bluuebunny/crossref_metadata_2025_split huggingface dataset:
Compressor Speed Impact on Positive-Slope and Second-Quadrant Performance
Authors: Pronk Ola Ilseth , Bakken Lars Eirik 
Published: January 2025

Abstract
Rotational speed is a critical process variable that dictates the performance and dynamic behaviour of a compressor. Speed control, when possible, is the most effective method of optimising compression systems, while speed decay strongly influences machine rundown and stability. The speed affects the system and compressor responses during transient events, including trips and process disruptions. An accurate description of speed impact on off-design operation is needed to predict instability development and rundown trajectories in the surge area.
This study examines the off-design performance of a single-stage centrifugal compressor at five different speeds, focusing on the positive-slope and second-quadrant areas. Instability behaviour, system interactions, and second-quadrant characteristics are discussed based on experimental data. The second-quadrant characteristics of a stationary impeller with both an interlocked and a free-spinning shaft are shown.
Modified affinity laws are used to predict the machine response to speed changes in the unstable operating area. Similitude-compliant blowdown tests demonstrate speed scaling in the first and second quadrants. The speed significantly impacts surge development and the amplitude of surge oscillations, while the plenum volume affects the oscillation amplitudes, frequencies, and phase shifts. Finally, rundown trajectories showcase speed-related system dynamics in the off-design area.
The Development of the Awesome STEM Lab for the 21st Century Students
Authors: Gharib Mohamed 
Published: January 2025

Abstract
In this paper, the state-of-the-art STEM and fabrication lab, which spans the learning and fabrication applications for high school and freshmen college students, is pioneered and presented in detail. The lab was crafted from scratch to make the most of the available space for both learning and developing prototypes in a safe, efficient, elegant design and attractive environment for the students. The developed lab is called the Innovation Lab, which is a part of the educational STEM and outreach initiatives at Texas A&M University at Qatar. It is a space precisely designed and implemented to encourage students to learn, create, and innovate in a safe and efficient environment. The Lab was designed to be a destination for learners and educators from the community to come and use the state-of-the-art rapid manufacturing equipment and benefit from the STEM experts at the university. The developed lab is divided into two stations: the design station and the fabrication station. This paper details the designs, functions, and advantages of the elements in the developed lab to provide a guide for STEM lab designers and developers in establishing the labs needed by 21st-century students.
The Exergy Entity Environmental Analysis Applied to the Air Refrigeration Machine
Authors: Castillo Paz Jorge Renzo , Tsatsaronis George 
Published: January 2025

Abstract
This study showcases the practical application of the Exergy Entity Environmental Analysis (EEEA) for evaluating and improving energy conversion systems, particularly those operating below ambient temperature. Environmental impacts or footprints of the studied process, consolidated in a single score, are objectively distributed among all involved exergy entities based on two categories: one linked to the exergy source (E-footprint) and the other tied to the plant infrastructure (Y-footprint). Notably, this allocation process does not necessitate auxiliary equations, as well as product/fuel definitions at the component level. The circulation pathways of exergy entities (plant products, degraded and recirculated exergy streams) depicted by the Castillo Paz diagram are fundamental for this allocation. Furthermore, this method diligently examines the influence of decision variables on thermodynamic and environmental data, providing a valuable set of exergetic and exergoenvironmental variables, along with parametric charts to find design solutions in line with exergetic and environmental objectives. The simple air refrigeration machine (ARM) process serves as an academic case study, with the Eco-indicator 99 approach used to quantify environmental footprints. Our findings suggest that mitigating the overall exergetic imperfection of the ARM process typically results in reduced associated plant environmental footprint, as well as diminished environmental inefficiency.
A Data-Driven Design Pipeline for the Structural Optimization of Quadcopters
Authors: Subramanya Rishab 
Published: January 2025

