bluuebunny/crossref_metadata_2025 huggingface dataset:
Adaptive and Scalable Approach for Time Series Anomaly Detection Using Transformer
Authors: Hu JieYi , Ding Sheng , Grimmeisen Philipp , Morozov Andrey
Published: January 2025

This paper introduces an innovative approach to time series anomaly detection, focusing on addressing the challenge of adapting to concept drift within input time series. Traditional time series anomaly detection methods often assume a stable environment for modeling. However, in practical applications, time series data may be influenced by concept drift, where the data distribution and features change over time. To effectively tackle this issue, we propose a dynamic adaptive model capable of real-time detection and adaptation to concept drift within time series data. Our algorithm leverages advanced time series anomaly detection techniques, particularly a Transformer-based online learning model, utilizing prediction errors as the basis for anomaly scores. Notably, the model incorporates a mechanism for concept drift detection and adaptation, continuously monitoring changes in the time series to adjust the learning rate accordingly. Additionally, our approach enhances the self-attention mechanism within the Transformer model by employing sparse self-attention, improving computational efficiency. Experimental results demonstrate that our algorithm exhibits superior accuracy in the presence of concept drift compared to traditional time series anomaly detection methods. This makes our approach particularly valuable in practical applications, especially in dynamic environments requiring adaptability. The combination of online learning, concept drift adaptation, and optimized self-attention contributes to the algorithm’s effectiveness in handling real-world time series data.
A Practical Approach to Evaluating the Adversarial Distance for Machine Learning Classifiers
Authors: Siedel Georg , Gupta Ekagra , Morozov Andrey
Published: January 2025

Robustness is critical for machine learning (ML) classifiers to ensure consistent performance in real-world applications where models may encounter corrupted or adversarial inputs. In particular, assessing the robustness of classifiers to adversarial inputs is essential to protect systems from vulnerabilities and thus ensure safety in use. However, methods to accurately compute adversarial robustness have been challenging for complex ML models and high-dimensional data. Furthermore, evaluations typically measure adversarial accuracy on specific attack budgets, limiting the informative value of the resulting metrics. This paper investigates the estimation of the more informative adversarial distance using iterative adversarial attacks and a certification approach. Combined, the methods provide a comprehensive evaluation of adversarial robustness by computing estimates for the upper and lower bounds of the adversarial distance. We present visualisations and ablation studies that provide insights into how this evaluation method should be applied and parameterised. We find that our adversarial attack approach is effective compared to related implementations, while the certification method falls short of expectations. The approach in this paper should encourage a more informative way of evaluating the adversarial robustness of ML classifiers.
Damage Detection and Imaging for Composite Storage Tanks Utilizing Ultrasonic Guided Waves
Authors: Jiang Houfu , Shen Yanfeng , Qu Yegao
Published: January 2025

Storage tanks, widely used for containing hazardous or high-pressure gases, are susceptible to damage due to factors like corrosion, cyclic inflations, and external impacts. The failure to promptly and accurately detect such damage can lead to catastrophic events, including leaks, explosions, and environmental contamination, posing severe risks to public health and safety. Moreover, timely detection plays a crucial role in maintaining the operational efficiency of storage facilities, avoiding costly downtime and ensuring compliance with strict safety regulations. Therefore, the detection of damage in storage tanks holds paramount importance in the realm of industrial safety and environmental conservation. Thus, this paper proposes the advancement of damage detection technology, utilizing the guided waves by detecting the variation of ultrasonic features for different damage conditions.

By carrying out an in-depth theoretical analysis via Finite Element Modeling (FEM), the sensitivity of the probing ultrasonic wave characteristics is studied systematically. The effective generation of guided wave modes is investigated, where the pure wave mode excitation is accomplished through a tuning procedure. To realize the damage localization, a three-dimensional numerical model is established by considering random defects placed on the surface. Two arrays of active sensors are placed on the tank, in order to record and evaluate the signal amplitude variation, while the propagating waves are travelling along the composite structure. Consequently, via the comprehensive consideration of transducer dimensions, center frequency, and propagating wave modes, the most effective damage detection setup is achieved and the sensing signals are recorded by the piezoelectric wafer active sensors (PWAS). Simulation results show that significant features such as the variation of the signal amplitude are fully captured, which are related to the presence of the damage along the wave path. Additionally, the Time of Flight (TOF) of sensing signals could further provide indicative information for localizing the particular defect on the tank. By changing the size, location, and quantities of defects, the feasibility and accuracy of this damage detection method is verified through the numerical analysis. The characteristic features extracted from multiple received signals are combined to reconstruct a color map of the damage condition on the storage tank. Finally, based on the quantification algorithm, damage can be imaged, utilizing crossing propagation paths of guided waves. The findings of this research possess superb application potential for the damage detection in composite tanks for enhancing the safety of storage systems. This paper finishes with summary, concluding remarks, and suggestions for future work.
Enhancing the Performance Measures of Abrasive Water Jet Machining on Cutting Titanium Alloy Specimens
Authors: Shanmugam Ragavanantham , Thangaraj Muthuramalingam , Ramoni Monsuru , Gaikwad Mahendra
Published: January 2025

