bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application of a Protocol to Observe Leadership Behaviors in Engineering Design Teams
Authors: Righter James , Wentzky Chase , Summers Joshua D. 
Published: November 2019

Abstract
This protocol study was conducted to increase understanding of the emergence and distribution of functional leadership behaviors in undergraduate engineering design teams. This study applies the protocol presented at the 2018 IDETC to observe design teams consisting of novice engineers constructing a function model during a video recorded session. The videos were then coded for leadership functions and analyzed to determine the distribution of informal leadership functions between the team members and the temporal emergence of the informal leadership structures within the teams. Leadership behaviors were observed to be predominantly transition and action functions with relational behaviors occurring less frequently. The behaviors were quantified by number of occurrences per quintile. The leaders observed to perform the most leadership behaviors early in the sessions often remained consistent. However, leadership functions were shared between team members as demonstrated by the leadership network graphs.
Design of an Electric Motor Transmission System Without Friction Synchronization
Authors: Alowayed Abdulmohsen , Fernandes Diarny , Jeunehomme Eric , Liu Siyang , Wang Zhishen , Yang Illina , Dorsch Daniel , Winter Amos 
Published: November 2019

Abstract
This paper investigates the design of a new transmission system without a friction synchronizer for high-performance hybrid vehicles. Manual and automated manual transmission systems traditionally use friction synchronizers to facilitate smooth transitions during a gearshift, ensuring speed matching and proper engagement of the gears. Active position sensing technology for dogteeth is being developed, along with the potential of speed matching using electric motors, eliminating the need for the friction synchronizer. However, in removing these friction synchronizer components, significant shock could be introduced to the transmission system with speed or position errors during a shift. This paper proposes a solution through a gear system that utilizes a face mesh design, torsional springs, and alternating teeth height. A prototype of this design was created and successfully tested as a proof of concept for a transmission system, which has the potential to improve hybrid, automated manual transmission design.
Stability Analysis of Periodic Solutions for Flexible Multibody Dynamics
Authors: Han Shilei , Bauchau Olivier A. 
Published: November 2019

Abstract
Discontinuous Galerkin formulation is developed for stability analysis of periodic solutions of flexible multibody dynamics. The proposed approach takes rigid-body motions of each structural nodes as the unknowns. The rigid-body motions are interpolated by using dual spherical linear interpolation (dual-SLERP). The analysis is composed of two steps: (1) the periodic solution is obtained by solving nonlinear equations resulting from Galerkin method; (2) a linearization about the periodic solution leads to a periodic linear system and its stability is assessed by using Floquet’s method.
Modeling and Control of New Multi-Mode Plug-in Hybrid Electric Vehicle
Authors: Raut Abhinandan , Phalke Suryaji , Peters Diane 
Published: November 2019

Abstract
Fuel economy and emission standards for internal combustion engine (ICE) vehicles lead to emergence of hybrid powertrain mechanisms. Hybrid powertrains can enable significant fuel economy improvements without sacrificing vehicle performance or utility. This requires optimization of engine operation, regenerative braking, and use of a wide range of possible combinations of engine and battery usage. The multi-mode hybrid powertrain in this paper combines many options to meet a complex driving requirement while maintaining the desired fuel economy. In this paper, a systematic design methodology is used to design a full-size hybrid vehicle with multiple components. This involves the modeling, simulation and development of optimal energy management strategy. This vehicle (full size car) has dual battery, dual fuel V6 engine with cylinder deactivation and bi-directional power flow in and from dual motor/generator. The design includes multiple gearboxes to connect these pieces. The vehicle model allows many degrees of freedom including various modes of operation depending upon the combination of degree of driver involvement, vehicle power requirement and optimized fuel economy resulting in automatic switching between modes. This model is tested for different Environmental Protection Agency (EPA) driving cycles. By integrating all components of this hybrid electric vehicle (HEV) and the highly coordinated energy management control system that performs optimum blending of torque, speed, and power from multiple power sources, the benefit from this hybridization is maximized.
Contactless Li-Ion Battery Voltage Detection by Using Walabot and Machine Learning
Authors: Wang Yanan , Niu Haoyu , Zhao Tiebiao , Liao Xiaozhong , Dong Lei , Chen Yangquan 
Published: November 2019

