Reduction In Computation Time Research Articles

A prediction method for blade deformations of large-scale FVAWTs using dynamics theory and machine learning techniques

There is renewed interest in floating vertical axis wind turbines (FVAWTs) as offshore wind turbines progressively increase in size and move into deeper waters. To explore the potential of large-scale FVAWTs for future commercialization, it is crucial to investigate blade deformations using an accurate and effective method. In this study, we developed a hybrid model, namely, the SVST-ANN, which integrates dynamic theory and machine learning techniques to predict blade deformations. Specifically, an artificial neural network (ANN) module is incorporated into the slack coupled vertical axis wind turbine simulation tool (SVST), which significantly reduces the total computational time. A comparative study was conducted between the SVST-ANN model and the traditional SVST model, employing a 10 MW helical-type FVAWT as an example. The results show that the SVST-ANN model can accurately and efficiently predict blade deformations. The maximum errors for the maximum value, average value, and standard deviation across all nodes are minimal, with a corresponding computational time reduction of approximately 60 %. This study provides a novel method for investigating the dynamic behavior of the FVAWTs, which is more effective for calculating the elastic deformations of blades than traditional numerical methods.

Optimizing architectural multi-dimensional forms; a hybrid approach integrating approximate evolutionary search, clustering and local optimization

Parametric tools in architecture allow for the design of complex and multi-dimensional forms on building facades. Among these, geometric patterns can mitigate direct sunlight and enhance energy efficiency. However, conventional simulation methods and available optimization tools are prohibitively expensive for optimizing such complex forms. To address this challenge, this study proposes two innovative hybrid workflows that integrate parametric modeling, evolutionary approximate and accurate models (NSGA-III), clustering through k-means algorithm, and local search (Tabu search) technique. The outcomes obtained from employing these hybrid approaches demonstrate a substantial reduction in computational time and costs while simultaneously achieving optimal results. Additionally, an extensive comparison between the two proposed methodologies is presented encompassing factors such as performance metrics and computational expenses incurred during implementation. The findings derived from pattern optimization reveal several key insights: increasing pattern counts; dispersing them across the facade; minimizing the distance between the pattern wall from windows; adopting a south-facing orientation with positive vertical rotation – all contribute towards diminishing energy consumption (measured by Energy Use Intensity or EUI) within cold climates. However, material selection for these patterns primarily affects visual comfort levels.

Damage detection of truss structures using meta-heuristic algorithms and optimized group method of data handling surrogate model

Optimization-based methods are increasingly being implemented for structural damage detection (SDD) problems through the minimization of the objective functions based on vibration data. Meanwhile, the challenge of high computational cost hinders the applied use of SDD methods, especially upon addressing large-scale structures. In this study, a two-stage damage detection approach is proposed for damage localization as well as extent quantification which reduces the computational time of SDD problems. In the first stage of the proposed framework, suspected locations of damage are identified by employing a residual force vector. Then in the second stage, damage extent of already identified elements will be assessed by model updating method through three different optimization algorithms, namely particle swarm optimization (PSO), differential evolution (DE), and enhanced colliding bodies optimization (ECBO). To spare time-consuming modal analysis in each iteration of optimization algorithms, a PSO-based group method of data handling (GMDH) polynomial neural network (PNN) surrogate model is proposed. Performance of GMDH PNN strongly depends on the input variables, the number of input variables, and the order of the polynomial. The PSO-based GMDH PNN, developed in this study, employs PSO for the optimum selection of these parameters. Furthermore, the efficiency of Elemental Energy Quotient Indicator (EEQI) as a new damage index proposed in this paper is compared with the approach based on the natural frequency-based index. This paper also deals with the incomplete modal data measured from limited number of sensors. This problem is addressed by employing the model reduction technique and solving the SDD problem with modal information obtained from the reduced model. Numerical examples show that the suggested PSO-based GMDH surrogate model is an accurate tool for various types of SDD problems, and it can provide promising results with a significant reduction in computational time.

