Acceleration Algorithm Research Articles

Bounding the speedup of the quantum-enhanced Markov-chain Monte Carlo algorithm

Bounding the speedup of the quantum-enhanced Markov-chain Monte Carlo algorithm

Accelerated strategies for impact identification of composite laminate using sparse sensor networks and model-based virtual time reversal

Barely visible impact damages are notorious threats to the health of composite structures. Detection of the impact is critical to promise the performance of composite structures. Recently, the authors proposed an impact identification method based on sparse sensor networks and virtual time reversal, which is verified on composite laminates. However, the computational demands of virtual time reversal simulations pose a notable limitation to this approach. To improve the efficiency of the method, several acceleration measures are developed in this article. First, the re-emitted wavefield analysis is accelerated using a frequency-domain method rather than the time-domain simulation, which largely reduces the computational costs. Subsequently, the GPU parallel acceleration algorithm is configured to further improve the efficiency. The proposed acceleration strategy is experimentally validated by various impact events on a composite laminate. The results render that the proposed acceleration strategy can promise the accuracy of the results of impact identification while significantly promoting computational efficiency, in which the time consumption plummets to only 1.94 s. Moreover, factors affecting the efficiency of the proposed method are discussed. The time consumption is positively correlated with the number of sensors involved in the identification. In addition, as the number of candidate points increases, the GPU encounters difficulty in handling all complex matrix operations simultaneously, leading to a substantial rise in computation time. Furthermore, the effect of signal lengths on the efficiency is minimal and largely negligible.

A fuzzy control based strategy for adjusting band blade feed rate in constant power sawing

Abstract The variable cross-sectional shape of the blank reduces the efficiency of the sawing machine. Additionally, the wear of the band saw blade causes a mismatch between the feed rate and the load, further exacerbating the wear. This study studied a feed rate control strategy considering sawtooth wear to balance the relationship between cutting efficiency and band life. Initially, speed parameters for the cut-in and cut-out areas, along with acceleration and deceleration control algorithms, were established to ensure the smooth transition of the saw blade during the machining process. Additionally, a constant power sawing feed rate model that considers the influence of wear was developed, enabling dynamic adjustments to the feed rate based on the saw blade's wear state. This study also proposes a fuzzy control-based target current correction method, which dynamically adjusts the target current according to the current wear state of the saw blade, ensuring that sawing requirements were met under varying levels of wear. The accuracy of the sawing model and control strategy was verified through experiments. The results demonstrated that this strategy improved efficiency by 44% compared to the traditional uniform feed rate method. This study introduces a novel feed rate control strategy for sawing and offers an effective solution to the problem of saw tooth wear.

Joint PP and PS AVA inversion using an acceleration algorithm and a multi-trace strategy

Abstract Exploration utilizing converted wave (PS) technology has garnered considerable interest in recent years owing to its ability to provide distinct insights into subsurface properties compared to compressional waves (PP). PS wave is particularly advantageous for accurately determining S-wave velocity (${V}_S$) and bulk density ($\rho $), whereas PP wave is better suited for assessing P-wave velocity (${V}_P$). Thus, theoretically, joint PP and PS AVA inversion can yield precise results for ${V}_P$, ${V}_S$, and $\rho $. However, a critical step before joint inversion is PP and PS registration, which is prone to causing lateral discontinuity in the inversion outcomes. To mitigate this issue, we present a simultaneous multi-trace joint inversion method. The method can improve the lateral consistency of inversion results by imposing lateral continuity constraints on the elastic parameters being inverted. Nevertheless, this simultaneous multi-trace inversion method inherently increases computational burden. To enhance algorithmic efficiency without sacrificing inversion precision, we implement an acceleration strategy. Synthetic and real data examples demonstrate that the proposed method offers remarkable performance in enhancing computational efficiency and lateral continuity compared to the conventional methods.

