Parallel Computation Techniques Research Articles

A GPU-accelerated 3dBEM supporting the optimization of actively morphing flapping-foil thrusters with application to AUV propulsion

In recent years, flapping thrusters have been getting attention as an eco-friendly alternative to conventional propellers for autonomous underwater vehicles (AUVs) mainly due to their low-frequency operation, advanced maneuverability, and high efficiency. Their operating principle mimics the thrust-producing kinematics found among efficient swimmers in the aquatic environment. The present work focuses on the analysis and optimization of flapping systems with active morphing features for increased performance. The two optimization studies refer to a realistic AUV propulsion scenario for a wing thruster with active hydrofoil-section adjustment, spanwise bend and twist morphing capabilities. A gradient descent approach based on sequential quadratic programming (SQP) is used for optimal tuning of design variables (planform shape, kinematics) targeting efficiency maximization under thrust and effective angle of attack constraints. For the evaluation of each candidate thruster the unsteady boundary element solver 3dBEM is developed and used for wing performance prediction. The solver exploits GPU parallel computation techniques to reduce computational time and enable fast optimization. Results indicate that the optimal thruster with chordline/spanwise bending is 25% more efficient, whereas the same result for the wing with spanwise bending and twist profiles is up to 8.8%. The 3dBEM solver can support the preliminary design process of morphing flapping-foil thrusters.

Ion Optical Optimization Method for an Ultrahigh Resolution Planar Multireflection Time-of-Flight Mass Analyzer Using the Hill Climbing Algorithm.

A novel ion optical optimization method for planar multireflection time-of-flight mass spectrometry (MR-TOFMS) is introduced in this paper. The multiparameters of the gridless mirror model, including geometric and voltage parameters, are automatically optimized using a self-made program created in SIMION 8.1. Combining with the hill climbing algorithm and parallel computing technique, this method substantially enhances optimization efficiency and accuracy. The fitting results demonstrated that the ion optical performance of the gridless mirror reached up to fourth-order isochronicity with respect to the energy spread and third-order isochronicity with respect to the spatial and angular spread. As a result, the gridless mirror model achieved an aberration limit resolution of 1.7 million under realistic ion beam conditions. Due to constraints of periodic lenses, the aberration limit resolution of the planar MR-TOFMS was optimized to 600k. These results indicate that the hill climbing algorithm is an effective method to search the optimal solutions in complex ion optical systems.

UP-DPC: Ultra-scalable parallel density peak clustering

Density Peak Clustering (DPC) is a highly effective density-based clustering algorithm, but its scalability is limited by the expensive Density Peak Estimation (DPE) step. To address this challenge, we propose UP-DPC: Ultra-Scalable Parallel Density Peak Clustering, a novel framework that employs approximate Density Peak Estimation and performs DPC on LDP-wise graphs. This approach enables UP-DPC to handle datasets of arbitrary scale without relying on spatial indexing for acceleration. Furthermore, we introduce a five-layer computational architecture and leverage parallel computation techniques to further enhance the speed and efficiency of UP-DPC.To evaluate the scalability and effectiveness of UP-DPC, we conduct extensive experiments on 14 datasets, including the large/web-scale datasets, and compare UP-DPC with 21 algorithms. Notably, on the MNIST8M dataset consisting of 8,000k data objects, UP-DPC achieves an NMI (Normalized Mutual Information) value of 0.6464 in just 35.41 seconds, outperforming the state-of-the-art GPU-based method, which only archives an NMI of 0.045 in 56.96 seconds. These results demonstrate the superior scalability and effectiveness of UP-DPC in handling large/web-scale datasets. The proposed framework offers significant improvements over existing methods and shows promise as a solution for density-based clustering tasks.

Ultrasound Entropy Imaging Based on the Kernel Density Estimation: A New Approach to Hepatic Steatosis Characterization.