Automatic Route Design by Stepwise Subdivision of Virtual Walls —Reduces Route Length and Speeds Up Execution Time—

With the development of autonomous driving technology utilizing machine learning, AI, and sensors, research on autonomous driving control has become more active, and a large number of innovative studies are underway. In the near future, all autonomous vehicle fleets will be able to communicate with each other for sharing information and overall optimal traffic control will be achieved. One of the vehicle control systems that are based on the premise of such a fully automated society is the “signal-less intersection.” There is an intersection traffic control method that achieves safe and rational route selection by using virtual walls (VWs), which are virtual obstacles, but there are issues in terms of total route length and reduction of computation time. To address the issues, we propose a method that (1) prunes unneeded paths and (2) arranges VWs in a stepwise manner. The effectiveness of the proposed method was evaluated by simulation, and the results showed that the total route length and execution time were reduced.

Low-complexity EVM estimation based on artificial neural networks for coherent optical systems

With continuous growth in modulation formats, the requirement for autonomous devices is becoming more important than ever. Predicting error vector magnitude (EVM) of m-ary quadrature amplitude modulation (mQAM) are intricate issue for the effective design of transmission systems. Existing estimation techniques have survived through repetitive processes that are frequently computationally expensive, and time-consuming. Recently deep learning approaches demonstrated good performance as useful computational tools, offering a different way for accelerating such mQAM simulations. This paper introduces an artificial neural network (ANN) architecture that aims to forecast the EVM of the popular modulation forms including 18 Gbaud 8QAM, 14 Gbaud 16QAM, and 10 Gbaud 64QAM under different transmission conditions. Amplitude histograms (AHs) are produced from constellation diagrams obtained with varying launch power, laser linewidth, OSNR, and transmission distance by an offline preprocessing flow. The fully trained framework exhibits superior performance in terms of computing cost compared to the simulation experiments. The overall execution time of the ANN-based modeling method is approximately 234 s as opposed to more than 23000 s when employing the simulation technique, resulting in a 99% reduction in computation time. As a result, this technology opens the door to quick, all-encompassing techniques for characterizing and analyzing optical fiber problems.

A generic approach for modelling hydrogen-methane-air detonation in hydrocode

Hydrogen is pivotal in the transition to a sustainable energy supply. The presence of hydrogen-methane mixtures is increasing as the feasibility of utilizing existing natural gas infrastructure for large-scale hydrogen transportation and distribution. However, recent explosion accidents underscore the need to understand the detonation hazards of hydrogen and methane. Current modelling techniques for hydrogen-methane-air detonations are computationally prohibitive due to extra-fine mesh and time step requirements to solve the chemical reaction coupling within detonation structure. This study presented a generic approach to address these challenges, enabling precise and rapid simulation of hydrogen-methane-air blast profiles within hydrocodes. Modified open-source codes helped bypass the time-consuming implicit coupling of detailed chemical reactions with detonation structure and thermodynamic properties based on Chapman-Jouguet (C-J) theory and equilibrium reactive flow assumption, yielding a 90% reduction in computational time. An empirical model, developed through theoretic calculations, provided C-J parameters for hydrocode input with high accuracy across typical industrial conditions. Then, these C-J parameters were employed in LS-DYNA hydrocode based on modified Jones-Wilkins-Lee (JWL) equation of state (EoS) to simulate the hydrogen-methane-air blast loading. This approach was validated against diverse experimental data, encompassing hydrogen-air, methane-air, and hydrogen-methane-air mixtures, and various fuel concentrations, experimental scales, confinement conditions, and fuel shapes. Although conservative results were observed in unstable detonations near the lower detonable limit, it outperformed traditional CFD methods in both accuracy and efficiency. Furthermore, the use of hydrocodes enables the analysis of blast loading as well as the dynamic behaviour of structures subjected to explosive forces. This approach is easily adaptable to other gaseous detonations beyond hydrogen-methane-air by simply replacing the chemical reaction models, making it a versatile tool across various fields.

