Scientific Simulations Research Articles

Molecular dynamics simulations guided the preparation of nano-silica/polyimide/cellulose composite insulating paper

Following the surge in power loads and continuous improvement in the voltage level, insulating papers' mechanical and electrical properties face new challenges. However, traditional inefficient “tentative” trial and error experiments are formidable to rapidly and efficiently prepare cellulose composite insulating paper because the direct scientific theory or simulation guidance is insufficient. To solve this problem, the nano-silica(nano-SiO2)/polyimide(PI)/cellulose composite models were designed and their mechanical, and dielectric properties were predicted by molecular dynamics (MD) simulation. The preparation of corresponding nano-SiO2/PI/cellulose insulating paper was guided by the MD simulation results and their surface morphology, mechanical properties, and electrical properties were investigated. The experimental results are in good agreement with the MD simulation results and confirmed that P3 insulating paper possesses the best comprehensive performance, and its tensile strength, relative permittivity, dielectric loss, volume resistivity, and breakdown strength are 71.44 MPa, 2.39, 0.2%, 5.16 × 1015 Ω∙m, and 75.8 kV∙mm−1, respectively. This work proves the feasibility and effectiveness of using MD simulation to guide the development of new insulating papers, and compared with the existing traditional preparation methods, this method is expected to be applied to study various new materials in the future and promote the vigorous development of new materials.

End-to-end differentiability and tensor processing unit computing to accelerate materials’ inverse design

Numerical simulations have revolutionized material design. However, although simulations excel at mapping an input material to its output property, their direct application to inverse design has traditionally been limited by their high computing cost and lack of differentiability. Here, taking the example of the inverse design of a porous matrix featuring targeted sorption isotherm, we introduce a computational inverse design framework that addresses these challenges, by programming differentiable simulation on TensorFlow platform that leverages automated end-to-end differentiation. Thanks to its differentiability, the simulation is used to directly train a deep generative model, which outputs an optimal porous matrix based on an arbitrary input sorption isotherm curve. Importantly, this inverse design pipeline leverages the power of tensor processing units (TPU)—an emerging family of dedicated chips, which, although they are specialized in deep learning, are flexible enough for intensive scientific simulations. This approach holds promise to accelerate inverse materials design.

Simulation Analysis of Land-Use Spatial Conflict in a Geopark Based on the GMOP–Markov–PLUS Model: A Case Study of Yimengshan Geopark, China

The foundation for accurately understanding regional land-use structures and pursuing the coordination of human–land relations is the scientific identification and simulation of temporal and spatial evolution patterns of land-use spatial conflict (LUSC). Based on the production–living–ecological space (PLES) perspective, a land-use spatial conflict identification and intensity diagnosis model (LUCSII) was constructed using a landscape ecology index. The methods of geographic information system (GIS), spatial autocorrelation analysis, and mathematical statistics were used to achieve the spatial pattern of LUSC over the last 20 years, and the GMOP–Markov–PLUS model was used to simulate the evolution of LUSC in the future under various scenarios. The results indicated that our established LUCSII could accurately identify potential land-use spatial conflict areas in geoparks. The GMOP–Markov–PLUS model constructed had also scientifically predicted the future land-use patterns under different scenarios, successfully demonstrating the changing process of spatial conflict pattern evolution. The research proposed three different plans for the long-term land use of YG, including ecological protection, economic development, and long-term development perspectives. Finally, the research further emphasized the importance for sustainable development of geoparks. More attention should be paid to the optimal allocation of land-use structure and the coordinated development of human–land relationships.

Spatiotemporal pattern and multi-scenario simulation of ecological risk in mountainous cities: a case study in Chongqing, China.

