bluuebunny/crossref_metadata_2025_split huggingface dataset:
Life Cycle Assessment of Pyrolysis-Derived Biochar From Organic Wastes and Advanced Feedstocks
Authors: Hersh Benjamin , Mirkouei Amin 
Published: November 2019

Abstract
Recent interest in reducing stress on the food-energy-water (FEW) nexus requires the use of renewable, organic products that can subsequently address environmental sustainability concerns, such as mitigating greenhouse gas emissions. Pyrolysis-derived biochar from organic wastes (e.g., nutrient-rich agricultural wastes and leftovers, forest harvest residues, and cattle manure) and advanced feedstocks (e.g., algae) is capable of addressing ever-increasing global FEW concerns. Biochar water-nutrient holding capacity and carbon sequestration are key attributes for improving organic farming and irrigation management. The major challenge to commercialize biochar production from organic wastes is the conversion process. Pyrolysis process is a cost-effective and successful approach in comparison to other conversion technologies (e.g., gasification) due to low energy requirement and capital cost, as well as high process efficiency and biochar quality. To determine the environmental impacts of the biochar production process, an analysis of the material, energy, and emission flows of a small-scale pyrolysis process is conducted for a real case study, using life cycle assessment method with the assistance of available life cycle inventory databases within OpenLCA software. The results demonstrate that this study is able to enhance sustainability aspects across FEW systems by (a) employing a portable refinery to address upstream challenges (i.e., collection, transportation, and preprocessing) of waste-to-biochar life cycle, (b) recycling domestic forest and agricultural residues (e.g., pine wood), (c) producing organic biochar-derived soil conditioners that can improve organic cropping and FEW systems. Ultimately, we conclude by discussing techno-economic and socio-environmental implications of biochar production from organic wastes and advanced feedstocks.
Virtual Sensing in High-Performance Vehicles With Artificial Intelligence
Authors: Bonfitto Angelo , Feraco Stefano , Amati Nicola , Tonoli Andrea 
Published: November 2019

Abstract
This paper presents a technique for the estimation of the sideslip angle and longitudinal speed of a high-performance four-wheel drive vehicle by means of Artificial Neural Networks. The proposed architecture relies on the combination of a pattern recognition neural classifier with two stages of cascaded regression neural networks. The classifier allows identifying the road condition and the regression stages perform the two estimation tasks. The networks are trained by means of datasets recorded on an instrumented vehicle. The strategy is tested in different road adherence conditions, namely dry, wet and icy, and in a variety of driving maneuvers. The effectiveness of the technique is demonstrated experimentally by deploying the algorithm on the vehicle and comparing the sideslip angle estimation with the measurement computed by an optical sensor and the longitudinal speed estimation with both the estimation already present on the vehicle and obtained as the mean value of the wheels’ velocity and with the optical sensor measurement.
Mechanical Design and Prototype of a Reconfigurable Actuated Universal Joint
Authors: Palpacelli M. C. , Carbonari L. , Palmieri G. , Callegari M. 
Published: November 2019

Abstract
This paper presents the mechanical design and prototyping of a reconfigurable universal joint. An electronically actuated mechanical switch allows to change joint’s configuration so that two different universal pairs can be obtained. One of the two revolute axes rotates freely whereas the other is driven by a servomotor: its direction can be chosen by the user, providing two different mechanical arrangements. It can be demonstrated that the use of this reconfigurable joint in a specific parallel kinematic manipulator equipped with three identical legs allows to change the mobility of its moving platform. The design proposed in the current work has been driven by analytical analyses and multibody simulations. A finite element analysis demonstrates the effectiveness of the mechanical design.
Modeling, Validation and Prototype Development of Electric Sail Tether Deployment Systems for CubeSats
Authors: Hargis Benjamin E. , Brandt Benjamin F. , Canfield Stephen L. , Tinker Michael 
Published: November 2019

Abstract
The Electric sail concept is based on a distributed tether satellite system with tether lengths on the order of thousands-of meters. The system must deploy from stowed arrangement into a selected flight configuration in which thrust forces are transmitted through the tether to the satellite body. The system must be stable through deployment procedure and maintain stable, desired configuration during flight operations. Understanding the dynamic behavior of the satellite bodies and distributed, conductive tether are critical to long-range design and development of the Electric Sail concept. This paper’s contribution is the presentation, development and validation of a mathematical model for simulating E-Sail deployment of a prototype system for testing on the MSFC Robotic Flat Floor Facility. A massed tether model is developed using the bead and string concept with equations of motion derived from Lagrange’s Method. The model is validated using infrared motion capture data produced by controlled experiments of a representative tether portion outfitted with IR targets. Further, a prototype is presented which will be used to investigate an E-Sail deployment approach and associated control. The design of this system will allow for deployment on specially designed flat floor facilities at MSFC. The prototype will be used to: 1) gather data for validation of system dynamic model, 2) evaluate alternative deployment strategies, 3) evaluate tether reel-out and damping control strategies.
Mahalanobis-Taguchi-Fukuda Approach to Human Motion Control
Authors: Fukuda Shuichi 
Published: November 2019

