bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigating the Challenges of Crowdsourcing for Engineering Design: An Interview Study With Organizations of Different Sizes
Authors: Forbes Hannah , Shaefer Dirk , Shergadwala Murtuza N. , Panchal Jitesh H.
Published: November 2020

Crowdsourcing has been identified as a valuable paradigm in the open design movement. In engineering design, it offers various benefits, such as the generation of diverse ideas and the involvement of consumers. Despite the potential benefits, there are many ways in which crowdsourcing initiatives may fail. An example of such a failure is when a previously successful initiative for a large organization fails to attract a suitable number of participants with diverse expertise for a start-up. Consequently, the start-up does not receive good sets of ideas, both in quantity and variety. Such failures of crowdsourcing initiatives are common due to the lack of appropriate design of crowdsourcing initiatives based on the organizational characteristics such as its size. While frameworks and guidelines exist for the design of crowdsourcing initiatives, whether these are useful for all sizes of organizations, is yet to be determined. Large organizations such as Procter & Gamble and NASA, now conduct crowdsourcing initiatives regularly. Furthermore, start-ups are emerging that leverage crowdsourcing as an integral part of their business model. On the contrary, small and medium enterprises (SMEs) have fallen behind in the adoption of crowdsourcing processes. In this paper, we aim to identify the challenges associated with crowdsourcing and how and whether these differ according to organizational size. We present the results of an interview study with industry professionals from five organizations of varying sizes, and yield key challenges associated with the application of crowdsourcing. This paper discusses suggested support mechanisms for crowdsourcing in SMEs and directions for further research for crowdsourcing in engineering design.
Dynamic Morphing of Elastic Plates via Principal Parametric Resonance
Authors: Arena Andrea , Lacarbonara Walter
Published: November 2020

Principal parametric resonances of elastic plates actuated by periodic in-plane stresses effected by embedded piezoelectric wires are investigated to describe the morphing scenarios of flexible, ultra-lightweight panels. A mechanical model of elastic plate including geometric nonlinearities and the parametric actuation provided by the piezoelectric wires, is adopted to formulate the nonlinear equation of motion. A bifurcation analysis is carried out by means of an asymptotic approach based on the method of multiple scales leading to a comprehensive parametric study on the effect of the wires width on the morphing regions (i.e., parametric instability regions) associated with the principal parametric resonances. The threshold voltages triggering the onset of the principal parametric resonances of the lowest three symmetric modes are also calculated as a function of the wires size so as to determine the voltage requirements for the morphing activation.
Design and Analysis of a Wire-Driven Multifunctional Robot for Single Incision Laparoscopic Surgery
Authors: Chowdhury A. M. Masum Bulbul , Cheng Jinsai , Cullado Michael J. , Shen Tao
Published: November 2020

Single Incision Laparoscopic Surgery (SILS) is a fast-growing method in the field of MIS (Minimally Invasive Surgery) that has the potential to represent the future of laparoscopic surgeries. The major benefits of SILS results from a single incision which makes surgeries essentially scar-less, and it can reduce wound infection substantially as well as recuperation time. Many new researches are now focusing on developing cutting edge technologies to support SILS; however, the practical applications of SILS are constrained by a number of intricacies such as space limitation, absence of dexterous multitasking tools, lack of sufficient actuation force and poor visualization. Deployment and retraction of surgical tools or robots are done manually in the absence of a multitasking tool manipulator which increases the surgery time, risk of injury and surgeon’s fatigue. Our research focuses on designing a novel operative hardware (multitasking manipulator) to facilitate the SILS technique with automatic tool changing capability. A wire driven mechanism has been implemented in the design to minimize the damage to the electronic hardware during sterilization since the electronic actuation and sensing components are located remotely from the end-effector which requires heat or chemical sterilization before surgery. And a wire-driven articulated robotic arm has also been designed to support the manipulator. The details of the robotic design and analysis are conducted in the paper. The feasibility of this robotic method has been demonstrated by experiments.
Exploration of the Timing of Introduction of Design Heuristic Cards to Early Design Brainstorming Sessions by Interdisciplinary Student Teams
Authors: Mercado Rivera Abimelec , Lugo José E.
Published: November 2020

