Physics

Three-dimensional concrete printing (3DCP) has gained a lot of popularity in recent years. According to many, 3DCP is set to revolutionize the construction industry: yielding unparalleled aesthetics, better quality control, lower cost, and a reduction of the construction time. In this paper, two finite element method (FEM) strategies are presented for simulating such 3D concrete printing processes. The aim of these models is to predict the structural behaviour during printing, while the concrete is still fresh, and estimate the optimal print speed and maximum overhang angle to avoid print failures. Both FE analyses involve solving multiple static implicit steps where sets of finite elements are added stepwise until failure. The main difference between the two methods is in the discretization of the 3D model. The first method uses voxelization to approximate the 3D shape, while the second approach starts from defining the toolpath and constructs finite elements by sweeping them along the path. A case study is presented to evaluate the effectiveness of both strategies. Both models are in good agreement with each other, and a comparable structural response is obtained. The model's limitations and future challenges are also discussed. Ultimately, the paper demonstrates how FEM-based models can effectively simulate complex prints and could give recommendations with regards to a better print strategy. These suggestions can be related to the maximum printing speed and overhang angle, but also the optimal layer height and thickness, the specific choice of the infill pattern, or by extension the mixture design. When print failures can be avoided, this methodology could save time, resources and overall cost. Future work will focus on the validation of these numerical models and comparing them to experimental data.

A Fourier-based Library of Unbounded Poisson Solvers (FLUPS) for 2D and 3D homogeneous distributed grids is presented. It is designed to handle every possible combination of periodic, symmetric, semi-unbounded and fully unbounded boundary conditions for the Poisson equation on rectangular domains with uniform resolution. FLUPS leverages a dedicated implementation of 3D Fourier transforms to solve the Poisson equation using Green's functions, in a fast and memory-efficient way. Several Green's functions are available, optionally with explicit regularization, spectral truncation, or using lattice Green's functions, and provide verified convergence orders from 2 to spectral-like. The algorithm depends on the FFTW library to perform 1D transforms, while Message Passing Interface (MPI) communications enable the required remapping of data in memory. For the latter operation, a first available implementation resorts to the standard all-to-all routines. A second implementation, featuring non-blocking and persistent point-to-point communications, is however shown to be more efficient in a majority of cases and especially while taking advantage of the shared memory parallelism with OpenMP. The scalability of the algorithm, aimed at massively parallel architectures, is demonstrated up to 73 720 cores. The results obtained with three different supercomputers show that the weak efficiency remains above 40\% and the strong efficiency above 30% when the number of cores is multiplied by 16, for typical problems. These figures are slightly better than those expected from a third party 3D Fast Fourier Transform (FFT) tool, with which a 20% longer execution time was also measured on average.

Is a deep learning model capable of understanding systems governed by certain first principle laws by only observing the system's output? Can deep learning learn the underlying physics and honor the physics when making predictions? The answers are both positive. In an effort to simulate two-dimensional subsurface fluid dynamics in porous media, we found that an accurate deep-learning-based proxy model can be taught efficiently by a computationally expensive finite-volume-based simulator. We pose the problem as an image-to-image regression, running the simulator with different input parameters to furnish a synthetic training dataset upon which we fit the deep learning models. Since the data is spatiotemporal, we compare the performance of two alternative treatments of time; a convolutional LSTM versus an autoencoder network that treats time as a direct input. Adversarial methods are adopted to address the sharp spatial gradient in the fluid dynamic problems. Compared to traditional simulation, the proposed deep learning approach enables much faster forward computation, which allows us to explore more scenarios with a much larger parameter space given the same time. It is shown that the improved forward computation efficiency is particularly valuable in solving inversion problems, where the physics model has unknown parameters to be determined by history matching. By computing the pixel-level attention of the trained model, we quantify the sensitivity of the deep learning model to key physical parameters and hence demonstrate that the inversion problems can be solved with great acceleration. We assess the efficacy of the machine learning surrogate in terms of its training speed and accuracy. The network can be trained within minutes using limited training data and achieve accuracy that scales desirably with the amount of training data supplied.

The calculation of interaction integrals is a bottleneck for the treatment of many-body quantum systems due to its high numerical cost. We conduct configuration interaction calculations of the few-electron states confined in III-V semiconductor 2D structures using a shallow neural network to calculate the two-electron integrals, that can be used for general isotropic interaction potentials. This approach allows for a speed up of the evaluation of the energy levels and a controllable accuracy.