Scientific descriptions and simulations of the ecological risks in mountainous areas can promote the sustainable use of land resources in these areas and improve the reliability of decision-making for ecological risk management. Taking Chongqing, China, as an example, we constructed a landscape ecological risk (LER) evaluation model based on land use data from 1995 to 2020 and analysed the spatiotemporal evolution characteristics of the LER pattern. Moreover, we coupled the patch-generating land use simulation (PLUS) model and multi-objective programming (MOP) method and input multiple scenarios (inertial development, ID; economic priority development, ED; ecological priority development, PD; and sustainable development, SD) to simulate the ecological risk pattern in 2030. The model coupling the "top-down" and "bottom-up" processes obtained optimal land use patterns in different contexts, and it was used to perform a spatially explicit examination of LER evolutionary trends in different contexts. The results showed that LER evolution in Chongqing has had obvious stage characteristics. The high-risk area decreased significantly under various constraints, including topographic, economic, and other constraints, and the distribution showed a trend of high in the west and low in the east. The LER spatial clustering characteristics were highly coupled with the risk level pattern. The ED scenario presented the most severe risk, the PD scenario presented a moderate risk, and the SD scenario balanced the land demand for economic and ecological development and had a better land use structure and LER compared with the other scenarios. The coupled model proposed in this study helps to obtain the optimal land use structure and mitigate ecological risks, thus providing a scientific basis for future urban development.

A quantum circuit to generate random numbers within a specific interval

Random numbers are of vital importance in fields such as cyptography and scientific simulations. However, it is well known how difficult it is for classical computers to generate random numbers. This is not the case for quantum computers, which are able to genuinely generate random numbers thanks to the property of superposition and their counter-intuitive concept of measurement. However, despite the simplicity of designing a circuit that generates a random number between 0 and 2^{N}-1 (being N the number of available qubits), designing a quantum circuit to generate a number within a specific interval is far from trivial. This paper proposes a customizable circuit design to generate random numbers. The circuit is non- hardware dependent, it allows fault-tolerance, and it can be used by current quantum devices. Therefore, it is a valuable tool for all those quantum applications and algorithms that need to work with random numbers. Moreover, a comparator circuit has also been designed as part of this work. This comparator is the best currently available in the literature in terms of qubits, T-count, and T-depth. It is therefore a useful tool for any other circuit or algorithm where this operation is needed.

A stochastic gradient method for a class of nonlinear PDE-constrained optimal control problems under uncertainty

The study of optimal control problems under uncertainty plays an important role in scientific numerical simulations. This class of optimization problems is strongly utilized in engineering, biology and finance. In this paper, a stochastic gradient method is proposed for the numerical resolution of a nonconvex stochastic optimization problem on a Hilbert space. We show that, under suitable assumptions, strong or weak accumulation points of the iterates produced by the method converge almost surely to stationary points of the original optimization problem. Measurability and convergence rates of a stationarity measure are handled, filling a gap for applications to nonconvex infinite dimensional stochastic optimization problems. The method is demonstrated on an optimal control problem constrained by a class of elliptic semilinear partial differential equations (PDEs) under uncertainty.

Customizable adaptive regularization techniques for B-spline modeling

B-spline models are a powerful way to represent scientific data sets with a functional approximation. However, these models can suffer from spurious oscillations when the data to be approximated are not uniformly distributed. Model regularization (i.e., smoothing) has traditionally been used to minimize these oscillations; unfortunately, it is sometimes impossible to sufficiently remove unwanted artifacts without smoothing away key features of the data set. In this article, we present a method of model regularization that preserves significant features of a data set while minimizing artificial oscillations. Our method varies the strength of a smoothing parameter throughout the domain automatically, removing artifacts in poorly-constrained regions while leaving other regions unchanged. The proposed method selectively incorporates regularization terms based on first and second derivatives to maintain model accuracy while minimizing numerical artifacts. The behavior of our method is validated on a collection of two- and three-dimensional data sets produced by scientific simulations. In addition, a key tuning parameter is highlighted and the effects of this parameter are presented in detail. This paper is an extension of our previous conference paper at the 2022 International Conference on Computational Science (ICCS) (Lenz et al., 2022) [1].

