bluuebunny/crossref_metadata_2025 huggingface dataset:
Experimental Evaluation of Heat Transfer and Pressure Drop in an Additively Manufactured Evaporator Channel Using Water as Working Fluid
Authors: Arif Mohammad , Aka Toochukwu , Ramakrishnan Vignesh , Hicken Jason Edward , Mishra Sandipan , Narayan Shankar
Published: January 2025

Additive manufacturing (AM) is gaining attention in thermal management due to its potential to fabricate complex internal cooling features. The non-conventional designs made possible with AM can be used to fabricate unique channel geometries, which could permit the removal of higher heat fluxes from electronic devices. This study conducts an experimental investigation of flow boiling occurring within a circular channel with a helical insert of helix ratio 0.29, constructed using AM technology with a length and diameter of 304.8 mm and 11.3 mm, respectively. The helical insert inside the circular channel creates a rotational flow not likely in conventional channel designs. This evaporator is tested in a system consisting of an accumulator, a variable-speed gear pump, a condenser maintained at a constant temperature using an externally cooled chiller, and multiple manual and electronic valves. The testbed has several sensors, including temperature, flow, and pressure sensors, integrated with a data acquisition system to capture the resulting flow and heat transfer. The flow boiling heat transfer is experimentally investigated under three different heat inputs (90, 100, 110 W) for a mass flow rate of 0.8–0.92 g/s. The performance of the evaporator section is compared with a baseline case, which consists of a smooth circular channel without the helical insert. It was found that the rotational flow enhanced the Nusselt number by nearly 181.5% relative to the baseline over the range of mass flow rates considered in this study. Likewise, the heat transfer rate is increased by 67.27% using the helical insert relative to the baseline test. The results indicate that pursuing similar channel designs within heat sinks could yield improvements in thermal dissipation.
Statistical Shape Modelling of the Nasosinusal Anatomy
Authors: Bertolini Michele , Tonghini Silvia , Rossoni Marco , Carulli Marina , Weise Susanne , Baldus Christian Jan , Colombo Giorgio , Bordegoni Monica
Published: January 2025

The human nose exhibits a huge variation in shape among individuals. All these variants alter the airflow through the nasal cavity and can impact how we smell odors. To acquire a better understanding of physiological and pathological functioning, it is important to study the effects of these modifications. SSM, or Statistical Shape Modelling, is a widely used methodology for considering morphological differences within a population.

In the literature, only a few studies analyze nasal anatomy in its entirety, including paranasal sinuses, whose segmentation is particularly challenging due to their complexity and variability. This work aims at creating a highly accurate SSM for the nasosinusal complex.

A traditional, greyscale thresholding-based segmentation approach was chosen, relying on Mimics software (Leuven, Belgium). 40 computed tomography (CT) datasets were considered. After segmentation, post-processing was performed to obtain watertight meshes using 3-Matic software (Leuven, Belgium), which was also used for the SSM generation.

The generated model shows all relevant landmarks that typically characterize a nasosinual complex. 32 different modes are needed to explain 95% of the total variance.

As a preliminary output, this study showed the feasibility and consistency of a statistical model of the nasosinusal anatomy. However, additional studies involving a larger sample size and a more robust validation process could be conducted.
Individual and Team Cognitive Biases That May Affect Engineers’ Assessments and Decisions on Risk
Authors: Gernand Jeremy M.
Published: January 2025

Cognitive biases play a significant role in decision-making processes within engineering, particularly when it comes to assessing risks associated with technology and making decisions regarding the mitigation of those risks, but while these topics have been explored in a variety of contexts like economics and medicine, current research of these processes in engineering is limited. This paper presents a review of research into engineering decision making and the effects that cognitive biases have on this process. Seventeen experimental studies were reviewed for their insights on these critical questions. While sample sizes are small, and professional engineers have rarely been involved in these experiments, several risk-relevant cognitive biases were confirmed, and some avenues for mitigation of those biases have been explored. These results highlight the critical need for further research in this area, and that more fundamentally sound organization, increasing diversity of research funding and management are needed. These include the investigation of engineering teams, of which only 4 of the 17 included studies have so far explored, with the functioning of teams in the identification and mitigation of risk being current standard engineering practice and critical to the continued safe implementation of technologies to make the world a better place.
An Adaptive Spatial-Terminal Iterative Learning Strategies in Roll-to-Roll Control Problems
Authors: Wang Zifeng , Jin Xiaoning
Published: January 2025

