bluuebunny/crossref_metadata_2025 huggingface dataset:
The CCA Missing Data Completion Algorithm for Machine Learning Purposes
Authors: Chełchowski Adam , Jarzebowska Elżbieta , Debowski Andrzej
Published: November 2024

The paper deals with multivariate time series classification, a widely used concept of supervised machine learning for automation in data analysis. Many algorithms have been proposed to deal with such classification, yet a majority of them require equal length data inputs, which often cannot be primarily satisfied. For this purpose different techniques of data adjustment are applied, such as truncation, padding or forecasting. In this paper we present a novel approach to data recovery, which yields an algorithm allowing to align variable-length time series, basing on cross-correlation. The paper presents the proposed algorithm architecture with its advantages and possible tasks to be adapted for. The novel algorithm is illustrated on a use case, which is a sequence of car-like vehicles driving through different road conditions. Two independent analyses using a specific time series classifier are derived to test it. The first one covers validation on the open-access data, while the second one focuses on verification on experimental data collected by ourselves. In both cases the incomplete data is recovered with the use of a few chosen techniques including the one proposed in this paper. The results are thereafter analysed and compared using fundamental statistical factors.
An Experimental Investigation of P-Bifurcation, Energy Harvesting and Vibration Suppression Using a Nonlinear Inerter Device
Authors: Cosner Joel A. , Tai Wei-Che
Published: November 2024

This work investigates the vibration suppression and energy harvesting capabilities of an inerter pendulum vibration absorber (IPVA) when applied to a single degree of freedom structure subjected to Gaussian broadband base excitation which was analytically treated as white noise. An IPVA which was previously studied by the authors was attached to a DC generator to experimentally investigate the energy harvesting and vibration mitigation capabilities of the device in connection with P-bifurcation. A formal characterization of the system’s unknown parameters was done and all measured, calculated, and experimentally determined parameters were presented along with four resistive load considerations including the open circuit configuration. Next, an analytically derived P-bifurcation boundary in the noise intensity versus electrical and friction damping plane was presented and the boundaries were verified with an experiment. The power spectral densities for electric power and relative suspended mass velocity were determined for the system with the IPVA and compared with that of the best tested linear system. A correlation between poor energy harvesting and a monomodal PDF was made apparent, while the mean and resonant peak power was enhanced by a factor of as much as four and two, respectively. The peak value for the relative velocity power spectral density was improved by as much as a factor of four near resonance and the mean square relative velocity by a factor of two.
Geometry-Driven Design of Morphable Surface Structures Using Topology Optimization and Circle Packing
Authors: Gao Lingfeng , Zhu Xiaoping , Tanaka Masato , Song Yuyang , Zhou Yuqing , Gu Xianfeng David , Chen Shikui
Published: November 2024

This paper presents a new computational framework for the co-optimization and co-control of morphable surface structures using topology optimization and circle-packing algorithms. The proposed approach integrates the design of optimized compliant components and the system-level control of the overall surface morphology. By representing the surface shape using circle packing and leveraging conformal mapping, the framework enables smooth deformation between 2D and 3D shapes while maintaining local geometry and global morphology. The morphing surface design problem is recast as designing circular compliant actuators using level-set topology optimization with displacements and stiffness objectives. The optimized component designs are then mapped back onto the circle packing representation for coordinated control of the surface morphology. This integrated approach ensures compatibility between local and global geometries and enables efficient actuation of the morphable surface. The effectiveness of the proposed framework is demonstrated through numerical examples and physical prototypes, showcasing its ability to design and control complex morphable surfaces with applications in various fields. The co-optimization and co-control capabilities of the framework are verified, highlighting its potential for realizing advanced morphable structures with optimized geometries and coordinated actuation. This integrated approach goes beyond conventional methods by considering both local component geometry and global system morphology and enabling coordinated control of the morphable surface. The general nature of our approach makes it applicable to a wide range of problems involving the design and control of morphable structures with complex, adaptive geometries.
Utilizing Reinforcement Learning to Improve Gait Training Performance and Development of Gravity-Compensating Control System for RoboREHAB Device
Authors: Anthony Jacob , Ballard Chad , Goh Chung-Hyun , Carr Jacob , Jeon Woohyoung
Published: November 2024