This research focuses on improving the outcome of idea generation sessions of interdisciplinary student teams working in the early design stages of a product or solution by measuring the effect of incorporating Design Heuristics Cards at different points of ideation sessions that adhere to the brainstorming guidelines. Using the design thinking methodology, an open-ended challenge was given to the participating teams for a Brainstorming exercise divided into a fifteen-minute individual segment followed by a thirty-minute team ideation segment. Three experimental treatments were designed where Design Heuristics Cards were introduced at different points of the ideation exercise: the start of the individual ideation segment, the start of the team ideation segment, or the second half of the team ideation segment. A fourth control treatment did not introduce the cards at any point but used the Brainstorming guidelines throughout. The metrics observed were Fluency, Novelty, Feasibility, and Market Fit of the ideas generated by the students. Eighty-four students participated in the experiment, with 58.3% being from majors in the College of Engineering, 28.6% from majors in the College of Business Administration, 7.1% from majors in the College of Arts and Sciences, and 6.0% from majors in the College of Agriculture. No significant difference was found among the experimental treatments; however the results are not considered final due to the explorative nature of the study. Recommendations are made on future work and possible improvements to the experiment.
Machine-Specific Energy Estimation Using the Unit Process Life Cycle Inventory (UPLCI) Model
Authors: Thoft Krogshave Johan , Boettjer Till , Ramanujan Devarajan
Published: November 2020

This paper discusses a method for machine-specific energy estimation in milling processes using the unit process life cycle inventory (UPLCI) model. To this end, we develop a standard methodology for constructing an adjusted UPLCI model that includes adjustment factors for uncertainties in machine tool specifications and the specific cutting energy of a workpiece material. The adjustment factors are calculated through experimental measurement of energy consumption for a standard test part on a specific machine tool. To validate the adjusted UPLCI model, we conducted a case study which experimentally measured the energy consumption for machining three parts made of Aluminum 6082 on a Chevalier QP2040-L three-axis vertical milling machine. Results show that the UPLCI model consistently overestimated the total energy consumption for machining the three parts and had significant estimation errors (314%, 499%, 286%). The largest sources of error in the UPLCI model were from overestimating the idle and basic power consumption of the machine tool. The adjusted UPLCI model significantly reduced the estimation errors for the same tests (27%, 0.3%, 36%).
Neuroadaptive Controller for Physical Interaction With an Omni-Directional Mobile Nurse Assistant Robot
Authors: Saadatzi Mohammad N. , Abubakar Shamsudeen , Das Sumit Kumar , Saadatzi M. Hossein , Popa Dan
Published: November 2020

Robot-assisted healthcare could help alleviate the shortage of nursing staff in hospitals and is a potential solution to assist with safe patient handling and mobility. In an attempt to off-load some of the physically-demanding tasks and automate mundane duties of overburdened nurses, we have developed the Adaptive Robotic Nursing Assistant (ARNA), which is a custom-built omnidirectional mobile platform with a 6-DoF robotic manipulator and a force sensitive walking handlebar. In this paper, we present a robot-specific neuroadaptive controller (NAC) for ARNA’s mobile base that employs online learning to estimate the robot’s unknown dynamic model and nonlinearities. This control scheme relies on an inner-loop torque controller and features convergence with Lyapunov stability guarantees. The NAC forces the robot to emulate a mechanical system with prescribed admittance characteristics during patient walking exercises and bed moving tasks. The proposed admittance controller is implemented on a model of the robot in a Gazebo-ROS simulation environment, and its effectiveness is investigated in terms of online learning of robot dynamics as well as sensitivity to payload variations.
Identifying Sustainable Solutions for Sanitation, Energy, and Water Needs in Off-Grid Indian Villages
Authors: Nair Anju Vijayan , Balu Nellippallil Anand , Das Ashok K. , Hall John , Allen Janet K. , Mistree Farrokh
Published: November 2020

Food, energy, and water are the significant factors necessary for the social and economic well-being and prosperity of people, particularly to accelerate rural development. In order to attain sustainable development in off-grid villages, the issues associated with the Food-Energy-Water (FEW) nexus must be addressed with respect to social, economic and environmental aspects. SunMoksha, a socio-technical enterprise, has proposed a model that includes smart technical solutions or intelligent assets, such as, Smart AQUAnet™, Smart Nanogrid™, Smart MEZ™, etc., addressing food, energy and water needs in off-grid villages. Its development model allows for multiple sustainable solutions to be introduced into the rural community, to address specific needs as the community moves up in the development process.

In this paper, a method is presented to identify such sustainable smart solutions. The method involves the use of the dilemma triangle and Go/No-Go analysis to address the needs in off-grid Indian villages. We demonstrate the efficacy of the method by first identifying the key issues in an Indian off-grid village from the perspective of the FEW nexus using the dilemma triangle construct. A Go/No-Go analysis is used to select the best feasible solution from a set of possible solutions addressing the dilemmas and issues. Additionally, the needs for sanitation in the village is also considered in the analysis, keeping in mind the health and well-beings of the communities. The method is generic and will support stakeholders/decision-makers in identifying and selecting the best suitable solution from a set of potential solutions. The method is illustrated for an off-grid village to identify an intelligent asset to simultaneously address the sanitation, energy and water needs.
Large-Scale Three-Dimensional Anisotropic Topology Optimization of Variable-Axial Composite Structures
Authors: Zhou Yuqing , Nomura Tsuyoshi , Zhao Enpei , Zhang Wei , Saitou Kazuhiro
Published: November 2020

