High Computational Efficiency Research Articles

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.

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.

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.

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.

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.

Influence of real particle morphology on single particle crushing behavior of rockfill based on FDEM

Particle morphology is critical in affecting the crushing behavior of rockfill materials. In contrast, most current single particle simulations lack satisfactory morphology accuracy, and the resulting crushing modes deviate from observations to some extent. Therefore, we reconstruct the real particle morphology with the spherical harmonic (SH) method and employ the finite-discrete element method (FDEM) to simulate the one-dimensional (1D) compressive crushing process of basalt particles commonly used in rockfill. The influences of four main morphological parameters, i.e. sphericity, aspect ratio, roundness, and convexity, on the single particle strength and the crushing modes are discussed. The results show that with the SH degree set to 15 and a mesh number of 20,480, the FDEM models of reconstructed particles achieve sufficient morphology accuracy and high computational efficiency. Based on the model, the simulation results demonstrate that the aspect ratio has the most significant impact on single particle strength, followed by sphericity. In contrast, roundness and convexity have a weaker effect than the above two parameters. Also, it is revealed that single particle strength decreases with increasing aspect ratio and sphericity, while it increases with higher roundness and convexity. Furthermore, aspect ratio significantly changes the initial crushing position, sphericity dominates post-crushing fragment size and quantity, and roundness mainly affects post-crushing morphology. The model results have been employed in establishing a support vector regression (SVR)-based predicted model, exhibiting good predictive performance and advantages for the optimization of rockfill particles in engineering.

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 5.45ms and 9.83ms, 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.

A three-phase model based on boundary element method for simulation of chloride diffusion in concrete

Chloride-induced corrosion of steel reinforcements has been identified as one of the main causes of deterioration of concrete structures. A feasible numerical method is required to predict chloride penetration in concrete structures. In this study, a three-phase model of concrete based on the Boundary Element Method (BEM) is proposed to investigate the diffusion and distribution behavior of chlorides in concrete. Compared with the finite element model (FEM), the proposed model has a higher computational efficiency, and a comparison of the simulated chloride concentration with the corresponding experimental data is proved to be reasonable. Furthermore, the parametric analysis is carried out to evaluate the effect of coarse aggregate parameters on chloride diffusion in concrete. The results show that chloride attack was more susceptible to the coarse aggregate content and less affected by the ITZ diffusion coefficient. This may be due to the fact that the volume fraction of aggregates in concrete is much higher than that in the ITZ. In addition, the low permeability of the coarse aggregate hindered the diffusion of chloride ions, while the ITZ around the aggregate accelerated the diffusion of chloride ions. when the aggregate content increases in a certain range from 12.56 % to 50.24 %, the hindering effect of aggregate on chloride ion diffusion is more obvious than the accelerating effect of ITZ.

A mesoscopic approach for nanoscale evaporation heat transfer characteristics

We propose a mesoscopic approach for investigating steady/transient nanoscale evaporation heat transfer characteristics, whose kinetic nature makes it an ideal tool to model the evaporation kinetics at the liquid-vapor interface and the microscopic wall-fluid interactions. Simulation of the evaporation of a flat nanoscale thin liquid film demonstrates its capability of resolving kinetically limited evaporation and liquid film adsorption. For the simulation of an evaporating meniscus in a nanochannel, our proposed mesoscopic approach considers the conservation of mass, momentum and energy of both liquid and vapor phases, thereby naturally incorporating both the multi-dimensional heat transfer effect and vapor transport resistance in nano-confined space. We demonstrate that solid-fluid interaction plays dominant roles on the interfacial transport during nanoscale evaporation, and vapor transport resistance has nonnegligible influences on the evaporation heat/mass transport in nano-confined spaces. By handling statistical behavior of molecule ensembles, this mesoscopic approach not only preserves microscopic physics but also has high computational efficiencies, enabling the simulation of space and time domains on the practical application level. This work paves the way for quantitative investigations of steady/transient nanoscale evaporation heat transfer characteristics using mesoscopic approach.

Hierarchical intelligent energy-saving control strategy for fuel cell hybrid electric buses based on traffic flow predictions

