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arXiv 2022年

作者： Joshi, Akshay Thakolkaran, Prakash Zheng, Yiwen Escande, Maxime Flaschel, Moritz De Lorenzis, Laura Kumar, Siddhant Department of Materials Science and Engineering Delft University of Technology Delft2628 CD Netherlands Department of Mechanical and Process Engineering ETH Zürich Zürich8092 Switzerland

Within the scope of our recent approach for Efficient Unsupervised Constitutive Law Identification and Discovery (EUCLID), we propose an unsupervised Bayesian learning framework for discovery of parsimonious and interpretable constitutive laws with quantifiable uncertainties. As in deterministic EUCLID, we do not resort to stress data, but only to realistically measurable full-field displacement and global reaction force data;as opposed to calibration of an a priori assumed model, we start with a constitutive model ansatz based on a large catalog of candidate functional features;we leverage domain knowledge by including features based on existing, both physics-based and phenomenological, constitutive models. In the new Bayesian-EUCLID approach, we use a hierarchical Bayesian model with sparsity-promoting priors and Monte Carlo sampling to efficiently solve the parsimonious model selection task and discover physically consistent constitutive equations in the form of multivariate multi-modal probabilistic distributions. We demonstrate and validate the ability to accurately and efficiently recover isotropic and anisotropic hyperelastic models like the Neo-Hookean, Isihara, Gent-Thomas, Arruda-Boyce, Ogden, and Holzapfel models in both elastostatics and elastodynamics. The discovered constitutive models are reliable under both epistemic uncertainties - i.e. uncertainties on the true features of the constitutive catalog - and aleatoric uncertainties - which arise from the noise in the displacement field data, and are automatically estimated by the hierarchical Bayesian model. Copyright © 2022, The Authors. All rights reserved.

关键词： Constitutive models

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Automated discovery of generalized standard material models with EUCLID

arXiv

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arXiv 2022年

作者： Flaschel, Moritz Kumar, Siddhant De Lorenzis, Laura Department of Mechanical and Process Engineering ETH Zürich Zürich8092 Switzerland Department of Materials Science and Engineering Delft University of Technology Delft2628 CD Netherlands

We extend the scope of our recently developed approach for unsupervised automated discovery of material laws (denoted as EUCLID) to the general case of a material belonging to an unknown class of constitutive behavior. To this end, we leverage the theory of generalized standard materials, which encompasses a plethora of important constitutive classes including elasticity, viscosity, plasticity and arbitrary combinations thereof. We show that, based only on full-field kinematic measurements and net reaction forces, EUCLID is able to automatically discover the two scalar thermodynamic potentials, namely, the Helmholtz free energy and the dissipation potential, which completely define the behavior of generalized standard materials. The a priori enforced constraint of convexity on these potentials guarantees by construction stability and thermodynamic consistency of the discovered model;balance of linear momentum acts as a fundamental constraint to replace the availability of stress-strain labeled pairs;sparsity promoting regularization enables the automatic selection of a small subset from a possibly large number of candidate model features and thus leads to a parsimonious, i.e., simple and interpretable, model. Importantly, since model features go hand in hand with the correspondingly active internal variables, sparse regression automatically induces a parsimonious selection of the few internal variables needed for an accurate but simple description of the material behavior. A fully automatic procedure leads to the selection of the hyperparameter controlling the weight of the sparsity promoting regularization term, in order to strike a user-defined balance between model accuracy and simplicity. By testing the method on synthetic data including artificial noise, we demonstrate that EUCLID is able to automatically discover the true hidden material model from a large catalog of constitutive classes, including elasticity, viscoelasticity, elastoplasticity, viscoplasticity

关键词： Elasticity

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Monolayer Graphene Interlayer Enhanced Flexible Gas Sensor with Ultrafast Response and High Strain Tolerance for Wearable Ammonia Monitoring

SSRN

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SSRN 2025年

作者： Wang, Chen Wang, Ziran Xu, Feng Zhang, Luyang Yuan, Haoyu Zhang, Zaiyu Gai, Chaqi Ren, Zhongjing Wang, Haipeng You, Rui Yan, Peng Key Laboratory of High-efficiency and Clean Mechanical Manufacture Ministry of Education School of Mechanical Engineering Shandong University Jinan250061 China Suzhou Research Institute Shandong University Suzhou215123 China The Sixth Scientific Research Department Shandong Institute of Nonmetallic Materials Jinan250031 China School of Instrument Science and Opto-Electronics Engineering Beijing Information Science and Technology University Beijing100192 China