Variable-axial fiber-reinforced composites allow for local customization of fiber orientation and thicknesses. Despite their significant potential for performance improvement over the conventional multiaxial composites and metals, they pose challenges in design optimization due to the vastly increased design freedom in material orientations. This paper presents an anisotropic topology optimization (TO) method for designing large-scale, 3D variable-axial composite structures. The computational challenge for large-scale 3D TO with extremely low volume fraction is addressed by a tensor-based representation of 3D orientation that would avoid the 2π periodicity of angular representation such as Eular angles, and an adaptive meshing scheme, which, in conjunction with PDE regularization of the density variables, refines the mesh where structural members appear and coarsens where there is void. The proposed method is applied to designing a heavy-duty drone frame subject to complex multi-loading conditions. Finally, the manufacturability gaps between the optimized design and the fabrication-ready design for Tailored Fiber Placement (TFP) is discussed, which motivates future work toward fully-automated design synthesis.
Decision-Based Design Method for Computing Marginal Cost of Durability
Authors: Pasquali Felipe M. , Meza Jonatan , Hall John F.
Published: November 2020

Product durability impacts both the environment and the economy. Companies are changing their business models to the circular economy. In this model, the ownership of the product remains with the manufacturer. With this new paradigm, determining the life of the product becomes even more important for the success of the business model. The metric defined as the Marginal Cost of Durability (MCD) determines the cost to increase or decrease the life of the system. For a system to last longer, more materials are needed to counteract the fatigue damage. While this metric has been defined and used in studies throughout the literature, there is a need for a formal method of collecting this data. This paper presents a novel method for measuring the MCD aided by Metamodel-Based Optimization. A case study is presented to demonstrate this method when applied to a wind turbine tower. The results indicate that there is an increasing linear relationship between life and cost. A wind turbine tower designed for 80 years has 34% more mass and cost than a 20-year design.
Muscle Contracture Modeling and Optimal Control for Crouch Gait Prediction
Authors: Tomasi Matilde , Artoni Alessio
Published: November 2020

Prediction of human movement, and especially of pathological gait, is nowadays an important and mostly unsolved research challenge. In this work, a recently developed computational framework based on optimal control was adopted and explored to assess its potential for predicting a pathological gait pattern, in particular the crouch gait typical of subjects affected by cerebral palsy. To this end, the generic musculoskeletal model on which this optimal control framework is based was made representative of such pathological case by modeling contracture of relevant muscle groups commonly associated with crouch gait, namely knee and hip flexors. All the conducted simulations succeeded in inducing the model into a crouch gait pattern, despite their diversity in cost functions. Moreover, the obtained joint angle trajectories correlated well with the experimental ones obtained from a CP child walking in crouch. These kinematic results suggest that optimal control techniques and proper tuning of musculotendon parameters are an important pairing for predictive simulations of human walking. On the other hand, the obtained results confirm that estimation of muscle activations is strongly dependent on the selected objective function and still requires deeper investigations.
RATS: A Robotic Arm Training System Designed for Rats
Authors: Zhang Haohan , Luna Tatiana , Yang Lillian , Martin John , Agrawal Sunil
Published: November 2020

This paper presents a novel robotic system to characterize and retrain reaching in rats. This robot is intended to be a research platform for rehabilitation of forelimb movements in rats. In this paper, we focus on the design of this robotic system. We present the design requirements, mathematical models, and details of the physical device. A parallel mechanism with a special alignment of the component chains is used to accommodate observed reaching motions of a rat’s forelimb. Additionally, we demonstrate the use of this robot to record forelimb trajectories. Three healthy rats were used to record repeated reaching motions while the robot applied nearly zero force. We believe that this robotic system can be used in future training studies with rats who have impaired arm motions due to a neurological insult.
Design for Flexibility: A Graph Coloring Technique to Study Design Changes in the Tethered Economy World
Authors: Estrada Spenser , Green Emilyn , Jahanbekam Sogol , Behdad Sara
Published: November 2020

Digitization, connected networks, embedded software, and smart devices have resulted in a major paradigm shift in business models. Transformative service-based business models are dominating the market, where advancement in technology has paved the way for offering not only a set of new services but also altering product functionalities and services over time. This paradigm shift calls for new design approaches. Designers should be able to design flexible products and services that can adapt to a wide range of consumer needs over time. To address the need for designing for flexibility, the objective of this study is to develop a graph coloring technique that can model changes in the functional requirements of a product and determine the minimum number of physical parts needed to meet future functionalities. This technique relies on vertex labeling by the designer and the construction of a core graph combining key elements of all desired iterations, which is then colored by label. One numerical example and one real-world example are provided to show the application of the proposed model.
Discontinuous Dynamics of a Frequency Up-Conversion Piezoelectric Harvester With an Impact Controlling Mechanism
Authors: Onsorynezhad Saeed , Wang Fengxia
Published: November 2020

In this study, an impact based frequency up-conversion mechanism is studied using discontinuous dynamics theory. The mechanism consists of a sinusoidal vibrating plastic beam as a driving element and a piezoelectric bimorph as a generator. In order to remove the unfavorable stick motion and enhance the performance of the energy harvester, two pairs of racks and pinion gears and a slider-crank have been added to the system, which makes us able to control the impact occurring time between the driving beam and the generator.

