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Surface deformation-dependent mechanical properties of bending nanowires:an ab initio core-shell model

Applied Mathematics and Mechanics(English Edition) 2022年第2期43卷 219-232页

作者： Ye XIAO J.SHANG L.Z.KOU Chun LI School of Mechanics Civil Engineering and ArchitectureNorthwestern Polytechnical UniversityXi’an710072China MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical UniversityXi’an710072China School of Mechanical Medical and Process EngineeringQueensland University of TechnologyBrisbaneQLD4000Australia

An ab initio core-shell model is proposed to evaluate the surface effect in bending nanowires,in which the elastic modulus depends on the surface relaxation and deformation induced by external *** using first-principles calculations based on the density functional theory(DFT),the surface and bulk properties are calculated for Ag,Pb,and Si *** obtained theoretical predictions of the effective Young’s modulus of nanowires agree well with the experimental data,which shows that the fixed-fixed nanowire is stiffened and the cantilevered nanowire is softened as the characteristic size of the cross section ***,the contrastive analysis on the two kinds of nanowires demonstrates that increasing the nanowire aspect ratio would enhance the surface *** present results could be helpful for understanding the size effect in nanowires and designing nanobeam-based devices in nanoelectromechanical systems(NEMSs).

关键词： surface effect density functional theory(DFT) surface relaxation surface stress nanowire

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Microbial electrosynthesis of acetate from CO_(2)under hypersaline conditions

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Environmental Science and Ecotechnology 2023年第1期13卷 86-95页

作者： Xiaoting Zhang Tyler Arbour Daijun Zhang Shiqiang Wei Korneel Rabaey College of Resources and Environment Southwest UniversityChongqing400715China Center for Microbial Ecology and Technology(CMET) Ghent UniversityCoupure Links 6539000GhentBelgium State Key Laboratory of Coal Mine Disaster Dynamics and Control Chongqing UniversityChongqing400044China Center for Advanced Process Technology for Urban Resource Recovery(CAPTURE) Frieda Saeysstraat 19052GhentBelgium

Microbial electrosynthesis(MES)enables the bioproduction of multicarbon compounds from CO_(2)using electricity as the *** high salinity can improve the energetic performance of bioelectrochemical systems,acetogenic processes under elevated salinity are poorly *** MES under 35e60 g L^(-1)salinity was *** production in two-chamber MES systems at 35 g L^(-1)salinity(seawater composition)gradually decreased within 60 days,both under-1.2 V cathode potential(***/AgCl)and^(-1).56 A m^(-2)reductive *** precipitation on cathodes(mostly CaCO3)likely declined the production through inhibiting CO_(2)supply,the direct electrode contact for acetogens and H2 *** decreasing Ca2t and Mg2t levels in three-chamber reactors,acetate was stably produced over 137 days along with a low cathode apparent resistance at 1.9±0.6 mU m^(2)and an average production rate at 3.80±0.21 g m^(-2)d^(-1).Increasing the salinity step-wise from 35 to 60 g L^(-1)gave the most efficient acetate production at 40 g L^(-1)salinity with average rates of acetate production and CO_(2)consumption at 4.56±3.09 and 7.02±4.75 g m^(-2)d^(-1),*** instantaneous coulombic efficiency for VFA averaged 55.1±31.4%.Acetate production dropped at higher salinity likely due to the inhibited CO_(2)dissolution and acetogenic *** up to 78%was enriched on cathodes as the main acetogen at 35 g L^(-1).Under high-salinity selection,96.5%Acetobacterium dominated on the cathode along with 34.0%Sphaerochaeta in *** research provides a first proof of concept that MES starting from CO_(2)reduction can be achieved at elevated salinity.

关键词： Carbon capture and utilization High salinity Carbonate precipitates Acetogenesis Marine bacteria

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A new propulsion system GUI based control amenable model development for high-power rockets

Advanced Control for Applications: Engineering and Industria...

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Advanced control for Applications: Engineering and Industrial Systems 2024年第3期6卷

作者： Prajapati, Soham Thakar, Parth S. Markana, Anilkumar Process Dynamics and Control Laboratory Pandit Deendayal Energy University Gandhinagar India Department of Electronics and Communication Engineering Pandit Deendayal Energy University Gandhinagar India Department of Electrical Engineering Pandit Deendayal Energy University Gandhinagar India

This paper proposes a new algorithm to model and characterize an autonomous high-power rocket using an indigenously developed graphical user interface (GUI) platform. This platform features a newly devised app, termed as THIEC Rocketry App which embeds the simulation based analysis to determine the design parameters of the rocket, required for a vertical flight. A solid propellant using potassium nitrate and sucrose, also known as rocket-candy, is considered for the GUI development. The GUI facilitates the designer to specify the desired flight parameters for the rocket propulsion system. Various characteristic plots for visualization and analysis are made available in GUI. The obtained parameters from the GUI are then utilized in computer-aided designing (CAD) for further identification of geometrical parameters like inertia tensor, center of gravity (CG) and center of pressure (CP). The mathematical control amenable model of the rocket is then developed using first principles so as to achieve an altitude up to 3 km. The overall system represents a complex nonlinear multi-input multi-output (MIMO) dynamics, having six degrees of freedom. The Newton-Euler formulation is employed to develop the equations of motion. The attitude control using canards is analyzed via simulations for the complete flight path - the boost and coast flights. Finally, the developed GUI based model is validated by practically manufacturing the components of the propulsion system for the small-scale high-power rocket. The proposed model will create the pathway for the development of some robust model-based control schemes for such autonomous rockets in future. © 2024 John Wiley & Sons Ltd.