Multilevel Robustness for 2D Vector Field Feature Tracking, Selection and Comparison

AbstractCritical point tracking is a core topic in scientific visualization for understanding the dynamic behaviour of time‐varying vector field data. The topological notion of robustness has been introduced recently to quantify the structural stability of critical points, that is, the robustness of a critical point is the minimum amount of perturbation to the vector field necessary to cancel it. A theoretical basis has been established previously that relates critical point tracking with the notion of robustness, in particular, critical points could be tracked based on their closeness in stability, measured by robustness, instead of just distance proximity within the domain. However, in practice, the computation of classic robustness may produce artifacts when a critical point is close to the boundary of the domain; thus, we do not have a complete picture of the vector field behaviour within its local neighbourhood. To alleviate these issues, we introduce a multilevel robustness framework for the study of 2D time‐varying vector fields. We compute the robustness of critical points across varying neighbourhoods to capture the multiscale nature of the data and to mitigate the boundary effect suffered by the classic robustness computation. We demonstrate via experiments that such a new notion of robustness can be combined seamlessly with existing feature tracking algorithms to improve the visual interpretability of vector fields in terms of feature tracking, selection and comparison for large‐scale scientific simulations. We observe, for the first time, that the minimum multilevel robustness is highly correlated with physical quantities used by domain scientists in studying a real‐world tropical cyclone dataset. Such an observation helps to increase the physical interpretability of robustness.

SZ3: A Modular Framework for Composing Prediction-Based Error-Bounded Lossy Compressors

Today's scientific simulations require a significant reduction of data volume because of extremely large amounts of data they produce and the limited I/O bandwidth and storage space. Error-bounded lossy compression has been considered one of the most effective solutions to the above problem. In practice, however, the best-fit compression method often needs to be customized or optimized in particular because of diverse characteristics in different datasets and various user requirements on the compression quality and performance. In this paper, we address this issue with a novel modular, composable compression framework named SZ3. Our contributions are four-folds. (1) We develop SZ3 which features an innovative modular abstraction for the prediction-based compression framework, such that compression modules can be plugged in easily to create new compressors based on characteristics of data and user requirements. (2) We create a new compression pipeline by SZ3 for GAMESS data, which significantly improves the compression ratios over state-of-the-art compressors. (3) We develop an adaptive compression pipeline by SZ3 for APS data with minimal efforts, which leads to the best rate-distortion among all existing error-bounded lossy compressors for any bit-rate. (4) We compare the sustainability of SZ3 with leading error-bounded prediction-based compressors, and then demonstrate the necessity of diverse pipelines by integrating and evaluating several compression pipelines on diverse scientific datasets from multiple disciplines. Experiments show that SZ3 incurs very limited overhead in compressor integration and our customized compression pipelines lead to up to 20% improvement in compression ratios under the same data distortion, when compared with the best existing approach.

Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics

Deep learning surrogate models are being increasingly used in accelerating scientific simulations as a replacement for costly conventional numerical techniques. However, their use remains a significant challenge when dealing with real-world complex examples. In this work, we demonstrate three types of neural network architectures for efficient learning of highly non-linear deformations of solid bodies. The first two architectures are based on the recently proposed CNN U-NET and MAgNET (graph U-NET) frameworks which have shown promising performance for learning on mesh-based data. The third architecture is Perceiver IO, a very recent architecture that belongs to the family of attention-based neural networks–a class that has revolutionised diverse engineering fields and is still unexplored in computational mechanics. We study and compare the performance of all three networks on two benchmark examples, and show their capabilities to accurately predict the non-linear mechanical responses of soft bodies.

Impact elements of feed grinder: a review

The article deals with the issue concerning the working bodies of technological equipment designed for grinding pieces and particles of feed raw materials. The most profiTable feed raw materials are by-products and waste materials of animal origin, which have a valuable high-protein content. An alternative way of mandatory waste disposal is their processing, including mechanical grinding to obtain feed products. In the process of grinding, particular importance is given to the working bodies, with the help of which the raw material is directly divided into parts. In this case, the destruction of the feed material often occurs by means of impact. Impact phenomena have proven to be highly effective in the process of intense cracking and chipping, which leads to the desired separation of the crushed particles into smaller ones. However, it is found that crushers have insufficient efficiency of impact elements. The work analyzes the processes of impact grinding from the standpoint of a number of scientific hypotheses, theories, modeling, simulation, experience and approbation, presented in various scientific publications. When studying and improving the theory of impact, attention is paid to nonlinear problems, cracking, modernization of the theory of brittle fracture, diagrams of force changes during impact force, impact equations, wave theory of impact, peridynamic theory. It should be noted that the main scientific results are directly reflected in the improvement of the design features of hammers. It has been revealed that the main improvement in the design of impact elements is in the direction of increasing the efficiency of working surfaces and developing the combination of impact with cutting, abrasion and crushing

