Scientific Simulations Research Articles

Optimization of building thermal environment in industrial heritage landscape regeneration design simulation based on image visual visualization

With the increasing attention paid to the regeneration design of industrial heritage landscape, how to effectively improve the thermal energy environment in these areas has become a research hotspot. The thermal efficiency of buildings not only affects the energy consumption, but also relates to the comfort of users and the sustainable development of the region. This study aims to explore the optimization method of building thermal energy environment based on image visual visualization, so as to achieve efficient regeneration of industrial heritage landscape, and improve the thermal energy performance and environmental adaptability of buildings through scientific design and simulation means. Through the use of computer aided design (CAD) and building information modeling (BIM) techniques, combined with heat flow simulation and visual image analysis, a detailed thermal environment assessment is carried out for selected industrial heritage buildings. Through data collection and analysis, we take into account factors such as sunlight, ventilation and heat loss in the building to develop an optimization plan. The simulation results show that the energy consumption of buildings with optimized thermal environment design is significantly reduced and the indoor thermal comfort is greatly improved. Visual tools effectively help the design team identify problem areas and provide intuitive data support for all parties during the design process. The optimization method of building thermal energy environment based on image visual visualization provides a new idea for the regeneration design of industrial heritage landscape, which helps to realize the dual goals of protecting the historical value and meeting the needs of modern environmental protection.

Enhancing security and randomness in cryptography and non-cryptographic applications with ORNA algorithm

Abstract In this paper, we advocate for using PRNGs to generate long sequences of statistically random numbers, also known as pseudorandom numbers. It has several practical applications, including cryptography, weather-based stimulation, procedural environment noise generation in games and casinos, and scientific simulations of real-world problems, like city traffic systems that use it to stimulate cars. One distinctive feature of our approach is the PRNG algorithm we propose; this algorithm has successfully produced statistically sound long-term sequences. The properties of the PRNG are assessed using several statistical methods. Our work presents the Oscillating Random Number Algorithm (ORNA), a new kind of random number generator, and examines both the statistical tests proposed by NIST and the results that ORNA has generated. The following also describes the several preceding algorithms developed, including the Mersenne Twister Algorithm (MT19937) and the Permuted Congruential Algorithm (PCG128), and compares ORNA’s graphical efficiency to theirs. In this work, we compared ORNA to MT19937 and PCG128, and found that ORNA excels according to all 16 statistical tests. ORNA has three primary benefits, namely, superior statistical performance, quicker code execution, and simpler code implementation due to the algorithm’s reduced complexity.

GPU Parallel Computing Architectures : Unlocking the Power of Parallelism for High-Performance Applications

Graphics Processing Units (GPUs) have evolved from specialized graphics rendering hardware to become powerful parallel computing architectures, revolutionizing high-performance computing across diverse domains. This comprehensive article explores the fundamental principles of GPU parallel computing architectures, their design, and their impact on modern computational challenges. We begin by examining the multi-core structure, memory hierarchy, and data processing capabilities of GPUs, including the SIMD execution model and thread organization. The article then delves into prominent programming models like CUDA and OpenCL, discussing their features and comparative advantages. We explore how GPUs are leveraged for general-purpose computing in scientific simulations, machine learning, and big data analytics, while also addressing the challenges inherent in GPU parallel computing, such as data transfer bottlenecks and load balancing. Recent technological advancements, including tensor cores, unified memory architecture, and ray tracing acceleration, are analyzed for their transformative potential. The article concludes by examining future directions in GPU technology, including integration with emerging technologies like quantum computing, advancements in energy efficiency, and the potential impact on solving complex global challenges. Through this comprehensive analysis, we illustrate the pivotal role of GPU parallel computing architectures in shaping the future of high-performance computing and their potential to address some of the world's most pressing computational problems.