In this work, the Rayleigh-Ritz method was applied to obtain the distributed-parameter models of the driving beam and the piezoelectric generator. Both the forward and the backward mechanical-electrical coupling effects were considered during the modeling of the generator. The electrical and mechanical dynamic behaviors of the proposed piezoelectric energy harvester were analytically studied to better understand the effect of system parameters on the performance of piezoelectric energy harvester. Discontinuous dynamics theory was applied to obtain the generated power and voltage. The stability of the periodic solutions was obtained, and the bifurcation diagrams of displacements, impact velocities, generated power, and voltage were obtained analytically as the excitation frequency varying.
Mechanical System Modelling Using Physics-Based and Data-Based Subsystems
Authors: Arbatani Siamak , Kövecses József
Published: November 2020

Mechanical systems have been traditionally represented using parametric physics-based models. In this work, we introduce a novel concept, in this part of the mechanical system is represented using data-based subsystem models, and the overall mechanical system model is composed of these data-based and other, physics-based subsystems. A core element is the interfacing of the subsystems, which gives rise to interaction forces. The interfacing problem is formulated in a way that makes it possible to give a general representation to the interaction forces. We demonstrate that from the point of view of the physics-based subsystems the important element is that the data-based models can represent the interaction force systems properly. The data-based subsystems are developed using deep recurrent neural networks, and the training data is generated based on simulations using the fully parametric physics-based model of the system. Such training data could also be obtained through physical experimentation.
Simulation of Hyper-Elasticity by Shape Estimation
Authors: Matte Christopher-Denny , Kwok Tsz-Ho
Published: November 2020

The simulation of complex geometries and non linear deformation has been a challenge for standard simulation methods. There has traditionally been a trade off between performance and accuracy. With the popularity of additive manufacturing and the new design space it enables, the challenges are even more prevalent. Additionally multiple additive manufacturing techniques now enable the use of hyperelastic materials as raw material for fabrication, and multi-material capabilities. This allows designers more freedom, but also introduces new challenges for control and simulation of the printed parts. In this paper, a novel approach to implementing non-linear material capabilities is devised with negligible additional computational for geometry based approaches. Material curves are fitted with a polynomial expression which can determine the tangent modulus, or stiffness, of a material based on strain energy. The moduli of all elements are compared to determine relative shape factors used to establish the blended shape of an element. This process is done dynamically to update the stiffness of a material in real-time, for any number of materials, regardless of linear or non-linear material curves.
The Role of Idea Fluency and Timing on Highly Innovative Design Concepts
Authors: Mirabito Yakira , Goucher-Lambert Kosa
Published: November 2020

Ongoing work within the engineering design research community seeks to develop automated design methods and tools that enhance the natural capabilities of designers in developing highly innovative concepts. Central to this vision is the ability to first obtain a deep understanding of the underlying behavior and process dynamics that predict successful performance in early-stage concept generation. The objective of this research is to better understand the predictive factors that lead to improved performance during concept generation. In particular, this work focuses on the impact of idea fluency and timing of early-stage design concepts, and their effect on overall measures of ideation session success. To accomplish this, we leverage an existing large-scale dataset containing hundreds of early-stage design concepts; each concept contains detailed ratings regarding its overall feasibility, usefulness, and novelty, as well as the completion time of each idea. Surprisingly, results indicate that there is no effect of idea fluency or timing on the quality of the output when using a holistic evaluation mechanism, such as the innovation measure, instead of a single measure such as novelty. Thus, exceptional concepts can be achieved by all generator segments independent of idea fluency. Furthermore, in early-stage concept generation sessions, highest-rated concepts have an equal probability of occurring early and late in a session. Taken together, these findings can be used to improve performance in ideation by effectively determining when and which types of design interventions future design tools might suggest.
Data-Driven Multiscale Topology Optimization Using Multi-Response Latent Variable Gaussian Process
Authors: Wang Liwei , Tao Siyu , Zhu Ping , Chen Wei
Published: November 2020

