Efficient Computational Method Research Articles

Novel hybrid power system and energy management strategy for locomotives

The traditional fuel locomotive is the primary type of locomotive currently in operation on non-electrified railways; however, it presents certain disadvantages including a low efficiency and high fuel consumption. Therefore, in this study, a multimode hybrid locomotive configuration scheme is designed to improve the system efficiency and reduce fuel consumption during locomotive operation; further, the power flow state under different modes is analyzed, the mathematical model of the hybrid locomotive is established, and a system optimal efficiency calculation method is developed. Moreover, an energy management strategy based on the optimal system efficiency and with a hierarchical architecture is proposed. In the upper layer of this proposed energy management strategy, the optimal efficiency of all operating points and operational state of each component are determined offline using the optimal efficiency calculation method. The lower layer selects and coordinates the mode online by identifying the condition of the wheel and distributes the torque of each power source and the state of each transmission system component. The simulation results indicate that the fuel economy of the proposed energy management strategy is improved by 27.22% compared with that of the traditional fuel locomotive, and the fuel economy is only 7.21% lower than the global optimization result obtained via dynamic programming. In addition, no frequent clutch switching is observed under this strategy, and the electric motor does not operate under non-rated conditions for prolonged periods; these advantages ensure the rationality of control and the reliability of component operation. Finally, the results of a hardware-in-the-loop simulation test confirm that the proposed energy management strategy demonstrates good real-time performance.

Investigation of Performance Difference between Photo‐Charging and Conventional Constant Current Charging for Energy Storage Batteries

AbstractSolar cells offer clean and abundant power sources for directly photo‐charging rechargeable batteries, which shows great potential for the development of integrated power supply. In order to deepen the understanding of the novel type of charging process, this research takes silicon solar cells and lithium cobalt oxide batteries as examples to compare the performance difference between photo‐charging and conventional constant current charging in detail. Surprisingly, the photo‐charging turns out to be superior in charging efficiency, polarization mitigation, and cyclic performance even with the intrinsic instability of the solar cells when compared with constant current charging mode. Finally, this research optimizes the calculation method of energy storage efficiency in the integrated power supply by calculating the actual power during the photo‐charging process by recording the voltage and current change with time. Besides, the power matching degree is quantified by the ratio of to η1, providing new ideas for the matching method of solar cells and rechargeable batteries in integrated power supplies.

Spatiochemical Characterization of the Pancreas Using Mass Spectrometry Imaging and Topological Data Analysis.

Mass Spectrometry Imaging (MSI) is a technique used to identify the spatial distribution of molecules in tissues. An MSI experiment results in large amounts of high dimensional data, so efficient computational methods are needed to analyze the output. Topological Data Analysis (TDA) has proven to be effective in all kinds of applications. TDA focuses on the topology of the data in high dimensional space. Looking at the shape in a high dimensional data set can lead to new or different insights. In this work, we investigate the use of Mapper, a form of TDA, applied on MSI data. Mapper is used to find data clusters inside two healthy mouse pancreas data sets. The results are compared to previous work using UMAP for MSI data analysis on the same data sets. This work finds that the proposed technique discovers the same clusters in the data as UMAP and is also able to uncover new clusters, such as an additional ring structure inside the pancreatic islets and a better defined cluster containing blood vessels. The technique can be used for a large variety of data types and sizes and can be optimized for specific applications. It is also computationally similar to UMAP for clustering. Mapper is a very interesting method, especially its use in biomedical applications.

A precisive calculation method of volumetric and hydraulic efficiency of centrifugal pumps

This paper proposes a new method to calculate the volumetric efficiency and hydraulic efficiency of centrifugal pumps based on the principle of energy balance. Two efficiencies are calculated by means of a low specific speed centrifugal pump handling media with different viscosities at best efficiency points and are compared with those of two existing methods. The results manifest that the definition of two efficiencies in the present paper is more precisive and sensitive to the change of liquid viscosity.

