bluuebunny/crossref_metadata_2025 huggingface dataset:
Unraveling the gender wage gap: Exploring early career patterns among university graduates
Authors: Sandner Malte , Yükselen Ipek
Published: November 2024

A large body of literature has shown that the gender wage gap is small in the first years after graduation and increases gradually with age, largely because of family decisions, often a penalty caused by childbirth. However, the gender wage gap immediately after graduation has received less attention. Using a unique dataset that links 5000 university graduates with master's degrees or equivalent from a large German university to detailed employment records from the German social security register, we specifically analyze the gender wage gap at the first job and its dynamics during the initial years of their careers after graduation. We find that a significant gender wage gap already exists in the first job after graduation, even before most young individuals make family decisions. However, this gender wage gap decreases in the first year after entering the labor market and then increases slowly over time. We attribute this initial decrease in the gender wage gap to female university graduates experiencing greater returns from firm and occupational changes than their male counterparts. This suggests that women may use these changes to address skill mismatches, which are more common among women than men in their first job.
Rebellion contagion: Embodying black radical love in the westernised academy
Authors: Hlabangane Nokuthula
Published: November 2024

Macro‐politics inform micro‐practices. As such, no institution and role‐player within can be thought outside of this framework. As such, the personal begets the political as the political begets the personal. Against the extractive posture of the university that imagines every‐body and every‐thing through colonial apparatus that are also individualising, with this work, I immerse myself in the communities that raised me, whose values I embody as I walk the university. I posit my travails and ruminations in the university as Black Radical Love. I argue, in a manner of speaking, that one cannot display cares against grand injustices while turning a blind eye to small ones. In this way, I make a distinction (following others) between “be‐ing in the university” and being of the university” to highlight the hidden curriculum of the university and how unscripted Black Radical Love always rises to unveil and engage with the violences embedded in the corporate, westernized university. I show glimpses of decolonial, Afrocentric futures that are intricately woven with and into Black Radical Love. They account for the robustness of my scholarship and general movement in the academy.
Discovery of the Novel HLA‐DRB1*16:02:13 Allele, a Variant of HLA‐DRB1*16, in a Taiwanese Individual
Authors: Yang Kuo‐Liang , Lin Py‐Yu
Published: November 2024

One nucleotide substitution in codon 206 of HLA‐DRB1*16:02:01:01 results in a novel allele, HLA‐DRB1*16:02:13.
High‐Accuracy Geological Deformation Monitoring Through Unmanned Aerial Vehicle Image Registration Without Control Points
Authors: Qin Lei , Liu Yawen , Tao Pengjie , Duan Yansong
Published: November 2024

Unmanned aerial vehicles (UAVs) have gained popularity in geological deformation monitoring due to their flexibility, cost‐effectiveness, and ease of image acquisition. However, accurate co‐registration of multi‐phase UAV images, essential for effective monitoring, requires numerous ground control points (GCPs), challenging in deformation‐prone areas. This study introduces a UAV‐based monitoring method that eliminates the need for GCPs. Initially, two‐phase images are integrated for joint aerial triangulation. The first‐phase image and tie points undergo bundle adjustment refinement. Control points are identified as tie points with minimal deviation and used to refine the second‐phase image. Three‐dimensional (3D) models are generated from these refined adjustments and differential analysis between the models reveals geological deformation. Experimental results show that this method automatically identifies stable areas and effectively captures both the location and extent of geological deformation, achieving a relative change accuracy of 0.06 m, which is twice the average ground resolution. Compared to traditional aerial triangulation methods without control points, this approach offers more than a tenfold increase in accuracy. The accuracy is on par with methods that utilize multiple control points, indicating the promising potential of this approach for geological deformation monitoring.
Entrance Lengths for Fully Developed Laminar Flow in Eccentric Annulus
Authors: Resell Åsmund Aamodt , Giljarhus Knut Erik Teigen , Skadsem Hans Joakim
Published: November 2024

We study the entrance length in eccentric annular geometries, where the axes of the inner and outer pipes forming the annulus are offset from each other. Such geometries are considered relevant for several industrial applications, such as drilling of wells for hydrocarbon production or geothermal energy recovery, as well as biomedical research involving annular vessels. Previous studies have shown that eccentricity increases the entrance length in annular geometries, and this has been attributed to azimuthal redistribution of fluid between the wide and narrow sides of the annulus. The present computational study aims to increase the understanding of entrance lengths in eccentric annuli for laminar flow with Reynolds numbers spanning the range from the creep limit and up to intermediate laminar values, below the regime of annular gap instabilities. We find that the entrance lengths in eccentric annuli generally increase for narrower annular gaps (higher aspect ratios). Moreover, the entrance lengths in the annuli with higher aspect ratios are also more sensitive to the degree of eccentricity compared to wider annuli (lower aspect ratios). We report empirical correlations for the dimensionless entrance length using the same form as earlier studies (Le=[C0n+(C1Re)n]1/n). We find that eccentricity, which breaks the axial symmetry of the annulus and causes azimuthal flow in the entrance region, impacts the value of the exponent n, which has been assigned the value of 1.6 in previous studies of axisymmetric conduits.
Phase Diagrams for Anticlastic and Synclastic Bending Curvatures of Hexagonal and Reentrant Honeycombs
Authors: Wei F. , He L. H.
Published: November 2024

