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11th International Conference on Functional Imaging and modeling of the Heart, FIMH 2021

作者： Li, David S. Mendiola, Emilio A. Avazmohammadi, Reza Sachse, Frank B. Sacks, Michael S. James T. Willerson Center for Cardiovascular Modeling and Simulation Oden Institute for Computational Engineering and Sciences Department of Biomedical Engineering The University of Texas at Austin AustinTX United States Computational Cardiovascular Bioengineering Lab Department of Biomedical Engineering Texas A&M University College StationTX United States Nora Eccles Harrison Cardiovascular Research and Training Institute Department of Biomedical Engineering The University of Utah Salt Lake CityUT United States

ISBN: (纸本)9783030787097

Pulmonary arterial hypertension (PAH) imposes a pressure overload on the right ventricle (RV), leading to myofiber hypertrophy and remodeling of the extracellular collagen fiber network. While the macroscopic behavior of healthy and post-PAH RV free wall (RVFW) tissue has been studied previously, the mechanical microenvironment that drives remodeling events in the myofibers and the extracellular matrix (ECM) remains largely unexplored. We hypothesize that multiscale computational modeling of the heart, linking cellular-scale events to tissue-scale behavior, can improve our understanding of cardiac remodeling and better identify therapeutic targets. We have developed a high-fidelity microanatomically realistic model of ventricular myocardium, combining confocal microscopy techniques, soft tissue mechanics, and finite element modeling. We match our microanatomical model to the tissue-scale mechanical response of previous studies on biaxial properties of RVFW and examine the local myofiber-ECM interactions to study fiber-specific mechanics at the scale of individual myofibers. Through this approach, we determine that the interactions occurring at the tissue scale can be accounted for by accurately representing the geometry of the myofiber-collagen arrangement at the micro scale. Ultimately, models such as these can be used to link cellular-level adaptations with organ-level adaptations to lead to the development of patient-specific treatments for PAH. © 2021, Springer Nature Switzerland AG.

关键词： Tissue

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Generating brick-like melt pools for Laser-Based Powder Bed Fusion using flexible beam shaping: a proof of concept on bead-on-plate single tracks

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Procedia CIRP 2024年 124卷 746-750页

作者： Jonas Grünewald Robin Prudlik Vijaya Holla Philipp Kopp Richard Off Thomas Stoll Stefan Kollmannsberger Katrin Wudy Professorship of Laser-based Additive Manufacturing TUM School of Engineering and Design Technical University of Munich Freisinger Landstraße 52 85748 Garching Germany Oerlikon AM Europe GmbH Freisinger Landstraße 52 85748 Garching Germany Chair of Computational Modeling and Simulation TUM School of Engineering and Design Technical University of Munich Arcisstraße 21 80333 Munich Germany Chair of Data Science in Civil Engineering Bauhaus University Weimar Coudraystraße 13A 99423 Weimar Germany

Despite the highest industrial maturity among additive manufacturing processes, laser-based powder bed fusion of metals (PBF-LB/M) lacks the productivity to further establish as an industrial manufacturing process. One approach for adjusting the melt pool shape to increase productivity in PBF-LB/M is spatial beam shaping. The freedom of beam shaping is almost unlimited, owing to modern optical concepts. The key is a beam shape optimized for a specific target. In this publication, the target is a brick-like melt pool cross-section. The publication demonstrates how the corresponding laser beam shape is designed using numerical optimization. The computed beam shape is reproduced using reflective spatial light phase modulators. The calculation is validated based on single-track experiments. Applying the optimized beam shape results in a melt pool shape similar to the optimization target. The width and depth of the melt track produced deviate by a maximum of 7 % from the specified target values.

关键词： Additive Manufacturing PBF-LB/M LCOS-SLM IN718 Beam Shape Optimization

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Estimation of Aortic Valve Interstitial Cell-Induced 3D Remodeling of Poly(Ethylene Glycol) Hydrogel Environments Using an Inverse Finite Element Approach

SSRN

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SSRN 2022年

作者： Khang, Alex Steinman, John Tuscher, Robin Feng, Xinzeng Sacks, Michael S. James T. Willerson Center for Cardiovascular Modeling and Simulation Oden Institute for Computational Engineering and Sciences The Department of Biomedical Engineering The University of Texas at Austin 201 East 24th St Stop C0200 AustinTX78712-1229 United States