As a patient gets older, the chances of sustaining a motor disability increase. Such disabilities impair the patient’s ability to perform daily activities and undergo rehabilitation to regain mobility. Exoskeleton devices and aqua therapy have been used to make this process easier. However, most exoskeletons do not replicate the natural gait pattern accurately and there does not exist a lower-limb rehabilitation device that provides gait training with zero-gravity assistance. The objective for this study is to design two control systems that govern motor control for the natural gait pattern using a redesigned leg assembly and create a weight-compensation control system for a traditional leg assembly. Reinforcement learning (RL) was used in Simulink to develop the gait pattern control system, and a physical prototype was created to test the zero-gravity control system using Arduino. The RL control system with the redesigned leg showed a X- and Y-deviation of 45.622 mm and 17.164 mm at the knee joint compared to the original two-segment design, which achieved an X- and Y-deviation of 133.333 mm and 38.832 mm at the knee joint. Zero-gravity was achieved via DC Motors for the knee and foot sections, but the motors were not strong enough to support the load at the hip joint. Using Servo Motors, a sit-to-stand movement was created, with only a slight reduction in the range of motion of the hip due to the 5V motors limitations. Finally, a walking motion was created using Servo Motors, achieving another method of zero-gravity. The zero-gravity controller worked for the walking and sit-to-stand transfer but the hip joint needs further improvement.
Modal Sensitivity Analysis of the In-Plane Rigid-Elastic Coupled Tire Model
Authors: Alobaid Faisal , Taheri Saied
Published: November 2024

This study uses the in-plane rigid elastic tire model and modal sensitivity analysis to explore the tire properties and its modal characteristics. The tire model integrated the Euler Bernoulli beam as a tire tread with sidewall lumped masses to extend the frequency range to 300 Hz. The finite Difference Method (FDM) is used to discretize the PDE’s equation of motion into ODE’s equations, create mass, stiffness, and damping matrix, and then build the tire modal model. The sensitivity analysis was applied to the tire modal model to identify the properties significantly impacting the tire’s natural frequencies in-phase and out-phase. Pretension and two sectional sidewall stiffnesses were chosen for sensitivity evaluation. Percentage variations of ±25% are imposed on each property to estimate their effects on the tire’s modal characteristics. The findings emphasize that the two sectional sidewall stiffness impact on the natural frequencies depends on the natural frequencies range (radial mode number) and whether it is in the in-phase or out-phase regions. However, the pretension generally significantly impacts natural frequencies linearly in all radial modes. These results offer valuable insights for understanding noise, vibration, and harshness and enhancing tire and vehicle performance for ride comfort over short-wavelength road profiles.
Static and Dynamic Behavior of Planetary Gears With Diametrically Opposite Planet Gears
Authors: Ryali Lokaditya , Talbot David
Published: November 2024

The majority of planetary gears in the industry have equally spaced planets, it’s very rare to find designs with unequally spaced planets unless there are assembly limitations or specific application needs. The literature on unequally spaced and diametrically opposite planet gear configurations is also quite sparse. Therefore, the current study presents a theoretical investigation into the impact of planet gear spacing on the performance of Epicyclic gear sets, with a design exploratory intention to understand if there are some benefits associated with diametrically opposite planet designs. To achieve these objectives the three-dimensional planetary load distribution model of Ryali and Talbot [1] is employed to investigate the quasi-static and dynamic behavior of diametrically opposite 4 planet gear sets. Various key planetary gear outputs such as planet load sharing, natural modes, overall transmission error, radial vibrations, and gear mesh force will be monitored and summarized in this paper.
Design of Rotating Nonlinear Pendulum Vibration Absorbers for Electrified Machinery
Authors: Singh Pratibha , Monroe Ryan , Geist Bruce
Published: November 2024

Centrifugal pendulum vibration absorbers (CPVAs) are passive devices and a proven technology for reducing torsional vibrations in rotating systems, including helicopter rotors and crankshafts of internal combustion engines. CPVAs consist of pendulums mounted on a rotor, driven by system rotation, and tuned to counteract engine-order fluctuating torques acting on the rotor, thereby smoothing vibrations. In this study, a unifilar CPVA configuration is proposed to address torsional vibrations in electric machines (EMs). A principal challenge in this application is the high-orders of torsional vibration inherent in current EM operation. As order increases, the path radius of curvature that the absorber mass is required to follow (for proper tuning) diminishes, which presents machining challenges. A dynamic model for a unifilar CPVA is developed and then linearized to compute the tuning orders of the system. A quadratic formula is derived whose roots govern the two natural orders of the system and initial results show a desirable large separation between these orders in a prototype design. The developed model will facilitate future simulation studies of the system forced vibration response to characterize the stability and vibration control performance of this design.
Time-Embedding GRBFNN: Learning Transient Response of Stochastic Dynamical Systems
Authors: Xing Siyuan , Sun Jianqiao
Published: November 2024

