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First-principles determination of the phonon-point defect scattering and thermal transport due to fission products in ThO2

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Physical Review Materials 2024年第2期8卷 025401-025401页

作者： Linu Malakkal Ankita Katre Shuxiang Zhou Chao Jiang David H. Hurley Chris A. Marianetti Marat Khafizov Computational Mechanics and Materials Department Idaho National Laboratory Idaho Falls Idaho 83415 USA Department of Scientific Computing Modeling and Simulation SP Pune University Pune 411007 India Idaho National Laboratory Idaho Falls Idaho 83415 USA Department of Applied Physics and Applied Mathematics Columbia University 500 West 120th Street New York New York 10027 USA Department of Mechanical and Aerospace Engineering The Ohio State University 201 West 19th Avenue Columbus Ohio 43210 USA

This work presents the first-principles calculations of the lattice thermal conductivity degradation due to point defects in thorium dioxide using an iterative solution of the Peierls-Boltzmann transport equation. We have used the nonperturbative Green's function methodology to compute the phonon-point defect scattering rates that consider the local distortion around the point defect, including the mass difference changes, interatomic force constants, and structural relaxation near the point defects. The point defects considered in this work include the vacancy of thorium (VTh) and oxygen (VO), substitutions of helium (HeTh), krypton (KrTh), zirconium (ZrTh), iodine (ITh), and xenon (XeTh) in the thorium site, and the three different configurations of the Schottky defects. The results of the phonon-defect scattering rate reveal that among all the considered intrinsic defects, the thorium vacancy and helium substitution in the thorium site scatter the phonon most due to the substantial changes in the force constant and structural distortions. The scattering of phonons due to the substitutional defects unveils that the zirconium atom scatters phonons the least, followed by xenon, iodine, krypton, and helium. This is contrary to the intuition that the scattering strength follows HeTh>KrTh>ZrTh>ITh>XeTh based on the mass difference. This striking difference in the zirconium phonon scattering is due to the local chemical environment changes. Zirconium is an electropositive element with valency similar to thorium and, therefore, can bond with the oxygen atoms, thus creating less force constant variance compared to iodine, an electronegative element, and the noble gases helium, xenon, and krypton. These results can serve as a benchmark for analytical models and help the engineering-scale modeling effort for nuclear design.

关键词： Carrier dynamics Energy materials Heat transfer Irradiation effects Lattice dynamics Nuclear power Phonons Thermal conductivity Thermal properties

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Fatigue prediction for metallic additively manufactured lattice components using FCM based on average strain energy density approach

Fatigue prediction for metallic additively manufactured latt...

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International Congress on Advanced Electromagnetic Materials in Microwaves and Optics (METAMATERIALS)

作者： R. De Biasi O. Oztoprak F. Zanini S. Carmignato F. Berto S. Kollmannsberger M. Benedetti Department of Industrial Engineering Via Sommarive 9 University of Trento Trento Italy School of Engineering and Design Chair of Computational Modeling and Simulation Arcisstraße 21 Technical University of Munich Munich Germany Department of Management and Engineering Stradella San Nicola 3 University of Padova Vicenza Italy Department of Chemical Engineering Materials Environment Via Eudossiana 18 University of Rome Sapienza Roma Italy Faculty of Civil and Environmental Sciences Chair of Data Science in Engineering Coudraystraße 13b Bauhaus-Universität Weimar Weimar Germany

ISBN: (数字)9798350373493

ISBN: (纸本)9798350373509

An accurate prediction of the fatigue life of metallic additively manufactured lattice structures is still a challenging task. The proposed work addresses this challenge of industrial relevance through the computation of an as-built geometry of a lattice structure directly from its X-ray computed tomography reconstruction. The computation is carried out by taking advantage of the Finite Cell Method and applies the average strain energy density method to predict fatigue failure. The proof of concept provided in this work shows an excellent agreement in the fatigue prediction, paving the way for a computationally accurate fatigue life prediction of other types of metamaterials.

