Scientific Simulations Research Articles

Perspectives on predicting and controlling turbulent flows through deep learning

The current revolution in the field of machine learning is leading to many interesting developments in a wide range of areas, including fluid mechanics. Fluid mechanics, and more concretely turbulence, is an ubiquitous problem in science and engineering. Being able to understand and predict the evolution of turbulent flows can have a critical impact on our possibilities to tackle a wide range of sustainability problems (including the current climate emergency) and industrial applications. Here, we review recent and emerging possibilities in the context of predictions, simulations, and control of fluid flows, focusing on wall-bounded turbulence. When it comes to flow control, we refer to the active manipulation of the fluid flow to improve the efficiency of processes such as reduced drag in vehicles, increased mixing in industrial processes, enhanced heat transfer in heat exchangers, and pollution reduction in urban environments. A number of important areas are benefiting from ML, and it is important to identify the synergies with the existing pillars of scientific discovery, i.e., theory, experiments, and simulations. Finally, I would like to encourage a balanced approach as a community in order to harness all the positive potential of these novel methods.

The application and challenges of artificial intelligence in the fashion and luxury industry

The concept of artificial intelligence (AI) involves the scientific and technological simulation of human intelligence, utilizing technologies such as deep learning, virtual reality, natural language processing, deep learning and more. Its core objective is to enable computers to process human- like abilities in perception, understanding, reasoning, learning, and decision-making. AI has achieved significant achievements and offers huge potential and applications across numerous areas, including the fashion and luxury industry. There, this article is to examine the application of AI technology in the fashion and luxury industry, specifically focusing on its utilization in personalized customer experience, market promotion and sales strategies, product design and innovation, as well as inventory and supply chain management. Additionally, this article aims to analyze the key existing issues and challenges brought about by these applications. This article a prospectus on the current focal points and future prospects of this research topic.

Randomness Certification from Multipartite Quantum Steering for Arbitrary Dimensional Systems.

Entanglement in bipartite systems has been applied to generate secure random numbers, which are playing an important role in cryptography or scientific numerical simulations. Here, we propose to use multipartite entanglement distributed between trusted and untrusted parties for generating randomness of arbitrary dimensional systems. We show that the distributed structure of several parties leads to additional protection against possible attacks by an eavesdropper, resulting in more secure randomness generated than in the corresponding bipartite scenario. Especially, randomness can be certified in the group of untrusted parties, even when there is no randomness in either of them individually. We prove that the necessary and sufficient resource for quantum randomness in this scenario is multipartite quantum steering when each untrusted party has a choice between only two measurements. However, the sufficiency no longer holds with more measurement settings. Finally, we apply our analysis to some experimentally realized states and show that more randomness can be extracted compared with the existing analysis.

An Efficient Explicit Moving Particle Simulation Solver for Simulating Free Surface Flow on Multicore CPU/GPUs

The moving particle simulation (MPS) method is a simulation technique capable of calculating free surface and incompressible flows. As a particle-based method, MPS requires significant computational resources when simulating flow in a large-scale domain with a huge number of particles. Therefore, improving computational speed is a crucial aspect of current research in particle methods. In recent decades, many-core CPUs and GPUs have been widely utilized in scientific simulations to significantly enhance computational efficiency. However, the implementation of MPS on different types of hardware is not a trivial task. In this study, we present an implementation method for the explicit MPS that utilizes the Taichi parallel programming language. When it comes to CPU computing, Taichi’s computational efficiency is comparable to that of OpenMP. Nevertheless, when GPU computing is utilized, the acceleration of Taichi in parallel computing is not as fast as the CUDA implementation. Our developed explicit MPS solver demonstrates significant performance improvements in simulating dam-break flow dynamics.

Self‐attention residual network‐based spatial super‐resolution synthesis for time‐varying volumetric data

AbstractIn the field of scientific visualization, the upscaling of time‐varying volume is meaningful. It can be used in in situ visualization to help scientists overcome the limitations of I/O speed and storage capacity when analysing and visualizing large‐scale, time‐varying simulation data. This paper proposes self‐attention residual network‐based spatial super‐resolution (SARN‐SSR), a spatial super‐resolution model based on self‐attention residual networks that can generate time‐varying data with temporal coherence. SARN‐SSR consists of two components: a generator and a discriminator. The generator takes the low‐resolution volume sequences as the input and gives the corresponding high‐resolution volume sequences as the output. The discriminator takes both synthesized and real high‐resolution volume sequence as the input and gives a matrix to predict the realness as the output. To verify the validity of SARN‐SSR, four sets of time‐varying volume datasets are applied from scientific simulation. In addition, SARN‐SSR is compared on these datasets, both qualitatively and quantitatively, with two deep learning‐based techniques and one traditional technique. The experimental results show that by using this method, the closest time‐varying data to the ground truth can be obtained.