This paper introduces the Time-Embedding Gaussian Radial-basis Function Neural Network (TE-GRBFNN), a novel architecture designed to learn the time-varying solutions of Fokker-Planck-Kolmogorov (FPK) equations, within the framework of Physics-informed Neural Networks (PINNs). TE-GRBFNN extends the structure of GRBFNN by integrating time-varying centers and widths of Gaussian neurons, determined by two separate time-embedding neural networks. This structural change effectively addresses the challenging unbounded boundary and normalization conditions associated with FPK equations, which have posed difficulties for other deep neural network approaches. As an initial step, we employ TE-GRBFNN to capture the response statistics of first-order nonlinear Stochastic Differential Equations (SDEs). Our results demonstrate strong agreement with Monte Carlo simulations, highlighting the efficacy of TE-GRBFNN. Once trained, the proposed TE-GRBFNN architecture can provide instantaneous predictions of FPK equation solutions at any arbitrary time and location. This feature can offer significant potential for advancing methodologies in structural optimization and control.
Microstructure-Aware Surrogate Models Based on Unbalanced Data for Material Design
Authors: Wu Yulun , Li Yumeng
Published: November 2024

Predicting material responses like strain and energy under various loading conditions is crucial for understanding structure-property relationships and guiding material design. However, this task can be computationally expensive and complex, especially for diverse materials with vast design spaces. Traditional methods like physics-based simulations can be time-consuming and costly, while experimental exploration across large spaces is impractical. Convolutional neural networks (CNNs) and fully connected neural networks (FNNs) offer promising alternative, enabling efficient and accurate material response predictions based on simulations or experimental data. This is particularly beneficial for materials with intricate microstructures that are difficult to characterize with conventional methods. However, CNNs and FNNs often face challenges due to limited training data, leading to poor generalization and low robustness. Additionally, material prediction tasks often encounter unbalanced data where acquiring different responses comes at varying costs. This unbalance can bias model predictions and hinder generalization to unseen material structures. To address these limitations, we propose employing multi-task learning (MTL) to enhance the understanding of material behavior in those deep learning models, specifically targeting the problem of unbalanced data. MTL leverages shared information between multiple interconnected learning tasks, allowing the model to learn from complementary information. In the context of material prediction, MTL can be employed to jointly train CNNs on predicting multiple responses, like displacement and strain energy. This shared learning approach enhances the model’s ability to identify underlying patterns and relationships, leading to more accurate and robust predictions.
A Noise Feedback Control Method for Parametrically Excited Mode-Localized Accelerometer
Authors: Tang Yinghai , Zhao Jian , Kacem Najib , Dong Zeyuan , Liu Pengbo
Published: November 2024

This article analyzes the effects of 1/f noise on the time-domain and frequency-domain responses of a parametrically excited mode-localized accelerometer. The impact of time-delay feedback control on 1/f noise is calculated, and with which, the noise feedback control method is proposed. The proposed method overcomes the drawback of the original time-delay feedback control that reduces the amplitude.
Density Measurement Using a Self-Excited Stainless-Steel Microcantilever With Electrostatic Force Actuation
Authors: Nakamura Takumi , Yabuno Hiroshi , Yamamoto Yasuyuki , Matsumoto Sohei
Published: November 2024

The present study proposes liquid density measurement using a self-excited microchannel resonator made of stainless steel. The sensor is made with metal etching and diffusion bonding. Thus, it enables us to reduce the sample amount compared to macroscale sensors while being easier to fabricate than MEMS. We propose using electrostatic force to excite the stainless-steel microcantilever directly to eliminate the dynamic coupling between a microcantilever and its holder. The actuation is applied under nonlinear velocity feedback control through voltage to employ the dynamics of the Rayleigh oscillator. The proposal’s validity is confirmed by distinguishing distilled water, ethanol, and salty water one by one. We observed distinguishable frequency shifts of self-excitation in the experiments.
A New Frequency-Modulation Input-Shaping Strategy Applied on a Double-Pendulum
Authors: Alhazza Khaled A.
Published: November 2024

In this work, a new frequency modulation technique is used to effectively control oscillations in multi-mode systems using single-mode command-shaping. It is well known that single-mode input shaper can effectively eliminate residual vibration of multi-mode system given that all frequencies within the system are odd-multiples of the shaped command frequency. Using this criterion, a new command shaping strategy specifically designed for multi-mode systems is developed. Our proposed strategy involves convolving a well defined formula with the original double-step command shaper. The resulting command modulates the modal frequencies such that the higher mode frequencies are odd-multiples of the first mode frequency. The equation of motion of the system is solved and the natural frequencies are determined analytically as a function of a controller gain. This gain is used to modify the trolley acceleration. To show the effectiveness of our strategy, two numerical simulation are conducted and validated experimentally on double pendulums.
Comparison of Robot Morphologies and Base Positioning for Welding Applications
Authors: Gautier Nicolas , Guillermit Yves , Sebsadji Yazid , Porez Mathieu , Chablat Damien
Published: November 2024

