bluuebunny/crossref_metadata_2025_split huggingface dataset:
Performance Analysis of AutomataScales to Support Early Design Decisions
Authors: Chaisiriroj Pongchalat , Stone Robert B. 
Published: November 2024

Abstract
In this paper, we present a comprehensive study and performance analysis on the AutomataScales simulations method focusing on electric propulsion systems for deep space missions. These applications require precise and time efficient simulations. However, traditional simulation methods such as Particle-In-Cell (PIC) method facing challenges from computationally intensive (2.5–21 days), memory demands (random-access memory or RAM and CPU), and steep learning curve for researchers. These limitations reduce their effectiveness in resource-constrained environments. For instance, each GB of RAM consumes approximately 0.1875 watts which resulting in more power consumption ranging from 87.1 to 145.2 MW per simulation run. The AutomataScales method combines discretization techniques with cellular automata and a multi-layer, multi-resolution approaches. This method offers a powerful tool to model complex multiphysics interactions and utilizing hybrid numerical scheme (discrete and continuous) with lower computational time and memory usage. The method depicts intricate and accurate behaviors in various types of particle trajectory (ionized particles, primary and secondary electrons) and plasma physics (particle collision and ionization). It provides a scalable and adaptable framework for multiphysics simulations with almost real-time simulation (0.1 second per time step). A key aspect of our research is the computational efficiency of AutomataScales. Our results show that the method can achieve up to 36.9 times faster, and 2.1 times less physical memory (RAM) compared to commercial simulation tools such as COMSOL Multiphysics® software. This substantial reduction in computational resources make AutomataScales more efficient and accessible for researchers to explore broader design variables in their early design process with or without computational constraints.
Simulation of Cutting Face-Hobbed Bevel Gears Based on a Ring-Dexel Model
Authors: Shih Yi-Pei , Sheen Bor-Tyng , Wei Yu-Hsien , Wei Bo-Lin 
Published: November 2024

Abstract
The face hobbing method, renowned for its high precision and production efficiency, is a primary technique for mass-producing hypoid gears in automatic industrial applications. This method can be implemented effectively on a modern six-axis CNC bevel gear-cutting machine. However, due to the complicated machining movement of multiple nonlinear axes, it is challenging but essential to conduct cutting simulations to validate the accuracy of the given machining positions. Even some commercial software can do cutting simulations for face hobbing machining; the settings are very complicated, and thus, integrating them into the human machine interface program of CNC machine presents many challenges. Additionally, the cutting feed rates provided by the ongoing CNC codes may not be optimal, necessitating manual adjustments by an engineer to improve machining efficiency. Material removal rate (MRR) is closely related to machining power, making it a valuable metric for optimizing cutting feed rates. However, there have been few studies in this area due to the complexity of face-hobbing processing. This paper aims to establish a mathematical model for face hobbing simulation based on a modified ring-dexel model. Many rings are arranged in the model to discretize the gear blank and to solve the tooth surface cutting points. These points can be used further to construct the surface geometry of the produced gear using triangle meshes. The volume of material removed is determined, enabling the calculation of the volume removal rate as a basis for optimizing cutting feed rates.
Applications With Autonomous Mobile Platforms and Robotic Arms for Experiential Learning and Research in Indoor and Outdoor Settings
Authors: Nagchaudhuri Abhijit , Pandya Jesuraj , Ward Lance , Butler Brian , Cuppett Jackson , Wilson Parker 
Published: November 2024

Abstract
Automation and robotics have been a dominant theme in the funded project efforts with acronyms ECPA (Environmentally Conscious Precision Agriculture), AIRSPACES (Autonomous Instrumented Robotic Sensory Platforms to Advance Creativity and Engage Students), and more recently DREAM (Developing Robotic Explorations with Agrobots and Moonbots). These projects have engaged undergraduate and graduate students from STEM disciplines. Robotic applications within and outside the laboratory have played a pivotal role in engaging the students and driving the educational and research objectives of the proposed projects. This paper will highlight some of the efforts undertaken by the project team for developing and utilizing mobile robots and fixed-base robotic arms.
Evaluation of Distributed Sensor Configurations for Rainwater Harvesting Monitoring Systems
Authors: Rudy Eren , Bilton Amy M. 
Published: November 2024

