Fast Computation Research Articles

Multi-Phase Stator Current Tracking with Gradual Penalization of Commutations

Energy efficiency in drives is an important issue. In converter-supplied variable-speed drives, switching losses can amount to a significant portion of all losses. This has been considered in Predictive Stator Current Control (PSCC), considering commutations at the power converter. However, in multi-phase drives, the computational burden limits the application of said techniques. Recent fast predictive algorithms have enabled shorter application times with enhanced tracking results. However, the switching frequency becomes larger with diminishing sampling periods. This paper presents a method that retains the fast computation of recent methods while reducing the switching frequency. The proposal revolves around a modification of the cost function to penalize commutations in a nonlinear way. For this task, a novel, gradual penalization is introduced. The method is experimentally applied to a five-phase induction motor. Experimental results show a significant reduction in switching frequency without compromising other control objectives. This results in an enhanced PSCC with a small sampling period and reduced switching losses.

Automated identification and long-term tracking of modal parameters for a super high-rise building

Structural modal parameter identification plays an important role in wind resistance design, damage identification, and health monitoring. At present, the modal identification process still suffers from low automation and low computational efficiency, which are not conducive to the continuous processing of monitoring data. To address this, an automated modal parameter identification approach is proposed by introducing the fast density and grid based (DGB) clustering, and applied to the Shanghai Tower, which is the tallest building in China. Moreover, in order to determine the optimal model order of the stochastic subspace identification (SSI), the trend of the logarithmic reduction rate for singular values is extracted by empirical mode decomposition (EMD). The numerical simulation of a cantilever beam and the field measurement of Shanghai Tower under the influence of Typhoon In-Fa are conducted to validate the effectiveness of the proposed approach. Results demonstrate that the approach can obtain modal parameters without human intervention and has the advantages of simple process, fast computation and high precision. Furthermore, based on the data collected from the structural health monitoring system of the Shanghai Tower over a period of 5.5 years, the approach is applied to analyze the correlation between natural frequencies and ambient temperature, as well as the long-term evolution law of modes, discovering that multi-order frequencies show a decreasing trend over time. The finite element analysis suggests that this fact might be attributed to the increase in structural mass caused by the rising usage rate and the degradation in material properties.

Control-oriented multiphysics model of a lithium-ion battery for thermal runaway estimation under operational and abuse conditions

This study proposes a control-oriented multiphysics model for lithium-ion batteries (LIBs) that can estimate electrochemical-thermal responses in real time under normal operation and abuse conditions. The proposed model integrates the simplified electrochemical model, the thermal resistance network, and the adaptive time-stepping method to ensure computational efficiency without sacrificing the accuracy. Specifically, the diffusion equation of the electrochemical model is simplified by addressing Padé approximation. The thermal resistance network estimates 3D temperature distribution through simple matrix multiplication to account for the entropic, ohmic, and chemical reaction during thermal runaway. The adaptive time-stepping method further secures accurate yet fast explicit calculation. Quantitative experimental validation reveals the high accuracy and robustness, and fast inference time of the proposed model to estimate the electrochemical-thermal responses with the average inference time of 0.0047 s per step. The application of the proposed model on the 26650 LFP cell also demonstrates the versatility on various shapes and types of LIBs. The systematic analysis on the 3D temperature distribution in the LIB of interest not only confirms the effectiveness of the internal temperature monitoring but also ensures the virtual sensing capability. The versatility of the proposed model underscores both design- and control- enabling solutions for battery thermal management.

Modeling a solar shading system that controls heat gain by using solar energy to change louver slat angles

In this study, we propose a model for a passive louver system that adjusts slat angles using solar heat energy. The model is structured in two stages: predicting the surface temperature of a solar collector plate and using this temperature to determine the slat angles. The system was installed and monitored in a building in Okinawa, Japan, to validate the model’s accuracy with field measurements. The validation showed that while the model accurately predicted the daily opening and closing times of the louvers, it faced limitations in minute-by-minute variations, making it less suitable for scenarios requiring instantaneous state evaluations, such as lighting environment assessments. Additionally, predicting the temperature for the double-skin façade was challenging and could affect overall model accuracy. Despite these issues, the model effectively achieved its primary goal of predicting annual energy consumption by accurately forecasting the daily opening and closing times of the louvers. The model’s fast computation speed and simplicity make it particularly suitable for annual energy simulations and easy to integrate with existing energy simulation programs. Further research is needed to enhance minute-by-minute reproducibility and integrate temperature predictions for double-skin façades, ensuring a comprehensive evaluation of both thermal and lighting environments.

