Applied sciences

Bulletin of the Polish Academy of Sciences Technical Sciences

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Bulletin of the Polish Academy of Sciences Technical Sciences | Early Access

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Abstract

The publication addresses the dynamic state challenges encountered during development of a Dual Active Bridge (DAB) converter within DC microgrid systems. The conventional startup method is identified as instigating a cascade of unfavorable outcomes, encompassing elevated starting current, transformer current asymmetry, DC voltage distortions, EMI and heightened thermal stress on semiconductor components. Additionally, it necessitates precise calibration of magnetic components and diodes. A proposed remedy to these issues is introduced, involving a control method based on an additional phase shift to modulate the current of the primary H bridge. This novel control methodology is posited as a means to mitigate the aforementioned undesirable effects associated with traditional converter initiation techniques. The research also delves into considerations of proper design procedure for the converter. Emphasis is placed on integrating the novel control methodology into the design framework to effectively address challenges arising during transient states. Validation of the proposed solution is substantiated through a series of laboratory tests, the results of which are comprehensively presented in the article. These tests affirm the efficiency of the system when incorporating the novel control methodology, thereby substantiating its practical utility in mitigating the identified issues during the initiation phase of the DAB converter in DC microgrid systems.
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Authors and Affiliations

Serafin Bachman
ORCID: ORCID
Marek Turzyński
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Abstract

In modern drive systems, the aim is to ensure their operational safety. Damage can occur not only to the components of the motor itself but also to the power electronic devices included in the frequency converter and the sensors in the measurement circuit. Critical damage to the electric drive that makes its further exploitation impossible can be prevented by using fault-tolerant control (FTC) algorithms. These algorithms are very often combined with diagnostic methods that assess the degree and type of damage. In this paper, a fault classification algorithm using an artificial neural network (ANN) is analysed for stator phase current sensors in AC motor drives. The authors confirm that the investigated classification algorithm works equally well on two different AC motors without the need for significant modifications, such as retraining the neural network when transferring the algorithm to another object. The method uses a stator current estimator to replace faulty sensor measurements in a vector control structure. The measured and estimated currents are then subjected to a classification process using a multilayer perceptron (MLP), which has the advantage of a small structure size compared to deep learning structures. The uniqueness of the method lies in the use of data in the training set that are not dependent on the parameters of a specific motor. Four types of current sensor faults were studied, namely total signal loss, gain error, offset, and signal saturation. Simulations were performed in a MATLAB/SIMULINK environment for drive systems with an induction motor (IM) and a permanent magnet synchronous motor (PMSM). The results show that the algorithm correctly evaluates the type of damage in more than 99.6% of cases regardless of the type of motor. Therefore, the results presented here may help to develop universal diagnostic methods that will work on a wide variety of motors.
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Authors and Affiliations

Krystian Teler
Maciej Skowron
Teresa Orłowska-Kowalska
ORCID: ORCID
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Abstract

Nowadays, cold-formed steel (CFS) has become widely used in the field of lightweight structures. In 2016, the Budapest University of Technology and Economics initiated a research on a unique structural system using CFS and utilized ultra-lightweight concrete as an encasing material. This material serves as a continuous bracing that improves CFS element resistance, stability behaviour, and performance, while also providing heat insulation capabilities, thus helping achieving sustainability goals. This paper is considered a continuation of previous research conducted by the authors. An experimental investigation was carried out on encased CFS columns subjected to eccentric loading. A total of fourteen stub-columns, with two distinct thicknesses, were subjected to various loading conditions for testing. The test results showed that local failure controlled the behaviour of all the tested elements. The reduction in capacity resulting from eccentricity with respect to centric resistance varied between 20% and 52%, depending on the applied load position and the core thickness of the tested steel elements. Moreover, the test outcomes were compared to the Eurocode analytical solution of pure steel elements. The overall load increment ranged from 46% to 18%, with a more noticeable bracing impact observed in the case of slender elements. Material test also supplement the results.
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Authors and Affiliations

Ahmed Alabedi
Péter Hegyi
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Abstract