Lightweight high-throughput true random number generator based on state switchable ring oscillator

True random number generators (TRNGs) perform an extremely critical role in cryptographic algorithms and security protocols, scientific simulation, industrial testing, privacy protection, and numerous other domains. Nevertheless, modern TRNGs have difficulty striking a reasonable balance between high throughput and low hardware consumption. In this paper, a novel lightweight high-throughput TRNG based on state switchable ring oscillators (SSROs) is proposed. Under the effect of flip-flops that are prone to entering the metastable region, the SSROs randomly switch between oscillatory and buffer states to create jitter and metastability. A feedback strategy is adopted to effectively eliminate the fixed point in the circuit, which further enhances the randomness of the structure. The proposed TRNG is implemented on Xilinx Artix-7 and Kintex-7 FPGAs, with support for automatic routing. It achieves a throughput of up to 400 Mbps while consuming only 16 LUTs and 13 DFFs, showing extremely high resource utilization efficiency. Experimental results show that the output random sequence passes the NIST SP800-22 test, the NIST SP800-90B test, and the AIS-31 test without any post-processing, exhibiting strong robustness against voltage and temperature variations as well as frequency injection attacks.

Enhancing Semantic Consistency in Image-to-Image Translation with an Improved CycleGAN Framework

Abstract. This paper presents an enhanced Cycle-Consistent Adversarial Networks (CycleGAN) model aimed at preserving semantic consistency during image-to-image translation, with a focus on complex tasks such as autonomous driving and scientific simulations. The study's key contribution is the incorporation of a pre-trained semantic segmentation model to preserve important characteristics during translation, such as license plates, traffic signs, and pedestrian structures. By introducing a semantic consistency loss alongside the traditional cycle-consistency loss, the proposed approach ensures that key features are retained, even in challenging scenes. Extensive experiments conducted on the Cityscapes dataset demonstrate the effectiveness in maintaining both visual fidelity and semantic accuracy, significantly improving upon the traditional CycleGAN. This method proves particularly valuable in domains where precision is essential, such as cross-domain image generation for autonomous systems and medical imaging. Future research will focus on optimizing the model for real-time applications and exploring multi-domain frameworks to further enhance its performance in diverse environments. Overall, this study offers an efficient image style-transfer solution for preserving semantic integrity without sacrificing translation accuracy.

A Set of Models for Device Positioning in Sixth Generation Networks. Part 2. Review of Algorithms and Accuracy Assessment

Relevance. This work is the second part of a series devoted to the study of a set of positioning models in sixth-generation terahertz networks and solves the problems of systematizing algorithms and assessing the accuracy of determining the location of a user device depending on the configuration and size of the antenna array at the base station.Purpose. Within the framework of the scientific problem of searching for means of achieving decimeter accuracy of coordinate estimates, outlined in the first part of the cycle, the analysis of accuracy assessment models, a review of algorithms and ways of their optimization, as well as a numerical experiment, performed in this study serve the purpose of justifying the configuration and dimensions of the antenna array used at the base station.The research method is an analytical review of the state of the problem based on current scientific publications, conceptual modeling, categorical approach, expert combination, comparative analysis, formalization, mathematical and simulation modeling.Solution / results. The paper presents models for assessing the accuracy of positioning in 6G terahertz networks, formalizes the relationship between primary measurements and coordinate estimates for multi-position and single-position positioning in the near and far zones. It provides an overview of algorithms for geometric positioning and positioning with training for cases of one-stage and two-stage processing; analyzes the specifics of implementing algorithms for simultaneous tracking and map construction. It provides an analysis of the features of optimizing algorithms in offline and online modes. Simulation modeling is used to assess the accuracy for a scenario of territorial distribution with direct visibility and ideal synchronization.Novelty. Using simulation modeling tools, the achievement of decimeter accuracy of coordinate and orientation estimates of 1° in the terahertz range for a far-field model using a 1 GHz band and a composite antenna array of more than half a thousand elements has been scientifically substantiated.The theoretical significance lies in establishing the dependence of the accuracy of coordinate and orientation estimates of the device on the configuration and dimensions of the antenna array at the base station.The practical significance of the developed simulation model lies in the numerical justification of the limits of device positioning accuracy in sixth-generation networks depending on the antenna array used at the base station for a given scenario.