The Titanium alloy is widely utilized in many bio medical application industries due its higher corrosion resistance and lower density. Since the Abrasive water jet machining (AWJM) process is used to cut such materials, it is essential to enhance the process mechanism as much as possible. The optimization of the AWJM technique can effectively improve the perfromance measures. In the present approach, an attempt was made to improve the performance measures of AWJM process with 40 mesh garnet particles while machining titanium (Ti-6Al-4V) specimens. The effect of input parameters such Water pressure (P), standoff distance (SD), abrasive particle flow rate (QA), and traversal speed (F) have been evaluated on performance measures such as surface roughness and material removal rate using Taguchi DEAR optimization approach. The optimal arrangement of input parameters in AWJM process were found as 1000 bar (P), 400 mg/min (QA), 4 mm (SD), and 400 mm/min (F) among the chosen factors with the error of 1.5%. It was also observed that the traversal speed possesses higher influential character on determining quality measures owing to its ability on modifying the impact energy.
A Conceptual Costing Software Tool Based on Machine Learning for Turbomachines
Authors: Sartini Mikhailo , Manuguerra Luca , Menchi Giacomo , Mandolini Marco , Lo Presti Giulio Marcello , Pescatori Francesco
Published: January 2025

The contemporary design landscape emphasizes the importance of product cost alongside performance, sustainability, and quality. Early determination of production costs, particularly in preliminary design phases like conceptual design, is crucial for maintaining competitiveness. Parametric cost estimation methods are preferred during these stages, leveraging identifiable relationships between design variables (cost drivers) and costs. Industry 4.0 innovations offer solutions to challenges posed by traditional Cost Estimation Relationship methods, with machine learning (ML) applications emerging as efficient tools for industrial processes. In the scientific literature, several approaches aim to create cost models using regression analysis, neural networks, and decision trees. The methods are implemented using data analysis tools that are not entirely suitable for product designers or cost engineers. The potential benefits of ML methods are not wholly exploited due to the limited effectiveness of the software tools.

Addressing this gap, the paper introduces a software tool with an administrative module enabling cost engineers to develop parametric cost models using ML algorithms based on the CRISP-DM method. The tool facilitates data collection, model training, evaluation, and benchmarking. Cost models are stored in a database accessible to design engineers, allowing quick and accurate cost estimations by providing relevant cost drivers. Feature importance algorithms highlight critical cost drivers.

The tool was tested in collaboration with a company that designs turbomachinery. During the experimentation, cost models were developed for one of the main rotor parts of an axial compressor (discs and spacer). The tool is a prototype to be further developed to manage an entire bill of materials, integrating risk management concepts.
Recruiting and Training the Future Workforce in Advanced Manufacturing Capabilities: A Case Study From the Tampa Bay
Authors: Amjad Muhammad Saad , Singh Pallavi , Zapata Valerie , Barger Marilyn , Diaz-Elsayed Nancy
Published: January 2025

The manufacturing sector is of paramount importance to a nation’s economy, as it creates employment opportunities, fosters innovation and enhances the competitive advantage in global markets. With the introduction of smart manufacturing technologies, the manufacturing sector has been able to boost its production performance, however, there is a skill gap that the manufacturing sector is facing when it comes to hiring a trained workforce. The US National Strategy for Advanced Manufacturing has underlined the importance of training an advanced manufacturing workforce with an opportunity to support historically underrepresented and underserved communities. We report the outcomes of a manufacturing training certificate program that is designed to provide equitable opportunities for high-quality, industry-relevant training that can lead to sustainable employment and economic growth. A holistic curriculum was developed to teach manufacturing education including CAD, CAM, 3D printing, AR/VR technologies, robotics and automation, assembly and production lines, and measurement systems. We report the outcomes of two cohorts that graduated in Summer 2023 and Fall 2023, in which a total of 86 training sessions were conducted. Twenty-five students enrolled and 17 students (14 males and 3 females) graduated across the two cohorts — among the graduates 47% identified as Hispanic or Latino, 41% identified as African American, and the rest identified as non-white/Hispanic; 16 of the graduates belonged to the low-to-middle income category. In the future, the program can be further expanded to include on the job training opportunities with local employers. We also provide recommendations to improve the learning experience of the students and enhance participation in the program.
Dense Spray-Coating Active Sensing Arrays for Health Monitoring of Composite Structures
Authors: Zhou Shulong , Shen Yanfeng , Wang Chunquan , Tian Yuan
Published: January 2025