In this paper, we present the kernel density estimation (KDE)-based parallelized ultrasound entropy imaging and apply it for hepatic steatosis characterization. A KDE technique was used to estimate the probability density function (PDF) of ultrasound backscattered signals. The estimated PDF was utilized to estimate the Shannon entropy to construct parametric images. In addition, the parallel computation technique was incorporated. Clinical experiments of hepatic steatosis were conducted to validate the feasibility of the proposed method. Seventy-two participants and 204 patients with different grades of hepatic steatosis were included. The experimental results show that the KDE-based entropy parameter correlates with log10 (hepatic fat fractions) measured by magnetic resonance spectroscopy in the 72 participants (Pearson's r = 0.52, p < 0.0001), and its areas under the receiver operating characteristic curves for diagnosing hepatic steatosis grades ≥ mild, ≥moderate, and ≥severe are 0.65, 0.73, and 0.80, respectively, for the 204 patients. The proposed method overcomes the drawbacks of conventional histogram-based ultrasound entropy imaging, including limited dynamic ranges and histogram settings dependence, although the diagnostic performance is slightly worse than conventional histogram-based entropy imaging. The proposed KDE-based parallelized ultrasound entropy imaging technique may be used as a new ultrasound entropy imaging method for hepatic steatosis characterization.

Sensitivity Study of Seismic Amplification Effect of Large-Scale Sichuan Basin on Key Parameters During the Great Wenchuan Earthquake

The effect of soil parameters on basin amplification effect has been widely investigated, especially for the small and medium-sized basins under plane wave incidence or point source ruptures. However, the sensitivities of seismic amplification effect of large-scale basins with respect to soil parameters during great earthquakes with tremendous rupture faults have obtained relatively less attention. In this paper, based on the established three-dimensional Sichuan basin model, the effects of four material parameters of the basin sediments, including the shear wave velocity, Poisson’s ratio, and the shear quality factor of the Quaternary sediment, as well as the velocity of the rock above the basin crystalline basement (which will be referred as SVQS, PRQS, QFQS, and VCBR, respectively), on the long-period wave amplification patterns of the Sichuan basin during the great Wenchuan earthquake are investigated by using the spectral element method and parallel computing technique. The results show the following: (1) SVQS has the greatest influence on the basin horizontal component ground motions, while the vertical component is comparably insensitive to it. (2) PRQS demonstrates comparable effects on the horizontal and vertical basin ground motions. With the increase of PRQS, the amplitudes and regions of the strong shakings both become smaller. However, the influence of PRQS is less significant compared with SVQS and VCBR. (3) QFQS mainly affects the relatively high-frequency motions inside the basin, and the low-frequency motions are generally insensitive to it. With the reduction of QFQS, the amplitudes and regions of the basin strong ground motions turn a little smaller, but the distribution features are generally unchanged. (4) Effects of VCBR on the horizontal and vertical component basin ground motions are opposite. With the growth of VCBR, the amplitudes and areas of strong horizontal motions increase consequently, but they decrease for the vertical component.

Extraction of Lagrangian Coherent Structures in the framework of the Lagrangian–Eulerian Stabilized Collocation Method (LESCM)

In recent years, the investigations on Lagrangian Coherent Structures (LCS) in complex flows have attracted increasing attention due to their ability to accurately describe flow field details. In this study, we propose a novel and accurate numerical technique based on the Lagrangian–Eulerian Stabilized Collocation Method (LESCM) for computing the Finite Time Lyapunov Exponents (FTLEs), which is essential for extracting LCSs in viscous incompressible flows. LESCM, previously employed for simulating fluid–structure interaction problems, was developed based on the Material Point Method (MPM). The hybrid Lagrangian–Eulerian​ description in LESCM enables explicit tracking of fluid flow trajectories throughout the simulation and direct evaluation of the FTLE field. Furthermore. The errors in FTLEs caused by the Particle Shifting Technique (PST) are completely avoided due to the Eulerian characteristic of LESCM as the deformation gradient is calculated based on fixed Eulerian nodes rather than fluid particles with unphysical shifting. Consequently, the novel technique based on LESCM surpasses the accuracy of pure Lagrangian particle methods and provides an accurate way of detecting complex LCSs in flow fields. Additionally, By harnessing the remarkable efficiency of LESCM, MATLAB can now handle up to 16 million particles with ease, eliminating the need for parallel computation techniques. Several numerical examples, such as 2D Taylor-Green​ vortices, flow passing a circular cylinder and a 3-dimensional shear driven cavity problem, have been tested to demonstrate the effectiveness of this novel approach under various conditions.