Abstract
Unmanned aerial vehicles (UAVs), particularly quadcopters, are increasingly prevalent in hazardous scenarios such as disaster rescue or military reconnaissance. To tackle these issues, optimizing their structural integrity is crucial for enhancing their effectiveness. Currently, designers make improvements incrementally, which is time-consuming and expensive. This research paper uses data-driven design, performance prediction models, and AI-driven design recommendation tools to structurally optimize quadcopters (a subset of UAVs). The presented methodology offers a design assistant for human designers, satisfying rigorous design constraints through a streamlined and replicable process. The methodology involves creating a fundamental quadcopter model with nine parameters representing common features. Thousands of models were generated (and eliminated) via SolidWorks and further analyzed using finite element analysis (FEA) to quantify structural performance under identified loads. An Automated Machine Learning (AutoML) model predicts structural performance values and is validated using a variety of statistical and graphical metrics. Sensitivity analysis using SHapley Additive exPlanations (SHAP) identifies the relationship between parameters and performance values. Multi-Objective Counterfactuals for Design (MCD) suggest actionable design recommendations to optimize multiple queries simultaneously. This data-driven approach serves as a useful design assistant for quadcopter designers, holding potential for broader applications beyond structural optimization. Its replicable nature ensures applicability across various optimization domains.
Exploring Nickel Based Layered Double Hydroxides (LDH) as an Alternative of Platinum for Fuel Cell Applications
Authors: Alshweiki Ali , Olukeye Tiwaloluwa , Udeochu Uche , Bari Bushra , Tyagi Pawan 
Published: January 2025

Abstract
With the aim of developing fuel cell applications that are capable of generating efficient electrical energy, much of the attention should be given to exploring new catalysts that could be less costly and more abundant than the most commonly used catalyst, platinum. This work explores nickel-based layered double hydroxide (Ni-LDH) as a fuel cell’s affordable catalyst for Oxygen Reduction Reaction (ORR). Ni-LDH was prepared and drop coated onto a Glassy Carbon Electrode (GCE) to conduct a 3-way electrode cyclic voltammetry using an oxygen-saturated aqueous sodium hydroxide (NaOH) solution as electrolyte. The electroanalytical study of the GCE-modified Ni-LDH working electrode showed an enhancement of both anodic and cathodic peaks because of the presence of oxygen at low temperatures. Data obtained from this experiment will serve as a reference for fuel cell systems in which Ni-LDH modified working electrodes are scaled up for comparison of area-specific properties.
Characterizing Laminated Composite Materials Utilizing the Reinforced Mixed-Mode Bending Apparatus
Authors: Boutin William , Yekani Fard Masoud , Westby Maximillian , Norkus Tyler 
Published: January 2025

Abstract
The accuracy of fracture toughness is pivotal for the safety of composite and nanocomposite systems, with various techniques developed to measure fracture energy across different modes. Apparatus compliance issues arising from geometric non-linearities remain a significant concern and warrant further investigation. In the late 1980s, NASA introduced the Mixed-Mode Bending (MMB) apparatus to assess mixed-mode I-II strain energy release rates. Very recently, Arizona State University (ASU) researchers refined MMB into the Reinforced Mixed-Mode Bending (RMMB) apparatus for improved accuracy. This advancement involves redesigning MMB components to enhance stiffness and interlaminar fracture toughness measurements, crucial for predicting crack initiation and propagation accurately. Over the past three decades, laminated composite and nanocomposite materials have seen increased demand in aerospace, nuclear, and space technology due to their high strength-to-weight and stiffness-to-weight ratios. However, delamination remains a persistent challenge despite manufacturing improvements. Carbon nanotube (CNT) membranes have been employed to enhance mechanical properties and mitigate delamination, prompting extensive research into their integration with composites. The interlaminar fracture toughness of laminated composites reinforced with CNT membranes using the MMB & RMMB fixtures will be examined. Finite element analysis (FEA) will aid in understanding fixture compliance, energy release rate initiation and propagation, and fracture toughness envelope functions. Fracture tests comparing MMB and RMMB methods will be conducted, with load-displacement curves analyzed across various mixed-mode ratios.
Real-Time Detection of Negative Road Objects for Autonomous Vehicles to Improve Safety
Authors: Hag Ahmed Yousif , Li Wenyao , Anwar Sohel 
Published: January 2025