This paper proposes a dense spray-coating active sensing arrays for composite structural health monitoring via the guided wave control. The actuation-sensing capability is realized by integrating piezoelectric nano-powder into a hosting matrix, followed by the formation of an active material layer on the composite surface through a spraying process. The actuation and sensing of guided wave mode can be achieved through the optimization of electrode configuration, in conjunction with dispersion effect. This research initiates with the analysis of the dispersion curves of composites to determine electrode parameters. Subsequently, three-dimensional numerical models in several array patterns, such as inter-digital type, comb types and irregular annular array, are meticulously constructed. Single-mode generation at one direction is achieved through the spatial distribution of array electrodes coupled with wavelength modulations in inter-digital array and comb array. At the same time, omnidirectional single-mode generation of irregular annular array is conducted through the electrode spatial distribution coupled with all direction wavelength modulations. Ultimately, the guided wave control performance is numerically investigated through exciting an anti-symmetric and a symmetric type guided wave mode in a multilayer composite plate. The numerical simulation shows that the proposed spray-coating system possesses great potential for future composite damage detection applications. The paper finishes with summary, concluding remarks, and suggestions for future work.
Toward Digital Twin of the Design Process: An Attempt to Capture Design Rationale From Spoken Words
Authors: Oizumi Kazuya , Nakazawa Ryo , Oda Yuya
Published: January 2025

While the drawings and other related data are shared among design teams, design rationale, such as the reasons behind the determination of design, is rarely expressed in these data. Moreover, they are rarely described even in documents such as design reports because it is too time-consuming to describe design rationale while designing a product. However, to reuse the information captured from design processes in the future, design rationale is indispensable. Therefore, a tool to capture design rationales with low workload is required. This paper attempted an automatic extraction of design rationales from conversations while designing a product. This attempt aims to establish a digital twin of a design process. Like other digital twins, sensors are installed to design processes, which are cameras, microphones and smart-glasses to detect when design rationales are spoken. The proposed method to extract design rationale detects where the design rationale is spoken based on multiple clues, which are important words, behaviors and physiological reactions. To verify the proposed method, design experiments were conducted in a controlled environment, which are designed to let designers speak out design rationales. As a result, one of the tests showed that 81% of design rationales could be extracted while 48% of where design rationales were not spoken was also extracted.
Sensitivity Analysis for the Effect of Misalignments on Performance Parameters of Harmonic Reducers
Authors: Guida Roberto , Bertolino Antonio Carlo , De Martin Andrea , Sorli Massimo
Published: January 2025

The rise of Urban Air Mobility (UAM) heralds a new era in transportation, offering efficient and sustainable aerial solutions tailored for urban settings. Crucial to this evolution are compact electromechanical actuators (EMA), facilitating precise control of electric Vertical Take-Off and Landing (eVTOL) aircrafts components with agility and responsiveness.

Compared to traditional hydraulic or pneumatic systems, EMA offer heightened efficiency, faster response times, and reduced maintenance needs, ideal for the UAM.

At the core of rotary compact EMA lies the harmonic reducer, ensuring high reduction ratios with minimal backlash and robust torque transmission. Its role in maintaining precision and minimizing losses is vital for system efficiency and reliability.

Reliability is paramount in UAM, necessitating redundancy measures and the adoption of Prognostics and Health Management (PHM) strategies. PHM integrates data-driven algorithms to predict component health, enabling proactive maintenance and mitigating failures.

This paper delves into the impact of misalignment on harmonic reducer performance, aiming to enhance the reliability and efficiency of EMA systems in UAM. By analyzing reducer dynamics, it aims to contribute to the advancement of UAM technologies, fostering safer and more dependable urban air mobility systems.
A Power Spectrum Bayesian Surrogate Model for a Geared Pair With a Cracked Tooth
Authors: Belli Olympio , Bittencourt Visnadi Laís , Fiori de Castro Helio
Published: January 2025

Even with the evolution of computation, the increased demand for high-quality models in engineering compromises its cost-effectiveness for use in several applications such as optimization, fault parameters, and uncertainty qualification. An example of this model is the dynamic simulation of gear trains and planetary. The computational cost is very high, and applications that require real-time results are impossible to obtain. To maintain the data quality, a solution to tackle this problem is to use surrogate data-driven models that can reproduce interpolated prediction. Artificial Intelligence is prominent in this area due to its unique ability to handle multi-dimensional data. However, the lack of explainability and interpretation breaks the advancements of this type of application in systems requiring high-reliability levels. In this sense, this work presents a reliable and interpretable surrogate model for a geared pair with a cracked tooth based on the combination of uncertainty quantification of Bayesian neural Networks with the interpretability of a Deep Operator Network.
Proposal for a Context-Aware Decision Support System for Waste Disposal Processes: Case Study Recycling at the Office
Authors: Ríos-Zapata David , Rendón-Vélez Elizabeth , Ruiz-Arenas Santiago , Vallejo Paola , Arbeláez-Estrada Juan Carlos , Villa-Espinal Jesus Hernando
Published: January 2025

