion resistance, and properties of the material, dynamic movement of the arm, beam deflection, vibration and resonance of the beam, and force of the water droplets. It is especially important to study the effect of water droplet impacts on the natural vibrations occurring within the system in multiple planes. The rotational motion of the apparatus causes the arm to deflect proportional to the moment generated by the motor, and the water droplets apply additional force in the same plane as the deflection. To explore the mode shapes of the system, computer simulations are being used to determine the theoretical range of speeds for operation. This range will then be tested with shakers installed along the arm of the apparatus to determine the mode shapes experimentally from the theoretical predictions. Through exploring the vibrations of the system both when water droplets are exciting the system and when they are not, the operating speeds of the apparatus to avoid resonance will be determined. Resonance occurs when the natural frequency of the system is reached, and a large amplitude is produced, making the system unstable. Exploring the mode shapes of this system determines if counterweights are needed to oppose the weight of the arm and coupon. Though, the effect of the counterweights on the drag force and the possible reduction of the arm speed should be considered as well. These considerations ensure the designed apparatus accurately and safely measures erosion on wind turbine blade material induced by water droplets.
Parametric Study of Viscoelastic Flow Simulations in Microfluidic Devices
Authors: Xue Guanyang , Caspar Justin , Asiri Jaber , Cheng Xuanhong , Oztekin Alparslan
Published: February 2023

The open-source C++ toolbox OpenFOAM is used to perform the Computational Fluid Dynamics (CFD) simulations in two-dimensional microfluidic devices to characterize the viscoelastic flow. The Oldroyd-B constitutive equation is coupled with continuity and momentum equations. Multiple stabilization methods are applied to the numerical simulation to simulate High Weissenberg Number Problem (HWNP) in the microchannel. We applied the Log Conformation Reformulation (LCR) method to guarantee the positive definiteness of the stress tensor. The CUBISTA scheme and the improved Both Side Diffusion (iBSD) method are applied to predict the flow behavior at high elasticity regions without numerical oscillations. Various microstructures, including circles and flat plates, are placed in the center of the channel as the confinement. Our previous work demonstrated that the polyhedral mesh with hexahedral inflation layers effectively meshes complex microstructures in microchannels. A viscoelastic fluid is injected from the inlet at varying flow rates, corresponding to the local Weissenberg number up to 25. A parametric study is conducted on the first normal stress difference (N1) in specific regions with an accurate prediction of the viscoelastic flow field near the microstructures.
The Dynamic and Control of a Robotic Platform for a Low-Cost Flight Simulator
Authors: Ji Xiaoxu , Alshaeri Ruba , Piovesan Davide , Conley Kaden
Published: February 2023

Robotic platforms have been prevalently used as a flight simulator in the commercial airlines and military aviation for pilot training to ensure that the flight crew members familiarize with the standard operating procedures. An ideal flight simulator can provide motions as they occur in the real airplane. However, it is difficult to provide an optimal simulation because of the interaction between the translational and rotational motions. Accordingly, in this study a low-cost Stewart platform is used to simulate different weather conditions via considering multiple variables, such as the frequency and amplitude of external perturbations. Its accuracy of acceleration will be evaluated. Once the result is validated, this affordable robotic platform can be widely applied in different types of simulators to achieve optimal stimulation.
Investigating the Tribological Aspects of Tool Wear Mechanism and Tool Life in Sustainable Lubri-Cooling Face Milling Process of Particle Reinforced SiCp/Al Metal Matrix Composites
Authors: Laghari Rashid Ali , Mekid Samir , Akhtar Syed Sohail , Laghari Asif Ali , Jamil Muhammad
Published: February 2023

