bluuebunny/crossref_metadata_2025_split huggingface dataset:
Resource-Aware Multi-Fidelity Multi-Objective Multidisciplinary Design Optimization
Authors: Tao Siyu , Sharma Chaitra , Devanathan Srikanth 
Published: November 2024

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.