Review of Continuous Time Markov Bridges: Models, Algorithms and Applications

Abstract. Continuous time Markov chains (CTMC) with start point and endpoint, i.e., a continuous time Markov Bridge, have become an important mathematical model in a wide variety of scientific disciplines and simulations. In this research, we review some classical Markov Bridge simulation algorithms and Markov Bridge's mathematical model, Kinetic Monte Carlo, Direct Sampling, Reject Sampling and Uniform Sampling with a potential application algorithm in biological protein folding evolution problem.

Electronic structure simulations in the cloud computing environment.

The transformative impact of modern computational paradigms and technologies, such as high-performance computing (HPC), quantum computing, and cloud computing, has opened up profound new opportunities for scientific simulations. Scalable computational chemistry is one beneficiary of this technological progress. The main focus of this paper is on the performance of various quantum chemical formulations, ranging from low-order methods to high-accuracy approaches, implemented in different computational chemistry packages and libraries, such as NWChem, NWChemEx, Scalable Predictive Methods for Excitations and Correlated Phenomena, ExaChem, and Fermi-Löwdin orbital self-interaction correction on Azure Quantum Elements, Microsoft's cloud services platform for scientific discovery. We pay particular attention to the intricate workflows for performing complex chemistry simulations, associated data curation, and mechanisms for accuracy assessment, which is demonstrated with the Arrows automated workflow for high throughput simulations. Finally, we provide a perspective on the role of cloud computing in supporting the mission of leadership computational facilities.

High-Performance Parallel Computing for Scientific Simulations

One essential strategy for overcoming the difficulties of contemporary scientific simulations is High-Performance Parallel Computing, or HPPC. In order to handle massive information and solve complicated equations, these simulations—which mimic complex phenomena like weather patterns, fluid dynamics, and biological systems—require enormous processing resources. By dividing tasks into smaller components and processing them concurrently, HPPC enables scientists to answer issues more quickly and precisely. Research in several disciplines, including biology, environmental science, engineering, and physics, is greatly advanced by this type of computer.

Lattice stick number 15 is unattainable for non-splittable links

In this paper, we explore mathematical links, defined as closed curves embedded in 3D space. Knot theory studies these structures, which also occur in real-world biopolymers like DNA. Lattice links are links in the cubic lattice. For scientific simulations or statistical studies, links are simplified to lattice links. The lattice stick number, denoted as s L (K), is the minimum number of lattice sticks needed to represent a link K in the cubic lattice. In previous study, it was shown that only two non-trivial knots and six non-splittable links have s L ≤ 14: specifically, sL(212)=8 , sL(31)=sL(212♯212) = sL(623)=sL(633)=12 , sL(412)=13 , and sL(41)=sL(512)=14 . Recent study has further revealed that no knot can have s L = 15. In this paper, we prove that lattice stick number 15 is not attainable for non-splittable links. As a corollary, eleven non-splittable links with s L =16 are presented.

A set of Models for Device Positioning in Sixth Generation Networks. Part 1. Methods Survey and Problem Statement

