bluuebunny/crossref_metadata_2025_split huggingface dataset:
Graphene Aerogels to Enhance the Effective Thermal Conductivity of Phase Change Materials
Authors: Park Jinho , Hankinson John , Ju Hyun , Haynes Comas 
Published: February 2024

Abstract
Despite their great potential for use in thermal load management and energy storage systems, applications of phase change materials (PCMs) have been limited in part due to the intrinsically low thermal conductivity of potential PCM materials, which limits timely thermal energy transfer into or out of the bulk of the material. In this work, a common organic PCM is infused into a three-dimensionally (3D) structured graphene to enhance effective thermal conductivity. To test the effectiveness of such a system, the T-history test and modified transient plane source (MTPS) technique were used to measure the thermal conductivity of the samples as the samples were thermally cycled through the phase transition temperature. The T-history test showed an increase in thermal conductivity of approximately 13% for a sample containing only 1.2% graphene by weight, and the MTPS testing showed results of thermal conductivity increasing by as much as 30% for samples containing 1.33% graphene by weight. The significant thermal conductivity enhancement resulting from only a small weight fraction of graphene aerogel highlights the potential of these graphene aerogel/PCM hybrids in thermal load management applications.
Dynamics of a Confined Cantilevered Pipe Concurrently Subjected to Internal and External Axial Flows: A Computational Study
Authors: Daneshmand Farhang , Liaghat Tahereh , Paidoussis Michael P. 
Published: February 2024

Abstract
This paper presents the results of several numerical simulations of two flow configurations involving a pipe conveying fluid and simultaneously subject to an external axial flow, using a coupled two-way Fluid-Structure Interaction (FSI) computational approach. The computational model is made up of two parts: a finite volume-based computational fluid dynamics (CFD) code for the fluid domain, and a finite element-based computational solid mechanics code for the structural domain. This approach solves the fluid and structural domains simultaneously, accounting for both the structural deformation caused by fluid pressure and the changes in fluid pressure resulting from structural deformation. To achieve accurate results, the process involves exchanging data between the solid and fluid models. In this technique, the pressure is transferred from CFD to structural analysis, and the deformation is transferred from structural to CFD analysis. This update occurs with each iteration until both solutions converge. Additionally, the simulation results are compared to those obtained from experiments for further validation.
Employing Machine Learning to Detect Post Resonance Backward Whirl in a Faulty Rotor System
Authors: Abdul Nasar Rafath , Al-Shudeifat Mohammad A. 
Published: February 2024

Abstract
Machine Learning (ML) algorithms have several applications in various fields of science. One of the major applications of ML is to recognize patterns in input signals and classify the input accordingly to various classes. Employing ML with structural health monitoring tools to identify faults has become an emerging area in research. Faults in rotor systems generally cause asymmetries in the rotor shaft stiffness which further results in generating higher harmonic components. One of the recent tools that has been prevalently used to identify faults is the post-resonance backward whirl phenomena (Po-BW). It can be said that any asymmetry in a rotor system should be affecting the dynamic of the whirling response of the system which can be further identified by detecting the Po-BW in the response. A two degree of freedom (2DOF) Jeffcott rotor (JC) model with a healthy and faulty shaft is used here to establish feature datasets. The obtained datasets are later used as input to train a deep learning network in the form of Alexnet to classify the input images based on the faults such as an open crack or/and unbalance related faults. The obtained results has indicated to classification accuracy of above 99%.
Structure-Reconsideration of Shell-Cushioning Materials of Helmet to Reduce the Impact Force of American Football Players by Finite Element Method
Authors: Terai Motoharu , Shimomura Kohei , Sakuma Atsushi , Zhang Yuelin 
Published: February 2024

Abstract
For further safety of American football players, this reports a helmet structure named endostructure helmet and describe its design method. It aimed at reducing the damage when the body is hit by a helmet while keeping the useful head protection gear. The proposed helmet has an outer pad on a shell. The outer diameter including the pad is fixed to a common football helmet. Some thickness patterns of the outer pad are proposed. Because the helmet size is fixed, when the outer pad becomes thicker, the inner pad becomes thinner. A thicker pad is expected to make the force travel through the body slowly but quickly reach the brain. Using the finite element method, a collision situation is simulated to analyze the effectiveness of this structure. Simple finite element models of the helmet, head, and body suffice to evaluate force absorption. Here, the decrease of the body pressure is shown by adding the outer pad. Making this pad thicker has a small impact in this decrease. When the outer pad is thicker than the inner pad, damage caused by vibration can be reduced.
Influence of Geometry and Mass on the Heat Transfer Properties of a Casted Heat Sink
Authors: Capela Paulina , Barbosa Flávia , Gomes Inês V. , Prior Filipe , Puga Hélder , Soares Delfim , Teixeira José Carlos 
Published: February 2024