关键词： Geometry Accuracy Lattices Fatigue Metamaterials Topology X-ray imaging Stress Strain Testing

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Reference Architecture and Ontology Framework for Digital Twin Construction

SSRN

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SSRN 2024年

作者： Schlenger, Jonas Urs Pluta, Kacper Mathew, Alwyn Yeung, Timson Sacks, Rafael Borrmann, André Computational Modeling and Simulation School of Engineering and Design Technical University of Munich Arcisstraße 21 Munich80333 Germany Faculty of Civil and Environmental Engineering Technion - Israel Institute of Technology Haifa32000 Israel Université Gustave Eiffel CNRS LIGM Marne-la-ValléeF-77454 France Inria Université Côte d’Azur ValbonneF-06902 France University of Cambridge CambridgeCB3 0FA United Kingdom

Digital twins, revolutionizing product and process management in manufacturing and transportation industry, face integration challenges to their application to the process of building construction. A significant obstacle is the lack of guidance on implementing essential data management layers. We propose a novel reference architecture for digital twin construction, focusing on the internal data structures required to address this gap. The architecture provides a concept for managing planning information, raw monitoring data, and derived information and knowledge as a holistic system. Moreover, we introduce the Digital Twin Construction Ontology, forming the internal data schema needed for interoperability between platform services. Unlike existing ontologies, the DTC Ontology facilitates direct comparison of the planned project evolution with the project status. To assess the proposed data structures, we conducted a case study using the ConSLAM dataset. This study showcases the practicality and relevance of the developed system architecture and data structures in real-world settings. © 2024, The Authors. All rights reserved.

关键词： Interoperability

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'Surgical Gps' Proof of Concept for Scoliosis Surgery

'Surgical Gps' Proof of Concept for Scoliosis Surgery

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2020 Spring simulation Conference, SpringSim 2020

作者： Tapp, Austin Audette, Michel Old Dominion University Electrical Computer Biomedical Engineering 5115 Hampton Blvd NorfolkVA United States Old Dominion University Computational Modeling Simulation Engineering 5115 Hampton Blvd NorfolkVA United States

ISBN: (纸本)9781565553705

Scoliotic deformities may be addressed with either anterior or posterior approaches for scoliosis correction procedures. While typically quite invasive, the impact of these operations may be reduced through the use of computer-assisted surgery. A combination of physician-designated anatomical landmarks and surgical ontologies allows for real-time intraoperative guidance during computer-assisted surgical interventions. Predetermined landmarks are labeled on an identical patient model, which seeks to encompass vertebrae, intervertebral disks, ligaments, and other soft tissues. The inclusion of this anatomy permits the consideration of hypothetical forces that are previously not well characterized in a patient-specific manner. Updated ontologies then suggest procedural directions throughout the surgical corridor, observing the positioning of both the physician and the anatomical landmarks of interest at the present moment. Merging patient-specific models, physician-designated landmarks, and ontologies to produce real-time recommendations magnifies the successful outcome of scoliosis correction through enhanced pre-surgical planning, reduced invasiveness, and shorted recovery time. © 2020 SCS.

关键词： Ontology

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Wearable Sensor Gait Analysis of Fall Detection using Attention Network

Wearable Sensor Gait Analysis of Fall Detection using Attent...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Haben Yhdego Jiang Li Christopher Paolini Michel Audette Computational Modeling and Simulation Engineering Old Dominion University Norfolk USA Electrical and Computer Engineering Old Dominion University Norfolk USA Electrical and Computer Engineering San Diego State University San Diego California

ISBN: (纸本)9781665429825

The statistical data from the National Council on Aging indicates that a senior adult dies in the US from a fall every 19 minutes. The care of elderly people can be improved by enabling the detection of falling events, especially if it triggers the pneumatic actuation of a protective airbag. This work focuses on detecting impending fall risk of senior subjects within the geriatric population, towards a planned approach to mitigating fall injuries through pneumatic airbag deployment. With the widespread adoption of wearable sensors, there is an increased emphasis on fall prediction models that effectively cope with accelerometry signal data. Fall detection and gait classification are challenging tasks, especially in differentiating falls from near falls. We propose to apply attention to the deep neural network (DNN) analysis of acceleration data where a fall is known to have occurred. We take the maximum value of the sensor signals to define the observation window of the detector. Powered by a transformer DNN with word embedding, attention networks have achieved a state-of-the-art in natural language processing (NLP) tasks. Besides the success of the transformer for efficiently processing long sequences, it supports parallel computing with fast computation. In this paper, we propose a novel transformer attention network for gait analysis of fall detection modeling with Time2Vec positional encoding-founded on a Masked Transformer Network. Using our dataset, we demonstrate that the proposed approach achieves better specificity and sensitivity than the present models.