Correlation-aware probabilistic data summarization for large-scale multi-block scientific data visualization

In this paper, we propose a correlation-aware probabilistic data summarization technique to efficiently analyze and visualize large-scale multi-block volume data generated by massively parallel scientific simulations. The core of our technique is correlation modeling of distribution representations of adjacent data blocks using copula functions and accurate data value estimation by combining numerical information, spatial location, and correlation distribution using Bayes’ rule. This effectively preserves statistical properties without merging data blocks in different parallel computing nodes and repartitioning them, thus significantly reducing the computational cost. Furthermore, this enables reconstruction of the original data more accurately than existing methods. We demonstrate the effectiveness of our technique using six datasets, with the largest having one billion grid points. The experimental results show that our approach reduces the data storage cost by approximately one order of magnitude compared to state-of-the-art methods while providing a higher reconstruction accuracy at a lower computational cost.

USING PINN TO SOLVE EQUATIONS OF EQUIDISTRIBUTIONAL METHOD FOR CONSTRUCTING 2D STRUCTURED ADAPTED NUMERICAL GRIDS

The Equidistributional method is a popular technique for constructing numerical grids in engineering and scientific simulations. It is based on the principle of equidistribution, which requires evenly spaced grid points to reduce numerical errors. However, traditional Equidistributional methods can become inefficient and inaccurate for complex geometries and boundary conditions. In this paper, we present a new approach for solving the Equidistributional method's equations using physics-informed neural networks (PINN). PINN is a type of machine learning algorithm that has been shown to be effective for solving partial differential equations (PDEs). Our findings suggest that the use of PINN has the potential to significantly enhance the performance of the Equidistributional method for constructing 2D structured adapted numerical grids.         TRANSLATE with x English Arabic Hebrew Polish Bulgarian Hindi Portuguese Catalan Hmong Daw Romanian Chinese Simplified Hungarian Russian Chinese Traditional Indonesian Slovak Czech Italian Slovenian Danish Japanese Spanish Dutch Klingon Swedish English Korean Thai Estonian Latvian Turkish Finnish Lithuanian Ukrainian French Malay Urdu German Maltese Vietnamese Greek Norwegian Welsh Haitian Creole Persian   //   TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back //     Язык этой страницы: Английский   Перевести на Русский         Азербайджанский Албанский Амхарский Английский Арабский Армянский Африкаанс Бенгальский Бирманский Болгарский Валлийский Венгерский Вьетнамский Греческий Гуджарати Датский Иврит Индонезийский Исландский Испанский Итальянский Казахский Каннада Каталанский Китайский (традиционный) Китайский (упрощенный) Корейский Креольский (гаити) Курманджи Кхмерский Лаосский Латышский Литовский Малагасийский Малайский Малаялам Мальтийский Маори Маратхи Немецкий Непальский Нидерландский Норвежский Панджаби Персидский Польский Португальский Пушту Румынский Русский Самоанский Словацкий Словенский Тайский Тамильский Телугу Турецкий Украинский Урду Финский Французский Хинди Хорватский Чешский Шведский Эстонский Японский   Всегда переводить Английский на РусскийPRO Никогда не переводить Английский Никогда не переводить jpcsip.kaznu.kz

Digital technology virtual restoration of the colours and textures of polychrome Bodhidharma statue from the Lingyan Temple, Shandong, China

This work proposes the virtual restoration of the Bodhidharma polychrome sculpture from the Lingyan Temple in China. Based on scientific analyses and simulation experiments, exterior colours and textures were virtually restored by combining 3D scanning and multi-view 3D reconstruction. At the same time, an efficient cultural relics high-fidelity information 3D modelling method was proposed. Colours and textures are essential for polychrome cultural relics, reflecting the historical appearance and technology. Due to long periods of natural ageing and the destruction of environmental factors, the colours and textures of polychrome sculptures are often altered or radically changed, making it difficult for people to appreciate the initial appearance. With the rapid development of digital technology and high-fidelity 3D modelling, virtual reality technology allows us to restore the appearance of cultural relics. This study expands the dimension of cultural relics exhibition, provides new perspectives for archaeology, art history and cultural heritage research, and provides a reference for the virtual restoration and digitalised archive of other cultural relics.

