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multiphysics modeling and simulation of 3-D Cu-Graphene Hybrid Nanointerconnects

IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES 2020年第2期68卷 490-500页

作者： Sun, Shuzhan Jiao, Dan Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

Cu-graphene (Cu-G) hybrid nanointerconnects are promising alternative interconnect solutions for the development of future integrated circuit technology. However, the modeling and simulation of their high-frequency electrical performance remains a challenging problem. To enable the design of complicated Cu-G interconnects, we propose a multiphysics modeling and simulation algorithm to cosimulate Maxwell's equations, dispersion relation of graphene, and Boltzmann equation. We also develop an unconditionally stable time marching scheme to remove the dependence of time step on space step for an efficient simulation of the multiscaled and multiphysics system. Extensive numerical experiments and comparisons with measurements have validated the accuracy and efficiency of the proposed work. This article has also been applied to predict the crosstalk effect and propagation delay of graphene-encapsulated Cu nanointerconnects.

关键词： Boltzmann equation Cu-graphene (Cu-G) hybrid nanointerconnects finite difference methods hybrid integrated circuits (ICs) Maxwell's equations multiphysics modeling and simulation time-domain analysis unconditionally stable algorithm

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First-Principles-Based multiphysics modeling and simulation of On-Chip Cu-Graphene Hybrid Nanointerconnects in Comparison With Simplified Model-Based Analysis

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IEEE JOURNAL ON MULTISCALE AND multiphysics COMPUTATIONAL TECHNIQUES 2019年 4卷 374-382页

作者： Sun, Shuzhan Jiao, Dan Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

Cu-Graphene (Cu-G) hybrid nanointerconnects, as well as many graphene-based devices, have a potential to significantly advance the technology of on-chip integrated circuits (IC). However, their modeling and simulation for a long time has relied on simplified steady-state models of graphene. To examine the validity and accuracy of existing simplified models in high-frequency and subnanometer applications, in this article, we theoretically analyze the assumptions made in existing simplified models. Meanwhile, we develop a first-principles-based multiphysics modeling and simulation algorithm to numerically assess the accuracy of the simplified model-based analysis of Cu-G interconnects. In this algorithm, the Maxwell's equations, the equation characterizing graphene, and the Boltzmann equation are cosimulated in time domain. A significant difference is observed between the first-principles-based analysis and the simplified model-based one when the frequency is high or/and the dimension of the Cu-G structure is small, which necessitates the first-principles-based multiphysics modeling and simulation for the design of advanced Cu-G interconnects.

关键词： Boltzmann equation cu-graphene hybrid nanointerconnects Drude model finite difference methods hybrid integrated circuits Maxwell's equations multiphysics modeling and simulation time-domain analysis

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Investigation on the modeling and simulation of hydrodynamics in asymmetric conduction laser micro-welding of austenitic stainless steel and its process optimization

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2025年第4期239卷 530-543页

作者： de la Hoz, Jose Luis Velazquez Cheng, Kai Brunel Univ London Coll Engn Design & Phys Sci Uxbridge England Brunel Univ London Coll Engn Design & Phys Sci Mech & Automot Engn Dept Uxbridge UB8 3PH Middx England

Laser micro-welding is a joining technology utilized across various high-value industries, like medical, automotive, e-mobility, and aerospace. A trial-and-error process to identify welding parameters does not necessarily lead to optimized quality levels. Furthermore, offline non-destructive examination methods often launched to verify welding quality may inadvertently trigger excessive costs and time delays, ultimately failing to guarantee defect-free welds. In response to these challenges, this article introduces an advanced multiscale model designed to unravel the intricate dynamics of hydrodynamics and the overarching physics within laser micro-welding melting pools. Developed using the COMSOL software package, the model adeptly demonstrates how surface tension gradients shape the geometry of welds, thus influencing their quality. This knowledge allows the mapping of welding defects. One of the novelties of the article is to introduce geometric dissimilar welding conditions by simulating an asymmetric edge joint. It shows a study on a new, unstudied way to laser weld with many applications in the field. The model further establishes its utility in design experiments to determine parameter, tolerance, and system design. Moreover, the insights garnered from understanding and controlling these drivers have far-reaching implications for the advancement of subsequent methodological research and the development of in-situ quality control practices by characterizing the welding defects. Finally, the results shows that the discouragingly high computational costs restrict its potential application to support a Digital Twin.