Vehicles' perception capability of environmental situations has been enhanced in the connected environment. However, the utilization of abundant and diverse traffic information poses a challenge in the formulation of energy-efficient strategies for current connected vehicles. To address this challenge, a hierarchical intelligent energy-saving control strategy for fuel cell hybrid buses based on traffic flow prediction is proposed. Diverging from traditional approaches that rely on surrounding vehicles status information, a more macroscopic perspective is introduced by incorporating traffic flow prediction information into the formulation of energy-saving control strategies for the first time, which enhances the adaptability of strategies. At the upper layer, a multi-objective intelligent eco-driving control strategy is designed, encompassing driving safety, energy consumption costs, traffic efficiency, and ride comfort. At the lower layer, an intelligent energy management strategy is developed to reduce hydrogen consumption and maintain stable battery state of charge. Simultaneously, action variables serve as the information bridge between the upper and lower layer strategies, and comprehensive analyses and validations of strategic performance are conducted from various perspectives. The research findings indicate that the introduction of traffic flow information enhances the cognitive capabilities of the intelligent system towards the traffic environment with little impact on the convergence process. The model excels in energy efficiency, driving smoothness, and passenger comfort compared to the baseline model, while also having the opportunity to surpass the traffic efficiency of the IDM model. The developed energy management strategy demonstrates an energy-saving benefit of 92.07 % of the offline optimal benchmark, and closely approaches the theoretical optimal performance of MPC with a prediction horizon of 5s. Additionally, the proposed strategy demonstrates high computational efficiency and effectively mitigates the degradation of the vehicle lifespan, making it conducive to real-time online applications.

Direct CALPHAD coupling phase-field model: Closed-form expression for interface composition satisfying equal diffusion potential condition.

We formulated two phase-field models to compute interfacial compositions, characterized by their high computational accuracy and efficiency. The inaugural model utilizes convergence calculations to fulfill the equal diffusion potential condition, while the subsequent model obviates the need for such calculations. Despite this, its computational outcomes strongly agree with those of the preceding model. Notably, in these models, the alteration in composition attributable to interfacial curvatures aligns with the Gibbs-Thomson effect within the equilibrium system. In this study, the solidification processes of Ni-Al-Cr and Ag-Cu-Sn alloys serve as case studies to underscore the computational precision and swiftness of the proposed models.

Enhanced noise resilience in passive tone detection via broad-receptive field complex-valued convolutional neural networks.

Tone detection is crucial for passive sonar systems. Numerous algorithms have been developed for passive tone detection, but their effectiveness in detecting weak tones is still limited. To enhance noise resilience in passive tone detection, a broad-receptive field complex-valued structure named attention-driven complex-valued U-Net is proposed. Concretely, two attention mechanisms, namely, temporal attention and harmonic attention, are proposed to broaden the receptive field with high computational efficiency. Complex-valued operators are then introduced to mine both amplitude and phase information of tones. Additionally, a symmetric downsampling and upsampling strategy is proposed to improve the reconstruction accuracy of detailed time-frequency information. Overall, the proposed method demonstrates a strong robustness to noise and a strong ability to generalize. Experimental results on both simulated data and real-world data validate the superiority of the proposed attention-driven complex-valued U-Net against conventional U-shaped structures.

A Granulation Strategy-Based Algorithm for Computing Strongly Connected Components in Parallel

Granular computing (GrC) is a methodology for reducing the complexity of problem solving and includes two basic aspects: granulation and granular-based computing. Strongly connected components (SCCs) are a significant subgraph structure in digraphs. In this paper, two new granulation strategies were devised to improve the efficiency of computing SCCs. Firstly, four SCC correlations between the vertices were found, which can be divided into two classes. Secondly, two granulation strategies were designed based on correlations between two classes of SCCs. Thirdly, according to the characteristics of the granulation results, the parallelization of computing SCCs was realized. Finally, a parallel algorithm based on granulation strategy for computing SCCs of simple digraphs named GPSCC was proposed. Experimental results show that GPSCC performs with higher computational efficiency than algorithms.

Damage detection and model updating of offshore jacket platforms using a direct sensitivity relation based on structural response

One of the challenges of the finite element model updating using time domain data is the lack of a direct relationship between changes in structural parameters and structural responses. Computational techniques, such as the Finite Difference Method (FDM) and Direct Differentiation Method (DDM), are used to calculate the sensitivity of the structural response to variations of structural parameters in the time domain. The use of these methods is time-consuming for large and complex structure. In the present study, an exact sensitivity equation is presented that establishes a direct and linear relationship between changes of structural parameters and structural response variations. The proposed closed form equations, have high computational efficiency in calculating the sensitivity of time domain responses. The measured responses directly contribute to the calculation of the sensitivity matrix, so the finite element model updating procedure is improved in terms of accuracy and reliability. The proposed method updates the finite element model even using a single excitation frequency that distinguishes the presented sensitivity relationship from frequency response-based methods which use wide ranges of excitation frequencies for model updating. To evaluate the effectiveness of the proposed method, different damage scenarios with different intensities have been simulated on two offshore jacket platform models. The performance of the proposed method in identifying the location and severity of induced damages in the presence of measurement and modeling errors confirms the effectiveness of the proposed method.

