bluuebunny/crossref_metadata_2025_split huggingface dataset:
Heat Exchanger Design, Analysis and Evaluation by Data-Driven Computational Fluid Dynamics
Authors: Zhang Zhifeng , Chen Yilun , Ma Jiefu 
Published: February 2021

Abstract
Heat exchanger design, analysis and evaluation are traditionally based on experimental data, empirical relationships and theoretical equations. Recently, due to the recent development of data-driven computational fluid dynamics, we are able to conduct “smart” analysis based on a large amount of data. In the present research, we developed a fast CFD data generation method. Based on the large amount of data, we developed two data-driven models: 1) a machine learning model that can predict the heat transfer behavior, and 2) a tree-based model that can capture the feature importance. This methodology can potentially be applied to future high-efficiency heat exchanger designs.
Effect of Additional Weight on Lyapunov Exponents for Human Squat Exercise
Authors: Galarza Jose , Caruntu Dumitru I. 
Published: February 2021

Abstract
This work deals with the stability of the dynamics of human squat exercise. This is a common exercise. The focus of this investigation is to analyze the knee angle time series and calculate the divergence for different weight added in the exercise. Experiments are conducted and motion capture used for each of the cases. Then the Lyapunov exponents of the time series of the knee joint angle are determined. Matlab software package is used for all calculations. Results are compared with data available in the literature. This work provides insight on the level of stability for added weight on the human squat exercise. This gives an insight on the level of safety of this exercise as weight is added to the human subject.
Clustering Algorithm Based Straight and Curved Crop Row Detection Using Color Based Segmentation
Authors: Khan Nazmuzzaman , Rajendran Veera P. , Al Hasan Mohammad , Anwar Sohel 
Published: February 2021

Abstract
Autonomous navigation of agricultural robot is an essential task in precision agriculture, and success of this task critically depends on accurate detection of crop rows using computer vision methodologies. This is a challenging task due to substantial natural variations in crop row images due to various factors, including, missing crops in parts of a row, high and irregular weed growth between rows, different crop growth stages, different inter-crop spacing, variation in weather condition, and lighting. The processing time of the detection algorithm also needs to be small so that the desired number of image frames from continuous video can be processed in real-time. To cope with all the above mentioned requirements, we propose a crop row detection algorithm consisting of the following three linked stages: (1) color based segmentation for differentiating crop and weed from background, (2) differentiating crop and weed pixels using clustering algorithm and (3) robust line fitting to detect crop rows. We test the proposed algorithm over a wide variety of scenarios and compare its performance against four different types of existing strategies for crop row detection. Experimental results show that the proposed algorithm perform better than the competing algorithms with reasonable accuracy. We also perform additional experiment to test the robustness of the proposed algorithm over different values of the tuning parameters and over different clustering methods, such as, KMeans, MeanShift, Agglomerative, and HDBSCAN.
Trajectory Control of Robotic Manipulators: A Comparison Study
Authors: Lee Ho-Hoon 
Published: February 2021

Abstract
In this paper, two different types of model-based trajectory control schemes are designed and compared for the control of robotic manipulators. First, two PD-based control schemes and one sliding-mode control scheme are designed, where Lyapunov stability theorem is used as a mathematical design tool. Then, the performances of the PD-based control schemes are compared to those of the sliding-mode control schemes with realistic computer simulations. The global asymptotic stability and the boundedness of all internal signals of the designed control schemes are shown with Lyapunov stability theorem.
Performance Analysis of Wind Tower Greenhouse Integration Using CFD
Authors: Sajjad Muhammad , Islam MD , Janajreh Isam 
Published: February 2021