Abstract
Learning from failures approach how to control human motion is developed by extending Mahalanobis Taguchi System. It enables quantitative measurement of how the learner is improving in his or her learning, It helps to acquire tacit knowledge such as swimming, for which we do not have valid approach. Since Mahalanobis Distance is a unitless measure for multi-dimensional variables, this approach can be extended to many adaptive network formation and management, because this approach let the learner recognize how he or she can coordinate their body pars to adapt to the changing situations. Thus, the approach ca be applied to development and operation of soft robots and adaptive network or team formation and management in the IoT connected society.
Multi-Fidelity Physics-Constrained Neural Network and its Application in Materials Modeling
Authors: Liu Dehao , Wang Yan 
Published: November 2019

Abstract
Training machine learning tools such as neural networks requires the availability of sizable data, which can be difficult for engineering and scientific applications where experiments or simulations are expensive. In this work, a novel multi-fidelity physics-constrained neural network is proposed to reduce the required amount of training data, where physical knowledge is applied to constrain neural networks, and multi-fidelity networks are constructed to improve training efficiency. A low-cost low-fidelity physics-constrained neural network is used as the baseline model, whereas a limited amount of data from a high-fidelity simulation is used to train a second neural network to predict the difference between the two models. The proposed framework is demonstrated with two-dimensional heat transfer and phase transition problems, which are fundamental in materials modeling. Physics is described by partial differential equations. With the same set of training data, the prediction error of physics-constrained neural network can be one order of magnitude lower than that of a classical artificial neural network without physical constraints. The accuracy of the prediction is comparable to those from direct numerical solutions of equations.
The Importance of Energy Criteria for Selecting Modes in Reduced Order Modeling
Authors: Bhattacharyya Suparno , Cusumano Joseph P. 
Published: November 2019

Abstract
We study the performance of the proper orthogonal decomposition when used for model reduction of an Euler-Bernoulli beam subjected to periodic impulses. We assess the accuracy of reduced order models (ROMs) obtained using steady-state displacement time series. The spatiotemporal localization of the applied impulses is tuned to control the number of excited modes in, and hence the effective dimensionality of, the system’s response. We find that when the impacts are significantly localized (i.e., are more impulsive), the conventional variance-based mode selection criterion can lead to inaccurate ROMs. We show that this arises when the reduced subspace capturing a fixed amount (say, 99.9%) of the total data variance underestimates the energy input and/or dissipated in the ROM, leading to energy imbalance. We thus propose a new energy closure criterion that provides an improved method for generating ROMs. The energetics of the resulting ROMs properly reflect those of the full system, and yield simulations that accurately represent the system’s true behavior.
System Reliability Analysis Using Hybrid Gaussian Process Model
Authors: Li Meng , Sadoughi Mohammad Kazem , Hu Zhen , Hu Chao 
Published: November 2019

Abstract
This paper proposes a system reliability analysis method based on the hybrid of multivariate Gaussian process (MGP) and univariate Gaussian process (UGP) models, named as hybrid Gaussian process-based system reliability analysis (HGP-SRA). MGP and UGP models are selectively constructed for the components of a complex engineered system: MGP models are constructed over the groups of highly interdependent components and the individual UGP models are built over the components which are relatively independent of one another. A nonlinear-dependence measure, namely the randomized dependence coefficient, is adopted to adaptively learn and quantify the pairwise dependencies of the components with both linear and nonlinear dependency patterns. In the proposed HGP-SRA method, initial hybrid Gaussian process (HGP) models are first constructed with a set of near-random samples and these surrogate models are then updated with new samples that are sequentially identified based on the acquisition function named as multivariate probability of improvement (MPI). The results of two mathematical and a real-world engineering case studies suggest that the proposed method can achieve better accuracy and efficiency in system reliability estimation than the benchmark surrogate-based methods.
Topological Semantics for Lumped Parameter Systems Modeling
Authors: Wang Randi , Shapiro Vadim 
Published: November 2019