High-Performance Krawtchouk Polynomials of High Order Based on Multithreading

Orthogonal polynomials and their moments serve as pivotal elements across various fields. Discrete Krawtchouk polynomials (DKraPs) are considered a versatile family of orthogonal polynomials and are widely used in different fields such as probability theory, signal processing, digital communications, and image processing. Various recurrence algorithms have been proposed so far to address the challenge of numerical instability for large values of orders and signal sizes. The computation of DKraP coefficients was typically computed using sequential algorithms, which are computationally extensive for large order values and polynomial sizes. To this end, this paper introduces a computationally efficient solution that utilizes the parallel processing capabilities of modern central processing units (CPUs), namely the availability of multiple cores and multithreading. The proposed multi-threaded implementations for computing DKraP coefficients divide the computations into multiple independent tasks, which are executed concurrently by different threads distributed among the independent cores. This multi-threaded approach has been evaluated across a range of DKraP sizes and various values of polynomial parameters. The results show that the proposed method achieves a significant reduction in computation time. In addition, the proposed method has the added benefit of applying to larger polynomial sizes and a wider range of Krawtchouk polynomial parameters. Furthermore, an accurate and appropriate selection scheme of the recurrence algorithm is introduced. The proposed approach introduced in this paper makes the DKraP coefficient computation an attractive solution for a variety of applications.

3D analytical magnetic model calculation for Halbach PM induction heater planar topology

In this work, a 3D analytical magnetic model based on hybridation of the sub-domain’s method and the image’s theory to compute magnetic field and translational motion eddy current in the conducting plate of the Halbach permanent magnet induction heater planar topology is developed. The main objective is to remedy the problem of the transverse edge effect, and hence improving the efficiency of analytical model and the accuracy of results. The developed model also allows fast and precise simulations of 3D magnetic phenomena, and presents an important reduction in computation time compared to 3D finite element simulations.

AN EFFICIENT ITERATIVE METHOD TO SIMULATE THE VARYING STATIC RECEPTANCE OF A THIN-WALLED WORKPIECE

This paper introduces an accelerated iterative Conjugate Gradient method (CG-method) for simulating the varying static receptance of a thin-walled workpiece. The proposed approach enhances computational efficiency by computing an optimal initial displacement guess for a given cutter location. This guess is based on the previously calculated displacement vector from the preceding cutter location, serving as the start iteration vector for the CG-method. The validation of efficiency gains through this method is demonstrated by a significant reduction in computation time for a numerical example. Moreover, to ensure the accuracy of the accelerated iterative CG-method, comparisons are made with the Cholesky method. The results confirm the precision of the proposed approach.

Energy management of renewable energy connected electric vehicles charging using Internet of things: Hybrid MRA-SDRN approach

This manuscript presents a novel hybrid technique, termed the MRA-SDRN method, for optimizing renewable energy-based electric vehicle (EV) charging within an Internet of Things (IoT) architecture. The key objective is to enhance the efficiency of PV-connected EV systems while concurrently reducing electricity costs and pollutant emissions. The MRA-SDRN approach leverages the Mud Ring Algorithm (MRA) and the Spiking Deep Residual Network (SDRN) to optimize several parameters critical for effective EV charging and renewable energy utilization. Comparative analysis with existing techniques conducted in MATLAB demonstrates notable reductions in computation time, with the MRA-SDRN method achieving a calculation time of 13.04 s, outperforming simulated annealing (SA), multi-objective particle swarm optimization and genetic algorithms (GA).Additionally, significant decreases in pollutant emissions are observed, with levels measured at 472.38 g compared to 570.48 g for GA, 528.35 g for SA, and 502.01 g for MOPSO. Regarding electricity costs, the MRA-SDRN approach achieves a competitive rate of 0.306 yuan/kWh, emphasizing its cost-effectiveness. These numerical results underscore the efficacy of the MRA-SDRN method in optimizing renewable energy management for EV charging, thereby contributing to enhanced reliability, efficiency, and sustainability in the transportation sector within the IoT framework.