3D hybrid fluid-particle jet simulations and the importance of synchrotron radiative losses

Relativistic jets in active galactic nuclei are known for their exceptional energy output, and imaging the synthetic synchrotron emission of numerical jet simulations is essential for a comparison with observed jet polarization emission. Through the use of 3D hybrid fluid-particle jet simulations (with the PLUTO code), we overcome some of the commonly made assumptions in relativistic magnetohydrodynamic (RMHD) simulations by using non-thermal particle attributes to account for the resulting synchrotron radiation. Polarized radiative transfer and ray-tracing (via the RADMC-3D code) highlight the differences in total intensity maps when (i) the jet is simulated purely with the RMHD approach, (ii) a jet tracer is considered in the RMHD approach, and (iii) a hybrid fluid-particle approach is used. The resulting emission maps were compared to the example of the radio galaxy Centaurus A. We applied the Lagrangian particle module implemented in the latest version of the PLUTO code. This new module contains a state-of-the-art algorithm for modeling diffusive shock acceleration and for accounting for radiative losses in RMHD jet simulations. The module implements the physical postulates missing in RMHD jet simulations by accounting for a cooled ambient medium and strengthening the central jet emission. We find a distinction between the innermost structure of the jet and the back-flowing material by mimicking the radio emission of the Seyfert II radio galaxy Centaurus\,A when considering an edge-brightened jet with an underlying purely toroidal magnetic field. We demonstrate the necessity of synchrotron cooling as well as the improvements gained when directly accounting for non-thermal synchrotron radiation via non-thermal particles.

Characterization of chaotic mixing effects in hydrometallurgical leaching process based on deep learning

Traditional stirring methods in the wet metallurgy leaching process suffer from low efficiency, high consumption, and low output, leading to increased production costs and energy consumption. Therefore, this study evaluates reactor performance using deep learning and introduces variable-speed stirring to enhance laminar mixing and reduce stirring reactor power consumption. An S-Type acceleration and deceleration control algorithm is constructed to ensure that stepper motors do not experience step loss, stalling, or overshoot when the frequency changes abruptly. A deep learning tracking model based on dual cameras is established to dynamically track tracer particles inside the stirring reactor, and a Euclidean distance evaluation method is proposed to characterize and evaluate the mixing performance of the stirring reactor. Experimental results demonstrate that the use of complex function variable-speed stirring and shortening the variable-speed cycle both contribute to improving mixing efficiency. Under a variable-speed cycle of 5 seconds, chaotic speed increases mixing efficiency by 53.1% compared to constant speed. This study provides a theoretical basis for optimizing the wet metallurgy leaching process.

CatchyCFDEM: Open-source microkinetic modeling of heterogeneous catalysis in Eulerian-Lagrangian CFD applied to oxidative coupling of methane

This work introduces catchyCFDEM, an OpenFOAM-based open-source CFD-DEM framework designed for simulating reactive gas–solid fluidized beds with both gas-phase chemistry and heterogeneous catalytic chemistry. A convective gas–solid heat transfer model is implemented and validated using experimental data gathered in a non-reactive pseudo-2D fluidized bed. Catalytic chemistry including different levels of complexity are included: pseudo-homogeneous, heterogeneous without mass transfer, and heterogeneous with diffusive mass transfer. Verification studies are performed by simulating a packed bed reactor using catchyCFDEM and comparing mass fraction profiles to a 1D plug flow simulation in Cantera. To enhance the computational efficiency of the resource-intensive reactive CFD-DEM simulations, speed-up algorithms such as Particle Agglomeration (PA) and in-situ adaptive tabulation (ISAT) are integrated in catchyCFDEM. As a case study, a gas–solid vortex reactor (GSVR) is simulated using the validated framework. The applicability of the GSVR for the oxidative coupling of methane (OCM) is evaluated based on an isothermal analysis evaluating the influence of several operational and geometrical parameters on the process. Finally, a proof-of-concept adiabatic simulation of the GSVR is presented. The catchyCFDEM framework is made publicly available on GitHub.

