Script Files Research Articles

Efficient Modeling of Ship Structure Based on Image Recognition and Data Retrieval

In order to significantly improve the efficiency of ship structure modeling, an efficient ship structure modeling method based on image recognition and data retrieval is proposed. First of all, the typical Panel has been designed, according to specific data standards, the data and drawings are saved to the database. Then when a new Panel needs to be created, compare the 2D drawings of the structure around the Panel through image recognition to get a similar structure and confirm which Panel's script file in the database is used. Finally, using the standard numbering method, the mapping relationship between the original Panel and the target Panel topology objects and the transformation logic of coordinate data are calculated, and then the target Panel script file is generated. This is the method of efficient modeling in this paper.

GlareShell: Graph learning-based PHP webshell detection for web server of industrial internet

With the explosive growth of the Industrial Internet scale, cyberattacks targeting industrial control systems also increased. The management and operation of Industrial Internet are usually performed via web servers which retain a large attack surface. In the Industrial Internet, attackers usually exploit vulnerabilities to inject malicious codes for remotely executing commands, stealing confidential data, and invading web servers. Existing approaches capture statistical and contextual dependence information from Webshell using machine learning (ML) or deep learning (DL) algorithms. However, the semantic feature mining of program code within Webshell is not sufficient when entering new types of Webshell. In this paper, we propose a graph learning-based PHP Webshell detection framework, GlareShell, using the word embedding technique, a risk weight allocation mechanism, and the graph neural network (GNN). First, GlareShell leverages static analysis to extract interprocedural control flow graphs (ICFGs) from PHP script files and then prunes these ICFGs to remove noisy statements. Then, word embedding techniques are employed to generate semantic representations from PHP statements. Next, we design a risk weight allocation mechanism to derive the risk levels of statements and concatenate them with word embeddings as attributions. The identified risk levels could guide the passing of potential attack patterns inside GNN models. Finally, GlareShell builds a GNN classifier directly from the ICFG with corresponding node attributions to identify the malicious PHP scripts. Experiment results on collected datasets prove the promise of our graph learning framework in the Webshell detection domain.

Multibody Models Generated from Natural Language

AbstractComputational models are conventionally created with input data, script files, programming interfaces, or graphical user interfaces. This paper explores the potential of expanding model generation, with a focus on multibody system dynamics. In particular, we investigate the ability of Large Language Model (LLM), to generate models from natural language. Our experimental findings indicate that LLM, some of them having been trained on our multibody code Exudyn, surpass the mere replication of existing code examples. The results demonstrate that LLM have a basic understanding of kinematics and dynamics, and that they can transfer this knowledge into a programming interface. Although our tests reveal that complex cases regularly result in programming or modeling errors, we found that LLM can successfully generate correct multibody simulation models from natural-language descriptions for simpler cases, often on the first attempt (zero-shot).After a basic introduction into the functionality of LLM, our Python code, and the test setups, we provide a summarized evaluation for a series of examples with increasing complexity. We start with a single mass oscillator, both in SciPy as well as in Exudyn, and include varied inputs and statistical analysis to highlight the robustness of our approach. Thereafter, systems with mass points, constraints, and rigid bodies are evaluated. In particular, we show that in-context learning can levitate basic knowledge of a multibody code into a zero-shot correct output.

Performance characterisation and design considerations for a domestic ammonia/salt resorption heat pump

Heat pumps will play a key role in the future provision of low carbon domestic heating and the re-use of industrial waste heat. Adsorption cycle heat pumps are advantageous in that they can use the existing natural gas network to avoid electricity supply limitations across the UK. A 2 kW domestic-scale ammonia/salt heat pump demonstrator is currently being tested at the University of Warwick as a replacement for a conventional condensing gas boiler. This paper describes analysis work in support of this testing which will lead to design refinements in follow-on developments.A Matlab-based 2D transient simulation package was developed to study heat transfer and reaction rate within a pair of linked reactors. Heat conduction and sorption rate are modelled together with inter-reactor gas flows and parasitic heat loss. Novel features include the use of Matlab's linked ODE solvers for convergence (ODE15S was found to be fastest) and the script file input configuration which combines clear visibility of parameters with the ability to run multiple simulations to show the effect of parametric variations. The code facilitates rapid design optimisation.Eleven cycle parameters have been investigated, including filling pressure, heat transfer coefficients, salt ratio, source temperatures, void space and heat capacity. The choice of cycle period involves a compromise between coefficient of performance and power output. A water/glycol heat transfer fluid gives better COP and output power than thermal oil. Insulation within the reactor shell has the potential to limit shell transient heat exchange but void space effects are likely to be more significant. The heat capacity of fluid in pipes and manifolds should be minimised.COP = 1.31 is achieved at 45 °C delivery; 60 °C for hot water is possible but with lower COP. The best results for space heating are obtained with source temperatures above -5 °C.