Recycling processes are complex for people, and elements such as regulation variations or waste conditions affect the decision. In the business context, companies take responsibility for the final waste management. Therefore, they invest resources in training their employees in recycling and implementing manual waste separation at their internal collection centres. A context-aware decision support system that aids the waste-disposal decision-making process at companies can support people’s recycling learning process. However, a deep knowledge of the variables involved in the waste disposal process is needed, including understanding the most common mistakes, the recycling training processes, among others. This research includes the analysis of the waste-separation processes at six Colombian companies, including interviews with managers, waste-separation operators, and employees. Grounded theory was implemented to identify and select the variables affecting the recycling process, allowing the definition of the common requirements. These are the inputs for developing an intelligent system that aims to support decision-making. In this article, a solution concept is proposed. It can be argued that implementing these solutions can help companies achieve their sustainability goals, influencing people’s environmental behaviour and enhancing a circular economy.
Preliminary Design of an Additively Manufactured Bio-Inspired Thermosyphon for Thermal Energy Storage
Authors: Spence Ryan , Hazlett Gavin , Nuszkowski John , Stagon Stephen , Oshman Christopher
Published: January 2025

Thermal Energy Storage materials, integrated with phase-change materials (PCMs), enable near-isothermal heat extraction through thermosyphons, reducing production costs and carbon emissions. Thermosyphons, with high thermal conductivities, efficiently transfer heat through evaporation, convection, and condensation, are widely utilized for various applications. Conventional PCM thermosyphons often lack sufficient contact surface areas for optimized PCM solidification. This paper proposes a thermosyphon design inspired by fractal geometry found in biological structures, specifically the corn plant, suitable for additive manufacturing. By utilizing fractal geometry, these thermosyphons promise enhanced heat exchange through increased exterior surface area and internal liquid wicking structures. A prototype, designed for household use, has been modeled with empirical and analytical methods, focusing on capillary pressure and effective thermal conductivity. Operating at 1 kW while submerged in a low-temperature wax-based PCM (melting temperature 40°C), it comprises one main vertical tube and six base tubes with leaves. Full-scale production will utilize stainless steel powder additive manufacturing, incorporating radially and axially patterned rectangular channel cubes for the wicking structure. Analytical modeling confirms the system’s viability with an excess pressure of 2353 Pa, showing that capillary pressure will efficiently drive liquid flow despite liquid viscous pressure losses. This innovation holds promise for significant contributions to industry and building heating system decarbonization efforts.
Full Thermal Runaway Simulation of 18650 NCA Li-ion Cell With Venting and Combustion in a Large Enclosure
Authors: Ostanek Jason K. , Parhizi Mohammad , Kwon Byoungchul , Jeevarajan Judith A.
Published: January 2025

Thermal runaway in lithium-ion (Li-ion) cells in a module or battery pack features a wide range of length and time scales as well as many complex physical phenomena, presenting significant challenges for numerical simulation. Including fluid domain effects, such as venting and combustion, increases computation time significantly beyond the transient heat conduction problem. There exists a need to develop and validate new, efficient computational strategies for these multiphysics problems. The present work examines thermal runaway of a single 18650 cell placed in a large open container and subjected to thermal abuse via an external heater. The model simulates the complete thermal runaway process and includes decomposition reactions, internal heat and gas generation, vent gas flow, and combustion of the vent gas. Using strategic modeling assumptions, such as switching to a lumped body formulation during the sharpest heat release, allowed an economical modeling approach. Using detailed cell geometric modeling and detailed thermophysical parameter identification resulted in good agreement with experiments. The distribution of heat through the cell body and into the surrounding space was captured, as well as the spatial distribution of the vented gases. The model approach may be extended to modules or battery packs to investigate the risk of thermal propagation, including effects of venting and combustion.
Kinetic Insights and Morphological Transformations in Coal Gasification: An In-Depth Analysis With Model Validation
Authors: Khan Haider , Savvopoulos Symeon , Janajreh Isam
Published: January 2025