On the intermolecular interactions and mechanical properties of polyvinyl alcohol/inositol supramolecular complexes

Hydrogen-bond (H-bond) cross-linking has recently been proven a promising strategy for simultaneously improving strength, toughness, and ductility of H-bonded polymers. However, there has been a lack of a fundamental understanding of how H-bond cross-linking works on a molecular level. To fill this knowledge gap, coarse-grained (CG) simulation provides a possibility because of its high computational efficiency and access to longer lengths and time scales. However, existing coarse-grained force fields and potential functions exhibit an inability to accurately describe H-bonded polymer systems. Herein, we report a modified CG model to understand the H-bond crosslinking effect of small molecules, inositol (IN) on polyvinyl alcohol (PVA), with reference to MARTINI 3.0 parameters and empirical data. The simulation results show that incorporating IN molecules results in a significant improvement in the strength, ductility, and toughness of PVA, which is in good agreement with experimental data. Moreover, the modified CG model establishes a close correlation between IN content, water content, tensile rate and glass transition, free volume, chain movement and mechanical properties of PVA. The results show that the yield strength of PVA initially increases and then decreases with the addition of IN. The maximum yield stress of PVA at IN-1.0 is approximately 155 MPa, representing a 33% increase compared to that of PVA. Additionally, the glass transition temperature (Tg) reaches 80.2 °C, ∼2.8 °C higher than that of pure PVA. This work develops a modified CG model for understanding intermolecular interactions and mechanical properties of H-bonded polymer systems on a molecular level. This understanding is expected to help expediate the material design and properties optimization of strong and tough polymeric materials.

Large-scale rain gauge network optimization using a kriging emulator

Rain gauge networks deliver crucial observations for many water-related applications but can be expensive to purchase and operate, which makes optimization of the number and locations of gauges an important task. Traditional optimization approaches often focus on kriging-based methods that are computationally expensive, which limits the scale of the optimization to small areas or a very limited number of gauges. This study presents a novel workflow with high computational efficiency that is able to handle optimization problems on large rain gauge networks. This is accomplished by developing a fast parametric emulator of the commonly used ordinary kriging approach. The results show that the developed emulator is able to accurately reproduce the original kriging uncertainty estimates with a computational speed up of a factor of 3000. In order to determine the best locations for new gauges, a greedy optimization heuristic that relies on sequential placement of gauges is developed. The sequential optimization can lead to sub-optimal solutions by itself, so a resubstitution mechanism is introduced to correct for this. The workflow is applied to the national gauging network of Denmark with 291 gauges, where it is able to optimally place 175 new gauges within 1 h of running time. A similar optimization with a traditional kriging approach would have taken approximately 125 days to complete highlighting the value the workflow.

Elastic reverse time migration based on nested triangular mesh in 2D isotropic media

Abstract Reverse time migration (RTM) based on two-way wave equation is an important imaging tool, which has been widely used in exploration geophysics. It offers distinct advantages in handling complex geological structures. Moreover, elastic reverse time migration (ERTM) can produce more physically meaningful images for subsurface and has received increasing attention in recent years. However, rugged topography presents challenges in the reconstruction of subsurface structures for ERTM. To overcome this problem, we introduce the grid method for topography ERTM, which is an unstructured mesh based algorithm. However, the conventional gird method for RTM is implementing by discretizing model into small meshes, leading to high computational cost. To address this problem, we introduce the grid method based on nested unstructured meshes to implement ERTM with complex surface topography. Low-order small meshes, due to their fine scale, can accurately depict complex surface topography. However, their generation process is relatively time-consuming. In contrast, high-order large meshes contain many similar small meshes internally, which can be processed in parallel, resulting in higher computational efficiency. Therefore, we propose the following strategy: for the receiver wavefield, several layers on surface discreted by the small meshes are used to place receivers and remaining layers discreted by the large meshes to improve computational efficiency. For the source wavefield, only the large meshes are used because the spacing of shot is relatively large. In this way, we ensure the accurate description of complex surface topography for the implementation of ERTM with high computational efficiency. Finally, the effectiveness of our method is validated through experiments on three models with complex topography.

Multiple scattering of local nonlinear resonators on a thin plate

Developing nonlinear resonant metamaterial plates requires an effective wave simulation method to analyze the wave interactions between multiple nonlinear scatterers. The multiple scattering method has the advantages of high computational efficiency and closed-form displacement solutions in modeling the finitely periodic scatterer array. However, the multiple scattering method of nonlinear scatterers is absent in the available studies of plate structures. In this paper, a nonlinear multiple scattering approach is formulated and analyzed for thin plates with nonlinear resonant scatterers. The resonant scatterer consists of an inner plate with a local resonator modeled by the nonlinear mass-spring system. The motion equation of resonant scatterers is solved using the perturbation technology and wave expansion method. The T-matrix of the resonant scatterer of each order is then derived at the arbitrary harmonic frequency from continuous interfacial conditions of resonant scatterers. Finally, the multiple scattering is formulated for a finitely periodic array of resonant scatterers on a thin plate. Numerical simulations capture the scattering characteristics and stop-band formations of fundamental and second-harmonic waves. Furthermore, the finitely periodic array of nonlinear and linear resonant scatterers achieves the nonreciprocal transmission of flexural waves. These numerical results demonstrate the potential applications of the proposed method in designing metamaterial-based plates with complex transmission properties.