Transformation between saddle- and dome-like bending shapes of regular and reentrant hexagonal honeycomb panels are explored. An analytical model is proposed to uncover the underlying mechanisms and identify the controlling parameter when the cell walls are slender beams. Then, 3D finite element simulations are performed to examine the architecture dependence of bending shape and construct the phase diagrams of anticlastic and synclastic curvatures when the cell walls have a general geometry. The results are believed helpful to the design and application of related honeycomb structures.
Automatic Knowledge Extraction for Decision Support in the Structural Design Process
Authors: Schlenz Sonja , Mößner Simon , Ek Carl Henrik , Duddeck Fabian
Published: November 2024

Much of an engineer’s implicit knowledge and experience is embedded in data from previous engineering processes. In the typical automotive structural design process, many simulation variants are created to incrementally improve relevant performance values. Engineers decide about the modifications to make to the geometry to achieve these improvements. The steps of this development process reflect the engineer’s decisions and thus her/his knowledge and experience. This implicit knowledge is potentially helpful for new projects, but is not easily accessible. This paper proposes a method to automatically extract past solutions from a database containing finite element simulation data from previous development processes. A reference model with high similarity to the problem at hand is identified in the database. Since the development process of this model is already completed, there must exist a later version of the model where the problem has been solved. The difference between the reference model and the identified improved model represents an engineer’s knowledge that was used to solve a similar problem in the past. Possible solutions identified this way are successfully used to support engineers in their decisions in new projects during the structural design process. The method is applied to pedestrian safety data from the structural design process of cars using real-world data.
Investigation on Transmission Error and Noise Improvement of Two-Stage Planetary Gearset Based on MBD and NVH Analysis
Authors: Yoo Ho-Gil , Min Dong Joon , Lee Min Sung , Kang Yong Goo , Shin Kyeongho
Published: November 2024

With the increasing demand for Electric Drive (E-drive) vehicles, the development of planetary gearset (PGS) has been actively pursued. In the development process of a PGS, meticulous considerations must be given not only to the reliability of the system but also to various performance aspects such as downsizing for packaging, noise, vibration, and harshness (NVH), as well as efficiency. In this study, multi-body dynamics and NVH analysis were conducted to improve the NVH performance of two-stage planetary geared E-drive reducer systems. The analysis explores the interplay between transmission error (TE) and NVH, with a comprehensive discussion based on the analysis results. The analysis results were validated by comparing them with the test results.

While TE has been extensively researched as a primary excitation source in reducers or transmission systems, the relationship between TE in PGS and NVH, especially at the system level, was inadequately studied. Furthermore, experimentally validated studies were rarely conducted on the TE of planetary gearsets. In this study, overall transmission error (OTE), individual gear meshing TE, and noise and vibration response of modal correlated two-stage PGS model were analyzed. The analysis results illustrate how TE manifests in the noise and vibration response. Additionally, gear microgeometry modification cases were suggested as NVH improvement strategies, and their effects have been analytically investigated. In conclusion, overall simulation and investigation works have given instrumental insights into the design and improvement of NVH performances across various PGS systems.
Rigiccordion Worm - A Linear Two-DOF Origami Robot
Authors: Freeman Michael , Schott Conor , Carr Andrew , Josef Carter , Cook Col , Hasan Mohammad H.
Published: November 2024

Origami robots offer great potential due to their compliant nature, relative ease of manufacturing, lightweight, and safety. They are considered in areas like collaborative robotics, space applications, navigation of a variety of terrains, and search and rescue. This paper focuses on robotic applications like the latter, in which an origami robot is desired to be small in size, lightweight, fast, simple to model, produce, and control. As such, the body of an origami robot — the origami structural spring — should be simple to fold and model. One way to achieve both of these properties is to use a simple origami pattern that is rigidly foldable. In this work, a two-degrees-of-freedom (two-DoF) worm robot is presented. The worm body is constructed from a simple rigidly deformable origami pattern, named ‘rigiccordion’, which exhibits linear response properties, as well as 3d printed rigid parts. The structure stiffness is experimentally attained for two different types of material (inkjet paper and Mylar). The procedure to laser cut the origami fold pattern for a Mylar sheet is presented. Finally, the two-DoF worm robot is designed and tested using the rigiccordion structure for compliance and energy storage. The results show that the two-DoF robot can move horizontally with a speed of up to 1.56 cm/s (0.12 body length/s) and turn at a rate of 1.8°/s.
Advancements in Stretchable and Washable Energy-Storage Devices
Authors: Pressly Addison , MajidiRad AmirHossein , Damircheli Mehrnoosh
Published: November 2024

