Speakers

To many of us, a perhaps overly simplified description of a Computational Mechanics method is one composed of a variational method and function spaces. It is even simplified further if we assume a Galerkin variational method, which only entails one function space. That is a paradigm that has had enormous practical benefits, being the basis of much large-scale computing done in engineering and science. Usually, this can be phrased in terms of the weak form of the problem, the starting point of discretization. It is the discretization of the function spaces that we deal with in practice, reducing an infinite dimensional problem to a finite dimensional one that can be solved on a computer. The Finite Element Method is obviously the predominant discretization method, and the first one to combine geometric and topologic versatility. In a sense, no matter how complicated an engineering design, it can be discretized using the Finite Element Method. However, it is well known that the exact geometry, perhaps defined by a Computer Aided Design (CAD) file, is almost never represented exactly by the Finite Element Method, the only exceptions being very simple cases. This is but one deficiency of the contemporary Finite Element Method in practice. One can add that building meshes is labor intensive, and a significant bottleneck in the design-through-analysis process. Other deficiencies are the introduction of geometry errors in computational models that arise due to feature removal, geometry clean-up and CAD “healing,” utilized to facilitate efficient mesh generation. Still other shortcomings of contemporary technology are the inability to “close the loop” with design optimization, and the lack of robustness of higher-order finite elements to achieve their full promise in industrial applications. What has been done to address these issues? 

Isogeometric Analysis [1] in its basic form represents a partial solution. It is based on the geometrical representations used in CAD, predominantly smooth splines, and is capable of more precise geometric descriptions, and more robust performance of higher-order spline elements, compared with standard higher-order C0-continuous finite elements, but the problem of developing boundary-fitted meshes remains laborious. Shortly after the introduction of Isogeometric Analysis, Ernst Rank and Alexander Düster proposed the Finite Cell Method, a cut-element or immersed method. In contrast with classical immersed methods, they showed how to obtain the same accuracy as the boundary-fitted method, and specifically higher-order accuracy with higher-order elements. There initial work was for standard higher-order elements but they soon after applied it to Isogeometic Analysis. It has been subsequently shown that Isogeometric Analysis has analytical advantages over standard finite elements in the immersed setting. This has facilitated the original dream of Isogeometric Analysis: To create exact geometries, expedite mesh generation and simplify local refinement. It seems the key concept is the introduction of a phase field that defines the geometry. In the case of an engineering design, the CAD file suffices to defines the phase field. It is binary in this instance, taking on the value 1.0 where there is material and 0.0 elsewhere. The phase field concept can be brought to life as a continuous function, which enables the integration of other types of analysis, such as topology optimization within CAD, additive manufacturing, and phase field fracture. 

Phase fields are everywhere in contemporary Computational Mechanics and are advocated as a standard device going forward. Immersed and phase field analysis will be illustrated through examples and applications, including Computational Medicine. I also hope to address the open question of whether we can immerse a geometry in an artificial neural net, say a Variationally Mimetic Operator Network (VarMiON), and obtain better, worse or equivalent results to standard Finite Element or Isogeometric Analysis Methods.

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[1] T.J.R. Hughes, J.A. Cottrell and Y. Bazilevs, “Isogeometric Analysis: CAD, Finite Elements, NURBS, Exact Geometry and Mesh Refinement,” Computer Methods in Applied Mechanics and Engineering, 194, (2005) 4135-4195.

In this lecture, unique techniques applied to linear Timoshenko and Bernoulli-Euler beam elements, and their applications in various engineering fields are presented. 

First, a brief outline of the Adaptively Shifted Integration (ASI)-Gauss code incorporated with linear Timoshenko beam elements and their applications are introduced. This code provides higher computational efficiency than the conventional code by the shifting numerical integration points of beam elements to appropriate positions according to the elasto-plastic properties. It can be applied to those problems with strong nonlinearities including phenomena such as member fracture and elemental contact. Several examples such as aircraft impact analysis of the WTC tower, seismic pounding analysis of the Nuevo Leon buildings, collapse analysis of a building subjected under tsunami wave and debris collision, and motion behaviour analyses of indoor non-structural components such as ceilings and furniture are presented.