This article undertakes a comprehensive examination of two distinct robot morphologies: the PUMA-type arm (Programmable Universal Machine for Assembly) and the UR-type robot (Universal Robots). The primary aim of this comparative analysis is to assess their respective performances within the specialized domain of welding, focusing on predefined industrial application scenarios. These scenarios encompass a range of geometrical components earmarked for welding, along with specified welding paths, spatial constraints, and welding methodologies reflective of real-world scenarios encountered by manual welders. The case studies presented in this research serve as illustrative examples of Weez-U Welding practices, providing insights into the practical implications of employing different robot morphologies. Moreover, this study distinguishes between various base positions for the robot, thereby aiding welders in selecting the optimal base placement aligned with their specific welding objectives. By offering such insights, this research facilitates the selection of the most suitable architecture for this particular range of trajectories, thus optimizing welding efficiency and effectiveness. A departure from conventional methodologies, this study goes beyond merely considering singularities and also delves into the analysis of collisions between the robot and its environment, contingent upon the robot’s posture. This holistic approach offers a more nuanced understanding of the challenges and considerations inherent in deploying robotic welding systems, providing valuable insights for practitioners and researchers alike in the field of robotic welding technology.
Facilitating Success: Exploring the Influence of Design Facilitators’ Behaviors on Team Members’ Responses and Perceptions of Team Climate and Trust
Authors: Witherell C. J. , Maar Aliaa , Dougherty Presley , Letting Cynthia , Menold Jessica
Published: November 2024

Teams are vital in engineering design as they harness collective problem-solving abilities and enhance creative outputs. However, complex social dynamics, cognitive biases, and individual traits hinder their overall effectiveness, rendering them less useful than the combined capabilities of their members. While previous research has recognized the importance of facilitation and favorable climate for team success, limited research has explored the specific behaviors employed by facilitators to achieve desired outcomes. Leveraging data from a Design Thinking workshop, our mixed-methods study investigates five facilitators’ behaviors and their impact on team dynamics. We develop Markov models to represent the team’s conversational dynamics. Similarities across models indicate recognizable interaction patterns despite group differences. High transition probabilities between facilitator and both participant engagement and support suggest that facilitator involvement fosters a collaborative atmosphere. The significant transition probability from facilitator clarification to participant question underscores the facilitator’s role in promoting comprehension. To assess the impact of facilitators’ behaviors on participants’ perceptions of trust and psychological safety, we examine the correlations between these factors. Significant correlations suggest facilitators positively influence psychological safety through engagement in the design task and promotion of reflective behaviors. Contrasting prior research, rapport building does not correlate with psychological safety. Participants express higher levels of trust in facilitators who provide more direction. It is crucial for facilitators to recognize their influence and employ tactics to foster trust and positive team climate. These findings hold significant implications for the effective facilitation of design teams, a necessary step to ensuring the success of teamwork.
Data-Defined Machine Intelligence for Mechanical Properties Identification
Authors: Iliopoulos Athanasios P. , Michopoulos John G. , Steuben John C. , Apetre Nicoleta
Published: November 2024

In this work we are introducing a Machine Intelligence (MI) concept that belongs to the set of approaches that emphasize data over artificial intelligence model. The development of the concept was motivated by the need to eliminate training as the means to enable fast or perpetual system adaptation and update, and also vastly reduce pre-processing times. This characteristic can be extremely valuable in situations when new data are regularly becoming available and utilized to increase the MI accuracy with minimal delay, or when the represented system changes with time. A characteristic of the proposed data-defined MI approach is that of introspection; it can be queried to locally estimate the system error at any data point that is part of its data set and potentially act accordingly. To provide evidence of the validity and performance of the approach, we first demonstrate on classical hand-writing classification of numerical digits. We then apply it to a challenging problem of identifying the properties of bi-linear elastoplastic material using a single full-field strain tensor image of a synthetic experiment. The validation test is performed for many untrained instances, and the histogram, mean and maximum errors are discussed. We also discuss several interesting capabilities that a data-defined concept enables.
Evaluation of Select Condition Indicators for Detecting Damage in Geared Transmissions Using a Finite Element/Contact Mechanics Model
Authors: Thunuguntla Suhas Gupta , Hood Adrian A. , Cooley Christopher G.
Published: November 2024