Understanding the reactivity and kinetics of feedstock particles is critical for process optimization, reactor design, and industrial gasifier scaling. This study investigates the impact of particle size on the gasification behavior of coal, focusing on the temperature and gas species concentration profiles during the process. Coal particles of varying sizes (1 mm, 4 mm, 6 mm, and 8 mm) were subjected to high-temperature gasification in a furnace maintained at 1073 K and under limited air supply. Temperature profiles are analyzed. The results indicate that smaller particles (4 mm) achieved higher peak temperatures suggesting faster reaction rates, while larger particles (6mm and 8mm) exhibited slower and lower peak temperatures. We also employed a sophisticated mathematical model integrating key equations for energy balance, heat flux, and char mass balance to simulate the residence time, core temperature and gas concentrations of O2, CO, and CO2 of the coal. Experimental data and model estimates indicate that thermal conductivity increases with particle size, while activation energy decreases at lower temperatures, suggesting higher reaction rates for smaller particles. Based on the validated numerical model, the sharper and faster reactions observed in the gas species concentration profiles for smaller particles indicate quicker gasification. The results show good alignment between experimental data and model predictions. These findings underscore the critical influence of particle size on thermal dynamics and reaction kinetics during coal gasification, highlighting the need for optimized particle size distribution to maximize gasification efficiency.
A Decarbonization Strategy for Preserving the Galapagos Islands With Green Hydrogen in Maritime Transportation: Empowering a Sustainable and Inclusive Green Tourism
Authors: Valarezo Alfredo , Ramos Emilia , Castro Westly , León Marco , Yépez Krutskaya , Bejarano M. Lorena , Escudero David , Thomson Camilla , Ingram David , Mignard Dimitri
Published: January 2025

This paper presents a proposal for implementing green hydrogen as a key component in decarbonizing maritime transportation in the Galapagos Islands. The objective is to transition interisland transportation for tourists using fuel-cell-powered ferries. Leveraging renewable energy sources, particularly wind and solar, as well as a mix of them, an integrated techno-economic analysis of the system is presented for producing, storing, and utilizing green hydrogen fuel. The approach addresses the unique challenges of the remote islands, focusing on the environmental impact and local sources of solar radiation and wind speeds in the vicinity of Baltra Island. The paper discusses a construction plan for a hydrogen plant capable of producing about 100 kg of hydrogen per day, utilizing AEM (anion-exchange membrane) modular electrolyzers for production and batteries and hydrogen tanks for storage. The feasibility and costs of three project scenarios (wind, solar, and hybrid) regarding energy coverage throughout the year, excess energy production fed to the grid, and cost sensitivity analyses of the energy market are thoroughly examined. This work contributes to the broader goal of achieving NetZero carbon for the Galapagos and preserving the pristine ecosystems of this UNESCO World Heritage site.
Investigating the Impact of Deposition Pressure on CRN Coating Properties and Machining Performance With Build Up Edge Formation
Authors: Chowdhury Mohammad Shariful Islam , Bose Bipasha , Arif Abul Fazal M. , Veldhuis Stephen
Published: January 2025

Coating properties, including hardness, elastic modulus, roughness, adhesion, and residual stress, significantly impact tool performance and wear patterns in machining. Physical vapor deposition (PVD) coatings offer tailored property combinations for specific applications, adjustable through deposition conditions like N2 gas pressure. This study focuses on customizing CrN coatings to enhance machining, particularly in reducing built-up edge (BUE) formation. Three CrN coatings with varying N2 gas pressures were deposited and characterized using X-ray diffraction (XRD) and nanoindentation testing. These techniques provided insights into the coatings’ structural and mechanical properties. The wear performance of these coatings was evaluated through a series of machining tests involving the finish turning of TiAl6V4 titanium alloy. Tool life studies were conducted to assess the coatings’ performance under machining conditions, while 3D wear volume measurements were executed to quantify the degree of tool wear and observe its progression. The results demonstrated that tailored CrN coatings with specific properties, such as a high elastic modulus, low H/E ratio and high plasticity index, were highly effective in reducing problems associated with sticking and built-up edge (BUE) formation.
Disruption Management of Interdependent Power Networks Using a Data-Driven Co-Design Approach for Enhanced System Resilience
Authors: Chung In-Bum , Luo Yi , Wang Pingfeng
Published: January 2025

With growing complexities and interdependencies, critical infrastructure systems such as power grids are likely to suffer from external disturbances and be vulnerable to disruptions. Hence, it is crucial for these systems to be designed and maintained with high resilience against disruptive events. A typical system undergoes three stages of operation: the normal stage, the degradation stage, and the recovery stage. To enhance the system’s resilience, the system can be designed so that the degree of degradation is minimal or the recovery strategy is operated effectively and efficiently to restore the nominal performance. In this paper, an integrated co-design framework is presented considering interactions between system design and recovery, where the post-disruption management scheme is considered in the pre-disruption design stage. This could result in a design that can easily be subjected to better restoration. For the optimization framework, a graph convolutional network was employed to reduce the computational burden in evaluating the performance of a power system during optimization. To show the efficacy of the co-design framework, it was applied and compared to a conventional sequential approach. For the case study, a distribution network of 37 buses based on an IEEE benchmark test network was employed. Both methods were able to create better designs compared to the original. While the sequential approach reduced the total length of connection lines by 0.11 percentage points more than the co-design approach, the latter performed approximately twice as well in terms of resilience. Although there was evidence of a trade-off between the network design and resilience, the case study results have proven that the developed co-design framework can enhance the network system’s resilience while improving the design.
Applying Binary Stiffness Mechanism to an Exoskeleton Arm for Casualty Evacuation
Authors: Chen Puning , Xie Yangming , Liu Chang , Wang Dahai , Dong Janet
Published: January 2025

