bluuebunny/crossref_metadata_2025_split huggingface dataset:
Data-Driven Multiscale Topology Optimization Using Multi-Response Latent Variable Gaussian Process
Authors: Wang Liwei , Tao Siyu , Zhu Ping , Chen Wei 
Published: November 2020

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.