The data-driven approach is emerging as a promising method for the topological design of the multiscale structure with greater efficiency. However, existing data-driven methods mostly focus on a single class of unit cells without considering multiple classes to accommodate spatially varying desired properties. The key challenge is the lack of inherent ordering or “distance” measure between different classes of unit cells in meeting a range of properties. To overcome this hurdle, we extend the newly developed latent-variable Gaussian process (LVGP) to creating multi-response LVGP (MRLVGP) for the unit cell libraries of metamaterials, taking both qualitative unit cell concepts and quantitative unit cell design variables as mixed-variable inputs. The MRLVGP embeds the mixed variables into a continuous design space based on their collective effect on the responses, providing substantial insights into the interplay between different geometrical classes and unit cell materials. With this model, we can easily obtain a continuous and differentiable transition between different unit cell concepts that can render gradient information for multiscale topology optimization. While the proposed approach has a broader impact on the concurrent topological and material design of engineered systems, we demonstrate its benefits through multiscale topology optimization with aperiodic unit cells. Design examples reveal that considering multiple unit cell types can lead to improved performance due to the consistent load-transferred paths for micro- and macrostructures.
A Vocabulary of Function Features for Computer Aided Modeling of Thermal-Fluid Systems
Authors: Venkatanarasimhan Lakshmi N. A. , Chowdhury Ahmed , Sen Chiradeep
Published: November 2020

Features are higher-level modeling entities that encapsulate multiple lower-level entities and relations into one easily usable unit. It has been shown previously that having CAD-like features in function modeling can increase the ease of modeling, model consistency, and reasoning. This paper presents and illustrates a protocol for extracting function features that can be used frequently in computer-aided function modeling within a given technical domain, and formally defines those features using graphical templates and grammar rules. A comprehensive set of six function features is thus created for the Thermal-Fluid Systems domain. Extendibility of the protocol is then illustrated by using it to extract two additional features from a different domain. The features thus produced, and their variants and usage in modeling are also discussed.
Quantifying Individuals’ Theory-Based Knowledge Using Probabilistic Causal Graphs: A Bayesian Hierarchical Approach
Authors: Hans Atharva , Chaudhari Ashish M. , Bilionis Ilias , Panchal Jitesh H.
Published: November 2020

Extracting an individual’s knowledge structure is a challenging task as it requires formalization of many concepts and their interrelationships. While there has been significant research on how to represent knowledge to support computational design tasks, there is limited understanding of the knowledge structures of human designers. This understanding is necessary for comprehension of cognitive tasks such as decision making and reasoning, and for improving educational programs. In this paper, we focus on quantifying theory-based causal knowledge, which is a specific type of knowledge held by human designers. We develop a probabilistic graph-based model for representing individuals’ concept-specific causal knowledge for a given theory. We propose a methodology based on probabilistic directed acyclic graphs (DAGs) that uses logistic likelihood function for calculating the probability of a correct response. The approach involves a set of questions for gathering responses from 205 engineering students, and a hierarchical Bayesian approach for inferring individuals’ DAGs from the observed responses. We compare the proposed model to a baseline three-parameter logistic (3PL) model from the item response theory. The results suggest that the graph-based logistic model can estimate individual students’ knowledge graphs. Comparisons with the 3PL model indicate that knowledge assessment is more accurate when quantifying knowledge at the level of causal relations than quantifying it using a scalar ability parameter. The proposed model allows identification of parts of the curriculum that a student struggles with and parts they have already mastered which is essential for remediation.
Utility Function Derived Off-Road Vehicle Path Planning
Authors: Pandey Vijitashwa , Slon Christopher , Mollan Calahan , Barthlow Dakota , Gorsich David , Jayakumar Paramsothy
Published: November 2020

Optimal navigation of ground vehicles in an off-road setting is a challenging task. One must accurately model the properties of the terrain and reconcile it with vehicle capabilities, while simultaneously addressing mission requirements. An important part of navigation is path planning, the selection of the route a vehicle takes between the start and end points. It is often seen that, given the starting and end points for a vehicle, the optimal path that the vehicle should take varies considerably with the mission requirements. While most commonly used algorithms use a local cost function, mission requirements are typically defined over the entire run of the vehicle. Utility theoretic methods provide a normative tool to model tradeoffs over attributes (mission requirements) that the operator cares about. It is critical therefore, that preferences embedded in the utility function influence the local cost functions used. In this paper, we provide a framework for a feedback-based method to update the parameters of the local cost-function. We do so by using a geodesic-based method for path planning given the terrain inputs, followed by a physics-based simulation of a vehicle to evaluate the attributes. These attributes are then combined into a multiattribute utility function. An optimization-based approach is used to find the parameters of the cost function that maximizes this multiattribute utility. We present our approach on a vehicle navigation example over a terrain acquired from United States Geological Survey data.
Early-Stage Uncertainty: Effects of Robust Convex Optimization on Design Exploration
Authors: Pillai Priya P. , Burnell Edward , Wang Xiqing , Yang Maria C.
Published: November 2020

