High Computational Efficiency Research Articles

Tracer transport in fractured porous media: Distribution of tracer concentration within the rock matrix and the implementation of time domain random walk algorithm

In this work, a new Time Domain Random Walk (TDRW) algorithm is proposed to estimate the tracer distribution profile within the rock matrix. The development of the new algorithm stems from the statistical properties of the analytical solution to a single fracture-matrix system, in which the particle position at a certain time is calculated and recorded. With the position of each particle determined, the resulting distribution will then provide an estimate of the tracer distribution profile directly. In addition, the newly developed algorithm can readily be extended to a case of more complicated fracture-matrix system, in which an arbitrary injection boundary condition may also be used. To verify the accuracy and applicability of the new algorithm, three benchmark simulations are made, in which the results of different approaches are found to be identical. Nevertheless, the new algorithm has a higher computational efficiency, due to its lower calculation demand.

Building extraction from remote sensing imagery: advanced squeeze-and-excitation residual network based methodology

Extracting buildings from remote sensing imagery (RSI) is an essential task in a wide range of applications, such as urban and monitoring. Deep learning has emerged as a powerful tool for this purpose, and in this research, we propose an advanced building extraction method based on SE-ResNet18 and SE-ResNet34 architectures. These models were selected through a rigorous comparative analysis of various deep learning models, including variations of residual networks (ResNet), squeeze-and-excitation residual networks (SE-ResNet), and visual geometry group (VGG), for their high performance in all metrics and their computational efficiency. Our proposed methodology outperformed all other models under consideration by a significant margin, demonstrating its robustness and efficiency. It achieved superior results with less computational effort and time, a testament to its potential as a powerful tool for semantic segmentation tasks in remote sensing applications. An extensive comparative evaluation involving a wide range of state-of-the-art works further validated our method’s effectiveness. Our method achieved an unparalleled intersection over union (IoU) score of 88.51%, indicative of its exceptional accuracy in identifying and segmenting buildings within the Wuhan University (WHU) building dataset. The overall performance of our method, which offers an excellent balance between high performance and computational efficiency, makes it a compelling choice for researchers and practitioners in the field.

Continuum and Discrete Analytical Methods for Vibration and Buckling Eigenvalues Shape Sensitivities

AbstractGradient-based optimization techniques need precise and efficient sensitivities. Numerical sensitivity methods such as finite differences are easy to implement but imprecise and computationally inefficient. In contrast, analytical sensitivity methods, such as the discrete and continuum ones are highly accurate and efficient. Continuum Sensitivity Analysis (CSA) is an analytical method used to calculate derivatives of shape or value parameters. While CSA has been successfully applied in static analysis and dynamic problems in the time domain, this work presents an extension of the approach to eigenvalue sensitivities for the first time. However, CSA revealed limitations, prompting the exploration of an alternative approach based on discrete analytical differentiation. This method is employed for the first time in shape sensitivities. The derivatives of the stiffness and mass matrices required by the method are calculated analytically, resulting in high accuracy and computational efficiency. In addition, an element agnostic approach has been developed leveraging primary analysis matrices to calculate their derivative. This characteristic, along with the nonintrusivity, makes the method employable with standard commercial software. Both approaches have been applied and validated in a wide range of scenarios, involving vibration and buckling problems.

Comparing Three Freeze-Thaw Schemes Using C-Band Radar Data in Southeastern New Hampshire, USA

Soil freeze-thaw (FT) cycles over agricultural lands are of great importance due to their vital role in controlling soil moisture distribution, nutrient availability, health of microbial communities, and water partitioning during flood events. Active microwave sensors such as C-band Sentinel-1 synthetic aperture radar (SAR) can serve as powerful tools to detect field-scale soil FT state. Using Sentinel-1 SAR observations, this study compares the performance of two FT detection approaches, a commonly used seasonal threshold approach (STA) and a computationally inexpensive general threshold approach (GTA) at an agricultural field in New Hampshire, US. It also explores the applicability of an interferometric coherence approach (ICA) for FT detection. STA and GTA achieved 85% and 78% accuracy, respectively, using VH polarization. We find a marginal degradation in the performance of STA (82%) and GTA (76%) when employing VV-polarized data. While there was approximately a 6 percentage point difference between STA’s and GTA‘s overall accuracy, we recommend GTA for FT detection using SAR images at sub-field-scale over extended regions because of its higher computational efficiency. Our analysis shows that interferometric coherence is not suitable for detecting FT transitions under mild and highly dynamic winter conditions. We hypothesize that the relatively mild winter conditions and therefore the subtle FT transitions are not able to significantly reduce the correlation between the phase values. Also, the ephemeral nature of snowpack in our study area, further compounded by frequent rainfall, could cause decorrelation of SAR images even in the absence of a FT transition. We conclude that despite Sentinel-1’s ~80% mapping accuracy at a mid-latitude site, understanding the cause of misclassification remains challenging, even when detailed ground data are readily available and employed in error attribution efforts.