Roll-to-Roll (R2R) systems, featuring a series of motorized or idle rollers, are pivotal for high-volume, continuous production of flexible substrates across various industrial applications. One of the significant challenges in R2R printing processes lies in maintaining tight alignment tolerances, as specified by the device resolution for multi-layer printed electronics. The alignment of the printed patterns in different layers is known as registration. However, the inherent deformability of flexible substrates, coupled with the complicated dynamics of rollers result in significant difficulties for high-precision registration control. The registration error (RE) is essentially the positional or dimensional deviation caused by undesired variations in substrate tensions and substrate speeds. Despite the employment of real-time feedback controllers such as Proportional-Integral-Derivative (PID) or Model Predictive Control (MPC) for R2R tension control, they are not responsive enough to transient disturbances and they fail to continually enhance performance in the presence of angle-periodic disturbances, a common scenario in R2R systems due to roller rotations. In this study, we introduce a Spatial-Terminal Iterative Learning Control (STILC) method, incorporating an adaptive basis function, aimed at eradicating RE in an R2R gravure printing process. This basis function, alongside the registration error, is utilized in the update of the STILC compensation profile via a P-type Iterative Learning Control law. Our numerical experiments show that the STILC with the adaptive basis function effectively eliminates the RE caused by roller-motor axis mismatches. Additionally, we also verify by simulations that our method provides better convergence performance than the STILC with an invariant basis function. This novel STILC approach is promising for various industrial applications involving spatially periodic disturbances, especially those with unknown dynamics and without given state trajectories to track rigorously, which is common in engineering practice.
Deep Q-Learning Based Optimal Energy Management of a Plug-in Hybrid Electric Vehicle
Authors: Narang Vikas , Neema Kartavya , Anwar Sohel
Published: January 2025

The Energy Management Strategy (EMS) of PHEV controls the motor power & engine power to reduce overall energy consumption and manage the battery state of charge (SOC). EMS strategies for PHEV are typically divided into two categories rule-based and optimization based. Rule-based EMS are easy to implement and simple to understand. However, their application is limited to specific driving cycles which makes it difficult to achieve the optimal fuel economy. Optimization based on the other hand requires a system model to compute the optimal solution with given constraints. Here we propose a value-based reinforcement learning approach called Deep Q learning-based (DQN) strategy to determine the optimal action to maximize the fuel economy, without the need to have a system model. A DQN algorithm is trained on a UDDS cycle using a PHEV simulation model. The agent interacts with the PHEV environment during training and stores the transactions St, at, rt, St+1, then random mini-batches are sampled to calculate the state-action values Q(s, a; θi), and the target network is used to generate target Q value. These two are then used to compute loss function to update neural network weights. Fuel economy and convergence characteristics of DQN are discussed and compared with rule-based EMS and performance is analyzed on unseen HWFET cycle.
Early Detection of Faulty Operation of Air Conditioning Systems in High Energy Consuming Manufacturing Sites
Authors: Allen-Magande Hugh , Khazaii Javad , Esmaeili Amin , Khazaei Ali
Published: January 2025

Manufacturing facilities that produce high-precision electronics, solar panels, pharmaceuticals, and other critical products demand a controlled environment with minimal airflow velocities at high airflow volumes to prevent product contamination. Consequently, the energy consumption of these facilities’ heating, ventilation, and air conditioning (HVAC) systems is substantial. This study focuses on assessing and enhancing the energy efficiency of HVAC systems in such high energy consuming manufacturing sites, aligning with ASHRAE guidelines using early fault prediction. The authors have developed a model of typical manufacturing site and have utilized pattern recognition approach to predict the faulty operation of the HVAC system in one type of such facilities that are generally referred to as clean space as soon as it happens. This will protect the system from lengthy and costly faulty operation, before it is recognized by the maintenance personnel, during pre-scheduled service plans. The findings of this research can significantly expedite the detection of HVAC system malfunctions when integrated into the manufacturing site’s automatic control system.
Formulation of Loosening Phenomena in Actual Machines and a Consideration of Loosening Visibility
Authors: Hareyama Soichi , Kobayashi Satoshi , Ozawa Kazumi , Tsuji Kenji , Yamamoto Shinya
Published: January 2025

The results of the previous study showed that the loosening phenomenon of bolted joints used in actual equipment has a strong linear relationship on both logarithmic coordinates. This linear relationship is also observed in the tendency of axial tension reduction in Junker-type loosening test machine. This relationship can be displayed as a regression line and regression equation, and can be utilized for loosening lifetime estimation. The axial tension reduction phenomenon usually can’t be measured without using ultrasonic measurement methods or strain gauges etc. Therefore, measurement required preparatory work, and loosening can’t be directly observed. In this study, the possibility of visualization of loosening on a practical level is examined using a bolt with an axial force indicator. At first, static tightening and loosening tests are conducted and the indications of the indicator on the bolt are in good agreement with the axial tension from the tightening side to the loosening side. In the dynamic loosening test, the test results are organized on both logarithmic coordinates, and a regression line can be drawn, confirming a strong linear relationship similar to the measurement results on the actual machine. The axial force indicator values measured intermittently can also be plotted on the regression line. We confirmed that the use of bolts with axial force indicators can be used to accurately grasp loosening (tendency in axial tension decrease) and that the regression line easily obtained by continuous measurement can also be obtained.
Experimental Study of Composite Prepreg Layup and Fused Filament Fabricated Hydrodynamic Thrust Bearings
Authors: Ali Muhammad , Yasko Isaiah
Published: January 2025