Abstract
Increasing risk of water scarcity in Mexico City has led to expansion of the practice of rainwater harvesting (RWH). However, it remains difficult to assess the benefits of the increased number of installations due to uncertainty in new users’ propsensity towards long-term adoption of these systems. This research proposes the use of sensors to aid in the study of RWH system adoption. A challenge that has presented itself in the instrumentation of RWH systems is the cost-effective collection of data from multiple sources that are physically separate at a common ‘gateway’ node for transmission. This study presents and evaluates three potential approaches to solving this problem: (1) the use of wires to connect sensors to the gateway node, (2) the use of LoRa-enabled wireless nodes configured in a self-organizing mesh network, and (3) LoRa-enabled nodes configured in a star network topology. An analysis of the cost of each configuration was performed, indicating that the wired system ($330.9 USD) was notably cheaper than the wireless options (&gt; $425.3 USD) due to the expense of multiple LoRa radios. However, the potential cost saving advantage of LoRa mesh nodes sharing a network between households allows for the possibility of a per-household deployment cost even cheaper than the wired system. A qualitative analysis also explores the substantial limitations of the wired system due to challenges in its installation.
Designing a Tractor for Small Farms in Resource Constrained Communities
Authors: Goldbach Collin , Velez Amber , Kleiner Tova , Kapoor Sam , Singh N. P. , Winter Amos G. 
Published: November 2024

Abstract
This paper presents the design, testing and user feedback of a new prototype of a tractor platform intended for use on small, resource-constrained farms. This development builds on past work by implementing several upgrades which promote market competitiveness and maximize functionality, ergonomics, and aesthetics. Stakeholder discussions, review of prior art and recommendations from past authors were used to draft new functional requirements for a better vehicle. Hydraulic power systems were implemented that significantly improve user comfort by automating repetitive or unwieldy tasks. Newly designed crop-spraying solutions based on feedback from farmers allowed the tractor to perform crop maintenance functions that larger vehicles cannot while also reducing worker exposure to harmful chemicals. A rear-oriented PTO was installed to allow the vehicle to power external implements. A redesign of a stabilizing solution increased the vehicle’s versatility in managing various crops and transit between properties. The upgraded vehicle was tested in Massachusetts. Farmers from Massachusetts and from The Philippines who tested the vehicle responded positively. They indicated the tractor would be a valuable addition to their small farms and would substantially reduce drudgery. Testers believed the format of the vehicle was familiar, easy to learn, and comfortable to ride. This paper demonstrates that two-wheeled tractors are not only viable, but can produce the same utility as conventional tractor layouts at a significantly lower cost.
Finding Installation Sequence of Polygonal Masonry Through Design and Depth Search of a Directed Acyclic Graph
Authors: Kim Taeyong 
Published: November 2024

Abstract
Polygonal masonry can be found in several historic buildings from various locations. There are several studies about the polygonal masonry techniques that are used during the construction of buildings: the wall of Inca Roca’s palace, Bomarsund fortress in Finland, and the fortress of Sacsayhuaman. However, when these polygonal stones are predesigned and the number of stones is substantial, it is hard to figure out the installation sequence due to the collisions between stones during the installation. This paper introduces an approach to solve this complicated problem by using a specific design method to define the shape of the polygonal stones, interpreting the relationship between the stones as a directed acyclic graph (DAG), and finding the depth of each node of this graph. The design of the stones is in ℝ2. The shapes of the stones are defined with the divided regions in ℝ2, divided by one or more functions and the boundary of the entire wall. Each stone is considered a node. The edges and directions of the graph are defined based on the character of adjacency between two regions. Depth search follows Kahn’s algorithm, but the algorithm searches in reversed order, removing the transitive reduction process of the graph. The conditions and assumptions about the design, collision, and graph are given in this paper.
Recalibration of Neural Networks Using Transfer Learning for Streamflow Forecasting
Authors: Qin Chloe , Zeng Yichao , Zhao Ying , Gugaratshan Guga , Hu Zhen 
Published: November 2024

