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Ionic polymer-metal composite torsional sensor: physics-based modeling and experimental validation

SMART MATERIALS AND STRUCTURES 2018年第7期27卷 075039-075039页

作者： Sharif, Montassar Aidi Lei, Hong Al-Rubaiai, Mohammed Khalid Tan, Xiaobo Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA Northern Technol Univ Tech Coll Kirkuk Dept Comp Engn Mosul Iraq SEL Pullman WA USA

Ionic polymer-metal composites (IPMCs) have intrinsic sensing and actuation properties. Typical IPMC sensors are in the shape of beams and only respond to stimuli acting along beam-bending directions. Rod or tube-shaped IPMCs have been explored as omnidirectional bending actuators or sensors. In this paper, physics-based modeling is studied for a tubular IPMC sensor under pure torsional stimulus. The Poisson-Nernst-Planck model is used to describe the fundamental physics within the IPMC, where it is hypothesized that the anion concentration is coupled to the sum of shear strains induced by the torsional stimulus. Finite element simulation is conducted to solve for the torsional sensing response, where some of the key parameters are identified based on experimental measurements using an artificial neural network. Additional experimental results suggest that the proposed model is able to capture the torsional sensing dynamics for different amplitudes and rates of the torsional stimulus.

关键词： ionic polymer-metal composite IPMC sensor modeling physics-based modeling tubular IPMC torsional sensor

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Neural modal ordinary differential equations: Integrating physics-based modeling with neural ordinary differential equations for modeling high-dimensional monitored structures

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DATA-CENTRIC ENGINEERING 2022年第1期3卷 e34-e34页

作者： Lai, Zhilu Liu, Wei Jian, Xudong Bacsa, Kiran Sun, Limin Chatzi, Eleni Internet Things Thrust Informat Hub HKUST GZ Guangzhou Peoples R China HKUST Dept Civil & Environm Engn Hong Kong Peoples R China Singapore ETH Ctr Future Resilient Syst Zurich Singapore Natl Univ Singapore Dept Ind Syst Engn & Management Queenstown Singapore Tongji Univ State Key Lab Disaster Reduct Civil Engn Shanghai Peoples R China Swiss Fed Inst Technol Dept Civil Environm & Geomatic Engn Zurich Switzerland Shanghai Qizhi Inst Shanghai Peoples R China

The dimension of models derived on the basis of data is commonly restricted by the number of observations, or in the context of monitored systems, sensing nodes. This is particularly true for structural systems, which are typically high -dimensional in nature. In the scope of physics-informed machine learning, this article proposes a framework-termed neural modal ordinary differential equations (Neural Modal ODEs)-to integrate physics-based modeling with deep learning for modeling the dynamics of monitored and high-dimensional engineered systems. In this initiating exploration, we restrict ourselves to linear or mildly nonlinear systems. We propose an architecture that couples a dynamic version of variational autoencoders with physics-informed neural ODEs (Pi-Neural ODEs). An encoder, as a part of the autoencoder, learns the mappings from the first few items of observational data to the initial values of the latent variables, which drive the learning of embedded dynamics via Pi-Neural ODEs, imposing a modal model structure on that latent space. The decoder of the proposed model adopts the eigenmodes derived from an eigenanalysis applied to the linearized portion of a physics-based model: a process implicitly carrying the spatial relationship between degrees-of-freedom (DOFs). The framework is validated on a numerical example, and an experimental dataset of a scaled cable-stayed bridge, where the learned hybrid model is shown to outperform a purely physics-based approach to modeling. We further show the functionality of the proposed scheme within the context of virtual sensing, that is, the recovery of generalized response quantities in unmeasured DOFs from spatially sparse data.

关键词： Deep learning dynamical systems neural ordinary differential equations physics-based modeling physics-informed machine learning

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physics-based Reliability modeling for Control Applications: Adaptative Control Allocation

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IEEE ACCESS 2024年 12卷 161054-161074页

作者： Liscouet, Jonathan Desrosiers, Joshua Heit, Zac Uwantare, Ishimwe Remoundos, Andrew Senouci, Anas Concordia Univ Mech Ind & Aerosp Engn Montreal PQ H3G 1M8 Canada

Unmanned Aerial Vehicles (UAVs) are increasingly utilized across various industries, necessitating high reliability to ensure safety and reduce operational costs. This paper introduces a systematic methodology for physics-based reliability modeling tailored for UAV control applications, focusing on adaptive control allocation to optimize reliability. The study addresses the limitations of conventional degradation-independent behavior factor-based models, which often rely on inaccurate degradation models due to the lack of parameterization data sources and methods. By replacing time with stress-derived variables in the reliability function, this approach enables the combination of any time-dependent reliability functions with any stress-life relationships, allowing for real-time physics-based reliability assessments. The approach is demonstrated through the development and application of models for electronic speed controllers within a hexarotor UAV, using a virtual prototype for flight simulation. Simulation results reveal that physics-based models improve reliability prediction accuracy compared to conventional proportional hazard models, particularly due to their reliance on published and manufacturer's data for parameterization. The paper concludes by highlighting the need for future research to simplify the integration of stress factor online measurements, addressing the complexity and data requirements inherent in physics-based models.