This research experimentally investigates the operating performance of composite hydrodynamic thrust bearings manufactured using prepreg layup and three-dimensional fused filament fabrication methods. Literature on the specific composite formulations being experimentally tested as hydrodynamic thrust bearing stock material in this study is not publicly available. Experimental analysis was conducted on a traditional aluminum-based, bisphenol resin and carbon fiber prepreg layup, and a three-dimensional fused filament fabricated chopped carbon fiber filled nylon hydrodynamic tapered-land thrust bearing. Each bearing was subjected to specific loads and sliding speeds ranging from 0.18–0.72 MPa and 4.11–8.23 m/s, respectively. Oil type and temperature was held constant throughout the analysis. Film pressure distribution, film thickness, and the change in bearing temperature due to frictional heating were recorded for each test. Results show that both composite bearings operated with improved performance when compared to a standard aluminum bearing. The composite layup bearing operated with pressures, minimum films, and bearing temperature elevations 43% higher, 30.5% thicker, and 25% lower than the aluminum bearing, respectively. The composite fused filament fabricated bearing operated with pressures, minimum films, and bearing temperature elevations 254% higher, 113% thicker, and 34% lower than the aluminum bearing, respectively. The results presented here provide justification for further research and potential deployment of composite layup and FFF materials in the hydrodynamic thrust bearing industry.
Prediction Current Control of Tubular Linear Permanent Magnet Synchronous Motor With Variable Coupling Length
Authors: Gao Qiantong , Li Bingran , Ye Peiqing
Published: January 2025

The tubular linear permanent magnet synchronous motor (TLPMSM) can directly provide large thrust without the need for transmission mechanisms like screws. However, the coupling length between the stator and mover in TLPMSM may change during operation, leading to parameter changes and thrust fluctuations. In this paper, we propose an improved deadbeat prediction current control method for linear motors with variable coupling length (VC-DPCC). To solve the model mismatch and parameter mismatch problems, we first analyze the variation of inductance and flux linkage and calibrate the fundamental component of the parameters based on the analytical model. Then, our proposed method suppresses the impact of harmonic components of parameters and eliminates the current prediction error caused by magnetic field distortion. Finally, our proposed method is verified on an experimental prototype. Compared to traditional control methods, the proposed method offers faster response and reduced current fluctuations, significantly improving the performance of TLPMSM in terms of current and thrust control.
Mechanical Properties of FDM Printed PLA-MXene Composites
Authors: Srinivas Pooja , Barsoum Imad , K. Abu Al-Rub Rashid , Zaki Wael
Published: January 2025

This study explores the integration of MXene nanomaterials with Polylactic Acid (PLA) for 3D printing via fused deposition modeling (FDM). Incorporating MXene at concentrations of 0.125, 0.25, 0.5, and 1 wt% into PLA was carried out to enhance the mechanical and thermal properties of the resultant composites. Mechanical testing, including tensile and dynamic mechanical analysis (DMA), showed notable improvements in mechanical strength with MXene inclusion. The most significant enhancements were observed at 0.25 wt% MXene, with an increase in tensile strength and DMA revealed increased stiffness and energy dissipation. Differential scanning calorimetry indicated minimal changes in transformation temperatures across all composites. SEM analysis of fracture surfaces confirmed the transition from brittle to ductile fracture modes with increasing MXene content, particularly evident at intermediate concentrations. However, further increases in MXene content (1 wt%) led to decreased tensile properties, likely due to agglomeration effects. These findings underscore the potential of PLA-MXene composites in applications requiring improved mechanical performance and vibration damping, demonstrating the effectiveness of 2D materials in enhancing polymer capabilities through additive manufacturing.
Uncertainty Analysis Methodology for Fluid Dynamic Gauging Characterization
Authors: Olufade Adesola
Published: January 2025

The in-situ characterization of soft deposits in liquid media is important in several scientific and industrial applications but presents substantial technological challenges. Nevertheless, fluid dynamic gauging (FDG), which is based on the principle of fluid flow through a nozzle that is in close proximity to a wall, can be used to estimate the thickness of wet deposits but its uncertainty analysis has not been rigorously addressed in the literature. This paper reports the development of a comprehensive device-agnostic methodology that can be used to characterize the uncertainty of FDG instruments. Furthermore, the uncertainty analysis of a calibrated instrument that has a horizontal configuration, in which the flow rate is controlled but the pressure drop is measured, is presented. The results indicate that the calibrated instrument can be used to estimate the thickness of deposits to ±81 μm at a 95% confidence. In addition, the uncertainty in thickness was found to be dependent on the confidence level of the expanded uncertainty coefficient, magnitude of the estimated thickness, and final clearance or height of the nozzle from the wall. The highest sensitivities of the instrument to the systematic uncertainties in the flow rate and pressure drop were observed at relatively low and high thickness estimates, respectively. An important contribution of the paper is the introduction of an uncertainty component from external mechanisms that may be used for independent displacement measurements or position control in the course of operating FDG instruments.
Evaluation of Biochar Production Through Slow Pyrolysis of Corn Agroindustry Residues in the Department of Córdoba, Colombia
Authors: Rhenals Hoyos Jorge Emilio , Cogollo Torres Cristina Isabel , Bula Antonio , Gómez Vásquez Rafael David , Rhenals Julio Jesús David , Mendoza Fandiño Jorge Mario , Arrieta Almario Álvaro Ángel
Published: January 2025