This paper offers a thorough literature review on stretchable and washable energy-storage devices, emphasizing their significance in healthcare and personal applications. We explored studies on carbon-based materials such as carbon nanotubes and carbon nanofibers for cost-effective solutions, along with fibrous electrical systems commonly utilized in creating batteries, supercapacitors, and triboelectric nanogenerators. Addressing challenges, the study provides practical recommendations for advancing research, focusing on enhancing energy and power density while maintaining mechanical robustness and comfort. The material choice, fabrication techniques, and mechanical properties for suitability in wearable technology are also discussed. In particular, the paper investigated several key items including omniphobic silk-based coils, advancements in lithium-ion batteries, and novel electrode designs that contribute to flexible and durable energy-storage solutions, printing methods such as core-shell fibrous structures, as well as encapsulation techniques. Additionally, we present a well-organized categorization of how washable and stretchable energy storage devices are fabricated, discussing their benefits, shortcomings, and standardized testing procedures. We highlight existing gaps in each technology and areas ripe for exploration. This work also includes valuable guidance and strategies for those entering the field of stretchable and washable batteries. Overall, we showcase recent advancements in the field, illustrating diverse possibilities for future power and energy devices while emphasizing the need for improved biocompatibility in various applications.
Hydrodynamic-Electrical-Control Co-Design of Hydrokinetic Turbines
Authors: Jiang Boxi , Liao Yingqian , Naik Kartik , Tariquzzaman Md , Martins Joaquim R. R. A. , Cao Yue , Sun Jing
Published: November 2024

The design optimization of a hydrokinetic turbine (HKT) is inherently multidisciplinary, involving hydrodynamic, mechanical, electrical, and control design. In the conventional sequential design process, the system-level problem is divided into sub-problems. These sub-problems are then solved in sequence, leading to sub-optimal solutions because it does not fully exploit cross-disciplinary synergy. This paper explores the optimal design of an HKT system by developing a control co-design (CCD) framework to simultaneously optimize the physical design variables, power transmission variables, and optimal control trajectory for a variable-speed fixed-pitch HKT. The CCD considers a system-level model that captures the couplings between the hydrodynamic and electrical efficiency of the HKT. Case studies are performed by analyzing the final designs and resulting hydrodynamic and electrical efficiencies. Electrical energy generation obtained through the proposed CCD approach is compared with a sequential design-control method. The cases show that the CCD approach, which considers hydrodynamics, electrical system, and control concurrently, leads to a different physical design and improved energy output of 1.76% more than that of the optimized system through a sequential method. The studies demonstrate that CCD benefits HKT design and achieving system-level optimality.
Realizability and System Architecture for Collaborative Large-Scale Mobile Platform 3-D Printing Robots
Authors: Hu Henry , Norris William R. , Soylemezoglu Ahmet , Nottage Dustin , Patterson Albert E.
Published: November 2024

Large-scale autonomous construction systems (ACSs) have become an important area of technology development in research years. Several areas remain to be comprehensively studied, including the design of both the construction equipment and final products, the science behind the materials used, system architecture, major applications (such space exploration, local disaster recovery, and expeditionary operations), and the realizability of the system. The realizability (manufacturability, assembly, and integration) of the system components is the most important of these at this point in the technology development, as it drives the feasibility and decision-making of the technology and enables the major applications. This article explores the realizability and system architecture of an ACS based on a large-scale collaborative autonomous robot (assumed to be a robotic arm) mounted in a mobile platform (such as a Warthog). The basic system configuration, the interfaces, and the realizability requirements driven by a formal concept-of-operations (CONOPS) diagram and known/designed system architecture. This work will be helpful in the further development, refinement, and application of this and similar systems by showing the major system architecture and formalizing the requirements for developing the system.
Well-Formed Quality of System Requirements for Verifying to ISO 29148-2018: A Natural Language Processing (NLP) Based Framework and Quantitative Metric
Authors: Kumar Sahu Chandan , Rai Vedant , Roshan Vinit
Published: November 2024