We propose a novel discrete method of constructing Gaussian Random Fields (GRF) based on a combination of modified spectral representations, Fourier and Blob. The method is intended for Direct Numerical Simulations of the V-Langevin equations. The latter are stereotypical descriptions of anomalous stochastic transport in various physical systems. From an Eulerian perspective, our method is designed to exhibit improved convergence rates. From a Lagrangian perspective, our method others a pertinent description of particle trajectories in turbulent velocity fields: the exact Lagrangian invariant laws are well reproduced. From a computational perspective, our method is twice as fast as standard numerical representations.

Collision between rigid three-dimensional objects is a very common modelling problem in a wide spectrum of scientific disciplines, including Computer Science and Physics. It spans from realistic animation of polyhedral shapes for computer vision to the description of thermodynamic and dynamic properties in simple and complex fluids. For instance, colloidal particles of especially exotic shapes are commonly modelled as hard-core objects, whose collision test is key to correctly determine their phase and aggregation behaviour. In this work, we propose the OpenMP Cuboid Sphere Intersection (OCSI) algorithm to detect collisions between prolate or oblate cuboids and spheres. We investigate OCSI's performance by bench-marking it against a number of algorithms commonly employed in computer graphics and colloidal science: Quick Rejection First (QRI), Quick Rejection Intertwined (QRF) and SIMD Streaming Extensions (SSE). We observed that QRI and QRF significantly depend on the specific cuboid anisotropy and sphere radius, while SSE and OCSI maintain their speed independently of the objects' geometry. While OCSI and SSE, both based on SIMD parallelization, show excellent and very similar performance, the former provides a more accessible coding and user-friendly implementation as it exploits OpenMP directives for automatic vectorization.

A model of high temperature superconducting dynamo, a promising type of flux pumps capable of wireless injection of a large DC current into a superconducting circuit, has recently been chosen as an applied superconductivity benchmark problem and solved using ten different numerical methods (Ainslie et al 2020 Supercond. Sci. Technol. 33 105009). Using expansions in Chebyshev polynomials for approximation in space and the method of lines for integration in time we derive a simple and accurate numerical method which is much faster. The proposed numerical method was applied also to problems with transport current and a field-dependent sheet critical current density.

In this paper, a detailed sensitivity analysis of the active manipulation scheme for scalar Helmholtz fields proposed in our previous works, in both free space and constant-depth homogeneous ocean environments, is presented. We apply the method of moments (MoM) together with Tikhonov regularization with Morozov discrepancy principle to investigate the effects of problem parameters variations on the accuracy and feasibility of the proposed active field control strategy. We discuss the feasibility of the active scheme (power budget and control accuracy) as a function of the frequency, the distance between the control region and the active source, the mutual distance between the control regions, and the size of the control region. Process error is considered as well to investigate the possibility of an accurate active control in the presence of manufacturing noise. The numerical simulations show the accuracy of the active field control scheme and indicate some challenges and limitations for its physical implementations.

This study introduces the concept of finite element network analysis (FENA) which is a physics-informed, machine-learning-based, computational framework for the simulation of complex physical systems. The framework leverages the extreme computational speed of trained neural networks and the unique transfer knowledge property of bidirectional recurrent neural networks (BRNN) to provide a uniquely powerful and flexible computing platform. One of the most remarkable properties of this framework consists in its ability to simulate the response of complex systems, made of multiple interconnected components, by combining individually pre-trained network models that do not require any further training following the assembly phase. This remarkable result is achieved via the use of key concepts such as transfer knowledge and network concatenation. Although the computational framework is illustrated and numerically validated for the case of a mechanical system under static loading, the conceptual structure of the framework has broad applicability and could be extended to the most diverse field of computational science. The framework is numerically validated against the solution provided by traditional finite element analysis and the results highlight the outstanding performance of this new concept of computational platform.

First principles simulations are conducted to shed light on the question of whether a two-dimensional topological insulator (2DTI) phase may be obtained at the interface between InAs and GaSb. To this end, the InAs/GaSb interface is compared and contrasted with the HgTe/CdTe interface. Density functional theory (DFT) simulations of these interfaces are performed using a machine-learned Hubbard U correction [npj Comput. Mater. 6, 180 (2020)]. For the HgTe/CdTe interface our simulations show that band crossing is achieved and an inverted gap is obtained at a critical thickness of 5.1 nm of HgTe, in agreement with experiment and previous DFT calculations. In contrast, for InAs/GaSb the gap narrows with increasing thickness of InAs; however the gap does not close for interfaces with up to 50 layers (about 15 nm) of each material. When an external electric field is applied across the InAs/GaSb interface, the GaSb-derived valence band maximum is shifted up in energy with respect to the InAs-derived conduction band minimum until eventually the bands cross and an inverted gap opens. Our results show that it may be possible to reach the topological regime at the InAs/GaSb interface under the right conditions. However, it may be challenging to realize these conditions experimentally, which explains the difficulty of experimentally demonstrating an inverted gap in InAs/GaSb.