Eulerian simulation of complex suspensions and biolocomotion in three dimensions

We present a numerical method specifically designed for simulating three-dimensional fluid–structure interaction (FSI) problems based on the reference map technique (RMT). The RMT is a fully Eulerian FSI numerical method that allows fluids and large-deformation elastic solids to be represented on a single fixed computational grid. This eliminates the need for meshing complex geometries typical in other FSI approaches and greatly simplifies the coupling between fluid and solids. We develop a three-dimensional implementation of the RMT, parallelized using the distributed memory paradigm, to simulate incompressible FSI with neo-Hookean solids. As part of our method, we develop a field extrapolation scheme that works efficiently in parallel. Through representative examples, we demonstrate the method’s suitability in investigating many-body and active systems, as well as its accuracy and convergence. The examples include settling of a mixture of heavy and buoyant soft ellipsoids, lid-driven cavity flow containing a soft sphere, and swimmers actuated via active stress.

Assessing in Silico Models and Methods for Non-Covalent Interactions between Graphene and Small Molecules

<p><a>Designing and optimising graphene-based gas sensors, which involve physisorption of analytes on the sensor surface, requires theoretical insights into the strength and nature of such non-covalent interactions. This modelling entails constructing appropriate atomistic representations for an infinite graphene sheet and its complex with the analyte, then selecting accurate yet affordable methods for geometry optimisations and energy computations. In this work, density functionals from the 2^nd to 5^th rungs of Jacob’s ladder, coupled cluster theory, and symmetry-adapted perturbation theory in conjunction with a range of surface models, from benzene to the periodic system, were tested for their ability to reproduce experimental adsorption energies of CO₂ on graphene in a low-coverage regime. The best agreement with the reference computations was found for global and double hybrid density functionals, while experimental adsorption energies were reproduced within chemical accuracy by extrapolating the SAPT0//DSD-BLYP-D3 interaction energies from finite clusters to infinity</a>. This simple yet powerful extrapolation scheme effectively removes size dependence from the data obtained using finite cluster models, and the latter can be treated at more sophisticated levels of theory relative to periodic systems.</p>

Assessing in Silico Models and Methods for Non-Covalent Interactions between Graphene and Small Molecules

<p><a>Designing and optimising graphene-based gas sensors, which involve physisorption of analytes on the sensor surface, requires theoretical insights into the strength and nature of such non-covalent interactions. This modelling entails constructing appropriate atomistic representations for an infinite graphene sheet and its complex with the analyte, then selecting accurate yet affordable methods for geometry optimisations and energy computations. In this work, density functionals from the 2^nd to 5^th rungs of Jacob’s ladder, coupled cluster theory, and symmetry-adapted perturbation theory in conjunction with a range of surface models, from benzene to the periodic system, were tested for their ability to reproduce experimental adsorption energies of CO₂ on graphene in a low-coverage regime. The best agreement with the reference computations was found for global and double hybrid density functionals, while experimental adsorption energies were reproduced within chemical accuracy by extrapolating the SAPT0//DSD-BLYP-D3 interaction energies from finite clusters to infinity</a>. This simple yet powerful extrapolation scheme effectively removes size dependence from the data obtained using finite cluster models, and the latter can be treated at more sophisticated levels of theory relative to periodic systems.</p>

Increase of ice discharge from Greenland's peripheral tidewater glaciers over recent decades