An accurate and efficient quasi-dynamic simulation method of electricity-heat multi-energy systems

Quasi-dynamic energy flow computation (EFC) has become a critical tool to determine and predict the states of the multi-energy system (MES), which helps improve MES’ operation efficiency and issues the security warning. However, methods in literature suffer numerical problems including fake oscillations, divergence, etc., Also, with the increasing of system dimensions, the computation efficiency can be hardly guaranteed due to the cross iterations between different nonlinear equations. This paper proposes an accurate and efficient method for quasi-dynamic energy flow computation. Using a scheme with total variation decreasing property, the numerical instability in solutions of thermal dynamics are effectively reduced. By estimating local truncation errors in a cheap way, the simulation step sizes are controlled adaptively and hence the overall simulation efficiency is greatly increased. Numerical tests were performed in a small system and the famous Barry Island system, which verified the advantages of the proposed method in both efficiency and accuracy.

Comparing the performance of machine learning methods in predicting soil seed bank persistence

Soil seed banks are pivotal for understanding plant population life histories and elucidating plant-environment relationships. Although experimental exploration of persistent seed banks yields valuable insights, it is often time-consming, labor-intensive, and costly. Consequently, the development of efficient computational methods like machine learning techniques is crucial to augment traditional fieldwork and enhance our capacity for effective seed bank data analysis and interpretation. In this study, we collated a dataset of 398 observations from the TRY database, encompassing measurements such as seed thickness, seed length, seed width, seed dry mass, and seed bank type. Notably, some species' seed bank types within this dataset are identified as having a soil seed bank, without distinguishing between persistent or transient seed banks. Our primary objective was to utilize this data to predict whether a species' soil seed bank is persistent or transient. To this end, we evaluated the performance of four classic machine learning methods—Adaptive Boosting (Adaboost), Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM)—in predicting seed bank types. We gauged the performance of each model using five metrics: Accuracy, AUC (Area Under the Curve), kappa, sensitivity, and specificity. Among the four models examined, the ANN model proved to be the most effective in predicting the persistence of soil seed banks, suggesting its efficacy in predicting seed bank types based on a limited number of independent variables. This study underscores the potential of machine learning methods in soil seed bank research and paves the way for future investigations. It can potentially aid in crafting conservation strategies by enhancing our understanding of plant population dynamics.

Density Functional Theory for Buckyballs within Symmetrized Icosahedral Basis

We have developed a highly efficient computation method based on density functional theory (DFT) within a set of fully symmetrized basis functions for the C60 buckyball, which possesses the icosahedral (Ih) point-group symmetry with 120 symmetry operations. We demonstrate that our approach is much more efficient than the conventional approach based on three-dimensional plane waves. When applied to the calculation of optical transitions, our method is more than one order of magnitude faster than the existing DFT package with a conventional plane-wave basis. This makes it very convenient for modeling optical and transport properties of quantum devices related to buckyball crystals. The method introduced here can be easily extended to other fullerene-like materials.

Developing software to assess the seismic risk of natural gas infrastructure: OpenSRA

As part of a multi-year study, the Pacific Earthquake Engineering Research (PEER) center in conjunction with Lawrence Berkeley National Laboratory (LBNL), the NHERI SimCenter at Berkeley, Slate Geotechnical Consultants (Slate), and several subcontractors, developed an open-source seismic risk analysis (OpenSRA) program that implements the performance-based earthquake engineering (PBEE) risk framework using a user-friendly interface, capable of operating on a desktop computer. This effort is made up of two elements: 1) research of seismic demand and capacity models, laboratory testing, and finite element modeling, and 2) implementing these findings into a backend calculation program with an easy-to-use interface. The NHERI SimCenter, Slate Geotechnical Consultants, and PEER researchers developed an integrative method to create an efficient code with a useful interface.OpenSRAimplements and updates current models addressing different forms of seismically induced ground deformation (e.g., fault rupture, liquefaction, and landslide) as well as ground shaking. It quantifies resistance to deformation in buried pipelines, well heads, caprocks, and aboveground components, and then provides a framework for estimating the uncertainty in both the models and parameters. The software allows the end-user to utilize these models according to the resolution of the available data (i.e., state-wide, regional, or site-specific). The interface provides a visualization of the inputs (e.g., ground motion, and liquefaction potential) and outputs (risk of leaks/breaks) overlaid on different maps of California.OpenSRAprovides the industry with updated seismic demand and capacity models, an efficient calculation method, and user-friendly software.OpenSRAenables end-users to estimate the risk of seismic damage to their infrastructure. Using risk-informed decisions enables utility owners to proactively and cost-effectively maintain infrastructure.