Abstract
Streamflow forecasting is a critical component in the design of resilient civil infrastructure, especially when considering extreme events related to river flows, such as flooding. While physics-based simulation models have traditionally been used for streamflow forecasting, their accuracy is often hindered by simplifications and assumptions made in the analysis. On the other hand, data-driven machine learning models leverage historical data from monitoring states, providing an alternative approach to streamflow prediction. However, purely data-driven models can be limited by the availability of monitoring data, which can lead to less accurate predictions. To address these challenges, this paper presents a novel hybrid modeling technique for streamflow forecasting by integrating the strengths of both physics-based and data-driven approaches. The key idea lies in the application of transfer learning to recalibrate neural networks. A long short-term memory (LSTM) model is first constructed using simulation data generated from a physics-based streamflow-forecasting model. This initial LSTM model is then fine-tuned using gauged monitoring data, transforming it into a recalibrated LSTM specially designed for streamflow forecasting. The results of this case study demonstrate the effectiveness of neural network recalibration using transfer learning, which significantly enhances the accuracy of LSTM-based streamflow forecasting and could greatly facilitate the design of resilient civil infrastructures in the face of climate change.
Vibro-Impacts of a Piecewise-Linear System With a Clearance
Authors: Donmez Ata , Kahraman Ahmet 
Published: November 2024

Abstract
Vibro-impacts are commonly observed in various engineering systems as a consequence of clearances they possess. Such motions are known to cause excessive noise issues posing a challenge in the design of such systems. Many of these systems can be formulated into a single-degree-of-freedom piecewise-linear system excited by force fluctuations. In this study, starting from a rattling gear pair example, such an equivalent piecewise-linear system will be defined and non-dimensionalized. A computationally efficient piecewise-linear solution technique will then be employed to solve the non-smooth dynamic equation. Parameter sensitivity studies will be conducted to map the overall dynamic response. Bifurcation diagrams will characterize P(m,n) type periodic and chaotic vibro-impact motions, demonstrating systematic bifurcations between P(m,n) and P(m,n+1) type periodic vibro-impact motions. A gear pair test setup will be utilized to demonstrate some of the predicted vibro-impact motions revealed by their bifurcation diagrams.
Exploring a Graph Theoretic Approach for the Quantitative Comparison of Digital Twin Designs
Authors: Kotecha Maulik C. , Xu Wanyu , McAdams Daniel A. 
Published: November 2024

Abstract
A graph theoretic approach for quantitatively comparing digital twin designs is explored in this paper, with a novel methodology being offered to evaluate the structural and functional similarities between these complex systems. Graph theory is leveraged to represent digital twins as mathematical graphs in our approach, with nodes represented by function-flow pairs and the interactions between the functions depicted by edges. The application of graph-based similarity measures, such as NetSimile and DeltaCon distance, for quantitatively assessing the resemblance between digital twin designs is allowed by this representation. Our method addresses the limitations of conventional vector-based metrics of design comparison, which focuses on the connectivity and interactions within these systems, offering a more nuanced and comprehensive analysis. The designs of six digital twins were collected, and functional models were created using the Functional Basis. The functional models were then converted to graphs to perform design similarity analysis. At the end of the study, it was concluded that graph similarity measures such as NetSimile and DeltaCon can be used as quantitative tools for comparing digital twins. However, further investigation is needed to test the effectiveness of such measures for complex designs such as digital twins. The limitations inherent in the current methodology are highlighted, such as challenges in capturing dynamic interactions and the evolving nature of digital twins. Finally, graph neural networks (GNN) are proposed for multiple feature-based similarity analysis and large language models (LLMs) to automate functional model generation.
Trade-Off Analysis for Time and Power Requirements in an Additive Manufacturing-Disrupted Manufacturing Cell
Authors: Jones Chad , Montoya Matthew , Strawser Larry , Patterson Albert E. 
Published: November 2024