&lt;p&gt;The Greenland Ice Sheet is losing mass at increasing rates. Substantial amounts of this mass loss occur by ice discharge. The ice sheet is surrounded by thousands of peripheral glaciers, which are dynamically decoupled from the ice sheet, and which account for ~10 % of the global glacier ice volume outside the two main ice sheets. Rather low-lying along the coasts, these peripheral glaciers are also losing mass at increasing, but disputed, rates. The total absence of knowledge about the role and share of solid ice discharge in this mass loss adds to the controversy. Since the quantification of ice discharge is still pending, a full understanding of ice mass loss processes in this globally important glacier region is substantially hampered.&lt;/p&gt;&lt;p&gt;Here, we present the first estimation of ice discharge from Greenland's peripheral tidewater glaciers. For each of these 760 glaciers, we combine an idealized rectangular flux gate cross sections derived from modelling with the Open Global Glacier Model with surface ice flow velocities derived from the ITS_LIVE and MEaSUREs remote sensing datasets to calculate glacier specific ice discharge on both annual and multi-annual time scales over the period 1985 to 2018. For the few glaciers not covered by either of the employed original datasets or modelling methods we use a regression tree-based extrapolation scheme to estimate the necessary input data for our calculation.&lt;/p&gt;&lt;p&gt;Our findings indicate a significant overall increase of ice discharge over the study period although several individual glaciers show contrasting developments. This increase became especially apparent across the southern parts of Greenland. Our results also show that the total of the ice discharge from Greenland's peripheral tidewater glaciers is dominated by few major contributors and that this dominance is completely time-independent.&lt;/p&gt;

Full-wave-equation depth extrapolation for migration using matrix multiplication

To investigate wavefield depth extrapolation using the full-wave equation, we have derived a new depth extrapolation scheme for migration using functions of the vertical wavenumber. We develop a complete matrix multiplication formulation and approach to calculate the related mathematical functions of the vertical wavenumber and perform depth extrapolation using matrix multiplication only. Because our depth extrapolation algorithm involves only matrix multiplication, it is naturally applicable to parallel computations. Impulse response experiments demonstrate that our proposed migration method can achieve the same accuracy as full-wave-equation migration using the finite-difference method, in terms of phase information, even for media with strong lateral velocity changes. In numerical experiments using a smoothed version of the 2D SEG/EAGE salt model, our migration method provides an equivalent imaging result compared with reverse time migration (RTM) and a more accurate imaging result than migration using one-way propagators. Our method has certain potential advantages over RTM using the same full-wave equation with fewer internal multiple scatterings and fewer data storage requirements. Our adopted method is a stable depth extrapolation scheme because the evanescent waves are well suppressed. The numerical experimental results on the synthetic model demonstrate the importance of suppressing evanescent waves in a full-wave-equation-based depth extrapolation scheme and migration for imaging quality and computation cost.

An Accelerated Symmetric Nonnegative Matrix Factorization Algorithm Using Extrapolation

Symmetric nonnegative matrix factorization (SNMF) approximates a symmetric nonnegative matrix by the product of a nonnegative low-rank matrix and its transpose. SNMF has been successfully used in many real-world applications such as clustering. In this paper, we propose an accelerated variant of the multiplicative update (MU) algorithm of He et al. designed to solve the SNMF problem. The accelerated algorithm is derived by using the extrapolation scheme of Nesterov and a restart strategy. The extrapolation scheme plays a leading role in accelerating the MU algorithm of He et al. and the restart strategy ensures that the objective function of SNMF is monotonically decreasing. We apply the accelerated algorithm to clustering problems and symmetric nonnegative tensor factorization (SNTF). The experiment results on both synthetic and real-world data show that it is more than four times faster than the MU algorithm of He et al. and performs favorably compared to recent state-of-the-art algorithms.

Computational investigation of the valid valence state contribution in calculating the electronic stopping power of a proton in bulk Al within the linear response approach

The electronic stopping power is a fundamental quantity to many technological fields that use ion irradiation. Here we investigate the validity of using a fully ab initio computational scheme based on linear response time-dependent density functional theory to predict the random electronic stopping power (RESP) of a proton in bulk aluminum. We verify the power of using the extrapolation scheme to overcome the expected convergence issue of the RESP calculations. We show that the calculated RESP of valence electrons compares well with experimental data for low proton velocity only when at full convergence and including the exchange-correlation effect. We demonstrate that the inclusion of valence states only is sufficient for calculating the electronic stopping power up to the stopping maximum.