Abstract
In recent years, manufacturing technologies have advanced, and producing parts with complex geometries is now possible. For example, a proper cooling system must be designed when casting is used in production of geometrically intricate component. An excellent example of a complex component to be manufactured are the heat sinks used in electric vehicles. Nevertheless, in this area of activity optimizing thermal dissipation is vital. Reducing component mass is also crucial for overall vehicle performance. Heat sink geometry and mass are key factors determining their heat transfer properties. In this context, the present study aims to explore the impact of heat sink geometry and mass on its heat transfer properties. Results showed that the heat sink’s mass can be reduced by 27 % while its thermal dissipation properties being improved. Also, the trapezoidal fin geometry is more effective in dissipating heat, leading to a 30% increase in the heat transfer coefficient of the heat sink at 50°C and a 36% increase at 90°C when compared to rectangular fins. The findings of this study can provide valuable insights for the improvement of the foundry design of this component, taking into account these critical factors.
Model of a Fluid-Level System for the Design and Analysis of Detention Basins Considering Recent Weather Extreme Events and Historic Precipitation Data
Authors: Hassanpour Pezhman 
Published: February 2024

Abstract
Engineering systems such as flood control dams and storm drain systems are designed to adhere to specific requirements outlined in engineering design codes. These codes heavily rely on local historic climate and precipitation data, which are updated every few years. However, the revision cycles of engineering codes and climate data are not necessarily synchronized. Consequently, it can take a decade or more for a shift in historic climate data to be reflected in the engineering codes. Meanwhile, climate change has increased the frequency and severity of extreme weather conditions. To address the impact of climate change until federal and state governments and regulatory agencies adopt new approaches, design engineers and firms may need to employ mathematical models to assess the resilience of their designs against recent changes in the local climate. The purpose of these models is not to replace the code requirements but to provide an additional layer of confidence that infrastructure can withstand future extreme weather conditions based on the most up-to-date data available. This paper utilizes a model of a fluid-level system comprising three interconnected tanks to simulate a runoff rainwater detention basin in Northern California. Each tank represents a pond within the detention basin and is modeled as a truncated cone, exhibiting nonlinear capacitance. The pipes connecting the tanks are modeled as resistances. Additionally, a bi-linear model is employed to represent a two-level pumping station that discharges water from the last pond into a man-made flood control channel. Sub-hourly rainfall intensity records from the National Weather Service (NWS) were obtained for the few days leading to the most recent historic highs. By integrating these data numerically, the inlet flow rate to each tank was determined as a function of time, enabling the calculation of rainfall water levels. The primary objective of this modeling exercise is to answer the question of whether the detention basin would overflow under specific rainfall intensity conditions. The significance of this question lies in the fact that overflow from one or more ponds can result in flooding events affecting adjacent buildings and infrastructure. Our model demonstrates that, despite meeting the construction code, the detention basin may experience overflows and cause damage to the serviced area under certain conditions. Consequently, future resilience may require a redesign of the basin.
A Framework for Human-Robot Teaming Performance Prediction: Reinforcement Learning and Eye Movement Analysis
Authors: Galvani Gustavo Martins , Korivand Soroush , Ajoudani Arash , Gong Jiaqi , Jalili Nader 
Published: February 2024

Abstract
In modern manufacturing environments, mass customization demands a balance between automation and human involvement. Human performance is influenced by the task load, with high loads leading to stress and low loads leading to disengagement, both of which can negatively affect performance. Predicting human engagement and performance can allow for adjustments in the team’s actions, leading to optimal performance. This paper proposes a framework for predicting the performance of a human-robot team in a quality control task based on task load, engagement, and eye movement data. The proposed framework employs a computational pipeline and eye movement data to monitor task load. It uses the NASA TLX questionnaire to assess participants’ task load and reinforcement learning to derive task-specific weights based on their performance. Then, the eye movement data is used to classify performance. The framework is evaluated with data collected from 16 participants performing a quality control task with a collaborative robot, in two scenarios. The study found that the framework predicts human-robot team performance with an accuracy of 96.88%. It also explores the potential of replacing the physiological data with a wristband with eye gaze for performance prediction due to challenges to record the eye movement data.
A Model-Based Identification Method of Variable Working Condition for Multi-Toothed Face Milling Process
Authors: Liu Shun , Jin Sun , Gu Qunfei , Du Xueming , Xiang Yang 
Published: February 2024