QDT — A Matlab toolbox for the simulation of coupled quantum systems and coherent multidimensional spectroscopy

We present QDT (“quantum dynamics toolbox”), an open-source Matlab software package that enables users to simulate coupled quantum systems in the subsystem energy eigenbasis using modular functions. QDT requires no user knowledge of operator matrix assembly and automatically performs all necessary operator constructions and Hilbert space expansions. Density matrix propagation is performed by numerically solving the Liouville–von-Neumann equation. In order to simulate dissipation and decoherence effects, the Lindblad formalism is implemented. Furthermore, QDT supplies practical analysis and plotting functions, such as visualization of density matrix and expectation value dynamics, that facilitate the evaluation of simulation results. QDT further provides a module for the simulation of coherent multidimensional spectroscopy. Program summaryProgram Title: QDT — A Quantum Dynamics ToolboxCPC Library link to program files:https://doi.org/10.17632/786vb4rgy3.1Code Ocean capsule:https://codeocean.com/capsule/4608215Licensing provisions: MITProgramming language: Matlab R2018a (and above) with parallel computing toolbox (optional) [1]Supplementary material: Available on the QDT website [2]Nature of problem: Coupled quantum systems can be easily set up and discussed as level schemes in the subsystem energy eigenbasis. The simulation of the time evolution of these systems by numerically solving differential equations is not straightforward, however. Simulation code that is written from scratch is time-consuming to create and prone to errors during implementation. Furthermore, if code bases deviate drastically between research groups, reproducibility of results becomes challenging, and communication is hindered.Solution method: QDT addresses the above issues by providing a modular toolbox that contains, among others, elements for the simulation of multi-level quantum systems, quantum harmonic oscillators, their interaction with external electric fields, and dissipation. These elements can be combined by the user to build a specific system of interest. All operators are created and expanded automatically based on a single script file provided by the user. Simulation tasks are automated by a single function call, and parallelized in case of extensive tasks.

Inglämnlagare: a tool for restructuring Swedish HER record site data for statistical analysis.

Background: This paper presents a new software tool, Inglämnlagare, developed to be open-source, that restructures information about ancient remains in Sweden for analysis. The background is a new version of the ancient sites database, the Historic Environment Record, curated by the Swedish National Heritage Board, that was launched in 2018 with a new database model that structures the information differently compared to previous versions. Methods: The program, written in Python programming language, has multicore support in order to improve performance for large files and uses regular expressions to extract information about individual features of composite sites. Such features, together with their summed amount, are written as new individual fields to a comma-separated value file. The program is delivered as a source script file that can be executed in any Python environment. Use cases: As an example of use, a case study of exploring graves of rectangular shape found within Sweden is provided. The use case also describes the different steps involved in preparing the data in QGIS to run the program, as well as some methods to efficiently analyse and visualize the output. Conclusions: Inglämnlagare will make more information from the Swedish record of ancient sites accessible for research and can be used to explore different content of the record more efficiently than previously possible. While the tool is written specifically for this dataset it also provides an example of how open-source tools can be used for data wrangling making information designed for a specific purpose, such as online dissemination, appropriate for analysis.

Can We Compare Attitudes Towards Crime Around the World? Assessing Measurement Invariance of the Morally Debatable Behavior Scale Across 44 Countries

ObjectivesWe aim to encourage scholars who conduct cross-national criminological studies to routinely assess measurement invariance (MI), that is, verify if multi-item instruments that capture latent constructs are conceptualized and understood similarily across different populations. To promote the adoption of MI tests, we present an analytical protocol, including an annotated R script and output file. We implement the protocol and, doing so, document the first test of configural, metric, and scalar invariance of the three-factor Morally Debatable Behavior Scale (MDBS).MethodsWe worked with data from wave seven of the World Values Survey (WVS). Applying multi-group confirmatory factor analyses, we, first, explored invariance of the MDBS in 44 countries (N = 59,482). Next, we conducted analyses separately for seven South-american, six South-east Asian, six East-asian, two North American and Australasian, and all four Anglophone countries.ResultsThe MDBS displays an overall lack of invariance. However, we confirmed configural invariance of the MDBS for the South-east Asian sample, metric invariance in the sample of Anglophone countries, and scalar invariance for the Australasian and North American countries.ConclusionsWave seven of the WVS can be used for latent mean score comparisons of the MDBS between the Australasian and North American countries. Associative relationships can be compared in the larger Anglophone sub-sample. Taken together, MI must be tested, and cannot be assumed, even when analyzing data from countries for which previous research has established cultural similarities. Our protocol and practical recommendations guide researchers in this process.