Riemannian Trust Region Method for Minimization of the Fourth Central Moment for Localized Molecular Orbitals.

The importance of localized molecular orbitals (MOs) in correlation treatments beyond mean-field calculation and in the illustration of chemical bonding (and antibonding) can hardly be overstated. However, the generation of orthonormal localized occupied MOs is significantly more straightforward than obtaining orthonormal localized virtual MOs. Orthonormal MOs allow facile use of highly efficient group theoretical methods (e.g., graphical unitary group approach) for calculation of Hamiltonian matrix elements in multireference configuration interaction calculations (such as MRCISD) and in quasi-degenerate perturbation treatments, such as the Generalized Van Vleck Perturbation Theory. Moreover, localized MOs can elucidate qualitative understanding of bonding in molecules, in addition to high-accuracy quantitative descriptions. We adopt the powers of the fourth moment cost function introduced by Jørgensen and coworkers. Because the fourth moment cost functions are prone to having multiple negative Hessian eigenvalues when starting from easily available canonical (or near-canonical) MOs, standard optimization algorithms can fail to obtain the orbitals of the virtual or partially occupied spaces. To overcome this drawback, we applied a trust region algorithm on an orthonormal Riemannian manifold with an approximate retraction from the tangent space built into the first and second derivatives of the cost function. Moreover, the Riemannian trust region outer iterations were coupled to truncated Conjugate Gradient inner loops, which avoided any costly solutions of simultaneous linear equations or eigenvector/eigenvalue solutions. Numerical examples are provided on model systems, including the high-connectivity H10 set in 1-, 2-, and 3-dimensional arrangements, and on a chemically realistic description of cyclobutadiene (c-C4H4) and the propargyl radical (C3H3). In addition to demonstrating the algorithm on occupied and virtual blocks of orbitals, the method is also shown to work on the active space at the MCSCF level of theory.

Contact dynamic analysis of tether-net system for space debris capture using incremental potential formulation

An efficient computational method is proposed to combine the discrete differential geometry (DDG) method with the multi-dimensional incremental potential (IP) formulation to simulate the contact dynamics between a shooting net and a defunct satellite. The geometrically nonlinearity of the elastic net is described using the DDG method, which discretizes the flexible net into multiple nodes and connected edges. The internal elastic forces and the associated Jacobians are derived analytically. The multi-dimensional incremental potential formulation is used to achieve the intersection-free condition during the physical collision between deformable tether-net and rigid space debris. The smooth IP barrier functional constructed for contact simulation is differentiable, such that the nonlinear elastodynamic system can be robustly solved through a fully implicit time-marching scheme. Our numerical approach is first applied to simple examples, and demonstrating its effectiveness in dealing with non-penetration contacts of elements with different dimensions, such as ‘edge-to-edge’ and ‘edge-to-surface’. Next, the dynamic interaction between the deploying net and defunct satellite is analysed. A parameter sweep is performed with our robust and efficient simulator to show the configuration evolution of the net shot from different relative locations of the target object. Our numerical results can provide an optimal deployment strategy for tether-net capture systems during the active debris removal missions.

Running a Sustainable Social Media Business: The Use of Deep Learning Methods in Online-Comment Short Texts

With the prevalence of the Internet in society, social media has considerably altered the ways in which consumers conduct their daily lives and has gradually become an important channel for online communication and sharing activities. At the same time, whoever can rapidly and accurately disseminate online data among different companies affects their sales and competitiveness; therefore, it is urgent to obtain consumer public opinions online via an online platform. However, problems, such as sparse features and semantic losses in short-text online reviews, exist in the industry; therefore, this article uses several deep learning techniques and related neural network models to analyze Weibo online-review short texts to perform a sentiment analysis. The results show that, compared with the vector representation generated by Word2Vec’s CBOW model, BERT’s word vectors can obtain better sentiment analysis results. Compared with CNN, BiLSTM, and BiGRU models, the improved BiGRU-Att model can effectively improve the accuracy of the sentiment analysis. Therefore, deep learning neural network systems can improve the quality of the sentiment analysis of short-text online reviews, overcome the problems of the presence of too many unfamiliar words and low feature density in short texts, and provide an efficient and convenient computational method for improving the ability to perform sentiment analysis of short-text online reviews. Enterprises can use online data to analyze and immediately grasp the intentions of existing or potential consumers towards the company or product through deep learning methods and develop new services or sales plans that are more closely related to consumers to increase competitiveness. When consumers experience the use of new services or products again, they may provide feedback online. In this situation, companies can use deep learning sentiment analysis models to perform additional analyses, forming a dynamic cycle to ensure the sustainable operation of their enterprises.