Next, the parallel solution scheme of inverse dynamics using Bernoulli-Euler beam elements and its application to a torque cancelling system (TCS) are introduced. The TCS calculates reaction moments generated by motors in robots by considering the dynamics of the numerically modelled system. The developed scheme can handle different types of configurations and can also consider elasticity of constituted links or passive joints by only changing the input numerical model. Once the reaction moment is known, it can be cancelled by applying an anti-torque to a torque generating device. Some applications of the system are presented in this lecture.

Biography:

Professor Daigoro Isobe received his Ph.D. degree from the University of Tokyo in 1994, and is currently a professor at University of Tsukuba, Japan. He has conducted various researches on collapse behaviors of building structures, and has also introduced unique approaches in the field of robotics using the essence of computational mechanics. He has published over 400 journal papers, conference papers, book chapters, and books. He served as a chairman of several international conferences and workshops such as COMPSAFE 2020, a special interest conference of IACM, and IWACOM-III, a unique set of international workshops held under JSCES. He received the Ichimura Award upon those achievements in the field of structural collapse simulations, in 2014, in presence of Princess Akiko of Japan. He also received the Kawai Medal from JSCES in 2015, and the Computational Mechanics Achievements Award from JSME in 2019. He is now a fellow of JSME and the President of JSCES.

The lecture provides an overview of recent research conducted by the speaker's group and collaborators on the automated discovery of material models. This research advocates for a paradigm shift, moving away from the traditional approach of calibrating unknown parameters within a preselected material model towards a new objective of model discovery. This entails the simultaneous selection, generation, or encoding of the most suitable model to interpret given experimental data, along with the calibration of its unknown parameters. To achieve this goal, a variety of tools are employed, ranging from sparse regression [1-4] to Bayesian learning [5], and from formal grammars to symbolic regression [6]. Each of these tools possesses distinct features but shares the common aim of ensuring the fulfilment of physics constraints and interpretability of the discovered model(s). Initially developed to discover a specific model within a predetermined category (i.e. hyperelasticity [1,4], viscoelasticity [7] or plasticity [2]), the approach was more recently extended to the general case of a material belonging to an unknown class of constitutive behavior [3]. Additional relevant aspects such as the type of data, specimen design, and experimental validation are also discussed.

[1] Flaschel, M., S. Kumar, & L. De Lorenzis (2021). Unsupervised discovery of interpretable hyperelastic constitutive laws. Computer Methods in Applied Mechanics and Engineering 381, 113852.

[2] Flaschel, M., S. Kumar, & L. De Lorenzis (2022). Discovering plasticity models without stress data. npj Computational Materials 8, 91.

[3] Flaschel, M., S. Kumar, & L. De Lorenzis (2023). Automated discovery of generalized standard material models with EUCLID. Computer Methods in Applied Mechanics and Engineering 405, 115867.

[4] Flaschel, M., H. Yu, N. Reiter, J. Hinrichsen, S. Budday, P. Steinmann, S. Kumar, & L. De Lorenzis (2023). Automated discovery of interpretable hyperelastic material models for human brain tissue with EUCLID. Journal of the Mechanics and Physics of Solids 180, 105404.

[5] Joshi, A., P. Thakolkaran, Y. Zheng, M. Escande, M. Flaschel, L. De Lorenzis, & S. Kumar (2022). Bayesian-EUCLID: discovering hyperelastic material laws with uncertainties. Computer Methods in Applied Mechanics and Engineering 398, 115225.

[6] Kissas, G., S. Mishra, E. Chatzi, & L. De Lorenzis (2024). The language of hyperelastic materials. arXiv:2402.04263.

[7] Marino, E., M. Flaschel, S. Kumar, & L. De Lorenzis (2023). Automated identification of linear viscoelastic constitutive laws with EUCLID. Mechanics of Materials 181, 104643.

Digital twins represent the next frontier in the impact of computational science on grand challenges across science, technology and society. A digital twin is a computational model or set of coupled models that evolves over time to persistently represent the structure, behavior, and context of a unique physical system, process, or biological entity. This talk will highlight progress and opportunities in achieving robust, reliable digital twins at scale, including the important role of graphical models, reduced-order modeling, scientific machine learning, and uncertainty quantification.

