bluuebunny/crossref_metadata_2025 huggingface dataset:
Design Optimization and Validation of Compliant Bidirectional Constant Force Mechanisms
Authors: Li Jing , Rehman Tanzeel Ur , Qaiser Zeeshan , Johnson Shane
Published: February 2024

Complaint constant force mechanisms (CFMs) have been applied in many applications, e.g., end effectors, micro grippers, etc., due to their inherent ability to maintain a constant force environment and increase energy storage efficiency. However, the typically designed uni-directional, i.e. tension or compression only, CFMs may not efficiently harvest energy from reversed cyclic loadings. Bidirectional compliant CFMs (Bi-CFM) are proposed to improve the energy storage efficiency for reversed loading conditions. Two different kinds of Bi-CFMs categorized based on their synthesis methods are proposed in this research: (1) Bidirectional Assembled CFM (BiAs) and, (2) Bidirectional Monolithic CFM (BiMo). The BiAs is designed with the combination of two existing compression CFMs in series. Alternatively, the newly proposed Incremental Complexity Design (ICD) method which involves gradually adding complexity to the graph-based topology selection and shape optimization is introduced to develop BiMo, leading to more efficient and systematic design optimization, while also enabling the exploration of simpler design solutions. Static and low frequency dynamic experimental validations were conducted on the Bi-CFMs with 3D-printed PLA. By iteratively increasing the complexity of the design, the variables associated with topology and shape optimizations are substantially reduced (89.5%–93.0%) compared with the literature, leading to enhanced computational efficiency. The BiAs, and the BiMo obtained from ICD both exhibit a high energy similarity index in analysis (0.95) and static experimental tests (0.83–0.88). Furthermore, the energy loss metric and energy similarity index for the BiMo is 26% lower and 6.3% higher on average than the BiAs during low frequency dynamic tests respectively. Usage of the proposed Bi-CFM would help increase energy storage efficiency and make a good contribution towards reversed cyclic loading fields, i.e. biomechanical engineering, wind energy storage, etc.
Review of Life Limitations for Acrylic Windows in Pressure Vessels
Authors: Hurd Daniel , Kemper Bart , Nappi Taylor , Williams Kaylie Kling
Published: February 2024

The standard ASME PVHO-1, Safety Standard for Pressure Vessels for Human Occupancy (PVHO), defines windows in pressure vessels. This includes specifying windows must be made of acrylic (Poly Methyl Methacrylate, or PMMA) and their service life. Windows can be some of the most expensive items regularly replaced in a pressure vessel. For human-occupied pressure vessels, catastrophic window failure generally results in injury or death. The design life has three aspects: total time under pressure, cyclic design life, and total chronological time from the date of initial manufacture. These limitations on these glassy polymer structures were set when the PVHO-1 code was first written based on limited experimental data back in the 1960’s and 1970’s. This paper will examine four decades of industry data using modern tools and material science to enable future development of service life standards for pressure vessel windows. Extending the life of windows will not only save the expense of replacing otherwise serviceable windows and reduce downtime, it can be a game changer in a number of potential acrylic window applications by making new applications feasible from a cost perspective.
On the Fatigue Capacity of a Subsea Intervention System Tool
Authors: Sepehri Ali , Bansal Gaurav
Published: February 2024

The offshore oil and gas industry is drilling into and producing from wells at pressures and temperatures greater than ambient conditions. Wells that are considered high pressure, high temperature (HPHT) experience even more challenging operating conditions. These HPHT wells have pressures greater than 15,000 psi (103.4 MPa) and/or temperatures of more than 350°F (450°K). To safely overcome the challenges in these operations, there is a greater demand for more advanced tools and new technology. Because of the sensitivity and potential impact on the environment, the industry is striving to homogenize the design and acceptance criteria. The Bureau of Safety and Environmental Enforcement (BSEE) requires equipment operating at high pressures and temperatures to pass a design verification analysis. Structural and fatigue capacity analyses are performed using finite element analysis (FEA) per ASME’s Boiler and Pressure Vessel Code, Section VIII, Division 3, API Standard 17G, and API TR 17TR8.