Improving filtering methods based on the Fast Fourier Transform to delineate objective relief domains: An application to Mare Ingenii lunar area

A recent study has proven that high-pass filtering (HPF) based on the Fast Fourier Transform (FFT) is a rapid and efficient computational method for the semi-automated detection of geomorphic features from high-resolution digital elevation models (DEM). Although this new approach shows great potential for cartographic purposes using remote sensing data, some methodological improvements are still required in the following areas: (i) to develop a robust criteria for filter radius selection; (ii) to test the relationship between filter vectors and landscape form, and explore how DEM artefacts (vegetation, anthropic structures, etc.) can interfere with landform detection; and (iii) to explore filter response regarding generalisation and blurring effects when working with landscapes composed of landforms of different scales that are superimposed on one another. These topics are addressed here through two experiments (Experiment_1 and Experiment_2) with synthetic digital relief models inspired in the lunar landscape. Finally, the improved methodology was applied on the Mare Ingenii lunar relief (Experiment_3) using the Lunar Orbiter Laser Altimeter DEM and the results were tested against ground truths (GTs) developed using the extensive database available at Astropedia website and an ad hoc crater map. The analysis of existing frequencies in a 2D DEM signal through the true magnitude-true frequency plot provides an objective method for filter radius selection, and the use of a Butterworth transference function enables a more versatile filtering. Experiment_1 demonstrates a close correspondence between vectors obtained by filtering called Filtered Geomorphic References (FGRs) and the synthetic landform selected. The accuracy indicators from Experiment_1 and 2 show the good results obtained in the correspondence between FGRs and crater depressions, either from flat-bottomed to bowl shapes. Experiments 2 and 3 confirm that in landscapes generated by superimposed geomorphic features of different sizes, the smaller the crater, the better the filters detect its boundaries. Moreover, the spatial repeatability of FGRs can be used as a cartographic criterion in the identification of crater shape depressions or hills. Besides, the criterion is useful to assess true reality mapped in the GT employed. Finally, the objective geomorphic units obtained by combining the FGRs demonstrate their usefulness for the objective characterisation of the moonscape. Using the synthetic landscapes, the FGRs identify those relief domains composed of depressions and hills.

An improved body force method for simulation of self-propulsion AUV with ducted propeller

The body force method (BFM) is an efficient computational method used for maneuvering simulations of marine vehicles with ordinary propellers. In this paper, an improved BFM is proposed to perform maneuvering simulations of a self-propelled AUV with a ducted propeller. Initially, the conventional BFM is applied to numerically investigate the open water performance of a ducted propeller(No. 19A+Ka4-70). However, significant prediction errors have been observed, which were mainly caused due to ignorance of the interaction between the duct and the propeller. To increase the prediction accuracy, an improved BFM is proposed to perform maneuvering simulations of the ducted propeller AUV by considering the mass flow correction. In addition, the external diameter of the actuator is proposed to be a function of real-time advance velocity. To investigate the efficacy of the proposed method, the simulated results have been compared with the model testing carried out in a towing tank and with the discretized propeller method. The proposed improved BFM is used to simulate the 20°/20° zigzag tests of AUV in both vertical and horizontal planes with different advance speeds. Simulated results, particularly propeller thrust, advance speed, pitch (or yaw), resistance, and especially duct thrust of AUV have significantly improved as compared to the conventional BFM. The improved BFM results were found in close agreement with discretized propeller method and thus, the proposed method is considered a highly efficient and computationally cost-effective method to perform maneuvering simulations of ducted-propeller marine vehicles. The present work can contribute to the fast optimization of the manoeuvrability of marine vehicles with ducted propeller.

