Acceleration Method Research Articles

AnACor2.0: a GPU-accelerated open-source software package for analytical absorption corrections in X-ray crystallography

Analytical absorption corrections are employed in scaling diffraction data for highly absorbing samples, such as those used in long-wavelength crystallography, where empirical corrections pose a challenge. AnACor2.0 is an accelerated software package developed to calculate analytical absorption corrections. It accomplishes this by ray-tracing the paths of diffracted X-rays through a voxelized 3D model of the sample. Due to the computationally intensive nature of ray-tracing, the calculation of analytical absorption corrections for a given sample can be time consuming. Three experimental datasets (insulin at λ = 3.10 Å, thermolysin at λ = 3.53 Å and thaumatin at λ = 4.13 Å) were processed to investigate the effectiveness of the accelerated methods in AnACor2.0. These methods demonstrated a maximum reduction in execution time of up to 175× compared with previous methods. As a result, the absorption factor calculation for the insulin dataset can now be completed in less than 10 s. These acceleration methods combine sampling, which evaluates subsets of crystal voxels, with modifications to standard ray-tracing. The bisection method is used to find path lengths, reducing the complexity from O(n) to O(log2 n). The gridding method involves calculating a regular grid of diffraction paths and using interpolation to find an absorption correction for a specific reflection. Additionally, optimized and specifically designed CUDA implementations for NVIDIA GPUs are utilized to enhance performance. Evaluation of these methods using simulated and real datasets demonstrates that systematic sampling of the 3D model provides consistently accurate results with minimal variance across different sampling ratios. The mean difference of absorption factors from the full calculation (without sampling) is at most 2%. Additionally, the anomalous peak heights of sulfur atoms in the Fourier map show a mean difference of only 1% compared with the full calculation. This research refines and accelerates the process of analytical absorption corrections, introducing innovative sampling and computational techniques that significantly enhance efficiency while maintaining accurate results.

A review on green waste composting, role of additives and composting methods for process acceleration.

Effective disposal of green waste has been a challenging task faced by urban bodies for a long time. Composting can be an effective method to manage green waste by recovering nutrients that can be used as organic manure. However, there are some limitations to green waste composting, such as a low degradation rate and the requirement for high manpower and space. Many researchers have studied ways to minimize the limitations of green waste composting through different approaches. These include the use of co-composting materials, inoculating agents, and process modifications such as multi-stage composting. In this review, we systematically summarized the physicochemical characteristics of green waste and green waste compost, optimum ratios of additives, and process modifications during the composting of green waste reported in various articles. This review is helpful for early-career researchers and individuals new to the field of green waste composting by providing them with key concepts and recent developments in the field. The study suggests that the sustainable selection of additives or methods for composting green waste should depend on resource availability, climatic conditions, and the characterization of the feedstock.

A Strain Acceleration Method to Identify the Onset of Diffuse Necking

A Strain Acceleration Method to Identify the Onset of Diffuse Necking

Multi Combustor Turbine Engine Acceleration Process Control Law Design

Abstract Focusing on the collaborative fuel supply and acceleration problem of multi combustor turbine engine, a variable geometry composite regulation acceleration control law design method based on the combination of compressor intermediate stage air bleed(CIB) and low-pressure turbine guide vanes(LTGV) is proposed. The multi combustor collaborative fuel supply law and variable geometry regulation law during acceleration process are obtained by Particle Swarm Optimization-Sequence Quadratic Program (PSO-SQP) optimization, and the composite regulation acceleration control strategy of dual/single combustion chamber idle acceleration process is explored. The multi combustor acceleration process simulation results indicate that the variable geometry composite regulation method can effectively alleviate the stability margin limitation of the compressor during the initial acceleration stage. Compared with conventional acceleration methods, the maximum relative reduction in acceleration time of our method reaches 26%; Enhancing the combustion exergy efficiency during the acceleration process can significantly improve the engine's acceleration performance.; For the acceleration process from single combustion chamber idle operation to normal operation of a dual combustion chamber, simultaneous fuel supply from both combustion chambers during acceleration has a shorter acceleration time. The research results of this paper can provide important references for the design of transition state control systems for multi combustor turbine engines.