The system requirements directly affect the quality, cost, and performance of system architecture developed in the detailed design phase, necessitating a comprehensive approach to ensure accuracy, completeness, and clarity. A well-defined set of requirements lays the foundation for developing effective and efficient systems, guiding the architectural design process toward meeting stakeholder expectations while optimizing resource utilization and minimizing potential risks associated with ambiguous or poorly specified requirements. Methodologies for assessing the quality of individual requirements generated during the initial conceptual system design phase are crucial. In this paper, we outline a rule-based natural language processing (NLP) framework for assessing the well-formed quality of individual requirements that conforms to the ISO/IEC/IEEE 29148:2018 (E) requirement standard. We leverage advanced linguistic analysis to automatically evaluate each of the nine essential characteristics — necessary, appropriate, unambiguous, complete, singular, feasible, verifiable, correct, and conforming — prescribed by ISO/IEC/IEEE 29148:2018 (E) to ensure the clarity, completeness, and consistency of individual requirements. The framework assesses requirements at various levels, including token, span, syntactic structure, and sentence levels, to determine compliance with the defined characteristics of well-formed requirements. Furthermore, this work introduces a quantitative metric, the well-formed requirement quality index, which amalgamates these nine characteristics into a scalar value within the range of [0, 1], thereby facilitating a comprehensive assessment of requirement quality. The efficacy of this framework is demonstrated through the evaluation of select requirements samples, offering a means to assess requirement quality and perform quantitative comparisons of individual requirements’ qualities.
Resource-Aware Multi-Fidelity Multi-Objective Multidisciplinary Design Optimization
Authors: Tao Siyu , Sharma Chaitra , Devanathan Srikanth
Published: November 2024

Modern simulation-based design optimization problems have evolved from being single-objective, single-discipline, and single-fidelity to being multi-objective, multi-disciplinary, and multi-fidelity. This evolution has been mainly driven by the elaborate design requirements and the availability of multi-disciplinary, multi-fidelity simulation models for complex products in industrial environments. In this paper, we formulate a more practical design optimization problem that considers the computation resources on top of the existing multi-objective, multi-disciplinary, and multi-fidelity problem. In the context of multi-disciplinary and multi-fidelity design optimization, the evaluation costs of simulation models can vary significantly across different disciplines and fidelities. Most existing algorithms for solving these problems only assume sequential or simply parallelized evaluation of the simulation models, which may be inefficient due to the mosaic cost structures of different simulation models. In our problem formulation, we incorporate the model evaluation environment and grant the optimization algorithm flexible control over the simulation model evaluations, so that it can potentially achieve higher efficiency than in traditional setups. Along with our new problem formulation, we propose an intuitive algorithm that solves the new problem and capitalizes on the flexible control over the model evaluations. We use a simulated case study to demonstrate the features of our problem formulation and algorithm.
Development and Evaluation of a Process to Advance Orphan Assistive Technology Prototypes for Small Batch Production
Authors: Sprigle Stephen , Simonelli Xavier , Misch Jacob , Allen Taylor , Chininis Stephen
Published: November 2024

Assistive technology (AT) is defined as any item or product system that is used to increase, maintain, or improve functional capabilities of persons with disabilities. Orphan AT has the defining characteristic of a potential user group that is too small to support adequate research and development. As a result, many Orphan AT devices might not be commercialized because development costs outweigh the expected sales volume. The objective of this project was to develop and evaluate a process to advance Orphan AT functional prototypes for small batch production. The process consists of four activities separated by a decision point. It begins by identifying an unique AT prototype that was designed for a person with a disability. It then clearly defines the target user group and identifies predicate technology and alternative strategies used to meet the functional goal. The outcome of these activities is used to decide whether to proceed. Further development proceeds by performing a functional assessment that includes both human factors and device functionality and a manufacturing assessment that optimizes production and manufacturing options for small batch production. The process was deployed in the design of four Orphan AT devices. After completion, the process was evaluated by three long-standing AT professionals. The process to advance Orphan AT functional prototypes for small batch production appears to be a pragmatic and cost-effective approach to reduce barriers associated with Orphan AT commercialization.
An Analysis of Concave and Convex Shaped Cantilever Beams On Vibration-Based Piezoelectric Energy Harvesting
Authors: Mohiuddin Md , Ahmed Zahir Uddin , Ahmed Riaz
Published: November 2024