Human periarticular bone defect is a difficult disease in orthopedics. There is challenge issue to recognize anisotropy, heterogeneity of bone tissue structure and graphics by low resolution clinic-CT image. In collaboration with clinical medicine, the data driving and mechanics modeling technique for bone defect reconstruction is proposed. Data driven micro-CT and clinical-CT images are used to obtain the characteristics of cancellous bone structure and graphics. The experimental technology and numerical method are developed for predicting the mechanics parameters of animal specimen on the multi-axial stress state. The constitutive model of heterogeneous anisotropy of bone tissue is established and the parameters are deduced by numerical simulation and specimen experiment. For designing the robust cancellous prosthesis bone, a kind of spinodal lattice is designed with random, indeterminate, aperiodic, asymmetry, irregular, large space for mechanical and biological function. The digital triplets with physical environment scanning CT image, virtual environment equivalent modulus and additive manufacturing lattice design are created to guide the clinical treatment of personalized bone defects. This work has been demonstrated in some clinical applications to the benefit of patients.

Biography:

Zhuo Zhuang is a Professor, School of Aerospace Engineering, Tsinghua University, China and Vice President of International Association for Computational Mechanics (IACM). He got Ph.D. in University College Dublin, Ireland, 1995 and was honored with Honorary Doctor Degree (EngD), Swansea University, UK, 2017. He has long engaged in computation and fracture mechanics for developing new theories, innovative models and methods for challenging problems in engineering involving multiple fields and scales, finite deformation, crystal plasticity and damage up to material and structural failure. He is a chief scientist of national fundamental research projects of China. He published more than 360 papers at International and National Journals, 2 books in English as "Extended Finite Element Method”, Elsevier, 2014, ISBN: 978-0-12-407717-1 and “Dislocation Mechanism-Based Crystal Plasticity Theory and Computation”, Elsevier, 2019, ISBN:978-0-12-814591-3, as well as more than 10 books in Chinese, with total 14,000 citations.

While IGA has significantly impacted much of computational mechanics, one area that has benefited the most from IGA research is computational methods for shell structures. Because geometrically complex, smooth surfaces are naturally represented in CAD systems, much of that technology could be directly employed in the discretization of existing shell theories, with increased accuracy and robustness in general-purpose nonlinear applications relative to traditional FEA representations. In addition, the increased smoothness of CAD surface representation (by means of B-Splines and their rational and unstructured variants) enabled the formulation, and use in general-purpose nonlinear applications, of thin shell theories previously unattainable in traditional FEA. Many more developments followed, making shells some of the most mature of IGA technologies today and a prime candidate for implementation in commercial FEA codes. This presentation will focus on key recent developments in IGA for thin shell structures and show a novel application of IGA to the modeling of architected materials and structures.

In recent years, architected materials and structures have gained significant popularity due to their ability to reach enhanced performance for use in multifunctional and multidisciplinary applications. Among numerous options investigated, architected structures based on Triply Periodic Minimal Surfaces (TPMS) have gained increasing attention because they exhibit exceptional properties in multiple disciplines simultaneously. However, because of the complexities involved in the geometry representation and mechanical response of these structures, physics-based modeling for this problem class engenders a set of challenges. In this paper we address some of these challenges by developing a first-of-its-kind IGA-based geometry modeling and simulation framework for architected materials and structures. We focus on sheet TPMS-based structures, for which we first develop an IGA-suitable geometry modeling pipeline and then evaluate their mechanical performance in crushing simulations.

Biography: Yuri Bazilevs is the E. Paul Sorensen Professor in the School of Engineering at Brown University. His research interests are in computational science and engineering, with emphasis on the modeling and simulation in solids and structures, fluids, and their coupling in HPC environments. For his research contributions Yuri received many awards and honors, including the 2018 Walter E. Huber Research Prize from the ASCE, the 2020 Gustus L. Larson Award from the ASME, and the Computational Mechanics Award from the International Association for Computational Mechanics (IACM). He is included in the lists of Highly Cited Researchers, both in the Engineering (2015-2018) and Computer Science (2014-2019) categories. Yuri recently completed his service as the President of the US Association for Computational Mechanics (USACM) and as the Chairman of the Applied Mechanics Division of the ASME. He currently serves on the US National Committee for Theoretical and Applied Mechanics (USNC/TAM).

Buckling phenomena are difficult to be computationally analyzed due to the high geometric nonlinearity, especially in the case of composite panels and shells. New design methodologies will be presented for the development of thin innovative aerospace composite structures, that work in the post-buckling field and that reach multi-stable configurations.