ELECTRON SOURCE BASED ON EMERGENCE OF SELF-INJECTED ELECTRON BUNCH AT PLASMA WAKEFIELD EXCITATION BY A TW LASER PULSE

Wakefield acceleration methods are known due to some their advantages. The main of them is the high accelerating gradient up to several teravolts per meter. In the paper another important advantage is concluded to the possibility of using a wakefield accelerator as a source of electrons by means of obtaining self-injected bunches and their acceleration. The result is the simulation of the process of plasma wakefield excitation by a laser pulse with an energy of tens of mJ and a power of 1…2 TW for obtaining the promising electron source. Homogeneous and Gaussian plasma profiles were investigated and compared to increase the energy of the self-injected bunches. The laser parameters were taken that corresponded to the parameters of the laser setup in the Institute of Plasma Electronics and New Methods of Acceleration of the National Scientific Center “Kharkiv Institute of Physics and Technology”. Based on the results of the simulation, the possibility of obtaining relativistic self-injected bunches that can be used for further laser acceleration experiments, including dielectric laser acceleration, was demonstrated.

A Harmonic Response Topology Optimization Method Based on Hybrid Cellular Automata

Abstract Hybrid cellular automaton (HCA) method is a topology optimization method with high convergence efficiency. Harmonic response topology optimization problem has been widely researched and common frequency-domain methods have been applied to solve this problem. Despite this, little work has so far been undertaken to utilize HCA method to improve the calculation efficiency of harmonic response topology optimization. In this paper we present a methodology which applies HCA method to solve harmonic response topology optimization problem. Mode displacement method (MDM), mode acceleration method (MAM) and full method (FM) are common frequency-domain methods which are respectively utilized as harmonic response analysis methods in this study. The examples under rotating loads demonstrate that the feasibility of this methodology at one specific frequency excitation and multiple frequencies excitation. This paper presents and implements a high convergence efficiency method for harmonic response topology optimization, this method can converge in 20–30 iterations. This method can extend the application range of HCA method and improve the optimization efficiency of harmonic response topology optimization.

Investigating the effective temporal resolution in a task-based functional MRI experiment at 7 T MRI using a dynamic phantom

Abstract An increasing number of human fMRI studies aim to discern the time delays between evoked responses under different stimuli conditions in different brain regions. To achieve that, a primary goal is to acquire fMRI data with high sampling rates. This task is now possible with ultra-high field (≥7 T) MRI and the advancement of imaging acceleration methods. Consequently, it becomes imperative to understand what is the actual or effective temporal resolution (ETR) that is realized in given settings of an fMRI experiment. In this study, we utilized a dynamic phantom to reliably repeat a set of scans, generating a “ground truth” signal with controllable onset delays mimicking fMRI responses in a task-based block-designed fMRI. Here, we define the ETR and quantify a scan’s ETR using the dynamic phantom. The quantification was performed for various scanning parameters, including echo time (TE), repetition time (TR), voxel size, and contrast-to-noise ratio (CNR). We further show that combining data from multi-echo EPI can improve the ETR (i.e., reduce it). In addition, parameters of the fMRI paradigm were examined, including the blocks’ length and density. As tissue properties (e.g., level of iron deposition) affect the CNR and thus change the ETR, we examined the signal rise mimicking not only the cortex, but also the basal ganglia (known for its high iron deposition). Combining multi-echo data, the estimated ETR for the examined scans was 151 ms for a cortex-mimicking setup and 248 ms for a basal ganglia-mimicking setup, when scanning with a sampling time (i.e., TR) of 600 ms. Yet, a substantial penalty was paid when the CNR was low, in which case the ETR was even larger than the TR. A feasibility set of experiments was also designed to evaluate how the ETR is affected by physiological signal fluctuations and the variability of the hemodynamic response. This study shows the viability of studying time responses with fMRI, by demonstrating that a very short ETR can be achieved. However, it also emphasizes the need to examine the attainable ETR for a particular experiment.