FCBench: Cross-Domain Benchmarking of Lossless Compression for Floating-Point Data

While both the database and high-performance computing (HPC) communities utilize lossless compression methods to minimize floating-point data size, a disconnect persists between them. Each community designs and assesses methods in a domain-specific manner, making it unclear if HPC compression techniques can benefit database applications or vice versa. With the HPC community increasingly leaning towards in-situ analysis and visualization, more floating-point data from scientific simulations are being stored in databases like Key-Value Stores and queried using in-memory retrieval paradigms. This trend underscores the urgent need for a collective study of these compression methods' strengths and limitations, not only based on their performance in compressing data from various domains but also on their runtime characteristics. Our study extensively evaluates the performance of eight CPU-based and five GPU-based compression methods developed by both communities, using 33 real-world datasets assembled in the Floating-point Compressor Benchmark (FCBench). Additionally, we utilize the roofline model to profile their runtime bottlenecks. Our goal is to offer insights into these compression methods that could assist researchers in selecting existing methods or developing new ones for integrated database and HPC applications.

Controlling Randomization in Astronomy Simulations

As the primary requirement, correctly implementing and controlling random number generation is vital for a range of scientific analyses and simulations across astronomy and planetary science. Beyond advice on how to set the seed, there is little guidance in the literature for how best to handle pseudo-random number generation in the current era of open-source astronomical software development. We present our methodology for implementing a pseudo-random number generation in astronomy simulations and software and share the short lines of python code that create the generator. Without sacrificing randomization at run time, our strategy ensures reproducibility on a per function/module basis for unit testing and for run time debugging where there may be multiple functions requiring lists of randomly generated values that occur before a specific function is executed.

The high energy X-ray probe (HEX-P): Resolving the nature of Sgr A* flares, compact object binaries and diffuse X-ray emission in the Galactic center and beyond

HEX-P is a probe-class mission concept that will combine high spatial resolution X-ray imaging (<10″ FWHM) and broad spectral coverage (0.2–80 keV) with an effective area far superior to current facilities’ (including XMM-Newton and NuSTAR). These capabilities will enable revolutionary new insights into a variety of important astrophysical problems. We present scientific objectives and simulations of HEX-P observations of the Galactic Center (GC) and Bulge. We demonstrate the unique and powerful capabilities of the HEX-P observatory for studying both X-ray point sources and diffuse X-ray emission. HEX-P will be uniquely equipped to explore a variety of major topics in Galactic astrophysics, allowing us to 1) investigate broad-band properties of X-ray flares emitted from the supermassive black hole (BH) at Sgr A* and probe the associated particle acceleration and emission mechanisms; 2) identify hard X-ray sources detected by NuSTAR and determine X-ray point source populations in different regions and luminosity ranges; 3) determine the distribution of compact object binaries in the nuclear star cluster and the composition of the Galactic Ridge X-ray emission; 4) identify X-ray transients and measure fundamental parameters such as black hole spin; 5) find hidden pulsars in the Galactic Center; 6) search for BH–OB binaries and hard X-ray flares from young stellar objects in young massive clusters; 7) measure white dwarf (WD) masses of magnetic CVs to deepen our understanding of CV evolution and the origin of white dwarf magnetic fields; 8) explore primary particle accelerators in the GC in synergy with future TeV and neutrino observatories; 9) map out cosmic-ray distributions by observing non-thermal X-ray filaments; 10) explore past X-ray outbursts from Sgr A* through X-ray reflection components from giant molecular clouds.

Progressive Tree-Based Compression of Large-Scale Particle Data.

Scientific simulations and observations using particles have been creating large datasets that require effective and efficient data reduction to store, transfer, and analyze. However, current approaches either compress only small data well while being inefficient for large data, or handle large data but with insufficient compression. Toward effective and scalable compression/decompression of particle positions, we introduce new kinds of particle hierarchies and corresponding traversal orders that quickly reduce reconstruction error while being fast and low in memory footprint. Our solution to compression of large-scale particle data is a flexible block-based hierarchy that supports progressive, random-access, and error-driven decoding, where error estimation heuristics can be supplied by the user. For low-level node encoding, we introduce new schemes that effectively compress both uniform and densely structured particle distributions. Our proposed methods thus target all three phases of a tree-based particle compression pipeline, namely tree construction, tree traversal, and node encoding. The improved efficacy and flexibility of these methods over existing compressors are demonstrated through extensive experimentation, using a wide range of scientific particle datasets.