Dynamics of a self-propelled capsule robot in contact with different folds in the small intestine

Considering the anatomy of small intestine involving lesions, circular folds and tumours are the major sources resisting the locomotion of capsule robots. By mimicking the small-bowel tumours as cone folds, this paper presents a comparative study on the dynamics of a vibro-impact capsule robot in contact with different circular and cone folds. With the aid of GPU parallel computing and path-following techniques, extensive bifurcation and basin stability analyses are performed to identify different capsule-fold interactions and unravel the parametric influences on the robot, such as fold shape, Young’s modulus and robot’s control parameters (e.g., excitation period and amplitude). It is found that fold shape and Young’s modulus may only affect capsule’s dynamics significantly when robot’s excitation period is large. Two essential locomotion modes, a period-one motion with capsule-fold contact in the small region of excitation amplitude and a fold crossing motion in the large region of excitation amplitude, dominating the dynamics of the robot regardless of fold shape and Young’s modulus are observed. In addition, the instability mechanism of this period-one motion is revealed in detail. The numerical study presented in this work will provide a solid basis for the locomotion control of the robot when encountering different types of circular folds and small-bowel tumours. It also offers the potential of utilising robot’s dynamics for bowel cancer detection.

POETS: An Event-driven Approach to Dissipative Particle Dynamics

HPC clusters have become ever more expensive, both in terms of capital cost and energy consumption; some estimates suggest that competitive installations at the end of the next decade will require their own power station. One way around this looming problem is to design bespoke computing engines, but while the performance benefits are good, the design costs are huge and cannot easily be amortized. Partially Ordered Event Triggered System (POETS)—the focus of this article—seeks to exploit a middle way: The architecture is tuned to a specific algorithmic pattern but, within that constraint, is fully programmable. POETS software is quasi-imperative: The user defines a set of sequential event handlers, defines the topology of a (typically large) concurrent ensemble of these, and lets them interact. The “solution” may be exfiltrated from the emergent behaviour of the ensemble. In this article, we describe (briefly) the architecture, and an example computational chemistry application, dissipative particle dynamics (DPD). The DPD algorithm is traditionally implemented using parallel computational techniques, but we re-cast it as a concurrent compute problem that is then ideally suited to POETS. Our prototype system is realised on a cluster of 48 FPGAs providing 50K concurrent hardware threads, and we report performance speedups of over two orders of magnitude better than a single thread baseline comparator and scaling behaviour that is almost constant. The results are validated against a “conventional” implementation.

Training-efficient and cost-optimal energy management for fuel cell hybrid electric bus based on a novel distributed deep reinforcement learning framework

Deep reinforcement learning (DRL) has become the mainstream method to design intelligent energy management strategies (EMSs) for fuel cell hybrid electric vehicles with the prosperity of artificial intelligence in recent years. Conventional DRL algorithms are suffering from low sampling efficiency and unsatisfactory utilization of computing resources. Combined with distributed architecture and parallel computation, DRL algorithms can be more efficient. Given that, this paper proposes a novel distributed DRL-based energy management framework for a fuel cell hybrid electric bus (FCHEB) to shorten the development cycle of DRL-based EMSs while reducing the total operation cost of the FCHEB. To begin, to make full use of the limited computing resources, a novel asynchronous advantage actor-critic (A3C)-based energy management framework is designed by innovatively integrating with the multi-process parallel computation technique. Then, a promising EMS considering the extra operation cost caused by fuel cell degradation and battery aging is designed based on this novel framework. Furthermore, EMSs based on a conventional DRL algorithm, advantage actor-critic (A2C), and another conventional distributed DRL framework, multi-thread A3C, are employed as baselines, and the performance of the proposed EMS is evaluated by training and testing using different driving cycles. Simulation results indicate that compared to EMSs based on A2C and multi-thread A3C, the proposed EMS can efficiently accelerate the convergence speed respectively by 87.46% and 88.92%, and reduce the total operation cost respectively by 44.83% and 41.19%. The main contribution of this article is to explore the integration of multi-process parallel computation in a distributed DRL-based EMS for a fuel cell vehicle for more efficient utilization of hydrogen energy in the transportation sector.