The most conventional configuration for vibration-based piezoelectric energy harvesting is cantilever beams. Researchers are continuously striving to enhance both the power capacity and bandwidth. While conventionally rectangular cantilever beams are used for cantilever energy harvesting, research has shown that rectangular shapes are not the ideal one for maximum power density. A lot of investigation has previously been carried out to optimize beam shapes. However, most of those previous studies cover trapezoidal and triangular beam shapes. This warrants the study of other beam shapes including concave and convex-shaped beams. In this work, concave and convex-shaped cantilever beam energy harvesters were investigated with varying geometric configurations. The output power from each shape was examined by altering the ratio of the beam’s middle width to the base width while keeping the thickness, surface areas, volume, and mass of the beam. The piezoelectric material attached to the beam followed a similar approach to mitigate the impact of PZT on the beam’s power capacity as it serves as the energy conversion medium. Such an approach was required to understand the true influence of concave and convex cantilever shapes on energy harvesting. One-third of the beam surface area was coated with piezoelectric material following the uni-morph setting. The coating of piezoelectric material on the beam was provided at its fixed end to obtain the maximum power out of piezoelectric material. Due to the significant influence of the geometric configuration of the beam on output power, it has been expressed in terms of power density. Furthermore, the resonant frequency of each shape was explored, as the output from the cantilever energy harvester attains its maximum at the resonant frequency of each specific shape. Results of the study revealed that a convex cantilever beam can achieve a superior output power density compared to concave and standard rectangular beams while utilizing an identical volume of beam and piezoelectric material. This holds particularly when the piezoelectric coating is applied near the fixed end. It is worth highlighting that in cases where the piezoelectric layer is near the fixed end, the power output is predominantly influenced by the base width of the beam, with a lower base width corresponding to higher output power density.
Upper Body Kinematic Analysis in Piano Playing Tasks
Authors: Lawrence Reece , Baus Juan , Cash Carla Davis , Yang James
Published: November 2024

The piano is one of the most popular instruments to learn among young musicians due to its versatility. As with any other physical activity, fluid piano performance requires practice to improve technique and reduce the injury risk in long playing sessions. One expert and one intermediate piano player performed three piano playing tasks at three different tempos while body kinematics data were collected with multiple inertial measurement units (IMUs). The maximum and minimum angular displacement of upper body joints and joint displacement were observed as a function of time. For all tempos, healthy joint angular displacements were observed. Across all tempos, more joint angular displacement was observed for the expert performer, showing better motor control and performance. The greater angular displacement in the hip joint for the experienced player shows a contrast in force production compared to the intermediate player. Less experienced players then have more forearm activity to keep tempo and loudness. We did not observe significant differences in joint displacement between the 100 and 130 BPM tasks. Generally, multi-joint motion is desired in piano playing to reduce biomechanical demands. Similar and future studies could benefit pedagogy, injury prevention, and rehabilitation processes.
Challenges in the Development and Integration of the Ground Vehicle Simulation Modeling Ontology
Authors: Louis Edward D. , Taylor Evan A. , Mocko Gregory M.
Published: November 2024

In this paper, challenges in authoring and implementing the Ground Vehicle Simulation Modeling Ontology, GVSMO, are presented. GVSMO seeks to address the need for advanced documentation, retrieval, integration and composition methods for high-fidelity, multi-physics, and multi-tool models and simulations. The US Army has identified simulation-based ground vehicle design methodologies and tools as key goals to speed the development of ground vehicles and implement next-generation mobility, autonomy, and electrification technologies. GVSMO addresses these needs by capturing domain knowledge about vehicle testing scenarios, ground vehicle architectures, operational environments, and simulation modeling and integrating the four domains within a fifth integration ontology. Challenges include maintaining ontological realism for a large domain, decomposing real-world testing documents into modelable units, drawing the distinction between classes and individuals within a complex domain, representing variant architectures and compound assertions within the OWL syntax, managing 150% modeling approaches, and merging several domain ontologies into one deployable ontology.
Real-Time Thermal Data Assimilation for Power Electronics at the Edge
Authors: Priddy Braden , Hainey Richard , Deese Tyler , Downey Austin R. J. , Khan Jamil , Ginn Herbert L.
Published: November 2024

Physical systems are often far too complex for a virtual model to fully encapsulate the behavior of a system. Requiring the user to fine tune parameters of the virtual model over the course of a system’s life cycle. Data assimilation addresses this problem by continuously updating the model with sensor data to construct a personalized model of the physical system. This personalized model is known as a digital twin. Digital twins of ship systems can provide insight into the future state of the ship to enable operators to make informed decisions to increase health of the ship. However, there are a few key challenges that need to be overcome while updating a model. The first problem is reducing latency between the physical system and the digital twin. While ensuring that the digital twin has enough time and data to update. The second problem is verifying the model is an accurate representation of the physical system. This paper proposes a methodology that uses real-time sensor data and a particle swarm optimization algorithm to update model parameters for an instrumented thermal loop developed as a stand-in for liquid-cooled power electronics. The swarm of particles represent different configurations of a multi-domain model that constitutes the digital twin of the thermal loop. All computations are done on the edge to emulate a real world system. Results demonstrate that the particle swarm algorithm can reliably update a digital twin of the thermal loop as external changes are made to the system (radiator turned on and off) with a root mean squared error of under 0.35 °C over the whole system. All models are updated in real-time with a maximum compute time of 38.4 s; demonstrating the proposed methodologies applicability for real-time data assimilation within a digital-twin framework.
Robustness Analysis of Asymmetric NES Via Nonlinear Normal Modes
Authors: Motato Eliot
Published: November 2024

