High Computational Efficiency Research Articles

Real-time three-level energy management strategy for series hybrid wheel loaders based on WG-MPC

The application of Hybrid Electric Wheel Loaders (HEWL) represents an attractive option for future industrial development. In order to reduce the equivalent fuel consumption and extend the lifetime of the battery, a real time whale optimal algorithm and general regression neural network (WOA-GRNN) based hierarchical model predictive control (WG-MPC) energy management strategy for HEWL is proposed. The structure of the WG-MPC consists of three layers, namely, the prediction layer, the economic layer and the control layer. In the prediction layer, a prediction model based on WOA-GRNN is proposed for the complex demand power prediction. In the economic layer, the equivalent diesel cost, diesel generator operation cost, the lifetime cost of the diesel generator and the battery pack are considered. The control layer is designed based on improved MPC. Through the simulation analysis, the proposed WOA-GRNN prediction model achieves the best prediction accuracy compared with the traditional prediction methods. Compared with FT-MPC and ECMS, the total economic cost of WG-MPC is reduced by 3.42% and 5.12%, respectively. The simulation results demonstrate that the proposed WG-MPC strategy has good control performance, high computational efficiency, low equivalent diesel consumption, longer battery lifetime and good use cost economy, which is of great significance for practical applications.

Ice volume and thickness of all Scandinavian glaciers and ice caps

Abstract We present a new map of bed topography and ice thickness together with a corresponding ice volume estimate representative of the years ~2010 for all Scandinavian ice caps and glaciers. Starting from surface observations, we invert for ice thickness by iteratively running an innovative ice dynamics model on a distributed grid and updating bed topography until modelled and observed glacier dynamics as represented by their rate of surface elevation change (dh/dt) fields align. The ice flow model used is the instructed glacier model (Jouvet and Cordonnier, 2023, Journal of Glaciology 1–15), a generic physics-informed deep-learning emulator that models higher-order ice flow with high-computational efficiency. We calibrate the modelled thicknesses against >11 000 ice thickness observations, resulting in a final ice volume estimate of 302.7 km3 for Norway, 18.4 km3 for Sweden and 321.1 km3 for the whole of Scandinavia with an error estimate of ~ $\pm 11\%$ . The validation statistics computed indicate good agreement between modelled and observed thicknesses (RMSE = 55 m, Pearson's r = 0.87, bias = 0.8 m), outperforming all other ice thickness maps available for the region. The modelled bed shapes thus provide unprecedented detail in the subglacial topography, especially for ice caps where we produce the first maps that show ice-dynamically realistic flow features.

Distributed identity management mechanism based on improved block-chain certificateless encryption algorithm

For solving the problem of the lack of full control over the identity data of network users, we propose an identity management method based on the certificateless encryption algorithm of block chain. We also propose a certificateless encryption algorithm combined with state secret algorithm SM2 to reduce the computational cost of the algorithm by using the decentralized advantages of block chain and the characteristics of data tamper-proof. The distributed user identity management mechanism is designed. The algorithm models of user identity registration, identity certificate issuance and identity authentication are optimized to improve the security of identity data management. The model is tested and verified. The results show that the model has fast information response ability, and the average delay is about 0. 4 s. Compared with other algorithms, the scheme in this paper has the highest computational efficiency. Under the same conditions, the computational time is about 6.9% of Su et al. (2020), 63.4% of Xu et al. (2021), 7.0% of Zhang et al. (2023) , and 76.9% of Cheng (2021). The computational performance is greatly improved.