This study analyzes the ability of several practical rotorcraft condition indicators for detecting damage in a two-stage rotor-craft transmission utilizing a finite element/contact mechanics (FE/CM) model. The transmission includes an input spiral-bevel stage and an output planetary stage. The vibrations of the transmission are calculated at typical operating torque and speed using a full dynamic solution of the finite element transmission model. In this solution, the contact conditions at all meshes are calculated at each time instant accounting for changes due to the kinematic motion of the individual gears; the elastic deformations of all teeth, blanks, shafts, and other components; and vibration. The elastic vibrations produced from the housing contain frequency content that appears in clusters near the mesh frequency harmonics of each gear stage. The planetary gear mesh frequency clusters have prominent upper and lower sidebands that occur at the planet-pass frequency. When damage (i.e., tooth root cracks or tooth surface pits) occurs on the individual gears of the planetary stage, additional transient vibrations occur that meaningfully alter the resulting vibrations and their spectrum. The frequency components near planetary gear mesh frequency clusters are not sensitive to damage, even when the damage is large. The use of condition indicators (CIs) for detecting damage is explored. The ability of the CIs to detect damage is shown to be sensitive to the input vibration signal used in the CI calculation. Time synchronous averaging is shown to improve the performance of CIs, provided the averaging does not artificially modify the underlying vibratory signal. We demonstrate that averaging over the planet-pass period effectively eliminates non-planetary gear vibration components and improves CI sensitivity. Multiple constructions of the difference signal are analyzed, and their impact on CI performance is quantified. Tooth root cracks with small lengths less than 50 percent through the tooth thickness generally do not lead to substantial increases in the individual CIs. It was observed that most CIs increased when more sidebands were removed for crack lengths above 50 percent. The results from this work will enable reliable damage detection techniques for geared transmissions, thus improving safety and reliability in rotorcraft propulsion.
Modeling and Control Co-Design of a Floating Offshore Vertical-Axis Wind Turbine System
Authors: Lee Yong Hoon , Bayat Saeid , Allison James T. , Hossain Md Sanower , Griffith D. Todd
Published: November 2024

This study investigates the modeling and design of a floating vertical-axis wind turbine (FloatVAWT) system with multidisciplinary design optimization (MDO) and control co-design (CCD) approaches. By integrating various associated disciplinary models, the study aims to holistically optimize the physical and control designs of the FloatVAWT system. Through the identification of impactful design elements and capitalizing on synergistic interactions, the study aims to provide insights to subsystem designers and aid their detailed decisions. The model developed for this CCD framework utilizes automated geometric manipulation and mesh generation to explore various FloatVAWT configurations during the early design stages. Surrogate models facilitate efficient design studies within limited computing resources by exchanging model information between disciplinary models and subsystems without running exhaustive simulations during the optimization loop. The model incorporates an aero-hydro-servo dynamic representation of the FloatVAWT system, considering physical and control constraints. Additionally, the study investigates the potential benefits of varying the average rotational speed of the vertical-axis wind turbine (VAWT) rotor to enhance energy production and minimize adverse platform motions, thus reducing the levelized cost of energy (LCOE). System-level design solutions are analyzed to identify design trade-offs and propose mitigation strategies for potential mechanical failures of the rotor. In conclusion, this study provides modeling strategies for the FloatVAWT system and analyzes the system design solutions through MDO and CCD approaches. The outcomes of the study offer insights into system-optimal solutions for subsystem-level decisions considering multidisciplinary couplings.
A Disassembly Score for Human-Robot Collaboration Considering Robots’ Capabilities
Authors: Liao Hao-Yu , Pulikottil Terrin , Peeters Jef R. , Behdad Sara
Published: November 2024

Product disassembly is essential for remanufacturing operations and recovery of end-of-use devices. However, disassembly has often been performed manually with significant safety issues for human workers. Recently, human-robot collaboration has become popular to reduce the human workload and handle hazardous materials. However, due to the current limitations of robots, they are not fully capable of performing every disassembly task. It is critical to determine whether a robot can accomplish a specific disassembly task. This study develops a disassembly score which represents how easy is to disassemble a component by robots, considering the attributes of the component along with the robotic capability. Five factors, including component weight, shape, size, accessibility, and positioning, are considered when developing the disassembly score. Further, the relationship between the five factors and robotic capabilities, such as grabbing and placing, is discussed. The MaxViT (Multi-Axis Vision Transformer) model is used to determine component sizes through image processing of the XPS 8700 desktop, demonstrating the potential for automating disassembly score generation. Moreover, the proposed disassembly score is discussed in terms of determining the appropriate work setting for disassembly operations, under three main categories: human-robot collaboration (HRC), semi-HRC, and worker-only settings. A framework for calculating disassembly time, considering human-robot collaboration, is also proposed.
Application of Machine Learning in Design and Optimization of MEMS Tunable Capacitors
Authors: Naderian Pooria , Shavezipur Mohammad
Published: November 2024