A paradigm shift in design concepts, considering buckling no more as a phenomenon to be avoided, but as a favorable behavior to be actively exploited will be presented, together with the new challenges related to the design and analysis of these structures. The developed design methodologies consist of an integrated mathematical formulation based on finite element analyses, that has also the potential to contribute to an increased role of modelling and simulation for aerospace composite structures from the preliminary design to the certification.

This design methodology represents the main goal of an ERC Advanced Grant funded by the European Commission, called NABUCCO with the duration of 5 years. NABUCCO covers all the aspects aforementioned and will include a series of experimental validations spanning from simple structural components to representative scaled wing models.

Biography

Chiara Bisagni is Professor at the Politecnico di Milano in Italy, Department of Aerospace Science and Technology, and she has a guest Professor position at the Delft University of Technology in the Netherlands, Faculty of Aerospace Engineering. She received her Ph.D. in Aerospace Engineering from Politecnico di Milano, where she started her academic career. Then, she was Professor at the University of California San Diego, before moving to the Delft University of Technology, where she was Professor from 2015 to 2023. 

Her research regards aerospace composite structures. Her projects span from buckling, post-buckling, and crashworthiness, to fatigue, damage tolerance and optimization, for aeronautical and space applications. 

Professor Bisagni received several awards, including an Amelia Earhart Fellowship, a Marie Curie Grant, a Young Researcher Fellowship from MIT, a Fulbright Grant, and recently was awarded an ERC Advanced Grant. She is Fellow of the American Institute of Aeronautics and Astronautics, and Knight of the Order of Star of Italy.

Current Finite Elements implementation, including those in commercial software, are characterized by a fixed/limited number of degree of freedom per nodes. Normally these are ‘six’ for structural elements and ‘three’ for 3D ones. These constraints could lead to severe limitations to solve ‘localized’ stresses/fields, laminated composite and/or metallic structures, electromechanical problems and structures subjected to multifield loadings.

In recent years, the speaker and co-workers have developed a version of finite elements in which the number of degrees of freedom in the node can vary within the element, from node to node: this is the NDK, Node Dependent Kinematic version of FE. In other words, each node can refer to a different structural theory and the FEM matrices are weighted not only with respect to classical shape functions but also with respect to structural theory. This was done for one-dimensional, two-dimensional plane and curved and three-dimensional elements. The key tool for the generation of the NDK formulation is the Carrera Unified Formulation, proposed by the speaker more than 25 years ago, which allows the writing of stiffness matrices in terms of a few fundamental 'nuclei' that are essentially independent of the type of structural theory and shape functions used in the node.

This lecture illustrates the NDK FEM method and propose applications to various linear and non-linear, static and dynamic problems, metallic and laminated composite materials, mechanical and electrical loadings. In particular, the possibility of applying NDK to global-local problems without the need to use transition elements and/or penalty procedures will be highlighted. The advantage in terms of both accuracy and computational cost reduction of the NDK-FEM method over traditional FEM will be clearly shown. As approximating functions for the structural part, reference will be made to polynomial (Taylor-based) expansions, use of Lagrange and Legendre polynomials or a combination of these.

Biography

Dr Erasmo Carrera is Professor of Aeronautics and Astronautics at Politecnico di Torino. He acts as President of the Italian Association of Aeronautics and Astronautics, A.I.D.A.A, member of Accademia delle Scienze di Torino and Academie de l’Air et de l’Espace He has been visiting professor at the University of Stuttgart, Virginia Tech, Royal Melbourne Institute of Technology, Tambov University, Supmeca and Ensam, PMU. Dr Carrera has been responsible for various research contracts granted by public and private national and international institutions, including the European Community, European Space Agency, Thales Alenia Space and Embraer. He is founder and Editor-in-Chief of Advances in Aircraft and Spacecraft Science, Editor-in-Chief of Mechanics of Advanced Materials Structures and Section Editor of Journal and Sound and Vibration. 