Abstract
The current study investigates the effect of wind catcher-greenhouse integration on the micro-climatic conditions inside the green house. The potential application of wind tower in greenhouse systems for hot climatic conditions with water mist injection was carefully evaluated. The effect of various parameters such as air flow velocity, relative humidity, injector mass flow rate and injector position in the wind tower was studied. The variations in the temperature and relative humidity was observed in the greenhouse. The computational fluid dynamic analysis was carried out using Fluent and k-ε turbulence model was adopted for the analysis. A non-isothermal, multiple species, viscous and turbulent flow is pursued. It was observed that the addition of water injectors inside the wind catcher significantly reduced the temperature and enhanced the relative humidity of the air.
Data-Based Modeling for Reactive Ion Etching: Effectiveness of an Artificial Neural Network Model for Estimating Tungsten Silicon Nitride Etch Rate
Authors: Walters Madeline A. , Fan Zhaoyan , Sencer Burak 
Published: February 2021

Abstract
This paper presents a data-based approach for modeling a plasma etch process by estimating etch rate based on controlled input parameters. This work seeks to use an Artificial Neural Network (ANN) model to correlate controlled tool parameters with etch rate and uniformity for a blanket 1100 Å WSiN thin film using Cl2 and BCl3 chemistry. Experimental data was collected using a Lam 9600 PTX plasma metal etch chamber in an industrial cleanroom. The WSiN film was deposited over 3000 Å TEOS to ensure adhesion, with an 8-inch bare silicon wafer as the base layer. Controlled tool parameters were radio frequency (RF) upper electrode power, RF lower electrode power, Cl2 gas flowrate, BCl3 gas flowrate, and chamber pressure. The full factorial design of experiment method was used to select the combinations of experimental configurations. The ANN model was validated using a subset of the training data.
Vibration-Based Early Crack Diagnosis With Machine Learning for Spur Gears
Authors: Karpat Fatih , Dirik Ahmet Emir , Kalay Onur Can , Doğan Oğuz , Korcuklu Burak 
Published: February 2021

Abstract
Gear mechanisms are one of the most significant components of the power transmission systems. Due to increasing emphasis on the high-speed, longer working life, high torques, etc. cracks may be observed on the gear surface. Recently, Machine Learning (ML) algorithms have started to be used frequently in fault diagnosis with developing technology. The aim of this study is to determine the gear root crack and its degree with vibration-based diagnostics approach using ML algorithms.
To perform early crack detection, the single tooth stiffness and the mesh stiffness calculated via ANSYS for both healthy and faulty (25-50-75-100%) teeth. The calculated data transferred to the 6-DOF dynamic model of a one-stage gearbox, and vibration responses was collected. The data gathered for healthy and faulty cases were evaluated for the feature extraction with five statistical indicators. Besides, white Gaussian noise was added to the data obtained from the 6-DOF model, and it was aimed at early fault diagnosis and condition monitoring with ML algorithms.
In this study, the gear root crack and its degree analyzed for both healthy and four different crack sizes (25%-50%-75%-100%) for the gear crack detection. Thereby, a method was presented for early fault diagnosis without the need for a big experimental dataset. The proposed vibration-based approach can eliminate the high test rig construction costs and can potentially be used for the evaluation of different working conditions and gear design parameters. Therefore, catastrophic failures can be prevented, and maintenance costs can be optimized by early crack detection.
A Hybrid Supervised Deep Learning and Nonlinear Finite Element Framework for Efficient Fatigue Life Predictions of Rotary Shouldered Threaded Connections
Authors: Song Fei , Li Ke 
Published: February 2021

Abstract
In this paper, a hybrid computational framework that combines the state-of-the art machine learning algorithm (i.e., deep neural network) and nonlinear finite element analysis for efficient and accurate fatigue life prediction of rotary shouldered threaded connections is presented. Specifically, a large set of simulation data from nonlinear FEA, along with a small set of experimental data from full-scale fatigue tests, constitutes the dataset required for training and testing of a fast-loop predictive model that could cover most commonly used rotary shouldered connections. Feature engineering was first performed to explore the compressed feature space to be used to represent the data. An ensemble deep learning algorithm was then developed to learn the underlying pattern, and hyperparameter tuning techniques were employed to select the learning model that provides the best mapping, between the features and the fatigue strength of the connections. The resulting fatigue life predictions were found to agree favorably well with the experimental results from full-scale bending fatigue tests and field operational data. This newly developed hybrid modeling framework paves a new way to realtime predicting the remaining useful life of rotary shouldered threaded connections for prognostic health management of the drilling equipment.
Simulation of Large-Scale Magnetic Manipulation of Micro/Nano-Material Based on Monte Carlo Method
Authors: Su Che-Fu , Xiang Xinrui , Wang Jirui , Fratto Edward , Charmchi Majid , Gu Zhiyong , Sun Hongwei 
Published: February 2021