Electrostatically actuated MEMS tunable capacitors have a nonlinear capacitance-voltage (C-V) response and limited tunability. Increasing the tunability and the linearity of the device’s response is of great desire. In this study, a design method, based on machine learning and optimization, is introduced that improves the tuning ratio and linearity of the device response. A capacitor with two deformation modes (rigid-body displacement of the moving electrode before pull-in and its deformations after pull-in) is used to demonstrate the technique. A set of simulation data for different design geometries is generated by ANSYS coupled-field multiphysics solver to create a three-level, three-factor central composite rotatable design (CCRD). To predict the linearity of the MEMS capacitor, a mathematical model using response surface methodology (RSM) is developed. The initial and final gap between the electrodes and the curvature of the moving electrode are considered as the input parameters and linearity as the outcome for the regression model development. The created model was evaluated for adequacy and significance. The model can predict linearity with more than 97% accuracy, according to confirmation tests, and the effect of the input parameters on the linearity of the response is studied. The optimization was conducted by Design-Expert 13 software and results show 0.96 for the maximum linearity of the C-V plot.
Defining a Modelling Language to Support Functional Hazard Assessment
Authors: Hulse Daniel , Mbaye Seydou , Irshad Lukman
Published: November 2024

Functional Hazard Assessment (FHA) is a key early-stage engineering process that supports the incorporation of safety in design by identifying the high-level functional hazards the system may encounter. While many FHA-like methodologies have been proposed in the design engineering literature, many of these methodologies have had difficulty becoming accepted industry practice. Industry standards, on the other hand, either provide too little recommendation on how to represent the function of the system to perform FHA, or rely on existing design artefacts which insufficiently support the goals of the process. This paper presents some of the problems with current modelling languages (both proposed and used) for FHA which limit the scope, expressiveness, flexibility, and precision of the analysis. It then outlines desirable principles an FHA-supporting analysis language should embody, and introduces the Functional Reasoning Design Language (FRDL), a formal modelling language for describing the functional elements of a system and their interactions, which aims to satisfy these principles. To demonstrate the use of this language, the modelling and hazard analysis of a disaster response drone is presented. While this case study is limited in scope, it highlights how FRDL can represent system function while reducing the ambiguity present in typical FHA-supporting functional modelling languages.
Generative Design for Power System Networks Using WGAN and Graph Performance Measures for Guided Generation
Authors: Chung In-Bum , Wang Pingfeng
Published: November 2024

Interdependent critical infrastructures are prone to be a target of disruptions from either natural disasters or deliberate attacks that can cause a great magnitude of negative consequences on the system. To withstand and reduce the impacts of disruptive events, it is beneficial to consider them in the early stage of design to create a configuration that can maintain its performance as much as possible. However, due to the complexity of modern systems, it is difficult to directly optimize the system design of large-scale with complicated constraints. Hence, in this research, a generative approach based on Wasserstein GAN is implemented to create intelligent designs. The power system networks are represented as graphs with features holding information on the nodes and edges that relate to electrical components. The trained generator can create a power system network consisting of generators and loads, where the buses are connected to transmission lines of a certain capacity. Afterwards, performance measures are employed to induce the generation of a population with more appealing characteristics. The framework was applied to a case study of a 57-bus system of synthesized power grid and the results have shown that the model was able to generate feasible designs of higher resilience.
Enhancing Long-Term Decision Making in Trajectory Planning: Deep Reinforcement Learning With Knowledge Transfer and Online Demonstrations
Authors: Hu Chuanhui , Jin Yan
Published: November 2024

Deep reinforcement learning is now widely applied in motion planning problems for autonomous systems due to its model-free nature and its ability to solve complex control problems through trial-and-error. However, the success of deep reinforcement learning depends heavily on the exploration policy and the design of the reward function. This dependence makes it challenging to solve long-range planning problems and requires careful reward function design to avoid the sparse reward problem. In this paper, we propose a dual-level off-policy deep reinforcement learning framework that combines high-level policy learning and low-level integration of a controller. The high-level policy enables the agent to make long-term decisions with a flexible horizon. Using a high-level policy also mitigates the sparse reward problem in long-range planning tasks. By storing only high-level actions and transitions in the experience buffer, the agent can efficiently learn in long-range trajectory planning tasks. The proposed parallel architecture of the neural network enables the utilization of high-level domain knowledge transfer. Online demonstrations can be integrated during training using global planning algorithms to enhance the quality of experiences. Experimental results show that transferring high-level geometry knowledge and applying online error correction can significantly enhance the agent’s performance in long-range trajectory planning tasks.
Recycling Automotive Shredder Residue: A Techno-Economic Analysis
Authors: Chapman Luke , Velez Amber , English Ashley , Gavilanes Melanie , Huynh Hung , Welsh Emily , Winter Amos G.
Published: November 2024