He has introduced the Unified Formulation, or CUF (Carrera Unifed Formulation), as a tool to establish a new framework in which to develop theories of beams, plates and shells for metallic and composite multilayered structures. He has been author and co-author of about 800 papers on the above topics. Carrera has been recipient of various 'best paper award' and of the 'JN Reddy Medal'. Professor Carrera has been Highly Cited Researchers (Top 100 Scientist) by Thompson Reuters in the two Sections: Engineering and Materials. He has been confirmed HiCI in 2015 in the Section Engineering. The only aerospace Engineering worldwide. Due to his scientific outcoming professor Carrera has been awarded by the President of Italian Republic, as 'Honoray Commendator'.

In recent years, there has been significant interest in using data-driven methods to solve problems in science and engineering, especially in the context of large coupled fluid flow and transport. Numerical simulations for these problems can be costly, making data-driven methods valuable for understanding and improving efficiency in quantifying and predicting states. This talk will review recent advancements in data-driven methods, such as dynamic mode decomposition, physics-informed neural networks, manifold learning, and neural operators, as applied to relevant problems involving coupled incompressible fluid flow with transport. These problems are of interest in sustainable resource exploration, geophysics, and various industrial applications. The talk will show how data-driven information can improve the efficiency of numerical simulation software for short-time prediction and adaptive time-stepping strategies, exploring parametric manifolds for unseen scenarios, and reconstructing high-dimensional simulations with lower-dimensional structures in feasible time.

In this talk, the design, modeling, and simulation on acoustics (vibration and noise, ultrasonic waves non-destructive testing, and structural health monitoring) and biomedical devices (vascular stents) is discussed. It is demonstrated that modeling and simulation plays a pivotal role for successful completion of projects. First an overview is given to the topics. The latter segment of the talk delves into a specialized exploration of ultrasonic waves. Specifically, it delves into the Rayleigh waves and the zero-group-velocity (ZGV) mode waves, and how to combine it with the sensor technology for effective defect detection. The application of machine learning (ML) with ultrasonic waves is also discussed. Finally, the development of a novel stent-graft, incorporating both computational structural analysis and fluid dynamics analysis, is presented.

Biography

Fangsen Cui received his B. S. and M. Eng. degrees from Xi'an Jiaotong University in 1984 and 1989, respectively. He joined the Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR) in 1998 after as PhD student in National University of Singapore. He is now a Senior Principal Scientist and Group Manager in Acoustics, Biomechanics &SHM. 

Fangsen was awarded the International Computational Investigator Award in 2019 and the IES Prestigious Engineering Achievement Award in 2021. Fangsen is active in academic/society activities: He is the co-editor or in the editorial board for a few journals. He is the General Council Member of International Association for Computational Mechanics (IACM, from 2012), Executive Council Member of Asia-Pacific Association for Computational Mechanics (APACM, from 2018), Fellow of International Association of Applied Mechanics (IAAM), and the President of Singapore Association for Computational Mechanics (SACM, from 2017).

The success of modern finite difference (FD) methods for numerical simulation of complex problems in computational mechanics is attributable to the development of summation-by-parts (SBP) finite difference. Traditionally, the design of SBP operators have been exclusively based on central FD stencils on co-located grids, as this has generally been accepted as necessary to ensure a skew-symmetric linear operator which is critical to prove linear stability. Recently, the dual-pairing (DP) SBP framework has shown that this is not necessarily true. The DP SBP operators are a dual-pair of backward and forward FD stencils which together preserve the SBP property. Because of the additional degrees of freedom, the DP SBP framework supports the design of SBP FD with improved properties, such as upwinding and dispersion relations preserving (DRP) properties, necessary for reliable simulation of nonlinear problems, including shocks, and wave propagation problems with high-frequency components. The result of this improvement is the absence of computationally fatal spurious wave modes in numerically computed solutions, and an efficiency increase that is exponential with the dimension of the problem. We will define and give explicit examples of DP SBP operators with a complete methodology to construct them.We will present numerical simulations of complex wave problems in 3D elastic solids and nonlinear atmospheric fluid flow, and demonstrate the efficiency of the DP SBP framework over traditional methods. 

Biography

Dr Kenneth Duru is a computational and applied mathematician. He is currently a Fellow at the Mathematical Sciences Institute, Australian National University. In 2012, he earned his PhD in Scientific Computing (major Numerical Analysis) from Uppsala University, Sweden. Dr Duru’s research lie at the interfaces of mathematical analysis, numerical analysis and high-performance computing (HPC), and contributes to the mathematical foundation of numerical methods and simulation tools for the solution of partial differential equations modelling complex real-world problems.