Abstract
Magnetic assembly of micro/nano materials are of great interest due to their unique properties. These nano-scale materials can be ensemble with other matrixes to prepare for new functional micro/nano composites with enhanced specific properties such as, thermal conductivity. In this study, we demonstrated the distribution and magnetic alignment of nickel (Ni) nanoparticle/nanowires inside of a non-magnetic matrix, (e.g., water or a molten wax), experimentally and computationally. A two-dimensional Monte Carlo simulation model is employed to investigate the aggregate structures of Ni nanoparticle/nanowires subjected to a one-directional static magnetic field. It is anticipated that the applied magnetic strength will influence the attractive forces between nanoparticle/nanowires that will produce chain-like cluster structures parallel to magnetic direction where the aligned chains will be separated by a range of distances that are also function of magnetic field strength.
Development of Manufacturing and Characterization Methods for Carbon Black-Based Conductive Polymer Composite Sensors
Authors: Albright Tyler B. , Hobeck Jared D. 
Published: February 2021

Abstract
This research details the process and methodology deployed to better understand the arrangement of colloidal carbon black (CB) nanoparticles in a spray-layer-by-layer (SLBL) manufactured conductive polymer composite (CPC). The effects of various SLBL parameters on the qualitative consistency and electromechanical performance of the resulting sensors are experimentally examined. Microscopy techniques are used to investigate the distribution and resulting CB structures of CPC films deposited on different types of substrates. Various substrate preparation methods and effects are discussed. Electrical characteristics of CPC films are investigated via deposition between copper electrode pairs on printed circuit boards. Practical applications of the characterized films are discussed along with future works regarding such sensors.
Naturalistic Male Skateboard User Speed Study
Authors: Buczek Sean , Eichaker Lauren , Graham Troy , Maull Thomas 
Published: February 2021

Abstract
Skateboards have been used as a means of transportation and extreme sport participation for decades. However, the prevalence of skateboards as a source of transportation is increasing. The laws that permit skateboard users to travel in roadways and in pedestrian walkways can vary by state, city, or county, allowing for a large variance in travel speed and user behavior. The amount of data available for the average speed of skateboard users during travel and trick initiation is limited. This study will preliminarily describe the natural travel and trick initiation speeds of skateboard users. The data that is presented in this study is beneficial to a vast audience including, but not limited to: traffic safety, road and intersection design, accident reconstruction, skateboard design, bearing design and useful life, and wheel design and useful life. This is an observational study of users on public spaces; no personal identification or biometric data was collected.
The Acquisition and Management of Healthcare Data, Within a Hospital Infrastructure
Authors: Darby-Taylor James , Luís-Ferreira Fernando , Sarraipa João , Jardim-Goncalves Ricardo 
Published: February 2021

Abstract
The quality of care provided to citizens by professionals and institutions depends on the quality and availability of information. Early commencement of treatment and medication, and the decisions on how to proceed, depend a lot on patients’ data in the different modalities available. It is also important to notice that large pools of data help inform health and wellbeing parameters for the largest possible community. To make that possible it is necessary both to have the best hospital practices but also to get consent and collaboration from patients. In order to accomplish such a goal, it is necessary to use practices, which adhere to legal constraints and are transparent while handling data and also to transmit those practices and protocols to professionals and patients. The present document aims to provide a framework envisaging the seamless application of the clinical procedures, following legal guidance and making the process known, secure and trustworthy. It aims to contribute to clinical practice, and clinical research, thereby contributing to big data analysis by ensuring trust and best clinical data handling.
Capacity Fade Estimation of a Lithium-Ion Battery Through an Integrated Electrochemical Battery Model and Empirical Cycle Aging Model
Authors: Anwar Sohel 
Published: February 2021