Uncertainty-aware blob detection with an application to integrated-light stellar population recoveries

Context. Blob detection is a common problem in astronomy. One example is in stellar population modelling, where the distribution of stellar ages and metallicities in a galaxy is inferred from observations. In this context, blobs may correspond to stars born in situ versus those accreted from satellites, and the task of blob detection is to disentangle these components. A difficulty arises when the distributions come with significant uncertainties, as is the case for stellar population recoveries inferred from modelling spectra of unresolved stellar systems. There is currently no satisfactory method for blob detection with uncertainties.Aims. We introduce a method for uncertainty-aware blob detection developed in the context of stellar population modelling of integrated-light spectra of stellar systems.Methods. We developed a theory and computational tools for an uncertainty-aware version of the classic Laplacian-of-Gaussians method for blob detection, which we call ULoG. This identifies significant blobs considering a variety of scales. As a prerequisite to apply ULoG to stellar population modelling, we introduced a method for efficient computation of uncertainties for spectral modelling. This method is based on the truncated Singular Value Decomposition and Markov chain Monte Carlo sampling (SVD-MCMC).Results. We applied the methods to data of the star cluster M 54. We show that the SVD-MCMC inferences match those from standard MCMC, but they are a factor 5–10 faster to compute. We apply ULoG to the inferred M 54 age/metallicity distributions, identifying between two or three significant, distinct populations amongst its stars.

A multibody dynamics approach on a tree-shaped acoustic black hole vibration absorber

Acoustic black hole (ABH) has shown great potential and aroused tremendous interest for vibration and sound control in the past two decades. However, efficient computational methods are still lacking for the design of ABH structures with practically required properties. Herein, a multibody system transfer matrix method is proposed to deal with a system comprising a host structure with an attached ABH cluster, exemplified by a novel tree-shaped ABH dynamic vibration absorber (ABH-DVA). Capitalizing on the flexibility offered by the method, the eigen-solutions and the steady-state vibration response of the combined system are derived and validated by finite element results. Observed vibration reduction and the underlying mechanisms are elucidated in terms of the vibrational energy ratio and vibration spatial distribution in the system. Owing to its rich modal dynamics and energy trapping capability, the proposed absorber demonstrates extraordinary and robust low-frequency and broadband vibration reduction performance from 25 Hz to 2500 Hz for different host structures. This can be ensured by designing its threshold frequency as the lower limit of the targeted frequency range. The proposed method and the vibration-reduction-effect are experimentally validated. This study offers a novel yet general approach for addressing complex ABH structures with various structural components and topologies.

Chaos Controllability in Non-Identical Complex Fractional Order Chaotic Systems via Active Complex Synchronization Technique

In this paper, we primarily investigate the methodology for the hybrid complex projective synchronization (HCPS) scheme in non-identical complex fractional order chaotic systems via an active complex synchronization technique (ACST). Appropriate controllers of a nonlinear type are designed in view of master–slave composition and Lyapunov’s stability criterion (LSC). The HCPS is an extended version of the previously designed projective synchronization scheme. In the HCPS scheme, by using a complex scale matrix, the system taken as slave system is asymptotically synchronized with another system taken as the master system. By utilizing a complex scale matrix, the unpredictability and security of communication are increased along with image encryption. An efficient computational method has been employed to validate and visualize the HCPS method’s efficacy by performing numerical simulation outcomes in MATLAB (version 2021).