Power management for fuel-cell electric vehicle using Hybrid SHO-CSGNN approach

The increasing connectivity of vehicles through intelligent transportation systems presents a significant research challenge: how to simultaneously optimize power management operations and vehicle dynamics to enhance energy efficiency. This paper proposes a novel approach to address this challenge by integrating the Sea-Horse Optimization Algorithm (SHO) and Contrastive Self-Supervised Graph Neural Network (CSGNN), referred to as the SHO-CSGNN technique. The primary objectives of this approach are to reduce computation time and improve energy efficiency in electric vehicles (EVs) powered by fuel cells. The SHO is employed to optimize the power consumption of the EV by dynamically adjusting power management operations based on real-time data and vehicle dynamics. On the other hand, the CSGNN is utilized to predict the vehicle's range performance by analyzing various factors such as road conditions, traffic patterns, and driving behavior. By combining these two approaches, the SHO-CSGNN technique can effectively optimize power management operations while considering the dynamic nature of the driving environment. The proposed technique is implemented and evaluated on the MATLAB platform, where it is compared with existing methods, including the Salp Swarm Algorithm (SSA), Firefly Algorithm (FFA), and Genetic Algorithm (GA). Experimental results demonstrate that the SHO-CSGNN technique outperforms all current methods, achieving a computation time reduction of 3.3 seconds compared to existing techniques. This study contributes to the advancement of power management strategies for EVs, highlighting the effectiveness of the SHO-CSGNN technique in enhancing energy efficiency and computational efficiency in real-world applications. The findings of this research have implications for the development of more efficient and sustainable transportation systems, paving the way for the widespread adoption of electric vehicles.

A simplified GNSS/LEO joint orbit determination method

Joint orbit determination (JOD) of Global Navigation Satellite Systems (GNSS) satellites and low Earth orbit (LEO) satellites has been substantiated as an efficacious approach to compensate for the ground station geometry. In the traditional JOD, the precise orbit determination (POD) of LEO satellite is mainly processed by the reduced-dynamic approach, however, this approach involves complex calculations and the credibility of the determined positions diminishes when LEO satellites undergo orbital maneuvers. Therefore, a simplified JOD method is designed that employs kinematic approach to determine the LEO satellites orbit. To verify the effectiveness of the proposed method, the orbit of GPS satellites and LEO satellites are jointly estimated utilizing the regional and global networks. 8 LEO satellites, including GRACE-C/D, SWARM-A/B/C, SENTINEL-3A/B, and JASON-3, are chosen for JOD. The comparative analysis between the proposed method and traditional method are achieved in terms of GPS orbit accuracy, LEO orbit accuracy, computation time and the JOD performance during LEO maneuvers. Under regional station scenario, the GPS orbit accuracy determined using the proposed method and the traditional method is 3.64 cm and 2.52 cm, respectively. In the case of global station scenario, the accuracies are 1.71 cm and 1.64 cm. Additionally, the traditional method yields superior enhancement and higher accuracy of the LEO orbits. However, it exhibits a noticeable increase in computation time compared to the proposed method and the performance of JOD declines significantly when LEO satellites undergo orbital maneuvers. Alternatively, although the accuracy of the LEO orbits using the proposed method is comparatively lower, it offers a substantial reduction in the overall network computation time compared to traditional method. Moreover, the proposed method based on LEO kinematic precise orbit determination (KPOD) is nearly unaffected by orbital maneuvers of LEO satellites, presenting unique advantages in practical data processing.

Extended finite element method with cell-based smoothing for modeling frictional contact crack-induced acoustic nonlinearity involving distorted mesh

This study proposes a 2D cell-based smoothed extended finite element method (CS-XFEM) for accurate and efficient simulation of nonlinear ultrasonic wave propagation in solid structures, specifically addressing the effects of frictional contact in cracks. Traditional mesh discretization methods for cracks often suffer from mesh distortion and computational instability owing to their high aspect ratios. To overcome this, CS-XFEM integrates a cell-based smoothing technique into XFEM to model the frictional contact of a crack. A comprehensive numerical example demonstrates the advantages of CS-XFEM. The results show that CS-XFEM exhibits a higher convergence rate and enables a larger critical time increment than XFEM. Specifically, the critical time increment of CS-XFEM was found to be twice that of XFEM, leading to a 50% reduction in the total computational time. These findings confirm that CS-XFEM is an efficient, accurate, and robust numerical method for studying the acoustic nonlinearity induced by crack-induced frictional contact.