Small failure probability analysis of stochastic structures based on a new hybrid approach

The small failure probability problem of stochastic structures is investigated by using two types of surrogate models and the subset simulation method in conjunction with parallel computation. To achieve high computational efficiency, the explicit expression of dynamic responses of stochastic structures is first derived in the form based on the explicit time-domain method. Then, the small failure probability analysis of stochastic structures is efficiently carried out through the Monte Carlo simulation method utilizing explicit expressions. To avoid the repeated calculation for the coefficient matrices or vectors of the explicit expression of stochastic structures, two types of surrogate models, e.g., the backpropagation neural network model and the Kriging model, are introduced to obtain these matrices or vectors for each parameter sample of the stochastic structures. The computational cost is further reduced by using the subset simulation method to generate conditional samples which follow the rule of Metropolis-Hastings. Furthermore, in virtue of the independence of the surrogate models for each time instant and the independence of dynamic analysis for each sample, parallel computation is embedded in the proposed approach, which can fully exploit the characteristics of the proposed approach and further improve the computational efficiency of dynamic reliability analysis. Numerical examples are given to illustrate the validity of the proposed hybrid approach.

Dynamic response of asymmetric suspended structure subjected to random wind excitation

SummarySuspended structures, regarded as the expression of structural beauty, have attracted the focus of many architects and engineers. With the complexity of dynamic responses of the asymmetric suspended structures subjected to the random alongside wind excitation, an innovative method was proposed through combining the finite element method (FEM), the complex mode method (CMM), the pseudo excitation method (PEM), and the quadratic decomposition method (QDM) of frequency response function. The method could obtain unified closed‐form solutions of spectral moment and variance of node displacement and velocity and acceleration of asymmetric structures and its correctness was verified by existing numerical approaches. The proposed method is applicable to the random wind‐induced vibration response analysis of diverse linear complex structures. Notably, its efficacy lies in its circumvention of numerical integration, endowing it with high computational efficiency and accuracy. Extended comparative studies and discussion were also performed on effect of suspended span and comparisons of normal framed structure and symmetry structure. The results showed that the suspended span presented positive relation to horizontal displacement and inverse tendency to horizontal acceleration of the whole structure and vertical and horizontal acceleration of the columns near to suspended part should also be considered in engineering design.

Optimal Space-Borne ISAR Imaging of Space Objects with Co-Maximization of Doppler Spread and Spacecraft Component Area

The Inverse Synthetic Aperture Radar (ISAR) has been proven to be an effective tool for space target sensing due to its capability of performing high-resolution imaging. Since the component information of the spacecraft is key to the identification of the target and diagnosis of its status, ISAR images with a clear and complete representation of the typical components are much desired. This requires a selection of the imaging time, during which a certain spacecraft component has a good projection on the ISAR image plane with the shape feature well conserved and a high resolution. In addition, a fully automated implementation with a high computational efficiency is also highly preferred for on-orbit operations so as to improve the intelligence level of the space-borne system. We propose a bicriterion-based automated optimal imaging time-selection method for the space-borne ISAR, which is seeking the slow time section of the data that result in the best image. A good image means a high azimuth resolution and the best presentation of the solar panels. One criterion is the Maximum Doppler Spread (MDS), which indicates the maximum Effective Rotational Velocity (ERV) leading to high image resolution, but it is influenced by the satellite attitude. of the spacecraft. The other is the Maximum Component Area (MCA), which is defined to indicate the completeness of the component considered. The radar echoes are processed sequentially by way of a sliding window. The interval with the co-maximization of the DS and CA is selected, and fine processing is performed further to obtain the best images. The results of the simulation experiments show that the proposed method can achieve spacecraft images with the solar panels presented the best. The computational complexity is low.

An adaptive finite element method for coupled fretting wear and fatigue crack propagation simulation

Fretting wear and fatigue crack propagation often occur at the same time in the service of mechanically connected structures. In order to analyze the effect of fretting wear on the process of fatigue crack propagation, a finite element simulation method for fatigue crack propagation considering fretting wear is proposed. In this method, the Archard model is used to predict the wear process, and the conventional Paris equation is modified based on the wear rate. In order to improve the accuracy of crack propagation prediction, a crack-tip super singular element (CSSE) is used to calculate the singular stress field and fracture control parameters of the crack tip. An adaptive meshing technique that automatically adjusts the mesh density according to the simulation process is designed to ensure the computational efficiency of the algorithm and the accuracy of the numerical results. The smoothing technique used in wear simulation can alleviate the influence of contact stress fluctuation on wear profile prediction. The usage of the new simulation method is introduced through benchmark examples. It is proved that the numerical results of crack propagation obtained by the adaptive finite element method (FEM) have high precision, good convergence and high computational efficiency. It is found that wear reduces the crack growth rate compared with the simulation results without considering wear. This method has a promising application in the coupling analysis of fretting wear and fatigue crack propagation of mechanically connected structures.