Abstract
This paper presents the preliminary results of our investigation in detecting negative road objects such as potholes and cracks in real-time for autonomous vehicles in order to improve safety. It is important to not only detect the existence of the negative road object from a safe distance, but also to identify the size, depth, and shape of such objects which plays a critical role in determining if the object must be avoided for safety or it can be ignored. We utilized open-source image databases such as ROBOFLOW (pothole) and RDD2020 of negative road objects for training a single layer convolutional neural network (CNN), such as YOLO. To test the trained algorithm, a small data set taken on the city streets in Indianapolis, IN was used in order to evaluate the accuracy of detection of the negative road objects. While the initial results show the ability of detecting the negative road objects through the proposed algorithm, more work needs to be done to improve the overall accuracy of detection with respect to size and shape of these negative road objects.
Performance Monitoring and Optimization of Robotic Production Lines: A Granular Approach for a KPIs-Based Study in Industrial Automation
Authors: Brunzini Agnese , Micucci Filippo Maria , Cappelletti Federica , Rossi Marta , Germani Michele 
Published: January 2025

Abstract
Technological innovation is profoundly transforming industrial production, notably through automated systems like robotic production lines. To harness their full potential, effective monitoring and evaluation are essential, facilitated by Key Performance Indicators (KPIs). This study conducts a thorough analysis of KPIs within industrial automation, focusing on a robotic production line assembling heat exchangers for refrigeration equipment. The aim is to measure, evaluate, and optimize performance in a smart, adaptable manner, creating a robust database that is not currently available as intended. Identified KPIs include standards such as cycle time, utilization, efficiency, and tailor-made metrics for this application, aligning with Industry 4.0 principles and sustainable manufacturing by monitoring energy and resource consumption as well. A granular data analysis approach captures the daily performance of each robot, enabling comprehensive assessment. Operational, daily, and line-level indicators provide insights into specific improvement areas. The methodology’s scalability allows for easy adaptation to larger production lines, highlighting its versatility and potential applicability across various industrial settings.
By proposing a granular and scalable KPIs framework, this research underscores the critical role of detailed performance monitoring in industrial robotics, driving continuous improvement and innovation while enhancing operational efficiency and competitiveness, in an ever-growing landscape of automated manufacturing environments.
Optimizing Aerodynamic Heating of a Mars Landing Vehicle: The Impact of Nose Angle and Aspect Ratio
Authors: Rhisat Fahad Nizam , Molki Majid 
Published: January 2025

Abstract
This paper presents a computational study of a Mars entry vehicle at three different Mach numbers: 5, 7.5, and 10. The study is two-dimensional and axis-symmetric. We simulated the entry vehicle for each Mach number with four different body lengths and nose angles. The primary objective of this study is to evaluate the impact of the Martian atmosphere’s viscous effects on aerodynamic heating. The computations used Unsteady Reynolds-Averaged Navier-Stokes (URANS) numerical simulations. The results demonstrate how nose angle and body length change the entry vehicle’s surface heat distribution. We conducted simulations to investigate the shockwave forming at the vehicle’s nose and the subsonic layer near its surface. The research highlights using computational models and simulations to explore aerodynamic heating during hypersonic flight and enhance the design of entry vehicles for secure landings on Mars.
Simulation of the Linearity and Sensitivity of Cantilever Beam-Based Relative Humidity Sensor With Peptidoglycan as the Functional Material
Authors: Fang Shuo , Chen Xi , Voiculescu Ioana 
Published: January 2025