The application of active adhesion control to the traction control system of an electric train holds great appeal for maximizing longitudinal acceleration force. Most of the currently reported works regulate the adhesion between wheel and rail by adjusting the torque reference of a cascade motor drive controller, which suffers from slow speed response and excessive start torque. This article proposes a cascade-free predictive adhesion control strategy for electric trains powered by an interior permanent magnet synchronous motor (IPMSM) to address these issues. The proposed control scheme utilizes an improved perturbation and observation method to predict the time-varying wheelrail adhesion state and determine the optimal slip speed. The initial setpoint reference command from the driver master is then adjusted to a dynamic reference that continuously adapts to the predicted adhesion conditions. Finally, the predictive speed control method is employed to ensure rapid convergence of the tracking objective. The simulation and hardware-in-the-loop testing results confirm that this approach achieves excellent dynamic performance, particularly during the train start-up phase and in the high-speed weak magnetic area of the IPMSM.
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Authors and Affiliations

Jiao Ren
Ruiqi Li
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Abstract

The working environment and objects of agricultural machinery are different from those of automobiles, andagricultural machinery is greatly affected by the working environment and working conditions, and the power output systemismore complex. Agricultural machinery not only has drive output, but also PTO output and hydraulic output, whichtogetherconstitute the output system of agricultural machinery. Agricultural machinery conditions can be dividedintoroadtransportation working conditions and field operation working conditions. The working conditions of agricultural machinerycan be divided into different load conditions according to the different traction tools and whether the hydraulic andPTOwork,such as ploughing, rotary tillage, fertilization, and transportation. Therefore, developing hybrid electric agricultural machinerysystems that are suitable for various complex working conditions holds great theoretical significance and practical value. Giventhe complex working conditions of agricultural machinery systems in agricultural work and the intricate challengesindesigning hybrid agricultural machinery systems. In this paper, the two-dimensional matrix is used to represent thephysicalstructure and dynamics of the multi-channel power output agricultural mechanism. A hierarchical two-dimensional matrixmethod for the generation and screening of hybrid electric agricultural machinery systems with multi-power output powerisproposed. The components of agricultural machinery are divided into an input layer and an output layer, and these componentsare coded and defined, and then transformed into a matrix. The hierarchical two-dimensional matrix method is usedtogenerateand screen the hybrid electric agricultural mechanism type. Through the stratification of the matrix, the complexityoftheconfiguration generation is reduced, and the constraints are applied to the basic screening of the generated configurations. Therationality of the configurations obtained after generation and screening is verified by Simulink simulation. The resultsshowthat the configuration screened by this method can meet the performance requirements of agricultural machinery.
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Authors and Affiliations

Baogang Li
Jinbo Pan
Rui Sun
Yuhuan Li
Zunmin Liu
Wanyou Huang
Hanjun Jiang
Fuhao Liu
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Abstract

Lithium-based battery systems (LBS) are used in various applications, from the smallest electronic devices to power generation plants. LBS energy storage technology, which can offer high power and high energy density simultaneously, can respond to continuous energy needs and meet sudden power demands. The lifetime of LBSs, which are seen as a high-cost storage technology, depends on many parameters such as usage habits, temperature and charge rate. Since LBSs store energy electrochemically, they are seriously affected by temperature. High-temperature environments increase the thermal stress on the LBS and cause its chemical structure to deteriorate much faster. In addition, the fast charging feature of LBSs, which is presented as an advantage, increases the internal temperature of the cell and negatively affects the battery life. The proposed energy management approach ensures that the ambient temperature affects the charging speed of the battery and that the charging speed is adaptively updated continuously. So, the two parameters that harm battery health absorb each other, and the battery has a longer life. A new differential approach has been created for the proposed energy management system. The total amount of energy that can be withdrawn from the LBS is increased by 14.18% compared to the LBS controlled with the standard energy management system using the genetic algorithm optimized parameters. In this way, the LBS replacement period is extended, providing both cost benefits and environmentally friendly management by LBSs turning into chemical waste later.
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Authors and Affiliations

Gökhan Yüksek
ORCID: ORCID
Timur Lale
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Abstract