Abstract
The introduction of additive manufacturing (AM) into modern production environments has provided new capabilities; however, these capabilities have disrupted manufacturing cells. The disruption of manufacturing through the addition of AM processes provides an opportunity to model a manufacturing cell, quantify the cells performance with respect to primary concerns, disrupt the cell, and lastly provide a qualitative analysis of the trade space between primary concerns. Using a previously developed cell model and convex optimization approach, the qualitative trade-offs between power and manufacturing time were explored. This trade-off analysis was then demonstrated using a realistic case study in which an existing cell was disrupted via the introduction of an additive manufacturing process with the requirement that the cell was not able to be re-designed or reconfigured. The optimal machine settings for the original cell were used as the starting point for an adaptive optimization to recover as much performance as possible from the cell without changing the order, number, or types of machines other than the introduction of the AM process. The results offer a large number of important lessons related to the design of manufacturing cells, the design of products, and the possible decision variables for integrating AM into end-user manufacturing systems.
A Numerical Investigation of Soil-Tire Interaction Using CEL and SPH Methods
Authors: Jasoliya Dhruvin , Untaroiu Alexandrina , Untaroiu Costin 
Published: November 2024

Abstract
The performance prediction of a tire on a rigid surface like a road can be done with appropriate friction law, which is relatively simple. However, in the case of off-road conditions, the terrain properties also affect the tire performance significantly. Therefore, accurate modeling of terrain is required for optimizing the key tire and vehicle design parameters. Soil, being a granular and elastic-plastic material, is difficult to model with traditional numerical methods due to its high deformation and material separation. Researchers have employed meshed and meshless methods for modeling high deformations occurring during the soil-tire interaction process. It was observed that meshed methods have numerical stability issues due to the accumulation of high plastic deformations in soil and the complex contact interface between tire and soil. The meshless methods can address these shortcomings, but they still lack extensive validation, especially for soil tire interaction simulations. In this study, a non-cohesive soil (Norfolk Sandy Loam) is modeled using meshed (Coupled Eulerian-Lagrangian) and meshless (Smooth Particle Hydrodynamics) methods. The Drucker-Prager material model was parameterized for soil modeling based on soil strength test results. The methodology for setting up soil-rigid tire numerical simulation for both numerical approaches is developed. Using experimental data, the numerical accuracy of both methods is assessed. The realistic stress data and good prediction of tire traction relative to experimental data suggest using the proposed methodology in future studies of tire-deformable soil interaction.
Optimizing Day-Ahead Market Scheduling: Policy Design for Renewable Energy Integration and Imbalance Cost Consideration
Authors: Chen Zhetao , Xi Zhimin , Wang Hao 
Published: November 2024

Abstract
This study introduces a Stackelberg game-theoretic approach to crafting an imbalance pricing strategy for the energy market operator, enhancing cost efficiency in a credit score-based energy market while adhering to policy-mandated renewable energy levels. Within this framework, the market operator takes the lead by introducing a novel hybrid charging station, which will participate in the energy market and be positioned as the follower in the game. This station utilizes solar photovoltaics and grid power to serve both battery and fuel cell electric buses. This approach optimizes the station’s capacity bidding strategy to maximize profits from electricity sales in the market, taking into account the intermittency of renewable sources and the fluctuation of energy prices. The research proposes a regulatory mechanism designed to manage imbalance prices for suppliers, ensuring that the daily cleared renewable power output meets a predetermined threshold using an iterative algorithm within the credit score-based day-ahead market. Numerical simulations validate the proposed strategy, suggesting that the market operator could enhance market efficiency while the station maximizes market profits from electricity sales under the new regulation.
Comparison Study on Two-DOF Broadband Energy Harvester for Varied Tram Vibration Frequency
Authors: Umar Hamza , Lee Soobum , Mullen Christopher , Kim Jaehoon , Lee Jaeyun 
Published: November 2024