A novel model order reduction technique for solving horizontal refraction equations in the modeling of three-dimensional underwater acoustic propagation

Modeling three-dimensional (3D) underwater acoustic propagation is vital for underwater detection, localization, and navigation. This article introduces a novel model order reduction (MOR) technique to solve horizontal refraction equations (HREs) in the modeling of 3D underwater acoustic propagation. This approach relies on an adiabatic approximation of the 3D sound field, representing the field as a combination of local vertical modes with their modal coefficients governed by a system of two-dimensional (2D) HREs. Inspired by normal mode theory, the coefficients in the expansion over vertical modes are determined by projecting them onto a lower-dimensional, orthogonal space defined by their transverse eigenfunctions. By artificially truncating the horizontal domain in the transverse directions using two perfectly matched layers (PMLs), the eigenproblem associated with the transverse eigenfunctions of the modal coefficients is closed and thus can be solved through a modal projection method. The modal projection method enables fast computation of modal coefficients in a longitudinally invariant environment within seconds, offering a naturally wide-angle solution covering ±90°. The MOR method is extended to encompass fully 3D cases by introducing an admittance matrix, a memory-saving strategy that prevents numerical overflow when the longitudinal range is large. Moreover, the fact that the outer boundaries of the PMLs are range-independent allows the proposed MOR technique to perform well on a coarse grid when employing the Magnus scheme, significantly saving the numerical cost for the fully 3D simulation. Numerical simulations are provided for both longitudinally invariant and fully 3D scenarios, demonstrating the high accuracy and efficiency of the proposed MOR technique in solving HREs.

Next-generation bioinformatics: An ultrafast and user-friendly tool for phylogenomic data exploration.

With increasingly large genomic datasets, even routine bioinformatic tasks can be arduous, computationally demanding, and time-consuming. Additionally, most bioinformatic programs do not have a graphical user interface (GUI) and thus, require users to be minimally competent in command-line. These impediments present significant economic and technological barriers for practitioners who do not have access to advanced computational resources and support. In this issue of Molecular Ecology Resources, Handika and Esselstyn (2024) developed an ultrafast and memory-efficient bioinformatic tool, SEGUL, that performs common manipulations and calculations of summary statistics on phylogenomic datasets. SEGUL has two main features that distinguish it from other bioinformatic programs: (1) it is based on the recently emergent, high-performance programming language Rust, and (2) it has a GUI written using Flutter, a cross-platform programming framework that also supports mobile operating systems (mobile iOS, iPadOS and Android). By leveraging and combining the power of Rust and Flutter, SEGUL achieves significantly faster computation times and lower memory usage across different platforms and CPU architectures compared to similar programs. The unique and innovative features of SEGUL pave the way for a new era of bioinformatics that can be more energy-efficient, inclusive, and available to a broader swathe of users.

A shape-independent analytical method for gear mesh stiffness with asymmetric spalling defects

Spalling, a common failure mechanism of gear systems, greatly affects the dynamics of gears operation, which is reflected in the time-varying mesh stiffness (TVMS). Current TVMS models often overestimate the asymmetric spalling phenomenon and may lead to inaccuracy in identifying and predicting the spalling failure. To address this problem, in this paper, a new stiffness, namely torsional stiffness, is introduced to quantify the effect of asymmetric spalling defects, and an equivalent stiffness calculation method for different asymmetric shapes is proposed. Based on this, a shape-independent TVMS model is constructed, which can realize the fast calculation of TVMS for spalling defects with different shapes at arbitrary asymmetric locations. Furthermore, a FEM-based validation method is developed by considering diverse loading states and improving the current result extraction method. Case studies are presented to illustrate the proposed model and to analyze the effects of different types of asymmetric spalling defects on gear dynamics. The FEM validation has shown that the proposed model has a good effectiveness.