In service, threaded connections are often subjected to dynamic loads, and to maintain a secure connection, they are generally preloaded. The combination of preload and cyclic loads results in a multiaxial stress distribution over the connection, where the coupling’s threads act to increase the stress, initiating fatigue cracks. The objective of this study is to evaluate fatigue service life per the methodologies outlined in API Standard 17G and DNV-RP-C203 for a subsea intervention system tool. The fatigue life evaluation of the subsea intervention system tool is performed with a finite element model using a 3D 180° geometry. In this study, the fatigue characteristics (ΔM-N curves) of the tool are evaluated in accordance with API Standard 17G and DNV-RP-C203 using elastic-plastic material properties. The tool design fatigue capacity curves are generated per the S-N method, with no reduction factor (RF = 1) and use of a fatigue life reduction factor of RF = 4 per DNV-RP-C203. The S-N curve for subsea steel forgings in air subjected to a high mean tensile stress from DNV-RP-C203 for base material hot spots is used in this analysis. A sensitivity study is also performed to determine the effect of the external tension (riser tension) on the fatigue capacity.
Ultrasonic Monitoring of Sensitization in Aluminum Alloys
Authors: Petculescu Gabriela
Published: February 2024

Sensitization of aluminum alloys lowers the safety of watercraft and is costly to mitigate. The development of new probes for determining the level of sensitization in alloys has been in focus during the last decade. This article argues the importance of the further development of a probe relying on ultrasonic detection. At least three physical quantities (shear wave velocity, longitudinal wave velocity, and longitudinal wave attenuation coefficient) of AA5083 andAA5456 aluminum alloys show sensitivity to material modification due to heat treatment at different temperatures. Resonant Ultrasound Spectroscopy and Pulse Echo were used for the measurements. The quantities vary monotonically with the heat treatment time, which correlates with the degree of sensitization in the alloy. The rate of change decreases as saturation is approached, in agreement with nucleation and growth kinetics. The 20% change between the as-received and the fully sensitized states, measured for the attenuation of longitudinal waves at 5 MHz, is already orders of magnitude higher than what other techniques have been capable of detecting. Stronger effects at higher frequencies are likely; the closer the wavelength to the size of the precipitate, the larger the attenuation. Extensions to determine sensitization in other alloys may be possible.
Effects of Cooling Rate on the Stress-Strain Behavior of SAC305 Solder: An Atomistic Study
Authors: Fardin Sadib , Moresalein Md. Jawarul , Motalab Mohammad
Published: February 2024

SAC305 (96.5Sn-3.0Ag-0.5Cu) is a lead-free solder alloy that has gained popularity as a replacement for traditional lead-based solders due to its lower melting point, environmental benefits, and compatibility with common electronic components. In this study, Molecular Dynamics simulations are used to investigate the effects of cooling rate on the mechanical properties and stress-strain behavior of SAC305 at the nanoscale. The simulations utilize an atomistic model of the material, where a beta-tin (β-Sn) matrix is modified with Ag and Cu atoms. Four different cooling rates such as 2.5 K/ps, 10 K/ps, 50 K/ps, and 100 K/ps are used to examine the effects on the micro structure and mechanical properties of the solder material. The results of the simulations are analyzed in terms of ultimate tensile strength, Young’s modulus, modulus of resilience, modulus of toughness, and coefficient of thermal expansion. The stress-strain behavior is found to be more strongly affected at slower cooling rates (2.5 K/ps and 10 K/ps), while at higher cooling rates (50 K/ps and 100 K/ps) have less of an impact. An inverse relationship is observed between cooling rate and the ultimate tensile strength of the material, as well as its Young’s modulus and modulus of resilience. Furthermore, as the cooling rate increases, the modulus of toughness of the material has also been observed to increase. The coefficient of thermal expansion has been observed to decline as the rate of cooling increases.
Multiscale Study on Fused Deposition Modeling and Stereolithography 3D Printed Parts: How to 3D Print Better?
Authors: O’Donahue Andrew , Mendenhall Ryan , Fouladi Kamran , Eslami Babak
Published: February 2024