关键词： Reliability Adaptation models Degradation Autonomous aerial vehicles Resource management Load modeling Mathematical models Predictive models Systematics Data models Adaptative control degradation model electronic speed controller (ESC) multirotor systems physics-based modeling reliability unmanned aerial vehicles (UAVs)

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Advances of physics-based precision modeling and simulation for manufacturing processes

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Advances in Manufacturing 2013年第1期1卷 75-81页

作者： Gang Wang Yi-Ming Rong Department of Precision Instruments and Mechanology Tsinghua University Department of Mechanical Engineering Worcester Polytechnic Institute

The development of manufacturing process concerns precision, comprehensiveness, agileness, high efficiency and low cost. The numerical simulation has become an important method for process design and optimization. physics-based modeling was proposed to promote simulations with a high accuracy. In this paper, three cases, on material properties, precise boundary conditions, and micro-scale physical models, have been discussed to demonstrate how physics-based modeling can improve manufacturing simulation. By using this method, manu- facturing process can be modeled precisely and optimized for getting better performance.

关键词： Material properties Multiscale analysis physics-based modeling Manufacturing process

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Linear unmixing using endmember subspaces and physics based modeling

Linear unmixing using endmember subspaces and physics based ...

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Conference on Imaging Spectrometry XII

作者： Gillis, David Bowles, Jeffrey Ientilucci, Emmett J. Messinger, David W. USN Res Lab Washington DC 20375 USA Rochester Inst Technol Rochester NY 14623 USA

ISBN: (纸本)9780819468093

One of the biggest issues with the Linear Mixing Model (LMM) is that it is implicitly assumed that each of the individual material components throughout the scene may be described using a single dimension (e.g. an endmember vector). In reality, individual pixels corresponding to the same general material class can exhibit a large degree of variation within a given scene. This is especially true in broad background classes such as forests, where the single dimension assumption clearly fails. In practice, the only way to account for the multidimensionality of the class is to choose multiple (very similar) endmembers, each of which represents some part of the class. To address these issues, we introduce the endmember subgroup model, which generalizes the notion of an 'endmember vector' to an 'endmember subspace'. In this model, spectra in a given hyperspectral scene are decomposed as a sum of constituent materials;however, each material is represented by some multidimensional subspace (instead of a single vector). The dimensionality of the subspace will depend on the within-class variation seen in the image. The endmember subgroups can be determined automatically from the data, or can use physics-based modeling techniques to include 'signature subspaces', which are included in the endmember subgroups. In this paper, we give an overview of the subgroup model;discuss methods for determining the endmember subgroups for a given image, and present results showing how the subgroup model improves upon traditional single endmember linear mixing. We also include results that use the 'signature subspace' approach to identifying mixed-pixel targets in HYDICE imagery.

关键词： hyperspectral linear mixing endmembers physics-based modeling

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High-Frequency physics-based Analytical modeling of Permanent Magnet Synchronous Motor 34

High-Frequency Physics-Based Analytical Modeling of Permanen...

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34th International Power System Conference (PSC)

作者： Rahimi, Alireza Kanzi, Khalil ACECR IREE Tehran Iran ACECR KN Toosi Branch Tehran Iran

ISBN: (纸本)9781728152738

In this paper, a physics-based analytical method is proposed in order to model the frequency behavior of laminated iron-core AC motors. The proposed model consists of frequency-dependent lumped circuit parameters representing two parts;Iron core and stator winding. These frequency-dependent components represent the skin effect and proximity effect in conductors and eddy-currents effect in the core. The total parasitic capacitance is considered to be frequency independent and estimated from impedance characteristics. The proposed method can be used in modeling various high-frequency issues such as electromagnetic interference (EMI), common-mode bearing currents, and long cable effects on motor terminals. Equivalent AC resistance and AC inductance of a Permanent Magnet Synchronous Motor (PMSM) calculated with the proposed method is compared with the Finite Element Method (FEM) results and shows a good agreement. Finally, the Impedance characteristic of the PMSM motor calculated using the proposed method is verified by the measurement data.

关键词： High-frequency PMSM modeling laminated iron magnetic cores physics-based modeling Analytical modeling eddy-currents effect

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The physics-based Manifold modeling Method for Free-form Surface Design

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Journal of Chongqing University 2002年第1期1卷 70-73页

作者：成思源 CollegeofResourcesandEnvironmentalScience ChongqingUniversityChongqing400044P.R.China

A new framework for free-form surface design is proposed. Using manifolds can generalize the spline scheme to surfaces of arbitrary topology. physics-based modeling incorporate physical laws into shape representation to provide direct shape interaction. The combination presents a new method inherits the attractive properties of the manifold surface as well as that of the physics-based models.