Green Software Engineering: Cloud-based Face Detection and Static Code Analysis

This paper presents an approach to green software engineering that integrates cloud-based face detection and static code analysis to promote sustainable software development. The proposed method uses OpenCV, a computer vision library, and a pre-trained Haar cascade classifier to detect faces in images. Faces are marked with green bounding frames that serve as visual indicators of their locations. In addition, the paper evaluates the quality of a distinct script file using Pylint library static code analysis techniques. The analysis evaluates code compliance with standards, identifies potential flaws, and identifies code odors. By integrating these practices, the proposed method seeks to reduce resource consumption, maximize energy efficiency, and enhance code maintainability, promoting environmentally friendly and sustainable software engineering practices. One outcome of our effort was creating the YasminNadiaArabcSocialMediaImages data collection, which includes faces of Arabic social media celebrities and is filled out to be accessible for public usage on the websites Kaggle and GitHub.

An open-source microscopy framework for simultaneous control of image acquisition, reconstruction, and analysis

We present a computational framework to simultaneously perform image acquisition, reconstruction, and analysis in the context of open-source microscopy automation. The setup features multiple computer units intersecting software with hardware devices and achieves automation using python scripts. In practice, script files are executed in the acquisition computer and can perform any experiment by modifying the state of the hardware devices and accessing experimental data. The presented framework achieves concurrency by using multiple instances of ImSwitch and napari working simultaneously. ImSwitch is a flexible and modular open-source software package for microscope control, and napari is a multidimensional image viewer for scientific image analysis.The presented framework implements a system based on file watching, where multiple units monitor a filesystem that acts as the synchronization primitive. The proposed solution is valid for any microscope setup, supporting various biological applications. The only necessary element is a shared filesystem, common in any standard laboratory, even in resource-constrained settings. The file watcher functionality in Python can be easily integrated into other python-based software.We demonstrate the proposed solution by performing tiling experiments using the molecular nanoscale live imaging with sectioning ability (MoNaLISA) microscope, a high-throughput super-resolution microscope based on reversible saturable optical fluorescence transitions (RESOLFT).

Film Studio as an Institution for War Memory? “Authoritative Viewers” and the Soviet Production of Cultural Trauma (1960–1980s)

The institutional approach in memory studies implies the study of stable organizational structures responsible for the production of cultural memory. Addressing the problem of late-Soviet institutions of memory of the Great Patriotic War in the 1960s-1980s, the author considers the film studio, using “Lenfilm” as an example, as a special institution of memory and cultural trauma of the war. Based on institutional sources (script files, artistic councils, etc.), the author proposes to study the production of Soviet films about the war as the interaction of a complex system of conflicting “authoritative viewers” who had (relative) autonomy, functions, and a different type of authority in judging the past – the “power of memory». The author substantiates a typology of such “memory power” (“author power”, “studio power”, “power of the cultural bureaucracy”, “party power”, “military power”, “corporate power”, and “media power”) and summarizes the main properties inherent in the system of cultural production that influenced the film memory of the war in the “long 1970s” – its stability, “institutional opacity”, “constant bargaining”, overlapping party-bureaucratic control, contestation and defense of the “autonomy of the author”.

A probabilistic approach for power cable cross section area selection: Most economic design by computer aided

This study presents a practical computer-based design program for a power cable network called “Power Cables Graphical User Interface” (PCGUI). This program is mainly for academic education, consulting electrical designers, primary engineers, and technical personnel with open-source code and a simple user interface. As a low/medium-voltage cable selection program, PCGUI will represent an essential part of the design for any electrical system, including different and complex analytic procedures based on various international standards (“IEEE, IEC, BS, NEC, NPFA 70, and local applied country standards.”). A MATLAB PCGUI program gives a new method to analyze and identify the optimized cable design depending on huge numbers of MATLAB script files and data appropriate for different factors and conditions. These factors and conditions include the type of insulation, temperature factor, grouping factor, accepted voltage drop, cable lifetime costs, etc. PCGUI is easily accomplished with the least effort and provides a fast and economical design with very high accuracy through limited manual input steps. After executing the program, the obtained results will contain the complete economic cable design, the circuit breaker standard rating and type, the actual cable current loading, the actual voltage drop, and the primary and the most economic cable cross-section area “CSA” based on the cost analysis.