Flexible gas sensors are rapidly emerging as a core component of the next generation of wearable electronics. Ideally, they should maintain a consistent performance under various mechanical deformations. However, it is challenging to achieve a semiconductor-based flexible gas sensor with high flexibility and sensitivity simultaneously. Here, we present an ammonia (NH₃) gas sensor with ultrafast response and high strain tolerance, using the design of a SnO₂ film integrating with an ultrathin graphene interlayer. Benefiting from the monolayer graphene, the flexible gas sensor achieves high conductivity of the conducting channel and fast response to NH₃, with a significant reduction of strain applied on the SnO₂ film when the sensor is bent. The sensor exhibits a wide detection range (0.5-100 ppm) and a low detection limit down to 500 ppb, with fast response times as low as 62 s and mechanical stability over 500 strain cycles. The sensor is integrated with a flexible circuitry to create a wearable sensing system with data-transmission and analysis, offering the NH₃ detection in various environments. This work demonstrates an effective way for the SnO₂-based flexible sensor to achieve wearable applications with high sensitivity and flexibility. © 2025, The Authors. All rights reserved.

关键词： Ammonia

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Microwave torrefaction of empty fruit bunch pellet:Simulation and validation of electric field and temperature distribution

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Journal of Bioresources and Bioproducts 2022年第4期7卷 270-277页

作者： Peter Nai Yuh Yek Sieng Huat Kong Ming Chiat Law Changlei Xia Rock Keey Liew Teck Sung Sie Jun Wei Lim Su Shiung Lam Centre for Research of Innovation and Sustainable Development University of Technology SarawakJalan UniversitySibuSarawak 96000Malaysia School of Foundation Studies University of Technology SarawakJalan UniversitySibuSarawak 96000Malaysia Department of Mechanical Engineering Faculty of Engineering and ScienceCurtin University Malaysia CampusMiriSarawak 98009Malaysia Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources International Innovation Center for Forest Chemicals and MaterialsCollege of Materials Science and EngineeringNanjing Forestry UniversityNanjing 210037China NV WESTERN PLT GeorgetownPenang 10400Malaysia Department of Fundamental and Applied Sciences HICoE-Centre for Biofuel and Biochemical ResearchInstitute of Self-Sustainable BuildingUniversity Technology PETRONASDarul RidzuanSeri IskandarPerak 32610Malaysia Higher Institution Centre of Excellence(HICoE) Institute of Tropical Aquaculture and Fisheries(AKUATROP)University Malaysia TerengganuKuala NerusTerengganu 21030Malaysia Sustainability Cluster School of EngineeringUniversity of Petroleum&Energy StudiesDehradunUttarakhand 248007India

Microwave simulation is significant in identifying a reactor design allowing the biomass to be heated and processed *** study integrated the radio frequency and transient heat transfer modules to simulate the microwave distribution and investigated the performance of microwave heating in the *** simulation results were compared with the experimental findings us-ing the finite element analysis software of COMSOL MULTIPHYSICS to predict the temperature profile and electric field of microwave in the biomass(empty fruit bunch pellets).The higher temperature distribution was observed at the bottom and centre section of the empty fruit bunch pellet bed in the reactor,showing the uniqueness of microwave *** to the simula-tion results,the temperature profile obtained through the specific cavity geometry and dielectric properties agreed with the experimental temperature *** simulated temperature profile demonstrated a logarithmic increase of 120°C/min at the first 50 s followed by 50°C/min until 350 *** experimental temperature profile showed three different heating rates before reaching 300°C,including 78.3°C/min(50-120°C),30.6°C/min(121-250°C),and 105°C/min(250-300°C).The results of this study might contribute to the improvement of microwave heating in biomass torrefaction.

关键词： Microwave Torrefaction Simulation Energy Temperature

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Integrating Machine Learning with Mechanistic Models for Predicting the Yield Strength of High Entropy Alloys

arXiv

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arXiv 2022年

作者： Liu, Shunshun Lee, Kyungtae Balachandran, Prasanna V. Department of Materials Science and Engineering University of Virginia CharlottesvilleVA22904 United States Department of Mechanical and Aerospace Engineering University of Virginia CharlottesvilleVA22904 United States

