Multiple Acceleration Research Articles

Seismic Response Analysis of Hydraulic Tunnels Under the Combined Effects of Fault Dislocation and Non-Uniform Seismic Excitation

Hydraulic tunnels are prone to pass through faults and high-intensity earthquake areas, which will cause serious damage under fault dislocation and earthquake action. Fault dislocation and seismic excitation are often considered separately in previous studies. For tectonic earthquakes with higher frequency in seismic phenomena, fault dislocation and ground motion are often associated, and fault dislocation is usually the cause of earthquake occurrence, so it is limiting to consider the two separately. Moreover, strong earthquake records show that there will be significant differences in the mainland vibration within 50 m. The uniform ground motion inputs in previous studies are not suitable for long hydraulic tunnels. This paper begins with the simulation of non-uniform stochastic seismic excitations that consider spatial correlation. Based on stochastic vibration theory, multiple multi-point acceleration time-history curves that can reflect traveling wave effects, coherence effects, attenuation effects, and non-stationary characteristics are synthesized. Furthermore, a fault velocity function is introduced to account for the velocity effect of fault dislocation. Finally, numerical analyses of the response patterns of the tunnel lining under four different conditions are conducted based on an actual engineering project. The results indicate the following: (a) the maximum lining response values occur under the combined effects of fault dislocation and non-uniform seismic excitation, indicating its importance in the seismic resistance of the tunnel. (b) Compared to uniform seismic excitation, the peak displacement of the tunnel under non-uniform seismic excitation increases by up to 6.42%, and the peak maximum principal stress increases by up to 28%. Additionally, longer tunnels exhibit a noticeable delay effect in axial deformation during an earthquake. (c) Under non-uniform seismic excitation, the larger the fault dislocation magnitude, the greater the peak displacement and peak maximum principal stress at the monitoring points of the lining. The simulation results show that the extreme response values primarily occur at the crown and haunches of the tunnel, which require special attention. The research can provide valuable references for the seismic design of cross-fault tunnels.

Are accelerators akin to breweries or wineries? A Bayesian variance decomposition of accelerator and cohort effects

AbstractResearch SummaryThe literature on startup accelerators uncovers multiple factors associated with accelerators’ advantages. Yet, we have a limited understanding of the relative magnitude of these factors. We ask: Are accelerators akin to breweries, where quality is mainly a function of the institution of origin (i.e., brewery for beer, accelerator for startups); or are they similar to wineries, where quality varies across cohorts (i.e., for a given winery, some vintages are of higher quality)? We explore this question using data from 1,350 tech‐startups graduating from dozens of accelerators in a global technology hub. A Bayesian hierarchical variance decomposition approach is introduced to account for the highly‐skewed zero‐inflated distribution in startups’ performance. We find that a notable fraction of startup performance is due to vintage; within‐accelerator, cross‐cohort variation.Managerial SummaryStartup accelerators (i.e., short‐term programs designed to help startups grow) are highly popular, with dozens of accelerators operating around the globe. Our focus is on accelerator programs aimed at catapulting technology ventures towards high growth. We ask: Are accelerators akin to breweries, where quality is mainly a function of the institution of origin (i.e., brewery for beer, accelerator for startups); or are they similar to wineries, where quality also varies across cohorts (i.e., for a given winery, some vintages are of higher quality)? A Bayesian hierarchical variance decomposition approach isused to study data from a global technology hub, detailing the performance of hundreds of startups that graduated across multiple accelerators. We find that a significant portion of startup success is linked to cohort‐specific factors within accelerators, highlighting the role of timing and dynamics of each accelerator cohort.