Biomass emerges as a promising source for bioenergy generation and valuable by-products, offering a sustainable alternative for utilizing agro-industrial waste. Among thermochemical conversion techniques, pyrolysis stands out for its capacity to transform biomass into three main products: Syngas, Bio-oil, and Biochar. Biochar, the result of biomass conversion, can represent between 4% and 40% of the original mass, depending on the conditions of the pyrolysis process. This solid material is of particular interest due to its multiple potential applications, which are regulated by international standards such as the International Carbon Standard and European EBC certification. In this study, a predictive model was developed to characterize the properties of corn cob biochar produced at different pyrolysis temperatures. The model is based on experimental thermogravimetric analysis and elemental analysis of the biomass, using empirical correlations to predict H/C ratios, OC content, inorganic content, heating value, and formation enthalpy. The results show that the pyrolysis temperature significantly affects the characteristics of biochar. It was observed that the thermochemical transformation of biomass reaches a maximum in gross calorific value at temperatures of 500°C, with an H/C ratio of 0.3 and an O/C ratio of 0.1. The formation enthalpies of biochar increase with the pyrolysis temperature, associated with the increase in organic carbon concentration. Furthermore, it is estimated that biochar begins to form at temperatures above 380°C, achieving a minimum H/C ratio of less than 0.7.
An Optimized Laser-Induced Graphene-Based Electrochemical Biosensor Modified With Graphene-Conductive Ink for Alpha-Fetoprotein Detection
Authors: Tabassum Ridma , Sarkar Pritu Parna , Jalal Ahmed Hasnain , Ashraf Ali , Islam Nazmul
Published: January 2025

In this work, a label-free electrochemical immunosensor was fabricated for the detection of alpha-fetoprotein (AFP) based on Laser-Induced Graphene (LIG) and modified with developed graphene polyaniline (G-PANI) ink. Alpha-fetoprotein (AFP) is a prominent biomarker for the diagnosis of primary liver cancer. For the prevention and treatment of hepatocellular carcinoma (HCC), it is essential to ensure the accurate detection of AFP. The performance of our designed immunosensor was evaluated by differential pulse voltammetry (DPV) and Chronoamperometry (CA) techniques were used to detect cancer proteins in the phosphate buffer saline (PBS). Both electrochemical analyses in different concentrations of AFP resulted in a linear relationship between peak current and the logarithmic plot of the concentrations with a correlation coefficient, R2 = 0.99. The limit of detection was also calculated to be ∼0.73 ng/mL in the range of 4 ng/mL-500 ng/mL. The nearly identical current outputs in the DPV measurements were obtained consecutive ten times which represents the sensor’s stability with an RSD of 1.66%. The results showed that the proposed immunosensor can be successfully used to detect AFP.
Enhancing Safety in Collaborative Workspaces: Defining Attention and Avoidance Zones Using Path Planning With Mobile Robotic Systems
Authors: Cheng Marvin H. , Camargo Hugo E. , Haney Justin M.
Published: January 2025

Ensuring the safety of human workers who work alongside mobile robots is extremely important. This study defines and delineates attention zones and avoidance zones for human workers to include both visual and audible aspects. Attention zones aims to keep workers aware of potential hazards. Specifically, visual attention zones focus on field of view and audible attention zones consider noise levels in the workspace. By establishing these zones, mobile robots can generate warning signals as human workers approach to minimize the risk of collisions. After determining optimal attention zones, researchers identify path planning strategies that can be adjusted based on visual and audible warnings to avoid potential accidents. The proposed attention zone-based warning system and the contact avoidance zone represent a critical advancement in enhancing worker safety in collaborative workspaces to promote a safer workspace.
Identification of Maxwell Model Parameters of Viscous Dampers With Elastic Traits
Authors: Farran Abdulrhman Mohmmed H. , Kashani Reza
Published: January 2025

Two methodologies for determining Maxwell model parameters for a viscous damper are introduced. Both approaches rely on experimental identification employing harmonic displacement loading (sinusoidal loading) across varied excitation frequencies. This entails simultaneous measurement of the damper’s displacement and the resultant force to construct its hysteresis curve. By integrating the force with respect to displacement along the hysteresis closed path — representing the area within this path — the energy dissipation of the damper is elucidated.