Relevance. Today, terahertz radio systems are considered as a technological basis for integrating methods and means of radio communication and radar in promising sixth-generation networks. If in 4G LTE networks the capabilities of positioning user equipment using the infrastructure of base stations were considered as auxiliary options, then in 5G NR networks, location determination technologies (LDTs) have become full-fledged services, the requirements for which are specified along with communication services. A new trend in positioning in 5G NR networks, compared to 4G LTE networks, has become a single-position assessment of the coordinates and orientation of the user equipment based on signals from a single base station with the ability to distinguish between direct and reflected signals. 6G networks are still in their infancy, but it can already be stated that they mark the next stage in the evolution of digital ecosystems, which is characterized by the convergence of communication technologies, localization and sensing of radio air and the surrounding space by radio engineering means.Purpose. This work opens a research cycle devoted to the review of models, methods and algorithms for positioning devices in 6G networks. The goal of the cycle is to find and justify new radio engineering means for achieving decimeter accuracy in 6G device coordinate estimates. The first part of the cycle provides an overview of the methods and formalization of the model for collecting primary measurements.Method is an analytical review of the state of the problem based on current scientific publications, conceptual modeling, categorical approach, expert combination, comparative analysis, formalization, mathematical and simulation modeling.Results. As a result of the review of device positioning methods during the transition to 6G networks, key performance indicators and LDT scenarios are updated. As a result of the comparative analysis of 5G and 6G networks, new factors, advantages and disadvantages of positioning technologies during the transition from millimeter wave networks to terahertz networks are systematized. A formalized mathematical model for collecting primary measurements is used in the simulation model for assessing the accuracy of device positioning in the second part of the cycle.Novelty. This cycle is the first such study in the Russian scientific segment on network positioning of the sixth generation of the terahertz range, in which the author's version provides an overview of methods and a systematization of a set of new factors of the OMP in communication networks.The theoretical significance of the review-analysis lies in the establishment of both technological obstacles and new opportunities for increasing positioning accuracy during the transition to 6G networks.The practical significance of the formalized mathematical model lies in its subsequent software implementation for numerical justification of the limits of positioning accuracy in 6G networks.

2023 IEEE Scientific Visualization Contest Winner: VisAnywhere: Developing Multi-platform Scientific Visualization Applications.

Scientists often explore and analyze large-scale scientific simulation data by leveraging two- and three-dimensional visualizations. The data and tasks can be complex and therefore best supported using myriad display technologies, from mobile devices to large high-resolution display walls to virtual reality headsets. Using a simulation of neuron connections in the human brain provided for the 2023 IEEE Scientific Visualization Contest, we present our work leveraging various web technologies to create a multi-platform scientific visualization application. Users can spread visualization and interaction across multiple devices to support flexible user interfaces and both co-located and remote collaboration. Drawing inspiration from responsive web design principles, this work demonstrates that a single codebase can be adapted to develop scientific visualization applications that operate everywhere.

Identification of ECA rules forming MACA in periodic boundary condition

Cellular automaton (CA) is a computing model which is emerging rapidly. It is largely used in different types of scientific applications and simulations due to its ability to solve complex problems using simple rule(s). Cellular automata (CAs) are used in different types of applications like cryptography, VLSI systems, fault detection, etc. Typically, most of these applications utilize one-dimensional, 2-state, 3-neighborhood CAs. This paper explores the concept of Next State RMT Transition Diagram (NSRTD) for characterization of all the Elementary Cellular Automata (ECA) rules in periodic boundary condition leading to the identification of all ECA rules forming more than two fixed points (referred to as Single Length Cycle Multi-Attractor CA (MACA)) for an arbitrary CA length (n). For this, the 88 Wolfram classification rules and their equivalent rules have been utilized to reduce the search complexity by avoiding exhaustive searching on all the 256 ECA rules.

A Prediction‐Traversal Approach for Compressing Scientific Data on Unstructured Meshes with Bounded Error

AbstractWe explore an error‐bounded lossy compression approach for reducing scientific data associated with 2D/3D unstructured meshes. While existing lossy compressors offer a high compression ratio with bounded error for regular grid data, methodologies tailored for unstructured mesh data are lacking; for example, one can compress nodal data as 1D arrays, neglecting the spatial coherency of the mesh nodes. Inspired by the SZ compressor, which predicts and quantizes values in a multidimensional array, we dynamically reorganize nodal data into sequences. Each sequence starts with a seed cell; based on a predefined traversal order, the next cell is added to the sequence if the current cell can predict and quantize the nodal data in the next cell with the given error bound. As a result, one can efficiently compress the quantized nodal data in each sequence until all mesh nodes are traversed. This paper also introduces a suite of novel error metrics, namely continuous mean squared error (CMSE) and continuous peak signal‐to‐noise ratio (CPSNR), to assess compression results for unstructured mesh data. The continuous error metrics are defined by integrating the error function on all cells, providing objective statistics across nonuniformly distributed nodes/cells in the mesh. We evaluate our methods with several scientific simulations ranging from ocean‐climate models and computational fluid dynamics simulations with both traditional and continuous error metrics. We demonstrated superior compression ratios and quality than existing lossy compressors.

