5,043 research outputs found

    Efficient first-principles electronic transport approach to complex band structure materials : the case of n -type Mg 3 Sb 2

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    We present an efficient method for accurately computing electronic scattering rates and transport properties in materials with complex band structures. Using ab initio simulations, we calculate a limited number of electron–phonon matrix elements, and extract scattering rates for acoustic and optical processes based on deformation potential theory. Polar optical phonon scattering rates are determined using the Fröhlich model, and ionized impurity scattering rates are derived from the Brooks-Herring theory. Subsequently, electronic transport coefficients are computed within the Boltzmann transport theory. We exemplify our approach with n-type Mg3Sb2, a promising thermoelectric material with a challenging large unit cell and low symmetry. Notably, our method attains competitive accuracy, requiring less than 10% of the computational cost compared to state-of-the-art ab initio methods, dropping to 1% for simpler materials. Additionally, our approach provides explicit information on individual scattering processes, offering an alternative that combines efficiency, robustness, and flexibility beyond the commonly employed constant relaxation time approximation with the accuracy of fully first-principles calculations

    Brittle-viscous deformation cycles at the base of the seismogenic zone in the continental crust

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    The main goal of the study was to determine the dynamical cycle of ductile-brittle deformation and to characterise the fluid pathways at different scales of a brittle-viscous fault zone active at the base of the seismogenic crust. Object of analysis are samples from the sinistral strike-slip fault zone BFZ045 from Olkiluoto (SW Finland), located at the site of a deep geological repository for nuclear waste. Combined microstructural analysis, electron backscatter diffraction (EBSD), and mineral chemistry were applied to reconstruct the variations in pressure, temperature, fluid pressure, and differential stress that mediated deformation and strain localization along BFZ045 across the BDTZ. Ductile deformation took place at 400-500° C and 3-4 kbar, and recrystallized grain size piezometry for quartz document a progressive increase in differential stress during mylonitization, from ca. 50 MPa to ca. 120 MPa. The increase in differential stress was localised towards the shear zone center, which was eventually overprinted by brittle deformation in a narrowing shear zone. Cataclastic deformation occurred under lower T conditions down to T ≥ 320° C and was not further overprinted by mylonitic creep. Porosity estimates were obtained through the combination of x-ray micro-computed tomography (µCT), mercury intrusion porosimetry, He pycnometry, and microstructural analysis. Low porosity values (0.8-4.4%) for different rock type, 2-20 µm pore size, representative of pore connectivity, and microstructural observation suggest a relationship to a dynamical cycle of fracturing and sealing mechanism, mostly controlled by ductile deformation. Similarly, the observation from fracture orientation analysis indicates that the mylonitic precursor of BFZ045 played an important role in the localization of the brittle deformation. This thesis highlights that the ductile-brittle deformation cycle in BFZ045 was controlled by transient oscillations in fluid pressure in a narrowing shear zone deforming at progressively higher differential stress during cooling

    Integrating numerical modeling and deep learning for stochastic microstructure reconstruction and multiscale mechanics

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    Designing materials with targeted properties requires efficient modeling of material behavior across different scales. Two key components to facilitating microstructure-resolved material modeling and design methods are (i) stochastic microstructure reconstruction and (ii) microstructure-resolved multiscale modeling for property prediction. However, conventional numerical approaches face significant challenges in the practical application of design, optimization, and uncertainty quantification. In this dissertation, an integrated machine learning (ML) and physics-based modeling framework is developed for 2D & 3D microstructure generation, micro-scale stress field prediction, and multiscale mechanics. Conventional stochastic microstructure reconstruction approaches are prohibitively slow and limiting for complex microstructure systems. Therefore, we framed microstructure reconstruction as a gradient-based optimization problem. In this approach, statistical descriptors and feature maps from a pre-trained deep convolutional neural network (CNN), are combined into an overall differentiable loss function. This approach is applied for efficient 3D reconstruction of bi-phase porous ceramic and multi-phase polycrystalline materials. Microstructural heterogeneity affects the macroscale behavior of materials. Conversely, loading at the macro-scale affects material behavior at the micro-scale. These up-scaling and down-scaling relations are often modeled using multiscale finite element (FE) approaches such as FE-squared (FE2). However, (FE2) requires numerous calculations at the micro-scale, which often renders this approach intractable. To address this, we developed an enormously faster ML-driven approach for multiscale modeling. This approach uses an ML-model, specifically a U-Net CNN, to predict stress in linear-elastic fiber reinforced composite materials. This ML-model is integrated in a discretization based multiscale approach to predict effective material properties for up-scaling and local stress tensor fields for subsequent down-scaling. Several numerical examples demonstrate a substantial reduction in computational cost using the proposed ML-driven approach when compared with the traditional multiscale modeling approaches such as full-scale FE analysis, and homogenization based (FE2) analysis