关键词： Asymmetric laser micro welding melt hydrodynamics multiphysics modeling and simulation smart manufacturing process optimization laser conduction regime multiscale modeling multiphysics simulation Digital Twin

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Optimizing Robotic Manipulation With Decision-RWKV: A Recurrent Sequence modeling Approach for Lifelong Learning

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JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2025年第3期25卷 031004页

作者： Dong, Yujian Wu, Tianyu Song, Chaoyang Southern Univ Sci & Technol Sch Design 1088 Xueyuan Rd Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Dept Mech & Energy Engn 1088 Xueyuan Rd Shenzhen 518055 Peoples R China

Models based on the transformer architecture have seen widespread application across fields such as natural language processing (NLP), computer vision, and robotics, with large language models (LLMs) like ChatGPT revolutionizing machine understanding of human language and demonstrating impressive memory capacity and reproduction capabilities. Traditional machine learning algorithms struggle with catastrophic forgetting, detrimental to the diverse and generalized abilities required for robotic deployment. This article investigates the receptance weighted key value (RWKV) framework, known for its advanced capabilities in efficient and effective sequence modeling, integration with the decision transformer (DT), and experience replay architectures. It focuses on potential performance enhancements in sequence decision-making and lifelong robotic learning tasks. We introduce the decision-RWKV (DRWKV) model and conduct extensive experiments using the D4RL database within the OpenAI Gym environment and on the D'Claw platform to assess the DRWKV model's performance in single-task tests and lifelong learning scenarios, showing its ability to handle multiple subtasks efficiently. The code for all algorithms, training, and image rendering in this study is available online (open source).

关键词： foundation models recurrent models lifelong learning robot learning artificial intelligence computational foundations for engineering optimization data-driven engineering engineering informatics machine learning for engineering applications multiphysics modeling and simulation

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On a multiphysics Digital Twin for Aging Prediction of Adhesive Step Lap Joints

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Journal of Computing and Information Science in Engineering 2025年第7期25卷 071001页

作者： Michopoulos, John G. Apetre, Nicole A. Iliopoulos, Athanasios P. Steuben, John C. Materials Science and Technology Division U.S. Naval Research Laboratory Washington DC 20375

The present work describes the early steps in creating a digital twin to predict aging for multi-material adhesive step lap joints (ASLJs), considering simultaneous mechanical and environmental influences. It begins by defining the digital twin context and its specific architecture known as the data-driven digital twin (D3T). For a D3T to work, a theoretical and computational framework must be established to understand how the properties of materials in ASLJs degrade due to environmental damage. The developed framework describes a thermodynamics-based theory for predicting material degradation. The computational implementation of the framework and its performance are evaluated using two models of multi-material ASLJs. The first model contains a single-step ASLJ made of titanium Ti-6Al-4V and carbon epoxy composite with FM-300K adhesive, featuring three variations to show different levels of homogenization. Studies on this model assess the impact of various parameters such as the finite element order, mesh density, damage parameters, and inclusion of damage models for the participating domains. The validation of this model is also provided against experimental data. The second model addresses a multistep ASLJ using the same materials. Predictions from this model are compared favorably with experimental results under two different environmental conditions to gain insights into the aging and performance degradation of the ASLJs. Finally, conclusions and plans close the present paper.

