Zn−MnO 2 batteries have attracted extensive attention for grid-scale energy storage applications, however, the energy storage chemistry of MnO 2 in mild acidic aqueous electrolytes remains elusive and controversial. U...
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Zn−MnO 2 batteries have attracted extensive attention for grid-scale energy storage applications, however, the energy storage chemistry of MnO 2 in mild acidic aqueous electrolytes remains elusive and controversial. Using α-MnO 2 as a case study, we developed a methodology by coupling conventional coin batteries with customized beaker batteries to pinpoint the operating mechanism of Zn−MnO 2 batteries. This approach visually simulates the operating state of batteries in different scenarios and allows for a comprehensive study of the operating mechanism of aqueous Zn−MnO 2 batteries under mild acidic conditions. It is validated that the electrochemical performance can be modulated by controlling the addition of Mn 2+ to the electrolyte. The method is utilized to systematically eliminate the possibility of Zn 2+ and/or H + intercalation/de-intercalation reactions, thereby confirming the dominance of the MnO 2 /Mn 2+ dissolution-deposition mechanism. By combining a series of phase and spectroscopic characterizations, the compositional, morphological and structural evolution of electrodes and electrolytes during battery cycling is probed, elucidating the intrinsic battery chemistry of MnO 2 in mild acid electrolytes. Such a methodology developed can be extended to other energy storage systems, providing a universal approach to accurately identify the reaction mechanism of aqueous aluminum-ion batteries as well.
The immersed finite element-finite difference (IFED) method is a computational approach to modeling interactions between a fluid and an immersed structure. The IFED method uses a finite element (FE) method to approxim...
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The immersed finite element-finite difference (IFED) method is a computational approach to modeling interactions between a fluid and an immersed structure. The IFED method uses a finite element (FE) method to approximate the stresses and forces on a structural mesh and a finite difference (FD) method to approximate the momentum of the entire fluid-structure system on a Cartesian grid. The fundamental approach used by this method follows the immersed boundary framework for modeling fluid-structure interaction (FSI), in which a force spreading operator prolongs structural forces to a Cartesian grid, and a velocity interpolation operator restricts a velocity field defined on that grid back onto the structural mesh. With an FE structural mechanics framework, force spreading first requires that the force itself be projected onto the finite element space. Similarly, velocity interpolation requires projecting velocity data onto the FE basis functions. Consequently, evaluating either coupling operator requires solving a matrix equation at every time step. Mass lumping, in which the projection matrices are replaced by diagonal approximations, has the potential to accelerate this method considerably. This paper provides both numerical and computational analyses of the effects of this replacement for evaluating the force projection and for the IFED coupling operators. Constructing the coupling operators also requires determining the locations on the structure mesh where the forces and velocities are sampled. Here we show that sampling the forces and velocities at the nodes of the structural mesh is equivalent to using lumped mass matrices in the IFED coupling operators. A key theoretical result of our analysis is that if both of these approaches are used together, the IFED method permits the use of lumped mass matrices derived from nodal quadrature rules for any standard interpolatory element. This is different from standard FE methods, which require specialized treatments to
Competition in various companies encourages practitioners, entrepreneurs and academics to examine the dynamic of business strategy. The spirited study of this business strategy was driven by its forceful environment. ...
Competition in various companies encourages practitioners, entrepreneurs and academics to examine the dynamic of business strategy. The spirited study of this business strategy was driven by its forceful environment. Dynamic moving environments require each company to be adaptive as quickly as possible. This adaptation encourages the company to have a certain advantage in facing competition. The winners of the market make the company can demonstrate a fast, flexible response, effective coordination and understanding of internal and external competencies. Similarly, in the manufacturing industry, the dynamic lifestyle of consumers encourages the company to adapt to creating its own excellence. The purpose of this article propose a research framework of the relationship between strategic agility and resource base view to the firm performance of manufacturing industry.
Deep learning has enjoyed much recent success, and applying state-of-the-art model learning methods to controls is an exciting prospect. However, there is a strong reluctance to use these methods on safety-critical sy...
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ISBN:
(数字)9781728173955
ISBN:
(纸本)9781728173962
Deep learning has enjoyed much recent success, and applying state-of-the-art model learning methods to controls is an exciting prospect. However, there is a strong reluctance to use these methods on safety-critical systems, which have constraints on safety, stability, and real-time performance. We propose a framework which satisfies these constraints while allowing the use of deep neural networks for learning model uncertainties. Central to our method is the use of Bayesian model learning, which provides an avenue for maintaining appropriate degrees of caution in the face of the unknown. In the proposed approach, we develop an adaptive control framework leveraging the theory of stochastic CLFs (Control Lyapunov Functions) and stochastic CBFs (Control Barrier Functions) along with tractable Bayesian model learning via Gaussian Processes or Bayesian neural networks. Under reasonable assumptions, we guarantee stability and safety while adapting to unknown dynamics with probability 1. We demonstrate this architecture for high-speed terrestrial mobility targeting potential applications in safety-critical high-speed Mars rover missions.