CHARM 2.0: Composing Heterogeneous Accelerators for Deep Learning on Versal ACAP Architecture

Dense matrix multiply (MM) serves as one of the most heavily used kernels in deep learning applications. To cope with the high computation demands of these applications, heterogeneous architectures featuring both FPGA and dedicated ASIC accelerators have emerged as promising platforms. For example, the AMD/Xilinx Versal ACAP architecture combines general-purpose CPU cores and programmable logic with AI Engine processors optimized for AI/ML. An array of 400 AI Engine processors executing at 1 GHz can provide up to 6.4 TFLOPS performance for 32-bit floating-point (FP32) data. However, machine learning models often contain both large and small MM operations. While large MM operations can be parallelized efficiently across many cores, small MM operations typically cannot. We observe that executing some small MM layers from the BERT natural language processing model on a large, monolithic MM accelerator in Versal ACAP achieved less than 5% of the theoretical peak performance. Therefore, one key question arises: How can we design accelerators to fully use the abundant computation resources under limited communication bandwidth for end-to-end applications with multiple MM layers of diverse sizes? We identify the biggest system throughput bottleneck resulting from the mismatch between the massive computation resources of one monolithic accelerator and the various MM layers of small sizes in the application. To resolve this problem, we propose the CHARM framework to compose multiple diverse MM accelerator architectures working concurrently on different layers within one application. CHARM includes analytical models that guide design space exploration to determine accelerator partitions and layer scheduling. To facilitate system designs, CHARM automatically generates code, enabling thorough onboard design verification. We deploy the CHARM framework on four different deep learning applications in FP32, INT16, and INT8 data types, including BERT, ViT, NCF, and MLP, on the AMD/Xilinx Versal ACAP VCK190 evaluation board. Our experiments show that we achieve 1.46 TFLOPS, 1.61 TFLOPS, 1.74 TFLOPS, and 2.94 TFLOPS inference throughput for BERT, ViT, NCF, and MLP in FP32 data type, respectively, which obtain 5.29 \(\times\) , 32.51 \(\times\) , 1.00 \(\times\) , and 1.00 \(\times\) throughput gains compared to one monolithic accelerator. CHARM achieves the maximum throughput of 1.91 TOPS, 1.18 TOPS, 4.06 TOPS, and 5.81 TOPS in the INT16 data type for the four applications. The maximum throughput achieved by CHARM in the INT8 data type is 3.65 TOPS, 1.28 TOPS, 10.19 TOPS, and 21.58 TOPS, respectively. We have open-sourced our tools, including detailed step-by-step guides to reproduce all the results presented in this article and to enable other users to learn and leverage CHARM framework and tools in their end-to-end systems: https://github.com/arc-research-lab/CHARM .

Designing and prototyping extensions to the Message Passing Interface in MPICH

As HPC system architectures and the applications running on them continue to evolve, the MPI standard itself must evolve. The trend in current and future HPC systems toward powerful nodes with multiple CPU cores and multiple GPU accelerators makes efficient support for hybrid programming critical for applications to achieve high performance. However, the support for hybrid programming in the MPI standard has not kept up with recent trends. The MPICH implementation of MPI provides a platform for implementing and experimenting with new proposals and extensions to fill this gap and to gain valuable experience and feedback before the MPI Forum can consider them for standardization. In this work, we detail six extensions implemented in MPICH to increase MPI interoperability with other runtimes, with a specific focus on heterogeneous architectures. First, the extension to MPI generalized requests lets applications integrate asynchronous tasks into MPI’s progress engine. Second, the iovec extension to datatypes lets applications use MPI datatypes as a general-purpose data layout API beyond just MPI communications. Third, a new MPI object, MPIX_Stream, can be used by applications to identify execution contexts beyond MPI processes, including threads and GPU streams. MPIX stream communicators can be created to make existing MPI functions thread-aware and GPU-aware, thus providing applications with explicit ways to achieve higher performance. Fourth, MPIX Streams are extended to support the enqueue semantics for offloading MPI communications onto a GPU stream context. Fifth, thread communicators allow MPI communicators to be constructed with individual threads, thus providing a new level of interoperability between MPI and on-node runtimes such as OpenMP. Lastly, we present an extension to invoke MPI progress, which lets users spawn progress threads with fine-grained control to adapt the communication performance to their application designs. We describe the design and implementation of these extensions, provide usage examples, and highlight their expected benefits with performance results.