Abstract
An electrochemical model based capacity fade estimation method for a Li-Ion battery is investigated in this paper. An empirical capacity fade model for estimating the state of health of a LiFePO4 electric vehicle battery was integrated with electrochemical battery model in Matlab/Simulink platform. This combined model was then validated against experimental data reported in the literature for constant current charge / discharge cycling. An HPPC current profile was then applied to the validated electrochemical-empirical battery prognosis model which reflected a real-time operating condition for charge and discharge current fluctuations in an electric vehicle battery. The combined model was simulated under the two different HPPC current inputs for three different cycle times. Additionally temperature was taken in account in estimating the cycle aging under the applied current profile to assess the present capacity remaining in the battery. The simulation results provided the state of health (SOH) of the battery for these cycling times which were comparable to the published experimental SOH values for constant current charge/discharge profiles. Thus this model can potentially be used to predict the capacity fade status of an electric vehicle battery.
A Comparison Between Numerical and Experimental High Reynolds Number Supersonic Jets Generated by Millimeter Scale Converging-Diverging Nozzles
Authors: Conahan Joseph M. , Ozdemir Ozan C. , Taslim Mohammad E. , Muftu Sinan 
Published: February 2021

Abstract
In thermal spray applications, such as cold spray, an inert gas jet (typically helium or nitrogen) is used to accelerate micron scale particles to supersonic velocities. The complex gas dynamics of these supersonic jets are critical to understand via computational methods for the control of the spray. This work compares supersonic jet waveforms visualized by schlieren imaging with those predicted by computational fluid dynamics (CFD) simulations. A supersonic nitrogen jet is produced by a millimeter scale converging-diverging nozzle with inlet pressures as high as 50 bars. The jet Reynolds numbers based on the nozzle exit diameter and stagnation gas properties range between 60,000 to 325,000. A schlieren visualization setup has been built which shows the first spatial derivative of densities within the flow field. The strong density gradients across the oblique shock waves in the jets allow for clear photographs of the flow pattern of the jets using this schlieren visualization setup. Comparisons between the experiments and the CFD results act as a validation technique for the accuracy of the simulations in terms of the positions and orientations of the oblique shock waves. Through this study, the nozzle internal surface roughness is determined to be a critical parameter in millimeter scale nozzles for the development of the boundary layer. The CFD surface roughness parameters inside the nozzle are incremented until the geometry of the oblique shock waves matches the schlieren images. This work validates the simulation techniques which will be used for future jet simulations, in which shock wave locations and orientations are important, such as jet impingement on a flat plate and particle-shock interactions.
Experimental Investigation and Data Analytics of Annular Cutting Velocity in Inclined and Horizontal Pipes
Authors: Seibi Abdennour C. , Salazar Brandon , Ben Hmida Jalel , Guillory Gordon 
Published: February 2021

Abstract
The lack of cutting transportation during drilling operations especially in horizontal and inclined wells can lead to large amounts of non-productive time and costly solutions. This problem has been encountered very often in the field due mostly to settlement of the cuttings at the bottom side of the hole and especially around pipe joints. Moreover, adopted rheological models are limited to 60 deg. inclination angle to predict the flow behavior of cuttings transport in directional wells. Therefore, the objective of this paper is to investigate the effect of various parameters related to the well configuration (inclined vs. horizontal), operating conditions, pipe/tool joints configurations, and flow conditions on the cutting velocity through an extensive experimental study with data analytics. The experimental approach was analyzed through film software, which allowed for the cutting velocities to be estimated. Regression models of cutting velocity with respect to each dimensionless group were formed and validated through a statistical analysis. A new empirical model for the cutting velocity was developed using multiple linear regression analyses. A sensitivity analysis was conducted to highlight the contribution of each dimensionless group on the variation of the cutting velocity. The newly proposed model for cutting velocity was tested and the calculated cutting velocity of 0.532 ft/s (.162 m/s) fell within the range of study between 0.188 ft/s (.057 m/s) and 0.690 ft/s (.210 m/s).
Conductive Polymer-Based Sensor for Soil Nutrient Detection
Authors: He Yue , Zhang Shenglong , Dong Ziqian , Li Fang 
Published: February 2021

