bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrated Design for Wave Energy Converter Farms: Assessing Plant, Control, Layout, and Site Selection Coupling in the Presence of Irregular Waves
Authors: Azad Saeed , Khanal Suraj R. , Herber Daniel R. , Jia Gaofeng 
Published: November 2024

Abstract
A promising direction towards reducing the levelized cost of energy for wave energy converter (WEC) farms is to improve their performance. WEC design studies generally focus on a single design domain (e.g., geometry, control, or layout) to improve the farm’s performance under simplifying assumptions, such as regular waves. This strategy, however, has resulted in design recommendations that are impractical or limited in scope because WEC farms are complex systems that exhibit strong coupling among geometry, control, and layout domains. In addition, the location of the candidate site, which has a large impact on the performance of the farm, is often overlooked. Motivated by some of the limitations observed in WEC literature, this study uses an integrated design framework, based on simultaneous control co-design (CCD) principles, to discuss the impact of site selection and wave type on WEC farm design. Interactions among plant, control, and layout are also investigated and discussed using a wide range of simulations and optimization studies. All of the studies were conducted using frequency-domain heaving cylinder WEC devices within a farm with a linear reactive controller in the presence of irregular probabilistic waves. The results provide high-level guidelines to help the WEC design community move toward an integrated design perspective.
Adaptive Agent-Based Control for Lithium-Ion Batteries in Naval Microgrids
Authors: Peskar Jarrett , Priddy Braden , Sado Kerry , Downey Austin R. J. , Booth Kristen , Khan Jamil 
Published: November 2024

Abstract
This paper presents an adaptive agent-based control scheme for operating distributed energy storage in naval power systems. The adaptive agent-based control scheme enables batteries distributed throughout an electrical grid to autonomously communicate and align each other to operate in the most effective way possible. To achieve this, each battery is assigned an agent capable of controlling the battery’s output with the goal of maximizing the life of the battery while supplying the required power to the load. Along with the goals of the individual agents, system-level alignment strategies are employed to maximize certain aspects of the battery operations. The employed system-level alignment strategies are duration, ready, and aggressive. The duration strategy maximizes the longevity of grid resources of expected battery degradation and electric generator component wear by utilizing the system in a manner that optimizes system-level longevity. The ready strategy maximizes the amount of power being kept in reserve for use when needed while the Aggressive strategy maximizes power output by allowing the batteries to be used to their limits to deliver as much power as possible. Moreover, an adaptive strategy was also created by mapping the previously mentioned static strategies to the levels of power demand. This adaptive strategy allows the batteries to operate in the most efficient manner while adapting to the power demand from the load. To analyze these strategies, the chosen performance metrics were 1) battery and generator remaining useful life (RUL), 2) average battery temperature, and 3) percent of time the requested load profile was achieved. Results demonstrate that the adaptive strategy operates with better overal efficiency by maintaining the 2nd highest battery RUL, the 3rd highest generator RUL and the 2nd lowest average temperatures. It was also the only strategy to fulfill the maximum pulse seen from the load.
Perception of Automation, AI, and Collaboration in Manufacturing
Authors: Adebayo Oredola , Kumar Pavan , Patel Apurva , Summers Joshua D. 
Published: November 2024

Abstract
This research investigates the perceptions and attitudes towards artificial intelligence (AI) and automation in manufacturing settings. A multimodal data collection approach is used to gather participant responses using surveys, focus group discussions, and interviews. Qualitative and quantitative data collected from six manufacturing companies are analyzed to explore the multifaceted dynamics influencing the adoption and acceptance of AI and automation technologies. The study investigates specific views, concerns, and expectations regarding the implementation of these technologies, shedding light on the nuanced perspectives of participants across different organizational contexts. By examining factors such as personal strategies, sentiment analysis, relevance to roles, and technology acceptance, the research offers insights into the complex interaction between technological advancement, organizational culture, and individual attitudes. Varying levels of understanding regarding technological relevance to roles are observed, alongside concerns about job displacement and decision-making transparency. Participants differ in their belief in AI and automation’s potential to address concerns, highlighting the need for tailored integration strategies. Unique observations, such as machine personalization and cultural influences, are uncovered. Conclusions stress the importance of addressing these complexities in adoption efforts, while future work aims to expand datasets and explore new data collection methods to inform integration strategies. Future work includes analysis of the focus group and interview transcripts for confirmation of these findings.
Refining Engineering Identity Framework for Implementation In Engineering Education – A Literature-Based Analysis
Authors: Kumar Pavan , Summers Joshua D. 
Published: November 2024