Runtime support for CPU-GPU high-performance computing on distributed memory platforms

IntroductionHardware heterogeneity is here to stay for high-performance computing. Large-scale systems are currently equipped with multiple GPU accelerators per compute node and are expected to incorporate more specialized hardware. This shift in the computing ecosystem offers many opportunities for performance improvement; however, it also increases the complexity of programming for such architectures.MethodsThis work introduces a runtime framework that enables effortless programming for heterogeneous systems while efficiently utilizing hardware resources. The framework is integrated within a distributed and scalable runtime system to facilitate performance portability across heterogeneous nodes. Along with the design, this paper describes the implementation and optimizations performed, achieving up to 300% improvement on a single device and linear scalability on a node equipped with four GPUs.ResultsThe framework in a distributed memory environment offers portable abstractions that enable efficient inter-node communication among devices with varying capabilities. It delivers superior performance compared to MPI+CUDA by up to 20% for large messages while keeping the overheads for small messages within 10%. Furthermore, the results of our performance evaluation in a distributed Jacobi proxy application demonstrate that our software imposes minimal overhead and achieves a performance improvement of up to 40%.DiscussionThis is accomplished by the optimizations at the library level and by creating opportunities to leverage application-specific optimizations like over-decomposition.

Analysis of Vibration Characteristics of Tractor–Rotary Cultivator Combination Based on Time Domain and Frequency Domain

A good planting bed is a prerequisite for improving planting quality, while complex ground excitation often leads to machine bouncing and operation vibration, which then affects the operation effect. In order to improve the quality of rotary tillage operations, it is necessary to study the effects of various vibration excitations on the unit during tractor rotary tillage operations and analyze the vibration interaction relationship among the tractor, the three-point suspension mechanism, and the rotary tiller. For this purpose, multiple three-way acceleration sensors were installed at different positions on the rotary tiller unit of a Lexing LS1004 tractor(Lexing Agricultural Equipment Co. Ltd., Qingdao, China) to collect vibration data at different operating speeds and conduct vibration characteristic analysis between different components. The test results showed that when the unit moved forward at 2.1 km/h, 3.6 km/h, and 4.5 km/h, respectively, the vibration acceleration of the tractor, the three-point suspension mechanism, and the rotary tiller increased with the increase in speed, and there was indeed interaction between them. The vertical acceleration change during the test in the three-point suspension mechanism was the most significant (5.914 m/s2) and was related to the increase in the speed of the vehicle and the vibration transfer of the rotary tiller. Meanwhile, the vertical vibration acceleration of the tractor’s symmetrical structure was not similar, suggesting the existence of structural assembly problems. From the perspective of frequency domain analysis, the resonant frequency at the cab of the tractor was reduced in a vertical vibration environment, with relatively low frequencies (0~80 Hz) and small magnitudes, which might be beneficial to the driver’s health. The rotary tillage group resonated around 350 Hz, and this characteristic can be used to appropriately increase the vibration of the rotary tiller to reduce resistance. The tractor cab resonated around 280 Hz, which must be avoided during field operations to ensure driver health and reduce machine wear. The research results can provide a reference for reducing vibration and resistance during tractor rotary tillage operations, as well as optimizing and improving the structure of rotary tillers and tractors.

Benchmarking parallel programming for single-board computers

Within the computing continuum, SBCs (single-board computers) are essential in the Edge and Fog, with many featuring multiple processing cores and GPU accelerators. In this way, parallel computing plays a crucial role in enabling the full computational potential of SBCs. However, selecting the best-suited solution in this context is inherently complex due to the intricate interplay between PPI (parallel programming interface) strategies, SBC architectural characteristics, and application characteristics and constraints. To our knowledge, no solution presents a combined discussion of these three aspects. To tackle this problem, this article aims to provide a benchmark of the best-suited parallelism PPIs given a set of hardware and application characteristics and requirements. Compared to existing benchmarks, we introduce new metrics, additional applications, various parallelism interfaces, and extra hardware devices. Therefore, our contributions are the methodology to benchmark parallelism on SBCs and the characterization of the best-performing parallelism PPIs and strategies for given situations. We are confident that parallel computing will be mainstream to process edge and fog computing; thus, our solution provides the first insights regarding what kind of application and parallel programming interface is the most suited for a particular SBC hardware.