Abstract
Behaviors of many engineering systems are described by lumped parameter models that encapsulate the spatially distributed nature of the system into networks of lumped elements; the dynamics of such a network is governed by a system of ordinary differential and algebraic equations. Languages and simulation tools for modeling such systems differ in syntax, informal semantics, and in the methods by which such systems of equations are generated and simulated, leading to numerous interoperability challenges.
We propose to unify semantics of all such systems using standard notions from algebraic topology. In particular, Tonti diagrams classify all physical theories in terms of physical laws (topological and constitutive) defined over a pair of dual cochain complexes and may be used to describe different types of lumped parameter systems. We show that all possible methods for generating the corresponding state equations within each physical domain correspond to paths over Tonti diagrams. We further propose a generalization of Tonti diagram that captures the behavior and supports canonical generation of state equations for multi-domain lumped parameter systems. The unified semantics provides a basis for greater interoperability in systems modeling, supporting automated translation, integration, reuse, and numerical simulation of models created in different authoring systems and applications. Notably, the proposed algebraic topological semantics is also compatible with spatially and temporally distributed models that are at the core of modern CAD and CAE systems.
Optimal Reconfiguration Path Decision Method for One RMT System
Authors: Huang Sihan , Zeng Cong , Wang Guoxin , Yan Yan 
Published: November 2019

Abstract
Reconfigurable manufacturing system (RMS) is designed around a specific part family. Reconfigurable machine tools (RMT) is the core facility of RMS. One RMT system is an RMS consisting of only one RMT. So, the RMT of one RMT system is designed for a specific part family, which integrates the characteristics of RMS and RMT. The reconfiguration path problem of one RMT system is an urgent problem to be solved. Therefore, an optimal reconfiguration path decision method for one RMT system is proposed in this paper. The objective is to produce the entire part family at the minimum reconfiguration effort. Reconfiguration time is adopted as the characteristic value of reconfiguration effort in this paper. The Tabu Search algorithm (TS) is used to solve this problem due to its excellent search ability. Process rule is considered during computation to obtained reasonable solution. A case study is given to implement the proposed method. The result shows that the proposed method can produce an optimal reconfiguration path for one RMT system satisfying the process rule.
Leveraging Animations to Enhance Learning Through Virtual Product Dissection
Authors: Kearney Kevin G. , Starkey Elizabeth M. , Miller Scarlett R. 
Published: November 2019

Abstract
Product dissection, in either physical or virtual form, has been found to be an effective learning tool. With the rapid growth of technology, effective virtual education tools have the potential to continue increasing in value. Although physical product dissection is often implemented in the classroom, there are some limitations to employing these tools. One such limitation is the inability to see into a product and view the internals of a product while it is functioning. Recent research has found that students who dissect physical or virtual products have similar conceptual understanding of products after dissection. Although there are promising findings for virtual dissection, there is still room for improvement in these virtual environments to enable better understanding of products. One way this may be possible is through the addition of animations, which show the functions of the internal components in a product, a capability that is currently not offered in virtual product dissection tools. These animations have the ability to show how a product works in ways unobtainable through physical product dissection and currently unavailable in virtual product dissection. Therefore, this research aims to explore the impact that animation in virtual product dissection has on learning and cognitive load for engineering students.
Electron Backscatter Diffraction-Enabled Anisotropic Thermo-Elastic Analysis of Additively Manufactured Single Tracks
Authors: Birnbaum Andrew J. , Michopoulos John G. , Steuben John C. , Iliopoulos Athanasios P. 
Published: November 2019

Abstract
Despite extensive efforts directed toward elucidating the connections between process, microstructure and performance of additively manufactured structures and components, a significant number of meaningful questions remain unanswered. Specifically, a large body of work has demonstrated that microstructural/sub-structural features in selectively laser melted (SLM) components give rise to a significant enhancement in strength. Furthermore, the change in associated ductility is comparable to that seen in post-processed, wrought annealed material. However, the origin and mechanism by which these features arise have remained elusive. This work is an initial step in leveraging computational capabilities for validating experiment-based theories that explain the basis for the above-mentioned phenomena. The present work describes a computational approach for utilizing spatially resolved crystal-lographic descriptions obtained via electron backscatter diffraction (EBSD) to define the domain geometry and material properties of an anisotropic thermo-elastic simulation. The resulting solution is used to ascertain the elastic strain energy state, and slip-system resolved shear stresses on a per-grain basis. This analysis is performed, in part, as a means for validating a hypothesis linking these characteristics with the development of sub-structural features, which are in turn, correlated with improvements in material performance. The results suggest that both strain energy density and grain boundary character play an important role in the formation of substructure in additively manufactured 316L stainless steels.
Conceptual Design and Prototype Development of a Solar-Powered Ground Robot for Energy-Autonomous Operation
Authors: Behjat Amir , Collins Leighton , Hoffman Andrew , Chidambaran Sharat , Dhameliya Maulikkumar , Chowdhury Souma 
Published: November 2019