Inglämnlagare - a tool for restructuring Swedish site data for statistical analysis

Background: This paper presents a new software tool, Inglämnlagare, developed to be open-source, that restructures information about ancient remains in Sweden for analysis. The background is a new version of the ancient sites database, the Historic Environment Record, curated by the Swedish National Heritage Board, that was launched in 2018 with a new database model that structures the information differently compared to previous versions. Methods: The program, written in Python programming language, has multicore support in order to improve performance for large files and uses regular expressions to extract information about individual features of composite sites. Such features, together with their summed amount, are written as new individual fields to a comma-separated value file. The program is delivered as a source script file that can be executed in any Python environment. Use cases: As an example of use, a case study of exploring graves of rectangular shape found within Sweden is provided. The use case also describes the different steps involved in preparing the data in QGIS to run the program, as well as some methods to efficiently analyse and visualize the output. Conclusions: Inglämnlagare will make more information from the Swedish record of ancient sites accessible for research and can be used to explore different content of the record more efficiently than previously possible. While the tool is written specifically for this dataset it also provides an example of how open-source tools can be used for data wrangling making information designed for a specific purpose, such as online dissemination, appropriate for analysis.

Multi-Objective Optimization Design for a Novel Parametrized Torque Converter Based on an Integrated CFD Cascade Design System

Advanced cascade design theories and methods are crucial to the rapid development of torque converters. Therefore, the study proposed a new parametric design method for a hydrodynamic torque converter cascade. The method is embodied by using a cubic non-uniform rational B-splines (NURBS) open curve and closed curve, respectively, to carry out the parametric design of the unit blade camberline and unit blade thickness distribution, and the curvature of the designed blade curve is continuous. Then, the author developed batch and script files in the Isight platform for a fully automated integrated design of the hydrodynamic torque converter, including cascade parametric modeling, meshing, computational fluid dynamics (CFD) simulation, post-processing, and optimization design. A three-dimensional cascade integrated optimization design system of the hydrodynamic torque converter is established with CFD technology as the bottom layer design, a control file as the middle layer, and an optimization algorithm as the top layer drive. Finally, multi-objective optimization was carried out for the key cascade parameters (camberline peak height). Compared with the original blade, the optimized NURBS blade increased by 7.207% in high-efficiency region width (Gη), and the optimized blade increased by 2.673% in peak efficiency (ηmax) to meet the actual engineering requirements. The new parametric design method of the blade shape and the integrated optimization design system of a three-dimensional cascade of torque converter proposed in this paper significantly reduces the design costs and shortens the design cycle of the torque converter, which will provide a valuable reference for engineers of turbomachinery.

Multi-Branch-CNN: Classification of ion channel interacting peptides using multi-branch convolutional neural network

Ligand peptides that have high affinity for ion channels are critical for regulating ion flux across the plasma membrane. These peptides are now being considered as potential drug candidates for many diseases, such as cardiovascular disease and cancers. In this work, we developed Multi-Branch-CNN, a CNN method with multiple input branches for identifying three types of ion channel peptide binders (sodium, potassium, and calcium) from intra- and inter-feature types. As for its real-world applications, prediction models that are able to recognize novel sequences having high or low similarities to training sequences are required. To this end, we tested our models on two test sets: a general test set including sequences spanning different similarity levels to those of the training set, and a novel-test set consisting of only sequences that bear little resemblance to sequences from the training set. Our experiments showed that the Multi-Branch-CNN method performs better than thirteen traditional ML algorithms (TML13), yielding an improvement in accuracy of 3.2%, 1.2%, and 2.3% on the test sets as well as 8.8%, 14.3%, and 14.6% on the novel-test sets for sodium, potassium, and calcium ion channels, respectively. We confirmed the effectiveness of Multi-Branch-CNN by comparing it to the standard CNN method with one input branch (Single-Branch-CNN) and an ensemble method (TML13-Stack). The data sets, script files to reproduce the experiments, and the final predictive models are freely available at https://github.com/jieluyan/Multi-Branch-CNN.

