bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of Distributed Sensor Configurations for Rainwater Harvesting Monitoring Systems
Authors: Rudy Eren , Bilton Amy M.
Published: November 2024

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 (> $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

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

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

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

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

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

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

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

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

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

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

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.
Examining Social and Environmental Factors for Wearable Data Integrity: a Case Study of Health-CPS
Authors: Wang Ruijing , Liao Ting
Published: November 2024

Wearable devices, with their ability to sense, collect, and transmit large amounts of physiological data, have experienced a recent surge in popularity, especially in the field of health monitoring. Ensuring data integrity is critical to effectively utilizing these devices, especially in the framework of Cyber-Physical Systems (CPS), where the seamless integration of computational algorithms with physical components plays a crucial role. Building on previous insights into the different contextual scenarios and their impact on data reliability, this paper examines the impact of social and environmental factors on the integrity of data collected by wearable devices, with a focus on human-related errors, such as improper wearing, and environmental conditions such as elevated humidity. Through a human-subject experiment, we simulated both prospective influential factors in a controlled lab setting and collected acceleration and heart rate data by utilizing the Apple Watch. To examine data integrity compromises, we proposed a multi-channel anomaly detection framework, and we demonstrated high accuracy rates in detecting anomalies, achieving 88.89% for improper wearing and a perfect 100% for elevated humidity. These findings confirm that human-related and environmental conditions affect data integrity. Our research enhances the reliability and robustness of CPS by quantifying the influence of social and environmental factors on wearable data integrity and providing insights into the system design beyond the technical elements of CPS.
Effects of Different Human-In-The-Loop Approaches on Human-AI Co-Design: A Comparison Between Human-Learning HITL Approach and Machine-Learning HITL Approach
Authors: Yin Yuan , Wang Boheng , Zuo Haoyu , Childs Peter
Published: November 2024

The speed at which the quantity of data is increasing stymies the deployment of some AI tools for design as the basis for training the tools is not stable. Also, selecting which data can be used as training data is a challenge. To address these issues, the concept of human-in-the-loop (HITL) approaches have been proposed. HITL includes human knowledge in the model-building processes to mitigate the high volume requirement of training data. Although how to apply AI in a design process and the effect of Generative AI tools (such as ChatGPT and Midjourney) in design processes has been explored, limited attention has been given to how different HITL approaches affect design performance. This study thus aimed to explore how different HITL approaches (Human-learning HITL approach and Machine-learning HITL approach) affect solutions of human-AI co-design. Fifteen participants were recruited to undertake two design tasks under the guidelines of Human-learning HITL and Machine-learning HITL approach respectively. The solutions from participants were assessed using four criteria (novelty, aesthetics, functionality, and feasibility). The results of the study indicated that the solution generated from Human-learning HITL approach is better compared with that of the Machine-learning HITL approach, especially in terms of aesthetics and functionality of the solution. The study supports the importance of expressing human insights through prompts on improving human-AI co-design solutions compared with providing more training data to AI.
Optimizing 3d Printing Process Parameters to Minimize Surface Roughness Using Bayesian Optimization
Authors: Kolte Akash , Bhaskaran Vignesh , Hoyle Christopher
Published: November 2024

Additive manufacturing techniques, particularly Fused Deposition Modeling have been extensively used in various industries due to their advantages in part design and flexibility over the conventional subtractive manufacturing processes. The process parameters play a crucial role in FDM 3D printing and need to be carefully tuned and optimized to achieve the desired quality, mechanical strength, and accuracy of the printed parts. This paper aims to optimize the process parameters in FDM 3D printing using a Bayesian Optimization (BO) method to minimize the surface roughness of the printed part. Nozzle temperature, layer height, bed temperature, print speed, and infill density are the five continuous input process parameters considered while the surface roughness is the output response. 34 Latin Hypercube Samples (LHS) are printed on a Prusa i3MK3S+ 3D FDM printer to generate the initial samples used in the optimization. Sequential BO with expected improvement as the acquisition function is used to find the optimal process parameters by balancing exploration and exploitation. The overall mean surface roughness (Ra) of the 57 experiments conducted was 6.09 μm and the optimizer produced an optimum value of 2.78 μm when the nozzle temperature was set to 216 °C, bed temperature to 66 °C, print speed to 73 mm/s, infill density to 20% and layer height to 0.123 mm respectively.
On the Prediction of Tremor Dynamics Motion Using Neural Network
Authors: Ding Zijian , Barry Oumar
Published: November 2024