The first method involves starting the modeling process at each frequency with Kelvin-Voigt (K-V) model. This entails evaluating the parameters of the K-V model and subsequently relating them back to the parameters of the Maxwell model. The second method involves determining the phase difference between the measured force and displacement within one cycle. This, coupled with the utilization of the hysteresis curve, enables the direct extraction of Maxwell model parameters. Both methodologies have been employed in identifying the parameters of an eddy current damper at two frequencies, yielding similar results.
Adjustable Pedals in Automotive Design: a Digital Human Modeling-Based Clearance, Clash, and Reach Design Study
Authors: Joffe Gabrielle B. , Demirel H. Onan
Published: January 2025

Although many interior vehicle components, including seats, steering wheels, and mirrors, are commonly adjustable in modern cars, many drivers and passengers still experience a poor occupant-vehicle fit. Subpopulations such as short, tall, and pregnant occupants encounter comfort- and safety-related concerns in the form of increased risk of airbag injury, a lack of legroom, and inadequate abdomen-steering wheel clearance. Despite the fact that pedal placement is a crucial factor that directly impacts a driver’s choice of seating position, pedal adjustability is only featured in a limited selection of modern vehicles. The early design approach proposed in this research work uses digital human modeling (DHM) to assess improvements to automotive occupant packaging as a result of implementing adjustable pedals. Three case studies are presented, depicting “what-if” scenarios that analyze clearance, clash, and pedal reach in generic, low-fidelity computer-aided design (CAD) vehicle models with short, tall, and pregnant occupants. The results of this study demonstrate how adjustable pedals benefit occupants and highlight how DHM can be used proactively to improve occupant-vehicle fit in the early design phases.
Dynamic Response of Soft Artificial Fabric Muscle Actuators for Launch and Recovery Systems
Authors: Smith Michael P. , Gamel Kaelyn , Redington Allison , Warner Eric , Cavallaro Paul V.
Published: January 2025

This research investigated the kinematics and vibrational performance of McKibben-type artificial soft fabric muscles (ASFMs). The ASFMs are constructed of High-Performance Fibers (HPF) encompassing an oversized, pleated, nonstructural fluid bladder. These ASFMs have potential applications for the launch, recovery, positioning and control of undersea and surface vehicles and interface platforms. The vibrational characteristics of ASFMs constructed of both glass and aramid braid materials were evaluated experimentally by exposing a fluid-pressurized ASFM to flow-induced vibration (FIV) and measuring the response.
Bi-Directional Locomotion of Asymmetric Curved Soft Millirobot Under Magnetic Fields in Different Frequencies
Authors: Bulang Christophe , Trinh Jessica , Scurry Jordan , Na Suk Joon , Park Jungkyu , Kim Dal Hyung
Published: January 2025

This paper presents the bi-directional swimming locomotion exhibited by both asymmetrical and symmetrical curved soft millirobots (SMR) and their characterization. Also, the controllability of these curved SMR is demonstrated via feedback control. The various curvatures of the robots are achieved by subjecting the robot to high temperatures while they are constrained using a circular mold containing a variety of hole sizes. When subjected to sweeping magnetic fields, the curved SMRs engage in distinctive swimming behavior, propelled forward within the low-frequency regime of magnetic fields while reversing their swimming direction at higher frequencies with the frequency ranges being determined by the thickness and curvature of the specific robot. The mathematical model of their locomotion is developed based on Taylor’s model but includes both forward and backward velocity terms and is validated with experimental data. The presented findings afford a profound insight into the complex locomotion dynamics of the curved SMR, thereby engendering a pivotal foundation for the prospective evolution of this robotic system.
Self-Excited Vibration Analysis of a Reduced-Order Model of a Braked Wheel With Tyre, Including Suspension Mechanism
Authors: Gualdi Daniele , Sorrentino Silvio , De Felice Alessandro
Published: January 2025

This study is aimed at deepening the stability analysis of a braked wheel of a road car, including suspension mechanism, and focusing on the interaction effects between the friction characteristic of the brake and the force characteristic of the tyre. It is well known that both contact forces can independently lead to instability in their respective rotation dofs. However, it is of practical interest to understand their combined effects on stability regions, which hasn’t been investigated in literature yet.

A minimal model with 5 dofs is proposed for the analysis, including the 2 dofs of the standard quarter car model (bounce of sprung and unsprung masses), plus rotation of the wheel rim and compliance of the suspension mechanism in both torsional and longitudinal directions. Stability analysis of the linearized model is performed with the aid of stability maps, highlighting sensitivity with respect to the governing parameters. In addition, the system’s modal behaviour is investigated as well.
Mechanical Failure Mode of Spinal Dura Mater Under Pressurized Loading Conditions
Authors: Tamura Atsutaka , Sakaue Chikano , Hongu Jun-ichi
Published: January 2025

It is believed that mechanical damage to the meninges protecting the spinal cord in case of blunt external forces can cause idiopathic cerebrospinal fluid leakage. This is probably because even a small meningeal failure leads to the leakage of cerebrospinal fluid (CSF) that fills the subarachnoid space. However, the dura mater, the outermost layer of the three-layered meninges, is especially resilient and is unlikely to be easily damaged. On the other hand, it is also known that CSF leakage can be caused by daily activities such as coughing, sneezing, and falling. Because of these contradicting facts, it is difficult to understand the essential mechanism of CSF leakage.

In recent years, extensive efforts have been made to elucidate the mechanism of traumatic and impact-related injuries through computational simulations. In order to enhance injury prediction accuracy, it is crucial to comprehend the actual failure mode of biological materials under injurious loading conditions.

