Forthcoming and Online First Articles

International Journal of Engineering Systems Modelling and Simulation

International Journal of Engineering Systems Modelling and Simulation (IJESMS)

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International Journal of Engineering Systems Modelling and Simulation (28 papers in press)

Regular Issues

  • Machine learning-based financial analysis of merger and acquisitions   Order a copy of this article
    by S. Kalaivani, K. Sivakumar, J. Vijayarangam 
    Abstract: Stock market analysis and forecasting is one of the most sought-after areas of study. As anyone who has observed stock market movements even as an outsider knows very well the enormous amount of risk involved with numerous factors affecting it, its study is quite an intriguing and interesting one, let alone a profitable one. So, it is imperative we look for prediction tools to help us through the process. As we dwell into already available tools in the fields of economics and statistics, we can sense a need of innovation from other evolving domains and the immediate one is the field of machine learning. This paper is a stock market price forecasting one, using neural network model, employed on financial data concerning pre and post-mergers of companies. We have collected data of pre-merger and post-merger states, formed a neural network model to fit it and used the model to forecast. The predictions were reasonably accurate.
    Keywords: neural network; financial forecasting; merger; acquisitions.
    DOI: 10.1504/IJESMS.2022.10046049
     
  • A modified hidden Markov model for outlier detection in multivariate datasets   Order a copy of this article
    by G. Manoharan, K. Sivakumar 
    Abstract: The processing of data is an essential part of any field. More than 80% of the study effort is focused on collecting meaningful information from the vast amounts of data available. However, in order to minimise calculation time and improve accuracy, it is necessary to keep track of any unused, redundant, or irrelevant data in the dataset. Because it’s tough to build up a data warehouse to separate homogeneous data, it will be inefficient and inappropriate in terms of deployment costs and performance metrics. Meanwhile, handling heterogeneous data consumes more time to process due to uneven data samples and missing data. Thus, identifying the data class and balancing the data is critical for improving the performance of classification models. Outlier detection is the process of detecting irrelevant, missing, or unequal data samples in a large database. The goal of this study is to employ a modified hidden Markov model to find such outliers in a big dataset. This method improves classification model performance while also reducing computation time and increasing classification accuracy. The proposed model is experimentally verified and compared with prominent existing technologies such as random forest and decision tree models.
    Keywords: outlier detection; hidden Markov model; HMM; classification; support vector machine; SVM; random forest; RF; decision tree; DT.
    DOI: 10.1504/IJESMS.2022.10046736
     
  • Ultra-low latency communication technique for augmented reality application in mobile edge computing   Order a copy of this article
    by S. Narayanan, Rakesh Kumar Arora, Sanjeev Gangwar, J. Pradeep Kandhasamy, T. Ratheesh, K. Murali 
    Abstract: In wireless communication, ultra low-latency communications (ULLC) services offer short packets that may coincide alongside enhanced mobile broadband (eMBB) services which send lengthy packets. In a mobile edge computing architecture with eMBB and URLLC services, we explore how to evaluate latency and enhance shorter packet offloading methods. Using eMBB and URLLC functions, we examine how to evaluate latency and enhance shorter packet offloading methods in mobile edge computing (MEC) approach. In the MEC system, a server called processor sharing (PS) is utilised to reduce computation delay for short packets by distributing the server’s whole processing power equally to all packets. The server ignores long packets in favour of shorter ones. With a small packet size, a closed-form formula for the complementary latency distribution function may be developed. To minimise the short packet’s end-to-end (E2E) latency, offloading probabilities are adjusted based on the dependable demand. In tests with short and long packets, the processor sharing server outperforms 2 types of first-come, first-serve servers.
    Keywords: mobile edge computing; MEC; processor sharing server; ultra low latency communication augmented reality.
    DOI: 10.1504/IJESMS.2022.10046829
     