Pathological tremors significantly affect the quality of life for patients worldwide. Rehabilitation exoskeletons serve as one of the solutions to alleviate these pathological tremors, and voluntary motion prediction-based motion planning has been employed to enhance the performance of these devices. This paper presents a method for predicting future voluntary movement in tremor-alleviating rehabilitation exoskeletons that use voluntary motion prediction-based motion planning. In this study, a Convolutional Neural Network and Transformer architecture based neural network work with EMG sensors to predict future voluntary movements. The results show that approach performs well in predicting future voluntary movements, but there is still a limitation to filter out the tremors completely. In summary, we provide a concept for predicting future voluntary movement, which has the potential to improve the effectiveness of rehabilitation exoskeletons in tremor alleviation.
Impact of Social Learning on Teamwork Efficiency in Learning-Based Self-Organized Systems
Authors: Huang Bingling , Ji Hao , Jin Yan
Published: November 2024

Self-organized systems (SOSs) possess the great potential to adapt to dynamic environments, and thus, advance the level of autonomy in engineering systems. Learning-based approaches, particularly multiagent reinforcement learning (MARL), have empowered the design freedom of SOSs while enhancing the system to be more intelligent. This is primarily because MARL encourages agents to learn from past failures and data through training. However, the training process is conducted in a “black box”, whose embeddings are uninterpretable for humans yet. This poses significant challenges for system designers, especially when task complexity reaches a certain extent, and agents experience a shortfall in learning due to limited available information. Therefore, in order to address how to steer more effective learning, we broaden the views of individual agents, enabling them to recognize the presence and actions of other members within a certain range of distance. This approach fosters social learning within AI agent groups. Prior work, however, has shown the intricacy of the social mechanism. Inappropriate social abilities can undermine a team’s performance. Thus, it is imperative to delve into the effects of social learning to facilitate the system design, further narrowing down the understanding gap between humans and AI agents. In this study, we train agent teams to engage in social learning to complete an assembly task involving collision avoidance. We aim to answer how social abilities influence teamwork at both the team and individual agent levels in terms of energy utilization. By examining the impact of team energy efficiency and teamwork division, our findings indicate a mapping linking appropriate social abilities to tasks of a certain complexity level. Suitable social abilities contribute to elevated team intelligence, more effective teamwork division, and higher energy efficiency, while insufficient social abilities may hinder cooperation and reduce energy efficiency.
Enhancing Vehicle Braking Performance on U-Split Road Through Active Steering Intervention
Authors: Hu Ruibo , Yu Liangyao , Huang Yong , Gao Dawei
Published: November 2024

Active steering technology offers an innovative approach to enhance vehicle safety by enabling maneuvers beyond the driver’s reaction speed. This proactive control not only prevents potential hazards but also maximizes ground friction utilization, thereby elevating overall safety. The focus of this paper is on improving vehicle braking performance through active front wheel steering control, using braking on uneven road surfaces as an illustrative example. While historical research primarily concentrated on post-instability measures in braking dynamics, specifically on returning a vehicle to a stable state after instability, limited exploration has been undertaken regarding control strategies when a vehicle encounters hazardous road conditions. At this initial stage, various vehicle states remain within a relatively stable range, allowing for rational intervention and more effective control. Utilizing obstacle recognition and distance estimation, considering estimable distances between the vehicle and the uneven road surface, the study introduces a Model Predictive Control (MPC) algorithm with a time margin for optimal steering input computation. The algorithm incorporates a high-fidelity nonlinear vehicle model and the Magic Formula tire model as state equation constraints, integrating the constraints of the actual steering actuation system. Simulation results demonstrate that implementing the MPC algorithm with a time margin effectively reduces lateral deviation and yaw angle during braking on uneven road surfaces, ultimately enhancing overall braking stability.
Design and Modeling of Compliant Four-Bar Mechanisms With Variable Stiffness Links for Multi-Output Trajectory
Authors: Luo Jiayu , Alvi Muhammad Hammad , Lin Han , Huang Xiaotong , Gan Dongming
Published: November 2024

This research presents a novel design of a four-bar mechanism featuring a variable stiffness link (VSL) as the output component, aimed at enabling diverse end-effector trajectories without modifying the link length or moment input. By employing both single-beam and multi-section beam configurations within a large deflection model, the study investigates the effect of varying link stiffness under constant load and geometric conditions on the mechanism’s trajectory outcomes. The proposed design was validated through both numerical modeling and experimental testing of a built prototype. The findings confirm the prototype’s alignment with theoretical predictions, highlighting the VSL’s key role in significantly enhancing the adaptability and application range of four-bar mechanisms. This advancement circumvents the traditional constraints of fixed-trajectory mechanisms, proposing a versatile, efficient, and cost-effective solution for complex motion applications in compliant mechanism design.
A Study on Combined Lithium and Sodium-Ion Hybrid Energy Storage Systems in Battery Electric Vehicles
Authors: Foti Orazio , Pakštys Saulius , Bonfitto Angelo
Published: November 2024