Therefore, in this study, we focused on the relationship between the intrinsic wrinkle shape formed on the dural surface and its mechanical failure mode. We measured the surface profile of the porcine dura mater and performed a pressurization test until dural mechanical failure occurred using a custom experimental test apparatus.
An Additively Manufactured and Optimized Bicycle Saddle Prototype for Low Weight
Authors: Yeole Shivraj Narayan , Kode Jaya Prakash , Maddineni Poojit , Bolisetti Lakshmi Mounika , Vajah Jyothi Bharadwaj
Published: January 2025

The world is transitioning from using conventional sources of energy to using renewable sources. As the transportation industry has a relatively large carbon footprint, people are opting for more sustainable modes of transport, such as bicycles for shorter distances. Bicycles not only are good for the environment but are also advantageous to humans in terms of health. Although the conventional bicycle saddle provides comfort to the rider, for longer distances or uneven terrain, the saddle can cause discomfort and health problems to the rider such as erectile dysfunction, soreness, and chafing on the inner thighs. These issues can be completely avoided by using a saddle that provides a good fit and right cushioning. The current study proposed an improved design of a bicycle saddle for a male rider of 100 kg weight and a sit bone distance of 125 mm. The objective was to reduce the saddle weight and improving the comfort. Material was determined by the stress exerted on it, as well as the material’s behavior under stress. TPU 95A was selected as the material due to its excellent mechanical and thermal properties. Cubic and hexagonal lattice structures were studied to determine the optimum structure. The saddle was also analysed by applying a range of loads from 700 N to 1400 N on TPU 95A material. The final design of the saddle was 3D printed to achieve a light weight prototype of 128 g.
Effect of Soaking Time on the Thermomechanical Response of Sintered NiTi Shape Memory Alloy
Authors: Alawwa Fares , Abu Al-Rub Rashid K. , El-Khasawneh Bashar , Zaki Wael
Published: January 2025

Out of all the several metal systems that can exhibit shape memory behavior, nickel-titanium (NiTi or nitinol) is still the most commonly used. An effective method for manufacturing nitinol components is by the process of sintering, which entails compressing elemental or pre-alloyed metal powder and using a suitable mold. Recent studies show that pressureless sintering of uncompacted pre-alloyed powder is a feasible method for fabricating nitinol. Using this technique omits the need to compress the powder into a green part prior to sintering. This study investigates the impact of soaking time on the production of nitinol by pressureless sintering of pre-alloyed NiTi shape memory alloy powder. The process entails heating and compacting nitinol at a specific rate and duration in a vacuum to facilitate its sintering. The material’s shape memory capability is evident from the phase transition peaks observed in the differential scanning calorimetry (DSC) thermograms obtained. It is shown that as the soaking time increases, the peak intensity decreases because of Ni rich area formation of Ni3Ti. In addition, energy dispersive spectroscopy (EDS) is conducted using scanning electron microscopy (SEM) to show that the phases present in the samples are mainly NiTi and Ni3Ti. Vickers hardness test indicated that the sample exhibits a hardness value that is similar to the values published in the literature. Using optical microscopy, the grain pattern of the polished samples shows a correlation between the sintering time and grain size. Overall, the results of this work show that the soaking time affects the grain size, and shape memory performance much more than it does to the hardness exhibited by the samples. furthermore, the process of pressureless sintering of pre-alloyed NiTi shape memory alloy powder can be an effective and efficient method for the fabrication of functional nitinol.
Numerical Investigation on Wave Energy Harvesting Based on the Vibration of a Membrane
Authors: Raihan Gazi Abu , Chakravarty Uttam
Published: January 2025

Wave energy harvesting has emerged as a promising sustainable energy resource in recent years, offering huge potential for future energy solutions. However, technological advancement in developing wave energy converters (WECs) is still insignificant due to several unsolved challenges. In recent years, rubber-like elastomeric composite topologies, that can streamline the PTO of WEC design, are becoming increasingly popular. This study presents a comprehensive numerical investigation into the potential of wave energy harvesting based on the vibration of dielectric elastomer membranes using frequency-based hydrodynamics modeling. The research aims to explore an innovative approach to energy by capitalizing on the vibrational energy of membranes in response to oscillation. The study employs hydrodynamics modeling techniques to simulate the interactions between ocean waves and designed floating membrane structures. The hydrodynamics data are mapped and calculated for the power generation of the wave energy converter in a specific time domain. The membrane, when subjected to wave action, exhibits specific vibrational patterns, which are then converted into usable electrical energy through an integrated energy conversion system. This research underlines the understanding of the mathematical modeling of wave energy converted using dielectric elastomer generators (DEGs) and provides value insights to design and optimize the wave energy converter using DEGs.
Optimised Design of Acoustic Black Hole for Broadband Noise Attenuation in Ducts
Authors: Pradhan Biren Kumar , Mohanty Amiya Ranjan
Published: January 2025