Abstract
This paper has proposed a contactless voltage classification method for Lithium-ion batteries (LIBs). With a three-dimensional radio-frequency based sensor called Walabot, voltage data of LIBs can be collected in a contactless way. Then three machine learning algorithm, that is, principal component analysis (PCA), linear discriminant analysis (LDA), and stochastic gradient descent (SGD) classifiers, have been employed for data processing. Experiments and comparison have been conducted to verify the proposed method. The colormaps of results and prediction accuracy show that LDA may be most suitable for LIBs voltage classification.
On Computation of Approximate Lyapunov-Perron Transformations
Authors: Sharma Ashu , Sinha Subhash C. 
Published: November 2019

Abstract
Many dynamical systems can be modeled by a set of linear/nonlinear ordinary differential equations with quasi-periodic coefficients. Application of Lyapunov-Perron (L-P) transformations to such systems produce dynamically equivalent systems in which the linear parts are time-invariant. In this work, a technique for the computation of approximate L-P transformations is suggested. First, a quasi-periodic system is replaced by a periodic system with a ‘suitable’ large principal period to which Floquet theory can be applied. Then, the state transition matrix (STM) of the periodic system is computed in the symbolic form using shifted Chebyshev polynomials and Picard iteration method. Finally, since the STM can be expressed in terms of a periodic matrix and a time-invariant matrix (Lyapunov-Floquet theorem), this factorization is utilized to compute approximate L-P transformations. A two-frequency quasi-periodic system is investigated using the proposed method and approximate L-P transformations are generated for stable, unstable and critical cases. These transformations are also inverted by defining the adjoint system to the periodic system. Unlike perturbation and averaging, the proposed technique is not restricted by the existence of a generating solution and a small parameter. Approximate L-P transformations can be utilized to design controllers using time-invariant methods and may also serve as a powerful tool in bifurcation studies of nonlinear quasi-periodic systems.
Analysis of Constraint Forces in Multibody Systems Based on the Differential Null Space Matrix Method
Authors: Kamiya Keisuke 
Published: November 2019

Abstract
This paper treats a problem to determine constraint forces in rigid mutibody systems. One of the most often applied algorithms for determination of constraint forces is based on the use of recursive Newton-Euler formalism. Another algorithm often applied for determination of constraint forces is based on the use of Lagrange multipliers. This paper presents a new method to determine constraint forces in rigid multibody systems. First relative displacements which violate the constraints, called anti-constraint relative displacements, are introduced, and governing equations which involve the constraint forces explicitly are derived. In the derived equations, the constraint forces appear independently from the Lagrange multipliers. Then, a method is proposed to determine the constraint forces by eliminating the Lagrange multipliers based on the methods proposed in previous papers by the author. The method is extended to have ability to treat systems with redundant constraints. Finally, validity of the proposed method is confirmed via numerical examples.
Manufacturing Vulnerability: How Resource Scarcity Hinders Team Coordination During Manufacturing in Nigerian Factories
Authors: Kusimo Abisola C. , Sheppard Sheri 
Published: November 2019