Fast simulation strategy for capacitively-coupled plasmas based on fluid model

Fluid simulations are widely used in optimizing the reactor geometry and improving the performance of capacitively coupled plasma (CCP) sources in industry, so high computation speed is very important. In this work, a fast method for CCP fluid simulation based on the framework of Multi-physics Analysis of Plasma Sources (MAPS) is developed, which includes a multi-time-step explicit upwind scheme to solve electron fluid equations, a semi-implicit scheme and an iterative method with in-phase initial value to solve Poisson's equation, an explicit upwind scheme with limited artificial diffusion to solve heavy particle fluid equations, and an acceleration method based on fluid equation modification to reduce the periods required to reach equilibrium. In order to prove the validity and efficiency of the newly developed method, benchmarking against COMSOL and comparison with experimental data have been performed in argon discharges on the Gaseous Electronics Conference (GEC) reactor. Besides, the performance of each acceleration method is tested, and the results indicated that the multi-time-step explicit Euler scheme can effectively decline the computational burden in the bulk plasma and reduce the time cost on the electron fluid equations by half. The in-phase initial value method can greatly decrease the iteration times required to solve linear equations and lower the computational time of Poisson's equation by 77 %. The acceleration method based on equation modification can reduce the periods required to reach equilibrium by two-thirds.

Influence of chloride acceleration methods on the electrochemical behaviour of superplasticiser admixed reinforced cementitious systems

Influence of chloride acceleration methods on the electrochemical behaviour of superplasticiser admixed reinforced cementitious systems

Accounting of weathering enhancement by mafic mineral spreading for CO 2 storage

The reduction of atmospheric CO 2 is an urgent global issue in the mitigation of global warming. Carbonate acts as a carbon sink in Earth's surface layer, and the weathering of terrestrial silicates and the deposition of oceanic calcium carbonate and organic matter mainly determine atmospheric and oceanic carbon dynamics on geological time scales. Various CO 2 reduction approaches have been evaluated, and the weathering acceleration method is expected to be a low-cost approach. This study attempts to estimate carbonate precipitation potential and dissolution rate from alkali leaching rates using weathering tests on mafic rocks. Based on Japan's annual average rainfall, the in situ carbonate precipitation potential obtained by spreading an olivine mineral or basalt rock powder at 40 ton-powder/ha was on the order of 10 -3 ton/year/ton-rock. This weathering rate could not be greatly accelerated under natural temperature conditions and fine granulation of the minerals.

Accelerating one-shot neural architecture search via constructing a sparse search space

Neural Architecture Search (NAS) has garnered significant attention for its ability to automatically design high-quality deep neural networks (DNNs) tailored to various hardware platforms. The major challenge for NAS is the time-consuming network estimation process required to select optimal networks from a large pool of candidates. Rather than training each candidate from scratch, recent one-shot NAS methods accelerate the estimation process by only training a supernet and sampling sub-networks from it, inheriting partial network architectures and weights. Despite significant acceleration, the supernet training with a large search space (i.e., the number of candidate sub-networks) still requires thousands of GPU hours to support high-quality sub-network sampling. In this work, we propose SparseNAS, an approach for one-shot NAS acceleration by reducing the redundancy of the search space. We observe that many sub-networks in the space are underperforming, with significant performance disparity to high-performance sub-networks. Crucially, this disparity can be observed early in the beginning of the supernet training. Therefore, we train an early predictor to learn this disparity and filter out high-quality networks in advance. Then, the supernet training will be conducted in this space sub-space. Compared to the state-of-the-art one-shot NAS, our SparseNAS reports a 3.1× training speedup with comparable network performance on the ImageNet dataset. Compared to the state-of-the-art acceleration method, SparseNAS reports a maximum of 1.5% higher Top-1 accuracy and 28% training cost reduction with a 7× bigger search space. Extensive experiment results demonstrated that SparseNAS achieves better trade-offs between efficiency and performance than state-of-the-art one-shot NAS.

Fasteners quantitative detection and lightweight deployment based on improved YOLOv8.