In this paper, the face milling experiments were performed to investigate the cutting process of SiCp/Al (SiCp 65%) volume percentage and their effect on tool life and tool wear mechanism. The study was performed based on different cutting parameters (cutting speed vc, feed per tooth fz, and axial depth of cut ap, 1mm, and width of cut ae 8mm,) and cutting environments adopted as Dry MQL and CO2 Snow to analyze the effect of lubri-cooling machining process using polycrystalline diamond (PCD) cutting tools. A total of 18 experiments were performed during milling of SiCp/Al (65%) with each experimental run involved the 321 mm3 of the volume of material removal. The study found that lubrication and cooling can effectively reduce the tool wear and improve the tool life up to 29% for SiCp65% on moderate cutting parameters. The major wear mechanisms of PCD cutting tools are perceived as abrasive wear and adhesive wear mechanisms, which develop the flank wear and build-up-edge formations. Seemingly, a sub-zero cooling environment and dry cutting are found convenient to produce a build-up edge on the rake face of the cutting tool while MQL-assisted machining provides the benefit to prevent the cutting tool from material adhesion.
Design and Numerical Analysis of Locally-Resonant Meta-Lattice Structure for Vibration Attenuation
Authors: Güngör Utku , Kurt Ahmet Arda , Lale Mert , Acar Furkan , Görgülüarslan Recep Muhammet , Ünver Hakkı Özgür
Published: February 2023

Meta-materials are artificial materials that perform superior properties in addition to natural behavior. Regardless of their chemical properties, metamaterials show high performance due to their geometric orientation. There are mechanical metamaterials that are used in vibration and acoustic fields, the main subject of which is wave transmission. The propagation of mechanical waves, which can cause problems such as structural damage, fatigue, poor performance, and discomfort in the industry, can be prevented or reduced by using meta-materials. Mechanical waves of various frequencies can be reduced or eliminated by locally resonant metamaterials obtained by adding various masses to viscoelastic materials. Metamaterials can be designed by modifying various lattice structures. Higher performance metamaterials are designed in this study by modifying the lattice structures used in the literature for vibration isolation. Finite element analyzes are carried out to show the transmissibility performance of the designed meta-lattice structures.
Revealing the Richtmyer-Meshkov Instability Within Gas Dynamic Detonations
Authors: Gao Yang , Feng Dehua , Ferguson Frederick
Published: February 2023

Computational Fluid Dynamics (CFD) analysis are widely used in modern risk assessment procedure in order to understand detonations during a given situation or an accident. Combustion regimes including deflagration, detonation transition and detonation are extremely important. Hydrodynamic instabilities during detonation make it even harder to simulated. Numerous lingering numerical challenges still exists in the areas of simulating gas detonation flows. Among these challenges is the inability of many high order numerical schemes to simulated gas denotation and wave propagation without getting into regions of negative density or negative pressure. Many existing high order schemes, which may have proven record of accomplishment in terms of their accuracies and efficiencies in handling complex flow fields, will often times facilitate the development of negative density or negative pressure in their efforts to simulate the physics associated with the time evolution of gas detonation flow fields. This effort describes the application of a positivity-preserving density and pressure scheme, named the Integro-Differential scheme (IDS), to the detonation gas dynamic problem. Among the problems of interest to this study are the 1-D shock tube problem, 2-D explosion problem and implosion detonations problems. The purpose of solve 1-D problem is to prove IDS has acceptable numerical stability and less dissipation as a computational fluid dynamics (CFD) scheme. Of particular interest to this paper is the implosion detonations problem. The implosion problem was analyzed on a square domain of dimension: 0 <= x <= 0.3; 0 <= y <= 0.3, with reflecting walls, and with zero initial velocities. The results indicated that the IDS was able to successfully capture the flow physics within the implosion problem. And the wall pressure and temperature data from the 2-D unsteady result and use extract line way to analysis.
Transactional Analysis Training for Improving Engineering Graduate Students’ Abilities to Understand Different Personalities
Authors: Tyagi Pawan
Published: February 2023