High-precision ultra-fast minimum cut approximation through aggregated hash of cut collection

Due to the wide application of s-t minimum cut (min-cut) in various scenarios, many acceleration algorithms have been proposed to solve it. However, the query times of the acceleration algorithms currently available are still high in large-scale graphs, rendering them useless in frequently solving scenarios. We re-examine the min-cut problem from a novel perspective of cut collection hash. By extracting aggregated hashes of mapped cut collections in one-dimensional space, a Monte Carlo-like method is used to quickly compare them and estimate the minimum cut between any two nodes with low computational effort and high accuracy. After the graph is preprocessed using a few hundred depth-first traversals, the time complexity of the min-cut solution can be logarithmic in terms of the average degree and capacity of the graph. Experiments on large-scale graphs show that compared to the fastest exact algorithm, the proposed algorithm can increase the speed of the min-cut solution by up to seven orders of magnitude, when only a few mathematical comparisons per pair are needed to obtain exact min-cut values of no less than 99.9% node pairs.

A High-Precision Sub-Grid Parameterization Scheme for Clear-Sky Direct Solar Radiation in Complex Terrain—Part I: A High-Precision Fast Terrain Occlusion Algorithm

In atmospheric modeling, sub-grid parameterization is an important method for studying the topographic effects of solar radiation using high-resolution Digital Elevation Model (DEM) data. For reducing the amount of computation, some approximate methods that can lead to errors are used in existing sub-grid parameterization schemes for clear-sky direct solar radiation (SPS-CSDSR). The lack of a high-precision fast terrain occlusion algorithm (HPFTOA) remains one of the biggest constraints in this field. This study proposed an HPFTOA. It mainly uses two kinds of acceleration algorithms. One method is to use a dynamic, lossless, and fast occlusion search radius. Another way is to use the rectangular grid for calculations within the accuracy of DEM data to avoid coordinate projection conversions. The test results indicate that the HPFTOA can carry out large-scale computation based on DEM data with a resolution of 90 m. Because it rarely uses approximation algorithms and considers the curvature of the Earth, SPS-CSDSR can achieve unprecedented precision. The HPFTOA can also be used in the fields of mountain solar energy assessment, remote sensing, and telemetry, including terrain-obscuring the probe. As computer performance improves and algorithms and execution code are optimized, the application prospects will be very broad.

Research and Implementation of FPGA Image Compression Parallel Algorithm Based on CCSDS

High-pixel, high-resolution spectral images are an important source of key graphical information obtained from space remote sensing, however, with the dramatically expanding amount of graphical information, the limited transmission bandwidth and hardware resources are difficult to meet the demand for massive data, which makes real-time image lossless compression and transmission technology an important solution. For this reason, this paper is based on the CCSDS image lossless compression algorithm, using FPGA for the construction of image compression system, and proposes a method strategy based on parallel acceleration algorithm to realize the real time processing of large-capacity hyperspectral images. The simulation results show that hyperspectral river and mountain range images can be compressed with greatly improved compression efficiency while maintaining real time performance and accuracy.

A Scalable Accelerator for Local Score Computation of Structure Learning in Bayesian Networks

A Bayesian network is a powerful tool for representing uncertainty in data, offering transparent and interpretable inference, unlike neural networks’ black-box mechanisms. To fully harness the potential of Bayesian networks, it is essential to learn the graph structure that appropriately represents variable interrelations within data. Score-based structure learning, which involves constructing collections of potentially optimal parent sets for each variable, is computationally intensive, especially when dealing with high-dimensional data in discrete random variables. Our proposed novel acceleration algorithm extracts high levels of parallelism, offering significant advantages even with reduced reusability of computational results. In addition, it employs an elastic data representation tailored for parallel computation, making it FPGA-friendly and optimizing module occupancy while ensuring uniform handling of diverse problem scenarios. Demonstrated on a Xilinx Alveo U50 FPGA, our implementation significantly outperforms optimal CPU algorithms and is several times faster than GPU implementations on an NVIDIA TITAN RTX. Furthermore, the results of performance modeling for the accelerator indicate that, for sufficiently large problem instances, it is weakly scalable, meaning that it effectively utilizes increased computational resources for parallelization. To our knowledge, this is the first study to propose a comprehensive methodology for accelerating score-based structure learning, blending algorithmic and architectural considerations.

