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Falcucci G, Amati G, Fanelli P, Krastev VK, Polverino G, Porfiri M, Succi S. Extreme flow simulations reveal skeletal adaptations of deep-sea sponges. Nature 2021; 595:537-541. [PMID: 34290424 DOI: 10.1038/s41586-021-03658-1] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 05/20/2021] [Indexed: 11/09/2022]
Abstract
Since its discovery1,2, the deep-sea glass sponge Euplectella aspergillum has attracted interest in its mechanical properties and beauty. Its skeletal system is composed of amorphous hydrated silica and is arranged in a highly regular and hierarchical cylindrical lattice that begets exceptional flexibility and resilience to damage3-6. Structural analyses dominate the literature, but hydrodynamic fields that surround and penetrate the sponge have remained largely unexplored. Here we address an unanswered question: whether, besides improving its mechanical properties, the skeletal motifs of E. aspergillum underlie the optimization of the flow physics within and beyond its body cavity. We use extreme flow simulations based on the 'lattice Boltzmann' method7, featuring over fifty billion grid points and spanning four spatial decades. These in silico experiments reproduce the hydrodynamic conditions on the deep-sea floor where E. aspergillum lives8-10. Our results indicate that the skeletal motifs reduce the overall hydrodynamic stress and support coherent internal recirculation patterns at low flow velocity. These patterns are arguably beneficial to the organism for selective filter feeding and sexual reproduction11,12. The present study reveals mechanisms of extraordinary adaptation to live in the abyss, paving the way towards further studies of this type at the intersection between fluid mechanics, organism biology and functional ecology.
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Affiliation(s)
- Giacomo Falcucci
- Department of Enterprise Engineering "Mario Lucertini", University of Rome "Tor Vergata", Rome, Italy. .,Department of Physics, Harvard University, Cambridge, MA, USA.
| | - Giorgio Amati
- High Performance Computing Department, CINECA Rome Section, Rome, Italy
| | | | - Vesselin K Krastev
- Department of Enterprise Engineering "Mario Lucertini", University of Rome "Tor Vergata", Rome, Italy
| | - Giovanni Polverino
- Centre for Evolutionary Biology, School of Biological Sciences, University of Western Australia, Perth, Western Australia, Australia
| | - Maurizio Porfiri
- Department of Biomedical Engineering, Tandon School of Engineering, New York University, New York, NY, USA.,Department of Mechanical and Aerospace Engineering, Tandon School of Engineering, New York University, New York, NY, USA.,Center for Urban Science and Progress, Tandon School of Engineering, New York University, New York, NY, USA
| | - Sauro Succi
- Department of Physics, Harvard University, Cambridge, MA, USA.,Italian Institute of Technology, Center for Life Nano- and Neuro-Science, Rome, Italy.,National Research Council of Italy - Institute for Applied Computing (IAC), Rome, Italy
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Coveney PV, Highfield RR. When we can trust computers (and when we can't). PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200067. [PMID: 33775149 PMCID: PMC8059589 DOI: 10.1098/rsta.2020.0067] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
With the relentless rise of computer power, there is a widespread expectation that computers can solve the most pressing problems of science, and even more besides. We explore the limits of computational modelling and conclude that, in the domains of science and engineering which are relatively simple and firmly grounded in theory, these methods are indeed powerful. Even so, the availability of code, data and documentation, along with a range of techniques for validation, verification and uncertainty quantification, are essential for building trust in computer-generated findings. When it comes to complex systems in domains of science that are less firmly grounded in theory, notably biology and medicine, to say nothing of the social sciences and humanities, computers can create the illusion of objectivity, not least because the rise of big data and machine-learning pose new challenges to reproducibility, while lacking true explanatory power. We also discuss important aspects of the natural world which cannot be solved by digital means. In the long term, renewed emphasis on analogue methods will be necessary to temper the excessive faith currently placed in digital computation. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.
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Affiliation(s)
- Peter V. Coveney
- Centre for Computational Science, University College London, Gordon Street, London WC1H 0AJ, UK
- Institute for Informatics, Science Park 904, University of Amsterdam, 1098 XH Amsterdam, The Netherlands
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Coveney PV, Highfield RR. From digital hype to analogue reality: Universal simulation beyond the quantum and exascale eras. JOURNAL OF COMPUTATIONAL SCIENCE 2020; 46:101093. [PMID: 33312270 PMCID: PMC7709487 DOI: 10.1016/j.jocs.2020.101093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 03/03/2020] [Indexed: 05/23/2023]
Abstract
Many believe that the future of innovation lies in simulation. However, as computers are becoming ever more powerful, so does the hyperbole used to discuss their potential in modelling across a vast range of domains, from subatomic physics to chemistry, climate science, epidemiology, economics and cosmology. As we are about to enter the era of quantum and exascale computing, machine learning and artificial intelligence have entered the field in a significant way. In this article we give a brief history of simulation, discuss how machine learning can be more powerful if underpinned by deeper mechanistic understanding, outline the potential of exascale and quantum computing, highlight the limits of digital computing - classical and quantum - and distinguish rhetoric from reality in assessing the future of modelling and simulation, when we believe analogue computing will play an increasingly important role.