Abstract
This paper offers a refined framework for Engineering Identity and its implementation in engineering education. Engineering is portrayed as a multifaceted discipline that leverages scientific, mathematical, and technical knowledge to design, create, and enhance various structures, machines, systems, and devices to address practical challenges and fulfill human needs. It is an action-oriented profession seeking change into a desired future state. The essential skills of critical thinking, problem-solving, creativity, and systematic thinking are those that engineers employ to innovate and implement projects across diverse industries, such as civil, mechanical, electrical, chemical, aerospace, bioengineering and computer engineering. This study emphasizes defining engineering identity within the context of undergraduate engineering education, recognizing this phase as pivotal for the development and evolution of an individual’s identity as an engineer. By scrutinizing definitions of engineering identity from the literature, a refined framework is distilled to support engineering education. The integrated definition for engineering identity is the activity of perception, identification, and corroboration by the student, validated through their interests, opportunities, competence, performance, and recognitions, that is influenced by their prior identity formed by personal, social, educational, and professional experiences. A deeper understanding and cultivation of engineering identity is needed to enrich the educational journey of students and equip them with the necessary skills and mindset for successful careers in the engineering field.
Requirements Mining From Engineering Standards – Development and Evaluation of a Standards Requirements Mining Framework
Authors: Luttmer Janosch , Ehring Dominik , Vinke Kathrin , Alazzazy Mohamed , Nagarajah Arun 
Published: November 2024

Abstract
Standards are an important source of knowledge and influence many decisions along the product development process. However, the capture, reuse, and management of standards content is highly inefficient and defined by a lot of manual steps. This paper presents the development of a holistic framework designed to automize the extraction and processing of requirements from engineering standards — one main element of standards. The plug-and-play framework consists of three core clusters, namely extraction of requirements, classification of requirements, as well as transfer of requirements. Due to the high importance of accurate requirements extraction, this research paper focuses on analyzing and optimizing existing requirements extraction techniques. For this, hyperparameter tuning as well as stacking is used. Using these methods, a neural network is developed with an overall recall of 97%. Moreover, different classification methods are implemented to enhance extracted requirements with additional attributes. The requirements are ultimately transferred and integrated into requirements management tools using the interchange format ReqIF. The framework’s functionalities are validated using three exemplary engineering standards. Future work will focus on the further optimization of extraction solutions with emphasis on user interaction. Moreover, user studies will be conducted to measure the efficiency and effectiveness of the proposed framework.
Exploring the Impact on Neurocognitive Process and Motivation Caused by Problem Modality
Authors: Kado Corey , Kames Elisabeth 
Published: November 2024

Abstract
Design based curriculum has been identified as a vital component for engineering education. In the decade since ASME Vision 2030 Project (V2030) strides have been achieved in the implementation of these guidelines into post-secondary institutions. Such is seen in the criteria for accreditation by ABET, which includes an explicit requirement for design to be present in engineering programs. This study is a continuation of previous work examining the effect of design problem modality. Design problem modality describes the method in which design problems are presented to the students. For this study, design problems were presented to the students through either an auditory description or a kinesthetic (hands-on) example. Participants are gathered from an engineering design cornerstone course at Florida Polytechnic University. The tool utilized to examine neural oscillations in this study is the electroencephalograph (EEG). To further examine the effects of these modalities, a modified version of the Motivated Strategies for Learning Questionnaire (MSLQ) and the learning style questionnaire VARK are utilized to extend the comprehension of the impact. Including demographic data of the participants, an encompassing understanding of problem modality on student’s can be achieved.
The results showed differences in neural oscillations in the beta high frequency range with differing design problem modality, however the order in which the design problems were presented showed no such correlation. Further examination found a correlation between the perceptions of a design problem, such as difficulty, and the design problem modality, as well as a participant’s satisfaction with the design problem order.
Dehomogenization for Multiscale Topology Optimization via Triangular Lattice Structures
Authors: Najmon Joel C. , Chaudhari Prathamesh , Tovar Andres 
Published: November 2024