关键词： Adhesives Materials properties data-driven engineering adhesive step lap joints Damage qualification Digital twin Shear (Mechanics) and validation of computational models verification multiphysics modeling and simulation Composite materials advanced computing infrastructure

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multiphysics simulation of Nucleation and Grain Growth in Selective Laser Melting of Alloys

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JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2020年第5期20卷 051002页

作者： Liu, Dehao Wang, Yan Georgia Inst Technol Woodruff Sch Mech Engn Atlanta GA 30332 USA

Selective laser melting (SLM) builds parts by selectively melting metallic powders layer by layer with a high-energy laser beam. It has a variety of applications in aerospace, medical device, and other low-volume manufacturing. Nevertheless, the lack of fundamental understanding of the process-structure-property relationship for better quality control inhibits wider applications of SLM. Recently, a mesoscale simulation approach, called phase field and thermal lattice Boltzmann method (PF-TLBM), was developed to simulate microstructure evolution of alloys in SLM melt pool with simultaneous consideration of solute transport, heat transfer, phase transition, and latent heat effect. In this paper, a nucleation model is introduced in the PF-TLBM framework to simulate heterogeneous nucleation at the boundary of the melt pool in SLM. A new method is also developed to estimate the thermal flux out of the SLM melt pool model given a constant cooling rate. The effects of latent heat and cooling rate on dendritic morphology and solute distribution are studied. The simulation results of AlSi10Mg alloy suggest that the inclusion of latent heat is necessary because it reveals the details of the formation of secondary arms, reduces the overestimation of microsegregation, and provides more accurate kinetics of dendritic growth.

关键词： selective laser melting nucleation dendritic growth phase field method thermal lattice Boltzmann method computational foundations for additive manufacturing multiphysics modeling and simulation

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Evolution of Sliding Contact Wear Between Deformable Conducting Bodies via a multiphysics Framework

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JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2020年第5期20卷 051007页

作者： Michopoulos, John G. Iliopoulos, Athanasios P. Apetre, Nicole A. Steuben, John C. Birnbaum, Andrew J. US Naval Res Lab Computat Multiphys Syst Lab Ctr Mat Phys & Technol Washington DC 20375 USA

A multiphysics computational framework is introduced and exercised to predict the wear behavior of two deformable, heat-conducting bodies under conditions of sliding contact. This framework enables the solution of a coupled system of partial differential equations (PDEs) expressing the conservation of energy and momentum along with two ordinary differential equations (ODEs) expressing mass conservation. This system is intended to capture wear evolution for each of the bodies forming a wear pair, in a self-consistent manner. Furthermore, an arbitrary-Lagrangian-Eulerian approach has been integrated to enable tracking the evolution of the wear fronts on both elements of the sliding contact pair through physics-informed mesh deformation. A theorem and a corollary are proved to indicate that most existing models describing wear that are expressed in the form of an ODE are actually manifestations of the law of conservation of mass. The framework is applied for two distinct slider-base pairs. The first involves an aluminum alloy slider and a copper alloy base. The second pair is identical to the first except it contains a thin strip of soda-lime glass embedded in the surface of the base. The effects of this glass layer on the wear of all participating bodies in comparison to the pair that does not contain this layer are presented. They indicate that while the glass layer has a wear mitigation effect for the stationary base it slightly increases the wear of the slider when compared with the respective bodies when the glass is not present.

关键词： multiphysics modeling and simulation physics-based simulations wear conservation laws coupled multiphysics

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Challenges in Geometry Assurance of Megacasting in the Automotive Industry

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JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2023年第6期23卷 060801页

作者： Warmefjord, Kristina Hansen, Josefin Soderberg, Rikard Chalmers Univ Technol Dept Ind & Mat Sci Gothenburg SE-41296 Sweden Volvo Cars Sweden Assar Gabrielssons Vag SE-41878 Gothenburg Sweden

Megacasting is a new concept in the automotive industry. A large number of sheet metal parts will be replaced with one large aluminum casting, i.e., a megacasting. This helps to reduce weight, opens up for larger design flexibility, allows for a more circular production, and takes away a large number of assembly steps in the production process. However, there are also challenges related to the use of megacastings. This position paper outlines challenges associated with the geometrical quality of the final product. It covers robust design and tolerancing in early product development phases as well as inspection preparation during pre-production and digital twin setup during full production to ensure the geometrical quality of a product containing a megacasting. simulations of both part-level and assembly-level deviation and variation are discussed. The paper outlines a geometry assurance process for products containing megacastings in the automotive industry, and what research challenges that are the most important ones to address in this area. It is concluded that computer-aided tolerancing tools must be able to predict the dimensional effects from joining methods such as flow-drill fasteners or self-pierced riveting, to use casting simulation as input, and to handle combinations of solid and surface meshes. Furthermore, there might be a need for adjustments to the joining process based on digital twins to achieve proper quality at a reasonable price. Experiences in using megacastings in the body-in-white are lacking and a fast learning curve is required.