Sample-Adaptive Classification Inference Network

Existing pre-trained models have yielded promising results in terms of computational time reduction. However, these models only focus on pruning simple sentences or less salient words, while neglecting the treatment of relatively complex sentences. It is frequently these sentences that cause the loss of model accuracy. This shows that the adaptation of the existing models is one-sided. To address this issue, in this paper, we propose a sample-adaptive training and inference model. Specifically, complex samples are extracted from the training datasets and a dedicated data augmentation module is trained to extract global and local semantic information of complex samples. During inference, simple samples can exit the model via the Sample Adaptive Exit Mechanism, Normal samples pass through the whole backbone model before inference, while complex samples are processed by the Characteristic Enhancement Module after passing through the backbone model. In this way, all samples are processed adaptively. Our extensive experiments on classification tasks datasets in the field of Natural Language Processing demonstrate that our method enhances model accuracy and reduces model inference time for multiple datasets. Moreover, our method is transferable and can be applied to multiple pre-trained models.

Efficient matrix profile computation with Euclidean distance using Eigen transformation: Performance evaluation based on beat-to-beat interval (BBI) data.

The matrix profile serves as a fundamental tool to provide insights into similar patterns within time series. Existing matrix profile algorithms have been primarily developed for the normalized Euclidean distance, which may not be a proper distance measure in many settings. The methodology work of this paper was motivated by statistical analysis of beat-to-beat interval (BBI) data collected from smartwatches to monitor e-cigarette users' heart rate change patterns for which the original Euclidean distance ( -norm) would be a more suitable choice. Yet, incorporating the Euclidean distance into existing matrix profile algorithms turned out to be computationally challenging, especially when the time series is long with extended query sequences. We propose a novel methodology to efficiently compute matrix profile for long time series data based on the Euclidean distance. This methodology involves four key steps including (1) projection of the time series onto eigenspace; (2) enhancing singular value decomposition (SVD) computation; (3) early abandon strategy; and (4) determining lower bounds based on the first left singular vector. Simulation studies based on BBI data from the motivating example have demonstrated remarkable reductions in computational time, ranging from one-fourth to one-twentieth of the time required by the conventional method. Unlike the conventional method of which the performance deteriorates sharply as the time series length or the query sequence length increases, the proposed method consistently performs well across a wide range of the time series length or the query sequence length.

Comparing regularized Kelvinlet functions and the finite element method for registration of medical images to sparse organ data

Image-guided surgery collocates patient-specific data with the physical environment to facilitate surgical decision making. Unfortunately, these guidance systems commonly become compromised by intraoperative soft-tissue deformations. Nonrigid image-to-physical registration methods have been proposed to compensate for deformations, but clinical utility requires compatibility of these techniques with data sparsity and temporal constraints in the operating room. While finite element models can be effective in sparse data scenarios, computation time remains a limitation to widespread deployment. This paper proposes a registration algorithm that uses regularized Kelvinlets, which are analytical solutions to linear elasticity in an infinite domain, to overcome these barriers. This algorithm is demonstrated and compared to finite element-based registration on two datasets: a phantom liver deformation dataset and an in vivo breast deformation dataset. The regularized Kelvinlets algorithm resulted in a significant reduction in computation time compared to the finite element method. Accuracy as evaluated by target registration error was comparable between methods. Average target registration errors were 4.6 ± 1.0 and 3.2 ± 0.8 mm on the liver dataset and 5.4 ± 1.4 and 6.4 ± 1.5 mm on the breast dataset for the regularized Kelvinlets and finite element method, respectively. Limitations of regularized Kelvinlets include the lack of organ-specific geometry and the assumptions of linear elasticity and infinitesimal strain. Despite limitations, this work demonstrates the generalizability of regularized Kelvinlets registration on two soft-tissue elastic organs. This method may improve and accelerate registration for image-guided surgery, and it shows the potential of using regularized Kelvinlets on medical imaging data.