On-chip source-device-independent quantum random number generator

Quantum resources offer intrinsic randomness that is valuable for applications such as cryptography, scientific simulation, and computing. Silicon-based photonics chips present an excellent platform for the cost-effective deployment of next-generation quantum systems on a large scale, even at room temperature. Nevertheless, the potential susceptibility of these chips to hacker control poses a challenge in ensuring security for on-chip quantum random number generation, which is crucial for enabling extensive utilization of quantum resources. Here, we introduce and implement an on-chip source-device-independent quantum random number generator (SDI-QRNG). The randomness of this generator is achieved through distortion-free on-chip detection of quantum resources, effectively eliminating classical noise interference. The security of the system is ensured by employing on-chip criteria for estimating security entropy in a practical chip environment. By incorporating a photoelectric package, the SDI-QRNG chip achieves a secure bit rate of 146.2 Mbps and a bare chip rate of 248.47 Gbps, with all extracted secure bits successfully passing the randomness test. Our experimental demonstration of this chip-level SDI-QRNG shows significant advantages in practical applications, paving the way for the widespread and cost-effective implementation of room-temperature secure QRNG, which marks a milestone in the field of QRNG chips.

Efficient mini-batch stochastic gradient descent with Centroidal Voronoi Tessellation for PDE-constrained optimization under uncertainty

The study of optimal control problems under uncertainty plays an important role in scientific numerical simulations. This class of optimization problems is frequently utilized in engineering, biology and finance. Although stochastic gradient descent is a well-known stochastic optimization technique for solving the approximate optimal control problem, it often exhibits a slow convergence rate due to the inherent variance in gradient approximation. To address this issue, we propose a more efficient stochastic optimization algorithm based on Centroidal Voronoi Tessellation sampling. This strategy reduces the error in gradient estimation compared to the conventional mini-batch stochastic gradient descent method. Our approach involves dividing the whole snapshot set into several Voronoi cells with low variance and extracting samples with good uniformity from each region to construct an unbiased estimation of the full gradient. Our method can economically and stably approximate numerical optimal control functions even with a fixed step size. Numerical results demonstrate that the proposed method is a reliable algorithm with great potential for applications in the field of optimization.

Wizards of Climate Science: Ancient Magicians in the Court of Big Science

Myths inform real-world decision making. Scientific simulation models can do the same. Neither reflects their real-world targets perfectly. They are most useful in an apophatic sense: employing them as the best tools available without confusing their indications with Truth. The actions and choices of wizards often reflect those of scientists; drawing parallels is informative and several questions will be explored within this framework. How is a climate scientist to respond when offered a Faustian agreement promising limited insights? As scientists, how might we better communicate scientific limits regarding which aspects of the future we can see clearly and which we cannot? Should we risk casting doubt on the as-good-as-it-gets science underlying anthropogenic climate change? If an electorate requires certainty of a threat before it will vote for action, are lies of omission or misrepresentation justified? Is it ethical for scientists whose research is relevant to the policy process to pause their typical vigorous scientific criticism of overinterpretation by others (particularly in sciences downstream from physical science) when their science is thought not adequate for purpose? Should scientists merely advise, presenting the relevant science as neutrally as they can, or advocate by emphasizing evidence that supports their preferred course of action, or become activists overselling their science to achieve well-motivated policy ends by whatever means required?