  • Scalable image compression mechanism for surveillance video summary   Order a copy of this article
    by T. Venkata Satya Vivek, Manoj Kumar Gupta, J. Pradeep Kandhasamy, Renu Kachhoria, Santwana S. Gudadhe, S. Lakshmi Narayanan 
    Abstract: The use of large-scale video surveillance systems is widespread in important areas such as home and public safety. Recognising and evaluating appropriate security measures is critical since these systems are vulnerable. A clear movie requires good compression. Lossy image compression may decrease the amount of bandwidth needed for picture transmission and the amount of storage available to a device, improving network performance. Neural networks have thrived in image processing thanks to deep learning. We present an image reduction technique based on semantic analysis based on the degree of human attention to each region of the picture. After evaluating the semantic images using a convolutional neural network (CNN), a compression bit-allocation algorithmic technique is used. This technique enhances video surveillance visual quality while keeping the same compression ratio.
    Keywords: convolutional neural network; CNN; image compression; recurrent neural network; scalable image; video surveillance.
    DOI: 10.1504/IJESMS.2022.10046832
     
  • A scheme based on ECDSA and its implementation for information security   Order a copy of this article
    by G. Mallikharjuna Rao, K. Deergha Rao 
    Abstract: Cryptography methods are means of securing digital data on a network. Digital signature on a document today is trendy in the digital world for authentication, authorisation, integrity, and non-repudiation. The elliptical curve digital signature algorithm (ECDSA) has been implemented and proved that it requires a key size of small length as compared to the RSA. However, its implementation for image and audio security is lacking in the literature. Hence, this paper has proposed a scheme based on ECDSA for text, image, and audio security over networks. Further, the proposed method is implemented on text, image, and audio using both LabVIEW software and myRIO hardware and verified for authentication.
    Keywords: digital signature; ECC; elliptical curve digital signature algorithm; ECDSA; cryptography; security.
    DOI: 10.1504/IJESMS.2022.10047195
     
  • Review of artificial intelligence techniques used in IoT networks   Order a copy of this article
    by Mujahid Tabassum, Kartinah Bt Zen, Sundresan Perumal, Veena Raj 
    Abstract: Artificial intelligence (AI) is an effective and efficient solution to manage and analyse data flow in any network. Internet of things (IoT) has quickly attracted significant global attention as an innovative, progressively growing technology. It has shown a rapid and successful involvement in many fields. Thus, IoT applications evolve exorbitantly and produce vast amounts of data required for intelligent data processing. It is approximately calculated that by 2025, IoT could make significant traffic of 79 zettabytes, and by 2030 around 25 billion active smart gadgets would be linked and woven through a single massive information network. It creates hurdles for the end-user to effectively evaluate and analyse the collected information. Therefore, IoT networks utilise robust and effective AI techniques such as machine learning (ML) and data analytics (DA), which examine large amounts of data and generate meaningful information promptly. ML is a self-learning process, and DA is another effective method for predicting the future behaviour of object or activities, using past data to improve productivity in different industries such as agriculture, transportation, online gaming, eHealth, etc. This paper discussed AI techniques such as ML and DA used in IoT networks and their impacts on productivity. Furthermore, we have discussed the future trends and challenges of IoT networks.
    Keywords: internet of things; IoT; artificial intelligence; data analytics; machine learning; internet.
    DOI: 10.1504/IJESMS.2022.10047758
     
  • Neural network-based optimisation of smart odometry classification in a self-governing robot for precise position and location estimation   Order a copy of this article
    by Shaik Mohammad Rafi, A. Prakash, Firdouse Banu, P. Muthu Krishnammal, K. Bhavana Raj, J.E. Anusha Linda Kostka 
    Abstract: The Verdino self-governing robot’s intelligent dummy device will benefit greatly from this study’s findings. An odometric mathematical model based on the robot’s trajectory equations determines the robot’s position. Odometer devices are system inputs, and a model is constructed using the wheel diameter and distance. This model determines the optimal nominal parameters by trying to conduct a restricted squares reduction. This model is computed using the current wheel diameter to assure the accuracy of the findings. A neural network model is used to train an odometric model using data. There is no doubt that the neural network works.
    Keywords: neural network; autonomous robot; position; and orientation estimate; odometry system.
    DOI: 10.1504/IJESMS.2022.10047953
     