With the growing interest in additive manufacturing utilizing topology optimization, it has recently become possible to fabricate optimized porous and lattice structures at the micro-scale (or meso-scale) level. For topology optimization at such small scales, a homogenization method based on the finite element method is generally used. However, in the optimal design of 3D microstructures considering material nonlinearity, the computational cost and memory usage increase dramatically, requiring treatments such as considerably coarsening the element mesh of the microstructures. As a result, a truly optimal topology of microstructure cannot be obtained.

Therefore, we propose a new multiscale topology optimization method using a homogenization method based on Fourier Fast Transform (FFT). Here, we address the problem of finding the optimal topology of a microstructure consisting of two different elastoplastic materials in order to maximize the energy absorption of the entire macrostructure.

It is shown that the proposed method can significantly reduce the computational cost and memory usage, with results that are almost identical to those of conventional homogenization methods based on finite element methods.

Short Bio:

• February 2010: Received Doctor of Engineering (Dr.-Ing.) from Institute for Structural Mechanics, University of Stuttgart, Germany (Supervisor: Professor Ekkehard Ramm)

• June 2010: Assistant Professor in the Department of Civil Engineering at Tohoku University, Japan

• April 2012: Assistant Professor in the International Research Institute of Disaster Science (IRIDeS) at the Tohoku University

• January 2015: Associate Professor in the Department of Civil Engineering at Tohoku University

• April 2018-current: A Full Professor in the Department of Civil Engineering at Nagoya University, Japan

• My research interests include topology optimization and optimal design of microstructures considering nonlinear mechanical behavior and its application to additive manufacturing. 

• I am also a board member of JSCES since 2018, a GC member of IACM since 2023, and an EC member of ASSMO since 2024.

Physics-based computational models of the cardiovascular system are increasingly used to simulate hemodynamics, tissue mechanics, and physiology in evolving healthy and diseased states. While predictive models using computational fluid dynamics (CFD) originated primarily for use in surgical planning, their application now extends well beyond this purpose. An increasingly wide range of modeling applications are aimed at uncovering fundamental mechanisms of disease progression and development, performing model-guided design, and generating testable hypotheses to drive targeted experiments. Increasingly, models are incorporating multiple physical processes spanning a wide range of time and length scales in the heart and vasculature. We will discuss recent advances in modeling methodology, including pivotal developments in image processing, multi-physics simulations, modeling under uncertainty, and vascular growth and remodeling. We argue that traditional CFD alone is insufficient to tackle increasingly complex clinical and biological problems across scales and systems. Rather, CFD should be coupled with appropriate multiscale biological, physical, and physiological models needed to produce comprehensive, impactful models of mechanobiological systems and complex clinical scenarios. 

Biography:

Alison Marsden is the Douglass M. and Nola Leishman Professor of Cardiovascular Disease in the Departments of Pediatrics, Bioengineering, and, by courtesy, Mechanical Engineering at Stanford University. She is a member of the Institute for Mathematical and Computational Engineering. From 2007-2015 she was a faculty member in Mechanical and Aerospace Engineering at UCSD. She graduated with a BSE degree in Mechanical Engineering from Princeton University in 1998, and a PhD in Mechanical Engineering from Stanford in 2005. She was a postdoctoral fellow at Stanford University in Bioengineering from 2005-07. She was the recipient of a Burroughs Wellcome Fund Career Award at the Scientific Interface in 2007, an NSF CAREER award in 2011. She was elected fellow of AIMBE and SIAM in 2018, the APS DFD in 2020, and BMES in 2021. She is the 2023 recipient of the Van C. Mow medal from the ASME Bioengineering Division. She has published over 160 journal articles and holds leadership roles in the ASME and APS scientific societies. Her research focuses on the development of numerical methods for cardiovascular biomechanics and application of engineering methods to impact patient care in cardiovascular surgery and congenital heart disease.