关键词： Sensitivity computational modeling Sociology Transformers Natural language processing Real-time systems Fall detection

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Ab initio phonon transport across grain boundaries in graphene using machine learning based on small dataset

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Physical Review Materials 2022年第4期6卷 044004-044004页

作者： Amirreza Hashemi Ruiqiang Guo Keivan Esfarjani Sangyeop Lee Computational Modeling and Simulation Program University of Pittsburgh Pittsburgh Pennsylvania 15261 USA Department of Mechanical Engineering and Materials Science University of Pittsburgh Pittsburgh Pennsylvania 15261 USA Department of Mechanical and Aerospace Engineering University of Virginia Charlottesville Virginia 22904 USA Department of Materials Science and Engineering University of Virginia Charlottesville Virginia 22904 USA Department of Physics University of Virginia Charlottesville Virginia 22904 USA Department of Physics and Astronomy University of Pittsburgh Pittsburgh Pennsylvania 15261 USA

Establishing the structure-property relationship for grain boundaries (GBs) is critical for developing next-generation functional materials but has been severely hampered due to its extremely large configurational space. Atomistic simulations with low computational cost and high predictive power are strongly desirable, but the conventional simulations using empirical interatomic potentials and density functional theory suffer from the lack of predictive power and high computational cost, respectively. A machine learning interatomic potential (MLIP) recently emerged but often requires extensive size of the training dataset, making it a less feasible approach. Here, we demonstrate that an MLIP trained with a rationally designed small training dataset can predict thermal transport across GBs in graphene with ab initio accuracy at an affordable computational cost. We employed a rational approach based on the structural unit model to find a small set of GBs that can represent the entire configurational space and thus can serve as a cost-effective training dataset for the MLIP. Only 5 GBs were found to be enough to represent the entire configurational space of graphene GBs. Using the atomistic Green's function approach and the MLIP, we revealed that the structure-thermal resistance relation in graphene does not follow the common understanding that large dislocation density causes larger thermal resistance. In fact, thermal resistance is nearly independent of dislocation density at room temperature and is higher when the dislocation density is small at sub-room temperature. We explain this intriguing behavior with the buckling near a GB causing a strong scattering of flexural phonon modes. In this paper, we show that a machine learning technique combined with conventional wisdom (e.g., structural unit model) can extend the recent success of ab initio thermal transport simulation, which has been mostly limited to single crystals, to complex yet practically important polycry

关键词： Phonons Graphene Landauer formula Machine learning

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Uncertainty quantification of microstructure variability and mechanical behaviour of additively manufactured lattice structures

arXiv

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arXiv 2021年

作者： Korshunova, Nina Papaioannou, Iason Kollmannsberger, Stefan Straub, Daninel Rank, Ernst Computational Modeling and Simulation Technische Universitat Munchen Germany Inc. of Engineering Risk Analysis Technische Universitat Munchen Germany Institute for Advanced Study Technische Universitat Munchen Germany

Process-induced defects are the leading cause of discrepancies between as-designed and as-manufactured additive manufacturing (AM) product behavior. Especially for metal lattices, the variations in the printed geometry cannot be neglected. Therefore, the evaluation of the inuence of microstructural variability on their mechanical behavior is crucial for the quality assessment of the produced structures. Commonly, the as-manufactured geometry can be obtained by computed tomography (CT). However, to incorporate all process-induced defects into the numerical analysis is often computationally demanding. Thus, commonly this task is limited to a predefined set of considered variations, such as strut size or strut diameter. In this work, a CT-based binary random field is proposed to generate statistically equivalent geometries of periodic metal lattices. The proposed random field model in combination with the Finite Cell Method (FCM), an immersed boundary method, allows to efficiently evaluate the inuence of the underlying microstructure on the variability of the mechanical behavior of AM products. Numerical analysis of two lattices manufactured at different scales shows an excellent agreement with experimental data. Furthermore, it provides a unique insight into the effects of the process on the occurring geometrical variations and final mechanical behavior. © 2021, CC BY-NC-ND.

关键词： Uncertainty analysis

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A computational Pipeline for Patient-Specific Prediction of the Postoperative Mitral Valve Functional State

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Journal of Biomechanical engineering 2023年第11期145卷 111002-111002页

作者： Liu, Hao Simonian, Natalie T. Pouch, Alison M. Iaizzo, Paul A. Gorman, Joseph H. Gorman, Robert C. Sacks, Michael S. James T. Willerson Center for Cardiovascular Modeling and Simulation The Oden Institute for Computational Engineering and Sciences Department of Biomedical Engineering The University of Texas at Austin AustinTX78712-1229 United States Departments of Radiology and Bioengineering University of Pennsylvania PhiladelphiaPA19104 United States Visible Heart Laboratories Department of Surgery University of Minnesota MinneapolisMN55455 United States Gorman Cardiovascular Research Group Department of Surgery Smilow Center for Translational Research Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States