    Ab-initio simulations of the intrinsic Deformation Mechanisms in Body Centered Cubic Al-Cr-Mo-Nb-Ti and Hf-Nb-Ta-Ti-Zr alloys

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    Hochentropie Legierungen (engl. High Entropy Alloys (HEA)) bestehen aus mehreren Legierungselementen. Diese Legierungsklasse ist seit einigen Jahren im Fokus aktueller Forschung, da einiger ihrer Verteter exzellente mechanische Eigenschaften besitzen. Kubisch raumzentrierte hochentropie Legierungen gelten als vielversprechende Kandidaten für Hochtemperaturanwendungen, da einige Vertreter auch bei hohen Temperaturen eine vergleichsweise hohe Festigkeit aufweisen. Jedoch ist neben der hohen Festigkeit, häufig auch eine hohe Sprödigkeit bei Raumtemperatur in dieser Legierungsfamilie zu finden, was einem Einsatz in kritischen Bauteilen entgegensteht. Bis heute sind die Hintergünde dieser Sprödigkeit nicht vollständig verstanden. Daher behandelt die vorliegende Arbeit systematisch die Deformationsmechanismen in den beiden Besipiellegierungen AlCrMoTi und HfNbTiZr. Beide weisen bei Raumtemperatur eine vergleichsweise hohe Festigkeit auf. AlCrMoTi ist jedoch bei Raumtemperatur spröde und HfNbTiZr duktil. Die Diskussion der Deformationsmechanismen efolgt auf Basis von Modellen aus der Versetzungstheorie. Die benötigten Modelparameter wurden mit Hilfe von ab-initio Rechnungen gewonnen, welche auf der Dichtefunktionaltheorie basieren

    Neuromorphic hardware for somatosensory neuroprostheses

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    In individuals with sensory-motor impairments, missing limb functions can be restored using neuroprosthetic devices that directly interface with the nervous system. However, restoring the natural tactile experience through electrical neural stimulation requires complex encoding strategies. Indeed, they are presently limited in effectively conveying or restoring tactile sensations by bandwidth constraints. Neuromorphic technology, which mimics the natural behavior of neurons and synapses, holds promise for replicating the encoding of natural touch, potentially informing neurostimulation design. In this perspective, we propose that incorporating neuromorphic technologies into neuroprostheses could be an effective approach for developing more natural human-machine interfaces, potentially leading to advancements in device performance, acceptability, and embeddability. We also highlight ongoing challenges and the required actions to facilitate the future integration of these advanced technologies

    Smart Gas Sensors: Materials, Technologies, Practical ‎Applications, and Use of Machine Learning – A Review

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    The electronic nose, popularly known as the E-nose, that combines gas sensor arrays (GSAs) with machine learning has gained a strong foothold in gas sensing technology. The E-nose designed to mimic the human olfactory system, is used for the detection and identification of various volatile compounds. The GSAs develop a unique signal fingerprint for each volatile compound to enable pattern recognition using machine learning algorithms. The inexpensive, portable and non-invasive characteristics of the E-nose system have rendered it indispensable within the gas-sensing arena. As a result, E-noses have been widely employed in several applications in the areas of the food industry, health management, disease diagnosis, water and air quality control, and toxic gas leakage detection. This paper reviews the various sensor fabrication technologies of GSAs and highlights the main operational framework of the E-nose system. The paper details vital signal pre-processing techniques of feature extraction, feature selection, in addition to machine learning algorithms such as SVM, kNN, ANN, and Random Forests for determining the type of gas and estimating its concentration in a competitive environment. The paper further explores the potential applications of E-noses for diagnosing diseases, monitoring air quality, assessing the quality of food samples and estimating concentrations of volatile organic compounds (VOCs) in air and in food samples. The review concludes with some challenges faced by E-nose, alternative ways to tackle them and proposes some recommendations as potential future work for further development and design enhancement of E-noses

    A data-driven framework for structure-property correlation in ordered and disordered cellular metamaterials