Abstract
In this paper we present the MATLAB modeling of a cantilever beam humidity sensor based on peptidoglycan as sensitive material. This humidity sensor is conceived to monitor the humidity required for an actuating device fabricated from water-responsive paper. In order to produce dynamic response of the water responsive paper, the relative humidity should alternate between high level and low level of humidity. A humidity sensor with short response time that is sensitive and linear is required for this application. Peptidoglycan was chosen for this type of humidity sensor, because of its short response time. As a disadvantage, experiments with peptidoglycan showed the mass change versus relative humidity as not being linear. In order to improve the peptidoglycan linearity, we intend to fabricated a cantilever beam as support for humidity absorbent peptidoglycan. The cantilever bending as a function of relative humidity will be monitored by strain gage type of resistors.
The model of a cantilever beam was analyzed with MATLAB to explore methods to enhance the humidity sensor sensitivity and to find optimal geometry for the cantilever beam.
A Study on the Modeling Method of 3C Products Precision Assembly Based on Data-Driven and First-Principles Coupling
Authors: Shao Hongyu , Li Quanfu , Mi Xuefei , Tian Jiahan , Dong Shujun 
Published: January 2025

Abstract
3C products (Computer, Communication and Consumer electronics) assembly precision affects product quality and production efficiency directly. Aiming at the assembly process of 3C products, especially smart phones, this paper is proposing a coupling modeling method of data-driven and first-principle. Through the systematic analysis of the assembly error source, the corresponding dual-drive solution based on the engineering practice is proposed. Innovative coupling mechanisms such as mechanism analysis assisted data analysis fitting and data-driven explanation of unknown physical relations in mechanism analysis are proposed. Based on this model, big data and artificial intelligence technology are used to find the optimal combination of the best technical parameters and the control strategy under given environmental conditions factors. The modeling method was verified by taking the mobile phone frame dispensing as an example. The experimental results show that the dual-drive modeling method can quickly and accurately complete the modeling and control tasks, and obviously improve the product quality. High practical value and application opportunity are verified.
Dynamic Prediction of Heart Beating Rhythms Based on a Novel Model
Authors: Wang Xinya , Xu Yeyin , Chen Yuanhao 
Published: January 2025

Abstract
In this paper, an improved model of a cardiac conduction system with consideration of main pacemakers and heart muscles is proposed. The pacemakers consist of Sinoatrial node, atrioventricular node and His-Purkinje are simulated through three modified van der Pol oscillators with time delayed velocity coupling. The atrial and ventricular muscles are modeled by four coupled Hindmarsh-Rose (HR) neuron models which are also stimulated by pacemakers. The synthetic electrocardiogram (ECG) constructed through a combination of electric signals of atrial and ventricular muscles. An implicit mapping topology for the complex cardiac firings is established. Bifurcation criterion is defined to predict the period-doubled waveforms and the corresponding stability is defined by eigenvalue analysis. The cardiac firing results are presented to provide the normal and pathological rhythms in the cardiac conduction system. The presented study gives a new model to explore the nonlinear dynamics of heart beating.
Improving Human Reliability Analysis in Probabilistic Risk Analysis: A Novel Method for Analyzing and Modifying Performance Shaping Factor Dependencies in SPAR-H
Authors: Liu Xin , Yan Shengyuan , Zhang Xiaodan , Ahmed Waqas 
Published: January 2025

Abstract
The Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) method is widely applied in human reliability analysis (HRA) within the nuclear industry. Through expert judgment and predefined multipliers, SPAR-H calculates human error probabilities (HEPs) by quantifying the impacts exerted by performance shaping factors (PSFs). These multipliers adjust the nominal HEPs to account for specific situational factors within nuclear power plant operations. However, SPAR-H’s neglect of dependencies among PSFs often leads to unrealistic HEP estimates, underscoring a critical limitation of this approach. This study introduces a novel method that both qualitatively and quantitatively analyzes PSF dependencies within the SPAR-H framework to enhance the accuracy of HEP estimations. The method first uses denoising autoencoders (DAEs) to remove noise and extract relevant features from expert judgment data, then employs the decision-making trial and evaluation laboratory (DEMATEL) approach to analyze the influence relationships among the PSFs, and finally proposes a relative weight calculation method to modify the PSF multipliers. The findings suggest that this method significantly minimizes redundant calculations arising from PSF overlaps within SPAR-H, yielding more reliable and accurate HEP estimates.
Processing and Structure-Property Relation of Polypropylene/Nanosilica Nanocomposite Porous Structures With Solid-State Foaming
Authors: Almufarraji Said , Al Jahwari Farooq , Pervez Tasneem , Al-Kharusi Moosa 
Published: January 2025