The basic measuring element of vibrating-wire strain gauges is a steel piano wire, functioning in the elastic range. This element is constantly under tension. Therefore, its material gradually deforms permanently. This deformation causes its stress to relax. This relaxation results in a measurement errors of the strain gauges. This error, as demonstrated by both in situ and laboratory tests, can reach values of even several percent of the strain gauge measuring range (FSR) over periods of 10 years. Therefore, a concept of a differential strain gauge was proposed, for the construction of which two measuring wires would be used. Changing the input value of the strain gauge, i.e. a displacement of one of its anchors in relation to the other one would cause one wire to lengthen while the other wire shortened identically. The measured displacement would be calculated on the basis of the difference in the frequency of the wires vibrations. In this way, the influence of the simultaneous relaxation of the wires on the measurement result would be greatly reduced. Based on this concept, a prototype differential strain gauge for measuring concrete deformation was realized. In addition to two wires, it also contains two electromagnets, placed together with the wires in a common body-housing. After the strain gauge was assembled, its first tests were carried out under laboratory conditions.
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Authors and Affiliations

Adam Kanciruk
Elżbieta Matus
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Abstract

Test artifacts, resembling real machine parts, allow quantitative evaluation of system performance and insight into individual errors, aiding in improvement and standardization in additive manufacturing. The article provides a comprehensive overview of existing test artifacts, categorized based on geometric features and material used. Various measurement techniques such as stylus profilometry and computed tomography are employed to assess these artifacts. It was shown that Selective Laser Melting (SLM) technology and titanium alloys are prevalent in artifact creation. Specific artifact categories include slits, angular aspects, length parameters, variable surfaces, and others, each accompanied by examples from research literature, highlighting diverse artifact designs and their intended applications. The paper critically discusses the main problems with existing geometries. It paper underscores the importance of user-friendly and unambiguous artifacts for dimensional control, particularly in surface metrology. It anticipates the continued growth of metrological verification in future manufacturing environments, emphasizing the need for precise and reliable measurement results to support decision-making in production conditions
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Authors and Affiliations

Patryk Mietliński
Bartosz Gapiński
Jolanta B. Królczyk
Piotr Niesłony
Marta Bogdan-Chudy
Anna Trych-Wildner
Natalia Wojciechowska
Grzegorz M. Królczyk
Michał Wieczorowski
Tomasz Bartkowiak
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Abstract

This study introduces a two-step reinforcement learning (RL) strategy tailored for "The Lord of the Rings: The Card Game", a complex multistage strategy card game. The research diverges from conventional RL methods by adopting a phased learning approach, beginning with a foundational learning step in a simplified version of the game and subsequently progressing to the complete, intricate game environment. This methodology notably enhances the AI agent’s adaptability and performance in the face of the unpredictable and challenging nature of the game. The paper also explores a multi-phase system where distinct RL agents are employed for various decision-making phases of the game. This approach has demonstrated remarkable improvement, with the RL agents achieving a winrate of 78.5 % at the highest difficulty level.
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Authors and Affiliations

Konrad Godlewski
Bartosz Sawicki
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Abstract

This study introduces an innovative algorithm that leverages Terrestrial Laser Scanning (TLS) and the Fuzzy Analytic Hierarchy Process (FAHP) for the optimization of building repair methodologies. Focusing on multi-criteria decision-making (MCDM), it showcases a methodology for evaluating and selecting the most effective repair strategy for building elements, balancing various conflicting criteria. The research applies TLS for rapid and accurate geometric data acquisition of engineering structures, demonstrating its utility in structural diagnostics and technical condition assessment. A case study on a single-family residential building, experiencing floor deformation in a principal ground-floor room, illustrates the practical application. Maximum deflection and floor deflection distribution were measured using TLS. Utilizing FAHP for analysis, the decision model identifies the most advantageous repair method from a building user’s perspective. This approach not only provides a systematic framework for selecting optimal repair solutions but also highlights the potential of integrating advanced scanning technologies and decision-support methods in the field of building materials and structural engineering.
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Authors and Affiliations

Zbigniew Walczak
Barbara Ksit
ORCID: ORCID
Anna Szymczak-Graczyk
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Abstract