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    Cellular solids and micro-lattices are a class of lightweight architected materials that have been established for their unique mechanical, thermal, and acoustic properties. It has been shown that by tuning material architecture, a combination of topology and solid(s) distribution, one can design new material systems, also known as metamaterials, with superior performance compared to conventional monolithic solids. Despite the continuously growing complexity of synthesized microstructures, mainly enabled by developments in additive manufacturing, correlating their morphological characteristics to the resulting material properties has not advanced equally. This work aims to develop a systematic data-driven framework that is capable of identifying all key microstructural characteristics and evaluating their effect on a target material property. The framework relies on integrating virtual structure generation and quantification algorithms with interpretable surrogate models. The effectiveness of the proposed approach is demonstrated by analyzing the effective stiffness of a broad class of two-dimensional (2D) cellular metamaterials with varying topological disorder. The results reveal the complex manner in which well-known stiffness contributors, including nodal connectivity, cooperate with often-overlooked microstructural features such as strut orientation, to determine macroscopic material behavior. We further re-examine Maxwell's criteria regarding the rigidity of frame structures, as they pertain to the effective stiffness of cellular solids and showcase microstructures that violate them. This framework can be used for structure-property correlation in different classes of metamaterials as well as the discovery of novel architectures with tailored combinations of material properties

    Vibration-based damage localisation: Impulse response identification and model updating methods

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    Structural health monitoring has gained more and more interest over the recent decades. As the technology has matured and monitoring systems are employed commercially, the development of more powerful and precise methods is the logical next step in this field. Especially vibration sensor networks with few measurement points combined with utilisation of ambient vibration sources are attractive for practical applications, as this approach promises to be cost-effective while requiring minimal modification to the monitored structures. Since efficient methods for damage detection have already been developed for such sensor networks, the research focus shifts towards extracting more information from the measurement data, in particular to the localisation and quantification of damage. Two main concepts have produced promising results for damage localisation. The first approach involves a mechanical model of the structure, which is used in a model updating scheme to find the damaged areas of the structure. Second, there is a purely data-driven approach, which relies on residuals of vibration estimations to find regions where damage is probable. While much research has been conducted following these two concepts, different approaches are rarely directly compared using the same data sets. Therefore, this thesis presents advanced methods for vibration-based damage localisation using model updating as well as a data-driven method and provides a direct comparison using the same vibration measurement data. The model updating approach presented in this thesis relies on multiobjective optimisation. Hence, the applied numerical optimisation algorithms are presented first. On this basis, the model updating parameterisation and objective function formulation is developed. The data-driven approach employs residuals from vibration estimations obtained using multiple-input finite impulse response filters. Both approaches are then verified using a simulated cantilever beam considering multiple damage scenarios. Finally, experimentally obtained data from an outdoor girder mast structure is used to validate the approaches. In summary, this thesis provides an assessment of model updating and residual-based damage localisation by means of verification and validation cases. It is found that the residual-based method exhibits numerical performance sufficient for real-time applications while providing a high sensitivity towards damage. However, the localisation accuracy is found to be superior using the model updating method

    CrunchGPT: A chatGPT assisted framework for scientific machine learning

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    Scientific Machine Learning (SciML) has advanced recently across many different areas in computational science and engineering. The objective is to integrate data and physics seamlessly without the need of employing elaborate and computationally taxing data assimilation schemes. However, preprocessing, problem formulation, code generation, postprocessing and analysis are still time consuming and may prevent SciML from wide applicability in industrial applications and in digital twin frameworks. Here, we integrate the various stages of SciML under the umbrella of ChatGPT, to formulate CrunchGPT, which plays the role of a conductor orchestrating the entire workflow of SciML based on simple prompts by the user. Specifically, we present two examples that demonstrate the potential use of CrunchGPT in optimizing airfoils in aerodynamics, and in obtaining flow fields in various geometries in interactive mode, with emphasis on the validation stage. To demonstrate the flow of the CrunchGPT, and create an infrastructure that can facilitate a broader vision, we built a webapp based guided user interface, that includes options for a comprehensive summary report. The overall objective is to extend CrunchGPT to handle diverse problems in computational mechanics, design, optimization and controls, and general scientific computing tasks involved in SciML, hence using it as a research assistant tool but also as an educational tool. While here the examples focus in fluid mechanics, future versions will target solid mechanics and materials science, geophysics, systems biology and bioinformatics.Comment: 20 pages, 26 figure
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