Performance Investigation of the Conjunction Filter Methods and Enhancement of Computation Speed on Conjunction Assessment Analysis with CUDA Techniques

The growing number of space objects leads to increases in the potential risks of damage to satellites and generates space debris after colliding. Conjunction assessment analysis is the one of keys to evaluating the collision risk of satellites and satellite operators require the analyzed results as fast as possible to decide and execute collision maneuver planning. However, the computation time to analyze the potential risk of all satellites is proportional to the number of space objects. The conjunction filters and parallel computing techniques can shorten the computation cost of conjunction analysis to provide the analyzed results. Therefore, this paper shows the investigation of the conjunction filter performances (accuracy and computation speed): Smart Sieve, CSieve and CAOS-D (combination of both Smart Sieve and CSieve) in both a single satellite (one vs. all) and all space objects (all vs. all) cases. Then, all the screening filters are developed to implement an algorithm that executes General-purpose computing on graphics processing units (GPGPU) by using NVIDIAs Compute Unified Device Architecture (CUDA). The analyzed results show the comparison results of the accuracy of conjunction screening analysis and computation times of each filter when implemented with the parallel computation techniques.

LISC Catalog of Open Clusters III. 83 Newly Found Galactic Disk Open Clusters Using Gaia EDR3

As groups of coeval stars born from the same molecular cloud, an open cluster (OC) is an ideal laboratory for studying the structure and dynamical evolution of the Milky Way. The release of high-precision Gaia Early Data Release 3 (Gaia EDR3) and modern machine-learning methods offer unprecedented opportunities to identify OCs. In this study, we extended conventional HDBSCAN (e-HDBSCAN) for searching for new OCs in Gaia EDR3. A pipeline was developed based on the parallel computing technique to blindly search for OCs from Gaia EDR3 within Galactic latitudes < 25°. As a result, we obtained 3787 star clusters, of which 83 new OCs were reported after cross-match and visual inspection. At the same time, the main star cluster parameters are estimated by color–magnitude diagram fitting. The study significantly increases the sample size and physical parameters of OCs in the catalog of OCs. It shows the incompleteness of the census of OCs across our Galaxy.

Adaptive density-based robust topology optimization under uncertain loads using parallel computing

This work presents an efficient parallel implementation of density-based robust topology optimization (RTO) using adaptive mesh refinement (AMR) schemes permitting us to address the problem with modest computational resources. We use sparse grid stochastic collocation methods (SCMs) for transforming the RTO problem into a weighted multiple-loading deterministic problem at the collocation points. The calculation of these deterministic problems and the functional sensitivity is computationally expensive. We combine distributed-memory parallel computing and AMR techniques to address the problem efficiently. The former allows us to exploit the computational resources available, whereas the latter permits us to increase performance significantly. We propose the parallel incremental calculation of the deterministic problems and the contribution to the functional sensitivity maintaining a similar memory allocation to the one used in the deterministic counterpart. The cumulative computing uses buffers to adapt the evaluation at the collocation points to the parallel computing resources permitting the exploitation of the embarrassing parallelism of SCMs. We evaluate the deterministic problems in a coarse mesh generated for each topology optimization iteration to increase the performance. We perform the regularization and design variable update in a fine mesh to obtain an equivalent design to the one generated in such a mesh. We evaluate the proposal in two- and three-dimensional problems to test its feasibility and scalability. We also check the performance improvement using computational buffers in parallel computing nodes. Finally, we compare the proposal to the same approach using different preconditioners without AMR schemes showing significant performance improvements.