With an end-goal of carbon neutrality set by many nations across the world, various industries face the challenging task of incorporating novel technologies in their products while remaining economically sustainable. This is particularly evident in the automotive sector, where diversifying the product portfolio of many Original Equipment Manufacturers to meet carbon emission targets is a primary goal, investing in research ranging from hydrogen fuel propulsion to energy storage systems. It is in this latter area in which this paper provides an insightful analysis in the coupling of two rechargeable energy storage systems based on different chemical technologies. The work herein evaluates a hybrid energy storage system for a subcompact crossover sport utility vehicle that includes a lithium-ion (LIB) and sodium-ion battery (NaIB) pack, with varying divisions of the total energy between the two. The aim is to exploit the NaIB pack to the greatest possible degree such that the longevity of the LIB pack is extended, given that NaIBs are more forgiving in their response to high C-rates. The division is performed on the basis of two methodologies: 1) maintaining a constant mass of the combined battery pack and 2) maintaining a constant energy of the hybrid energy storage system. The analysis considers two energy management strategies that are both deterministic rule-based, the first termed as a Load Follower and the second as a Range Extender. The analysis of a selection of energy division and energy management strategies shows that a hybrid battery system comprising of 70% of energy provided by a LIB and 30% by a NaIB coupled with a Range Extender energy management provides the best trade-off between C-rate reduction, range penalization and cost reduction. Such a solution allows for up to 46% reduction in LIB C-rate and 7.6% reduction in cost with respect to the vehicle mounted with the baseline LIB.
Path Optimization From En Route 3d Printed Aircraft Using Physics-Based Estimation Methods and Surrogate Models
Authors: Dickerson Tevin , Salmon John L. , Mattson Christopher A.
Published: November 2024

In a previous study, a general approach to increasing the range capability of an unmanned aerial system was assessed by 3D printing components or parts for the UAS while a carrier platform, of both the printer and UAS systems, travels to a launch point for the UAS. As a result, the platform could launch the UAS from a distance further away from the location of interest. This paper outlines the parameters of a physics-based model to determine the actual range increase of an attritable aircraft, constrained by the amount of time available for printing while en route. It develops the formulation of the UAS range increase from the lift-to-drag ratio increases caused by the physical wing section enhancements printed in a constrained amount of time while the carrier platform travels from one location to another. The relationship is leveraged to optimize the shortest path necessary for the carrier platform to access or reach the set of locations of interest in an operational environment. Scenarios of up to 15 locations are explored by first optimizing the sequence of the locations in a traveling salesman problem and then optimizing the actual path taken by the carrier platform along that sequence. Statistical results are presented to summarize the benefits of 3D printing en route across different scenarios.
Metareasoning to Adapt Path Planning Decision Making for Different Modalities of Uncertainty
Authors: Sathyamurthy Eashwar , Richardson John , Herrmann Jeffrey W. , Raglin Adrienne
Published: November 2024

Determining the appropriate level and composition of reasoning for decision-making is a critical component of variable autonomy, as an autonomous system might shift the responsibility for decision-making to its human teammate when it cannot solve the problem that has appeared. Metareasoning (reasoning about reasoning) can provide the autonomous system with a way to adapt its own reasoning (path planning) processes so that it relies less upon its human teammate. A use case was selected to study metareasoning for the problem of path planning in the presence of different types of uncertainty. We created and evaluated the performance of metareasoning policies that changed the path-planning approach for a set of simulated autonomous systems for diverse and dynamic uncertainties. The initial simulation included a single vehicle navigating from a start location to a specified goal while avoiding obstacles. Various iterations of the simulation were conducted, including missions with no uncertainty, uncertainty of obstacle locations, and uncertainty of obstacle status. Our simulation results showed that the vehicle used shorter paths when employing metareasoning, which also reduced total computational effort. These results suggest that metareasoning is a feasible strategy for variable autonomy human-robot teaming and that autonomous systems can use metareasoning to reduce the dependency on human intervention.
Improving Climbing Performance Using Passive Tail in Lizard-Inspired Robot
Authors: Pranav Kodidasu , Sankar Kadali Mani , Thakur Atul
Published: November 2024