A new binary structure is designed and summarized, which can be applied to the auxiliary wearable robotic exoskeleton design for casualty transfer. This binary structure, substituting for a conventional spring, utilizes the material’s elastic deformation characteristics to produce a significant increase in resistive force abruptly once a certain displacement threshold is exceeded, creating a spring-like effect. The binary structure provides flexibility for arm motion and substantial pulling force for maintaining the arm in position, transitioning smoothly from high flexibility to supportive rigidity as needed. Human elbow joints achieve a maximum range of motion within a ±20 mm displacement when the upper and lower arms are at a 90-degree angle, which also corresponds to the position where the device is connected to the forearm and the binary structure is not under load. The exoskeleton system, integrated with this mechanism, assists soldiers in transporting casualties with an additional support force of up to 320N while reducing arm injuries and fatigue. An approach is summarized, combining simulation-based theoretical analysis with experimental validations, demonstrating the potential of this binary structure to change casualty evacuation efforts in military operations.
Damage Mechanics–Based Simulations of Cutter-Rock Interaction With an Integrated Cloud CAE Platform
Authors: Gan Xiaoge , Song Fei , Zhao Shun , Panayirci Huseyin Murat , Gu Xuan , Villareal Steven , Li Ke , Alteirac Laurent , Zhang Youhe , Gjertsen Ole
Published: January 2025

Enhancing bit rock-breaking efficiency is the prerequisite for improving the rate of penetration (ROP) in drilling. The investigation of the rock-breaking mechanism in cutting is essential to evaluate and design the drill bit. Over the years, the oil and gas industry has developed a deep knowledge of cutter-rock interaction (CRI) that is vital for optimizing drilling and excavation processes. Drill bit design and drilling optimization requires a labor-intensive process to study CRI, which usually requires significant effort and resources based on experimental characterization. Therefore, the design and implementation of a high performing platform to enable efficient prediction of cutter-rock interaction is highly desired.

In this study, a new virtual CRI platform-digital rock laboratory (DRL) has been developed for accelerating accurate simulation of cutter-rock interaction at scale. The nonlinear finite element analysis (FEA) models that can reasonably describe the cutter-rock interaction mechanism with damage mechanics are first developed and serve as the compute engine for the computer aided engineering (CAE) platform. It has been shown that the developed FEA models agree favorably with experimental results in terms of cutting forces for various rocks, which has validated the modeling approach. We then introduce the process of developing a cloud-based computation platform for virtual cutter-rock interaction and showcase its advantages over traditional on-premises computing solutions. We highlight how the cloud-based platform replaces a years-long laborious laboratory testing process that is necessary to drill bit and cutting element development. This study aims to demonstrate the benefits of our cloud-based virtual CRI solution, including its novelty, efficiency, and sustainability advantages. Furthermore, the use of the platform can significantly reduce the cost and time for physical verification and validation (V&V) process that is necessary to minimum new product risks in the development, making it a more sustainable solution.
Multiscale Modeling and Simulation of Silicon Anode
Authors: Bansal Parth , Li Yumeng
Published: January 2025

The growing demand for portable battery power has led to ongoing innovations in battery design, with Silicon (Si) emerging as a promising material for Lithium-Ion Batteries (LIBs) due to its advantages over traditional graphite anodes. However, Si anodes face challenges such as volumetric changes during charge cycles, which cause internal stresses, delamination, and reduced capacity. To tackle these issues, this study examines the potential of using an Inverse Opal (IO) structure in Si anode design. Investigations are conducted on its effects on cracking, delamination, and capacity loss through a multiscale modeling approach. The multiscale solid mechanics model reveals different patterns of cracking and delamination for various thicknesses of Si anodes, highlighting the role of cohesive zones between Si grains. Additionally, an electrochemical finite element (FE) model, based on the results of solid mechanics simulations, shows that thicker Si anodes experience faster capacity degradation due to increased growth of the solid electrolyte interface (SEI) layer on their larger exposed surfaces. This combined approach provides insights into structural design strategies to address Si anode challenges and enhance LIB performance.
Role of Median Curves and Surfaces and Their Computations in Coordinate Metrology
Authors: Shakarji Craig , Yin Kexin , Qi Qunfen , Morse Edward , Srinivasan Vijay
Published: January 2025