  • A novel hybrid supervised machine learning model for real-time risk assessment of floods using concepts of big data   Order a copy of this article
    by Tegil J. John, R. Nagaraj 
    Abstract: Risk assessment (RA) modelling refers to combinatorial development of identification and assessment of the potential for the occurrence of an event that causes a negative impact on an entity of interest. With recent advances in data acquisition and archival methods, concepts of big data have been a great boon to RA development. It is primarily due to the fact that the accuracy of RA relies on the volume of historical data analysed. Based on this, a RA model is designed as a hybrid model using differential evolution and an adaptive neuro-fuzzy inference system to assess risk in real-time. The performance ability of the proposed hybrid model is compared with conventional ANFIS and neural network models by analysing the rainfall status in India. Data from the expert systems are collected by analysing various case study areas from India to validate the performance of the proposed hybrid system. The proposed model performance is validated through parameters like precision, recall, f1-score and accuracy. With maximum accuracy of 94.65% proposed model attains better performance than conventional approaches.
    Keywords: neural network; autonomous robot; position and orientation estimate; odometry system.
    DOI: 10.1504/IJESMS.2022.10048225
     
  • Improved performance for Alzheimer's disease earlier detection and diagnosis using deep learning algorithms   Order a copy of this article
    by T. Deenadayalan, S.P. Shantharajah 
    Abstract: As our society ages, cognitive impairment and dementia in the elderly, particularly Alzheimer’s disease (AD), have become more prevalent. After clinical symptoms arise, senile cognitive impairment will advance to irreversible dementia, finally leading to death as a multifactor, multistage, and clinical syndrome with concomitant disorders. Alzheimer’s disease is currently irreversible, and scientific trials for effective treatments are lacking. The progression of a patient’s condition will go through numerous stages, so early detection is critical. Early Alzheimer’s disease treatments can effectively decrease disease development while also reducing the burden on patients’ families and society. The research presents a deep learning-based strategy for early detection and screening of Alzheimer’s disease. The method involves slicing a three-dimensional magnetic resonance picture of the human brain into a two-dimensional image, then using an object recognition network called faster region with convolutional neural networks (R-CNN) to detect shrinkage in the hippocampal region of the brain to make an AD diagnosis. To get feature maps and to get 100% high-precision detection of AD samples, a new network is constructed and optimised based on VGG16, which is the basic network of faster R-CNN. At the same time, the validation set achieves 97.67% of the detected picture correctness.
    Keywords: disease detection; feature extraction; CNN; deep learning algorithm; high-precision.
    DOI: 10.1504/IJESMS.2022.10048226
     
  • Effect of sliding friction on torsional vibration of wind turbine drivetrain system under constant wind load   Order a copy of this article
    by Rishi Kumar, Sankar Kumar Roy 
    Abstract: Sliding friction is an unavoidable phenomenon that occurs between two contacting bodies under motion. In this paper, dynamic analysis of wind turbine drivetrain system (WTDS) is performed and sliding friction is incorporated in the model. Key tooth meshing points along path of contact like pitch point, single and double pair region are identified; reversal of force due to sliding friction at pitch point is explained. The drivetrain system contains three stages of gear drive. To study the dynamic characteristics of the system, governing equations of motion of lumped parameter model is derived using Lagrange’s formulation. Time varying mesh stiffness (TVMS) is estimated using the analytical method and frictional torque are incorporated in the drivetrain model. Finally, the governing equations are numerically solved using the Houbolt discretisation method in MatLab. Torsional vibration signals and frictional torque are obtained in MatLab. The simulation results with and without friction are fast Fourier transformed. The dynamic effect of sliding friction on the WTDS is investigated in the frequency domain and comparison is made with no friction condition.
    Keywords: wind turbine drivetrain system; WTDS; mathematical modelling; sliding friction; pitch point; time varying mesh stiffness; TVMS; fast fourier transform.
    DOI: 10.1504/IJESMS.2022.10048371
     
  • Experimental study on frost heave deformation characteristics of rock and soil based on pore distribution model   Order a copy of this article
    by Guo-xing Pang, Lin-ping Fu, Diandian Ding 
    Abstract: The traditional rock and soil frost heave deformation characteristics analysis test is time-consuming and expensive. Therefore, an experimental study method of rock and soil frost heave deformation characteristics based on the pore distribution model is proposed. The pore distribution model was constructed to calculate the stress intensity factor formed by the point force and the distributed gravity, and then the frost heave displacement was obtained and the frost heave deformation characteristics were analysed. The test results show that the running time of this study is saved by at least 2 min, and the average test cost is 14,951 million Yuan. Through the results of soil strain energy release rate and soil moisture content obtained, the effectiveness of the frost heave deformation characteristic test of rock and soil is fully verified.
    Keywords: pore distribution model; frost heave of rock and soil; deformation characteristics; experimental study.
    DOI: 10.1504/IJESMS.2022.10048586
     