Significant recent progress has been made in flow modeling using both equation-driven and data-driven techniques. We focus here on the intersection of these two approaches, using data to complete the details of known flow dynamics. We utilize the classical approaches and tools of the modern day – theoretical analysis and data-driven methods, respectively –to illuminate features responsible for the sustenance of turbulence associated with nonlinear interactions in the Navier-Stokes equations. Focusing on a spatio-temporal representation of turbulence near walls – an omnipresent phenomenon in large-scale transport and transportation – we identify and quantify key scale interactions. Methods to obtain data-driven representations of both linear and nonlinear dynamics will be discussed, along with some implications for the modeling of wall turbulence. The work has benefited from funding by the US ONR, ARO and AFOSR over a period of years, which is gratefully acknowledged.

The arbitrary Eulerian Lagrangian (ALE) formulation is a common approach to tracking fronts in finite element simulations. It is, however, tricky to track fronts over long distances, as the mesh density generally becomes too low on one side of the front (increasingly large elements). Moreover, traditional ALE front tracking cannot cope with changes in front topology. To remedy the above problems (at least the first one), remeshing is required from time to time to maintain correct mesh approximation capability on both sides of the front. This remeshing requires projection of the field and updating of the database in the simulation, which is detrimental to the speed of the code and the accuracy of the solution.

We introduce a new approach in which the set of nodes located on the front evolves over time. This allows the front to migrate through the mesh without breaking the approximation capability of the mesh. Topological changes are also easily taken into account. For example, a small front can form, propagate and merge with other fronts as it propagates. The small front may be represented initially by three or four nodes, then by hundreds of nodes as it lengthens.

For the new approach to work properly, we have to accept that some elements become very small and possibly of zero measure. This means that the elements can deform in extreme ways, hence the acronym X-MESH. Surprisingly, as we shall show, this situation does not prevent simulations from being carried out. 

In short, X-MESH simply uses node movements to propagate fronts over long distances, even in the event of topological changes. The mesh topology remains unchanged during simulation. The size and sparsity of the finite element matrices are therefore fixed throughout the simulation, and no field projection is required. As the simulation progresses, nodes arrive and depart from the front.

X-MESH's capability will be demonstrated for several important applications in mechanics and physics, such as front tracking in the Stefan phase change model or the simulation of immiscible two-phase flows.

The work is funded by a European Research Council (ERC) Synergy Grant whose co-pI is J.F. Remacle.

References:

- N. Moës, J.-F. Remacle, J. Lambrechts, B. Lé and N. Chevaugeon. The eXtreme Mesh Deformation Approach (X-MESH) for the Stefan Phase Change Model. Journal of Computational Physics 2023, 477, 111878. https://doi.org/10.1016/j.jcp.2022.111878.

- A. Quiriny, J. Lambrechts, N. Moës and J.-F. Remacle, X-Mesh: A new approach for the simulation of two-phase flow with sharp interface, Journal of Computational Physics 2024, 112775. https://doi.org/10.1016/j.jcp.2024.112775.

Recently, supercomputing has been changing dramatically. Integration/convergence of Simulation/Data/Learning (S+D+L) is important towards Society 5.0 proposed by Japanese Government, which enables integration of cyber space & physical space. In 2015, we started the BDEC project (Big Data & Extreme Computing) for development of supercomputers and software for integration of (S+D+L). In May 2021, we started operation of the Wisteria/BDEC-01. It is the first BDEC system, which consists of computing nodes for computational science and engineering with A64FX (Odyssey), and those for Data Analytics/AI with NVIDIA A100 GPU's (Aquarius). We also develop a software platform "h3-Open-BDEC" for integration of (S+D+L) on the Wisteria/BDEC-01, which is designed for extracting the maximum performance of the supercomputers with minimum energy consumption focusing on (1) Innovative method for numerical analysis by adaptive precision, accuracy verification and automatic tuning, (2) Hierarchical Data Driven Approach based on machine learning, and (3) Software for heterogeneous systems. Integration of (S+D+L) by h3-Open-BDEC enables significant reduction of computations and power consumption, compared to those by conventional simulations. In this talk, achievements in this project and future perspectives towards the next stage will be described. 

Aspects of fluid dynamics appear often in Leonardo da Vinci's notebooks: sketches of water flow, plans for flying machines, studies of bird flight.  He seemed fascinated by the eddying movement of water, and designed ingenious experiments to try and understand the causes of these complex motions.  He lacked the advanced mathematical tools required to study this subject properly, however, and his attempts to use geometrical reasoning for the analysis of fluid flows were unsuccessful. This limitation is reflected in many of the machines he designed, which we now know cannot work. His observational powers, however, allowed him to make some exceptionally perceptive remarks that foreshadow techniques used today, both in the experimental and the theoretical analysis of flow problems, observations illustrated by striking drawings and sketches.  In this talk, some of Leonardo's reflections on turbulence will be discussed, vis a vis the present understanding of this captivating but baffling subject, perhaps the last unsolved problem in classical physics.