Abstract
This study examined the development of polypropylene (PP)/nanosilica nanocomposite porous structures using solid-state foaming for advanced damping applications. By employing nanosilica, which is known for enhancing mechanical properties, and nitrogen as the blowing agent, this research aimed to create finely controlled porous structures within the PP matrix. The optimized extrusion process ensured a homogeneous nanoparticle dispersion that is important for consistent mechanical properties. Our findings demonstrated that adjusting foaming parameters, such as pressure up to 5000 psi, temperature at approximately 164°C, and nitrogen saturation time significantly affected the porosity and cell morphology. Specifically, the optimized conditions led to an average pore size reduction to 60 microns and a compression strength at 0.4% strain to 0.69 MPa with 0.1 wt% nanosilica. The foaming process resulted in a substantial weight reduction of approximately 86.43%, indicating a significant decrease in the material density which enhanced the composite’s potential application in lightweight structures and damping applications.
Advancements in Computational Modeling of Mechanical Properties of TPMS Lattice Structures
Authors: Abdelaal Ahmed F. , Al-Athel Khaled , Abubakar Abba , Akhtar Syed Sohail 
Published: January 2025

Abstract
Additive Manufacturing (AM) has revolutionized the fabrication of lattice structures, offering unparalleled customization of mechanical, thermal, and fluid-dynamic properties compared to traditional subtractive techniques. These intricate lattice configurations play a crucial role across diverse industries, such as medical, automotive, and aerospace, serving various functions including lightweight componentry, expansive surface area structures, and robust energy absorption systems. AM’s inherent flexibility allows for the production of highly intricate parts, including periodic lattice structures seamlessly integrated into complex surface geometries, thereby pushing the boundaries of conventional manufacturing methodologies. Within the realm of AM and lattice structure engineering, the utilization of Triply Periodic Minimal Surfaces (TPMS) stands out for its ability to confer unique mechanical characteristics. Recent research has focused on enhancing mechanical properties by introducing density gradients across TPMS lattice structures. However, a critical gap remains in understanding how these innovative designs influence compressive behavior. This study aims to address this gap by conducting a comprehensive comparative analysis of TPMS lattice structures fabricated via AM. Our investigation seeks to elucidate the influence of different TPMS structures on structural response under compressive loading conditions. our aim is to advance the understanding of AM-based lattice structure design and contribute to the development of high-performance components.
Surface Quality Enhancement of Additively Manufactured Parts Through Hybrid Additive Manufacturing Incremental Forming Approach
Authors: Tiwari Mithilesh Kumar , Ponappa K. , Tandon Puneet 
Published: January 2025

Abstract
Hybrid manufacturing (HM), integration of multiple manufacturing technologies, has revolutionized production methods. This paper introduces one such hybrid manufacturing method, Hybrid Additive Manufacturing Incremental Forming (HAMIF) approach, that seamlessly merges additive manufacturing (AM) and incremental forming (IF) in a single setup to enhance surface quality of additively printed parts. The experimental trials are conducted using a Tin alloy on the in-house developed HAMIF setup to fabricate specimens of AM sand HAMIF process. These specimens have undergone surface roughness testing using the Mitutoyo SURFTEST-SJ-500. Results demonstrated a remarkable improvement, reducing average surface roughness (Ra) from 0.3514 μm to 0.0555 μm. The optical microscopy analysis emphasizes the superiority of the HAMIF process in surface roughness and unidirectional grain formation, outperforming AM methods. The compacting effect induced during the HAMIF process reduces porosity and minor cracks, elevating surface quality. This offers a transformative solution for industries by advancing production capabilities and ensuring high precision of finished components. The HAMIF approach emerges as a promising avenue for augmenting surface quality in additively manufactured parts, presenting a significant step towards efficient and effective manufacturing processes.