An efficient finite element approach was recently developed to analyse encased cold-formed steel (CFS) structures. This new technique replaced encasing material with unidirectional springs, analogous to the Winkler foundation concept, to shorten the analysis time while ensuring accuracy and reliability in predicting the structural behaviour of encased CFS components. In this paper, the validity, and limitations of the simplified spring model to represent outstanding plates were assessed. The investigation demonstrated that the simplified spring model could effectively predict the ultimate load for a wide range of ultra-lightweight concrete moduli (50-250 MPa) with an acceptable error. The analysis indicated that plate elements initially in cross-section class 4 without encasing material become at least class 3, or better as a consequence of encasing. Previously reported experiments were used to evaluate the performance of the ESM. The analysis demonstrated that the ESM can accurately predict the local failure ultimate load of encased CFS sections with an acceptable error percent and significantly less computational effort than a 3D solid model.
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Authors and Affiliations

Ahmed Alabedi
Péter Hegyi
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Abstract

The paper concerns the problem of minimization of the total potential energy of trusses subjected to static loads in the presence of prescribed displacements of selected supporting nodes. The positions of the internal (free) nodes are fixed and the supporting nodes are imposed, the member stiffnesses being design variables, while the truss volume represents the cost of the design. Due to the assumption of the stiffnesses being non-negative, the problem is reduced to a problem of optimization of structural topology. Upon eliminating all the design variables analytically the optimum design problem is eventually reduced to the two mutually dual problems expressed either in terms of member forces or in terms of displacements of free nodes. The problem setting concerning the case when the prescribed displacements of supports are the only loads applied (i.e. kinematic loads) assumes a particularly simple form. A specific numerical method of solving the stress-based auxiliary problem has been developed for the selected 2D and 3D optimal designs. The study is the first step towards topology optimization of trusses with distortions
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Authors and Affiliations

Sławomir Czarnecki
ORCID: ORCID
Tomasz Lewiński
ORCID: ORCID
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Abstract

With a continued strong pace of artificial intelligence, the way of formulating the flight day plan has a significant impact on the efficiency of flight training. However, through extensive research we find that the scheduling of flight days still relies on manual work in most military aviation academies. This method suffers from several issues, including protracted processing times, elevated error rates, and insufficient degree of optimization. This article provides a comprehensive analysis of automated flight scheduling using Goal Programming algorithm and details the implementation of the corresponding algorithm on the LINGO platform. The study enhances the flexibility and robustness of the model by setting bias variables, wherein the flight courses for students and instructors can be automatically and reasonably scheduled.
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Authors and Affiliations

Pengfei Sun
Jia Liu
ORCID: ORCID
Hao Nian
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Abstract

Sealing is an important prerequisite for downhole heater work. This paper proposes a combination of soft and hard and welding sealing programs, which were analyzed using theoretical calculations, numerical simulation, and in-situ testing. The results show that 316 stainless steel can meet the stuffing seal requirements; The first stuffing compresses and gradually reduces, while the second stuffing essentially does not deform. Stuffing deformation to fill the gap in the sealing hole, resulting in a sealing layer. The compression rate is 0.43%, 8.45%, and 12.64%, indicating that the locking stress should be more than 2000 N; The temperature at the weld is heated by heat conduction and distributed in a concentric circle. Thermal stress will influence the 50mm barrier, but the 100mm boundary will be mostly unaffected. Actually, the thermal stress that destroys the weld seal may be reduced by adjusting the heater's output or raising the gas injection rate. During the beginning of the in-situ heat injection, the temperature of the heating rods rises simultaneously with the outlet temperature. As consequently, the two show opposite tendencies. The heat generated by the heating rods will cause the injected gas to be preheated in advance.
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Authors and Affiliations

Qiang Li
ORCID: ORCID
Qingfeng Bu
Xiaole Li
Hao Zeng
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Abstract