Complex Standard Eigenvalue Problem Derivative Computation for Laminar–Turbulent Transition Prediction

As a high-fidelity approach to transition prediction, the coupled Reynolds-averaged Navier–Stokes (RANS) and linear stability theory (LST)-based method is widely used in engineering applications and is the preferred method for laminar flow optimization. However, the further development of gradient-based laminar flow wing optimization schemes is hindered by a lack of efficient and accurate derivative computation methods for LST-based eigenvalue problems with a large number of design variables. To address this deficiency and to compute the derivatives in the LST-based solution solver, we apply the adjoint method and analytical reverse algorithm differentiation (RAD), which scale well with the number of inputs. The core of this paper is the computation of the standard eigenvalue and eigenvector derivatives for the LST problem, which involves a complex matrix. We develop an adjoint method to compute these derivatives, and we couple this method with RAD to reduce computational costs. In addition, we incorporate the LST-based partial derivatives into the laminar–turbulent transition prediction framework for the computation of total derivatives. We verify our proposed method with reference to finite difference (FD) results for an infinite swept wing. Both the intermediate derivatives from the transition module and total derivatives agree with the FD reference results to at least three digits, demonstrating the accuracy of our proposed approach. The fully adjoint and the coupled adjoint–RAD methods both have considerable advantages in terms of computational efficiency compared with iterative RAD and FD methods. The LST-based transition method and the proposed method for efficient and accurate derivative computations have prospects for wide application to laminar flow optimization in aerodynamic design.

An efficient calculation method of large-region dynamic traffic noise maps based on hybrid modeling

The construction of noise maps is of great significance for the management and control of urban noise and the protection of residents’ physical and mental health. The European Noise Directive recommends using computational methods to construct strategic noise maps when possible. The current noise maps based on model calculation rely on complex noise emission and propagation models, and their huge number of regional grids needs to consume a lot of calculation time. This seriously restricts the update efficiency of noise maps, making it difficult to realize large-scale application and real-time dynamic update of noise maps. In order to improve the computational efficiency of noise maps, based on big data-driven technology, this paper combines the traditional CNOSSOS-EU noise emission modeling method with the multivariate nonlinear regression modeling method, and proposes an efficient calculation method of large-region dynamic traffic noise maps based on hybrid modeling method. First, this paper constructs the (daily and nightly) noise contribution prediction models of road sources with different classes, considering the daily and nightly periods and different urban road classes. Parameters of the proposed model are evaluated by using the multivariate nonlinear regression method to replace the complex nonlinear acoustic mechanism modeling. On this basis, in order to further improve the computational efficiency, noise contribution attenuations of the constructed models are parameterized and evaluated quantitatively. And then, the database containing the index table of the road noise sources-receivers and the corresponding noise contribution attenuations is constructed. The experimental results show that compared with the traditional calculation methods based on acoustic mechanism model, the noise map calculation method based on hybrid model proposed in this paper greatly reduces the model computations of noise map, improves the efficiency of noise mapping. It will provide technical support for constructing dynamic noise maps of large urban regions.

Minimum-Lap-Time Planning of Multibody Vehicle Models via the Articulated-Body Algorithm

Minimum lap-time planning (MLTP) is a well-established problem in the race car industry to provide guidelines for drivers and optimize the vehicle’s setup. In this paper, we tackle the 3D nature of the problem in its full extension, making no simplifying assumptions on the mechanics of the system. We propose a multibody vehicle model, described by rigorous dynamical equations. To effectively handle the resulting complexity, we devised an efficient direct dynamics computational method based on Featherstone’s articulated-body algorithm (ABA). To solve the MLTP, we employed a direct-collocation technique, discretizing the problem so that all information of the 3D track is pre-processed and directly embedded into the discrete problem. This discretization approach turns out to be perfectly compatible with our vehicle model, leading to a solution in accessible computational time frames. The high level of detail of the model makes the proposed approach most useful for in-depth vehicle dynamics analyses on complex tracks. To substantiate the analysis, we provide a comparison with the results obtained by a double-track model on the Nürburgring Nordschleife circuit. Consistently with the average trend defined by the double track, the proposed model features a more dynamically rich behavior, realistically capturing the higher-order effects elicited by the sharp corners and the highly variable slope of the track.

Method and computer library for calculation of the Boltzmann collision integrals on discrete momentum lattice.

We present a general and numerically efficient method for calculation of collision integrals for interacting quantum gases on a discrete momentum lattice. Here we employ the original analytical approach based on Fourier transform covering a wide spectrum of solid-state problems with various particle statistics and arbitrary interaction models, including the case of momentum-dependent interaction. The comprehensive set of the transformation principles is given in detail and realized as a computer Fortran 90 library FLBE (Fast Library for Boltzmann Equation).