Accelerating the design of materials with targeted properties is one of the key materials informatics tasks. The most common approach takes a data-driven motivation, where the underlying knowledge is incorporated in the form of domain-inspired input features. Machine learning (ML) models are then built to establish the input-output relationships. An alternative approach involves leveraging mechanistic models, where the domain knowledge is incorporated in a predefined functional form. These mechanistic models are meticulously formulated through observations to validate specific hypotheses, and incorporate elements of causality missing from data-driven ML approaches. In this work, we demonstrate a computational approach that integrates mechanistic models with phenomenological and ML models to rapidly predict the temperature-dependent yield strength of high entropy alloys (HEAs) that form in the single-phase face-centered cubic (FCC) structure. Our main contribution is in establishing a quantitative relationship between the HEA compositions and temperature-dependent elastic constants. This allows us to improve the treatment of elastic constant mismatch to the solid solution strengthening effect in the mechanistic model, which is important for reliable prediction of yield strength as a function of temperature in single-phase FCC-based HEAs. We accomplish this by combining Bayesian inference with ensemble ML methods. The outcome is a probability distribution of elastic constants which, when propagated through the mechanistic model, yields a prediction of temperature-dependent yield strength, along with the uncertainties. The predicted yield strength shows good agreement with published experimental data, giving us confidence in applying the developed approach for the rapid search of novel FCC-based HEAs with excellent yield strength at various temperatures. © 2022, CC BY.

关键词： Inference engines

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NN-EUCLID: deep-learning hyperelasticity without stress data

arXiv

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arXiv 2022年

作者： Thakolkaran, Prakash Joshi, Akshay Zheng, Yiwen Flaschel, Moritz De Lorenzis, Laura Kumar, Siddhant Department of Materials Science and Engineering Delft University of Technology Delft2628 CD Netherlands Department of Mechanical and Process Engineering ETH Zürich Zürich8092 Switzerland

We propose a new approach for unsupervised learning of hyperelastic constitutive laws with physics-consistent deep neural networks. In contrast to supervised learning, which assumes the availability of stress-strain pairs, the approach only uses realistically measurable full-field displacement and global reaction force data, thus it lies within the scope of our recent framework for Efficient Unsupervised Constitutive Law Identification and Discovery (EUCLID) and we denote it as NN-EUCLID. The absence of stress labels is compensated for by leveraging a physics-motivated loss function based on the conservation of linear momentum to guide the learning process. The constitutive model is based on input-convex neural networks, which are capable of learning a function that is convex with respect to its inputs. By employing a specially designed neural network architecture, multiple physical and thermodynamic constraints for hyperelastic constitutive laws, such as material frame indifference, material stability, and stress-free reference configuration are automatically satisfied. We demonstrate the ability of the approach to accurately learn several hidden isotropic and anisotropic hyperelastic constitutive laws - including e.g., Mooney-Rivlin, Arruda-Boyce, Ogden, and Holzapfel models - without using stress data. For anisotropic hyperelasticity, the unknown anisotropic fiber directions are automatically discovered jointly with the constitutive model. The neural network-based constitutive models show good generalization capability beyond the strain states observed during training and are readily deployable in a general finite element framework for simulating complex mechanical boundary value problems with good accuracy. Copyright © 2022, The Authors. All rights reserved.

关键词： Constitutive models

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Injectable, Anti-Collapse, Highly Adhesive and Plasticity Bioactive Artificial Bone Promotes Bone Regeneration by Moderating Oxidative Stress in Osteoporotic Bone Defect

SSRN

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SSRN 2023年

作者： Huang, Lei Zhang, Shihao Bian, Mengxuan Xiao, Lan Chen, Weisin Wang, Jiayi Lu, Shunyi Xiang, Xingdong Li, Yulin Jiang, Libo Zhang, Jian Department of Orthopedic Surgery Zhongshan Hospital Fudan University Shanghai200032 China The Key Laboratory for Ultrafine Materials of Ministry of Education Frontiers Science Center for Materiobiology and Dynamic Chemistry Engineering Research Center for Biomedical Materials of Ministry of Education School of Materials Science and Engineering East China University of Science and Technology Shanghai200237 China Centre for Biomedical Technologies School of Mechanical Medical and Process Engineering Queensland University of Technology Brisbane4059 Australia

The treatment of osteoporotic bone defect remains a big clinical challenge because osteoporosis (OP) is associated with oxidative stress and high levels of reactive oxygen species (ROS), a condition detrimental for bone formation. Anti-oxidative nanomaterials such as selenium nanoparticles (SeNPs) have positive effect on osteogenesis owing to their pleiotropic pharmacological activity which can exert anti-oxidative stress functions to prevent bone loss and facilitate bone regeneration in OP. In the current study a strategy of one-pot method by introducing Poly (lactic acid-carbonate) (PDT) and β-Tricalcium Phosphate (β-TCP) with SeNPs, is developed to prepare an injectable, anti-collapse, shape-adaptive and highly adhesive artificial bone material (PDT-TCP-SE). The PDT-TCP-SE artificial bone exhibits sufficient adhesion in biological microenvironments and good biocompatibility, osteogenic effect, angiogenic effect and anti-inflammatory as well as anti-oxidative effect in vitro and in vivo. Moreover, the PDT-TCP-SE can protect BMSCs from erastin-induced ferroptosis through the Sirt1/Nrf2/GPX4 antioxidant pathway, which, in together, demonstrated the artificial bone as a novel biomaterial with potential clinical application for the future treatment of osteoporotic bone defects. © 2023, The Authors. All rights reserved.