The paper presents chosen results of experimental tests performed on physical mock-ups of tensegrity triplex modules, approximately 1.2 m tall and of 0.5 m diameter, made of steel. A uniform and uniaxial static compressive loading is applied to three upper nodes of the modules at six different self-stress levels. Cable forces are measured using specially crafted force transducers of an electro-resistive strain-gauge type. Two types of struts with different slenderness are incorporated to analyse the influence of buckling on the modules behaviour. A simple three-parameter mathematical model is presented in order to explain the modules’ behaviour and discuss the obtained experimental data. The results show nonlinear behaviour in the equilibrium path, as well as, rapidly decreasing axial stiffness in the post-critical phase. Increasement of prestress has a small influence on the stiffness in the chosen range of compressive loading. The experimental results are valuable for purposes of verification and validation of numerical studies and fill the lack of experimental data in the literature.
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Authors and Affiliations

Andrzej Rutkiewicz
Leszek Małyszko
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Abstract

The paper proposes a deep-learning approach to the recognition of melanoma images. It relies on the application of many different architectures of CNN combined in the form of an ensemble. The units of the highest efficiency are selected as the potential members of the ensemble. Different methods of arrangement of the ensemble members are studied and the limited number of the best units are included in the final form of an ensemble. The results of numerical experiments performed on the ISIC2017 database have shown the very high efficiency of the proposed ensemble system. The best accuracy in recognition of melanoma from non-melanoma cases obtained by the ensemble was 96.54% at AUC=0.9909, sensitivity 94.71%, and specificity 97.67%. These values are superior to the results presented for this ISIC2017 database.
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Authors and Affiliations

Fabian Gil
Stanisław Osowski
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Abstract

With the increasing proportion of renewable energy power generation, its accompanying intermittency and volatility problems are becoming increasingly prominent, and the frequency fluctuation of the power system is becoming increasingly severe. Participation in frequency regulation services can be economically rewarding for generating units. The flywheel energy storage system can effectively improve the frequency regulation capability of coal-fired units. In this paper, the improvement of the FM capability of coal-fired units in the operation of a two-area interconnected power system containing wind power is investigated, and a model of a two-area interconnected power system comprising a turbine generator, wind power, and flywheel energy storage is established. The enhancement of the FM capability of coal-fired units by adding a flywheel energy storage system is analysed. The simulation results show that adding the flywheel energy storage system improves the FM capability of the coal-fired unit to a considerable extent, and the coal-fired unit can decide the flywheel capacity it needs to be equipped with through detailed economic calculations.
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Authors and Affiliations

Shunyi Song
Tianshu Qiao
Rui Zhang
Shuangyin Liang
Yibing Liu
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Abstract

Traffic accident prediction is a crucial component of the Intelligent Traffic System, which is important to maintain citizen safety and decrease economic losses. Current deep learning based methods for traffic accident prediction fail to consider the driving mechanism of traffic accidents, so a novel traffic accident prediction method based on multi-view spatial-temporal learning is proposed, which represents the driving mechanism of traffic accidents from multiple views. Firstly, for the urban regions divided by grids, a new augmentation has been designed to augment the spatial semantic information of regions through learnable semantic embedding, then Deformable Convolutional Networks with non-fixed convolution kernels are used to learn dynamic spatial dependencies between regions and Gated Recurrent Units are used to learn temporal dependencies, which can capture dynamic spatial-temporal evolution patterns of traffic accidents. Secondly, Long Short-Term Memory is employed to learn the traffic flow breakdown from the flow difference of adjacent time steps in each region to recognize the traffic accident precursor in the risk environment. Thirdly, accident patterns in different regions are learned from historical traffic flow to determine whether the flow is the dominant factor and capture the spatial heterogeneity of traffic accidents. Finally, the above features are fused for accident prediction at the regional level. Experiments are conducted on 2 real datasets, and the experimental results show that the proposed method outperforms 8 benchmark methods
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Authors and Affiliations

Jian Feng
ORCID: ORCID
Tianchao Liu
Yuqiang Qiao
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Abstract

The aim of this article is to present the results of tunnel tests and field tests of small-scale horizontal-axis wind turbines. The article proposes a new concept of turbine rotor adapted to improve efficiency at low wind speeds. The methodology for calculating the rotor and generator is shown. The turbine construction solution is presented briefly, along with the technology for manufacturing turbine components and assembly. An analysis of the obtained results is also carried out.
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Authors and Affiliations