Discretization Analysis of Fluid Mechanical Flow Field Grid

The design basis of the performance of special industrial fluid machinery is to obtain the distribution of air flow parameters through the solution of flow field. In the numerical solution of flow field, the discretization of computational domain grid is the key factor affecting the calculation results. In this paper, the two-dimensional axisymmetric flow field analysis model were adopted. By establishing the software interface and automatic execution script file, the grid discreteness analysis process was established. The Richardson extrapolation method was used to infer the accurate solution of the flow field, and the grid independence and convergence analysis and the grid spacing analysis of the first layer of the wall were carried out. The results showed that the numerical results gradually approached the exact solution with the increase of the total number of grids, but the correlation between the numerical results and the total number of grids decreased in this process; By changing the normal spacing of the first layer of grid on each wall and making mathematical statistical analysis, the sensitive area of grid spacing could be determined. The method established in this paper could significantly improve the overall efficiency of the fluid mechanical mesh discreteness analysis and flow field analysis, and recommended the configuration of the optimal mesh discretization mode for the subsequent simulation calculation, which was conducive to further improve the calculation accuracy and efficiency of the flow field.

Research on safety of the intended functionality of automobile AEB perception system in typical dangerous scenarios of two-wheelers

The statistical analysis was conducted on data of accident scenarios between cars and two-wheelers from National Automobile Accident In-depth Investigation System (NAIS) database in order to study safety of intended functionality of Autonomous emergency braking (AEB) perception system in typical dangerous scenarios of cars and two-wheelers. 11 scenario-related variables were selected, and 6 types of typical scenarios were obtained through cluster analysis and manual classification. The 6 types of typical scenarios were built by the automatic driving simulation software PreScan, and the AEB longitudinal control algorithm was built in Matlab/Simulink. Batch simulation script files were written, and the relative location distribution of car and two-wheeler with different time to collision (TTC) was obtained by batch simulation. Furthermore, the effects of car velocity, two-wheeler velocity and cyclist casualties on the parameter configuration of the perception system were analyzed. Under the premise of satisfying safety of intended functionality of the perception system, the optimal sensor detection scheme at different TTCs was obtained by comprehensively considering the death accident detection rate, detection area, and standard deviation. The results show that when the detection rate is 90%, the AEB system can adopt the detection scheme of long-range radar and short-range radar. The field of view (FOV) and detection range of the short range radar are 133.6° and 38.1 m, and those of the long range radar are 84.5° and 74.3 m. And when the detection rate was close to 100%, a single sensor can be used, and the detection parameters are 150° and 77.6 m. It provides reference for parameter optimization of AEB perception system.

Numerical Regge pole analysis of resonance structures in state-to-state reactive differential cross sections

This is the third (and the last) code in a collection of three programs [Sokolovski et al. (2011), Akhmatskaya et al. (2014)] dedicated to the analysis of numerical data, obtained in an accurate simulation of an atom-diatom chemical reaction. Our purpose is to provide a detailed description of a FORTRAN code for complex angular momentum (CAM) analysis of the resonance effects in reactive angular scattering [for CAM analysis of integral reactive cross sections see [Akhmatskaya et al. (2014)]. The code evaluates the contributions of a Regge trajectory (or trajectories) to a differential cross section in a specified range of energies. The contribution is computed with the help of the methods described in [Dobbyn et al. (1999), Sokolovski and Msezane (2004), Sokolovski et al. (2007)]. Regge pole positions and residues are obtained by analytically continuing S-matrix element, calculated numerically for the physical integer values of the total angular momentum, into the complex angular momentum plane using the PADE_II program [Sokolovski et al. (2011)]. The code represents a reactive scattering amplitude as a sum of the components corresponding to a rapid “direct” exchange of the atom, and the various scenarios in which the reactants form long-lived intermediate complexes, able to complete several rotations before breaking up into products. The package has been successfully tested on the representative models, as well as the F + H2→ HF+H benchmark reaction. Several detailed examples are given in the text. Program summaryProgram title:DCS_ReggeCPC Library link to program files:https://doi.org/10.17632/gf4gm82n6m.1Licensing provisions: GPLv3Programming language:FORTRAN 90Supplementary material:PADE_II, MPFUN and QUADPACK packages, validation suites, script files, input files, readme files, Installation and User GuideNature of problem: The package extracts the positions and residues of resonance poles from numerical scattering data supplied by the user. This information is then used for the analysis of resonance structures observed in reactive differential cross sections.Solution method: The S-matrix element is analytically continued in the complex plane of either energy or angular momentum with the help of Padé approximation of type II. Resonance Regge trajectories are identified and their contributions to a differential cross section are evaluated at different angles and energies.Additional comments including restrictions and unusual features: Use of multiple precision MPFUN package.