Currently, research on on-board real-time quantitative detection of rail fasteners is few. Therefore, this paper proposes and validates an improved YOLOv8 based method for quantitative detection of rail fasteners, leveraging the capabilities of edge miniaturized artificial intelligence (AI) computing devices. First, the lightweight MobileNetV3 is employed as the backbone network for our model to increase detection speed, and the SA attention mechanism is integrated at the end of the backbone network to enhance the feature of the fasteners. Then the deformable convolution is introduced to reconstruct the bottleneck structure of the neck network, which can segment fasteners without compromising accuracy. Subsequently, the optimized network model is utilized on a Jetson AGX Xavier edge AI computing device by the TensorRT acceleration method. Segmentation results are then extracted at the pixel level for quantitative analysis of fastener breakage degree and deflection angle, so as to correct the detection results. Experimental results show that the size of the improved lightweight network volume is reduced by 28% compared to the original YOLOv8 model, and the frame rate on the edge AI computing device is lifted by 71.87%, i.e., 55 f/s. Furthermore, the model is refined based on quantitative analysis results, resulting in an mAP0.5 of 97.0%, and real-time quantitative detection of rail fasteners is realized.

Agreement and Sensitivity of the Acceleration–Velocity Profile Derived via Local Positioning System

Sprint performance is commonly assessed via discrete sprint tests and analyzed through kinematic estimates modeled using a mono-exponential equation, including estimated maximal sprinting speed (MSS), relative acceleration (TAU), maximum acceleration (MAC), and relative propulsive maximal power (PMAX). The acceleration–velocity profile (AVP) provides a simple summary of short sprint performance using two parameters: MSS and MAC, which are useful for simplifying descriptions of sprint performance, comparison between athletes and groups of athletes, and estimating changes in performance over time or due to training intervention. However, discrete testing poses logistical challenges and defines an athlete’s AVP exclusively from the performance achieved in an isolated testing environment. Recently, an in situ AVP (velocity–acceleration method) was proposed to estimate kinematic parameters from velocity and acceleration data obtained via global or local positioning systems (GPS/LPS) over multiple training sessions, plausibly improving the time efficiency of sprint monitoring and increasing the sample size that defines the athlete’s AVP. However, the validity and sensitivity of estimates derived from the velocity–acceleration method in relation to changes in criterion scores remain elusive. To assess the concurrent validity and sensitivity of kinematic measures from the velocity–acceleration method, 31 elite youth basketball athletes (23 males and 8 females) completed two maximal effort 30 m sprint trials. Performance was simultaneously measured by a laser gun and an LPS (Kinexon), with kinematic parameters estimated using the time–velocity and velocity–acceleration methods. Agreement (%Bias) between laser gun and LPS-derived estimates was within the practically significant magnitude (±5%), while confidence intervals for the percentage mean absolute difference (%MAD) overlapped practical significance for TAU, MAC, and PMAX using the velocity–acceleration method. Only the MSS parameter showed a sensitivity (%MDC95) within practical significance (<5%), with all other parameters showing unsatisfactory sensitivity (>10%) for both the time–velocity and velocity–acceleration methods. Thus, sports practitioners may be confident in the concurrent validity and sensitivity of MSS estimates derived in situ using the velocity–acceleration method, while caution should be applied when using this method to infer an athlete’s maximal acceleration capabilities.

A hybrid PCA acceleration method for rapid real-time 2D MRI.

Objective.To develop a 2D MR acceleration method utilizing principal component analysis (PCA) in a hybrid fashion for rapid real-time applications.Approach.Retrospective testing was performed on 10 lung, 10 liver and 10 prostate 3T MRI data sets for image quality and target contourability. Sampling of k-space is performed by acquiring central (low-frequency) data in every frame while the high-frequency data is incoherently undersampled such that all of k-space is acquired in a pre-determined number of frames. Firstly, principal components (PCs) representative of intra-frame correlations between central and outer k-space data are used to estimate unsampled data in the frame of interest. Then to add further stability, PCs representative of time-domain fluctuations within a reconstruction window of the most recent frames are fit to outer k-space data (including above estimations) to obtain final estimates in the frame of interest. Accelerated reconstructions between 3x and 8x were tested for image quality and contourability along with the optimal number of PCs for fitting.Main results.It was found that at higher acceleration rates, image quality did not deteriorate significantly. Similarly, it was found that the images were of sufficient quality to contour a target using auto-contouring software at all tested acceleration rates and sites. SSIM values were found to be ⩾0.91 at all accelerations tested. Similarly dice coefficients at the different sites were found to be ⩾0.89 even at 8x accelerations which is on par with or better than intra-observer variation.Significance.This method appears to produce improved image quality and contourability compared to previous PCA methods while also allowing a greater number of PCs to be used in reconstruction. The method can be run using a simple single-channel coil and does not require significant computing power to meet real-time interventional standards (reconstruction times ∼60 ms/frame on Intel i5 CPU).