  • Numerical simulation based radial laser cladding process optimisation for annular thin-walled parts   Order a copy of this article
    by Xuhui Xia, Yuding Gao, Lei Wang, Zelin Zhang, Ping Yi, Tong Wang, Baotong Chen 
    Abstract: In order to effectively reduce the deformation and improve the forming quality, the radial laser cladding process optimisation for annular thin-walled parts with numerical simulation and orthogonal experiment is carried out in this paper. A three-dimensional thermal-mechanical coupling finite element model for laser cladding of the annular thin-walled part is established. Based on the orthogonal experiment, the influence of laser power, scan speed and laser spot radius on the formation quality of radial single-layer cladding layer is investigated. The residual stress result shows that the maximum value of the residual stress in each direction appears at the junction of cladding layer and substrate. The optimal process parameters combination is laser powder of 1,400 W, scan speed of 21 mm/s, laser spot radius of 2.5 mm with smaller deformation and well forming quality. The results can provide some scientific and theoretical guidance for actual laser cladding of annular thin-walled parts.
    Keywords: radial laser cladding; thermal-mechanical coupling model; annular thin-walled part; process optimisation.
    DOI: 10.1504/IJESMS.2022.10048707
     
  • Performance analysis of doubly-fed induction generator using PID controller optimised by whale optimisation   Order a copy of this article
    by Ashutosh Kashiv, H.K. Verma 
    Abstract: The large-scale installation of wind turbines equipped with a doubly powered induction generator (DFIG) has been promoting the carrying out of several studies related to potential solutions for their integration into the electrical grid. In this paper, a control technique is presented that allows to regulate the active and reactive powers of DFIG in a stable and independent way. Its feasibility is supported by simulation results of models developed using MATLAB/Simulink software. To optimise the gains of the proportional, integral, derivative (PID) controller, a metaheuristic optimisation methodology called whale optimisation is used, where the operation of the system will be considered in the design stage to increase the robustness of the control. The behaviour of the control loops is evaluated after the occurrence of three-phase short circuits in the distribution network.
    Keywords: doubly-fed induction generator; DFIG; GSC; PID; rotor-side controller; RSC; wind turbine; whale optimisation algorithm; WOA.
    DOI: 10.1504/IJESMS.2022.10048733
     
  • A comprehensive review on different optimisation components for hybrid renewable energy sources   Order a copy of this article
    by Muleta Negasa, Altaf Q.H. Badar 
    Abstract: Renewable energy sources (RES) are natural energy sources that do not deplete with time. These energy sources are characterised as decentralised, modular, flexible technologies, closer to the load, and having smaller production capability. RES suffers from some drawbacks like reliability owing to its intermittent nature and high initial investment costs. To mitigate these drawbacks, hybrid renewable energy systems (HRES) are proposed in research studies. Different methodologies that operate these HRES at the optimal with their mathematical modelling, different algorithms (artificial intelligence and meta-heuristic) are reviewed in this paper. According to the literature survey, more researchers are giving attention to adjusting reliability and cost of energy separately or in the combination of both. On the other hand, environmental effects are critical issues for the globe in this century but have not given more attention.
    Keywords: hybrid renewable energy system; HRES; optimisation techniques; reliability; cost; environmental impacts.
    DOI: 10.1504/IJESMS.2022.10048772
     
  • Polarisation maintaining square shape photonic crystal fibre with high nonlinearity   Order a copy of this article
    by Monika Kiroriwal, Poonam Singal 
    Abstract: A highly birefringent square shape photonic crystal fibre (S-PCF) with high nonlinearity has been simulated and studied. AlGaAs infiltrated slotted elliptical and rectangular cores are considered to identify the impact of core shape on optical properties. The light managing behaviour of the triangular meshed S-PCF is studied by employing the finite element method (FEM). Simulated results indicate that the slotted elliptical core is more compelling than the slotted rectangular core. Proposed PCF with high birefringence nearly to 0.46, high nonlinearity of 1.2 x 105 W-1km-1, and high numerical aperture of 0.873 at 2 um can be a prominent contender for a wide span of uses such as in nonlinear optics, polarisation-maintaining, sensing, and medical imaging.
    Keywords: photonic crystal fibre; semiconductor nonlinear material; birefringence; nonlinearity; nonlinear optics; shape photonic crystal fibre; S-PCF; finite element method; FEM.
    DOI: 10.1504/IJESMS.2022.10048773
     