Neural operators for accelerating scientific simulations and design

Neural operators for accelerating scientific simulations and design

PRINCIPLES OF MATHEMATICAL SIMULATION OF MINING VEHICLES USING APPLIED MATHEMATICAL SOFTWARE

The transportation of rock mass and coal in mining operations relies heavily on the efficiency and reliability of mining transport systems. This study delves into the intricate dynamics of chassis functions during transportation, emphasizing the critical role of transmitting dynamic stresses and mass to the road or rail structures. With a focus on increasing productivity, modern mining locomotives and heavy dump trucks now carry substantial adhesion adhesive weights, allowing for the hauling of heavier loads even on steep slopes. The integration of scientific simulations and research-based design methods aids in identifying and managing dynamic loading effects within mining vehicle chassis. This approach minimizes the transfer of dynamic loads onto the bolster structure, enhancing system reliability and operational efficiency. Computer-aided software plays a vital role in stream-lining the development process, reducing the need for costly physical prototypes and extensive testing in challenging mining environments. The study outlines simplified calculation schemes for mining transport utilities, balancing the need for accuracy with computational efficiency. By utilizing mathematical models and simulation techniques, the dynamics of mine locomotives and dump trucks can be accurately evaluated, guiding design decisions and operational strategies. The application of Lagrange equations and software tools like “Wolfram Mathematica” facilitates the generation of differential equations for dynamic analysis, providing insights into vehicle dynamics and road interactions. Overall, this research underscores the importance of advanced modeling and simulation techniques in optimizing mining transport systems, enhancing safety, productivity, and reliability in demanding mining environments. Purpose. To highlight the application of mathematical software as an instrument for mechanical system dynamics study, that allows scientifically substantiate the usage of new technical solutions during their development. Method. Development a system of differential equations using Lagrange equations of the 2nd order, which are solved in Wolfram Mathematica. Preparation of initial conditions and mass-inertia data can be done by any known software. The generalized mathematical model can be used for any vehicle suspending unnecessary coordinates. Results. Implementation of new technical solutions without scientific ground could be resulted in difficulties while exploitation. For its removal necessary to receive dynamical characteristics. Existing engineering calculation methods are not suitable, especially because of tight time and limited financing. The customized mathematical models can be developed and solved on demand using available software. A scientific novelty. Enhancement of theoretical and experimental research become possible owing to the usage of mod- ern approaches of dynamic systems simulation using applied mathematical software Wolfram Mathematica. Obtained model can be modified in few operation from one mass task into multibody system with maximum 69 free displacements (coordinates). Practical value. The possibility to receive customized simulation model fast, verified with increased quality for scientific purposes.

PyOED: An Extensible Suite for Data Assimilation and Model-Constrained Optimal Design of Experiments

This paper describes PyOED, a highly extensible scientific package that enables developing and testing model-constrained optimal experimental design (OED) for inverse problems. Specifically, PyOED aims to be a comprehensive Python toolkit for model-constrained OED . The package targets scientists and researchers interested in understanding the details of OED formulations and approaches. It is also meant to enable researchers to experiment with standard and innovative OED technologies with a wide range of test problems (e.g., simulation models). OED, inverse problems (e.g., Bayesian inversion), and data assimilation (DA) are closely related research fields, and their formulations overlap significantly. Thus, PyOED is continuously being expanded with a plethora of Bayesian inversion, DA, and OED methods as well as new scientific simulation models, observation error models, and observation operators. These pieces are added such that they can be permuted to enable testing OED methods in various settings of varying complexities. The PyOED core is completely written in Python and utilizes the inherent object-oriented capabilities; however, the current version of PyOED is meant to be extensible rather than scalable. Specifically, PyOED is developed to “enable rapid development and benchmarking of OED methods with minimal coding effort and to maximize code reutilization.” This paper provides a brief description of the PyOED layout and philosophy and provides a set of exemplary test cases and tutorials to demonstrate the potential of the package.