Abstract
The purpose of this research is to learn how less-skilled workers (LSWs) and highly-skilled workers (HSWs) coordinate in Nigerian factories, where the formal education gap between these two groups can be quite significant. The study takes place in two factories and two universities across four Nigerian states over the summer of 2018. Drawing on methods of ethnography (i.e. a collection of qualitative methods to closely observe social interactions and practices as to interpret and build theory), it was discovered that resource scarcity hinders team coordination through the occurrence of four obstacles: (1) unclear role boundaries and work processes, (2) poor attitude towards work, (3) under- and overutilization of employees, and (4) worker demoralization and feelings of being undervalued. This paper builds upon previous work on factory coordination in cross-occupational functional groups in Silicon Valley. Theoretically, this paper reveals how the studied manufacturing firms in Nigeria currently perform team coordination and the challenges that prevent them from reaching high efficiency. Additionally, it offers premises for future field experiments to test the generalizability of the findings and interventions to enhance coordination effectiveness and the product development process in Sub-Saharan African manufacturing firms. Implications for the literature on engineering for global development is discussed. Ultimately, this research is a small step towards enabling independent African firms to have the skills, tools, and resources to design and create their own unique solutions for challenges faced in-country.
Influence of Omitted Variables in Consumer Choice Models on Engineering Design Optimization Solutions
Authors: Gowharji Waleed , Whitefoot Kate S. 
Published: November 2019

Abstract
This paper examines the impact of Omitted Variable Bias (OVB) within consumer choice models on engineering design optimization solutions. Engineering products often have a multitude of attributes that influence consumers’ purchasing decisions, many of which are difficult to include in revealed-preference models due to a lack of data. Correlations among these omitted variables and product attributes included in the model can bias demand parameter estimates. However, engineering design optimization studies typically do not account for this bias. We examine the influence consumer-choice OVB can have on design optimization results. We first mathematically derive how OVB propagates into biased optimal design solutions and characterize properties of optimization problems that affect the magnitude of this bias. We then demonstrate the impact of OVB on optimal designs using a more-realistic engineering optimization case study of automotive powertrain design. In the demonstration, we estimate two sets of choice models: one using only “typically observed” vehicle attributes commonly found in the literature, and one with an additional set of “typically unobserved” attributes gathered from Edmunds.com. We find that the model with omitted variables leads to, in some scenarios, substantial bias in parameter estimates (5–143%) which propagates up to 21% bias in the optimal engine size.
Discontinuous Dynamics of a Frequency Up-Conversion Piezoelectric Harvester
Authors: Onsorynezhad Saeed , Wang Fengxia 
Published: November 2019

Abstract
This study investigates an impact based frequency up-conversion mechanism via discontinuous dynamics analysis. The mechanism composed of a stopper and a piezoelectric bimorph. The piezoelectric beam is subjected to a sinusoidal base excitation and impacts with the stopper. In this system, complex dynamics are induced by impacts, hence to better understand the energy harvesting performance of the piezoelectric beam, we seek the steady state periodic motions of the system. As the excitation frequency varies, the output voltage and power of the piezoelectric beam with periodic motions were obtained.
The piezoelectric bimorph was modeled as an Euler-Bernoulli beam, and the linear piezoelectric constitutive equations were used to obtain the equations of the piezoelectric beam. The generated voltage and power were obtained using discontinuous dynamics analysis. In order to better analyze the energy harvesting performance of the piezoelectric energy harvester, the stable and unstable periodic motions were obtained. The bifurcation diagram of the period-1 and period-2 motions were obtained analytically as the excitation frequency varying.
Critical Stability of a Hinged Beam With Dynamic Moment: With and Without External Flow
Authors: Abdullatif Mahmoud , Mukherjee Ranjan , Hellum Aren 
Published: November 2019

Abstract
The stability characteristics of a hinged beam subjected to a dynamic moment is investigated. The moment is proportional to the curvature of the beam at some point along its length. The stability investigations are carried out using a Galerkin approximation, both in the presence and absence of external flow. In the absence of external flow, stability is lost through divergence and flutter depending on the location of the point of measurement of curvature and the sign of the applied moment. In the presence of external flow, additional terms are introduced in the dynamic model. This alters the mechanism of flutter, reduces the value of the parameter at the critical point, and changes the nature of oscillations from standing waves to traveling waves.
Command Shaping Control of a Vertically Rotating Flexible Beam
Authors: Alhazza Khaled A. 
Published: November 2019