This paper explores a novel role for median curves and surfaces in coordinate metrology, and the techniques to compute these curves and surfaces. The role includes the important problem of identifying potential outliers in two- and three-dimensional geometrical measurements and grouping the measured data into zones of containment without any prejudice to the assumption of underlying probabilistic models. It is shown that using the orthogonal l1-norm in the computation of median curves and surfaces leads to several desirable equivariant and invariant properties. It is also shown that using widely available, free, and open-source software packages renders the task of computing median straight lines, planes, circles, and spheres both easy and efficient. This opens a new avenue for interesting research and industrial applications for coordinate metrology.
Void Analysis of Continuous Carbon Fiber Reinforced 3D Printed Thermoset Composites
Authors: Islam Md Zahirul , Aryal Bibek , Rahman Md Atikur , Gibbon Luke , Hall Eric , Ulven Chad
Published: January 2025

Light assisted 3D printing of continuous carbon fiber reinforced thermoset composites has gained attention due to their ability to print with low-viscosity resin. Void formation during the 3D printing of continuous carbon fiber reinforced thermosets poses a challenge to improving the mechanical strength of the printed composites. The primary objective of this study is to characterize the voids present within continuous fiber reinforced 3D printed thermoset composites and define their influence on fracture or crack propagation. This study conducted 3D printing of continuous carbon fiber reinforced thermoset composites using a light-assisted printing process. Micro-CT analysis was performed to obtain information about the voids present within the 3D printed composites. The effect of a void on fracture or crack propagation depends on both the size and shape of the void. Results indicate that smaller voids were more spherical in shape, intra-tow in position and thus less conducive for crack propagation. However, inter-tow voids are cylindrical in shape. Proper impregnation of fibers with the thermoset resin is helpful to reduce irregularly shaped cylindrical inter-tow voids, which are conducive to crack propagation.
Minimizing Annealing and Growth Time for Consistent Multilayer Graphene Production
Authors: Velasquez Morquecho Enrique , Li Wei
Published: January 2025

This study explores the possibility of minimizing the time required for synthesis, particularly the annealing and growth time, while maintaining consistent production of high-quality multilayer graphene using chemical vapor deposition (CVD). Annealing and growth in conventional thin film growth processes are often lengthy and energy intensive. The investigation presented in this study controlled the growth of multilayer graphene on a suitable substrate. Characterization is done via Raman Spectroscopy. Samples produced with annealing and growth times as minimal as 30 minutes each were able to yield similar quality multilayer graphene as those with longer annealing and growth times of 2 hours each. The samples all had a 2D/G ratio of 0.51 or less indicating multilayer graphene, and the D/G ratio was below 0.12 indicating that minimal defects could be detected on the surface of the samples. This result opens the opportunity for increasing the production rate of large-scale high quality multilayer graphene samples using chemical vapor deposition.
Multi-Physics Simulation Study of 3-D Bi-Continuous Silicon Anode for Li-Ion Batteries
Authors: Bansal Parth , Li Yumeng
Published: January 2025

Lithium-ion batteries (LIBs) are indispensable for various applications due to their high energy density, yet the energy storage capacity of current carbon-based anodes has plateaued. Silicon (Si) emerges as a promising alternative due to its high capacity, abundance, and low toxicity. However, Si anodes face challenges such as significant volume changes during charge cycles, leading to issues like material pulverization and electrical isolation. To address these challenges, nano-sized Si and diverse microstructures, including 3D porous Si, have been investigated. Triply periodic minimal surfaces (TPMSs), notably Inverse Opal and Gyroid structures, offer potential advantages in Si anode design due to their customizable pore sizes and interconnected networks. This paper compares the performance of these structures in mitigating stresses, strains, cracking, and delamination using multiscale finite element models. By establishing a link between macroscale stress development and microscale failure mechanisms, the study aims to enhance understanding of anode morphology’s impact on LIB performance and reliability, offering insights crucial for advancing battery technology.
Performance Assessment of a Novel PNIPAM Polymer Desiccant Coated Heat Exchanger
Authors: Islam Md , Choudhari Mahmadrafik , Abdalla Ahmad , Mittal Hemant , Qureshi O. A. , Al Alili Ali
Published: January 2025