A Guided Evolutionary Strategy Based-Static Var Compensator Control Approach for Interarea Oscillation Damping

Static Var compensator (SVC) has often been used in the power system industries to regulate the bus voltage and reactive power. With an increasing concern of the power system transient stability, the SVC has also been used to dynamically regulate the power flow for oscillation dampings in recent years. A guided surrogate-gradient-based evolutionary strategy (GSES)-based SVC control approach is proposed in this article to damp power system interarea oscillations. The GSES algorithm is used to train a reinforcement learning agent to learn the best decisions that control the SVC, with an objective of quickly damping interarea oscillations. The GSES algorithm brings many benefits such as easier, more robust, and efficient training with less required hyperparameters. Because the GSES can engage with multiple individual workers during the training process, parallel computation techniques are implemented in this research to accelerate the computational speed. To test the performance of the proposed GSES-based SVC controller, we compared this novel method with a supplementary SVC damping controller on two different test systems. Results indicate that the proposed GSES-based SVC control approach can effectively damp power system interarea oscillations. Especially, in some case studies, the proposed approach successfully saved the test system from a transient-instability-caused breaking down.

Dynamic reliability analysis of stochastic structures under non-stationary random excitations based on an explicit time-domain method

For the analysis of the dynamic reliability of stochastic structures subjected to non-stationary random excitations, an effective hybrid approach is proposed. In this approach, the explicit time-domain method is employed to obtain the dynamic responses of each deterministic structure. Dynamic responses for the explicit time-domain method can be expressed as a series of products of deterministic coefficient matrices and random load vectors, which can significantly enhance computational efficiency. For the issue of stochastic structures, the double-hidden-layer backpropagation neural network is introduced as a surrogate model for reconstructing the coefficient matrices to avoid repeated construction of the coefficient matrices for each sample with different structural parameters using the extremely time-intensive regular method. For more effective training of the backpropagation neural network, the Latin hypercube sampling technique is introduced to generate representative samples of random structural parameters. In consideration of the intrinsic benefit of the explicit time-domain method, parallel computation is incorporated into the method. Finally, the explicit time-domain method is used to perform the Monte Carlo simulation in conjunction with the parallel computing technique to resolve the problem of small failure probabilities. To demonstrate the efficacy of the proposed method, numerical examples are presented.

High-Performance and Parallel Computing Techniques Review: Applications, Challenges and Potentials to Support Net-Zero Transition of Future Grids

The transition towards net-zero emissions is inevitable for humanity’s future. Of all the sectors, electrical energy systems emit the most emissions. This urgently requires the witnessed accelerating technological landscape to transition towards an emission-free smart grid. It involves massive integration of intermittent wind and solar-powered resources into future power grids. Additionally, new paradigms such as large-scale integration of distributed resources into the grid, proliferation of Internet of Things (IoT) technologies, and electrification of different sectors are envisioned as essential enablers for a net-zero future. However, these changes will lead to unprecedented size, complexity and data of the planning and operation problems of future grids. It is thus important to discuss and consider High Performance Computing (HPC), parallel computing, and cloud computing prospects in any future electrical energy studies. This article recounts the dawn of parallel computation in power system studies, providing a thorough history and paradigm background for the reader, leading to the most impactful recent contributions. The reviews are split into Central Processing Unit (CPU) based, Graphical Processing Unit (GPU) based, and Cloud-based studies and smart grid applications. The state-of-the-art is also discussed, highlighting the issue of standardization and the future of the field. The reviewed papers are predominantly focused on classical imperishable electrical system problems. This indicates the need for further research on parallel and HPC approaches applied to future smarter grid challenges, particularly to the integration of renewable energy into the smart grid.

Bifurcation analysis of a vibro-impacting capsule robot in contact with a circular fold

The lining of the small intestine consists of a series of circular folds, which is the main source of intestinal resistance for capsule endoscopy. This paper presents an in-depth bifurcation analysis for a vibro-impacting self-propelled capsule robot encountering a circular fold. Using the GPU parallel computing and path-following techniques, one- and two-parameter bifurcation analyses are performed to reveal the capsule–fold dynamics of a newly proposed model. It is found that the excitation parameters of the capsule and the fold’s mechanical properties can significantly influence bifurcation scenarios. Then basin stability analysis is further conducted to uncover the capsule–fold dynamics from a probability perspective. Numerical results indicate that the period-one motion of the capsule–fold interaction and the crossing motion can dominate the global dynamics of the system in the small and large excitation amplitude regions, respectively. Once the mechanical properties of the circular fold are varied, a significant change in multistability is also observed and demonstrated via the basins of attraction. The results presented in this work are intended to provide a solid basis for the locomotion control of the capsule robot in the small intestine when encountering different types of circular folds.