Translational eigenstates of He@C60 from four-dimensional abinitio potential energy surfaces interpolated using Gaussian process regression.

We investigate the endofullerene system 3He@C60 with a four-dimensional potential energy surface (PES) to include the three He translational degrees of freedom and C60 cage radius. We compare second order Møller-Plesset perturbation theory (MP2), spin component scaled-MP2, scaled opposite spin-MP2, random phase approximation (RPA)@Perdew, Burke, and Ernzerhof (PBE), and corrected Hartree-Fock-RPA to calibrate and gain confidence in the choice of electronic structure method. Due to the high cost of these calculations, the PES is interpolated using Gaussian Process Regression (GPR), owing to its effectiveness with sparse training data. The PES is split into a two-dimensional radial surface, to which corrections are applied to achieve an overall four-dimensional surface. The nuclear Hamiltonian is diagonalized to generate the in-cage translational/vibrational eigenstates. The degeneracy of the three-dimensional harmonic oscillator energies with principal quantum number n is lifted due to the anharmonicity in the radial potential. The (2l + 1)-fold degeneracy of the angular momentum states is also weakly lifted, due to the angular dependence in the potential. We calculate the fundamental frequency to range between 96 and 110 cm-1 depending on the electronic structure method used. Error bars of the eigenstate energies were calculated from the GPR and are on the order of ∼±1.5 cm-1. Wavefunctions are also compared by considering their overlap and Hellinger distance to the one-dimensional empirical potential. As with the energies, the two abinitio methods MP2 and RPA@PBE show the best agreement. While MP2 has better agreement than RPA@PBE, due to its higher computational efficiency and comparable performance, we recommend RPA as an alternative electronic structure method of choice to MP2 for these systems.

A novel hybrid machine learning model for rapid assessment of wave and storm surge responses over an extended coastal region

Storm surge and waves are responsible for a substantial portion of tropical and extratropical cyclones-related damages. While high-fidelity numerical models have significantly advanced the simulation accuracy of storm surge and waves, they are not practical to be employed for probabilistic analysis, risk assessment or rapid prediction due to their high computational demands. In this study, a novel hybrid model combining dimensionality reduction and data-driven techniques is developed for rapid assessment of waves and storm surge responses over an extended coastal region. Specifically, the hybrid model simultaneously identifies a low-dimensional representation of the high-dimensional spatial system based on a deep autoencoder (DAE) while mapping the storm parameters to the obtained low-dimensional latent space using a deep neural network (DNN). To train the hybrid model, a combined weighted loss function is designed to encourage a balance between DAE and DNN training and achieve the best accuracy. The performance of the hybrid model is evaluated through a case study using the synthetic data from the North Atlantic Comprehensive Coastal Study (NACCS) covering critical regions within New York and New Jersey. In addition, the proposed approach is compared with two decoupled models where the regression model is based on DNN and the reduction techniques are either principal component analysis (PCA) or DAE which are trained separately from the DNN model. High accuracy and computational efficiency are observed for the hybrid model which could be readily implemented as part of early warning systems or probabilistic risk assessment of waves and storm surge.