Key influence factors in magneto-controlled motion of micro-nano graphite flakes

Magneto-controlling micro-nano materials’ motion is a promising way that enable the noncontact, remote, and nondestructive controlling of their macrostructure as well as functionalities. Here, an optical microscope with an electromagnet was constructed to in-situ monitor the magneto-controlled motion process microscopically. Taking micro-nano graphite flake (MGF) as a model system, we experimentally demonstrate the key factors that influence the magneto-controlling of materials’ motion. First, the product of intensity and gradient of the magnetic field (B∇B) has been confirmed as the dominant driving force and the flipping direction of the MGFs is accordingly determined by the vector direction of B×∇B. Second, quantitatively comparative experiments further revealed that the threshold driving force has an exponential relationship with the structural aspect ratio (b/a) of MGFs. Third, the critical magneto-driving force is found as proportional to the viscosity of the solvent. Accordingly, a dynamic model is developed that describes the flip of the diamagnetic flake under external magnetic field excitation considering the shape factor. It is shown experimentally that the model accurately predicts the flip dynamics of the flake under different magnetic field conditions. In addition, we also discovered the delay effect, multiple cycle acceleration effect, and the fatigue effects due to gas adsorption in magneto-controlled MGFs flipping. These findings can be used to achieve magneto-controlling materials’ macrostructure as well as their functionalities.

DDN3.0: determining significant rewiring of biological network structure with differential dependency networks.

Complex diseases are often caused and characterized by misregulation of multiple biological pathways. Differential network analysis aims to detect significant rewiring of biological network structures under different conditions and has become an important tool for understanding the molecular etiology of disease progression and therapeutic response. With few exceptions, most existing differential network analysis tools perform differential tests on separately learned network structures that are computationally expensive and prone to collapse when grouped samples are limited or less consistent. We previously developed an accurate differential network analysis method-differential dependency networks (DDN), that enables joint learning of common and rewired network structures under different conditions. We now introduce the DDN3.0 tool that improves this framework with three new and highly efficient algorithms, namely, unbiased model estimation with a weighted error measure applicable to imbalance sample groups, multiple acceleration strategies to improve learning efficiency, and data-driven determination of proper hyperparameters. The comparative experimental results obtained from both realistic simulations and case studies show that DDN3.0 can help biologists more accurately identify, in a study-specific and often unknown conserved regulatory circuitry, a network of significantly rewired molecular players potentially responsible for phenotypic transitions. The Python package of DDN3.0 is freely available at https://github.com/cbil-vt/DDN3. A user's guide and a vignette are provided at https://ddn-30.readthedocs.io/.

Cannibals in PARADISE: The Effect of Merging Interplanetary Shocks on Solar Energetic Particle Events

Gradual solar energetic particle (SEP) events are associated with shocks driven by coronal mass ejections (CMEs). The merging of two CMEs (so-called cannibalistic CMEs) and the interaction of their associated shocks has been linked to some of the most powerful solar storms ever recorded. Multiple studies have focused on the observational aspects of these SEP events, yet only a handful have focused on modeling similar CME–CME interactions in the heliosphere using advanced magnetohydrodynamic (MHD) models. This work presents, to our knowledge, the first modeling results of a fully time-dependent 3D simulation that captures both the interaction of two CMEs and its effect on the acceleration and transport of SEPs. This is achieved by using an MHD model for the solar wind and CME propagation together with an integrated SEP model. We perform different simulations and compare the behavior of the energetic protons in three different solar wind environments, where a combination of two SEP-accelerating CMEs are modeled. We find that particle acceleration is significantly affected by the presence of both CMEs in the simulation. Initially, less efficient acceleration results in lower-energy particles. However, as the CMEs converge and their shocks eventually merge, particle acceleration is significantly enhanced through multiple acceleration processes between CME-driven shocks, resulting in higher particle intensities and energy levels.