Oracle-efficient estimation and global inferences for variance function of functional data

A new two-step reconstruction-based moment estimator and an asymptotically correct smooth simultaneous confidence band as a global inference tool are proposed for the heteroscedastic variance function of dense functional data. Step one involves spline smoothing for individual trajectory reconstructions and step two employs kernel regression on the individual squared residuals to estimate each trajectory variability. Then by the method of moment an estimator for the variance function of functional data is constructed. The estimation procedure is innovative by synthesizing spline smoothing and kernel regression together, which allows one not only to apply the fast computing speed of spline regression but also to employ the flexible local estimation and the extreme value theory of kernel smoothing. The resulting estimator for the variance function is shown to be oracle-efficient in the sense that it is uniformly as efficient as the ideal estimator when all trajectories were known by “oracle”. As a result, an asymptotically correct simultaneous confidence band for the variance function is established. Simulation results support our asymptotic theory with fast computation. As an illustration, the proposed method is applied to the analyses of two real data sets leading to a number of discoveries.

Weber–Maxwell electrodynamics: classical electromagnetism in its most compact and pure form

ABSTRACT Weber–Maxwell electrodynamics is a modernized, compressed, cleansed and, in many respects, advantageous representation of classical electrodynamics that results from the Liénard-Wiechert potentials. In the non-relativistic domain, it is compatible with both Maxwell’s electrodynamics and Weber electrodynamics. It is suitable for all electrical engineering tasks, ranging from electrical machines to radar and high-frequency technologies. Weber–Maxwell electrodynamics also simplifies access to quantum physics and other areas of modern physics, such as optics and atomic physics. Particular advantages of Weber–Maxwell electrodynamics are its simple and fast computability in computer calculations and, as it is based on point charges, in the simulation of plasmas. The latter is particularly important for fusion research. Moreover, Weber–Maxwell electrodynamics is also highly suited to academic and post-primary education, as it allows an easy comprehension of both magnetism and electromagnetic waves. Due to the novelty of Weber–Maxwell electrodynamics, there are currently no articles that summarize its most important aspects. The present article aims to achieve this.

Distributionally robust chance-constrained kernel-based support vector machine

Support vector machine (SVM) is a powerful model for supervised learning. This article addresses the nonlinear binary classification problem using kernel-based SVM with uncertainty involved in the input data specified by the first- and second-order moments. To achieve a robust classifier with small probabilities of misclassification, we investigate a distributionally robust chance-constrained kernel-based SVM model. Since the moment information in the original problem becomes unclear/unavailable in the feature space via kernel transformation, we develop a data-driven approach utilizing empirical moments to provide a second-order cone programming (SOCP) reformulation for efficient computation. To speed up the required computations for solving large-size problems in higher dimensional space and/or with more sampling points involved in estimating empirical moments, we further design an alternating direction multipliers-based algorithm for fast computations. Extensive computational results support the effectiveness and efficiency of the proposed model and solution method. Results on public benchmark datasets without any moment information indicate that the proposed approach still works and, surprisingly, outperforms some commonly used state-of-the-art kernel-based SVM models.

Fast Calculation for Vehicle–Road Coupled Response Based on Cyclic Road Modeling Method

In order to improve the computational efficiency of the vehicle–road coupled response in long-distance driving, this paper proposes a cyclic road modeling method to equivalent an infinitely long road. The road is built using a closed model, and the vehicle–road coupled response is calculated assuming that the vehicle numerically cyclic driving. First of all, the concept of the cyclic road model is introduced, and the modal analysis is performed. Secondly, the principle for determining the optimal length of the cyclic road model is proposed based on the attenuation properties of the road response in time and space. Next, the vibration analysis of the vehicle–road coupled system is conducted, and its equation of motion is constructed using the mode superposition method (MSM). Further, the optimal length of the cyclic road model is derived regarding to road parameters via numerical analysis. At last, the application of this cyclic road model on computing the vehicle–road coupled response in long-distance driving is discussed via numerical simulation. The results indicate that this model has a consistent performance with the widely used models, while significantly improves computational efficiency. The proposed method also provides a new idea for the fast modeling of roads.