A batch-filling method of VIE-MoM matrix for inhomogeneous dielectric target with full- and half-SWG function

A batch-filling method (BFM) for generating the volume-integral-equation-methods of moment (VIE-MoM) matrix for the scattering of inhomogeneous objects by using the full- and half-SWG basis function is proposed. The BFM is based on the summation of contributions of all integrals over tetrahedrons and boundary faces, and the contributions are arranged into a column vector that represents the interactions of the source basis function with all the test basis functions. The time of the BFM for filling VIE-MoM matrix can be reduced over 95% compared with the conventional basis-basis (CBB) matching filling scheme due to the vector filling scheme, which avoids the repeated traversal between the source tetrahedron and test tetrahedron to fill the impedance matrix elements. Details for the numerical process and the filling scheme of impedance matrix are elaborated. Numerical results show that the batch-filling method has high computational efficiency and accuracy for VIE-MoM.

The subloading surface model in hyperelastic-based plasticity with time integration algorithms in intermediate and current configurations

This paper introduces a novel application of subloading surface concept to effectively model the elastoplastic behavior of materials in the finite strain regime. The subloading surface approach allows for a smoother and more seamless transition from the elastic to the plastic region. Two time integration schemes are presented in this study, one in the intermediate configuration and the other in the current configuration. In the first time integration scheme, co-rotational objective time rates are not required, and the formulation utilizes the Mandel stress. In the second time integration scheme, implemented in Eulerian description, the Kirchhoff stress defined in the current configuration is utilized.The results obtained for the simple shear deformation case demonstrate the absence of oscillations. Additionally, these results show excellent agreement with experimental data and existing findings from the literature. Furthermore, a computational comparison between the time integration schemes using the subloading surface model and a model discussed in the literature reveals that the proposed algorithms in this paper require significantly fewer increments in the given time integrations. This indicates a higher computational efficiency and reduced computational time for the proposed methods. Overall, the predicted responses of all the analyzed cases from the provided time integrations exhibit excellent agreement with each other, confirming the accuracy and effectiveness of the presented model.

A novel evasion guidance for hypersonic morphing vehicle via intelligent maneuver strategy

This paper presents a novel evasion guidance law for hypersonic morphing vehicles, focusing on determining the optimized wing's unfolded angle to promote maneuverability based on an intelligent algorithm. First, the pursuit-evasion problem is modeled as a Markov decision process. And the agent's action consists of maneuver overload and the unfolded angle of wings, which is different from the conventional evasion guidance designed for fixed-shape vehicles. The reward function is formulated to ensure that the miss distances satisfy the prescribed bounds while minimizing energy consumption. Then, to maximize the expected cumulative reward, a residual learning method is proposed based on proximal policy optimization, which integrates the optimal evasion for linear cases as the baseline and trains to optimize the performance for nonlinear engagement with multiple pursuers. Therefore, offline training guarantees improvement of the constructed evasion guidance law over conventional ones. Ultimately, the guidance law for online implementation includes only analytical calculations. It maps from the confrontation state to the expected angle of attack and the unfolded angle while retaining high computational efficiency. Simulations show that the proposed evasion guidance law can utilize the change of unfolded angle to extend the maximum overload capability. And it surpasses conventional maneuver strategies by ensuring better evasion efficacy and higher energy efficiency.

A fuzzy rough granular ensemble learning based on the feature selection with chi-square1

Neighborhood granulation is a classical granulation method. Although it is adequate for clustering and classification tasks, its granules are more complex, and the data representation is binary. This paper proposes a new granulation method based on the neighborhood granulation. Firstly, a detailed definition of the granular form is given with fuzzy rough set theory. Then, a modified fuzzy rough discriminant function is proposed based on neighborhood systems. The samples are globally granulated on single features to construct granules and on multiple features to construct granular vectors. Also, a feature selection technique based on the Chi-square, which strikingly reduces the complexity of the fuzzy rough granular vectors, is introduced to address the disadvantage of the fuzzy rough granular vectors. An ensemble model structure is also proposed in the paper for the mixed nature of fuzzy rough granular vectors. The paper makes a detailed comparison between the fuzzy rough granulation and the neighborhood granulation. The results show that fuzzy rough granulation has higher computational efficiency and classification performance. Finally, a detailed comparison is made between the fuzzy rough granular ensemble model and various classical ensemble algorithms. The final results show that the fuzzy rough granular ensemble model has better robustness and generalization.