Efficient Parallel FDTD Method Based on Non-Uniform Conformal Mesh

The finite-difference time-domain (FDTD) method is a versatile electromagnetic simulation technique, widely used for solving various broadband problems. However, when dealing with complex structures and large dimensions, especially when applying perfectly matched layer (PML) absorbing boundaries, tremendous computational burdens will occur. To reduce the computational time and memory, this paper presents a Message Passing Interface (MPI) parallel scheme based on non-uniform conformal FDTD, which is suitable for convolutional perfectly matched layer (CPML) absorbing boundaries, and adopts a domain decomposition approach, dividing the entire computational domain into several subdomains. More importantly, only one magnetic field exchange is required during the iterations, and the electric field update is divided into internal and external parts, facilitating the synchronous communication of magnetic fields between adjacent subdomains and internal electric field updates. Finally, unmanned helicopters, helical antennas, 100-period folded waveguides, and 16 × 16 phased array antennas are designed to verify the accuracy and efficiency of the algorithm. Moreover, we conducted parallel tests on a supercomputing platform, showing its satisfactory reduction in computational time and excellent parallel efficiency.

An Incremental Interpolation Scheme With Discrete Cosine Series Expansion for Multimaterial Topology Optimization

Abstract Topology optimization is a powerful tool for structural design, while its computational cost is quite high due to the large number of design variables, especially for multilateral systems. Herein, an incremental interpolation approach with discrete cosine series expansion (DCSE) is established for multilateral topology optimization. A step function with shape coefficients (i.e., ensuring that no extra variables are required as the number of materials increases) and the use of the DCSE together reduces the number of variables (e.g., from 8400 to 120 for the optimization of the clamped–clamped beam with four materials). Remarkably, the proposed approach can effectively bypass the checkerboard problem without using any filter. The enhanced computational efficiency (e.g., a ∼89.2% reduction in computation time from 439.1 s to 47.4 s) of the proposed approach is validated via both 2D and 3D numerical cases.

A computational efficient approach for distributionally robust unit commitment with enhanced disjointed layered ambiguity set

AbstractTo achieve the sustainable development of the society, renewable energy dominated power systems are gradually formed. However, the uncertainty of renewable power poses challenges for power system operations, such as balancing the load and generation in day‐ahead unit commitment problems. To address this issue, an enhanced disjointed layered ambiguity set is proposed in this paper to effectively capture the uncertainty of renewable generation forecast errors. On this basis, a two‐stage distributionally robust unit commitment optimization problem is formulated, which is computational challenge. To this end, a novel reformation approach and a worst‐case support set identification method are proposed to derive the optimal solution in a computationally efficient way. Simulation results demonstrate that the proposed ambiguity set brings lower operation costs of unit commitment while maintaining similar reliability compared to existing methods. In detail, the proposed ambiguity set achieves a reduction in operating costs of approximately 10.01%. The proposed novel reformation approach and solution method are also shown to have higher computation efficiency than the existing Benders decomposition method, resulting in a reduction in computation time to less than 3.78% of the original duration.

In Search of the Best Low-Cost Methods for Efficient Screening of Conformers.

Locating the lowest energy conformer is crucial for the accurate computation of equilibrium properties of molecular systems. This paper examines the performance of efficient low-cost methods in terms of the alignment and relative energies of their energy minima against the benchmark revDSD-PBEP86-D4/def2-TZVPP//MP2/cc-pVTZ potential energy surface. The low-cost methods considered include GFN-FF, GFN2-xTB, DFTB3, HF-3c, B97-3c, PBEh-3c, and r2SCAN-3c composite methods against a diverse test set of 20 compounds including alkanes, perfluoroalkyl molecules, peptides, open-shell radicals, and Zn(II) complexes of varying sizes. The "3c" composite methods are generally more accurate, but are at least 2-3 orders of magnitude more expensive than tight-binding methods which have energy minima that align well with the benchmark potential energy surface. The findings of this paper were further exploited to introduce a simple strategy involving Grimme's CENSO energy-sorting algorithm that resulted in up to an order of magnitude reduction in computational time for locating the lowest energy conformer on the revDSD-PBEP86-D4/def2-TZVPP//MP2/cc-pVTZ surface.