关键词： physics-based modeling Spline Manifold Geometric modeling

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physics-based probabilistic analysis of corrosion initiation in alkali-activated slag concrete assisted by machine learning

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CONSTRUCTION AND BUILDING MATERIALS 2025年 471卷

作者： Dong, Bin Zhao, Shaoyu Zhang, Yingyan Zhang, Yihe Yu, Yuguo Yang, Jie RMIT Univ Sch Engn POB 71 Bundoora VIC 3083 Australia RMIT Univ Sch Engn Civil & Infrastruct Engn Melbourne Vic 3000 Australia

With the pressing demand for sustainability in infrastructure construction, alkali-activated slag (AAS) concrete has emerged as a viable green alternative to carbon-intensive cementitious materials. However, the long-term durability prediction of this material, particularly regarding chloride-induced corrosion, remains underexplored, which limits its broader practical applications. To address this gap, this study presents an innovative machine learning (ML)-assisted framework tailored for probabilistic analysis of corrosion initiation in AAS concrete. The framework incorporates an advanced physics-based approach that integrates multi-ionic transport analysis with hydrate-based modeling of chloride binding, thereby delivering a precise chloride ingress evaluation. To overcome computational challenges, a novel ML algorithm, extended support vector regression (XSVR), is applied to develop metamodels that serve as surrogates for the computationally demanding physical analyses. This metamodeling technique accelerates sampling-based probabilistic analysis, enabling rapid assessment of time-dependent corrosion probability under various uncertainties. The accuracy, efficiency and versatility of the proposed framework are assessed via systematic investigations. The effectiveness of the physics- based model in capturing chloride resistance of AAS concrete is confirmed using experimental data. With accurate metamodels generated by X-SVR, the ML-assisted workflow demonstrates substantial efficiency gains in obtaining detailed statistical information over traditional Monte Carlo simulation, without sacrificing precision. Additionally, the framework proves effective in sensitivity assessments and real-time response updates as new information becomes available. Overall, this study provides a practical approach to leveraging computational materials science for investigating the risk of chloride-induced corrosion in AAS concrete, thereby contributing to the development of sustainabl

关键词： Alkali-activated slag concrete Corrosion initiation Machine learning physics-based modeling Probabilistic analysis

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physics-based Lumped-Parameter modeling of Automotive Canister Fuel Purge

Physics-Based Lumped-Parameter Modeling of Automotive Canist...

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American Control Conference

作者： Francheck, Matthew Ebrahimi, Behrouz Grigoriadis, Karolos Makki, Imad

ISBN: (纸本)9781479986842

A physics-based canister fuel purge flowrate model based on fluid mechanics and thermodynamics principles is presented in this paper to improve the tailpipe emission control in automotive applications. The aim of the paper is to derive a lumped-parameter model for the canister that estimates the flowrate out of the canister purged into the intake manifold. The lumped parameters of the model, including canister capacitance and flow resistance are employed to obtain a first-order multi-input and single-output (MISO) dynamic model. The vacuum pressure in the intake manifold and the fuel tank pressure serve as inputs and the mass flowrate is considered as the model output. Gradient descent system identification method is used to estimate the model parameters based on experimental data collected at Ford Motor Company. The advantage of the model is that it does not require cumbersome integral or differential computational methods and is easy to implement for fueling control purposes. Hence, it allows direct implementation in the fueling control to compensate for the extra fuel benefiting the stoichiometric air/fuel regulation in the engine catalyst.

关键词： Canister Purge Model physics-based modeling Multi-Input Single-Output System Parameter Estimation

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physics-based near-earth radiowave propagation modeling and simulation

Physics-based near-earth radiowave propagation modeling and ...

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作者： Liao, DaHan University of Michigan

学位级别：Ph.D.

Both the efficacy and accuracy of existing algorithms for predicting radiowave coverage are often compromised for the region proximate to the ground surface where grazing incidence (and wave cancellation) occurs, surface wave propagation predominates, and intricate higher order reflection and diffraction phenomena become important. A challenge of ongoing interest is to develop high-fidelity electromagnetic models that can reliably evaluate wave interaction with a realistic terrain over long distances with the inclusion of ground proximity effects—in order to support channel performance assessment and grid planning of near-ground (or even sub-surface) communication systems and sensing-oriented networks. In the featured study, physics-based propagation models enabling accurate calculation of propagation path-loss among the nodes of the VHF (30 MHz–300 MHz) near-earth wireless system deployed in natural scenes are assembled through a compilation of analytical, numerical, experimental, and hybrid approaches. Wave propagation issues and their physical interpretations pertinent to the modeling of assorted terrain conditions are presented within the scope of the following: (1) Demonstration of the relevancy and significance of various types of surface waves defining near-grazing radiowave interactions with a dielectrically-covered terrain through the derivation of second order asymptotic solutions. (2) Treatment of the diffraction effect of a vegetation layer discontinuity using Kirchhoff-Huygens approach and validation of obtained results with measurements from an experimental setup. (3) Simulation of long-distance propagation over undulating terrain surfaces with a high order numerical solver achieving accurate solutions with as few as one unknown per linear wavelength for highly rough profiles (rms slope up to 15°). (4) Characterization of ground wave propagation over random rough surfaces with closed-form effective, near-grazing reflection coefficients formulated from

关键词： Radiowave Propagation Electromagnetics Near-earth Radiowave Propagation physics-based modeling Unattended Ground Sensors Antennas Thesis

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