Nonlinear Energy Sink (NES) absorbers excel in comparison to traditional Tuned Mass Dampers (TMD) due to their superior efficiency over a wider range of frequencies. However, despite the advantages of NES over TMDs, their susceptibility to variations in system energy poses a challenge, particularly in applications where the primary structure is subjected to random excitations. A modified NES, referred to as Asymmetric NES (ANES), has previously demonstrated a substantial enhancement in NES robustness, opening up new possibilities for this particular case. This study investigates the factors contributing to the superior robustness of ANES in comparison to the performance of traditional cubic NES. Through an analysis utilizing Nonlinear Normal Modes (NNM) and Frequency Energy Plots (FEP), this research illustrates that the improved effectiveness of Asymmetric NESs results from a more intricate interaction of the ANES nonlinear normal modes with the kinetic energy FEP of the primary structure. In this work, the NNMs of tuned ANES and NES absorbers with similar mass are generated and plotted alongside the kinetic energy FEP of a three-story shear building subjected to various earthquake ground accelerograms. The findings reveal that the NNM of the ANES absorber intersects more effectively with the system’s energy FEP, explaining the superior robustness of the ANES against variations in the input spectrum power.
Symmetry-Induced Mechanical Metamaterials Design Framework and Dataset Generation
Authors: Abu-Mualla Mohammad , Huang Jida
Published: November 2024

The surge in machine learning research and recent advancements in 3D printing technologies have significantly enriched materials science and engineering, particularly in the domain of mechanical metamaterials, which commonly consist of periodic truss materials. Despite the extensive exploration of their tailorable properties, truss-based metamaterial design has predominantly adhered to cubic and orthotropic unit-cells, a limitation arising from the conventional design method, where the type of symmetry related to the designed truss-based material is determined after the design process is done. To overcome this issue, this work introduces a groundbreaking 3D truss material designing framework that departs from this constraint by employing six distinctive material symmetries (cubic, hexagonal, tetragonal, orthotropic, trigonal, and monoclinic) within the design process. This innovative approach represents a versatile paradigm shift compared to previous design approaches. Furthermore, we are able to integrate anisotropy into the design framework, thus enhancing the property space exploration capability of the proposed design framework. Probing materials property space using our design framework demonstrates its capacity to achieve a diverse range of mechanical properties, surpassing even the most extensive datasets available in the literature. The proposed method facilitates the generation of a comprehensive truss dataset, which can be represented in a trainable continuous format suitable for machine learning and data-driven approaches. This advancement paves the way for the development of robust inverse design tools for truss materials, marking a significant contribution to the mechanical metamaterial community.
Optimizing Artificial Landmark Placement for Improving Indoor Localization of Mobile Robots
Authors: Devarakonda Venkata Naren , Satya Sai Tankala , Sebastian Bijo
Published: November 2024

Precise localization in GPS-denied indoor environments can be achieved using artificial landmarks. However, the majority of existing artificial landmark techniques have a low detection range and have to be used in large numbers to allow for standalone pose estimation. This is often not possible in cluttered indoor spaces like factories. In this paper, we propose an analytical framework to determine the minimum number of artificial landmarks and their placement for a given environmental layout and robot path, for a maximum allowable uncertainty in robot pose, anywhere on the predefined path. The proposed approach is validated with an Extended Kalman Filter (EKF) based localization on a differential drive robot. The EKF implementation uses odometry and Inertial Measurement Unit (IMU) data for prediction and AprilTags as sparse artificial landmarks for correction. The proposed approach was validated by driving a mobile robot with EKF localization in different simulated and real-world scenarios. Based on the desired path to be followed by the robot, the minimum number and placement of artificial landmarks were determined for each scenario. The maximum uncertainty in the robot pose at any point on its path was less than the allowable maximum threshold in both simulated and real-world testing. While the validation is presented using AprilTags with an EKF localization framework, the proposed approach could be applied to other passive and active tags, beacons, or marker-based localization approaches.
Data-Driven Sizing and Shaping of Topology Optimization Concepts Using Implicit Surfaces, Free Form Deformations and Multifidelity-Based Surrogate Models
Authors: Strömberg Niclas
Published: November 2024