Broadband noise attenuation remains a challenging problem in ducts. The development of acoustic metamaterials in recent times has made it possible to control and manipulate sound waves in novel ways. Acoustic black holes (ABH) are passive type absorbers. The two major parameters affecting the sound absorption characteristics of ABH are known as power-law decay of duct radius and tailored wall admittance. In theory, the tailored geometry induces power-law decay of wave propagation velocity, and the incident wave gets trapped inside the geometry without any reflections. The current study aims to develop an optimised ABH design targeting broadband frequency. The transfer matrix method (TMM) approach is used to characterise the reflection coefficient and validated using numerical approach considering thermoviscous effect along the thin walls. The developed basic design is further optimised for the influencing parameters and validated using numerical analysis. The result shows a broadband noise attenuation for the optimised design of the black hole.
Cavity Wall Undulations to Manage Unsteady Airflow
Authors: Gabel Matthew , Lee Peter C. , Sarigul-Klijn Nesrin
Published: January 2025

We investigate the use of cavity wall features to delay cavity transition from the shear mode to the wake mode and obtain more favorable cavity conditions across the flight envelope. Undulations referred to as “juts” are added to the cavity wall to catch internal cavity vortices and delay their growth and shedding into the freestream. We use Large Eddy Simulation (LES) to model the flow over the cavity for compressible flow conditions. The frequencies of the undulations are adjusted with an optional trailing edge lip to investigate an optimum configuration. The juts are compared based on their complexity, noise profile, drag, and flow unsteadiness inside the bay. We also propose possible applications of these juts to real world cavities used on existing aircraft.
Optimized Sizing of Solar PV Cells, Battery, and Aircraft Design of PV-Powered Surveillance UAV
Authors: Almheiri Salama , Alhammadi Abrar , Khan Aijaz , Zafar Sayem , Dos Santos Gustavo
Published: January 2025

The scope of this paper is to optimize the design of a fully solar-powered surveillance Unmanned Aerial Vehicle (UAV). Iterative modifications of the Photovoltaic (PV) power system design and the UAV airframe design were adopted to ensure optimal results. ANSYS Fluent was used to run Computational Fluid Dynamics (CFD) simulations on the UAV to obtain results of aerodynamic forces. These results were used to estimate the weight of the UAV and the thrust which were found to be 12.6 kg and 7 N, respectively. Contours of the velocity, pressure, wall y+, and turbulence intermittency, were produced to study the flow regime around the UAV. A MATLAB script was programmed to obtain irradiance data specific to Abu Dhabi and validate it with actual irradiance data to ensure accuracy. A comprehensive calculation of the UAV’s power consumption of 113W throughout its forward-flight was conducted and led to the decision to use 80 SunPower C60 solar cells, 2 Lithium-Polymer 3S batteries, and a Maximum Power Point Tracking (MPPT) system. MATLAB Simulink was used to model the MPPT using the incremental conductance method for the effective extraction and tracking of the maximum power point under the varying irradiance conditions obtained in Abu Dhabi.
Enhancing Water Leakage Detection in Transmission Lines Through Deep Learning-Analyzed Ground Penetrating Radar Images
Authors: Alnaqbi Mariam , Rezk Mahmoud , Almaeeni Khuloud , Ebisi Fahed
Published: January 2025

Underground water leakage is a critical problem that water utilities are facing. Efficient and timely detection of water leaks is necessary to mitigate consequences like operational cost loss and infrastructure damage and ensure water systems sustainability. This paper introduces a novel approach to detecting water leaks in transmission lines, utilizing deep learning algorithms to analyze Ground Penetrating Radar (GPR) images. Conventional leak detection methods often lack precision and early detection capabilities. The proposed method automates GPR image analysis, enhancing detection speed and accuracy through Convolutional Neural Networks (CNNs). The researchers collected the data from controlled leakage setups, augmented them, and used them to train a CNN model optimized for GPR image features. Results demonstrate superior detection performance compared to traditional methods, offering early leak discovery and potential cost savings.
Research on the Influence of Characteristics of Secondary Suspension on Vehicle Vertical Vibration in High-Speed Maglev Vehicle
Authors: Xue Zhao , Zhang Jimin
Published: January 2025

Aiming at the phenomenon that the vertical vibration acceleration amplitude of the vehicle body of a certain type of EMS high-speed maglev train increased significantly after being put into operation for a long time, theoretically analysis and numeral calculation were carried out about the influencing factors of the vertical vibration characteristics of the vehicle body in this type of high-speed maglev train, determining that the secondary suspension device in the form of symmetrical closed-chain six-linkage this type of vehicle had obvious influence on the vertical vibration characteristics of the vehicle body. Then with the vehicle system dynamics model considering the dynamic characteristics of the symmetrical closed-chain six-bar linkage mechanism in the secondary suspension, the influence of the slewing resistance in the rotating pair of the mechanism on the vertical vibration of the vehicle body was deeply studied. The research results show that: the operating speed of the vehicle and the vertical stiffness of the primary suspension have little influence on the vertical vibration of the vehicle body; the vertical stiffness of the air spring in the secondary suspension and the slewing resistance at the rotating pair of the symmetrical closed-chain six-link mechanism both affect the vertical vibration characteristics of the vehicle body, but the latter has a more obvious impact, whose influence on the vertical acceleration amplitude of the vehicle body is up to more than 50%, compared with the test results; in order to improve the vertical dynamic performance of this type of maglev train, it is suggested to keep the slewing resistance at each rotating pair of the symmetrical closed-chain six-link mechanism in the secondary suspension at a low level during daily operation and maintenance of the vehicle.
Vibration-Induced Degasification of Trapped Gas From the Liquids: Low-Cost Solution
Authors: Karim Saad , Haider Syed Mustafa , Hussain Abbas , Najib Antash , Majeed Syed Umer
Published: January 2025