Lizards are excellent climbers and effectively utilize their tail to actively counter the destabilizing pitching moment during climbing. Taking inspiration from the use of a tail for imparting stability we designed a compliant passive tail to improve the climbing performance of a wall climbing lizard-inspired robot developed in the past by our research group [1, 2]. This paper presents the incorporation of the spring-actuated tail into climbing robots to improve their stability and climbing capabilities. A spring-actuated tail helps in regaining the stability during momentary adhesive failure making the robot more reliable. However, there is no external support for the tail to avoid slipping during momentary adhesive failure. To improve the climbing performance and reduce the chances of failure, A flexible pressure-sensitive adhesive tip has been incorporated on the compliant passive tail to enhance the climbing stability. The climbing capabilities of the proposed design are compared with two different conditions: a robot with and without a tail under two different terrains: hard (Acrylic sheet) and soft (Fitness mat). The experimental results demonstrate that the robot equipped with a tail featuring a flexible adhesive tip achieves the best climbing performance on hard terrains with a maximum slope of 65°, while a tail lacking a flexible adhesive tip achieves superior climbing performance on soft terrains with a maximum slope of 40°. This approach enables a climbing robot to navigate and ascend various terrains, thereby expanding its operational capabilities to encompass areas with different terrains. This versatility is especially valuable in tasks such as inspection, exploration, and construction.
Bluff Body, Piezoelastic Oscillator: Reduced-Order Model, Vortex-Induced Vibrations, and Energy Harvesting
Authors: Bellei Andrés , Balachandran Balakumar
Published: November 2024

Energy harvesting from flow-induced vibrations has gained substantial attention in the last two decades due to the rising demand for renewable and sustainable energy sources, as well as the widely availability of these sources, offering a viable alternative in areas where other ambient energy sources may not be readily accessible. Flow-induced vibrations of bluff bodies are characterized by complex nonlinear dynamics, for which accurate models are currently lacking.

In this work, a circular cylinder attached to the free end of a piezoelastic cantilever is considered for energy harvesting. When placed in a flow, this system undergoes vortex-induced vibrations. A reduced-order model is developed to understand fluid-structure interactions of this system. A wake oscillator has been used to describe vortex-induced vibrations and a finite-element model has been used to model the piezoelastic cantilever. The developed model is used to explore the interplay amongst the fluid, structure, and piezoelectric element. The results obtained are compared to experimental data from literature, in terms of the vibration amplitude, vibration frequency, and power obtained. Modifications to the wake oscillator model are examined to better reflect the fluid-structure interactions. It is found that there is a trade-off between accurately predicting the vibration amplitude and accurately predicting the vibration frequency.
Extending the Product Architecture Strategies and Effects Database Through Large Language Models
Authors: Rice Scott E. , Sannar Benjamin C. , Mattson Christopher A. , Anderson Michael A.
Published: November 2024

There are more than 4000 archival publications on the topic of product architecture. Manually synthesizing such a vast body of literature is prohibitive. To ease the burden and bring insight from the literature to product design engineers, we introduced the Product Architecture Strategies and Effects (PASE) database in a previous publication. This database is a collection of relationships between product architecture decisions and their effects as contained in a selection of the literature. Despite the discovery of over 400 relationships, the PASE database was constructed manually from only 17 publications. The present paper describes a way to expand the PASE database, utilizing the data it currently contains to train a set of cascading large language models (LLMs) to rapidly expand the PASE database. In this paper, we carry out the first two steps of the proposed approach and show that it has potential to reduce the effort required to add to the PASE database by orders of magnitude, compared to the manual approach.
Navigating the Tradespace: Empowering Decision-Making With Data Manipulation
Authors: Turner Cameron J. , Raj Nikhil , Hartman Greg , Castanier Matt , Rapp Stephen
Published: November 2024

In engineering design and decision-making, the exploration of tradespace is crucial for arriving at optimal solutions. Traditional methods, such as morphological charts, have been instrumental in organizing design possibilities. Our approach integrates functional objectives, enhancing focus on performance and efficiency. While morphological charts have been fundamental, our work introduces advanced techniques like ANOVA and PCA, expanding tradespace exploration. This empowers researchers to uncover insights and correlations and make informed decisions, driving innovation.

We illustrate how methodologies like ANOVA, PCA and correlation open new horizons in tradespace exploration, capturing the richness of design alternatives and understanding relationships between variables. Embracing functional objectives aligns engineering design with evolving technological demands. As graduate students and researchers tackle complex challenges, our work bridges the past and future of design methodologies.