Multi-Adaptive Strategies-Based Higher-Order Quantum Genetic Algorithm for Agile Remote Sensing Satellite Scheduling Problem.

The agile remote sensing satellite scheduling problem (ARSSSP) for large-scale tasks needs to simultaneously address the difficulties of complex constraints and a huge solution space. Taking inspiration from the quantum genetic algorithm (QGA), a multi-adaptive strategies-based higher-order quantum genetic algorithm (MAS-HOQGA) is proposed for solving the agile remote sensing satellites scheduling problem in this paper. In order to adapt to the requirements of engineering applications, this study combines the total task number and the total task priority as the optimization goal of the scheduling scheme. Firstly, we comprehensively considered the time-dependent characteristics of agile remote sensing satellites, attitude maneuverability, energy balance, and data storage constraints and established a satellite scheduling model that integrates multiple constraints. Then, quantum register operators, adaptive evolution operations, and adaptive mutation transfer operations were introduced to ensure global optimization while reducing time consumption. Finally, this paper demonstrated, through computational experiments, that the MAS-HOQGA exhibits high computational efficiency and excellent global optimization ability in the scheduling process of agile remote sensing satellites for large-scale tasks, while effectively avoiding the problem that the traditional QGA has, namely low solution efficiency and the tendency to easily fall into local optima. This method can be considered for application to the engineering practice of agile remote sensing satellite scheduling for large-scale tasks.

Investigation of a novel hydraulic tunnel composite lining with polyurea coating interlayer

PurposeMultilayer composite liner structures are the primary structural form of hydraulic tunnels. However, the bearing mechanism of multilayer composite liners has not been investigated thoroughly. Many existing design schemes do not properly achieve a balance between structural safety, anti-seepage capacity, and cost effectiveness. Thus, a new composite liner structure type and its theoretical model was proposed.Design/methodology/approachA novel hydraulic tunnel composite liner structure with a polyurea spray coating interlayer was proposed in this study. A theoretical model based on the state-space method was developed and verified using FEM models and existing theoretical models. Parametric analysis was conducted based on the theoretical model to investigate the influence of various variables, including interfacial shear stiffness, inner liner thickness, and outer liner elastic modulus.FindingsIt was concluded that the proposed theoretical model can be used successfully to calculate multilayer composite liner structures with high calculation efficiency. The overall deformation stiffness of the composite liner system increased with the interfacial shear stiffness. The sprayed coating interlayer significantly affects the residual force distribution between the outer and inner liners, which can also be affected by the adjustment of the thickness of the outer and inner liners. Thus, attention should be paid to these factors in the rational design of the proposed composite liner system.Originality/valueWith the development of China’s water conservancy projects, complex geological conditions, high surrounding rock stress, high internal and external water pressures, and other unique application scenarios have gradually increased. This places higher requirements on the bearing performance and impermeability of hydraulic tunnel lining structures. On the other hand, conventional hydraulic tunnel lining structures can hardly achieve a satisfactory balance between economy, structural safety, and impermeability. Thus, the proposed structure has the potential to be used in a wide range of applications.

Computationally efficient dominant load-based local bone microstructure reconstruction method using topology optimization

The bone microstructure of the human proximal femur is clinically crucial for diagnosing skeletal pathologies, such as osteoporosis and bone metastases. The topology optimization-based bone microstructure method obtains these bone microstructures by converting low-resolution (LR) images into high-resolution images. However, this method is inherently computationally inefficient as it requires numerous finite elements, iterative analyses, and parallel computations. Therefore, this study proposes a novel topology optimization-based localised bone microstructure reconstruction method using the dominant load, which highly affects the selected region of interest (ROI), for efficient resolution enhancement. The load dependency of selected ROIs is quantified with a load dependency score. Then, the localised finite element model is constructed based on the local load estimation. Finally, the selected dominant load is applied as an input for the topology optimization-based bone microstructure reconstruction method. The reconstructed bone microstructure was similar to that of the conventional method. The localised finite element model applied by the dominant load effectively and accurately reconstructed the bone morphology and exhibited high computational efficiency. In conclusion, the dominant load-based approach can be used to construct a reasonable trabecular bone structure for ROI with high computational efficiency. The predictive performance of the proposed method was validated and showed promise for accurate trabecular bone structure prediction without additional radiation exposure.