关键词： megacasting geometry assurance tolerancing variation simulation dimensional control computer-aided engineering geometric assurance/tolerance modeling multiphysics modeling and simulation

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Sustained CAD/CAE Application Integration: Supporting Simplified Models

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JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2021年第1期21卷 011002页

作者： Kirkwood, Robert Sherwood, James A. Univ Massachusetts Lowell Dept Mech Engn 15 MacDonald Dr Nashua NH 03062 USA Univ Massachusetts Lowell Dept Mech Engn 1 Univ Ave Lowell MA 01854 USA

Computer-aided design/computer-aided manufacturing/computer-aided engineering (CAD/CAM/CAE) integration offers designers, analysts, and manufacturers the opportunity to share the data throughout the product development process. Finite element (FE) meshing applications integrated with the solid model data from CAD systems represent a major subset of CAD/CAM/CAE integration. In an earlier paper, it was demonstrated that virtual persistent identifiers (VPIs) can be used to assure or repair sustained integration with successive versions of neutral-format solid models. From that article, several follow-on issues become apparent. The geometry as per the CAE model often differs from the CAD model, so even with cross-format issues resolved, significant obstacles to sustained CAD/CAE integration remain. Along with simplification, the current article investigates additional techniques for further automating the recognition of changes between CAD models, reducing the manual interaction to just a few minutes. The article goes on to demonstrate how associativity can be sustained when using current versions of neutral formats like STEP and IGES. The overall point of the paper is to show that given a precise recognition of the differences between two solid models, a generalized means of ad-hoc integration is possible. This point is demonstrated through two case studies where simplifications of the CAD geometry are made to facilitate the meshing of the part. The integration is shown to be maintained across successive versions and to address a range of simplification processing. A summary of best practices for efficiently accommodating sustained CAD/CAE integration is also presented.

关键词： computer aided design data-driven engineering multiphysics modeling and simulation virtual prototyping computer aided engineering

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Monotonic Gaussian Process for Physics-Constrained Machine Learning With Materials Science Applications

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JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2023年第1期23卷 011011页

作者： Tran, Anh Maupin, Kathryn Rodgers, Theron Sandia Natl Labs Sci Machine Learning Albuquerque NM 87185 USA Sandia Natl Labs Optimizat & Uncertainty Quantificat Albuquerque NM 87185 USA Sandia Natl Labs Computat Mat & Data Sci Albuquerque NM 87185 USA

Physics-constrained machine learning is emerging as an important topic in the field of machine learning for physics. One of the most significant advantages of incorporating physics constraints into machine learning methods is that the resulting model requires significantly less data to train. By incorporating physical rules into the machine learning formulation itself, the predictions are expected to be physically plausible. Gaussian process (GP) is perhaps one of the most common methods in machine learning for small datasets. In this paper, we investigate the possibility of constraining a GP formulation with monotonicity on three different material datasets, where one experimental and two computational datasets are used. The monotonic GP is compared against the regular GP, where a significant reduction in the posterior variance is observed. The monotonic GP is strictly monotonic in the interpolation regime, but in the extrapolation regime, the monotonic effect starts fading away as one goes beyond the training dataset. Imposing monotonicity on the GP comes at a small accuracy cost, compared to the regular GP. The monotonic GP is perhaps most useful in applications where data are scarce and noisy, and monotonicity is supported by strong physical evidence.

关键词： artificial intelligence data-driven engineering engineering informatics machine learning for engineering applications multiphysics modeling and simulation

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