关键词： Oxidative stress

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Discovering plasticity models without stress data

arXiv

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arXiv 2022年

We propose a new approach for data-driven automated discovery of material laws, which we call EUCLID (Efficient Unsupervised Constitutive Law Identification and Discovery), and we apply it here to the discovery of plasticity models, including arbitrarily shaped yield surfaces and isotropic and/or kinematic hardening laws. The approach is unsupervised, i.e., it requires no stress data but only full-field displacement and global force data;it delivers interpretable models, i.e., models that are embodied by parsimonious mathematical expressions discovered through sparse regression of a potentially large catalogue of candidate functions;it is one-shot, i.e., discovery only needs one experiment. The material model library is constructed by expanding the yield function with a Fourier series, whereas isotropic and kinematic hardening are introduced by assuming a yield function dependency on internal history variables that evolve with the plastic deformation. For selecting the most relevant Fourier modes and identifying the hardening behavior, EUCLID employs physics knowledge, i.e., the optimization problem that governs the discovery enforces the equilibrium constraints in the bulk and at the loaded boundary of the domain. Sparsity promoting regularization is deployed to generate a set of solutions out of which a solution with low cost and high parsimony is automatically selected. Through virtual experiments, we demonstrate the ability of EUCLID to accurately discover several plastic yield surfaces and hardening mechanisms of different complexity. Copyright © 2022, The Authors. All rights reserved.

关键词： Fourier series

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A Fully Additive Fabrication Approach for sub-10-Micrometer Microvia Suitable for 3-D System-in-Package Integration

A Fully Additive Fabrication Approach for sub-10-Micrometer ...

引用

Electronic Components and Technology Conference (ECTC)

作者： Roghayeh Imani Shailesh Chouhan Jussi Putaala Olli Nousiainen Juha Hagberg Sami Myllymäki Sarthak Acharya Heli Jantunen Jerker Delsing Department of Computer Science Electrical & Space Engineering EISLAB Luleå Technical University Luleå Sweden Microelectronics Research Unit University of Oulu Oulu Finland Materials and Mechanical Engineering Research Unit University of Oulu Oulu Finland Department of Information Technology and Electrical Engineering University of Oulu Oulu Finland

The semiconductor industry demands high input/output (I/O) density, requiring sub-l0-micrometer microvia. Here we propose a novel, fully additive, economical approach for creating and copper plating of microvias. The experimental process consisted of three stages. In Stage I, a polyurethane layer was spin-coated onto a FR-4 PCB base, followed by target copper layer deposition using the sequential build-up-covalent bonded metallization (SBU -CBM) method. In Stage II, first another layer of polyurethane was spin-coated on the top of the target copper layer, and then a microvia was created on the polyurethane layer using a picosecond pulsed ultraviolet (UV) laser. Finally, in Stage III, the SBU-CBM method was used to selectively copper plating of the microvia. Optical microscopy and cross-section scanning electron microscopy (SEM) images confirmed the successful formation and copper plating of sub-l0 micrometer microvia.

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Helical Covalent Polymers with Unidirectional Ion Channels as Single Lithium-Ion Conducting Electrolytes

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CCS Chemistry 2021年第12期3卷 2762-2770页

作者： Yiming Hu Nathan Dunlap Hai Long Hongxuan Chen Lacey J.Wayment Michael Ortiz Yinghua Jin Abdulrahiman Nijamudheen Jose L.Mendoza-Cortes Se-hee Lee Wei Zhang Department of Chemistry University of Colorado BoulderBoulderCO 80309 Department of Mechanical Engineering University of Colorado BoulderBoulderCO 80309 National Renewable Energy Laboratory GoldenCO 80401 Department of Chemical Engineering and Materials Science Michigan State UniversityEast LansingMI 48824

Single-ion conducting polymer electrolytes have attracted great attention as safe alternatives to liquid electrolytes in high energy density lithium-ion ***,we report the first example of a crystalline anionic helical polymer as a single lithium-ion conducting solid polymer electrolyte(SPE).Single-crystal X-ray analysis shows that the polymer folds into densely packed double helices,with bundles of unidirectional negatively charged channels formed that can facilitate lithium-ion transportation.

关键词： helical covalent polymer spiroborate single-ion conducting electrolyte

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