Bayesian time domain approach for damping identification and uncertainty quantification in stay cables using free vibration response

The dynamic characteristics, particularly damping, play crucial roles in the design, monitoring, and vibration control of stay cables. The free vibration test, commonly used for damping identification of stay cables, demonstrates notable technical advantages in specific mode excitation and improving the signal-to-noise ratio. Nevertheless, challenges arise from uncertainties associated with ineluctable ambient vibration influences and noise contamination in measured responses. In recent years, there has been a growing interest in Bayesian modal identification methods due to their remarkable ability to quantify uncertainties. However, further advancements are needed in the time domain, particularly when addressing the relatively short duration of free vibration response data. This is essential to minimize the impact of background ambient vibration and enhance the accuracy of modal identification. This paper proposes a novel Bayesian modal identification approach specifically crafted to address free vibration data affected by ambient vibrations, aiming to achieve high-precision modal identification and effective uncertainty quantification from free vibration response of stay cables. Additionally, a speedup algorithm is used for determining the most probable values (MPVs) to enhance computational efficiency. The efficacy of the proposed approach is validated through simulated scenarios, encompassing both a single-degree-of-freedom structure and a finite element model of a stay cable. Furthermore, the effectiveness of the proposed method is validated through its application in a laboratory test involving a scale model of the stay cable, as well as in a field test of actual stay cables of Sutong Bridge. The results demonstrate that the proposed method achieves high accuracy in identifying modal damping ratios, and effectively quantifies uncertainties in the identification results.

On the efficient non-linear solver for hydraulic fracturing and well cementing simulations based on Anderson acceleration

The aim of this study is to create a fast and stable iterative technique for numerical solution of a quasi-linear elliptic pressure equation. We developed a modified version of the Anderson acceleration (AA) algorithm to fixed-point (FP) iteration method. It computes the approximation to the solutions at each iteration based on the history of vectors in extended space, which includes the vector of unknowns, the discrete form of the operator, and the equation's right-hand side. Several constraints are applied to AA algorithm, including a limitation of the time step variation during the iteration process, which allows switching to the base FP iterations to maintain convergence. Compared to the base FP algorithm, the improved version of the AA algorithm enables a reliable and rapid convergence of the iterative solution for the quasi-linear elliptic pressure equation describing the flow of particle-laden yield-stress fluids in a narrow channel during hydraulic fracturing, a key technology for stimulating hydrocarbon-bearing reservoirs. In particular, the proposed AA algorithm allows for faster computations and resolution of unyielding zones in hydraulic fractures that cannot be calculated using the FP algorithm. The quasi-linear elliptic pressure equation under consideration describes various physical processes, such as the displacement of fluids with viscoplastic rheology in a narrow cylindrical annulus during well cementing, the displacement of cross-linked gel in a proppant pack filling hydraulic fractures during the early stage of well production (fracture flowback), and multiphase filtration in a rock formation. We estimate computational complexity of the developed algorithm as compared to Jacobian-based algorithms and show that the performance of the former one is higher in modelling of flows of viscoplastic fluids. We believe that the developed algorithm is a useful numerical tool that can be implemented in commercial simulators to obtain fast and converged solutions to the non-linear problems described above.