Charge transfer plasmons in nanoparticle arrays on graphene: Theoretical development

The properties of charge transfer plasmons (CTPs) in periodic metallic nanoparticle arrays (PMNPAs) on the single-layer graphene surface are studied within a computationally efficient original hybrid quantum-classical model. The model is based on the proven assumption that the carrier charge density in doped graphene remains unchanged under plasmon oscillations. Calculated CTP frequencies for two PMNPA geometries are shown to lie within the THz range and to be factorized, i.e., presented as a product of two independent factors determined by the graphene charge density and the PMNPA geometry. Equations are derived for describing the CTP frequencies and eigenvectors, i.e., oscillating nanoparticle charge values. It is shown that the CTP plasmons having a band structure containing a wave vector and a band number, like to phonons in periodic media, can be divided into an acoustic mode and optical CTP modes. For the acoustic modes, the CTP group velocity tends to zero at k→0, but reaches a value of ∼VFermi in graphene inside the Brillouin zone, while for the optical modes, the group velocity dispersion is extremely weak, although their energy is higher than the acoustic plasmon energies. It is shown that the calculated dependence of CTP frequencies on the carrier concentration in graphene is in good agreement with experimental data. We believe that the proposed model can help in designing various graphene-based terahertz nanoplasmonic devices of complex geometry due to very high computational efficiency.

Unlocking slip-mediated bending in multilayers: Efficient modeling and solutions with high precision and simplicity

This work introduces a novel approach to understanding the bending behavior of multilayer structures with weak interfaces. Despite the existence of various theoretical models, achieving high accuracy and computational efficiency remains a challenge. To address these limitations, we propose a Non-uniform Slip Model (NSM) governed by two essential dimensionless parameters: the number of layers and the shear factor. Utilizing the Semiparametric Hybrid Variational (SHV) method, we derive theoretical solutions that require less computational effort and offer enhanced accuracy. We further simplify the NSM through homogenization to the Uniform Slip Model (USM), yielding clear and concise analytical solutions for deflection curves and effective bending stiffness with high precision. The USM is also extended to account for nonlinear slipping interfaces with continuous shearing flow, providing an analytical stiffness expression that includes the bending angle. This extension explains the experimental observation of multilayered graphene’s bending stiffness, which decreases from cubic to linear with increasing number of layers due to continuous slip and dislocation between atomic layers. Our study delivers theoretical insights for analyzing slip-mediated bending in laminated materials, paving the way for the design and optimization of such structures.

Exponential Convergence and Computational Efficiency of BURA-SD Method for Fractional Diffusion Equations in Polygons

In this paper, we develop a new Best Uniform Rational Approximation-Semi-Discrete (BURA-SD) method taking into account the singularities of the solution of fractional diffusion problems in polygonal domains. The complementary capabilities of the exponential convergence rate of BURA-SD and the hp FEM are explored with the aim of maximizing the overall performance. A challenge here is the emerging singularly perturbed diffusion–reaction equations. The main contributions of this paper include asymptotically accurate error estimates, ending with sufficient conditions to balance errors of different origins, thereby guaranteeing the high computational efficiency of the method.

Furniture design based on image color extraction algorithm

With the increasing demand for personalized and customized home products, how to realize the innovative design of furniture and improve the design efficiency has become a research hotspot for related professionals. Aiming at these problems, the study extracts the main color of furniture images by optimizing the K-mean clustering algorithm, uses the simulated annealing algorithm to color-match the furniture, and reconstructs the image by edge detection to design a furniture design method based on image color extraction. The results revealed that in the foreground part, the correct rate of color match based on the design method was 95.7%, and in the background part, the correct rate of color match based on the design method was 94.81 %, which proved its effectiveness. The average feature point extraction time and the average feature point matching time of the design-based algorithm were 5.45 ms and 9.83 ms, respectively, which proved its high computational efficiency. In furniture color edge detection and overall color match, the image obtained based on the design method was significantly clearer, and the overall coherence, saturation and brightness were closer to the input image. In addition to raising the standard of furniture design, the study's design methodology increases design efficiency and offers solid technical support for the area.