This study presents a novel “sorter-at-shredder” business model for the pyrolysis of Automotive Shredder Residue (ASR) plastics, addressing the challenge of low plastic recycling rates in end-of-life vehicles (ELVs). ASR comprises 25% of ELV material, and ASR consists of about 43% polypropylene (PP) and polyethylene (PE) plastics, which can be separated using sink-float sorting technology and pyrolyzed into olefin-rich product. The “sorter-at-shredder” business model strategically places small-scale pyrolysis plants that process up to 280 tons per day at existing shredders, integrating with ferrous and non-ferrous processing chains and enabling daily processing of ASR plastics. The model focuses on PP and PE plastics, creating olefin-rich product that can be re-entered into the supply cycle. Economic analysis reveals a yearly revenue of $46.1M for olefins-rich product, and a promising payback period of 6.3 years for the shredder, falling within acceptable ranges. The model uses Dallas, Texas, to capitalize on regulatory, economic, and geographical advantages, including proximity to a cluster of 5 shredders which process 165 tons of PP and 119 tons of PE each day, and proximity to oil refineries which can purchase olefin-rich product from the shredder. Future work involves optimizing sorting and pyrolysis technologies, ensuring the model’s success and contributing to a sustainable automotive industry.
Identifying Human Errors and Error Mechanisms From Accident Reports Using Large Language Models
Authors: Irshad Lukman , Walsh Hannah
Published: November 2024

Emerging operational concepts for aviation hinge on novel paradigms for human machine interaction. Critical to their safe operation is early consideration of human error into the design process. Existing methods for consideration of human error require significant expert input, which is challenging both in early design and in novel systems for which there is little existing safety expertise. In this research, we propose a methodology for identifying human error, error producing factors, and mechanisms in early design from historical incident reports. Additionally, we hypothesize that cross-domain sharing of lessons learned can aid with early design human considerations in circumstances where data is not relevant or incomplete. This is addressed by identifying causes of human error in aviation and railway domains through applying state-of-the art natural language processing techniques to historical incident reports. Using this method, it is possible to extract extensive reports on human error from past incidents. Using the proposed approach, we identify nine human errors from railway reports and fourteen from aviation reports, with three errors common to both domains. There is at least one error producing conditions for each human error while a majority of the errors have more than one error mechanism. We also found that a majority of the human errors, error producing factors, and error mechanisms (even if they are not common between the domains) can be used to inform safe operations across domains as long as the errors are not domain specific and are interpreted and contextualized using engineering judgement.
DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Graph-Based Drag Prediction
Authors: Elrefaie Mohamed , Dai Angela , Ahmed Faez
Published: November 2024

This study introduces DrivAerNet, a large-scale high-fidelity CFD dataset of 3D industry-standard car shapes, and RegDGCNN, a dynamic graph convolutional neural network model, both aimed at aerodynamic car design through machine learning. DrivAerNet, with its 4000 detailed 3D car meshes using 0.5 million surface mesh faces and comprehensive aerodynamic performance data comprising of full 3D pressure, velocity fields, and wall-shear stresses, addresses the critical need for extensive datasets to train deep learning models in engineering applications. It is 60% larger than the previously available largest public dataset of cars, and is the only open-source dataset that also models wheels and underbody. RegDGCNN leverages this large-scale dataset to provide high-precision drag estimates directly from 3D meshes, bypassing traditional limitations such as the need for 2D image rendering or Signed Distance Fields (SDF). By enabling fast drag estimation in seconds, RegDGCNN facilitates rapid aerodynamic assessments, offering a substantial leap towards integrating data-driven methods in automotive design. Together, DrivAerNet and RegDGCNN promise to accelerate the car design process and contribute to the development of more efficient vehicles. To lay the groundwork for future innovations in the field, the dataset and code used in our study are publicly accessible at https://github.com/Mohamedelrefaie/DrivAerNet.
Surrogate Models for Compliant Joints in Programmable Structures
Authors: Pieber Michael , Zhang Zhaowei , Manzl Peter , Gerstmayr Johannes
Published: November 2024

This work focuses on compliant joints in autonomous cells. These cells are able to preserve the underlying geometry of a mesh and enable self-reconfiguration through the utilization of six-bar linkages. The six-bar linkages, essential for maintaining triangular mesh geometry, are realized as 3D-printable compliant mechanisms. However, challenges arise from compliance in the hinges and deviation from the desired center of rotation which results in positioning errors. To address these issues, a surrogate model which is derived from beam elements, is used to predict positioning errors during self-reconfiguration. Finally, these errors are corrected by solving the inverse kinematics of a hyper-redundant manipulator. Our study illustrates the benefits of utilizing a surrogate model, which reduces computation time compared to beam elements. For the first time, we successfully correct positioning errors within a system of cells.
Nonlinear Static Modeling of a Large Range XY-Nanopositioning System
Authors: Moeenfard Hamid , Rath Siddharth , Awtar Shorya
Published: November 2024