While mitral valve (MV) repair remains the preferred clinical option for mitral regurgitation (MR) treatment, long-term outcomes remain suboptimal and difficult to predict. Furthermore, pre-operative optimization is complicated by the heterogeneity of MR presentations and the multiplicity of potential repair configurations. In the present work, we established a patient-specific MV computational pipeline based strictly on standard-of-care pre-operative imaging data to quantitatively predict the post-repair MV functional state. First, we established human mitral valve chordae tendinae (MVCT) geometric characteristics obtained from five CT-imaged excised human hearts. From these data, we developed a finite-element model of the full patient-specific MV apparatus that included MVCT papillary muscle origins obtained from both the in vitro study and the pre-operative three-dimensional echocardiography images. To functionally tune the patient-specific MV mechanical behavior, we simulated pre-operative MV closure and iteratively updated the leaflet and MVCT prestrains to minimize the mismatch between the simulated and target end-systolic geometries. Using the resultant fully calibrated MV model, we simulated undersized ring annuloplasty (URA) by defining the annular geometry directly from the ring geometry. In three human cases, the postoperative geometries were predicted to 1 mm of the target, and the MV leaflet strain fields demonstrated close agreement with noninvasive strain estimation technique targets. Interestingly, our model predicted increased posterior leaflet tethering after URA in two recurrent patients, which is the likely driver of long-term MV repair failure. In summary, the present pipeline was able to predict postoperative outcomes from pre-operative clinical data alone. This approach can thus lay the foundation for optimal tailored surgical planning for more durable repair, as well as development of mitral valve digital twins. © 2023 American Society of Mec

关键词： Geometry

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Scalability of Sensor simulation in ROS-Gazebo Platform with and without Using GPU

Scalability of Sensor Simulation in ROS-Gazebo Platform with...

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Spring simulation Conference (SpringSim)

作者： Ahmet Saglam Yiannis Papelis Department of Computational Modeling and Simulation Engineering Old Dominion University Norfolk VA USA Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk VA USA

ISBN: (数字)9781565553705

ISBN: (纸本)9781728163642

Autonomous mobile robots depend heavily on sensors for interpreting the external environment and planning their movement. Given the complexity of even simple environments, the use of simulation is necessary to test sensory perception and movement strategies efficiently. Accurate and efficient sensor simulation is key to the successful development and fielding of autonomous mobile robots. In this paper, we investigate the scalability of LIDAR-type sensor simulation in ROS-supported Gazebo, an open-source multi-robot simulator. Specifically, the performance and scalability of the simulator are assessed with and without support from GPU in two virtual worlds, one highly-complex in terms of the number of collision targets and one not-complex. In both cases, we increase the number of sensors and evaluate system performance. The results show that the use of GPU helps improve performance, providing better speedups in the complex scene. However, the number of sensors that can be used is limited when using GPU.

关键词： Robot sensing systems Graphics processing units computational modeling Complexity theory Scalability Analytical models

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Concrete Defect Classification Based on a Digital Twin Platform

SSRN

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SSRN 2024年

作者： Calcagni, Maria Teresa Salerno, Giovanni Martarelli, Milena Schlenger, Jonas Urs Hassan, Thomas Heinikainen, Rene Borrmann, André Fies, Bruno Revel, Gian Marco Department of Industrial Engineering and Mathematical Sciences Università Politecnica delle Marche Via Brecce Bianche 12 Ancona60131 Italy Computational Modeling and Simulation Technical University of Munich Arcisstraße 21 München80333 Germany Orange Rue Olivier de Serres 78 Paris75015 France Fira Group Oy Teknobulevardi 3-5 Vantaa01530 Finland Centre Scientifique et Technique du Batiment Avenue Jean Jaures 84 Champs sur Marne77420 France

Ensuring the durability of civil buildings is critical to preventing long-term structural failure. Traditional damage assessment methods, based on visual inspections, are labor-intensive and subjective. The adoption of Digital Building Twin (DBT) platforms emerges as a solution for comprehensive monitoring of the building throughout its life cycle. This study, part of the European BIM2TWIN project, focuses on concrete surface quality monitoring using vision techniques and deep learning algorithms. Four neural network models are created for cracks, honeycombing, pitting and exposed bars, collectively analyzing the images to identify and quantify defects. Project managers can assess the criticality of defects based on accurate pixel counts and geometric features, converting measurements from pixels to millimeters. The data are then stored in the DBT platform, providing a historical record for future reference and decision-making by project managers. © 2024, The Authors. All rights reserved.

关键词： Pixels

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