Piotr Strojny
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Abstract

Recently, structural adhesives have become significant in the shaping of structural elements, especially in thin-walled structures, where they replace or supplement traditional connection methods. However, adhesive-bonded joints are highly susceptible to internal structural imperfections due to their application technique and the nature of the adhesive. These material inconsistencies impact the strength parameters and the mechanical behavior of the entire connection. This study proposes a simplified method for the probabilistic numerical modeling of structural imperfections in an adhesive layer. The adhesive is modeled as an uncorrelated random field with weakened elements representing structural imperfections randomly scattered throughout its entire volume. The percentage of these imperfections (in relation to the total volume) is adopted a random variable. By conducting experimental tests on dogbone specimens of a selected adhesive and comparing them to adequate numerical tests with varying volumes of weakened elements, the determination of the representative imperfection volume of the investigated adhesive was possible. Based on these tests, the calibration of the probability density function to describe the volume of the imperfections may be performed. Furthermore, the application of the random model for an adhesive-bonded single lap-joint is shown to be viable. Finally, the calculation of a probability-based mechanical response (in this case, the normal force at critical elongation) of the single lap-joint with structural imperfections is performed, and its resultant reliability is assessed and evaluated.
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Authors and Affiliations

Karol Winkelmann
Jan Faizullah
Łukasz Smakosz
ORCID: ORCID
Violetta Konopińska-Zmysłowska
Victor Eremeyev
Marcin Kujawa
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Abstract

The paper is devoted to the numerical analysis of the roof truss subjected to upward wind loading and braced at the tensioned top chord. The linear buckling analysis were performed for the beam and shell model of the structure. As the result the influence of rotational connection stiffness between the brace and the top chord on the truss stability was appointed. The biaxial strength testing machine was used to conduct the experimental tests of the rotational connection stiffness between selected steel profiles. The results in the form of measured structural displacements and rotations were presented. The static nonlinear analysis results performed for the shell model of the structural connection were compared to the results obtained on the experimental set-up.
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Authors and Affiliations

Marcin Krajewski
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Abstract

The related works of the diamond burnishing processes focused on improvements in surface quality. The study aims to optimize burnishing factors, including the spraying distance of the nozzle (S), the inlet pressure of the cold air (I), and the quantity of the liquid CO2 (L) of the cool and cryogenic-assisted diamond burnishing operation for minimizing energy consumed (EC) and arithmetical mean surface height roughness (Sa). Burnishing responses are modeled based on the radial basis function network and full factorial data. The entropy method, improved grey wolf optimizer, non-dominated sorting genetic algorithm II, and technique for order of preference by similarity to ideal solution were implemented to calculate the weights, produce solutions, and select the best outcome. As a result, the optimal data of the S, I, and L were 15.0 mm, 3.0 bar, and 11.0 L/min, respectively. The Sa and EC were reduced by 20.4% and 3.8%, respectively, at the optimality. The optimized outcomes could be employed to improve energy efficiency and machining quality for the internal diamond burnishing process. The optimizing technique could be used to solve complicated issues for different burnishing operations. The cool and cryogenic-assisted diamond burnishing process could be utilized for machining different internal holes.
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Authors and Affiliations

An-Le Van
Truong-An Nguyen
Xuan-Ba Dang
Trung-Thanh Nguyen
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Abstract

Gas turbines are widely used for power generation globally, and their greenhouse gas emissions have increasingly drawn public attention. Compliance with environmental regulations necessitates sophisticated emission measurement techniques and tools. Traditional sensors used for monitoring emission gases can provide inaccurate data due to malfunction or miscalibration. Accurate estimation of gas turbine emissions, such as particulate matter, carbon monoxide, and nitrogen oxides, is crucial for assessing the environmental impact of industrial activities and power generation. This study used 5 different machine learning models to predict emissions from gas turbines, including adaboost, xgboost, k-nearest neighbor, linear and random forest models. Random search optimization was used to set the regression parameters. The findings indicate that the adaboost regressor model provides superior prediction accuracy for emissions compared to other models, with an accuracy of 99.97% and a mean squared error of 2.17 on training data. This research offers a practical modeling approach for forecasting gas turbine emissions, contributing to the reduction of air pollution in industrial applications.
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Authors and Affiliations