Molecular dynamics simulation of mechanical and thermal properties of nano-zinc oxide modified cellulose insulating paper

With the surge in electrical loads and increasing voltage levels, the mechanical performance and thermal stability of insulating paper are facing severe challenges. However, due to the lack of direct scientific theories or simulation guidance, traditional inefficient “trial-and-error” experiments are difficult to effectively develop new types of cellulose composite insulating papers. For solving this problem, in this work we are to enhance the effects of nanoscale zinc oxide (nano-ZnO) on the mechanical and thermal properties of cellulose through molecular dynamics simulations. Initially, we model the nano-ZnO/cellulose composite material , then carry out a microscopic analysis of the mechanical performance and thermal stability of modified cellulose with varying nano-ZnO content, thus determining the optimal ratio of nano-ZnO to cellulose. The results indicate that compared with the outcomes from the unmodified model, the mechanical performance, cohesive energy density, glass transition temperature, and thermal conductivity of the nano-ZnO-modified cellulose model are all improved, with the highest increase in elastic modulus reaching 45.31% and the highest increase in thermal conductivity attaining 41.49%. The addition of nano-ZnO effectively fills the gaps in the fiber network and enhances the interactions between cellulose chains and thermal conduction channels, thereby improving the thermodynamic performance of cellulose. This work provides valuable theoretical references for rapidly preparing modified cellulose insulating papers with excellent thermodynamic performance.

Kiwaku, a C++20 library for multidimensional arrays. Application to ACTS tracking

C++ is a vital part of particle physics as it allows for high level abstractions while offering state of the art performance. In this article we will first introduce the C++ 20 concepts, a major C++ usability enhancement. We will then introduce Kiwaku, a new multidimensional arrays library taking advantage of the most advanced C++ features at the time of writing, providing the user with intuitive API while retaining state of the art performance. Multidimensional arrays are a basic building block for many scientific experiments and simulations, and particle physics is no exception. Using examples borrowed from Covfie and ACTS libraries, and using data from the ATLAS CERN experiment, we will show how Kiwaku offers good usability while having a negligible impact on performance compared to using classic C++ std::arrays.

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

OpenMC is an open source Monte Carlo neutral particle transport application that has recently been ported to GPU using the OpenMP target offloading model. We examine the performance of OpenMC at scale on the Frontier, Polaris, and Aurora supercomputers, demonstrating that performance portability has been achieved by OpenMC across all three major GPU vendors (AMD, NVIDIA, and Intel). OpenMC’s GPU performance is compared to both the traditional CPU-based version of OpenMC as well as several other state-of-the-art CPU-based Monte Carlo particle transport applications. We also provide historical context by analyzing OpenMC’s performance on several legacy GPU and CPU architectures. This work includes some of the first published results for a scientific simulation application at scale on a supercomputer featuring Intel’s Max series “Ponte Vecchio” GPUs. It is also one of the first demonstrations of a large scientific production application using the OpenMP target offloading model to achieve high performance on all three major GPU platforms.

Enhancing Short Track Speed Skating Performance through Improved DDQN Tactical Decision Model.

This paper studies the tactical decision-making model of short track speed skating based on deep reinforcement learning, so as to improve the competitive performance of corresponding short track speed skaters. Short track speed skating, a traditional discipline in the Winter Olympics since its establishment in 1988, has consistently garnered attention. As artificial intelligence continues to advance, the utilization of deep learning methods to enhance athletes' tactical decision-making capabilities has become increasingly prevalent. Traditional tactical decision techniques often rely on the experience and knowledge of coaches and video analysis methods that require a lot of time and effort. Consequently, this study proposes a scientific simulation environment for short track speed skating, that accurately simulates the physical attributes of the venue, the physiological fitness of the athletes, and the rules of the competition. The Double Deep Q-Network (DDQN) model is enhanced and utilized, with improvements to the reward function and the distinct description of four tactics. This enables agents to learn optimal tactical decisions in various competitive states with a simulation environment. Experimental results demonstrate that this approach effectively enhances the competition performance and physiological fitness allocation of short track speed skaters.