Machine Learning-Based Traffic Flow Prediction and Intelligent Traffic Management

With the rapid development of information technology, multiple time series forecasting, which is typical of traffic flow forecasting, has become increasingly important in big data analysis. As the cornerstone of intelligent transportation system, traffic flow forecasting has important scientific research value and practical application value for urban traffic operation scheduling, quality and efficiency improvement of logistics transportation industry and public travel planning. Traffic flow prediction is always an important task of intelligent transportation system. Due to the complex temporal and spatial dependence of traffic flow sequence, it is very challenging to construct accurate traffic flow prediction in its ring neural network, graph network and Transformer model. Much of the existing work is based on very good models. Considering the advantages of convolutional networks, such as high computational efficiency and strong feature extraction ability, a traffic flow prediction model based on multi-view spatiotemporal convolution is proposed.

A human-machine interaction method for rock discontinuities mapping by three-dimensional point clouds with noises

Rock discontinuities control rock mechanical behaviors and significantly influence the stability of rock masses. However, existing discontinuity mapping algorithms are susceptible to noise, and the calculation results cannot be fed back to users timely. To address this issue, we proposed a human-machine interaction (HMI) method for discontinuity mapping. Users can help the algorithm identify the noise and make real-time result judgments and parameter adjustments. For this, a regular cube was selected to illustrate the workflows: (1) point cloud was acquired using remote sensing; (2) the HMI method was employed to select reference points and angle thresholds to detect group discontinuity; (3) individual discontinuities were extracted from the group discontinuity using a density-based cluster algorithm; and (4) the orientation of each discontinuity was measured based on a plane fitting algorithm. The method was applied to a well-studied highway road cut and a complex natural slope. The consistency of the computational results with field measurements demonstrates its good accuracy, and the average error in the dip direction and dip angle for both cases was less than 3°. Finally, the computational time of the proposed method was compared with two other popular algorithms, and the reduction in computational time by tens of times proves its high computational efficiency. This method provides geologists and geological engineers with a new idea to map rapidly and accurately rock structures under large amounts of noises or unclear features.

Integrated entry guidance with no-fly zone constraint using reinforcement learning and predictor-corrector technique

This paper presents an integrated entry guidance law for hypersonic glide vehicles with no-fly zone constraint. Existing methods that employ predictor-corrector technique and lateral guidance logic for both guidance and avoidance, may have limitations in response time and maneuverability when facing sudden threats, because the guidance cycle is limited by computational efficiency and the bank angle magnitude cannot be adjusted according to the urgency of the avoidance. To overcome these challenges, the proposed method divides the entry process into safe flight stages and no-fly zone avoidance stages, and introduces reinforcement learning to develop an intelligent avoidance strategy for the latter. This division reduces the complexity of the learning problem by restricting the state space and increases the applicability in the presence of multiple no-fly zones. The trained avoidance strategy can directly output continuous bank angle command through a single forward calculation, considering both guidance and avoidance requirements. This enables the full utilization of the vehicle’s maneuverability and supports a high command update frequency to effectively handle threats. Additionally, a network trained via supervised learning is employed to generate reference commands, accelerating the training convergence of reinforcement learning. Simulation results demonstrate the effectiveness of the proposed guidance law, highlighting its high computational efficiency, command stability, and robustness. Importantly, the approach offers convenience in extending to multiple no-fly zones and accommodating vast initial state spaces.

Perturbatively including inhomogeneities in axion inflation

Axion inflation, i.e. an axion-like inflaton coupled to an Abelian gauge field through a Chern-Simons interaction, comes with a rich and testable phenomenology. This is particularly true in the strong backreaction regime, where the gauge field production heavily impacts the axion dynamics. Lattice simulations have recently demonstrated the importance of accounting for inhomogeneities of the axion field in this regime. We propose a perturbative scheme to account for these inhomogeneities while maintaining high computational efficiency. Our goal is to accurately capture deviations from the homogeneous axion field approximation within the perturbative regime as well as self-consistently determine the onset of the non-perturbative regime.