Emrah Aslan
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Abstract

The main causes of aviation accidents in recent years are mostly related to pilot operational errors and pilot operational characteristics directly reflect flight quality, so flight quality and flight safety are inseparable. Improving the assessment method of flight quality is of great significance for building a competency-based and evidence-based flight training system as well as enhancing flight safety. However, some of the existing researches have the problems of being one-sided and the assessment accuracy is not high. We propose a flight quality assessment method based on KOA-CNN-GRU-Self-Attention for the whole flight phase to accurately assess the flight quality and to improve and supplement the existing system. Firstly, the QAR data of the whole flight phase is selected and divided into three data sets according to the three indexes of operational smoothness, accuracy, promptness, which are respectively substituted into the PCA comprehensive evaluation model to assess the flight quality. Then, the evaluation results are labelled with the rating as the input of CNN-GRU-Self-Attention, and the parameters are optimized using KOA. Finally, the evaluation of flight quality for the three indexes was achieved by training the KOA-CNN-GRU-Self-Attention model. The test results show that the accuracy of operational smoothness, accuracy, and promptness reaches 98.73%, 95.07%, and 97.18%, respectively, and the assessment effect is better and higher than the existing model. The model is also compared and analyzed with three base models CNN, QDA, XGBoost and three fusion models CNNSelf-Attention, GRU-Self-Attention, CNN-GRU-Self-Attention, which show overall better results in accuracy, recall, precision and F1-Score.
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Authors and Affiliations

Tianyi Wu
Zichun Lin
Jianan Huang
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Abstract

Green energy transformation requires comprehensive strategies that include both innovations in energy production and more efficient energy use. This article investigates the potential for saving electrical energy in industrial automation systems by utilizing bistable switching/relay. Compared to traditional systems, these innovative solutions demonstrate significant reductions in energy consumption. A market analysis of available bistable relays, along with experimental determination of their control conditions, highlights their application potential and indicates the benefits of their implementation. The findings suggest that replacing classical relays with their bistable counterparts could significantly contribute to global sustainability efforts. The article presents the process of redesigning a standard industrial relay into a bistable design. Adding two additional elements achieved the intended bistable functionality. The article calls for increased research and investment in such technologies, emphasizing that the energy-saving potential offered by bistable switching/relay circuits should not be overlooked.
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Authors and Affiliations

Piotr Tetlak
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Abstract

In a very broad range of industrial applications, especially in electric vehicles, permanent magnet synchronous motors (PMSMs) play an important role. Any failure in PMSMs may cause possible safety hazards, a drop in productivity, and expensive downtime. Therefore, their reliable operation is essential. Accurate failure identification and classification allow for addressing problems before they escalate, which helps ensure the seamless operation of PMSMs and reduces the likelihood of equipment failure. Therefore, in this paper, novel failure identification methods based on gated recurrent unit (GRU) and long short-term memory (LSTM) from recurrent neural network (RNN) methods are proposed for early identification of stator interturn short circuit failure (ISCF) and demagnetization failure (DF) occurring in PMSMs under multiple operating conditions. The proposed methods use three phase current signals recorded from the experimental study under multiple operating conditions of the motor as input data. In the proposed methods, both feature extraction and classification are executed within a unified framework. The experimental outcomes obtained demonstrate that the proposed methods are able to identify a total of six unique motor conditions, including three ISCF variations and two DF variations, with high accuracy. The LSTM and GRU approaches predicted the identification of failures with 98.23% and 98.72% accuracy, respectively. Compared to existing methods, the success of the proposed approaches is satisfactory. In addition, LSTM and GRU-based failure identification methods are also compared in detail for accuracy, precision, sensitivity, specificity and training time in this study.
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Authors and Affiliations