Abstract
Dehomogenization is a post-processing step for generating physically realizable structures from homogenized designs. Typically employed in conjunction with homogenization-based topology optimization (HBTO), dehomogenization is used to derive a binary representation of the initially homogenized (gray) design. Currently, most dehomogenization approaches rely on an implicit representation of microscale topologies using periodic functions, which limits their applicability to specific parameterized microstructures, such as rectangular voids. This work introduces a novel dehomogenization approach that allows the projection of a homogenized mechanical property field onto a discrete lattice structure of arbitrary shape. This results in a well-connected, functionally graded lattice structure featuring control over the length scale, orientation, and complexity of the final design. The proposed approach incorporates an optimization algorithm to find the lattice thickness distribution that minimizes the difference between a local target homogenized property and a corresponding lattice homogenized stiffness tensor. The definition of a signed distance function between connected nodes yields tapered beam designs with smoothly varying thicknesses. The effectiveness of the approach is demonstrated on a half MBB beam, a clamped beam, and an L-bracket. The compatibility of the resulting non-periodic structures indicates that the performance of the dehomogenized design is maintained or improved compared to the homogenized design.
Cooling-Guided Diffusion Model for Battery Cell Arrangement
Authors: Sung Nicholas , Liu Zheng , Wang Pingfeng , Ahmed Faez 
Published: November 2024

Abstract
Our study introduces a Generative AI method that employs a cooling-guided diffusion model to optimize the layout of battery cells, a crucial step for enhancing the cooling performance and efficiency of battery thermal management systems. Traditional design processes, which rely heavily on iterative optimization and extensive guesswork, are notoriously slow and inefficient, often leading to suboptimal solutions. In contrast, our innovative method uses a parametric denoising diffusion probabilistic model (DDPM) with classifier and cooling guidance to generate optimized cell layouts with enhanced cooling paths, significantly lowering the maximum temperature of the cells. By incorporating position-based classifier guidance, we ensure the feasibility of generated layouts. Meanwhile, cooling guidance directly optimizes cooling-efficiency, making our approach uniquely effective. When compared to two advanced models, the Tabular Denoising Diffusion Probabilistic Model (TabDDPM) and the Conditional Tabular GAN (CTGAN), our cooling-guided diffusion model notably outperforms both. It is five times more effective than TabDDPM and sixty-six times better than CTGAN across key metrics such as feasibility, diversity, and cooling efficiency. This research marks a significant leap forward in the field, aiming to optimize battery cell layouts for superior cooling efficiency, thus setting the stage for the development of more effective and dependable battery thermal management systems.
Distributed Multi-Agent Bayesian Optimization for Unknown Design Space Exploration
Authors: Chen Siyu , Bayrak Alparslan Emrah , Sha Zhenghui 
Published: November 2024

Abstract
In multi-agent Bayesian optimization for Design Space Exploration (DSE), identifying a communication network among agents to share useful design information for enhanced cooperation and performance, considering the trade-off between connectivity and cost, poses significant challenges. To address this challenge, we develop a distributed multi-agent Bayesian optimization (DMABO) framework and study how communication network structures/connectivity and the resulting cost would impact the performance of a team of agents when finding the global optimum. Specifically, we utilize Lloyd’s algorithm to partition the design space to assign distinct regions to individual agents for exploration in the distributed multi-agent system (MAS). Based on this partitioning, we generate communication networks among agents using two models: 1) a range-limited model of communication constrained by neighborhood information; and 2) a range-free model without neighborhood constraints. We introduce network density as a metric to quantify communication costs. Then, we generate communication networks by gradually increasing the network density to assess the impact of communication costs on the performance of MAS in DSE. The experimental results show that the communication network based on the range-limited model can significantly improve performance without incurring high communication costs. This indicates that increasing the density of a communication network does not necessarily improve MAS performance in DSE. Furthermore, the results indicate that communication is only beneficial for team performance if it occurs between specific agents whose search regions are critically relevant to the location of the global optimum. The proposed DMABO framework and the insights obtained can help identify the best trade-off between communication structure and cost for MAS in unknown design space exploration.
Advancing Ground Vehicle Dynamics: Dynamic Morphing Technology Integration and Analysis for the Autonomous Utility Truck in High-Crosswind Conditions
Authors: Patel Parth Y. , Koomullil Roy , Zhang Siyuan , Moradi Lee , Vantsevich Vladimir 
Published: November 2024