Non-stationary Anderson acceleration with optimized damping

Anderson acceleration (AA) has a long history of use and a strong recent interest due to its potential ability to dramatically improve the linear convergence of the fixed-point iteration. Most authors are simply using and analyzing the stationary version of Anderson acceleration (sAA) with a constant damping factor or without damping. Little attention has been paid to nonstationary algorithms. However, damping can be useful and is sometimes crucial for simulations in which the underlying fixed-point operator is not globally contractive. The role of this damping factor has not been fully understood. In the present work, we consider the non-stationary Anderson acceleration algorithm with optimized damping (AAoptD) in each iteration to further speed up linear and nonlinear iterations by applying one extra inexpensive optimization. We analyze the convergence rate this procedure and develop an efficient and inexpensive implementation scheme. We show by extensive numerical experiments that the proposed non-stationary Anderson acceleration with optimized damping procedure often converges much faster than stationary AA with constant damping, adaptive damping or without damping, especially in the cases larger window sizes are needed. We also observe that simple strategies like using constant damping factors and adaptive damping factors, sometimes, work very well for some problems while sometimes they are even worse than AA without damping. Our proposed method is usually more robust than AA with constant damping and adaptive damping. Moreover, we also observed from our numerical results that damping can be good, but choosing the wrong damping factors may slow down the convergence rate. Theoretical analysis of the effects of damping factors are needed and important.

Rapid calculation of computer-generated holograms for line-drawn 3D objects with varying thicknesses

Accelerating the calculation of computer-generated holograms (CGHs) is an important requirement for three-dimensional (3D) electro-holography display systems. Among the several sophisticated algorithms for CGH acceleration, the CG-line method for projecting 3D wire-frame objects is highly promising due to its high acceleration capability. However, conventional algorithms cannot account for line thickness, and only lines with a constant thickness corresponding to the pixel pitch of the spatial light modulator can be drawn. This restricts the expressiveness and visibility of 3D images, particularly for high-resolution holograms. Therefore, we propose a method for widening the line thickness of the CG-line method. We successfully generated a 3D object comprising bold lines of different thicknesses without sacrificing speed.

A new method for motion stability improving of dual-robotic ultrasonic nondestructive testing system

The dual-robotic ultrasonic nondestructive testing (NDT) system equipped with extension arm is an effective means to realize automatic ultrasonic testing of composite semi-enclosed workpieces. However, this cantilever structure amplifies the tiny vibrations of the inspection system, which seriously affects the coupling effect and the resolution of ultrasonic inspection. To solve the problem, this paper proposes a S-shaped acceleration and deceleration feature control algorithm to improve the motion stability and further to improve the inspection resolution of dual-robotic ultrasonic NDT system. Furthermore, the specific steps of S-shaped acceleration and deceleration features control algorithm in trajectory planning of the dual-robotic ultrasonic NDT system are described in detail. Comparative experiments of motion stability show that the acceleration in the x direction at the left and right ends of the detection area decreased significantly, from 0.15 to 0.08 m/s2 and the ultrasonic image of the internal defects have a higher resolution and smoother edges. The improvement plays an important role in promoting the resolution at the left and right ends of the detection area.

GPU-based parallel programming for FEM analysis in the optimization of steel frames

ABSTRACT Optimization of large-scale frame structures consumes a vast amount of time since the analysis of such complex systems contains several iterative processes. Mitigating computational burden and reducing this time to a reasonable level is possible by running GPU (Graphical Processing Unit) processors, which can be found on standard computers. This study presents an algorithm for the acceleration of size optimization of steel frames by using the BBO (Biogeography-Based Optimization) method that is suitable for GPU architecture. The GPU-based parallel algorithm, designed for FEM (Finite Element Method) analysis, is applied to three hypothetical steel-frame case structures with different numbers of members and nodes; and processed on four different computers which are available on the market. The presented case studies revealed that the proposed solution’s efficiency increases as the number of members increases and confirmed the ability of the acceleration algorithm for optimization of large-scale frame structures and provided time efficiency.