In this work, a framework for data-driven sizing and shaping of topology optimization (TO) concepts is developed, implemented and demonstrated. The density field from a solid isotropic material with penalization (SIMP)-based TO solution is converted to an implicit surface-based geometry (ISG) by using regularized radial basis function networks (RBFN) with Wendland’s compactly supported radial basis functions. Sizing of the ISG is done locally by morphing operations and shaping is performed by applying free form deformations (FFD) on the stl-mesh which is generated from the ISG by a marching cube algorithm. The smooth FFD-based shaping is represented as a RBFN with cubic splines using a set of control points with corresponding prescribed deformations. Data-driven sizing and shaping of TO concepts are then performed by using multifidelity non-linear computer experiments and surrogate model-based design optimization. The developed and implemented framework is demonstrated for the well-known Messerschmitt-Bölkow-Blohm (MBB)-beam as well as an application of a flywheel to a compactor machine.
Two-Dimensional (2D) Modeling of Engine Mounted Hoses: Using Cosserat Theory of Rods
Authors: Borra Chaitanya , Kaul Sudhir
Published: November 2024

The use of hoses is widely prevalent in multiple mechanical, aeronautical, and automotive applications involving the use of coolants, lubricants, etc. However, most system models often fail to completely represent the dynamic and hyperelastic behavior of hoses. Though hoses generally undergo large deformations and offer non-linear damping, these characteristics are not fully leveraged in traditional analytical approaches. Fatigue and vibration analyses performed on the integration systems, which may include various metal components along with the hoses, are generally linear and elastic. The assumption of linearity may not account for actual strains in the components attached to these hoses. This may result in poor correlation between test and finite element analysis (FEA) results, leading to higher cost and sub-optimal designs. This paper discusses the formulation, modeling, and analysis of two-dimensional (2D) engine mounted hoses. The Cosserat theory of rods has been used for modeling. Multiple boundary conditions have been used to verify the robustness of the model. Free vibration and forced vibration response has been evaluated in this study along with an initial investigation of stability. It is observed that the Cosserat theory of rods in conjunction with a hyper elastic material model can be successfully used for modeling the structural dynamics of hoses. Such a model is expected to be beneficial in many applications.
Investigating Pro-Environmental Attitude for Designing Affective Eco-Feedback
Authors: Wilson Sara L. , Khan Zainab J. , Lee Bianca , Krishnan Supreetha , Fernández John E. , Yang Maria C.
Published: November 2024

This research examines the relationship between attitudes towards individual pro-environmental behavior and emotional intensity in response to eco-feedback designs for sustainable behavior change. This user-centered approach was performed in three phases. In the first phase, workshops were conducted with users who generated visual and written content in response to prompts on environmental attitudes. For the second phase, the research team created ten eco-feedback product sketches for electricity conservation, which were informed by qualitative analysis of 67 workshop participants’ content. Experimental sketches included positive and negative message framing, while control sketches provided generic baselines for comparison. Finally, for the third phase, 60 workshop participants were surveyed for emotional intensity in response to the eco-feedback sketches. Significant correlations were found between users’ attitudes and emotional intensity towards experimental designs compared to control designs. Users with positive attitudes towards individual conservation behaviors exhibited the strongest positive and negative emotions in response to designs with positive and negative message framing, respectively. While experiencing lesser degrees of emotional intensity, users with neutral or negative attitudes responded with statistically significant negative emotional intensity to experimental designs with negative message framing. These findings demonstrate that strategic message framing, tailored to users based on their pre-existing attitudes, can drive emotional responses and, consequently, motivation for sustainable behavior change across diverse attitude profiles.
To Quantize or Not to Quantize: Effects on Generative Models for 2D Heat Sink Design
Authors: Drake Arthur , Wang Jun , Chen Qiuyi , Nejat Ardalan , Guest James , Fuge Mark
Published: November 2024

In Topology Optimization (TO) and related engineering applications, physics-constrained simulations are often used to optimize candidate designs given some set of boundary conditions. However, such models are computationally expensive and do not guarantee convergence to a desired result, given the frequent non-convexity of the performance objective. Creating data-based approaches to warm-start these models — or even replace them entirely — has thus been a top priority for researchers in this area of engineering design. In this paper, we present a new dataset of two-dimensional heat sink designs optimized via Multiphysics Topology Optimization (MTO). Further, we propose an augmented Vector-Quantized GAN (VQGAN) that allows for effective MTO data compression within a discrete latent space, known as a codebook, while preserving high reconstruction quality. To concretely assess the benefits of the VQGAN quantization process, we conduct a latent analysis of its codebook as compared to the continuous latent space of a deep AutoEncoder (AE). We find that VQGAN can more effectively learn topological connections despite a high rate of data compression. Finally, we leverage the VQGAN codebook to train a small GPT-2 model, generating thermally performant heat sink designs within a fraction of the time taken by conventional optimization approaches. We show the transformer-based approach is more effective than using a Deep Convolutional GAN (DCGAN) due to its elimination of mode collapse issues, as well as better preservation of topological connections in MTO and similar applications.
Implementation of Network Optimization and Resiliency Analysis Towards Mission Assurance
Authors: Scott Daniel J. , Jensen David C.
Published: November 2024

Network optimization and resiliency analysis are pivotal domains revealing network functionality, strength, and resilience. Despite their promise, these methodologies often encounter integration limitations, scalability issues, or functional gaps, prompting the need for further development.