Reconstructing higher-resolution four-dimensional time-varying volumetric data

We have witnessed substantial growth in super-resolution research within the computer vision community. Unlike previous works that mainly focus on the super-resolution synthesis of images, videos, or single volumes, our research is dedicated to the super-resolution synthesis of time-varying volumetric data, which are generated from scientific simulations and are crucial for domain scientists to understand and analyse complex scientific phenomena. Compared to previous works, our research presents a greater challenge: the time-varying volumetric data have higher dimensions, making it more difficult to synthesise super-resolution that maintains good spatio-temporal consistency while achieving high visual quality. To tackle this challenge, we introduce a new GAN-based network called SSR-DoubleUNetGAN, which includes novel network architecture and loss functions, allowing for accurate synthesis of spatial super-resolution for time-varying volumetric data with relatively fast training time. Our method can be applied in the context of in-situ visualisation to aid domain scientists in analysing more time-varying volumetric data more efficiently. In addition, it can be used in the compression-decompression pipeline to recover the super-resolution time-varying volumetric data from their low-resolution counterpart. To demonstrate its effectiveness, we applied various time-varying volumetric datasets from different scientific simulations to it. To demonstrate its advantages, we compared it qualitatively and quantitatively with five state-of-the-art super-resolution techniques, namely SSR-TVD, Tricubic, SRResNet, Cubic, and Linear. Furthermore, we conducted an ablation study to validate its important modules. The experimental results show that our method outperforms the compared state-of-the-art techniques.

Simulating strong gravity-lensing effect using python with 10 source and 20 lensing galaxies

This report explores using Python, a coding language, to create simulated images of a gravitational lens system, using the Hubble Space Telescope (HST) parameters. With Pythons helpful tools, like NumPy for math operations and Astropy for astronomy tasks, we build algorithms that recreate the interactions within our chosen group of galaxies and take into account HSTs unique imaging capabilities. Our method combines theory of gravitational lensing with practical coding strategies to make simulations show these complex light-bending interactions. The report walks through how the algorithms are developed with specific scientific simulation models like Sersic profile and point-spread function (PSF), showcasing the important role of computer simulations in deepening our understanding of space. In this report, I will introduce how we can use python code to create simulation images of a gravitational lens system. This system involves with 10 source galaxies ,20 lensing galaxies and with consideration of dark matter halo.

Testing Modules for Experiments in Stellar Astrophysics (MESA)

Regular, automated testing is a foundational principle of modern software development. Numerous widely used continuous integration systems exist, but they are often not suitable for the unique needs of scientific simulation software. Here we describe the testing infrastructure developed for and used by the Modules for Experiments in Stellar Astrophysics (MESA) project. This system allows the computationally demanding MESA test suite to be regularly run on a heterogeneous set of computers and aggregates and displays the testing results in a form that allows for the rapid identification and diagnosis of regressions. Regularly collecting comprehensive testing data also enables longitudinal studies of the performance of the software and the properties of the models it generates.

Hermes-3: Multi-component plasma simulations with BOUT++

A new open source tool for fluid simulation of multi-component plasmas is presented, based on a flexible software design that is applicable to scientific simulations in a wide range of fields. Hermes-3 is built on plasma simulation framework BOUT++, consolidating earlier SD1D and Hermes models into a single code that can be configured at run-time to solve plasma models in 1D, 2D or 3D, either for transport (steady-state) or turbulent (time-evolving) problems, with an arbitrary number of ion and neutral species. We describe the improved numerical algorithms and software design that have been implemented in Hermes-3.To demonstrate the capabilities of this tool, applications relevant to the boundary of tokamak plasmas are presented: 1D simulations of diveror plasmas evolving equations for all charge states of neon and deuterium; 2D transport simulations of tokamak equilibria in single-null X-point geometry with plasma ion and neutral atom species; and simulations of the time-dependent propagation of plasma filaments (blobs).Hermes-3 is publicly available on Github under the GPL-3 open source license. The repository includes documentation and a suite of unit, integrated and convergence tests. Program summaryProgram Title: Hermes-3CPC Library link to program files:https://doi.org/10.17632/rtkvc3r743.1Developer's repository link:https://github.com/bendudson/hermes-3Licensing provisions: GPLv3Programming language: C++14Nature of problem: Simulation of dynamics and steady-state solutions of multiple ion and neutral species in magnetised plasmas using drift-reduced fluid plasma models in 1 to 3 spatial dimensions. Focused on but not restricted to the simulation of turbulence in the boundary of tokamaks.Solution method: Hermes-3 implements a system of flexible software components, that are configured at run-time and coupled by passing a state object between them (the Encapsulate State and Command patterns). Hermes-3 builds on BOUT++ [1], employing the Method of Lines with implicit and explicit time-integration methods in curvilinear coordinates on block-structured meshes, making use of libraries including SUNDIALS, PETSc and Hypre. Hermes-3 implements drift-reduced plasma fluid equations using conservative finite difference methods, and atomic processes that couple plasma and neutral fluids.