Accelerated multi-b-value multi-shot diffusion-weighted imaging based on EPI with keyhole and a low-rank tensor constraint

PurposeMulti-Shot (MS) Echo-Planar Imaging (EPI) may improve the in-plane resolution of multi-b-value DWI, yet it also considerably increases the scan time. Here we explored the combination of EPI with Keyhole (EPIK) and a calibrationless reconstruction algorithm for acceleration of multi-b-value MS-DWI. MethodsWe firstly analyzed the impact of nonuniform phase accrual in EPIK on the reconstructed image. Based on insights gained from the analysis, we developed a calibrationless reconstruction algorithm based on a Space-Contrast-Coil Locally Low-Rank Tensor (SCC-LLRT) constraint for reconstruction of EPIK-acquired data. We compared the algorithm with a modified SPatial-Angular Locally Low-Rank (SPA-LLR) algorithm through simulations, phantoms, and in vivo study. We then compared EPIK with uniformly undersampled EPI for accelerating multi-b-value DWI in 6 healthy subjects. ResultsThrough theoretical derivations, we found that the reconstruction of EPIK with a SENSE-encoding-based algorithm, such as SPA-LLR, may cause additional aliasing artifacts due to the frequency-dependent distortion of the coil sensitivity. Results from simulations, phantoms, and in vivo study verified the theoretical finding by showing that the calibrationless SCC-LLRT algorithm reduced aliasing artifacts compared with SPA-LLR. Finally, EPIK with SCC-LLRT substantially reduced the ghosting artifacts compared with uniform undersampled multi-b-value DWI, decreasing the fitting errors in ADC (0.05 ± 0.01 vs 0.10 ± 0.01, P < 0.001) and IVIM mapping (0.026 ± 0.004 vs 0.06 ± 0.006, P < 0.001). ConclusionThe SCC-LLRT algorithm reduced the aliasing artifacts of EPIK by using a calibrationless modeling of the multi-coil data. The dense sampling of k-space center offers EPIK a potential to improve image quality for acceleration of multi-b-value MS-DWI.

Finite element iterative algorithm based on Anderson acceleration technique for incompressible MHD equations

In this paper, an Anderson’s acceleration algorithm for the finite element Oseen iterative scheme of the steady incompressible magnetohydrodynamics (MHD) problem is designed and analyzed. The Anderson acceleration algorithm based on solving an optimization problem in each iteration receives great attention to accelerate linear and nonlinear iterations. We prove that the proposed algorithm has higher convergence rate than that of the Oseen iterative method for the steady incompressible MHD, and give the accelerating convergence theory under different acceleration depths m. Numerical experiments including the singular solution and benchmark problems are carried out to verify the correctness and effectiveness of the algorithm. The tests show the marked improvement in the high physical parameters (including the hydrodynamic and magnetic Reynolds numbers, and the coupling number) when the Oseen iteration fails to converge.

The application of improved densenet algorithm in accurate image recognition

Image recognition technology belongs to an important research field of artificial intelligence. In order to enhance the application value of image recognition technology in the field of computer vision and improve the technical dilemma of image recognition, the research improves the feature reuse method of dense convolutional network. Based on gradient quantization, traditional parallel algorithms have been improved. This improvement allows for independent parameter updates layer by layer, reducing communication time and data volume. The introduction of quantization error reduces the impact of gradient loss on model convergence. The test results show that the improvement strategy designed by the research improves the model parameter efficiency while ensuring the recognition effect. Narrowing the learning rate is conducive to refining the updating granularity of model parameters, and deepening the number of network layers can effectively improve the final recognition accuracy and convergence effect of the model. It is better than the existing state-of-the-art image recognition models, visual geometry group and EfficientNet. The parallel acceleration algorithm, which is improved by the gradient quantization, performs better than the traditional synchronous data parallel algorithm, and the improvement of the acceleration ratio is obvious. Compared with the traditional synchronous data parallel algorithm and stale synchronous parallel algorithm, the optimized parallel acceleration algorithm of the study ensures the image data training speed and solves the bottleneck problem of communication data. The model designed by the research improves the accuracy and training speed of image recognition technology and expands the use of image recognition technology in the field of computer vision.Please confirm the affiliation details of [1] is correct.The relevant detailed information in reference [1] has been confirmed to be correct.