Application of computer information technology in food quality safety and detection

Nowadays, food safety problems have occurred frequently, and how to ensure food quality safety has become the focus of attention of the whole world. While computer information technology has become a necessary means to control food quality safety along with the continuous improvement of information degree. Computer information technology is the general term mainly used for the management and processing of information, including acquisition, processing, expression, communication, management and evaluation of information. In this paper, the application of computer information technology applied in food industry at home and abroad was mainly summarized, including query and retrieval related information of food science, stimulation of the production process to effectively control the potential risk factors, fast calculation in the process of food production and consumption, network platform design of sensory evaluation instead of complex manual work, and realization the whole process of full traceability for food industrial chain, so as to provide effective supervision channels for the government, establish food industrial traceability system, improve the information degree in food management and play the role of information technology in food safety management and realize the value.

Text-independent voiceprint recognition via compact embedding of dilated deep convolutional neural networks

In order to process speech, most state-of-the-art experimental methods employ convolutional neural networks (CNNs), which operate on a continuous, 1-dimensional (1-D) time stream. In an audio signal, the mel-spectrogram facilitates the representation of attributes of the utterances' in the frequency domain (which corresponds to the speech spectrum). Moreover, for a time-series speaker signal, CNNs are superior to machine or transfer learning models in capturing characteristics from long-form talks. This paper introduces a jump-connected 1-D CNN that employs a combined loss function for speaker recognition. The suggested model uses a 1-D convolutional layer combined with jump connections to extract speaker-specific characteristics; this reduces time-based and frequency-based variability for faster computing. A combined softmax loss, stable L2-norm, and smooth L1-norm loss function guide the proposed compact convolutional neural networks (CCNN) to identify the correct spokesman with improved efficacy. We evaluated the proposed framework using various standard and real-time audio datasets. The experimental findings demonstrate that the proposed CCNN outperforms existing approaches by reducing the equal error rate by 9.02 %. Also, our recommended voiceprint identification model achieves an impressive average speaker recognition rate of 98.76 %. Simultaneously, the reliability of the 1-D CCNN is tested under various conditions. Other fields of study, like language modelling, could employ this approach after some fine-tuning.Relevance of the work: Speaker recognition is an area of interest in which machine learning (ML) and deep learning (DL) schemes, when combined, have the potential to make history in the areas of forensic sciences, automation, and authentication. Using a modest CNN can enhance the identification and verification process by ignoring many issues such as false positives, background noise, and so on. Expanding this process would facilitate raga identification and disease treatment therapies.

Deformation Analysis of Existing Buildings Affected by Shield Tunnels Based on Intelligent Inversion and Measured Data

In the construction of urban underground shield tunnels, uneven deformation can easily occur when the shield passes through soft soil and other poor strata. Such deformation has a significant impact on surface settlement and may cause potential safety hazards to the surrounding existing buildings, directly affecting the safety of urban operation. When simulating and predicting surface settlements, the small-strain soil hardening model can more accurately characterize the mechanical parameters of soil. Nevertheless, its parameters are numerous and complicated to determine accurately, so parameter inversion is needed to determine the accurate parameters of the soft soil layer in order to more accurately predict the surface settlement. This study uses the EFAST method to analyse the sensitivity of the HSS model parameters of soft soil strata. It is determined that the parameters that have the most significant impact on the surface settlement are the reference tangent modulus, rebound modulus, and effective cohesion. Then, XGBoost’s fast calculation speed and high precision of SSA inversion are used to inverse and optimize the parameters with high sensitivity. Finally, according to the parameters of the soft soil layer obtained from inversion and measured data, the settlement deformation and safety behaviour of existing buildings are analysed. Combined with the actual shield tunnel project in a city along a river, the inversion calculation shows that the overall average error of the transverse monitoring section is 1.04 mm, and the average maximum error of each monitoring point in the overall shield process is 2.87 mm. The prediction effect is significantly improved compared with the original parameters. The accuracy of the inversion of soil layer parameters is verified from the perspective of time and space. The average settlement of the river embankment foundation is 2.5 mm. Compared with the original parameter data, the prediction results have been greatly improved, and the settlement deformation results are more consistent with the measured data.