High-precision and high-speed requirements in flexure-based positioning systems require stringent dynamic performance. In the presence of geometric non-linearities, which are relevant in large stroke flexure mechanisms, a standard approach is to linearize the system dynamics about an operating point and then design suitable controllers. Therefore, the first step in this subsequent dynamic analysis, is to have static equilibrium conditions of the flexure mechanism that can serve as the operating point. With this motivation, the objective of this paper is to propose a nonlinear static model for a specific XY flexure mechanism capable of decoupled motion along two orthogonal axes. This XY flexure mechanism is of practical use in high-precision high-speed positioning systems. Initially, the generalized coordinates of the flexure mechanism are chosen from the various displacement coordinates of the system. The strain energy and generalized forces of the flexure depend not only on the generalized coordinates but also on other displacement coordinates. This complicates the application of the principle of virtual work. To address this complexity, we utilize the fact that other displacement components of the system implicitly depend on the generalized coordinates due to nonlinear constraints arising from the geometric nonlinearities associated with beam arc-length conservation. By considering this implicit dependence when applying derivatives in the principle of virtual work, we can derive the nonlinear equilibrium equations governing the static behavior of the flexure mechanism. This approach for arriving at a static model for the XY flexure is then generalized into a step-by-step procedure, capable of deriving the governing nonlinear static equations of a diverse class of flexure mechanisms. A physical understanding of the system is then employed to solve these equations to obtain the operating point for uniaxial loading. Resulting expressions are then used in a subsequent analysis with perturbation expansions to obtain closed-form, highly accurate solutions for the operating point of the XY flexure under biaxial loading. The results are validated through numerical simulations and Finite Element Analysis.
Navigating Team Dialogues: How Team Dynamics Affect Convergent and Divergent Design Actions During Concept Generation and Selection
Authors: Dougherty Presley , Letting Cynthia , Witherell C. J. , Soria Zurita Nicolás F. , Starkey Elizabeth , Menold Jessica
Published: November 2024

Communication plays a pivotal role in design teams and can impact team dynamics and design outcomes. Understanding the relationships between design communication and design trajectory can significantly improve our ability to track and optimize design processes. While prior work has investigated design convergence, there is limited work on how team climate and communicative patterns impact the design process. This study investigates the interactions between team climate, communication, and design actions. We estimate team climate as an individual’s perception of their team’s psychological safety, or how comfortable they feel sharing thoughts and ideas freely. Design actions were measured using a behavioral coding schema and audio recordings of design team interactions. In this work, 28 participants and 5 design thinking experts engaging in a design thinking workshop were studied to understand the relationship between design communication, process, and team climate. Contrary to expectations, this mixed-methods study found no significant correlation between perceived psychological safety and design behaviors in engineering design teams, challenging prior research suggesting a positive link. This finding highlights the complexity of team dynamics and underscores the need for further exploration to understand how psychological safety, conversational behaviors, and design actions interact to shape collaborative processes and innovation in design teams.
System Tautochronic Path for Pendulum Vibration Absorber Based on Simple Harmonic Oscillator Transformation
Authors: Valadbeigi Nima , Monroe Ryan , Geist Bruce
Published: November 2024

Centrifugal pendulum vibration absorbers (CPVAs) are essentially collections of pendulums attached to a rotor or rotating component or components within a mechanical system for the purpose of mitigating the typical torsional surging that is inherent to internal combustion engines and electric motors. The dynamic stability and performance of CPVAs are highly dependent on the motion path defined for their pendulous masses. Assemblies of absorbers are tuned by adjusting these paths such that the pendulums respond to problematic orders (multiples of average rotation speed) in a way that smooths the rotational accelerations arising from combustion or other non-uniform rotational acceleration events. For most motion paths, pendulum tuning indeed shifts as a function of the pendulum response amplitude. For a given motion path, the tuning shift that occurs as pendulum amplitude varies produces potentially undesirable dynamic instabilities. Large amplitude pendulum motion that mitigates a high percentage of torsional oscillation while avoiding instabilities brought on by tuning shift introduces complexity and hazards into CPVA design processes. Therefore, identifying pendulum paths whose tuning order does not shift as the pendulum amplitude varies, so-called tautochronic paths, may greatly simplify engineering design processes for generating high-performing CPVAs.

This paper expands on the work of Sabatini [1], in which a mathematical condition for tautochronicity is identified for a class of differential equations that includes those that arise in the modeling of the motion of a pendulum in a centrifugal field. The approach is based on a transformation from the physical coordinate to a standard Hamiltonian system. We show that transforming a nonlinear oscillator made tautochronic through path modification actually transforms the nonlinear oscillator into a simple harmonic oscillator. To illustrate the new approach and results, the technique is applied to the simplified problem of determining the cut-out shape that produces tautochronic motion for a mass sliding in the cut-out of a larger mass that is free to translate horizontally without friction. In the simplified problem, centrifugal acceleration is replaced by constant gravitational acceleration and rotation of the rotor inertia is replaced by the translation of the large base mass.