Abstract
Vibration energy is widespread in tram systems with high density, and it is a prevalent option of passive energy generation to enable self-powering wireless sensors for structural health monitoring. This paper presents a broadband vibrational energy harvesting device design that utilizes a varied frequency from a tram vehicle using a 2-degree-of-freedom vibrational system combined with electromagnetic energy conversion. Specifically, this paper will compare the power output for two design configurations: open-end and closed-end designs. For each design, a stepwise optimization process is applied to determine (1) mechanical parameters for frequency tuning to adjust to the trams’ operational conditions, and (2) electromagnetic parameters for the whole system design to maximize power output. That is, the first step optimization will determine mechanical design parameters for frequency tuning: masses (m1, m2) and spring constants (k1, k2, k3). The second step will use these parameters as initial guesses and present electrical parameters (number of coil turns, external resistor) to maximize the power output in the frequency band of interest. The design results indicate that the 2-degree-of-freedom closed-end has a preferable performance in terms of broadband vibration energy harvesting with higher average power as well as manufacturability within design constraints.
Barriers to Sustainable System Evolution: a Simulation Study Exploring the Transition From Private to Public Transportation
Authors: Gracia Otalvaro Rogelio , Watson Bryan C. 
Published: November 2024

Abstract
Road transport networks are complex distributed systems that present the most challenges in the management and use phase of their lifecycles. As traffic congestion and air pollution within urban city centers rise, authorities are implementing more restrictions and regulations to address these challenges. Additionally, public transportation, car-sharing, sustainable vehicles, and Mobility-as-a-Service have all increased in popularity as solutions to these issues. However, there is evidence that these new trends might not help as expected. The main objective of this study is to understand the dynamics of transportation network evolution from a systems perspective. Focusing on system capacity, and population characteristics, we seek to evaluate how different ratios of users commuting with either public transport or private car affect network congestion. The hypothesis tested is: as the ratio between public buses and private cars is modified to include more buses, the dynamics of the transportation system change, impacting system evolution towards more sustainable modes of transportation. Contributions are conceptual models documented through the overview, design concepts, and details protocol, as well as an analysis of the results obtained. The results indicate the evolution path taken toward more sustainable transportation can severely mitigate system-level objectives. Significant recommendations to city planners are included to help manage the complexity of transport systems, leading to the design and operation of smarter and sustainable cities. Future research will focus on customizing the model to represent real cities and determine if the same system evolution guidance applies.
Evaluating the Effectiveness of Heuristic Cards With Varying Example Specificity in Design for Additive Manufacturing Education
Authors: Pearl Seth , Meisel Nicholas 
Published: November 2024

Abstract
Additive manufacturing (AM) is an emerging technology capable of producing intricate designs. Despite the advantages that AM affords, such as geometric complexity and material reduction, designers often leverage simple geometries and standard components instead. These design decisions lead to inefficient designs that do not leverage the capabilities of AM. There is a need to help designers improve their designs so they can be better suited for AM. This work explores ways to support DfAM thinking using specific and abstract DfAM exemplars. To understand the relevancy of the content needed to improve designs for AM, an experiment was conducted where students were tasked with a design challenge where they were told that the designs would theoretically be manufactured using AM. Throughout the design challenge, the students received design considerations for AM in the form of paper cards that contained visual examples that were either abstract or product-specific. The students evaluated their own designs, while expert raters were tasked with determining if the visual example relevancy influenced the manufacturability of the designs. This work found that the design considerations regarding “Mass Customization” and “Feature Size” significantly varied in the designs when the students were exposed to the varying intervention content material.