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Affiliation(s)
- Peter V. Coveney
- Centre for Computational Science, University College London, Gordon Street, London, WC1H 0AJ, UK
- Institute for Informatics, Science Park 904, University of Amsterdam, 1098 XH, Amsterdam, Netherlands
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Hoekstra AG, Portegies Zwart S, Coveney PV. Multiscale modelling, simulation and computing: from the desktop to the exascale. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2019; 377:20180355. [PMID: 30967039 PMCID: PMC6388007 DOI: 10.1098/rsta.2018.0355] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/11/2018] [Indexed: 05/02/2023]
Abstract
This short contribution introduces a theme issue dedicated to 'Multiscale modelling, simulation and computing: from the desktop to the exascale'. It holds a collection of articles presenting cutting-edge research in generic multiscale modelling and multiscale computing, and applications thereof on high-performance computing systems. The special issue starts with a position paper to discuss the paradigm of multiscale computing in the face of the emerging exascale, followed by a review and critical assessment of existing multiscale computing environments. This theme issue provides a state-of-the-art account of generic multiscale computing, as well as exciting examples of applications of such concepts in domains ranging from astrophysics, via material science and fusion, to biomedical sciences. This article is part of the theme issue 'Multiscale modelling, simulation and computing: from the desktop to the exascale'.
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Affiliation(s)
- Alfons G. Hoekstra
- Computational Science Laboratory, Institute for Informatics, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands
- ITMO University, Saint Petersburg, Russia
| | | | - Peter V. Coveney
- Centre for Computational Science, Department of Chemistry, University College London, London, England
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Hoekstra AG, Chopard B, Coster D, Portegies Zwart S, Coveney PV. Multiscale computing for science and engineering in the era of exascale performance. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2019; 377:20180144. [PMID: 30967040 PMCID: PMC6388008 DOI: 10.1098/rsta.2018.0144] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/09/2018] [Indexed: 05/18/2023]
Abstract
In this position paper, we discuss two relevant topics: (i) generic multiscale computing on emerging exascale high-performing computing environments, and (ii) the scaling of such applications towards the exascale. We will introduce the different phases when developing a multiscale model and simulating it on available computing infrastructure, and argue that we could rely on it both on the conceptual modelling level and also when actually executing the multiscale simulation, and maybe should further develop generic frameworks and software tools to facilitate multiscale computing. Next, we focus on simulating multiscale models on high-end computing resources in the face of emerging exascale performance levels. We will argue that although applications could scale to exascale performance relying on weak scaling and maybe even on strong scaling, there are also clear arguments that such scaling may no longer apply for many applications on these emerging exascale machines and that we need to resort to what we would call multi-scaling. This article is part of the theme issue 'Multiscale modelling, simulation and computing: from the desktop to the exascale'.
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Affiliation(s)
- Alfons G. Hoekstra
- Computational Science Laboratory, Institute for Informatics, Faculty of Science, University of Amsterdam, The Netherlands
- High Performance Computing Department, ITMO University, St Petersburg, Russia
| | - Bastien Chopard
- Department of Computer Science, University of Geneva, Switzerland
| | | | | | - Peter V. Coveney
- The Centre for Computational Science, Department of Chemistry, University College London, UK
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Melnikova NB, Svitenkov AI, Hose DR, Hoekstra AG. A cell-based mechanical model of coronary artery tunica media. J R Soc Interface 2018; 14:rsif.2017.0028. [PMID: 28679664 DOI: 10.1098/rsif.2017.0028] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Accepted: 06/05/2017] [Indexed: 12/23/2022] Open
Abstract
A three-dimensional cell-based mechanical model of coronary artery tunica media is proposed. The model is composed of spherical cells forming a hexagonal close-packed lattice. Tissue anisotropy is taken into account by varying interaction forces with the direction of intercellular connection. Several cell-centre interaction potentials for repulsion and attraction are considered, including the Hertz contact model and its neo-Hookean extension, the Johnson-Kendall-Roberts model of adhesive contact, and a wormlike chain model. The model is validated against data from in vitro uni-axial tension tests performed on dissected strips of tunica media. The wormlike chain potential in combination with the neo-Hookean Hertz contact model produces stress-stretch curves which represent the experimental data very well.
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Affiliation(s)
- N B Melnikova
- Saint Petersburg National Research University of Information Technologies, Mechanics and Optics, Saint Petersburg, Russia .,Peter the Great State Polytechnic University, Saint Petersburg, Russia
| | - A I Svitenkov
- Saint Petersburg National Research University of Information Technologies, Mechanics and Optics, Saint Petersburg, Russia
| | - D R Hose
- University of Sheffield, Sheffield, UK
| | - A G Hoekstra
- Saint Petersburg National Research University of Information Technologies, Mechanics and Optics, Saint Petersburg, Russia.,University of Amsterdam, Amsterdam, The Netherlands
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