Parametric investigation and scale testing on accelerated CO2 sequestration of cement-based materials by utilizing industrial waste heat

The increasing CO2 emissions from heavy industries have presented a growing threat to the global environment. The critical solution is to develop CO2 sequestration technologies, especially the fast speed CO2 fixation methods. In this study, the CO2 uptake performances of hydrated cement paste under various environment parameters coupling with high temperature were evaluated through accelerated carbonation experiments. The results show that high temperature is an effective way to improve CO2 sequestration efficiency, in which the waste heat accompanied by the emitted CO2 can be used as the source of high temperature. Based on this, appropriate vapor and liquid water addition during carbonation can further improve the CO2 sequestration performance of powder samples. Moreover, the hydration and carbonation products and the porosity before and after carbonation were evaluated, high temperature and vapor acceleration method facilitated aragonite generation and higher porosity reduction. Scale-up test of recycled aggregates on CO2 sequestration under optimum carbonation conditions was evaluated, and the results show the CO2 sequestration performance of recycled aggregates was stable in different input scales, which could sustain the CO2 sequestration amount of 80kg/t-cem.

Phase field thermal shock analysis of rotating porous cracked pretwisted FGM microblade using exact shear correction factor

The present work is an attempt to develop a simple and accurate finite element formulation for the thermal shock analysis of the rotating porous cracked pretwisted functionally graded material (FGM) microblade using modified coupled stress theory in conjunction with phase-field and first-order shear deformation theory (FSDT). The physical neural surface is taken as the reference plane and the exact value of the shear correction factor is calculated from the shear stiffness. The elastic properties are assumed to be temperature-dependent and the upper ceramic layer is subjected to a high thermal shock whereas the bottom metallic layer is maintained at room temperature or is thermally insulated. The governing differential equation for the present analysis is derived using Hamilton’s principle and Newmark average acceleration method is used to obtain the transient response of the rotating porous cracked pretwisted FGM microblade subjected to thermal shock. The results obtained from the present finite element formulation are first validated with several benchmark examples available in the literature. New results are presented investigating the effect of crack depth, crack location, crack angle, rotational velocity and material scale ratio on the transient response of the cracked rotating porous pretwisted FGM microblade subjected to thermal shock. It is shown here that the parameters like crack depth, crack location and crack angle have a significant influence on the transient response of the rotating porous cracked pretwisted FGM microblade.

Model optimization and acceleration method based on meta-learning and model pruning for laser vision weld tracking system

Purpose This paper aims to propose a lightweight, high-accuracy object detection model designed to enhance seam tracking quality under strong arcs and splashes condition. Simultaneously, the model aims to reduce computational costs. Design/methodology/approach The lightweight model is constructed based on Single Shot Multibox Detector (SSD). First, a neural architecture search method based on meta-learning and genetic algorithm is introduced to optimize pruning strategy, reducing human intervention and improving efficiency. Additionally, the Alternating Direction Method of Multipliers (ADMM) is used to perform structural pruning on SSD, effectively compressing the model with minimal loss of accuracy. Findings Compared to state-of-the-art models, this method better balances feature extraction accuracy and inference speed. Furthermore, seam tracking experiments on this welding robot experimental platform demonstrate that the proposed method exhibits excellent accuracy and robustness in practical applications. Originality/value This paper presents an innovative approach that combines ADMM structural pruning and meta-learning-based neural architecture search to significantly enhance the efficiency and performance of the SSD network. This method reduces computational cost while ensuring high detection accuracy, providing a reliable solution for welding robot laser vision systems in practical applications.