In a century where additive manufacturing (AM), specifically 3D printing, is significantly more than a prototyping technique, there is a gap in understanding how to enhance the quality and strength of 3D printed parts. Material characterization techniques in solid mechanics are well-established. However, there is still a need to develop a material characterization regimen for 3D-printed parts from different techniques, such as fused deposition modeling (FDM) or stereolithography (SLA). In this study, we perform a comprehensive benchmarking study comparing the quality and strength of prints using FDM and SLA for a given wall thickness, infill percentage, and overhang angle. The dogbone samples printed in each configuration are tested under tensile testing to measure their rupture strength. The same polymers formed as thin films are studied at the micro-scale using atomic force microscope (AFM) measurements. The results of this study will provide a detailed guideline for both general and technical users of FDM and SLA 3D printers to optimize printing time and material strength and quality for the given condition.
A Multiphysics Approach to Understanding Chemoreception in Bio-Robotic Fish Schools
Authors: Menzer Alec , Lei Menglong , Li Chengyu , Dong Haibo
Published: February 2024

By performing numerical simulations of the governing fluids and odorant transport equations, this study identified a relationship between the hydrodynamic interactions and olfactory chemoreception in a fish-like school. Simulations were performed using an in-house developed immersed-boundary method based incompressible Navier-Stokes flow solver coupled with the odorant advection-diffusion equation. From a hydrodynamic performance perspective, it was found that the drag of a trailing fish in a synchronous school could be reduced by up to 18% (relative to an in-line school) by introducing a lateral spacing (L) of 0.2 or 0.4 BL relative to the leader. Detrimental wake interactions in the form of strong high velocity regions produced by the leader intercepting the follower head were eliminated at these larger lateral spacings. However, large lateral spacing also led to suppressed surface odor concentration on the trailing fish hence reducing the trailing fish’s ability to detect the presence of the leading fish through chemoreception. At a moderate lateral spacing of L = 0.2BL, the drag induced by the follower is still lower than for the in-line follower (but slightly higher than for L = 0.4BL) and provides a high odor concentration on the body surface. Thus, the moderate lateral spacing serves as a balance between avoiding the detrimental high velocity regions behind the leader and increasing the odor interaction in an in-line synchronous school.
Adaptive Control of Curved Ship Blocks Welding Process Using Laser Active Vision System
Authors: Zhang Sen , Li Liangfeng , Zhang Yansong
Published: February 2024

Ship-curved blocks are mainly welded by arc welding, which are susceptible to deformation and processing errors but essential to the quality of the ship, with a complex spatial distribution and several meters of the weld length. Vision-based intelligent robotic welding, which extracts feature points from images even in massive disturbances and controls weld trajectory with standardized commodities and low labor intensity, is increasingly replacing conventional manual welding for flattened blocks. However, it is challenging for robotic welding to accommodate curved surfaces with changing gaps and large deformation during curved block processing. To address this challenge, we report on a developed intelligent robotic welding system accompanying an adaptive control method that tracks seam feature points, adjusts torch posture according to the curvature of the piece, and adapts welding parameters based on the gap values. The system comprises a welding machine, a 6-axis robot, an industrial computer, and a laser profile camera. The adaptive control method uses images from the laser active-vision camera to suppress welding interference and extract the laser profile, which controls the welding process based on posture and parameter adjustment criteria. The welding parameter adaptive control criterion is obtained from welding experiments, focusing on the gap value that impacts the welding process but changes during curved surface welding, deriving the optimum wire feed speed. The verification experiment focuses on Y-groove butt welding curved ship panels with a radius of curvature of around 1100m and a gap of less than 2mm. The welding piece, whose fill width and caps height is uniform, demonstrated that our intelligent welding method could produce high-quality welding of complex workpieces. The proposed system sets the stage for integrated implement seam tracking, posture adjustment, and adapting welding parameters. The proposed method also opens an avenue for welding the curved or easily deformed piece.
Effect of Laser Power and Diamond Tool Parameters for Micro Laser-Assisted Ductile Mode Material Removal on Fused Silica
Authors: Shirzadi Jahromi Hassan , Mohammadi Hossein , Kode Sai K. , Ellis Jonathan D. , Ravindra Menon Deepak
Published: February 2024