Refactoring BZIP2 on the new‐generation sunway supercomputer

AbstractHigh‐performance computing is progressively assuming a fundamental role in advancing scientific research and engineering domains. However, the ever‐expanding scales of scientific simulations pose challenges for efficient data I/O and storage. The data compression technology has garnered significant attention as a solution to reduce data transmission and storage costs while enhancing performance. In particular, the BZIP2 lossless compression algorithm has been widely used due to its exceptional compression ratio, moderate compression speed, high reliability, and open‐source nature. This paper focuses on the design and realization of a parallelized BZIP2 algorithm tailored for deployment on the New‐Generation Sunway supercomputing platform. By leveraging the unique cache patterns of the New‐Generation Sunway processor, we propose the highly tuned multi‐threading and multi‐node implementations of the BZIP2 applications for different scenarios. Moreover, we also propose the efficient BZIP2 libraries based on the management processing element and computing processing element which support the commonly used high‐level (de)compression interfaces. The test results indicate that the our multi‐threading implementation achieves maximum speedup of 23.09 (8.57) in decompression(compression) compared to the sequential implementation. Furthermore, the multi‐node implementation achieves 50.81% (26.35%) parallel efficiency and peak performance of 16.6 GB/s (52.8 GB/s) for compression(decompression) when scaling up to 2048 processes.

A high throughput pseudo‐random number generator driven by four‐dimensional discrete hyper‐chaotic system

AbstractPseudo‐random number generators (PRNGs) are the cornerstone of various fields including computer science, cryptography, and scientific simulation. To generate high‐quality and unpredictable pseudo‐random sequences, many works draw attention to the use of chaos theory and dynamical systems. However, most of them focus on continuous or low‐dimensional chaotic systems with strong temporal correlations or insufficient nonlinear dynamics. Thus, in this study, a new PRNG based on a four‐dimensional discrete system is proposed to solve the aforementioned flaws. The PRNG has been implemented on Xilinx Artix‐7 xc7a100tfgg484‐2, which can generate pseudo‐random sequences at a high speed of 12811.53 Mb/s without additional post‐processing. All these sequences have successfully passed the NIST SP800.22 standard test. Both throughput and randomness quality of the proposed PRNG outperform the state‐of‐the‐art.

Convex optimization-based structure-preserving filter for multidimensional finite element simulations

In simulation sciences, capturing the real-world problem features as accurately as possible is desirable. Methods popular for scientific simulations such as the finite element method (FEM) and finite volume method (FVM) use piecewise polynomials to approximate various characteristics of a problem, such as the concentration profile and the temperature distribution across the domain. Polynomials are prone to creating artifacts such as Gibbs oscillations while capturing a complex profile. An efficient and accurate approach must be applied to deal with such inconsistencies to obtain accurate simulations. This often entails dealing with negative values for the concentration of chemicals, exceeding a percentage value over 100, and other such problems. We consider these inconsistencies in the context of partial differential equations (PDEs). We propose an innovative filter based on convex optimization to deal with the inconsistencies observed in polynomial-based simulations. In two or three spatial dimensions, additional complexities are involved in solving the problems related to structure preservation. We present the construction and application of a structure-preserving filter with a focus on multidimensional PDEs. Methods used such as the Barycentric interpolation for polynomial evaluation at arbitrary points in the domain and an optimized root-finder to identify points of interest, improve the filter efficiency, usability, and robustness. Lastly, we present numerical experiments in 2D and 3D using discontinuous Galerkin formulation and demonstrate the filter's efficacy to preserve the desired structure. As a real-world application, implementation of the mathematical biology model involving platelet aggregation and blood coagulation has been reviewed and the issues around FEM implementation of the model are resolved by applying the proposed structure-preserving filter.