Aspiring graduate students in science and technology generally lack formal training in understanding human behavior and traits that can adversely impact their ability to perform and innovate at the highest level. Positive intelligence (PI) and Transactional Analysis are two practical methods in human psychology that millions of people have tested for self-growth. The author previously published the application of PI for enhancing engineering students (Tyagi, P., Positive Intelligence Education for Unleashing Student Potential. ASME 2019 International Mechanical Engineering Congress and Exposition 2019, Volume 5: Engineering Education, V005T07A009). This paper focuses on training graduate students about the popular and practical transactional analysis science and assessing their competence in utilizing this knowledge to decipher their own and other people’s. Transactional analysis was taught to students via Student presentation-based effective teaching (SPET) methodology developed by the author. Under this approach, graduate students enrolled in the MECH 500 Class were provided a set of questions to answer by self-reading of the recommended textbook “I am Okay You are Okay by Thomas Harris”. Each student individually answered the assignment questions and then worked in the group to prepare a group presentation for the in-class discussion. Three group discussions were conducted to present different views about the four types of transactions and underlying human traits. Before transactional analysis training, students were also trained in Positive intelligence psychology tools for a similar objective. After the discussion, students were surveyed about the depth of their understanding. Students also reflected their views on the utility of transactional analysis with respect to positive intelligence. More than 75% of students mention that they gain high competency in understanding, defining, and utilizing transactional analysis. This study presents insights for positively impacting graduate students’ mindsets as they pursue an unpredicted course of research that can sometimes become very challenging.
Non-Newtonian Blood Flow in a Diseased Abdominal Aortic Arterial Segment Under the Effect of an External Magnetic Field: A Numerical Study
Authors: Elhanafy Ahmed , Abuouf Yasser , Ookawara Shinichi , Ahmed Mahmoud
Published: February 2023

Numerical simulation of blood flow modeling in cardiovascular system has been one of the most efficient tools in understanding and diagnosing many diseases in the recent few decades. Recent studies have been performed to investigate the effect of the external magnetic fields on the biomagnetic fluids such as blood. There are many applications of magnetic field which are closely related to hemodynamics such as the magnetic resonance imaging (MRI) devices, magnetic drug targeting (MDT) and cell separation. These applications have a direct connection with hemodynamics parameters such as the wall shear stress, pressure drop and recirculation zones. In this study, numerical simulation of blood flow in an abdominal aortic aneurysm under an external magnetic field is performed. Blood is considered as a non-Newtonian fluid with shear-thinning properties, in which the blood viscosity is a function of both shear rate and hematocrits using the Casson model. The three-dimensional axisymmetric flow is considered in the arterial segment. Results at different flow conditions such as the strength of the magnetic field, different inlet velocities and different hematocrits are estimated. The results indicate the significant effect of the magnetic strength on the wall shear stress and the pressure drop which reached 200 % and 600 %, respectively.
Machine Learning Potentials for Graphene
Authors: Singh Akash , Li Yumeng
Published: February 2023

Graphene has been one of the most researched material in the world for the past two decades due to its unique combination of mechanical, thermal and electrical properties. Graphene exists in a stable two dimensional (2D) structure with hexagonal carbon rings. This special 2D structure of graphene enables it to exhibit a wide range of peculiar material properties like high Young’s modulus, high specific strength, and electrical conductivity etc. However, it is extremely challenging and costly to investigate graphene solely based on experimental tests. Atomistic simulations are powerful computational techniques for characterizing materials at small length and time scales with a fraction of cost relative to experimental testing. High fidelity atomistic simulations like Density Functional Theory (DFT) simulations, and ab initio molecular dynamic simulations have higher accuracy in predicting 2D material properties but are computationally expensive. Classic molecular dynamics (MD) simulations adopt empirical interatomic potentials which drastically reduce the computational time but has lower simulation accuracy. To bridge the gap between these two type of simulation techniques, a new artificial neural network potential is developed, for graphene in this study, to enable the characterization of 2D materials using classic MD simulations with a comparable accuracy of first principles simulation. This is expected to accelerate the discovery and design of novel graphene based functional materials. In the present study mechanical and thermal properties of graphene are investigated using the machine learning potentials by conducting MD simulations. To validate the accuracy of machine learning potentials mechanical properties such as Young’s modulus, ultimate tensile strength and thermal properties such as coefficient of thermal expansion and lattice parameter are evaluated for graphene and compared with existing literature.