Rapid and Near-Analytical Planning Method for Entry Trajectory under Time and Full-State Constraints

A rapid trajectory-planning method based on an analytical predictor–corrector design of drag acceleration profile and a bank-reversal logic based on double-stage adaptive adjustment is proposed to solve the entry issue under time and full-state constraints. First, an analytical predictor–corrector algorithm is used to design the profile parameters to satisfy the terminal of altitude, velocity, range, time, and flight-path angle constraints. Subsequently, an adaptive lateral planning algorithm based on heading adjustment and maintenance is proposed to achieve the flight stage adaptive division and determination of the bank-reversal point, thereby satisfying the terminal position and heading angle constraints. Concurrently, a rapid quantification method is proposed for the adjustable capacity boundary of the terminal heading angle. On this basis, a range-and-time correction strategy is designed to achieve high precision and the rapid generation of a three-degree-of-freedom entry trajectory under large-scale lateral maneuvering. The simulation results demonstrated that compared with the existing methods, the proposed method can adaptively divide flight stages, ensuring better multitask applicability and higher computational efficiency.

A Novel Head-following Algorithm for Multi-Joint Articulated Driven Continuum Robots

Abstract Head-following (tracking) issue is a challenge in developing multi-joint continuum robots. However various approaches have been developed in head-following algorithm for articulated-driven mechanism (ADM) continuum robots, problems still exist such as low end-accuracy, large trajectory deviation, and low computational efficiency. This paper presents a novel head-following algorithm(NHF) with high precision, small trajectory deviation, and high computational efficiency for multi-joint ADM continuum robots. The proposed algorithm first uses the follow-the-leader (FTL) method to search for planning points. Secondly, the end-effector errors are calculated, split, and adjusted. Thirdly, the error judgment set is assigned based on the error rate of the end-effector, and also the joints that need to be adjusted are determined. Finally, the joint angles are iteratively adjusted. In this paper, the NHF algorithm is simulated on ADM continuum robots with saparately 10, 20 and 31 joints. The result shows that, compareing with other FTL algorithms, NHF algorithm has the highest end accuracy, and the smallest trajectory deviation.

Study on Rapid Simulation of the Pre-Cooling Process of a Large LNG Storage Tank with the Consideration of Digital Twin Requirements

The pre-cooling of a large LNG storage tank involves complex phenomena such as heat transfer, low-temperature flow, gas displacement, and vaporization. The whole pre-cooling process could take up to 50 h. For large-scale, full-capacity storage tanks, it is particularly important to accurately control the pre-cooling temperature. Digital twin technology can characterize and predict the full life cycle parameters from the beginning of pre-cooling development to the end and even the appearance of damage in real time. The construction of a digital twin platform requires a large number of data samples in order to predict the operating state of the device. Therefore, a simulation method with high computational efficiency for the pre-cooling process of LNG tanks is of great importance. In this paper, the mixture model and discrete phase model (DPM) are applied to simulate the pre-cooling process of a large LNG full-capacity tank. Following Euler–Lagrange, the DPM greatly simplifies the solution process. Compared with the experimental results, the maximum error of the DPM simulation results is less than 11%. Such a highly efficient simulation method for the large LNG full-capacity storage tank can make it possible to build the digital twin platform that needs hundreds of data model samples.

Sampling rare events using nanostructures for universal Pt neural network potential

The density functional theory (DFT) data-driven approach to generating potential energy surfaces using machine learning has been proven to quickly and accurately predict the molecular and crystal structures of various elements. However, training databases consisting of hundreds of well-known symmetric structures have shown fatal weaknesses in calculating amorphous or nano-scale structures. Ab-initio molecular dynamics (AIMD) simulations create a training set that compensates for these shortcomings, but there are still many rare event structures. Here we introduce a new method to easily enlarge the data diversity and dramatically reduce data points based on the highly defected nano structures for universal machine learned potential. Our potential applies to bulk and nano systems and has been shown to high accuracy and computational efficiency while requiring minimal DFT training data. The developed potential is expected to help observation of structural changes in the Pt-based nano-catalysts that have been difficult to simulate at the DFT-level.