Abstract
Initially pioneered in aircraft design to optimize maneuverability and aerodynamic efficiency, dynamic morphing technology is now revolutionized in ground vehicles to address the stability and maneuverability challenges. Inspired by the success of morphing solutions in aviation and a few other ground vehicles, this paper dynamically analyzes a novel approach that the authors proposed previously in their published work to advance ground vehicle dynamics by integrating dynamic morphing technology into utility trucks equipped with robotic manipulator boom equipment and to manage their traction capability and the stability during the motion under high-crosswind conditions. This paper aims to optimize the tire-road/terrain contact by dynamically adjusting the truck’s shape through coordinated movements of the boom equipment and mitigate the adverse effects of crosswinds on vehicle dynamics. Using advanced modeling techniques and simulation studies, we analyzed the dynamic morphing process and its impact on the truck’s traction performance in high-crosswind conditions. Utility trucks can achieve traction and stability by optimizing the distribution of the truck’s weight and aerodynamic forces and moments, thus improving safety and performance in challenging environments.
Advanced Tire Testing by an Innovative Moving Laboratory for Automotive Components Safety Assessment (MoLAS)
Authors: Ballo Federico Maria , Biffi Alessio , Gobbi Massimiliano , Mastinu Giampiero 
Published: November 2024

Abstract
The paper presents an innovative moving laboratory (MoLAS) for tire testing. The MoLAS system is mounted on a semitrailer to be pulled for outdoor tire characterization under realistic grip condition. The vertical load is controlled by means of a pneumatic spring actuator mounted on top of a moving platform guided by linear ball sliders. A double-stage suspension system allows to filter out the vertical disturbance induced by road irregularity. Two electric actuators are used to precisely control steering and camber angles. Tire forces and moments are measured by an innovative six-axis force sensor specifically designed for the purpose. The main innovation introduced by the MoLAS with respect to the state-of-art test systems relies in the possibility to test the tire both in traction and braking longitudinal slip conditions. This is achieved by an internal combustion engine and a retarder electromagnetic brake mounted on the driveline. A complete tire characterization has been performed by placing the MoLAS system on the RuotaVia drum of the LaST laboratory at Politecnico di Milano. The main results are presented in the paper.
Physics-Informed Data-Driven Approaches to Electric Vehicle Battery State-of-Health Prediction: Comparison of Parallel and Series Configurations
Authors: Zhao Yixin , Haapala Karl R. , Natarajan Arun , Behdad Sara 
Published: November 2024

Abstract
Battery lifetime and reliability depend on accurate state-of-health (SOH) estimation, while complex degradation mechanisms and varying operating conditions strengthen this challenge. This study presents two physics-informed neural network (PINN) configurations, PINN-Parallel, and PINN-Series, designed to improve SOH prediction by combining an equivalent circuit model (ECM) with a long short-term memory (LSTM) network. PINN-Parallel process input data through parallel ECM and LSTM modules and combine their outputs for SOH estimation. On the other hand, the PINN-Series uses a sequential approach that feeds ECM-derived parameters into the LSTM network to supplement temporal data analysis with physics information. Both models utilize easily accessible voltage, current, and temperature data that match realistic battery monitoring constraints. Experimental evaluations show that PINN-Series outperforms the PINN-Parallel and the baseline LSTM model in accuracy and robustness. It also adapts well to different input conditions. This demonstrates that the simulated battery dynamic states from ECM increase the LSTM’s ability to capture degradation patterns and improve the model’s ability to explain complex battery behavior. However, the trade-off between the robustness and training efficiency of PINNs is also discussed. The research findings show the potential of PINN models (particularly the PINN-Series) in advancing battery management systems, but the required computational resources need to be considered.
Multi-Split Configuration Design for Fluid-Based Thermal Management Systems
Authors: Bayat Saeid , Shahmansouri Nastaran , Peddada Satya RT , Tessier Alex , Butscher Adrian , Allison James T. 
Published: November 2024

Abstract
High power density systems require efficient cooling to maintain their thermal performance. Despite this, as systems get larger and more complex, human expertise and insight may not suffice to determine the desired thermal management system designs. To this end, a framework for automatic architecture exploration is presented in this article for a class of single-phase, multi-split cooling systems. For this class of systems, heat generation devices are clustered based on their spatial information, and flow splits are added only when required and at the location of heat devices. To generate different architectures, candidate architectures are represented as graphs. From these graphs, dynamic physics models are created automatically using a graph-based thermal modeling framework. Then, an optimal fluid flow distribution problem is solved by addressing temperature constraints in the presence of exogenous heat loads to achieve optimal performance. The focus in this work is on the design of general multi-split heat management systems. The methods presented here can be used for diverse applications in the domain of configuration design. The multi-split algorithm can produce configurations where splitting can occur at any of the vertices. The results presented include three categories of problems and are discussed in detail.