Abstract
The changing of working conditions, such as effective material removal rate (MRR) and cutting force, is an inevitable phenomenon during practical milling process because of tool wear and variable cutting parameter resulting in the variations of machined surface quality. It is an important indicators of machined surface quality which can be used for process online monitoring. However, for a multi-toothed face milling process the cutting force will be changing along with gradually increasing tool wear and designed variable cutting parameter both in feed direction and circumferential direction, which increases the difficultly for decoupling and identifying the changing portions of working conditions from the nominal curves in time domain. To address this issue, this paper attempts to provide a model-based methodology to identify practical parameters of variable working condition for multi-toothed milling process in real time. Based on a simulation model of instantaneous milling forces functions to the milling parameters and instantaneous MRR, the nominal milling force curves in time domain can be generated with the calculating of designed nominal MRR. Then an inverse problem-solving method is proposed to minimize the corresponding deviation curves between simulated and measured milling force curves, thus to identify the deviation of practical MRR from the nominal values. Therefore, the proposed model-based identification method of variable working condition can be applied to identify the deviations of cutting forces and practical MRR from their nominal values which are directly related with cutting teeth runout or changing tool wear. It provides an effective way and a theoretical guidance to identify the practical deviations from a normally irregular process of variable cutting parameter, and can be used for process monitoring.
Molybdenum Disulfide Solid-State Nanopores for Single-Molecule Biosensing
Authors: Saharia Jugal , D. Y. Bandara Y. M. Nuwan , Saharan Lokesh 
Published: February 2024

Abstract
Solid-state nanopore (SSN) is a propitious class of sensors delivering single-molecule level readouts with advantages such as high-throughput and label-free detection. While biological nanopores have shown great promise as a tool for fast and low-cost DNA/RNA sequencing, their artificial counterparts — SSNs — are yet to achieve the base-to-base signal resolution required for such high-precision genomic applications. Silicon nitride is the ubiquitous membrane material for SSNs and even the thinnest silicon nitride membranes are still a few nm thick (housing several bases at a given time), thus, unable to achieve single base resolution. In recent years, 2D materials such as graphene, hexagonal boron nitride (h-BN), and molybdenum disulfide (MoS2) have gained considerable traction as suitable membrane materials for sequencing applications. 2D materials can range from a single layer to a few atomic layers of thickness. The main advantage of 2D materials over relatively thick silicon nitride membranes is their ability to achieve high spatial resolution. They are also accompanied by superior mechanical strength and favorable optical and electronic properties compared to silicon nitride. Among the 2D materials, MoS2 has emerged as a promising option with several advantages over graphene and h-BN. Here, we review the application and potential of MoS2 as SSN membrane material for DNA and protein sensing in addition to the mechanical properties of MoS2 membranes, fabrication methods of MoS2 layers, and the process of MoS2 transfer on silicon substrates. We have also critically reviewed various theoretical and experimental studies that have reported using MoS2 nanopores for single molecule studies. While the overall number of MoS2 nanopore devices reported is low, the results obtained promise greater application of such devices for single molecule sensing.
Predicting Drop Dynamics in Sub-Critical Weber Number Regime: High-Fidelity Simulation and Data-Driven Modeling
Authors: Mahmood Taofiqhasan , Tonmoy Md Amanullah Kabir , Sevart Chad , Wang Yi , Ling Yue 
Published: February 2024

Abstract
Accurate prediction of the dynamics of a deformable and freely-moving drop in a uniform gas stream is essential for numerous applications involving droplets, such as spray cooling and liquid fuel injection. When the droplet Weber number is finite but moderate, the drop deviates from its spherical shape and deforms as it is accelerated by the gas stream. Since the drag depends on the drop shape, rigorously resolving the drop shape evolution is necessary for accurate predictions of the drop’s velocity and position. In this study, 2D axisymmetric interface-resolved simulations were performed using the Basilisk solver. The sharp gas-liquid interface is resolved using a geometric Volume-of-Fluid (VOF) method. The quadtree mesh is used to discretize the 2D domain, providing flexibility to dynamically refine the mesh in user-defined regions. The adaptation criterion is based on the wavelet estimate of the discretization errors of the color function and all velocity components. Parametric simulations are conducted by systematically varying the Weber and Reynolds numbers. The instantaneous drop shapes are characterized using spherical harmonic modes. The temporal evolution of the drag and the spherical harmonic mode coefficients are investigated to identify correlations between the drag and the spherical harmonic mode coefficients. The simulation data are also utilized to develop point-particle models for Euler-Lagrange simulations of sprays consisting of a large number of drops. Due to the complex interplay between droplet drag and deformation, accurate models cannot be developed through conventional physics-based approaches. Therefore, a data-driven approach will be adopted. The spherical harmonic mode coefficients up to the sixth mode are used to characterize the drop shape. The evolutions of the spherical harmonic mode coefficients from the simulation results for cases in the test set are used to train the Non-linear Auto-Regressive with Exogenous input Neural Network (NARXNN) model. The predicted mode coefficients are then used as input to train an additional NARXNN model for the drop acceleration.
Solar Driven Polygeneration System Using sCO2 Cycle, Multi-Effect Desalination and Absorption Cooling
Authors: Alharbi Sattam 
Published: February 2024