Fused silica is an essential material used in various applications due to its excellent optical and mechanical properties. However, processing it can be challenging due to its high hardness and brittleness, leading to sub-surface damage during grinding and polishing operations. Micro laser-assisted ductile mode material removal is a promising technique for achieving high levels of precision and accuracy in material removal. This technique leads to controlled and precise removal of material without inducing cracks or fractures. Despite its advantages, there are several challenges associated with the process, such as selecting the appropriate laser power and diamond tool geometry. In this study, we employed a Universal Mechanical Tester equipped with modified OPTIMUS, a laser-assisted machining technology to investigate the impact of laser and diamond tool geometry on scratch cut quality. Introducing and increasing the laser power demonstrated an improvement of around 251.7% in cut ductility, leading to a decrease in the severity of sub-surface damage (SSD). Altering the rake angle from −25° to −45° resulted in a 45.3% reduction in the critical depth of cuts (DOCs). However, when laser was employed, the critical DOCs increased around 34.4% in ductile mode material removal on fused silica samples, which underscores the criticality of the laser in this process.
Investigating the Interlaminar Shear Strength of Short Carbon Fiber-Reinforced PEEK Composites Fabricated by Fused Filament Fabrication
Authors: Yavas Denizhan , Sosa Luis
Published: February 2024

Carbon fiber (CF)-reinforced polyetheretherketone (PEEK) composites are highly valued for their exceptional physical and mechanical properties, making them suitable for various industrial applications. This study aims to investigate the interlaminar shear strength (ILSS) of short CF-reinforced PEEK composites fabricated by the fused filament fabrication (FFF) technique. Short beam shear (SBS) tests were performed to quantify the ILSS of 3D-printed CF-reinforced PEEK composites and unreinforced PEEK as the baseline. The digital image correlation (DIC) technique was used during the tests to measure in-situ full-field strain maps, enabling the observation of interlaminar failure mechanisms. The role of CF reinforcement and infill line orientation between adjacent layers on the ILSS of the PEEK composites was analyzed. The research findings reveal two main points: firstly, by adding short carbon fibers to 3D-printed PEEK composites, known as CF-PEEK, their ILSS can be improved by up to 100%, making it more effective than standard PEEK. Secondly, increasing the mismatch angle θ between infill lines of neighboring layers enhances ILSS in both PEEK and CF-PEEK by up to 50%. These outcomes could enhance the mechanical properties of 3D-printed PEEK and CF-PEEK composites in industries that require strong, lightweight materials.
Investigating the Role of Infill Geometry and Density on the Mode-I Fracture Toughness of Polymeric Materials Fabricated by Fused Filament Fabrication
Authors: Yavas Denizhan
Published: February 2024

The Fused Filament Fabrication (FFF) method is a 3D printing technology that allows for the creation of thermoplastic materials with various infill geometries in cellular and lattice forms, which facilities designing high-performance lightweight materials. This study aims to investigate the effect of infill geometry and density on the mode-I fracture toughness of thermoplastic materials fabricated by the FFF method. Polylactic acid (PLA) is chosen as the model material. Compact tension fracture tests are performed to measure the mode-I fracture toughness. The digital image correlation technique is used to measure the crack tip strain field varying with the examined parameters. The results of the study suggest that the elastic fracture toughness scales with the infill density in a power-law relationship. For the examined range of relative density, the correlation factor exhibits minor dependence on the infill geometry, with a value of 3. However, when considering inelastic fracture energy dissipation, the triangle infill geometry was found to have the highest crack resistance due to toughening arising from crack tip wandering. These findings have broad applications, and the proposed scaling laws can help predict the fracture toughness of polymeric materials made through the FFF method.
Graphene Aerogels to Enhance the Effective Thermal Conductivity of Phase Change Materials
Authors: Park Jinho , Hankinson John , Ju Hyun , Haynes Comas
Published: February 2024

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

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

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

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

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

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

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

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

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

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

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

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

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.
Investigation of Dynamic Hybrid RANS-LES Turbulence Modeling for CFD Simulation of a Normal Jet in Crossflow
Authors: Simmonds Cole C. , Walters D. Keith , Leylek James H.
Published: February 2024