Thanks to the recent discovery of the magic-angle bilayer graphene, twistronics is quickly becoming a burgeoning field in condensed matter physics. This Rapid Communication expands the realm of twistronics to acoustic...
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Thanks to the recent discovery of the magic-angle bilayer graphene, twistronics is quickly becoming a burgeoning field in condensed matter physics. This Rapid Communication expands the realm of twistronics to acoustics by introducing twisted bilayer phononic graphene, which remarkably also harbors the magic angle, evidenced by the associated ultraflat bands. Beyond mimicking quantum-mechanical behaviors of twisted bilayer graphene, we show that their acoustic counterpart offers a considerably more straightforward and robust way to alter the interlayer hopping strength, enabling us to unlock magic angles (>3∘) inaccessible in classical twisted bilayer graphene. This study not only establishes the acoustical analog of twisted (magic-angle) bilayer graphene, providing a test bed more easily accessible to probe the interaction and misalignment between stacked two-dimensional materials, but also points out the direction to a new phononic crystal design paradigm that could benefit applications such as enhanced acoustic emission and sensing.
作者:
Dankowicz, HarryKumar, VijayKrovi, VenkatASME Applied Mechanics Reviews
Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Transactions of the ASME
Journal of Mechanisms and Robotics Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania PhiladelphiaPA19104 United States Transactions of the ASME
Journal of Mechanisms and Robotics Department of Automotive Engineering Clemson University GreenvilleSC29607 United States
Applied Mechanics Reviews (AMR) is an international review journal that serves as a premier venue for state-of-the-art and retrospective survey articles and reviews of research areas and curricular developments across...
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Applied Mechanics Reviews (AMR) is an international review journal that serves as a premier venue for state-of-the-art and retrospective survey articles and reviews of research areas and curricular developments across all subdisciplines of applied mechanics and engineering science, including fluid and solid mechanics, heat transfer, dynamics and vibration, and applications. AMR also provides a forum for dissemination of original tutorial articles. These organize novel insight and reflection on the use of one or several methods of modeling, design, analysis, or synthesis in an accessible format that can be used for guided instruction or self-study.
The Midwest Mechanics Seminar (MMS) Series brings distinguished scholars from across the broad subdisciplines of mechanics to ten institutions within the Midwest of the United States during two, one-week tours. Speakers give a research seminar at each host institution (Tour A: University of Michigan, Michigan State University, University of Notre Dame,...
We propose a visual servoing framework for learning to improve grasps of objects. RGB and depth images from grasp attempts are collected using an automated data collection process. The data is then used to train a Gra...
We propose a visual servoing framework for learning to improve grasps of objects. RGB and depth images from grasp attempts are collected using an automated data collection process. The data is then used to train a Grasp Quality Network (GQN) that predicts the outcome of grasps from visual information. A grasp optimization pipeline uses homography models with the trained network to optimize the grasp success rate. We evaluate and compare several algorithms for adjusting the current gripper pose based on the current observation from a gripper-mounted camera to perform visual servoing. Evaluations in both simulated and hardware environments show considerable improvement in grasp robustness with models trained using less than 30K grasp trials. Success rates for grasping novel objects unseen during training increased from 18.5% to 81.0% in simulation, and from 17.8% to 78.0% in the real world.
Pushrim-activated power-assisted wheels (PAPAWs) are assistive technologies that provide on-demand assistance to wheelchair users. PAPAWs operate based on a collaborative control scheme. Therefore, they rely on accura...
ISBN:
(数字)9781728160757
ISBN:
(纸本)9781728160764
Pushrim-activated power-assisted wheels (PAPAWs) are assistive technologies that provide on-demand assistance to wheelchair users. PAPAWs operate based on a collaborative control scheme. Therefore, they rely on accurate interpretation of the user's intent to provide effective propulsion assistance. This paper presents a learning-based approach to predict wheelchair users' intention when performing a variety of wheelchair activities. We obtained kinematic and kinetic data from manual wheelchair users when performing standard wheelchair activities such as turns and ascents. Our measurements revealed variability in physical capabilities and propulsion habits of different users, therefore, highlighting the need for the development of personalized intention inference models. We used Gaussian Mixture models to label different phases of user-pushrim interactions based on individual user's wheeling behaviour. Supervised classifiers were trained with each user's data and these models were used to predict the user's intentions during different propulsion activities. We found random forest classifiers had high accuracy (>92%) in predicting different states of individual-specific wheelchair propulsion and user intent for 2 participants. This proposed framework is computationally efficient and can be used for real-time prediction of wheelchair users' intention. The outcome of this clustering-classification pipeline provides relevant information for designing user-specific and adaptive PAPAW controllers.
Human-robot collaboration (HRC) is becoming increasingly important as the paradigm of manufacturing is shifting from mass production to mass customization. The introduction of HRC can significantly improve the flexibi...
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