Abstract
Vertically rotating flexible beam under the effect of gravity adds some challenges to the existing input- and command shaping techniques. Classical shapers usually have zero initial conditions while vertically rotating beam may have initial or final deflections. These conditions may introduce large residual vibrations at the end of rest-to-rest maneuvers. Furthermore, the system contains some nonlinearities that may reduce the effectiveness of classical input- and command-shapers. In this work, a waveform command shaping profile is used initially and then optimized to reduce rest-to-rest residual vibrations of a vertically rotating flexible beam. The system equation of motion is determined, discretized, and then linearized to find an initial command shaper. The parameters of the proposed command shaper is then optimized to find a better performance. Only the first mode is considered in this work since higher modes usually have negligible amplitudes. To show the importance of the proposed work, comparisons between the uncontrolled shaper, double step shaper, smooth wave form command shaping, and the optimized command shaping are performed. The proposed technique is tested numerically using two cases, with different maximum velocities, flexible beam length, and acceleration times. Results show that the effectiveness of the proposed technique in reducing residual vibrations in rest-to-rest maneuvers.
Indoor Testbed for Vector Field Multirobot Adaptive Navigation: Feasibility Study
Authors: Rajabhandharaks Danop , McDonald Robert T. , Neumann Michael A. , Kitts Christopher A. 
Published: November 2019

Abstract
Multirobot adaptive navigation maneuvers a multi-vehicle system based on characteristics of the environment to autonomously localize features of interest. This navigation method can be more time and energy efficient than conventional navigation methods. Most work in this area explores scalar fields, where a single characteristic value is associated with every point in the environment. This work is an initial testbed exploration of adaptive navigation for vector fields, where every point in the environment is associated with a multi-parameter value. A vector field can represent a single physical quantity such as water/air flow or multiple simultaneous and collocated scalar quantities such as temperature and gas concentration. The contribution of this work is the extension of an existing adaptive navigation testbed to support vector field representations, navigation, and further research. Vector fields are generated using a large-format printer to print 8-bit colored floor mats. Mobile robots, equipped with RGB sensors, sense the color and, through calibration, estimate the underlying vector field. This paper will walk through our process of generating vector fields and a calibration method to be used for adaptive navigation. Successful results from two adaptive navigation experiments are shown in the paper: finding a source with a single robot and using a two-robot formation to straddle a crest of high velocity flow in a vector field.
A Neuroevolution-Based Learning of Reciprocal Maneuver for Collision Avoidance in Quadcopters Under Pose Uncertainties
Authors: Behjat Amir , Gabani Krushang , Chowdhury Souma 
Published: November 2019

Abstract
This paper focuses on the idea of energy efficient cooperative collision avoidance between two quadcopters. Two strategies for reciprocal online collision-avoiding actions (i.e., coherent maneuvers without requiring any real-time consensus) are proposed. In the first strategy, UAVs change their speed, while in the second strategy they change their heading to avoid a collision. The avoidance actions are parameterized in terms of the time difference between detecting the collision and starting the maneuver and the amount of speed/heading change. These action parameters are used to generate intermediate way-points, subsequently translated into a minimum snap trajectory, to be executed by a PD controller. For realism, the relative pose of the other UAV, estimated by each UAV (at the point of detection), is considered to be uncertain — thereby presenting substantial challenges to undertaking reciprocal actions. Performing supervised learning based on optimization derived labels (as done in prior work) becomes computationally burden-some under these uncertainties. Instead, an (unsupervised) neuroevolution algorithm, called AGENT, is employed to learn a neural network (NN) model that takes the initial (uncertain) pose as state inputs and maps it to a robust optimal action. In neuroevolution, the NN topology and weights are simultaneously optimized using a special evolutionary process, where the fitness of candidate NNs are evaluated over a set of sample (in this case, various collision) scenarios. For further computational tractability, a surrogate model is used to estimate the energy consumption and a classifier is used to identify trajectories where the controller fails. The trained neural network shows encouraging performance for collision avoidance over a large variety of unseen scenarios.
Numerical Study of Superharmonic Resonances in a Mistuned Horizontal-Axis Wind-Turbine Blade-Rotor Set
Authors: Sapmaz Ayse , Acar Gizem D. , Feeny Brian F. 
Published: November 2019