The jet in crossflow is a canonical flow feature in many natural and engineered systems, ranging from pollutant dispersal in exhaust discharge to film cooling of high-temperature components in modern aerospace propulsion systems. The ability to computationally predict the flow features of jets in crossflow accurately and efficiently is therefore important for analysis and design for a wide variety of applications. In this study the capabilities of the dynamic hybrid RANS-LES (DHRL) turbulence modeling approach are investigated and compared to an industry standard Reynolds-averaged Navier-Stokes model (k-ω SST) in order to validate the accuracy of the two approaches. CFD simulations of a row of square vertical jets issuing into a wall-bounded crossflow were performed. Simulations were run for three different jet-to-crossflow velocity ratios using both models. To validate the computational results, the jet flow Reynolds number, density ratio, and velocity ratios were selected to match those used in a previously published experimental study [1]. Velocity measurements obtained from the experimental data plots were compared to the corresponding velocity measurements obtained from the results of both the DHRL and the RANS model. Results highlight the advantages of a hybrid RANS-LES modeling approach for jet-in-crossflow simulations.
Investigating the Effects of Feedback Time Delay in Human Upright Stability Using Virtual Reality
Authors: Neupane Kushal , Chagdes James R.
Published: February 2024

The study of human postural instability and its mechanism is critical for understanding the fall mechanics and the underlying factors engendering the instability in human postural balance. This is especially crucial given that the identification of these instabilities aid in the identification of fall risk, fall prevention strategies, and rehabilitation post-fall. In this study, we analyze the postural instability of upright human stance and the mechanisms responsible for the induction of this instability through artificial manipulation of the visual sensory feedback time through the application of virtual reality (VR). Previous studies have established the existence of instability via limit-cycle oscillations (LCOs) when the combination of neuromuscular gain and delay is excessive in a mathematical model, but experimental proof remains elusive. Through the manipulation of the visual sensory feedback in VR, we try to understand and evaluate specific neuromuscular mechanics, such as delay in feedback and its effects on muscle strength resulting in instability. Also, we try to observe the existence of compensating behavior in upright balance in subjects after the time of sustained delay.
Thermal Characteristics of Computer CPU Coolers Operating With a Pressurized Working Fluid
Authors: Das Shuva , Molki Majid
Published: February 2024

Working fluids used in the CPU coolers significantly affect the cooling performance. While many aspects of working fluids have been explored, only a few studies explain the effect of pressurized working fluids. The model cooler investigated in this study represents an air-cooled type of desktop CPU cooler. A computational domain from the whole cooler is selected, taking advantage of symmetry. With Reynolds number and temperature ranging from 100 to 2000 and 35°C to 95°C, a conjugate heat transfer analysis on the grid-independent domain brings out the Nusselt number as a function of the Reynolds number at a fixed Prandtl number. The Prandtl number remains constant here because the atmospheric pressure of the air was considered. Then further investigation is performed with an increase in air pressure from 10 to 100 atm. And associated thermal characteristics are analyzed. Results suggest that heat transfer increases with the increase in air pressure only up to a certain point of 20 atm. Heat transfer is maximum for the same air pressure when the Reynolds number is maximum. This way, the present study helps define the working fluids’ optimum design pressure limit in such coolers.
Exploring Efficacy of Machine Learning (Artificial Neural Networks) for Enhancing Reliability and Resilience of Thermal Energy Storage Platforms Utilizing Phase Change Materials for Sustainability and Mitigating Food-Energy-Water (FEW) Nexus
Authors: Sudhir Pinjala Sai , Banerjee Debjyoti
Published: February 2024

Thermal Energy Storage (TES) platforms can help balance the difference between energy consumption and supply. Phase change materials (PCMs) can enhance the performance of TES platforms by improving their resilience and reliability, as the high latent heat values of PCMs allow for more compact form factors. Inorganic PCMs have higher latent heat values than organic PCMs, but their reliability is often compromised due to their requirement for a high degree of supercooling for initiating nucleation for freezing. The “Cold Finger Technique (CFT)” can mitigate this issue by leaving a small portion of the PCM un-melted during the melt cycle, facilitating spontaneous nucleation during the freezing cycle. This study uses machine learning (ML) techniques to leverage the effectiveness of CFT by training an Artificial Neural Network (ANN) model to predict the time required to reach a designated melt-fraction of the PCM with outstanding accuracy. However, the fidelity of the dataset used to train the ANN algorithm can impact the accuracy of the predictions. The prediction errors were in the order ≤ 10% of the time required to reach the desired melt-fraction and are drastically bigger at 99% and 100% melt-fractions.