Reconstruction of global surface ocean <i>p</i>CO<sub>2</sub> using region-specific predictors based on a stepwise FFNN regression algorithm

Abstract. Various machine learning methods were attempted in the global mapping of surface ocean partial pressure of CO2 (pCO2) to reduce the uncertainty of the global ocean CO2 sink estimate due to undersampling of pCO2. In previous research, the predictors of pCO2 were usually selected empirically based on theoretic drivers of surface ocean pCO2, and the same combination of predictors was applied in all areas except where there was a lack of coverage. However, the differences between the drivers of surface ocean pCO2 in different regions were not considered. In this work, we combined the stepwise regression algorithm and a feed-forward neural network (FFNN) to select predictors of pCO2 based on the mean absolute error in each of the 11 biogeochemical provinces defined by the self-organizing map (SOM) method. Based on the predictors selected, a monthly global 1∘ × 1∘ surface ocean pCO2 product from January 1992 to August 2019 was constructed. Validation of different combinations of predictors based on the Surface Ocean CO2 Atlas (SOCAT) dataset version 2020 and independent observations from time series stations was carried out. The prediction of pCO2 based on region-specific predictors selected by the stepwise FFNN algorithm was more precise than that based on predictors from previous research. Applying the FFNN size-improving algorithm in each province decreased the mean absolute error (MAE) of the global estimate to 11.32 µatm and the root mean square error (RMSE) to 17.99 µatm. The script file of the stepwise FFNN algorithm and pCO2 product are distributed through the Institute of Oceanology of the Chinese Academy of Sciences Marine Science Data Center (IOCAS, https://doi.org/10.12157/iocas.2021.0022, Zhong, 2021.

Detection of correct pregnancy status in lactating dairy cattle using MARS data mining algorithm

In this study, it is aimed to determine pregnancy outcomes by using multivariate adaptive regression splines (MARS) algorithm for classification type problems. For this purpose, data obtained from a private dairy farm in the Konya region of Türkiye in 2020 were used to determine pregnancy outcomes in Holstein dairy cattle. It has been determined how to perform statistical analyses on solving classification-type problems with the MARS algorithm and how to use R packages (caret and earth) by creating an R script file. After the analysis, the MARS estimation equation was created and in finding the probability of being pregnant: While lactation period, cow age, number of lactations, insemination number, and total lactation milk yield variables are important, it was seen that 7-day mean milk yield and last lactation milk yield were not significant. Using the train function of the caret package, the number of terms that produce the highest accuracy and the degree of interaction are determined. Goodness-of-fit tests of the optimum model were calculated. Within the scope of the evaluation of the generalization ability of the model, training and test sets were created, the classification success graph of the MARS algorithm, the model building phase were summarized, and the generalization ability of the established model was measured. When the pregnancy status is taken as a positive reference, the correct classification rate (sensitivity) of the animal with positive pregnancy status was found to be 0.9574. The correct classification rate (specificity) of pregnant animals was found to be 0.8370. The overall classification ratio of the training set (accuracy) was found to be 0.8777. The area under the ROC curve (AUC) was found to be 0.947, which indicates that the optimum specificity value is close to 1.

Electromagnetic-Thermal-Coupled Fault Analysis of PMASynRM With Turn-to-Turn Short-Circuit Involving a Few Strands

This article assesses the electromagnetic (EM) and thermal behaviors of a triple-redundant, 3×3-phase, permanent-magnet-assisted synchronous reluctance machine (PMASynRM) under a short circuit (SC) involving a few strands across a number of turns. Directly coupled EM-thermal simulation based on 2-D transient EM and 3-D thermal model with aid of a scripting file is adopted for analyzing thermal behavior under the most severe interstrand SC fault. The results provide a better understanding of more realistic SC behaviors in machines with stranded winding. It is shown that EM-thermal-coupled simulations are necessary to access the machine's thermal behavior under the fault conditions.