Quantum information theory transforms the very foundations of information theory and computing by replacing pre-quantum, or ‘classical’, informational foundation of binary strings into superpositions thereof, utilising quantum theory’s wave-particle duality. In a sense, bits capture the particle-like behaviour with the bit being zero or one like a particle being there or not there (half a particle is forbidden). Superposition bits, such as allowing a 0 and a 1 to co-exist asa superposition of waves representing each, relies on the wave-like property. From this wave-and-particle representation of information is introduced, even the logical rules such as for Boolean operations, manifested as concatenations of one-bit operations such as NOT and two-bit operations such as NAND, gives way to quantum logic, which respects and preserves wave-and-particle-like properties. From this new paradigm of information processing, disruptive changes occur to the notion of whether problems such as number factorisation are even hard in the sense of whether the subexponential cost for solving with respect to the size of computational input, and a provable advantage exists for a kind of unstructured search problem. Building on these notions, I provide a perspective on quantum computing for data science, including a dive into state-of-the-art for both hardware and algorithms.

Brief Bio: Barry Sanders is Scientific Director of Calgary’s “Quantum City”, hosted by the University of Calgary and tasked with building a strong quantum ecosystem in Alberta. Barry was awarded two Diplomas of Imperial College in 1985 and 1987 and a Doctor of Philosophy in 1988 from the University of London. In recognition of career achievement, Barry was awarded a Doctor of Science from Imperial College London in 2018. His postdoctoral positions were in Australia and New Zealand, and he was a professor at Macquarie University Sydney for 12 years before moving back to his Alma Mater University of Calgary in 2003. Barry’s theoretical research includes quantum sensing and metrology, quantum and quantum-resilient communication, quantum computing and quantum optics. He has held numerous distinguished international visiting professorships and affiliations in Canada, the USA, China, India and elsewhere, and is a Scientist with the Creative Destruction Lab at both the Universities of Toronto and Calgary; the Creative Destruction Lab’s mission is to accelerate the commercialization of science for the betterment of humankind. Sanders serves as an Expert with the Canadian Council of Academies and is a member of the Scientific Board for the Banff International Research Station. He is a former member of the Open Quantum Institute Incubation Advisory Board of the Geneva Science and Diplomacy Anticipator and is co-lead of the International Research Network: Canada-France Quantum Alliance involving France’s Centre National de la Recherche Scientifique. Barry serves on expert panels in Canada, the USA and Europe. He is a Fellow of the Royal Society of Canada, of the United Kingdom Institute of Physics, of the American Physical Society, and of Optica, and he received the City of Calgary International Achievement Award in 2022.

The inception of the Space–Time Isogeometric Analysis (ST-IGA) in 2010 was major milestone in the Space–Time Computational Flow Analysis (STCFA). It enabled first-of-its-kind solutions in many classes of problems ranging from flapping-wing aerodynamics of an actual locust to tsunami-shelter vertical-axis wind turbines, ventricle-valve-aorta flow analysis to car and tire aerodynamics with near-actual geometries, road contact, and tire deformation. We will provide an overview of how the ST-IGA evolved in the solutions it can deliver in connection with the STCFA and reached where it is in 2024. We focus on tire aerodynamics with complex tread pattern and rod contact as one of the latest examples of what the ST-IGA can do now.

Bio:

Professor Takizawa is the Head of the Mathematics and Physics Unit “Multiscale Analysis, Modelling and Simulation” at Waseda University. He received his B.S., M.S., and Ph.D. degrees from Tokyo Institute of Technology in 2001, 2002, and 2005. He has been conducting computational fluid mechanics research since 2000, computational FSI and mesh generation research since 2003, and IGA research since 2010. He has published nearly 130 Web-of-Science- indexed journal articles on these subjects. He is a Web of Science Highly Cited Researcher. He coauthored a textbook titled Computational Fluid–Structure Interaction: Methods and Applications.