During the processing, transferring, and handling of liquids, a lot of changes occur such as pressure change, temperature change, and chemical reactivities. These changes often result in the formulation or detachment of gas which remains stuck inside the liquid in the form of bubbles. The formation of bubbles in liquids causes variations in pressure and volume resulting in poor processing and are difficult to remove. This bubble formation phenomenon is widespread during the processing of water, wax of candles, aluminum castings, epoxy adhesives, lubricants, coatings, drinks, polymers, emulsions, and suspension products. Moreover, medical applications such as droplet digital polymerase chain reaction require the precise removal of trapped bubbles for its successful completion. This current work investigated the effectiveness of a low-cost bubble removal method (degasification) from water through vibrations. Two metal plates, each with distinct surface roughness values (Ra = 0.89μm & Ra = 1.6μm), were immersed in a boiling water beaker. They were then subjected to various vibration frequencies to investigate the removal of gas bubbles. During boiling, gas trapped inside the water separated into bubbles, adhering to the metal plates similar to heat exchangers or vessels. These bubbles are removed using vibrations applied to the plates. From experimentation, it has been found that the metallic plate with lower surface roughness removed bubbles more rapidly than the other. Furthermore, among the seven vibration frequencies tested (20, 50, 70, 100, 150, 200, & 250Hz), a frequency of 150Hz proved most effective for both plates. This concluded that for efficient degasification, a lower surface roughness area coupled with a vibration frequency of 150Hz is optimal.
Synthesis of Large Stroke Constant Torque Mechanisms via Iterative Structural Optimization and Nonsymmetric Design for Enhanced Strain Relief
Authors: Bi Shun , Rehman Tanzeel Ur , Johnson Shane
Published: January 2025

Compliant constant torque mechanisms (CTMs) offer a novel approach to maintain torque levels with their nonlinear torque rotation relation in a monolithic design. Current CTM designs have limited stroke (20–70 degrees) and coefficient of variance (COV) (±4%). It is hypothesized that both complex geometry and ineffective compliant members together increase computational cost and reduce strain relief capacity. In this study, an iterative optimization approach to address ineffective members in CTMs is proposed, aiming to enhance stroke range while minimizing complexity. Leveraging design methods related to strain relief from constant force mechanisms (CFMs), our methodology employs the graph method with nodal perturbation to introduce more slenderness and tortuosity efficiently into the geometry. Through iterative optimization with FEA models for CTMs achieved extended stroke ranges of 20–130 and maintained COV to about ± 2%, overcoming limitations of traditional CTMs. The experimental results show 40–120 stroke ranges and around ± 3% COV. This study offers a new methodology to efficiently design CTM while overcoming certain limitations existing in current methods. Such method may further be utilized in surgical and robotics applications.
Geometry Prediction Simulation for Metal Additive Manufacturing: a Calibration-Free Approach
Authors: Lorin Samuel , Sagar Vaishak Ramesh , Cromvik Christoffer , Edelvik Fredrik , Wärmefjord Kristina , Söderberg Rikard
Published: January 2025

Metal additive manufacturing has significantly impacted the manufacturing industry, yet ensuring geometric quality remains a challenge during the design phase, with geometric distortion proving difficult to alleviate through post-treatment methods. In numerous manufacturing processes, simulations have proven effective; however, accurately simulating geometric outcomes in metal additive manufacturing necessitates the mathematical representation of various physical scales, spanning from the microscopic intricacies of powder and laser melting to the macroscopic deformation of components. Notably, existing simulation methods often fall short in accounting for the fluctuations in thermal history resulting from the cyclical heating and cooling inherent in layer-by-layer manufacturing processes. Also, as a design tool, there is a need for simple yet accurate simulations to aid the designer. This paper delves into the incorporation of thermal history variations and their influence on the accuracy of geometry predictions. This research introduces a new approach for using the inherent strain method. By employing micro-scale simulations, we conduct meso-scale simulations to estimate the plastic strain changes resulting from repeated heating-cooling cycles. The calculated plastic strain values are then used to determine inherent strains, which are applied layer by layer within block structures. This method considers the shrinkage experienced in previous layers, enabling accurate deformation simulations at the part scale. Validation of this approach involved a comparative analysis with five strain bridges, each featuring varying bridge thicknesses, fabricated through the Selective Laser Melting process. It is noted that the printed parts undergo a rotation after the support structure is cut off. When compensating for this rotation, the prediction of geometric distortion exhibited exceptional accuracy after cut-off. Consequently, the result suggests this method facilitates precise geometry simulation for components produced using the Selective Laser Melting process without the need for extensive inherent strain parameter tuning. The authors believe that this approach holds great promise as an aid for designers in the additive manufacturing industry.