Development of Monte Carlo based treatment planning system for BNCT

A treatment planning system (TPS) plays a major role for a successful radiotherapy, directly influencing the calculated dose accuracy, and thus the treatment quality. Multifunctional Arithmetic for Neutron Transportation Analysis (NeuMANTA), a TPS especially designed for complete workflow for BNCT, which aims to provide high precision dose calculation with maximizing the efficacy. The dedicated MC engine COMPASS seamlessly embedded in NeuMANTA can be called for dose distribution calculation in background. Optimized cross-section library, parallel computing, and various variance reduction techniques are applied to speed up the particle transportations. A benchmark was performed between NeuMANTA and MCNP6, and results agreed well with no significant statistical difference, however, NeuMANTA needs only half of the calculation time. The accuracy of estimated doses, and complete treatment workflow are the main features in NeuMANTA.

Effective Viscoplastic-Softening Model Suitable for Brain Impact Modelling

In this paper, we address the numerical aspects and implementation of a nonlinear viscoplastic model of the mechanical behaviour of brain tissue to simulate the dynamic responses related to impact loads which may cause traumatic injury. Among the various viscoelastic models available, we deliberately considered modifying the Norton–Hoff model in order to introduce non-typical viscoplastic softening behaviour that imitates a brain’s response just several milliseconds after a rapid impact. We describe the discretisation and three dimensional implementation of the model, with the aim of obtaining accurate numerical results in a reasonable computational time. Due to the large scale and complexity of the problem, a parallel computation technique, using a space–time finite element method, was used to facilitate the computation boost. It is proven that, after calibrating, the introduced viscoplastic-softening model is better suited for modelling brain tissue behaviour for the specific case of rapid impact loading rather than the commonly used viscoelastic models.

3D Numerical Analysis Method for Simulating Collapse Behavior of RC Structures by Hybrid FEM/DEM

Recent years have seen an increase in demand for the demolition of obsolete and potentially hazardous structures, including reinforced concrete (RC) structures, using blasting techniques. However, because the risk of failure is significantly higher when applying blasting to demolish RC structures than mechanical dismantling, it is critical to achieve the optimal demolition design and conditions using blasting by taking into account the major factors affecting a structure’s demolition. To this end, numerical analysis techniques have frequently been used to simulate the progressive failure resulting in the collapse of structures. In this study, the three-dimensional (3D) combined finite discrete element method (FDEM), which is accelerated by a parallel computation technique incorporating a general-purpose graphics processing unit (GPGPU), was coupled with the one-dimensional (1D) reinforcing bar (rebar) model as a numerical simulation tool for simulating the process of RC structure demolition by blasting. Three-point bending tests on the RC beams were simulated to validate the developed 3D FDEM code, including the calibration of 3D FDEM input parameters to simulate the concrete fracture in the RC beam accurately. The effect of the elements size for the concrete part on the RC beam’s fracture process was also discussed. Then, the developed 3D FDEM code was used to model the blasting demolition of a small-scale RC structure. The numerical simulation results for the progressive collapse of the RC structure were compared to the actual experimental results and found to be highly consistent.

Damping inter‐area oscillation using reinforcement learning controlled TCSC

Inter-area oscillation is a serious problem that threatens a power system. Appropriate damping control of the inter-area oscillation would ensure the grid stability and maintain the tie-line power transfer capability. In this paper, a novel reinforcement learning (RL) based power oscillation damping (POD) controller is proposed that uses Thyristor Controlled Series Compensators (TCSC) to damp inter-area oscillations. By leveraging the unbiased gradient direction estimation of the natural evolution strategy (NES), the power flows on the tie-lines were successfully regulated and inter-area oscillations were damped through dynamically modulating the inserted reactance of the TCSC. Furthermore, parallel computation techniques were adopted to speed up the training process of the NES. The proposed RL-based POD controller has been tested on both two-area four-machine system and North American MinniWECC system. Extensive studies have demonstrated the excellent performance of the proposed RL-based TCSC POD controller in damping inter-area oscillations.