Abstract
To increase the production of crops, chemical fertilizers are used in crop fields. However, underuse or overuse cannot increase crop yields but even decrease them and cause severe environmental problems. Thus, the detection and monitoring of chemical concentration are increasingly important. To build up and monitor a data-based system for a large area, such a method is costly and time-consuming. In this research, we developed a conductive polymer-based sensor to detect nitrate concentrations in soil water. Conducting polymer poly(3,4-ethylenedioxythiophene) (PEDOT) was used as our sensing material. To increase its conductivity, we used the vacuum phase polymerization method to achieve a high conductive and stable polymer film. The conductivity of the polymer film is 500 S/cm. Our results have demonstrated that the conductive polymer-based sensors have high sensitivity to nitrate solution. The response to 1000 ppm nitrate solution is 47.2% (Response = (Initrate - IDIwate) / IDIwater). The sensors can detect nitrate range from 1ppm to 1000 ppm. The response time is less than 1 minute. This impedance-based sensor will eventually be integrated with the surface acoustic wave sensors, combined with an antenna and a GPR unit for low maintenance, autonomous, and in-situ soil nutrient sensing.
Pre-Feasibility Study and Unit Sizing of Hybrid Renewable Energy System for a Global System for Mobile Communications (GSM) Station in Tabriz, Iran
Authors: Amini Reza , Pourgol Mohammad Mohammad 
Published: February 2021

Abstract
This research, a part of more extensive research, presents pre-feasibility and unit sizing analysis of a hybrid system equipped with renewable energy resources in Tabriz, Iran (46°26/E, 38°06/N). This case study examines a GSM telecommunication station as a stand-alone load system. Renewable energy resources include photovoltaic (PV), a wind turbine as primary resources and fuel cell, and a battery stack as backup and energy storage systems, while an electrolyzer is used to provide the required hydrogen in the fuel cell system. Feasibility analysis and unit sizing are conducted based on climatic data such as solar irradiance and wind speed to derive an optimal model during the study. HOMER software is used to simulate the feasibility analysis, measure the size, and optimize the system. The results showed that the hybrid system performs cost-effectively. According to wind energy data and fuel-cell costs, sensitivity analysis is conducted. The load is a stand-alone GSM station that consumes 60kW per day with a maximum of 3.8kW per hour. This analysis showed that wind, PV, electrolyzer, battery, and fuel cells are not currently the best solutions. Shortly, according to improved prediction in fuel-cell technology, it is expected that this source as a backup will be the best option because it has decreased fuel-cell costs up to 65 percent. Some criteria such as size, application, and cost, have been mainly examined. These results provide excellent opportunities for this hybrid system. The result shows using fuel cells in a hybrid system is not affordable by considering current fuel cell price, and the main suggestion hybrid system configuration is PV, wind turbine, and battery, while for the nearby location of Tabriz by decreasing wind speed, using wind turbines is not feasible and more PV array should be added to the system.
A Linear Acoustic Phased Array for Nonreciprocal Transmission and Reception
Authors: Adlakha R. , Moghaddaszadeh M. , Attarzadeh M. A. , Aref A. , Nouh M. 
Published: February 2021

Abstract
Acoustic phased arrays are capable of steering and focusing a beam of sound via selective coordination of the spatial distribution of phase angles between multiple sound emitters. Here, we propose a controllable acoustic phased array with space-time modulation that breaks time-reversal symmetry, and enables phononic transition in both momentum and energy spaces. By leveraging the dynamic phase modulation, the proposed linear phased array is no longer bound by the reciprocity principle, and supports asymmetric transmission and reception patterns that can be tuned independently. Through theoretical and numerical investigations, we develop and verify a mathematical framework to characterize the nonreciprocal phenomena, and analyze the frequency conversion between the wave fields. The space-time acoustic phased array facilitates unprecedented control over sound waves in a variety of applications including underwater telecommunication.