Trigonometric-spline dwell time scheduling and real-time interpolator under dynamic constraints for deterministic polishing.

Dwell time scheduling is a critical stage of deterministic polishing for ultra-precision fabrication of optics. Recently the dwell time algorithms for deterministic polishing have been widely studied. Nevertheless, there exist some shortcomings when those methods were applied in the industry, including low computational efficiency, large memory consumption, insufficiently-considered dynamic constraints, poor smoothness of the feedrate profile, and reliance on non-open CNC interpolator. To overcome those deficiencies, this work proposes a highly-efficient dwell time algorithm under the dynamic constraints of machine tools. The method calculates the initial dwell time density (DTD) sequence through non-blind deconvolution algorithm, and provides the feasible set of DTD profiles based on trigonometric-spline model. And the DTD repairing tactics are developed based on a self-adaptive offset algorithm under confined feedrate and acceleration. Finally, a C1-continuous DTD profile satisfying dynamic constraints is generated. A real-time interpolator based on trigonometric-spline DTD profile is developed. The simulation results show that the proposed method generates a C1-continuous feedrate profile rigidly respecting dynamic constraints, and preserves the ideal dwell time gradient distribution, achieving a more ideal residual error with high computational efficiency compared with the previous methods. The comparative experiments demonstrate that the proposed method performs better in suppressing the multi-frequency errors compared with the previous methods, and achieves high computational efficiency. The algorithm is applicable to highly-precise and highly-efficient fabrication of large-aperture optical components.

A consistent second order ISPH for free surface flow

The Incompressible Smoothed Particle Hydrodynamics (ISPH) is now a popular numerical method for modelling free surface flows, in particular the breaking waves and violent wave-structures interaction. The ISPH requires the projection approach, leading to solving a pressure Poisson's equation (PPE). Although the accuracy and convergence of the numerical scheme to discretise the Laplacian operator involved in PPE is critical for securing a satisfactory solution of the PPE, the overall performance of the ISPH is also influenced by other key numerical implementations, including (1) estimation of the viscous terms; (2) calculation of the velocity divergence; (3) discretisation of the boundary conditions for the PPE; and (4) evaluation of the pressure gradient. In our previous paper [29], the quadratic semi-analytical finite difference interpolation scheme (QSFDI), which has a leading truncation error at third order derivatives, has been adopted to discretise the Laplacian operator. In this paper, the QSFDI will be adopted, not only for discretising the Laplacian operator, but also for approximating viscous terms, velocity divergence, boundary conditions and pressure gradient. The performance of the newly formulated consistent second order ISPH is assessed by various cases including the oscillating liquid drop, the wave propagation, and the liquid sloshing. The results do not only demonstrate a second order convergence over a limited range of conditions and a higher computational efficiency, i.e., requiring less computational time to achieve the same accuracy, but also show a better mass/energy conservation property and capacity of reproducing a smooth pressure field, than other ISPH models considered in this study.

Functional connectivity analysis for modeling flow in infrastructure

The performance loss of infrastructure caused by hazards can disrupt economic activities, obstruct emergency responses, and be detrimental to society's well-being and recovery. Network analysis methods, including topology-based and flow-based methods, are valuable tools for infrastructure functionality assessment. Topology-based methods capture connectivity patterns of network components with relatively low computational costs. However, topology-based methods do not model the flow of resources from source facilities to consumers. On the other hand, flow-based methods are computationally intensive but can provide information on the flow performance of infrastructure by modeling the operational dynamics. This paper introduces a novel hybrid approach, Functional Connectivity Analysis (FCA), to provide comparable functionality assessment to flow-based methods with computational efficiency similar to the connectivity analysis by introducing flow-related characteristics into topological connectivity metrics. This approach is illustrated with a realistic example modeling the functionality of potable water infrastructure in Shelby County, Tennessee. Comparing the results of connectivity analysis, flow analysis, and FCA shows that FCA can provide information on functionality comparable to flow analysis with high computational efficiency.