Spatially explicit carbon emissions by remote sensing and social sensing

Scientific simulation of carbon emissions is an important prerequisite for achieving low-carbon green development and carbon peak and carbon neutralization. This study proposed a carbon emissions spatialization method based on nighttime light (NTL) remote sensing and municipal electricity social sensing. First, the economics-energy comprehensive index (EECI) was proposed by integrating the NTL and municipal electricity consumption (EC) data. Second, the carbon emissions were spatialized at a fine scale based on NTL, EC, and EECI, respectively. Finally, the geographical detector model was applied to quantify the influencing factors on carbon emissions from the perspectives of individuals and interactions. Results show that combining remote sensing and social sensing data helps depict carbon emissions accurately. The factor analysis found that GDP and population were the basis of carbon emissions, while the secondary industry and urbanization rate were the direct factors. This study is expected to provide constructive suggestions and methods for emission reduction, carbon peak, and carbon neutrality in high-density cities in China.

GamaBox-One: A Proposed Architecture for Cloud-based Big Data Management Platform for Multipurpose Computation using Hybrid Architecture

The architecture of the data center is the main key for producing a highly functional demand big data management platform for multipurpose uses. Nowadays various technologies have come to provide construction of that purpose, providing several use cases for big data analytics and processing. In this paper, we want to explore possibilities of architecture that had to be built in answer to the multipurpose data center, such as analytical research, scientific simulation, machine learning, deep data learning, and data orchestration. We discover how Hadoop and its element supporter can be used alongside cloud orchestrators such as Terraform or Occopus and container orchestrators such as Kubernetes or Docker Swarm. We also provide possible supporting components that can handle the different jobs in High-Performance Computing and how the system can be secured. Our proposed approach in this research has developed the architecture for cloud-based big data management for multipurpose computation.

Machine-Learning Methods on Noisy and Sparse Data

Experimental and computational data and field data obtained from measurements are often sparse and noisy. Consequently, interpolating unknown functions under these restrictions to provide accurate predictions is very challenging. This study compares machine-learning methods and cubic splines on the sparsity of training data they can handle, especially when training samples are noisy. We compare deviation from a true function f using the mean square error, signal-to-noise ratio and the Pearson R2 coefficient. We show that, given very sparse data, cubic splines constitute a more precise interpolation method than deep neural networks and multivariate adaptive regression splines. In contrast, machine-learning models are robust to noise and can outperform splines after a training data threshold is met. Our study aims to provide a general framework for interpolating one-dimensional signals, often the result of complex scientific simulations or laboratory experiments.

Parallel and Distributed Computing for High-Performance Applications

The study of parallel and distributed computing has become an important area in computer science because it makes it possible to create high-performance software that can effectively handle challenging computational tasks. In terms of their use in the world of high-performance applications, parallel and distributed computing techniques are given a thorough introduction in this study. The partitioning of computational processes into smaller subtasks that may be completed concurrently on numerous processors or computers is the core idea underpinning parallel and distributed computing. This strategy enables quicker execution times and enhanced performance in general. Parallel and distributed computing are essential for high-performance applications like scientific simulations, data analysis, and artificial intelligence since they frequently call for significant computational resources. High-performance apps are able to effectively handle computationally demanding tasks thanks in large part to parallel and distributed computing. This article offers a thorough review of the theories, methods, difficulties, and developments in parallel and distributed computing for high-performance applications. Researchers and practitioners may fully utilize the potential of parallel and distributed computing to open up new vistas in computational science and engineering by comprehending the underlying concepts and utilizing the most recent breakthroughs.