Elastodynamic shape derivative: focus on the sampling of a circular distribution

The detection and quantification of defects plays a crucial role in several industries. Assessing the severity enables to schedule maintenance appropriately, optimizing costs and reducing risks. Guided elastic waves are particularly suitable for Structural Health Monitoring applications on thin structures thanks to their long range and high sensitivity. Guided wave tomography, based on an acoustic propagation hypothesis, has been studied over the last decade to quantitatively image defects in waveguides. The acoustic approximation of this family of methods allows fast computation and good estimation of defects for simple geometries such as plates or pipes. However, this hypothesis induces that a single guided mode is considered, without mode conversion, which limits its use to defects larger than two wavelengths in structures close to ideal waveguides. Hence the need of new imaging algorithms for smaller defects in less ideal structures such as pipes with welded supports. We present here an adaptation of the shape derivative method to ultrasounds in order to reconstruct surface defect on structures with complex geometries. The physical model used is the full elastodynamic problem. It allows taking into account all physical phenomena occuring during wave propagation. Iterative methods such as shape derivative are well known to give precise results but at a high computational cost as the method needs to solve the full elastodynamic problem at each iteration, and a sensitivity to local minima, which may prevent convergence toward the true defect. To suppress these two drawbacks, a spectral finite element method is used to reduce the computation and memory costs. The result of acoustic guided wave tomography is also used as a first guess to reduce the number of iterations and the sensitivity to local minima. After explaining the principle of the shape derivative, validations on simulated and experimental data will be presented in this talk.

NDT Data-Supported Structural Reassessments based on Regression Relations in Measuring Data Analysis

Non-destructive Testing (NDT) yields valuable data on actual structural elements, supporting the assessment of existing infrastructures while minimizing additional structural damage caused by inspections. Simultaneously, the uncertainty in measurement, which quantifies the quality of measurement results, plays a crucial role in decisions aimed at, e.g., optimizing maintenance strategies, rehabilitation works and Structural Health Monitoring (SHM) implementations. The Guide to the Expression of Uncertainty in Measurement (GUM) framework has been already applied to non-destructive concrete testing. This necessitates an obligatory process in which extensive measurements with strictly limited scopes need to be conducted by expert personnel. This study aims to simplify and optimize existing NDT data analysis procedures by employing regression analysis, enabling the detection of structural features. The regression analysis yields a modifier for determining the correct depth of an object within the structural element as well as an intercept in the linear input-output formulation, with time of flight serving as the variable and wave velocity as its slope. The method is validated through laboratory experiments, including the use of an ultrasonic measurement system. The introduced method provides unbiased and accurately measured results, while the measurement data can be uncorrelated. Major advantages are faster computations, a wider scope and a notable reduction in the amount of needed information about the measuring process. Furthermore, the study illustrates how the regression relation approach, in combination with ultrasonic data describing the inner structure of a concrete element, can effectively support the reassessment of bridges in operation. To achieve this, the measurable positions of the resisting longitudinal tendons of a bridge structure are varied to investigate the displacement change under dynamic service loads. The findings reveal that the application of the proposed NDT analysis method, with its increased practicability, can enhance the efficiency of NDT data-supported structural reassessments in different scenarios.

JASPER: Fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene expression

Joint association analysis of multiple traits with multiple genetic variants can provide insight into genetic architecture and pleiotropy, improve trait prediction, and increase power for detecting association. Furthermore, some traits are naturally high-dimensional, e.g., images, networks, or longitudinally measured traits. Assessing significance for multitrait genetic association can be challenging, especially when the sample has population sub-structure and/or related individuals. Failure to adequately adjust for sample structure can lead to power loss and inflated type 1 error, and commonly used methods for assessing significance can work poorly with a large number of traits or be computationally slow. We developed JASPER, a fast, powerful, robust method for assessing significance of multitrait association with a set of genetic variants, in samples that have population sub-structure, admixture, and/or relatedness. In simulations, JASPER has higher power, better type 1 error control, and faster computation than existing methods, with the power and speed advantage of JASPER increasing with the number of traits. JASPER is potentially applicable to a wide range of association testing applications, including for multiple disease traits, expression traits, image-derived traits, and microbiome abundances. It allows for covariates, ascertainment, and rare variants and is robust to phenotype model misspecification. We apply JASPER to analyze gene expression in the Framingham Heart Study, where, compared to alternative approaches, JASPER finds more significant associations, including several that indicate pleiotropic effects, most of which replicate previous results, while others have not previously been reported. Our results demonstrate the promise of JASPER for powerful multitrait analysis in structured samples.