The US Army DEVCOM GVSC invested years to develop a comprehensive tradespace encompassing vast vehicle configurations. A ‘Testbed’ tradespace with about 13 variables has been formulated to facilitate smaller-scale exploration. It serves as a resource for future studies, providing a standardized framework for advancing engineering design optimization. This collaborative effort highlights the need for effective tradespace exploration approaches to inform decision-making.
Implementing the Risk Management Framework for Additive Manufacturing Security: A Model-Based Approach
Authors: Gibbons Duncan W. , Lubell Joshua , Witherell Paul
Published: November 2024

Metal additive manufacturing machines are complex and inherently digital and often cyber-physical systems. As the adoption of this manufacturing technology increases and it becomes increasingly industrialized, concerns about security are evermore prevalent. Both cyber and non-cyber related attacks on critical infrastructure such as additive manufacturing production systems are causes for concern for industry and government. Both the public and private sectors need to focus on securing their information systems to reduce the risk of security attacks and their adverse effects. This research aims to apply the National Institute of Standards and Technology’s Risk Management Framework to the metal additive manufacturing production scenario. The Risk Management Framework defines a rigorous, yet flexible and repeatable, process for managing security risk. A model-based assessment approach is proposed to leverage the digital nature of this manufacturing technology. A case study is performed to demonstrate this approach for a commercial laser powder bed fusion machine in its operating environment. This case study focuses on the technological security risks. This study demonstrates how a model-based approach maximizes the benefits of the Risk Management Framework by improving information and decision traceability for addressing metal additive manufacturing security risks.
Exploring the Effects of a Problem Typology Framework on Student Engagement in a Selection Problem
Authors: Retzlaff Lisa , Ghoreyshi Sama , Olewnik Andrew , Ferguson Scott
Published: November 2024

In most engineering curricula, students are often not given opportunities to solve design problems outside their introductory engineering courses and capstone design. This inhibits them from exercising their ability to navigate ill-structured and complex problem elements inherent to design problems. Problem-based learning (PBL) can help provide opportunities to exercise these abilities by allowing students to gain experience in problem framing and decision-making. However, implementing PBL is often challenging because faculty must navigate problem design, facilitation, and assessment. In this study, we investigate the impact of using an explicit problem typology framework in facilitating student progression in a PBL environment where they engage with a selection problem, a common subproblem in design. One student section was exposed to this problem typology framework (intervention group), while another section was not (control). Using retrospective interview data from students, we analyzed how students talked about their engagement with the selection problem. Results of interview analysis were supplemented by analysis of student assignment submissions. We find that students introduced to the problem typology framework focused less on the reporting of equations and calculations, were more articulate in developing and describing the use of a ranking system in support of selection, and focused on aspects of mathematical and procedural reasoning contributing to the confidence in their final solution. Our interpretation of these findings is that students who used the framework began to view design as sociotechnical, not technorational, in nature. These facets may be missed, even in more open pedagogies like PBL, if facilitation strategies are not carefully considered. We believe these findings lend support for continuing efforts to operationalize Jonassen’s design theory of problem solving to develop PBL environments.
Requirements for Designing a Fail-Safe Defense Supply Network
Authors: Carpenter Samantha L. , Klier John , Allen Janet K. , Mistree Farrokh
Published: November 2024

Defense supply networks are critical for national security. These supply networks are currently designed using the cyber-physical construct to be lean. An unintended consequence of a lean supply network is that it is fragile and not fail-safe. We propose to design the defense supply network as a cyber-physical-social system that will result in a resilient fail-safe supply network that accommodates both variations and disruptions thus allowing us to dynamically manage lead times, address a systemic response for variations and disruptions to reduce or eliminate failure. We recognize the Government, Supplier and User to be the stakeholders. To frame the problem in the context of Dilemmas faced by the three stakeholders we use the Dilemma Triangle Method. The Dilemmas are then analyzed and resolved via the development of a Requirements List within the current defense sustainment “Machine” model outlined in the Art of the Possible (AoP). The application and examination of Dilemmas through the lens of the AoP based constraints management system is further specified to include satisficing resolutions to the identified Dilemmas rooted in a preliminary Requirements List. The Requirements List is foundational for designing a defense supply network that is failsafe. In this paper, we propose a method for identifying the requirements for designing fail-safe supply networks to satisfy multiple stakeholders. We illustrate the efficacy of the approach using the defense supply network as an example. Our focus is on the method rather than the results per se.