Abstract
In current work, a new poly-generation system using a solar tower with thermal storage is proposed. The molten salts which is the heat transfer fluid (HTF) provides input energy to the supercritical carbon dioxide (sCO2) cycle in the main heat exchanger (MHX) to run the sCO2 recompression Brayton cycle. Some of the waste heat in the precooler from the sCO2 recompression cycle is recovered in the generator to operate a lithium bromide-water (LiBr-H2O) absorption cooling cycle (ACC) in order to enhance the overall performance of the poly-generation system. Furthermore, the exit temperature of the molten salts from the MHX is still high and it has been used to generate motive steam for a multi-effect desalination plant with thermal vapor compression (MED-TVC) to produce distilled water. A details energy, exergy and simple economic analyses of the poly-generation system with power, freshwater and cooling production are performed using Engineering Equation Solver (EES) software. From the results, the poly-generation system produces a 100 MW of power, 780 kg/s of freshwater and 23.5 MW of cooling. From the simple economic analysis, the electricity and freshwater costs are 6.7 cent/kWh and 0.89 $/m3, respectively.
Full-Field Vibration Measurement on a Hollow Cylinder Using a Mirror-Assisted 3D CSLDV System
Authors: Yuan Ke , Zhu Weidong 
Published: February 2024

Abstract
A novel general-purpose three-dimensional (3D) continuously scanning laser Doppler vibrometer (CSLDV) system was recently developed by the authors to measure 3D vibration of a structure with a curved surface. As a non-contact system, it can avoid the mass-loading problem in 3D vibration measurement using triaxial accelerometers. In previous studies, the 3D CSLDV system was used to measure 3D full-field vibration of a turbine blade with a curved surface and identify its operating deflection shapes (ODSs) and mode shapes. It had the same level of accuracy as that with a commercial 3D scanning laser Doppler vibrometer (SLDV) system, but had much higher efficiency than the latter. However, the 3D CSLDV system can be limited by its field of view, which is a common problem for optical-based measurement devices. Moving the 3D CSLDV system to different positions to measure different parts of a test structure is not practical during 3D CSLDV measurement, since the system has to be re-calibrated once it has been moved, which can be time-consuming and introduce measurement errors. This work proposes a novel mirror-assisted testing methodology for 3D CSLDV measurement that aims to measure vibration of difficult-to-access areas of a structure without moving the 3D CSLDV system during the test, and stitch ODSs of its different parts to obtain its panoramic 3D ODSs. The proposed methodology includes a novel scan trajectory design method that uses virtual areas of the structure behind the mirror to conduct continuous and synchronous scanning of three laser spots, and a novel velocity transformation method that uses virtual positions of three laser heads behind the mirror to stitch ODSs of different parts of the structure. To demonstrate the proposed methodology, 3D CSLDV measurement is conducted on an aluminum hollow cylinder specimen, which has difficult-to-access areas such as its side and back surfaces, with the assistance of the mirror to obtain its panoramic 3D ODSs corresponding to its first two modes. Comparison between identified ODSs of the hollow cylinder specimen from the experiment and mode shapes from its finite element model is made and modal assurance criterion values are larger than 0.98.
Vibration Loosening Performance of Additively-Manufactured Bolted Joints
Authors: Gerini-Romagnoli Marco , De Agostinis Massimiliano , Nassar Sayed A. , Tedlapu Khushboo 
Published: February 2024

Abstract
The vibration loosening performance of threaded joints made of Additively Manufactured (AM) and Cast AlSi10Mg clamped members is investigated. Measurements of under-head friction coefficient are correlated to the vibration loosening results. AM parts are manufactured in 3 different orientations, affecting the angle between the deposition planes and the underhead surface. Loosening rates of AM parts are compared to cast and AM samples with machined surfaces. Conventional flange head M8x1.5 Class 8.8 fasteners are tightened to 5 levels of initial clamp load, ranging from 1 to 18 kN. All samples demonstrate comparable steady-state loosening rates, albeit with different wear patterns. Non-machined AM samples exhibit lower underhead friction compared to machined surfaces at lower preloads. At higher preloads, non-machined AM samples experience a notable increase in the bearing friction coefficient due to surface alterations caused by the sliding bolt head. Furthermore, non-machined AM specimens display significantly higher initial preload loss than machined specimens whereas machined AM joints exhibit similar vibration loosening characteristics to machined cast joints, with comparable loosening rates and sample-to-sample variance. Data variance is also higher for non-machined AM samples. More significant differences between the groups of specimens are observed at lower preloads; higher bolt tension, together with the underhead fretting, alters the surface of the AM samples, and its tribological characteristics. No significant load relaxation/creep is observed for any of the tested specimens. Discussion of the results and conclusions are provided.