Aortic valve interstitial cells (AVICs) reside within the leaflet tissues of the aortic valve and function to replenish, restore, and remodel extracellular matrix components. AVIC contractility is brought about through the contractile properties of the underlying stress fibers and plays a crucial role in processes such as wound healing and mechanotransduction. Currently, it is technically challenging to directly investigate AVIC contractile behaviors within the dense leaflet tissues. As a result, optically clear poly (ethylene glycol) (PEG) hydrogel matrices have been used to study AVIC contractility through means of 3D traction force microscopy (3DTFM). However, the stiffness of the hydrogel material within the vicinity of the AVIC is difficult to measure directly and is further confounded by the remodeling activity of the AVIC. Ambiguity in the local hydrogel mechanical properties can lead to large errors in computed cellular tractions. Herein, we developed an inverse computational approach to estimate AVIC-induced remodeling of the hydrogel material. The capabilities of the model were validated with a ground truth data set generated via a test problem comprised of an experimentally measured AVIC geometry and a prescribed modulus field containing unmodified, stiffened, and degraded regions. The inverse model was able to estimate the ground truth data set with high accuracy. When applied to AVICs assessed via 3DTFM, the model estimated regions of significant stiffening and degradation local to the AVIC. We observed that stiffening was largely localized at AVIC protrusions and was likely a result of collagen deposition as confirmed by immunostaining for collagen type 1. Degradation was more spatially uniform and present in regions further away from the AVIC surface and likely a result of enzymatic activity. Our results indicate that AVICs substantially modify the local hydrogel mechanics, which were successfully quantified by our computational model. Looking forward

关键词： Tissue

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Neuro-symbolic Explainable Artificial Intelligence Twin for Zero-touch IoE in Wireless Network

arXiv

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arXiv 2022年

作者： Munir, Shirajum Kim, Ki Tae Adhikary, Apurba Saad, Walid Shetty, Sachin Park, Seong-Bae Hong, Choong Seon Virginia Modeling Analysis and Simulation Center Department of Computational Modeling and Simulation Engineering Old Dominion University SuffolkVA23435 United States Department of Computer Science and Engineering Kyung Hee University Yongin-si17104 Korea Republic of Department of Computer Science and Engineering School of Computing Kyung Hee University Kyung Hee University Yongin-si17104 Korea Republic of Wireless@VT Group Bradley Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA22203 United States

Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks. A reliable XAI twin system for ZSM requires two composites: an extreme analytical ability for discretizing the physical behavior of the Internet of Everything (IoE) and rigorous methods for characterizing the reasoning of such behavior. In this paper, a novel neuro-symbolic explainable artificial intelligence twin framework is proposed to enable trustworthy ZSM for a wireless IoE. The physical space of the XAI twin executes a neural-network driven multivariate regression to capture the time-dependent wireless IoE environment while determining unconscious decisions of IoE service aggregation. Subsequently, the virtual space of the XAI twin constructs a directed acyclic graph (DAG)-based Bayesian network that can infer a symbolic reasoning score over unconscious decisions through a first-order probabilistic language model. Furthermore, a Bayesian multi-arm bandits-based learning problem is proposed for reducing the gap between the expected explained score and current obtained score of the proposed neuro-symbolic XAI twin. To address the challenges of extensible, modular, and stateless management functions in ZSM, the proposed neuro-symbolic XAI twin framework consists of two learning systems: 1) implicit learner that acts as an unconscious learner in physical space, and 2) explicit leaner that can exploit symbolic reasoning based on implicit learner decisions and prior evidence. Experimental results show that the proposed neuro-symbolic XAI twin can achieve around 96.26% accuracy while guaranteeing from 18% to 44% more trust score in terms of reasoning and closed-loop automation. © 2022, CC BY-NC-ND.

关键词： Internet of Everything

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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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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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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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Space-time hp finite elements for heat evolution in laser-based additive manufacturing

arXiv

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arXiv 2021年

作者： Kopp, Philipp Calo, Victor Rank, Ernst Kollmannsberger, Stefan Computational Modeling and Simulation Technische Universität München Munich Germany Computational Geoscience & Applied Mathematics School of Electrical Engineering Computing & Mathematical Science Curtin University Perth Australia Institute for Advanced Study Technische Universität München Munich Germany

The direct numerical simulation of metal additive manufacturing processes such as laser powder bed fusion is challenging due to the vast differences in spatial and temporal scales. Classical approaches based on locally refined finite elements combined with time-stepping schemes can only address the spatial multi-scale nature and provide only limited scaling potential for massively parallel computations. We address these shortcomings in a space-time Galerkin framework where the finite element interpolation also includes the temporal dimension. In this setting, we construct four-dimensional meshes that are locally refined towards the laser spot and allow for varying temporal accuracy depending on the position in space. By splitting the mesh into conforming time-slabs, we recover a stepwise solution to solve the space-time problem locally in time at this slab;additionally, we can choose time-slab sizes significantly larger than classical time-stepping schemes. As a result, we believe this setting to be well suited for large-scale parallelization. In our work, we use a continuous Galerkin-Petrov formulation of the nonlinear heat equation with an apparent heat capacity model to account for the phase change. We validate our approach by computing the AMB2018-02 benchmark, where we obtain an excellent agreement with the measured melt pool shape. Using the same setup, we demonstrate the performance potential of our approach by hatching a square area with a laser path length of about one meter. © 2021, CC BY.

关键词： 3D printers

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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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