Timur Lale
Gökhan Yüksek
ORCID: ORCID
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Abstract

t. Hard-facing alloys increase the service life of components exposed to abrasive, erosive, or metal-to-metal wear conditions. Hard-facing is a metalworking process in which layers of a harder material are arc-welded onto a base metal. In particular, high-chromium hard-face weld deposit layers form a strong metallurgical bond with the substrate steel plate, enhancing the resistance to abrasive loadings. Metallurgical and microstructural analysis is conducted to improve the performance of such bi-layered metal structures. The discussion of an HC-O hard-face alloy deposited on S235 steel substrate plates is hereby presented, focusing on the characterization of the coating’s microstructure. The study establishes the relationship among the chemical composition, ‘as-cladded’ microstructure, and hardness properties of the investigated high chromium Fe–27 wt.% Cr–5 wt.% C hard-facing alloy.
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Authors and Affiliations

Teresa Faras
Benjamin Koenig
Paul P. Meyer
Ibra Diop
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Abstract

The purpose of this paper is to study unique solution and iterative sequence of approximate solution for uniformly approaching unique solution to a new class of singular fractional differential equations with two kinds of Riemann-Stieltjes integral boundary value conditions by using some fixed point theorems. Because of different properties of the nonlinear terms and complexity of the boundary conditions in equations, we first probe several fixed point theorems of sum-type operators which expand many existing works in this research area. It is essential to point out that some conditions in our works greatly simplify the proof process of fixed point theorems. By applying the operator conclusions obtained in this paper, some sufficient conditions that guarantee the existence and uniqueness of solutions to singular differential equations are obtained, two iterative schemes that uniformly converges to the unique solution are given which provide computational methods of approximating solutions. As applications, some examples are provided to illustrate our main results.
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Authors and Affiliations

Nan Zhang
Lingling Zhang
ORCID: ORCID
Hongwei Liu
Hui Wang
Huimin Tian
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Abstract

Pedestrian trajectory prediction provides crucial data support for the development of smart cities. Existing pedestrian trajectory prediction methods often overlook the different types of pedestrian interactions and the micro-level spatial-temporal relationships when handling the interaction information in spatial dimension and temporal dimension. The model employs a spatial-temporal attention-based fusion graph convolutional framework to predict future pedestrian trajectories. For the different types of local and global relationships between pedestrians, it first employs spatial-temporal attention mechanisms to capture dependencies in pedestrian sequence data, obtaining the social interactions of pedestrians in spatial contexts and the movement trends of pedestrians over time. Subsequently, a fusion graph convolutional module merges the temporal weight matrix and the spatial weight matrix into a spatial-temporal fusion feature map. Finally, a decoder section utilizes TimeStacked Convolutional Neural Networks to predict future trajectories. The final validation on the ETH and UCY datasets yielded experimental results with an Average Displacement Error(ADE) of 0.34 and an Final Displacement Error(FDE) of 0.55. The visualization results further demonstrated the rationality of the model.
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Authors and Affiliations

Guihong Lui
Chenying Pan
Xiaoyan Zhang
Qiangkui Leng
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Abstract

The DC-DC converter represents a crucial component in the renewable energy sources. The stability and dynamic capability enhancement of the DC/DC converter have emerged as a significant research topic in the current era. Model predictive control (MPC) is particularly prevalent due to its high dynamic response speed, simplicity of the controller design, and capacity for multi-objective optimization. However, the traditional finite control set model predictive control (FCS-MPC) method is suffer to variable switching frequency and vast computing. To improve the dynamic performance of the converter, a novel nonlinear control strategy named fixed switching frequency MPC and passivity-based control (PBC), named FSFPBMPC, is proposed, which could achieve fixed switching frequency and enhance the system's dynamic response speed. Firstly, the Euler-Lagrange (EL) model of the boost converter is established. Secondly, the relationship between duty cycle and MPC is established. Ultimately, the output voltage of PBC is incorporated into the cost function of the FCS-MPC. The characteristics of PBC power shaping and damping injection can enhance the system's immunity to interference, improve the system's dynamic response speed, and thus reinforce the system's stability. Then, depending on MATLAB, the simulation results proved that the proposed strategy has the same effect as we expected.
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Authors and Affiliations

Yajing Zhang
Yuqing Shen
BaoYing Huang
Jiangchao Zhang
Haojing Chang

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