Engineers design for an inherently uncertain world. In the early stages of design processes, they commonly account for such uncertainty either by manually choosing a specific worst-case and multiplying uncertain parameters with safety factors or by using Monte Carlo simulations to estimate the probabilistic boundaries in which their design is feasible. The safety factors of this first practice are determined by industry and organizational standards, providing a limited account of uncertainty; the second practice is time intensive, requiring the development of separate testing infrastructure. In theory, robust optimization provides an alternative, allowing set based conceptualizations of uncertainty to be represented during model development as optimizable design parameters. How these theoretical benefits translate to design practice has not previously been studied. In this work, we analyzed present use of geometric programs as design models in the aerospace industry to determine the current state-of-the-art, then conducted a human-subjects experiment to investigate how various mathematical representations of uncertainty affect design space exploration. We found that robust optimization led to far more efficient explorations of possible designs with only small differences in an experimental participant’s understanding of their model. Specifically, the Pareto frontier of a typical participant using robust optimization left less performance “on the table” across various levels of risk than the very best frontiers of participants using industry-standard practices.
Measuring Human Learning in Design Space Exploration to Assess Effectiveness of Knowledge Discovery Tools
Authors: Bang Hyunseung , Selva Daniel
Published: November 2020

While there exist various knowledge discovery tools to support designers’ learning during design space exploration, there is no established definition of what is expected to be learned and how it should be measured. Measuring learning is important, as it enables assessing and comparing different knowledge discovery methods. In this paper, we review the major categories of learning goals that are introduced in the field of education. Then, 7 different measures are developed to target specific learning goals relevant to design space exploration. Different learning goals are targeted by modifying the domain of the knowledge that is tested by each measure, and the specific task that the user is asked to perform. A human-subject experiment is conducted to measure how these metrics are related. Specifically, the consistency and correlations between different combinations of the measures are examined. Based on the observations made in this study, we discuss the implications and issues for future usage.
Deformable Blade Element and Unsteady Vortex Lattice Fluid-Structure Interaction Modeling of a 2D Flapping Wing
Authors: Reade Joseph , Jankauski Mark A.
Published: November 2020

Flapping insect wings experience appreciable deformation due to aerodynamic and inertial forces. This deformation is believed to benefit the insect’s aerodynamic force production as well as energetic efficiency. However, the fluid-structure interaction (FSI) models used to estimate wing deformations are often computationally demanding and are therefore challenged by parametric studies. Here, we develop a simple FSI model of a flapping wing idealized as a two-dimensional pitching-plunging airfoil. Using the Lagrangian formulation, we derive the reduced-order structural framework governing wing’s elastic deformation. We consider two fluid models: quasi-steady Deformable Blade Element Theory (DBET) and Unsteady Vortex Lattice Method (UVLM). DBET is computationally economical but does not provide insight into the flow structure surrounding the wing, whereas UVLM approximates flows but requires more time to solve. For simple flapping kinematics, DBET and UVLM produce similar estimates of the aerodynamic force normal to the surface of a rigid wing. More importantly, when the wing is permitted to deform, DBET and UVLM agree well in predicting wingtip deflection and aerodynamic normal force. The most notable difference between the model predictions is a roughly 20° phase difference in normal force. DBET estimates wing deformation and force production approximately 15 times faster than UVLM for the parameters considered, and both models solve in under a minute when considering 15 flapping periods. Moving forward, we will benchmark both low-order models with respect to high fidelity computational fluid dynamics coupled to finite element analysis, and assess the agreement between DBET and UVLM over a broader range of flapping kinematics.
Design Space Analysis Method for Support of System Design Under the Consideration of Uncertainties in the Early Design Stage
Authors: Aoyama Kazuhiro , Uchibori Yoshihiro , Oizumi Kazuya , Hiramatsu Shigeki , Unesaki Hiroshi , Kondo Shyuichi
Published: November 2020

In this study, following the concept of set-based design, after preparing global calculation results, we introduced the approach of setting the design solution area that satisfies the product performance goals of the system design. In this approach, from the viewpoint of considering uncertainty, we aimed to develop an analysis method that can get the organic relationship between target variables and design variables. And more, under the assumption that it is difficult to comprehend the full picture of products that are becoming more sophisticated and complex with the knowledge that has been fostered by skilled engineers, the proposed system uses the objective calculation indices that is provided knowledge of the designer. Specifically, the following method are proposed to solve the problem.

- Implementation of meta-modeling of design space.

- Classified solution space using a density-based clustering method to detect that the solution spaces are divided into multiple disconnected space.

- Defined an index called distribution concentration and expressed the possibility of dealing with the uncertainty of the solution domain.