ИССЛЕДОВАНИЕ ПОВЫШАЮЩЕГО ПРЕОБРАЗОВАТЕЛЯ ПОСТОЯННОГО НАПРЯЖЕНИЯ НА БАЗЕ МНОГОФУНКЦИОНАЛЬНОГО ИНТЕГРИРОВАННОГО ЭЛЕКТРОМАГНИТНОГО КОМПОНЕНТА

The relevance The methods of functional integration of discrete electrical and electromagnetic components allow increasing the reliability and efficiency of electrical energy conversion systems, reducing their weight and dimensions. The authors proposed a circuit solution based on the replacement of some individual discrete components of a DC boost converter with a multifunctional integrated electromagnetic component (MIEС). The component performs the functions of an inductor and a filter capacitor. Aim of research This article discusses the investigation of a DC voltage booster converter based on a multifunctional integrated electromagnetic component. The main task is to investigate a DC voltage booster converter based on a multifunctional integrated electromagnetic component. Research methods In this study, the following methods were used: the study of scientific literature and computer simulation in the Matlab environment. Results A computer model of a step-up DC voltage converter based on MIEС in the Circuit Simulator software package has been developed. Graphs of the output voltage are obtained. The circuits of step-up DC voltage converters are analyzed. A circuit of a DC voltage boost converter based on MIEС has been assembled. Output voltage waveforms were taken. An analysis of the computer model and laboratory test sample was carried out. A set of works has been completed to improve the output characteristics of a step-up converter based on MIEС. The optimal scheme has been chosen to implement the replacement of the inductor and filter capacitor with a multifunctional integrated electromagnetic component. Conducted laboratory studies confirmed the operability of MIEС as a replacement for individual discrete components of the DC voltage boost converter circuit. The optimal mode of operation of the boost converter based on MIEС is determined, which makes it possible to expand the output voltage range and reduce ripples. Expansion of the output voltage range is feasible due to the near-resonant or resonant mode of operation of the converter, which is feasible due to the MIEС structure, which is a resonant LC circuit.

МОДЕЛИРОВАНИЕ ЭЛЕКТРОМАГНИТНЫХ ПРОЦЕССОВ В ФИЛЬТРОКОМПЕНСИРУЮЩЕМ УСТРОЙСТВЕ В СОСТАВЕ СИСТЕМЫ ЗАРЯДА ЕМКОСТНОГО НАКОПИТЕЛЯ

The relevance Secondary power supplies, frequency converters, and inverters are actively used in modern rapidly developing power engineering. The trend towards a decrease in the weight and size of devices and an increase in their power explains the need to use pulse modes of electrical energy converters. The oscillatory circuits of resonant inverters contain elements such as an inductor and a capacitor, i.e. electromagnetic elements, as a rule, constituting up to 85–90 % of the mass and dimensions of secondary power supplies. The main technical means of reducing the mass and dimensions of electromagnetic elements are increasing the conversion frequency and functional integration. Resonant modes of operation of inverters during switching can reduce interference and losses. Aim of research This article discusses the development of an efficient capacitive storage charging system and simulation of electromagnetic processes in a filtercompensating device as part of an efficient capacitive storage charging system. The main task is to develop an efficient capacitive storage charging system. Research methods Research methods. In this study, the following methods were used: the study of scientific literature and computer simulation in the Matlab environment. Results The authors have developed a computer model in MatLab of a capacitive storage charging system based on a half-bridge circuit of an inductivecapacitive converter. A multifunctional integrated electromagnetic component (MIEC) is used as a pre-absorber. Investigations of voltage levels at various points of the four-terminal network have been carried out. The voltage on the plates and capacitance MIEC was determined. The voltage in the diagonals of the inverter was measured to find the maximum voltage. Because of computer modeling and experimental research, graphs of voltages on the elements of the half-bridge circuit of the capacitive storage charging system were obtained. The developed computer model will allow the investigation of various modes of operation of a secondary power source based on the proposed component in high-voltage charging systems for energy storage devices. The developed model allows you to reduce the financial costs of experimental research. Also, this model allows you to explore more complex systems based on MIEC, which have different properties and indicators.