Healthy Japanese dietary pattern is associated with slower biological aging in older men: WASEDA’S health study

Aging is the greatest risk factor for numerous diseases and mortality, and establishing geroprotective interventions targeting aging is required. Previous studies have suggested that healthy dietary patterns, such as the Mediterranean diet, are associated with delayed biological aging; however, these associations depend on nationality and sex. Therefore, this study aimed to investigate the relationship between dietary patterns identified through principal component analysis and biological aging in older men of Japan, one of the countries with the longest life expectancies. Principal component analysis identified two dietary patterns: a healthy Japanese dietary pattern and a Western-style dietary pattern. Eight epigenetic clocks, some of the most accurate aging biomarkers, were identified using DNA methylation data from whole-blood samples. Correlation analyses revealed that healthy Japanese dietary patterns were significantly negatively or positively correlated with multiple epigenetic age accelerations (AgeAccel), including AgeAccelGrim, FitAgeAccel, and age-adjusted DNAm-based telomere length (DNAmTLAdjAge). Conversely, the Western-style dietary pattern was observed not to correlate significantly with any of the examined AgeAccels or age-adjusted values. After adjusting for covariates, the healthy Japanese dietary pattern remained significantly positively correlated with DNAmTLAdjAge. Regression analysis showed that healthy Japanese dietary pattern contributed less to epigenetic age acceleration than smoking status. These findings suggest that a Western-style dietary pattern may not be associated with biological aging, whereas a healthy Japanese dietary pattern is associated with delayed biological aging in older Japanese men. Our findings provide evidence that healthy dietary patterns may have mild beneficial effects on delayed biological aging in older Japanese men.

Variable transformations in combination with wavelets and ANOVA for high-dimensional approximation

We use hyperbolic wavelet regression for the fast reconstruction of high-dimensional functions having only low-dimensional variable interactions. Compactly supported periodic Chui-Wang wavelets are used for the tensorized hyperbolic wavelet basis on the torus. With a variable transformation, we are able to transform the approximation rates and fast algorithms from the torus to other domains. We perform and analyze scattered data approximation for smooth but arbitrary density functions by using a least squares method. The corresponding system matrix is sparse due to the compact support of the wavelets, which leads to a significant acceleration of the matrix vector multiplication. For non-periodic functions, we propose a new extension method. A proper choice of the extension parameter together with the piecewise polynomial Chui-Wang wavelets extends the functions appropriately. In every case, we are able to bound the approximation error with high probability. Additionally, if the function has a low effective dimension (i.e., only interactions of a few variables), we qualitatively determine the variable interactions and omit ANOVA terms with low variance in a second step in order to decrease the approximation error. This allows us to suggest an adapted model for the approximation. Numerical results show the efficiency of the proposed method.