关键词： Rockets

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Synthesis of Invariant Systems under Uncoordinated Disturbances Based on the Theory of Sliding Modes 14

Synthesis of Invariant Systems under Uncoordinated Disturban...

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14th International Conference Management of Large-Scale System Development, MLSD 2021

作者： Gulyukina, Svetlana Utkin, Victor Laboratory of Information Process Analysis and Modeling V.A. Trapeznikov Institute of Control Sciences of RAS Moscow Russia Laboratory of Dynamics of Nonlinear Control Processes by E. S. Pyatnitsky V.A. Trapeznikov Institute of Control Sciences of RAS Moscow Russia

ISBN: (纸本)9781665412308

This paper proposes a new philosophy for the synthesis of sliding modes using the method of expanding the state space and the block approach in control. An important feature of the developed algorithms for the synthesis of invariant systems, in contrast to systems with deep feedbacks, is the modulus constraint of controls and state vector coordinates. © 2021 IEEE.

关键词： Invariance

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Synthesis of Invariant Systems of a Given Accuracy Based on the Sliding Modes Theory

Synthesis of Invariant Systems of a Given Accuracy Based on ...

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2021 International Russian Automation Conference, RusAutoCon 2021

作者： Utkin, Victor Gulyukina, Svetlana Pyatnitsky V.A. Trapeznikov Institute of Control Sciences of Ras Laboratory of Dynamics of Nonlinear Control Processes by E. S. Moscow Russia V.A. Trapeznikov Institute of Control Sciences of Ras Laboratory of Information Process Analysis and Modeling Moscow Russia

ISBN: (纸本)9780738131566

In this paper, we propose original methods for the synthesis of invariant systems considering the constraints on the components of the state vector and control. A two-step procedure for the synthesis of the problem of ensuring invariance with a given accuracy is developed by expanding the space of states. We used discontinuous functions at the input of integrators, which generate sliding motions in each of the subsystems over a finite time span to solve the stabilization problem. The synchronous generator is considered as an example of application;the numerical simulation results obtained in Matlab showed the high efficiency of the proposed approach. © 2021 IEEE.

关键词： Invariance

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Physics-informed neural networks for parameter learning of wildfire spreading

arXiv

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arXiv 2024年

作者： Vogiatzoglou, Konstantinos Papadimitriou, C. Bontozoglou, V. Ampountolas, K. System Dynamics Laboratory Department of Mechanical Engineering University of Thessaly Volos38334 Greece Transport Processes & Process Equipment Laboratory Department of Mechanical Engineering University of Thessaly Volos38334 Greece Automatic Control & Autonomous Systems Laboratory Department of Mechanical Engineering University of Thessaly Volos38334 Greece

Wildland fires pose a terrifying natural hazard, underscoring the urgent need to develop datadriven and physics-informed digital twins for wildfire prevention, monitoring, intervention, and response. In this direction of research, this work introduces a physics-informed neural network (PiNN) designed to learn the unknown parameters of an interpretable wildfire spreading model. The considered modeling approach integrates fundamental physical laws articulated by key model parameters essential for capturing the complex behavior of wildfires. The proposed machine learning framework leverages the theory of artificial neural networks with the physical constraints governing wildfire dynamics, including the first principles of mass and energy conservation. Training of the PiNN for physics-informed parameter identification is realized using synthetic data on the spatiotemporal evolution of one- and two-dimensional firefronts, derived from a high-fidelity simulator, as well as empirical data (ground surface thermal images) from the Troy Fire that occurred on June 19, 2002, in California. The parameter learning results demonstrate the predictive ability of the proposed PiNN in uncovering the unknown coefficients of the wildfire model in one- and two-dimensional fire spreading scenarios as well as the Troy Fire. Additionally, this methodology exhibits robustness by identifying the same parameters even in the presence of noisy data. By integrating this PiNN approach into a comprehensive framework, the envisioned physics-informed digital twin will enhance intelligent wildfire management and risk assessment, providing a powerful tool for proactive and reactive strategies. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

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Constrained Gaussian process Learning for Model Predictive control ⁎

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IFAC-PapersOnLine 2020年第2期53卷 971-976页

作者： Janine Matschek Andreas Himmel Kai Sundmacher Rolf Findeisen Laboratory for Systems Theory and Automatic Control Otto von Guericke University Magdeburg Germany Process Systems engineering Otto von Guericke University Magdeburg Germany Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany

Many control tasks can be formulated as tracking problems of a known or unknown reference signal. examples are motion compensation in collaborative robotics, the synchronisation of oscillations for power systems or the reference tracking of recipes in chemical process operation. Both the tracking performance and the stability of the closed-loop system depend strongly on two factors: Firstly, they depend on whether the future reference signal required for tracking is known, and secondly, whether the system can track the reference at all. This paper shows how to use machine learning, i.e. Gaussian processes, to learn a reference from (noisy) data while guaranteeing trackability of the modified desired reference predictions within the framework of model predictive control. Guarantees are provided by adjusting the hyperparameters via a constrained optimisation. Two specific scenarios, i.e. asymptotically constant and periodic REFERENCES, are discussed.

关键词： Gaussian processes trajectory tracking learning supported model predictive control

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Graphical Minimax Game and On-Policy Reinforcement Learning for Consensus of Leaderless Multi-Agent Systems *

Graphical Minimax Game and On-Policy Reinforcement Learning ...

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International Conference on control and Automation (ICCA)

作者： Wei Dong Chunyan Wang Jinna Li Jianan Wang Key Laboratory of Dynamics and Control of Flight Vehicle within Ministry of Education School of Aerospace Engineering Beijing Institute of Technology Beijing China School of Information and Control Engineering Liaoning Shihua University Fushun China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

In this paper, we study the adaptive optimal consensus control of leaderless multi-agent systems (MASs) with heterogeneous dynamics. First, the consensus control problem is converted into a graphical minimax game problem and the corresponding algebraic Riccati equation (ARE) for each agent is obtained. Then, an on-policy reinforcement learning algorithm is proposed to online learn the optimal control policy without requiring the system dynamics. A certain rank condition is established to guarantee the convergence of the proposed online learning algorithm to the unique solution of the ARE. Finally, the effectiveness of the proposed algorithm is demonstrated through a numerical simulation.

关键词： Games Heuristic algorithms Reinforcement learning Mathematical model Vehicle dynamics Performance analysis Optimal control

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Spontaneous imbibition in porous media: From pore scale to Darcy scale

arXiv

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arXiv 2021年

作者： Qin, Chao-Zhong Wang, Xin Hefny, Mahmoud Zhao, Jianlin Guo, Bo State Key Laboratory of Coal Mine Disaster Dynamics and Control Chongqing University Chongqing China School of Resources and Safety Engineering Chongqing University Chongqing China Geothermal Energy and Geofluids Institute of Geophysics ETH Zürich Switzerland Geology Department South Valley University Egypt Department of Mechanical and Process Engineering ETH Zürich Switzerland Department of Hydrology and Atmospheric Sciences University of Arizona TucsonAZ United States

Spontaneous imbibition has been receiving much attention due to its significance in many subsurface and industrial applications. Unveiling pore-scale wetting dynamics, and particularly its upscaling to the Darcy scale are still unresolved. In this work, we conduct image-based pore-network modeling of cocurrent spontaneous imbibition and the corresponding quasi-static imbibition, in homogeneous sintered glass beads as well as heterogeneous Estaillades. A wide range of viscosity ratios and wettability conditions are taken into account. Based on our pore-scale results, we show the influence of pore-scale heterogeneity on imbibition dynamics and nonwetting entrapment. We elucidate different pore-filling mechanisms in imbibition, which helps us understand wetting dynamics. Most importantly, we develop a non-equilibrium model for relative permeability of the wetting phase, which adequately incorporates wetting dynamics. This is crucial to the final goal of developing a two-phase imbibition model with measurable material properties such as capillary pressure and relative permeability. Finally, we propose some future work on both numerical and experimental verifications of the developed nonequilibrium permeability model. © 2021, CC BY.

关键词： Porous materials

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Constrained Gaussian process learning for model predictive control

arXiv

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arXiv 2019年

作者： Matschek, Janine Himmel, Andreas Sundmacher, Kai Findeisen, Rolf Laboratory for Systems Theory and Automatic Control Otto-von-Guericke University Magdeburg Germany Process Systems Engineering Otto-von-Guericke University Magdeburg Germany Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany

Many control tasks can be formulated as a tracking problem of a known or unknown reference signal. Examples are movement compensation in collaborative robotics, the synchronisation of oscillations for power systems or reference tracking of recipes in chemical process operation. Tracking performance as well as guaranteeing stability of the closed loop strongly depends on two factors: Firstly, it depends on whether the future desired tracking reference signal is known and, secondly, whether the system can track the reference at all. This paper shows how to use machine learning, i.e. Gaussian processes, to learn a reference from (noisy) data, while guaranteeing trackability of the modified desired reference predictions in the framework of model predictive control. Guarantees are provided by adjusting the hyperparameters via a constrained optimization. Two specific scenarios, i.e. asymptotically constant and periodical references, are discussed. Copyright © 2019, The Authors. All rights reserved.

关键词： Gaussian distribution

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