Desiccant assisted air conditioners provide an approach to meet the humidity and temperature needs of buildings by decoupling latent and sensible loads, hence improving the efficiency of air conditioning systems for hot and humid environments. The desiccant coated heat exchangers are used to control the latent load. This research work was carried out to evaluate desiccant coated heat exchangers (DCHX) system performance in terms of moisture removal rate, moisture removal capacity, moisture mass ratio, regeneration specific heat input. Poly (N-isopropyl acrylamide) (PNIPAM), a novel thermo-responsive polymer, has been produced and coated to heat exchangers in order to absorb atmospheric water vapor. DCHX went through a cyclic test (adsorption/desorption process) that may be regulated by adjusting its temperature with thermoelectric devices, such as Peltier devices. The development and investigation of cyclic performance of desiccant coated heat exchanger is made as per ASHRAE standard 139. The experiments were performed under different conditions by altering the temperature and humidity using a controlled environment chamber. The performance parameters like moisture removal capacity, moisture removal rate, moisture mass ratio, and regeneration specific heat input are calculated for measuring the cyclic performance of DCHX. It is observed that at higher relative humidity conditions, the moisture removal capacity and moisture removal rate are higher. At 90% relative humidity, the observed maximal moisture removal capacity and moisture removal rate are 0.121 kg/hr and 0.116 kg/hr respectively. Furthermore, the values of regeneration specific heat input are calculated, and they are dependent on humidity levels. Its values are lower at high relative humidity.
Multiphysics Modeling and Simulation of Gas Sensor for NO2 Detection
Authors: Bansal Parth , Jiang Yuan , Li Zhou , Cordero Sergio , Heussen Zahra , Senesky Debbie , Wang Pingfeng , Li Yumeng
Published: January 2025

Amid rising environmental concerns, this study looks into the importance of advanced gas sensing technologies through computational modeling and simulation. Utilizing the capabilities of COMSOL Multiphysics software, an examination is conducted on the performance of an innovative gas sensor featuring SnO2/reduced graphene oxide (rGO) nanocomposites designed for Nitrogen Dioxide (NO2) detection. The integration of modules for laminar flow, electric current, heat transfer, and electric species transport facilitates a thorough analysis of the sensor’s behavior under varied operational conditions. Through finite element simulations, the complex interaction between material characteristics, surface morphology, and environmental factors is explored, offering predictive insights into NO2 sensing mechanisms. Anchored in experimental data, the sensor design serves as the foundation for simulation, enabling virtual exploration of performance improvements and optimization strategies. Validation against experimental results, reported by Zhang et al., ensures the credibility and reliability of the computational model, confirming its usefulness as a predictive tool for sensor development. This approach shows promise in accelerating the advancement of gas sensing technologies, thus aiding in the creation of high-performance sensors with enhanced sensitivity and selectivity for critical environmental monitoring and industrial applications.
Variable Rate Sprayer System
Authors: Zhuta Largim , Ott Katie , Sharma Manoj , Kitts Christopher
Published: January 2025

Precision spraying is a technique that relies on a precisely targeted metered delivery of a desired fluid. It utilizes sensors and automation to optimize the application of chemicals. By adjusting the application rate in real-time based on specific crop needs variable rate sprayers minimize chemical use, reduce environmental impact, and increase crop yield. In this paper, we present a sprayer system for our in-house modular rover that can administer fluid in a controlled manner. It offers discrete left and right independent spray control, each within the range of 120–400 L/h. Tests conducted in the field show the performance within 0.1–4.8 % of the commanded value. Furthermore, we lay out the framework for a high-level sprayer-rover interface controller to intelligently adapt and command the desired flow rates.
Unveiling Structural Integrity Through Data Driven Reconstruction
Authors: Zhang Yang , Tang Jiong
Published: January 2025

Full-field online sensing provides dense spatial information about vibrating structures in real-time. Such compact sensing is crucial for accurately pinpointing potential damage locations in the structures. To effectively utilize these dense time histories, the sensors must be densely distributed. However, when structures are in operation, long-term deployment of such technologies can be cost-inefficient and often infeasible in real-life scenarios. Additionally, sensor malfunctions in structural health monitoring systems can result in missing measured data, reducing the accuracy of damage detection. In this regard, we propose a framework to estimate full-field vibration responses from the time histories of a few sensors based on the compressive sensing technique. Specifically, under controlled conditions, we obtain dense measurement data of the structure, and through these data, learn the spatial basis functions needed for compressive sensing operations using the Dictionary learning technique. This framework does not require any prior information on the structural model, making it entirely data driven. In the data reconstruction phase, we use the measurement data from a small number of sensors, combined with the learned dictionary, to reconstruct the full-field structural responses. However, the challenge lies in how to determine the number of limited sensors and how to arrange them. To tackle these challenges, this study transforms full-field data reconstruction into a sparse binary multi-objective optimization problem. One objective is to maximize the reconstruction correlation, and the other objective is the sparsity constraint on the number of sensors, i.e., the L0 norm. Through tests on a three-story building structure, our proposed method yields multiple solutions and can reconstruct full-field structural responses with high precision. Simultaneously, we emulate structural damage cases and detect structural damage through reconstruction indicators.