Dual-domain faster Fourier convolution based network for MR image reconstruction

Deep learning methods for fast MRI have shown promise in reconstructing high-quality images from undersampled multi-coil k-space data, leading to reduced scan duration. However, existing methods encounter challenges related to limited receptive fields in dual-domain (k-space and image domains) reconstruction networks, rigid data consistency operations, and suboptimal refinement structures, which collectively restrict overall reconstruction performance. This study introduces a comprehensive framework that addresses these challenges and enhances MR image reconstruction quality. Firstly, we propose Faster Inverse Fourier Convolution (FasterIFC), a frequency domain convolutional operator that significantly expands the receptive field of k-space domain reconstruction networks. Expanding the information extraction range to the entire frequency spectrum according to the spectral convolution theorem in Fourier theory enables the network to easily utilize richer redundant long-range information from adjacent, symmetrical, and diagonal locations of multi-coil k-space data. Secondly, we introduce a novel softer Data Consistency (softerDC) layer, which achieves an enhanced balance between data consistency and smoothness. This layer facilitates the implementation of diverse data consistency strategies across distinct frequency positions, addressing the inflexibility observed in current methods. Finally, we present the Dual-Domain Faster Fourier Convolution Based Network (D2F2), which features a centrosymmetric dual-domain parallel structure based on FasterIFC. This architecture optimally leverages dual-domain data characteristics while substantially expanding the receptive field in both domains. Coupled with the softerDC layer, D2F2 demonstrates superior performance on the NYU fastMRI dataset at multiple acceleration factors, surpassing state-of-the-art methods in both quantitative and qualitative evaluations.

Observation of Energetic Electron Near the Electron Diffusion Region of Magnetic Reconnection

AbstractMagnetic reconnection can effectively convert magnetic energy into plasma energy, and accelerate electrons. In this article, the electrons with energies up to 150 keV are observed in the separatrix region and near the electron diffusion region (EDR) detected by the Magnetospheric Multiscale mission in the magnetotail. Combined with the electron pitch‐angle distribution, the electrons in these two regions have a striking behavior: the low‐energy electrons (<∼10 keV) move mainly toward the X‐line, while the energetic electrons (69–139 keV) move mainly away from the X‐line. In the EDR, the energy of electrons can reach up to 10 keV and there is a notable enhancement in the flux of electrons in the direction perpendicular to the magnetic field, which implies the presence of acceleration processes occurring in the EDR, leading to the energization of electrons. Furthermore, the energy spectrum of non‐thermal electron with energies above 6 keV shows a power law distribution in this event, suggesting the occurrence of multiple acceleration processes rather than a single energization mechanism. These findings underscore the EDR's role as a crucial region for electron acceleration during magnetic reconnection. The study provides essential clues about the mechanisms driving electron acceleration, contributing to our understanding of space weather phenomena and the broader dynamics of plasma physics in space environments.

Toward Energy-efficient STT-MRAM-based Near Memory Computing Architecture for Embedded Systems

Convolutional Neural Networks (CNNs) have significantly impacted embedded system applications across various domains. However, this exacerbates the real-time processing and hardware resource-constrained challenges of embedded systems. To tackle these issues, we propose spin-transfer torque magnetic random-access memory (STT-MRAM)-based near memory computing (NMC) design for embedded systems. We optimize this design from three aspects: Fast-pipelined STT-MRAM readout scheme provides higher memory bandwidth for NMC design, enhancing real-time processing capability with a non-trivial area overhead. Direct index compression format in conjunction with digital sparse matrix-vector multiplication (SpMV) accelerator supports various matrices of practical applications that alleviate computing resource requirements. Custom NMC instructions and stream converter for NMC systems dynamically adjust available hardware resources for better utilization. Experimental results demonstrate that the memory bandwidth of STT-MRAM achieves 26.7 GB/s. Energy consumption and latency improvement of digital SpMV accelerator are up to 64× and 1,120× across sparsity matrices spanning from 10% to 99.8%. Single-precision and double-precision elements transmission increased up to 8× and 9.6×, respectively. Furthermore, our design achieves a throughput of up to 15.9× over state-of-the-art designs.

Configurable sparse matrix - matrix multiplication accelerator on FPGA: A systematic design space exploration approach with quantization effects

High-performance sparse matrix multipliers are essential for deep learning applications, and as big data analytics continues to evolve, specialized accelerators are also needed to efficiently handle sparse matrix operations. This paper proposes a modified, configurable, outer product based architecture for sparse matrix multiplication, and explores design space of the proposed architecture. The performance of various architecture configurations has been examined for input samples with similar characteristics. The proposed architecture has been implemented on Xilinx Kintex-7 FPGA using 32-bit single precision floating-point arithmetic and also in 8-bit, 16-bit and 32-bit fixed-point arithmetic formats. The effect of quantization in the proposed architecture has been analyzed extensively and the results have been reported. The performance of the proposed architecture has been compared with state-of-the-art implementations, and an improvement of 9.21% has been observed in the performance.