- The network diagram based on the calculated index values was proposed to confirm the change in the characteristics of the solution space when the performance target of the product was changed.

Finally, the effectiveness of the proposed method was verified by applying it to actual simulation results.
3D Build Melt Pool Predictive Modeling for Powder Bed Fusion Additive Manufacturing
Authors: Yang Zhuo , Lu Yan , Yeung Ho , Kirshnamurty Sundar
Published: November 2020

Melt pool size is a critical intermediate measure that reflects the outcome of a laser powder bed fusion process setting. Reliable melt pool predictions prior to builds can help users to evaluate potential part defects such as lack of fusion and over melting. This paper develops a layer-wise Neighboring-Effect Modeling (L-NBEM) method to predict melt pool size for 3D builds. The proposed method employs a feedforward neural network model with ten layer-wise and track-wise input variables. An experimental build using a spiral concentrating scan pattern with varying laser power was conducted on the Additive Manufacturing Metrology Testbed at the National Institute of Standards and Technology. Training and validation data were collected from 21 completed layers of the build, with 6,192,495 digital commands and 118,928 in-situ melt pool coaxial images. The L-NBEM model using the neural network approach demonstrates a better performance of average predictive error (12.12%) by leave-one-out cross-validation method, which is lower than the benchmark NBEM model (15.23%), and the traditional power-velocity model (19.41%).
Evaluating Plane Curves As Constraints for Locomotion on Uneven Terrain
Authors: Liu Chang , Plecnik Mark
Published: November 2020

This paper focuses on preliminary work related to the discovery of single degree-of-freedom mechanism paths useful for dynamic locomotion tasks. The objective is to bridge a gap between kinematic specifications and emerging dynamic behaviors. This is accomplished by formulating a set of ordinary differential equations that includes essential mechanism characteristics (path traced, mechanical advantage) but excludes all physical mechanism parameters (topology, link lengths). The dynamics represent a rotation constrained body propelled by a foot that is attached to that body by a user-defined path. The foot is powered by a series-elastic actuator acting through a mechanical advantage function that is defined across the length of the path. Through this framework, a range of user-defined paths were tested for effective locomotion on flat and complex terrains. Foot paths and mechanical advantage functions exist outside of any mechanical design, with the goal to discover paradigms worth instantiating into physical mechanisms, a task reserved for kinematic synthesis. This work would empower existing kinematic synthesis techniques to achieve dynamic requirements. In other words, kinematic requirements are transformed from an end to a means. Their dynamic utility would be evaluated by the framework presented in this paper rather than pursued by themselves.
TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain
Authors: Nie Zhenguo , Lin Tong , Jiang Haoliang , Kara Levent Burak
Published: November 2020

In topology optimization using deep learning, load and boundary conditions represented as vectors or sparse matrices often miss the opportunity to encode a rich view of the design problem, leading to less than ideal generalization results. We propose a new data-driven topology optimization model called TopologyGAN that takes advantage of various physical fields computed on the original, unoptimized material domain, as inputs to the generator of a conditional generative adversarial network (cGAN). Compared to a baseline cGAN, TopologyGAN achieves a nearly 3× reduction in the mean squared error and a 2.5× reduction in the mean absolute error on test problems involving previously unseen boundary conditions. Built on several existing network models, we also introduce a hybrid network called U-SE(Squeeze-and-Excitation)-ResNet for the generator that further increases the overall accuracy. We publicly share our full implementation and trained network.
Perineum Pressure Distribution Among Various Bicycle Saddles
Authors: Cruz Jazmin , Garcia Mario , Jackson Elizabeth , Yang James
Published: November 2020

Cycling is a widely popular exercise that is known to provide great health benefits. However, it has been questioned if cycling is responsible for genital numbness or Erectile Dysfunction (ED) due to compression of the perineum between the rider and the bicycle saddle. This study compares the perineal pressure distribution between three saddles (ISM, 3 West, and Fizik) for healthy, active male cyclists and a saddle recommendation is made. Using their own bikes, participants perform six randomized cycling trials (two per saddle) while sitting on a piezo-resistive pressure mat. Participants were asked to qualitatively rate the saddles after each trial. The quantitative results favor the ISM saddle due to its lower perineal pressure values, but the qualitative perceived comfort from the cyclists is split.
A Novel Two-Stage Design Framework for 2D Spatial Packing of Interconnected Components
Authors: Peddada Satya R. T. , James Kai A. , Allison James T.
Published: November 2020