Research on the Protection System for Smart Grid Based on Phasor Information at Circuit Breakers

A smart grid protection system based on phasor information at circuit breakers is proposed in this paper. Phasor data for fault diagnosis is obtained from phasor measurement units or intelligent electronic devices installed near circuit breakers, without the need for additional measurement equipment. When a fault occurs, the protection system first locates the core protection circuit breaker closest to the fault point. It then identifies the faulty component by analyzing the current phasor differences between this breaker and adjacent ones. This scheme offers high computational efficiency, enabling rapid identification of faulty equipment and its precise location. The proposed protection system structure can be divided into two types: centralized and decentralized, and the most suitable scheme should be selected based on the actual situation. Finally, the feasibility of the proposed scheme was verified through the IEEE 39-bus system and a simulated actual area power grid model, demonstrating its applicability to practical smart grid scenarios. The simulation network validates the implementation method of the proposed protection strategy.

Multi-time-step coupling of peridynamics and classical continuum mechanics for dynamic brittle fracture

Peridynamics (PD), as a nonlocal theory, is well-suited for solving problems with discontinuities, such as cracks. However, the nonlocal effect of peridynamics makes it computationally expensive for dynamic fracture problems in large-scale engineering applications. As an alternative, this study proposes a multi-time-step (MTS) coupling model of PD and classical continuum mechanics (CCM) based on the Arlequin framework. Peridynamics is applied to the fracture domain of the structure, while continuum mechanics is applied to the rest of the structure. The MTS method enables the peridynamic model to be solved at a small time step and the continuum mechanical model is solved at a larger time step. Consequently, higher computational efficiency is achieved for the fracture domain of the structure while ensuring computational accuracy, and this coupling method can be easily applied to large-scale engineering fracture problems.

TFAN: A Task-adaptive Feature Alignment Network for few-shot website fingerprinting attacks on Tor

Few-shot website fingerprinting (WF) attacks aim to infer which website a user browsed through anonymity networks, such as Tor, using limited labeled traces. Recent methods either adopt complex metric strategies or perform time-consuming transfer learning, neither of which yields the most efficient performance in dynamic network environments. In this paper, we introduce a novel Task-adaptive Feature Alignment Network (TFAN) following the meta-learning paradigm. TFAN regards the few-shot WF attack as a feature alignment problem in class latent space, aiming to depict each location in the query feature map as a weighted sum of support features of a given class. Ridge regression provides a closed-form solution without extra parameters or techniques, ensuring high computational efficiency. Moreover, we also propose a Task-adaptive Modulation Unit (TMU), which activates the differences between support prototypes to generate task-level channel weights, making channels with significant discriminative details for each task contribute more to alignment. Extensive experiments on public Tor datasets demonstrate the superiority of TFAN in different scenarios. Notably, it is the only method that maintains over 90% accuracy in the 1-shot setting even 42 days later. Our code is available at https://github.com/Crybaby98/TFAN.

Adaptive multi-patch isogeometric analysis with truncated hierarchical B-splines in isotropic/orthotropic media

We present an adaptive multi-patch isogeometric analysis on the basis of truncated hierarchical B-splines (THB-splines) for solving two-dimensional complex isotropic/orthotropic elasticity. The THB-splines have local refinement property and their basis functions exhibit linear independence, so these features are highly applicable for adaptive isogeometric analysis. Guided by a posterior error estimator based on stress recovery, the adaptive algorithm is utilized in isogeometric analysis. In order to further extend the proposed method to solve complex geometry problems, the multi-patch technique is adopted to achieve exact modeling with Nitsche’s method as a multi-patch coupling approach. An isotropic numerical example with exact analytical solutions and three orthotropic numerical examples are presented to verify the effectiveness and accuracy of the developed method. Numerical solutions show that the developed adaptive isogeometric analysis method has high computational efficiency.

Velocity divergence—Generalized adaptive probability density evolution method

AbstractThis study proposes a novel velocity divergence‐generalized adaptive probability density evolution method (VD‐GAPDEM) for calculating the probability density function of the stochastic response process of stochastic structures under stochastic dynamic loads. First, based on the principle of probability conservation, the velocity divergence‐generalized adaptive probability density evolution equation (VD‐GAPDEE) is derived for a stochastic system that can effectively consider the shape and location changes of the joint transitional probability density of representative points (RPs) in the stochastic response process. Second, a novel VD‐GAPDEM is proposed to solve the VD‐GAPDEE directly using the point selection technique based on the generalized F discrepancy and the second‐order Runge–Kutta method with a smoothing kernel method (Runge–Kutta‐SKFAM). Furthermore, the differences and connections between VD‐GAPDEM and the existing probability density evolution method are analyzed. Additionally, the high computational efficiency and accuracy of the proposed VD‐GAPDEM are demonstrated through three typical examples of stochastic response analysis, involving stochastic systems subjected to stochastic dynamic loads.