Abstract
This paper is on a simplified model of an in-plane blade-hub dynamics of a horizontal-axis wind turbine with a mistuned blade. The model has cyclic parametric and direct excitation due to gravity and aerodynamics. This work follows up a previous perturbation study applied to the blade equations written in the rotor-angle domain and decoupled from the hub, in which superharmonic and primary resonances were analyzed. In this work, the effects of mistuning, damping, and forcing level are illustrated. The first-order perturbation solutions are verified with comparisons to numerical simulations at superharmonic resonance of order two. Additionally, the effect of rotor loading on the rotor speed and blade amplitudes is investigated for different initial conditions and mistuning cases.
Design and Kinematics of a Modular Robot for Assistive Tasks for the Disabled
Authors: Nelson Carl A. , Bruckner Mitchell A. , Chae Jay S. , Burnfield Judith M. , Buster Thad W. , Cesar Guilherme M. , Pfeifer Chase M. , Dasgupta Prithviraj 
Published: November 2019

Abstract
This paper presents a modular robot for adaptively providing assistance to individuals with spinal cord injury or other similar physical limitations. The design rationale is given, and details of prototyping are described. The robot’s kinematic model is elaborated, and preliminary testing and validation are presented.
Rule of Mixtures Model to Determine Elastic Modulus and Tensile Strength of 3D Printed Carbon Fiber Reinforced Nylon
Authors: Deng Kaiyue , Nejadkhaki Hamid Khakpour , Pasquali Felipe M. , Amaria Anosh P. , Armstrong Jason N. , Hall John F. 
Published: November 2019

Abstract
A model to compute the elastic modulus and tensile properties of 3D printed Carbon Fiber Reinforced Polymers (CFRP) is presented. The material under consideration is Carbon Fiber Reinforced Nylon (CFRN) produced in a Fused Deposition Modeling (FDM) process. A relationship between the nylon raster in each layer and the carbon fiber volume fraction was devised with the help of a scanning electron microscope (SEM). Thirteen groups with different layer configurations and carbon-fiber percentages were formulated and tested to obtain the elastic modulus and tensile strength. This study focused only on the properties along the printed fiber direction. The results from these tests were analyzed within the rule of mixtures framework. The results suggest that the rule of mixtures can be successfully applied to unidirectional CFRP fabricated using additive manufacturing.
Gaussian Process Emulation for Big Data in Data-Driven Metamaterials Design
Authors: Bostanabad Ramin , Chan Yu-Chin , Wang Liwei , Zhu Ping , Chen Wei 
Published: November 2019

Abstract
Our main contribution is to introduce a novel method for Gaussian process (GP) modeling of massive datasets. The key idea is to build an ensemble of independent GPs that use the same hyperparameters but distribute the entire training dataset among themselves. This is motivated by our observation that estimates of the GP hyperparameters change negligibly as the size of the training data exceeds a certain level, which can be found in a systematic way. For inference, the predictions from all GPs in the ensemble are pooled to efficiently exploit the entire training dataset for prediction. We name our modeling approach globally approximate Gaussian process (GAGP), which, unlike most largescale supervised learners such as neural networks and trees, is easy to fit and can interpret the model behavior. These features make it particularly useful in engineering design with big data. We use analytical examples to demonstrate that GAGP achieves very high predictive power that matches or exceeds that of state-of-the-art machine learning methods. We illustrate the application of GAGP in engineering design with a problem on data-driven metamaterials design where it is used to link reduced-dimension geometrical descriptors of unit cells and their properties. Searching for new unit cell designs with desired properties is then accomplished by employing GAGP in inverse optimization.