Packing and routing problems separately are each challenging NP-hard problems. Therefore, solving the coupled packing and routing problem simultaneously will require disruptive methods to better address pressing related challenges, such as system volume reduction, interconnect length reduction, ensuring non-intersection, and physics (heat, fluid pressure or electromagnetic) considerations. Here we present a novel two-stage sequential design framework to perform simultaneous physics-based packing and routing optimization. Stage 1 is comprised of generating interference-free initial layouts that are fed to stage 2 as starting points to perform continuous physics-based optimization. Three distinct strategies for stage 1 have been introduced recently, 1) the force-directed layout method (FDLM), 2) an extension of the shortest path algorithms (SPAs) and 3) a unique geometric topology (UGT) generation algorithm. In stage 2, a gradient-based topology optimization method is used to simultaneously optimize both component locations and routing paths of component interconnects. In addition to geometric considerations, this method supports optimization based on system behavior by including physics-based objectives and constraints (e.g., modeled using 1D lumped parameter and 2D finite element physics models). The three layout generation methods developed for stage 1 are compared here with respect to system performance metrics obtained from stage 2. In summary, the design automation framework presented here integrates several elements together as a step toward a more comprehensive solution of 3D packing and routing problems with both geometric and physics considerations.
Experimental Study on Lane Change Maneuvers With a Motorcycle
Authors: Ecker Horst , Litschauer Stefan
Published: November 2020

In this study, an innovative concept to measure the dynamic parameters of lane change maneuvers with motorcycles is presented and measurement results and conclusions are discussed An instrumented motorcycle is used for the experiments. Two independent measurement systems provide redundant data for obtaining precise readings of the vehicle position and the speed, for 3-axis accelerations and angular speeds, and more. Among these signals, the roll angle and the roll rate are especially important signals in this context.

Measurements were carried out on public roads in a city and on highways, to allow for a broad range of speeds. The time series of the numerous tests were examined and analyzed with respect to the aforementioned signals and their correlation. Because of a regression analysis, analytical expressions could be derived for the lane change duration depending on the offset, for the maximum roll rate depending on the manoeuver intensity and finally for the roll rate and the roll angle as a function of time.
Digitizing Dissection: A Case Study on Augmented Reality and Animation in Engineering Education
Authors: Kearney Kevin G. , Starkey Elizabeth M. , Miller Scarlett R.
Published: November 2020

Advancing virtual education through technology is an important step for engineering education. This has been made evident by the educational difficulties associated with the 2020 Covid-19 pandemic. Maintaining educational standards while using virtual learning is something possibly solved through researching new educational technologies. A potential technology that can enhance virtual education is Augmented Reality, since it can show information that would otherwise not be easily experienced or obtained. Traditional learning tools fail to offer the ability to control objects and explore numerous perspectives the way augmented reality can. Augmented reality can be even further enhanced through the addition of animation. Animation could add the ability to see motion, increasing overall understanding as well as increasing the motivation to learn. When motion is not visualized, it must be perceived, which can increase cognitive load and cause the limitations of working memory to be met. Reaching the limits of working memory has been shown to negatively affect learning. Therefore, the purpose of this study was to identify the impact of digitizing product dissection on engineering student learning and cognitive load. Specifically, we sought to identify the impact of Augmented Reality and Animations through a full factorial experiment with 61 engineering students. The results of the study show that the virtual condition with animation exhibited increased effectiveness as a learning tool. It also showed that augmented reality is not significantly different than a virtual environment in the context of product dissection. The results of this study are used to explore future uses of augmented reality and animation in education, as well as lay the groundwork for future work to further explore these technologies.
Restoring Harmony in the Lifeworld? Identity, Learning, and Leaving Preelite Sport
Authors: Ronkainen Noora J. , Ryba Tatiana V. , Allen-Collinson Jacquelyn
Published: November 2020

Sport provides many youth participants with a central life project, and yet very few eventually fulfill their athletic dreams, which may lead them to disengage from sport entirely. Many studies have explored the processes of athletic retirement, but little is known about how youth athletes actually reconstruct their relationship with sport and embodiment postretirement. The authors explored these issues in the story of “Pilvi,” a Finnish alpine skier who disengaged from sport in her late adolescence. Employing an existential-phenomenological approach, they conducted six low-structured interviews with Pilvi, combined with visual methods, and identified key themes relating to the body, space, culture, and time. Their findings highlight the difficulty of building a new relationship with sport and the often restrictive cultural horizons of sport and exercise culture that limit the “possible selves.” The authors discuss the significant implications for applied practitioners helping youth athletes and effectively supporting them in leaving their sport.
COVID-19 recovery funds dwarf clean energy investment needs
Authors: Andrijevic Marina , Schleussner Carl-Friedrich , Gidden Matthew J. , McCollum David L. , Rogelj Joeri
Published: November 2020

A modest fraction of current global stimulus funds can put the world on track to achieve Paris Agreement goals
Reconstituting tissue patterning
Authors: Barkai Naama , Shilo Ben-Zion
Published: November 2020

A synthetic morphogen reveals quantitative principles of tissue patterning