This paper explores the characteristics of network optimization and resiliency analysis, presenting existing methodologies, discussing their limitations, and proposing a new approach. Our proposed method integrates genetic algorithms (GAs) and weighted degradation analysis to address these shortcomings effectively.

Our research emphasizes the integration of GAs for network optimization and weighted degradation analysis for resiliency assessment. Through a case study focusing on an electrical grid system, we elucidate the efficacy and challenges of our proposed methodologies. While GAs efficiently generate optimized networks, they encounter inconsistencies due to algorithmic issues. Similarly, weighted degradation analysis demonstrates forward propagation capabilities but faces challenges in assessing back-propagation issues adequately.

Network optimization and resiliency analysis play a crucial role in understanding network characteristics, contributing to overall network assurance and mission success. This study underscores the importance of ongoing refinement and resolution of algorithmic issues to fully leverage these methodologies. Further research is essential to overcoming existing challenges and seamlessly integrating these methodologies into mission-critical systems, thereby enhancing network understanding and optimization capabilities.
The Use of Narrative Methods to Generate Engineering Design Knowledge: A Scoping Literature Review
Authors: Loweth Robert P.
Published: November 2024

Narrative inquiry is a form of qualitative research in which researchers derive meaning from narratives of individuals’ experiences. This scoping literature review explored how narrative methods have been used to generate engineering design knowledge. Sixteen papers met the author’s inclusion criteria. The author reviewed these papers to identify their research questions, the types of narratives employed to answer these research questions, and the methods that researchers used to derive meaning from their narratives. Five papers involved arts-based narratives: four analyzed visualizations of students’ design processes while one synthesized student design processes into a descriptive timeline. Five papers analyzed documentary narratives comprised of audio or video recordings of designers engaged in design work. Five papers analyzed biographical narratives derived from interviews with designers or from ethnographic study of design projects. Only one paper used an autobiographical narrative to examine liberatory design practices. Fourteen papers addressed their study aims by applying categorizing analysis processes to narratives. Only two papers created narratives from data as a method to explain their findings. Opportunities for deeper engagement with narrative methods in engineering design research include leveraging arts-based narratives to study experienced designers, synthesizing explanatory narratives, leveraging narratives to study design equity, and leveraging autobiographical narratives.
Measurement of Chaotic Complexity in Multibody Dynamics Planar Mechanism With Clearance Using Entropy Representation
Authors: Yousuf Louay S.
Published: November 2024

A planar mechanism of a two cam and concentric flat faced follower and oscillated roller follower in tis guides with clearance is considered in this paper. There is a clearance between the roller follower and its guides while the two cam is connected to the concentric flat faced follower. The roller follower is concentric with flat faced follower at a revolute joint. The contact and impact is happened between the two cam and concentric flat faced follower and between the roller follower and the two guides at different cam speeds and different guide’s follower gap. The time delay is determined from average mutual information algorithm while the embedding dimensions is calculated using global false nearest neighbors algorithm. The novel feature of chaotic complexity is measured using spectral entropy, permutation entropy, approximate and sample entropy at different cam speeds and different guide’s follower gap. SolidWorks program is used in the simulation of the follower motion. The roller follower is moved with three degrees of freedom inside its guides which causes the chaotic complexity. MatLab algorithm is used to extract the value of the entropy. The high entropy value the more chaos phenomenon in the dynamical system.
Applied AI in Assessing Design Education: A Comparison of Design Progress Evaluation Between AI Dashboard and Conventional Human Evaluators
Authors: Chiu Matt , Silva Arlindo
Published: November 2024

The process of design in education is generally broad, which makes it difficult to measure. It is human-centric in nature, thus assessment is often qualitative and involves comparing students’ performance or grading. The dynamic nature of design processes also takes a lot of time from educators to fully understand the progress made by student design teams in order to provide critical and essential feedback. As such, time becomes a valuable and important resource that educators need to ration to keep the pace of the class while ensuring students meet their learning objectives and milestones. This paper showcases a method of visualizing the design process of students through a dashboard using divergent and convergent thinking as metrics across individual, team, and cohort levels, with the use of Natural Language Processing (NLP) models. Such a dashboard allows educators a quantitative means to receive “real-time” feedback on the performance made in a large cohort of students and enables some optimization of the time spent assessing their progress. An evaluation of this approach compares the difference between the visualization of results produced by an AI-enabled dashboard and conventional human evaluators, showcasing similarities and contrasts. This paper ends with a discussion about the viability of the AI tool, presenting the current limitations and future potential.