Railway vibration – fast physics-based models for the prediction of ground vibration and the identification of track damage

The following applications of machine learning/AI for railway vibration will be discussed: 1. The prediction of the wave propagation from a railway line (completely physics based for surface lines, for tunnel lines (partially) physics-based ML for surface lines). 2. The track behaviour for the emission of train-induced ground vibration (physics based for homogeneous soil, ML for layered soil). 3. Track damage detection and quantification from FRF and moving load response. 4. Bridge damage detection and localisation from modal analysis and moving load response. 5. The use of axle-box acceleration for the identification of track/sub-soil condition and bridge resonances. Prediction calculation of railway vibration usually needs time-consuming finite element, boundary element and wavenumber domain calculations. For a user-friendly prediction software, fast calculations are needed. Several time-consuming detailed calculations should be used to develop simpler and fast models. The dynamic stiffness of isolated or un-isolated railway tracks from detailed calculations with a continuous soil is approximated with the simpler Winkler soil. The vehicle-track resonance (P2 resonance) rules the effect of the mitigation measures, and it can also be used for the on-board monitoring of the track and sub-soil condition. For the identification of track damage such as gaps between sleepers, track slabs and layers, detailed models with a continuous soil have been updated to get the best fit to the measured frequency response functions from hammer tests and the deformation pattern from the moving load response. Whereas the track damage can be locally identified, this is more difficult for bridges where the modal analysis gives mainly global information. The influence lines of the inclination for statically passing vehicles (locomotive, truck, compaction ) have been used to localise bridge damage (stiffness variations). The on-board monitoring of rail bridges needs special conditions (regular trains with special speeds) to excite and measure the bridge resonance.

Wave Propagation for Structural Health Monitoring (WaveProSHM): open software with GUI

Guided wave–based SHM methods have been explored extensively in recent decades. The wave propagation modelling including piezoelectric transducers and various damage types helps understand intricacies of guided wave behavior and for designing a robust SHM system. However, modelling aspects are still challenging. Moreover, commercially available software for the modelling of guided waves often lacks appropriate high-order elements and code optimization for fast GPU computations. This research aimed to develop and disseminate to the SHM community the open software based on highly efficient vectorised code of the time domain spectral element method which can be run on GPU. The developed code is written in MATLAB with Graphical User Interface (GUI) and communicates with GMSH – open source finite element mesh generator. It can model signals of propagating guided waves in structures which are excited and registered by an array of piezoelectric transducers. The capabilities of the software are shown in an example of a large-scale problem of interacting guided waves with delamination.

Improving tally efficiency and accuracy of multi-group scattering matrix calculations in the Monte Carlo code NECP-MCX

Two issues arise in the calculation of the multi-group scattering matrix when employing a continuous-energy Monte Carlo code for generating homogenized multi-group cross-sections. Firstly, the analog estimator is used to evaluate group-to-group elements, which leads to large statistical uncertainty. Secondly, employing the scalar flux as the weighting function in generating the high-order scattering matrix introduces errors in fast reactor calculations. For the first issue, the repeated collision approach and pre-tabulated cross-section approach are adopted to improve the tally efficiency. For the second issue, the average scattering cosine is calculated based on the conservation of the mean square displacement of neutrons, which is then used to correct the first-order self-scattering cross-section. To evaluate the effectiveness of the above approaches, a PWR pin-cell problem and fast reactor core problems are tested. The results demonstrate that: 1) The figure of merit for multi-group scattering matrix calculations was improved by 8–12 times with the pre-tabulated cross-section approach. 2) Biases of keff were reduced from over 500 pcm to less than 300 pcm when using the corrected self-scattering cross-section. 3) The corrected self-scattering cross-section also yielded higher accuracy for the assembly power calculations, where the maximum biases are reduced from 5 % to 1 %.