Optimization of multi-target continuous dynamic trajectory for unmanned aerial vehicles

In the problem of trajectory optimization for unmanned aerial vehicles (UAVs) passing through multiple target points, continuous velocity and acceleration in the process of optimization are essential for maintaining a feasible trajectory. This paper proposes a multi-segment holistic Bezier shaping method (MHBSM) for dynamic trajectory optimization. The MHBSM not only achieves holistic optimization of UAVs passing through a multi-target trajectory but also ensures high-order continuity at the junctions of trajectory segments. Unlike methods that generate non-dynamic trajectories by connecting arcs and straight lines (such as Dubins curves) without considering speed changes, MHBSM optimizes the trajectory while taking into account the dynamics of UAVs. The method optimizes the differential flat output space of a fixed-wing UAV dynamic model and transforms the dynamic optimization problem into a three-dimensional position optimization problem. The problem is then further transformed into an optimization of control points using the Bezier curve. The resulting trajectory satisfies the dynamic constraints. In comparison to the Gaussian pseudospectral method (GPM), MHBSM achieves similar optimization results with only a 2.58% difference, while requiring approximately 1.36% of the computational time on average. The MHBSM demonstrates excellent convergence performance while satisfying the constraints of continuous acceleration and obstacle avoidance.

Thermally Driven Multi-Objective Packing Optimization Using Acceleration Fields

Abstract The packing optimization of three-dimensional components into a design space is a challenging and time-intensive task. Of particular concern is the thermal performance of the system, as tightly packed components typically exhibit poor heat dissipation performance which can result in overheating and system failure. As temperature modeling can be quite complex, there is a growing demand in the industry for software tools that aid designers in the packing process whilst considering heat transfer. This work outlines a novel multi-objective algorithm that considers temperature and thermal effects directly within the packing optimization process itself using thermal optimization objectives. In addition, the algorithm can consider functional objectives such as a desired center of mass position and minimizing rotational inertia. The algorithm packs components from initial to optimal positions within a design domain using a set of dynamic acceleration fields. There are multiple accelerations, each designed to improve the objective values for the systems (e.g., minimize temperature variance). Component temperatures are calculated using thermal finite element analyses modeling conduction and natural convection. Forced convection is approximated via computational fluid dynamics simulations. Numerical results for two academic and one real-world case studies are presented to demonstrate the efficacy of the presented algorithm.

Research on the interaction between energy consumption and power battery life during electric vehicle acceleration

Most studies on the acceleration process of electric vehicle focus on reducing energy consumption, but do not consider the impact of the power battery discharge current and its change rate on the battery life. Therefore, this paper studied the interaction between electric vehicle energy consumption and power battery capacity attenuation during acceleration. First, a power battery life model for electric vehicle under driving conditions is established, and the percentage of battery capacity loss per kilometer is used to measure the capacity loss under different acceleration conditions. Then, the relationship between the percentage of battery capacity loss per kilometer and velocity and acceleration is explored, and the capacity attenuation mechanism of power battery under different acceleration processes is analyzed. Finally, the energy consumption and battery capacity attenuation is studied when the electric vehicle accelerated with multiple accelerations curves, and the interaction of the first acceleration and acceleration time on the electric vehicle energy consumption and the power battery capacity attenuation characteristics is discussed. The research results indicate that when the electric vehicle accelerates with different multiple accelerations curves, the change of energy consumption per kilometer and percentage of battery capacity loss per kilometer with acceleration and acceleration time is different, and the change of the two is basically opposite.

Point Multiplication Accelerator for Arbitrary Montgomery Curves

Point Multiplication Accelerator for Arbitrary Montgomery Curves