CO2 Electrolysis Technologies: Bridging the Gap toward Scale-up and Commercialization.

CO2 electroreduction (CO2E) converts CO2 into carbon-based fuels and chemical feedstocks that can be integrated into existing chemical processes. After decades of research, CO2E is approaching commercialization with several startups, pilot plants, and large initiatives targeting different products. Here, we analyze the global efforts in scaling up CO2E, addressing implementation challenges and proposing methods for acceleration. We present a comparative analysis of key performance indicators (KPIs) between laboratory and industrial settings and suggest a stepwise technoeconomic analysis (TEA) framework, supported by industrial data, exploiting interactions within the academic and industrial communities. We identify the lack of systems-oriented standardization and durability as the main bottlenecks slowing down progress in the lab-to-prototype-to-market pathway of CO2E technologies. Inspired by electrolysis and fuel cell technologies, we outline protocols to advance fundamental research and aid catalyst development progress in performance, upscaling, and technology readiness level of CO2E.

Enabling SENSE accelerated 2D CSI for hyperpolarized carbon-13 imaging

As hyperpolarized (HP) carbon-13 (13C) metabolic imaging is clinically translated, there is a need for easy-to-implement, fast, and robust imaging techniques. However, achieving high temporal resolution without decreasing spatial and/or spectral resolution, whilst maintaining the usability of the imaging sequence is challenging. Therefore, this study looked to accelerate HP 13C MRI by combining a well-established and robust sequence called two-dimensional Chemical Shift Imaging (2D CSI) with prospective under sampling and SENSitivity Encoding (SENSE) reconstruction. Due to the low natural abundance of 13C, the sensitivity maps cannot be pre-acquired for the reconstruction. As such, the implementation of sodium (23Na) sensitivity maps for SENSE reconstructed 13C CSI was demonstrated in a phantom and in vivo in the pig kidney. Results showed that SENSE reconstruction using 23Na sensitivity maps corrected aliased images with a four-fold acceleration. With high temporal resolution, the kidney spectra produced a detailed metabolic arrival and decay curve, useful for further metabolite kinetic modelling or denoising. Metabolic ratio maps were produced in three pigs demonstrating the technique’s ability for repeat metabolic measurements. In cases with unknown metabolite spectra or limited HP MRI specialist knowledge, this robust acceleration method ensures comprehensive capture of metabolic signals, mitigating the risk of missing spectral data.

An Innovative and Efficient Implementation of Matrix-Free Newton Krylov Method for Neutronics/Thermal-hydraulics Coupling Simulation

The core physical behavior of reactors is essentially the result of multi-physical fields coupling feedback. High-fidelity neutronics/thermal-hydraulics (N/TH) analysis can simulate and predict nuclear reactor core phenomena realistically, providing advanced and reliable technical means during the design and safety analysis of nuclear reactor. In this work, an efficient and robustness coupling method using power density as the coupling parameter, Matrix-Free Newton Krylov (MFNK) method, is successfully developed and innovatively implemented in HNET for high-fidelity N/TH coupling simulation. To enhance the efficiency and stability, the multi-level generalized equivalence theory-based CMFD (ML-gCMFD) iterative acceleration method and ML-gCMFD coupling acceleration method are proposed. In addition, the nonlinear preconditioning and hybrid perturbation size formula are implemented to further improve the convergence. Finally, to evaluate the numerical accuracy, convergence, efficiency and stability of MFNK method, a series of representative problems, including a three-dimensional (3D) single fuel pin problem, VERA Benchmark Problem 6, and VERA Benchmark Problem 7, are analyzed by comparing with the current N/TH coupling methods. Numerical results indicate that MFNK method can obtain strong stability, high convergence performance, and relatively high computational efficiency while ensuring high accuracy. It demonstrates that MFNK method has significant performance advantages and potential for high-fidelity N/TH coupling simulation.