Abstract
This paper presents the conceptual design and fabrication/assembly of an autonomous solar powered small unmanned ground vehicle (UGV) platform for operation in outdoor environments. The contribution lies in the ability of the proposed design to offer uninterrupted operation in terms of endurance, to facilitate educational and research applications that are otherwise challenging to perform with a typical UGV (that needs significant downtime for recharging). A high incident area for solar PV panels is required to be able to support the complete energy needs of a ∼ 46 lb UGV (i.e., fully recharge the suitably sized battery powering the UGV). This makes it challenging to develop a stable platform that can carry solar panels much larger than the surface area of the platform itself (an aspect receiving minimal attention in other similar purpose platforms). To address this challenge, a novel umbrella-like folding mechanism is conceived, designed and successfully incorporated in the baseline prototype. This mechanism allows incorporating a remarkable ∼1 sq.m of incident solar PV with a net rated capacity of 200 W, one that remains folded to facilitate mobility, and can open/unfold to different extents for energy capture when needed. At the same time, the proposed design facilitates static and dynamic stability in spite of the significant solar PV incorporation. With the reference of the baseline prototype, an optimization approach is taken to develop a conceptual design of the next generation of this solar UGV. Specifically, the incident angle of the solar panels (enabled by the umbrella mechanism) at complete-open stage and the dimensions of the mechanism links and associated supports are separately optimized to respectively maximize the energy capture and the range of the UGV (assuming operation in Buffalo, NY), subject to stability and nominal velocity (of 2km/hr) constraints. The optimum design is found to provide an estimated range of 19.8 km/day.
Risk Perception and Product Safety in User-Centered Design
Authors: Cai Mingzhi , Seligsohn Erin N. , Zhang Zhinan , Wang Yan 
Published: November 2019

Abstract
When customers decide which product to buy, the perceived risks associated with the purchase are typically part of consideration. The customer’s purchase decision is directly related to the perceived risk about finance, safety, reputation, or others. It is important to incorporate the customer’s perception of risks in user-centered product design. Existing research of risk perception in product development focuses on the warning label design to ensure that consumers are aware of product safety and potential hazards. There is limited work on how to design the product itself with the consideration of risk factors. In this research, the effects of risk perception from consumers on design are studied. The results show that the perceived product safety by customers can be independent from objective measurements of risks. The perceptions are influenced by individual experiences, information obtained from government regulations and standards, as well as personal characteristics. Design variables related to the levels of comfort, aesthetics, and performance for automobile could affect the customers’ perception about vehicle safety.
Modeling, Control and Planning for Multiple Mobile Microrobots
Authors: Johnson Benjamin V. , Cappelleri David J. 
Published: November 2019

Abstract
We present the modeling, control and planning for multiple magnetic mobile microrobots actuated on a planar array of coils that generates local magnetic fields. The system is capable of actuating multiple microrobots independently. Such systems have a future in micromanufacturing and biomedical applications. The coils are modeled extensively to understand the forces generated by various coil combinations of the array, and solutions for different actuation force directions are discovered. The path planning problem is formulated as a Markov decision process that solves a policy to reach a goal from any location in the workspace. The presence of multiple robots in the workspace can interfere with their motion. Hence, the coil models are used concurrently with models of interaction force between multiple magnetic robots to plan efficient paths to reach a goal in the workspace in the presence of other robots.
Manipulating Trust of Autonomous Products With Affective Priming
Authors: Liao Ting , MacDonald Erin 
Published: November 2019

Abstract
User-to-product trust has two notable aspects: (1) the user’s propensity to trust, and (2) the product’s trustworthiness as assessed by the user. Autonomous products, which perform many functions on their own with limited user input, require the user to exhibit trust at an appropriate level before use. Research in product trust thus far has focused on the product trustworthiness: manipulating the product’s design, for example, anthropomorphizing an autonomous vehicle and measuring changes in trust. This study flips the usual approach, manipulating a person’s propensity to trust and measuring response to an existing autonomous product, the Amazon Echo. We build on our past successes with priming exercises to reveal insights into the user-related factors of product trust. In this study, we used visual stimuli that evoked either positive, neutral or negative emotions as affective primes to influence users’ trust propensity before the interaction. The participants interacted with a mock-up of the Amazon Echo via ten pre-determined question-and-answer (Q&A) sets. During the interaction, the participants evaluated the Echo’s competence and if it met participants’ expectations. They also reported trust towards the Echo after the Q&A sets. Holistically, the affective primes show no significant effect on the trust propensity. For the subgroup of participants whose expectations of the product’s performance were met, both the perceived product competence and the affective primes have significant effects on trust propensity. These results demonstrate the complex nature of trust as a multidimensional construct and the critical role of product performance in trust formation. They also suggest that it will be difficult for a product to build trust with users who expect the product to perform in a different way than its intent — if one wants to design a product that builds trust, they should understand user expectations and design to meet them. This learning can facilitate the intentional design of the affective process in trust formation that helps build a healthy level of trust with autonomous products.