  • Elephant sound classification using machine learning algorithms for mitigation strategy   Order a copy of this article
    by T. Thomas Leonid, R. Jayaparvathy 
    Abstract: Conflicts between humans and elephants have become a wide problem in the agricultural and forest sectors, posing a threat to human lives and inflicting significant resource loss. This paper presents and compares the results of feature extraction techniques for detecting elephant voice signal. Support vector machine (SVM) classifiers, K-nearest neighbour (KNN) classifiers, nave Bayes classifiers and convolutional neural network (CNN) classifiers all use the recovered features as inputs. The performance of all feature extraction techniques are validated and compared on elephant voice signals. The experimental results have confirmed that highest testing classification accuracy of 84% is resulted from CNN classifier with discriminatory features from the voice. This signifies that the different techniques of feature extraction technique have immense potential than other techniques in Identifying elephant voice signal.
    Keywords: classification; convolutional neural network; CNN; accuracy; elephant; feature extraction.
    DOI: 10.1504/IJESMS.2022.10049166
     
  • Review on sentiment analysis of movie reviews using machine learning techniques based on data available on Twitter   Order a copy of this article
    by Dharmendra Dangi, Amit Bhagat, Jeetendra Kumar Gupta 
    Abstract: Opinion mining or sentiment analysis is the study to extracted useful information from the given datasets like tweets on Twitter or opinions of people on other social blogs or portals related to a particular topic. Sentiment analysis aims to predict the type of opinion like positive, somewhat positive, or negative somewhat negative and neutral. Sentiment analysis based on machine learning techniques has more importance as it gives better outputs. The study of these kinds of datasets with the help of machine learning techniques can be used in many different forms like to make predictions, to study the patterns, to analyse the sentiments, to study the reviews the movies, to predict the way stock market may behave. Data available on microblogging sites like Twitter have certain hidden indications which are useful to solve many research problems. This article is the review article that will highlight some recent studies in the field of sentiment analysis based on the movie review available on websites like Twitter.
    Keywords: machine learning; sentiment analysis; positive; negative; Twitter.
    DOI: 10.1504/IJESMS.2022.10049764
     
  • Design and experimental analysis of novel window mill vertical axis wind turbine   Order a copy of this article
    by N. Suthanthira Vanitha, L. Manivannan, A. Karthikeyan, K. Radhika, T. Meenakshi 
    Abstract: A novel window mill vertical axis wind turbine (VAWT) is introduced to utilise the maximum wind power to produce the electricity. The novel design improves the conversion ratio by overcoming the pressure imbalance on the existing blade design with maximum utilisation of wind energy and is capable of generating power which is three times greater than the existing windmill design. The proposed window mill is enveloped by metal case with subways and huge walls on both sides to run the turbine even during low wind head for ensuring higher efficiency than existing windmills. A complete layout of VAWT blade design is presented including the calculation of theoretical maximum efficiency, practical efficiency, propulsion and blade loads. The ANSYS simulation and experimental results are presented. These results encourage and reinforce the conviction that vertical axis wind energy conversion systems are practical and potentially very contributive renewable energy system to produce the electricity. In addition, artificial intelligence-based vertical axis wind turbine is found to provide higher performance for wind speed with economical. The proposed window mill with metal case is capable of improving the efficiency by 52% even for low heads of wind speed.
    Keywords: vertical axis wind turbine; VAWT; computational fluid dynamics; finite element analysis anemometer; blower; rotor; analysis system; solid works.
    DOI: 10.1504/IJESMS.2022.10051749
     
  • An extensive review on use of CFD simulation technique for assessment of performance of various rib geometries and arrangements in heated fluid ducts   Order a copy of this article
    by Harshad N. Deshpande, Vaijanath N. Raibhole 
    Abstract: The use of ribs is one of the vital methods of improvement of the performance of thermal systems, for example, the cooling channel of the heat exchanger, solar air heater, etc. There are many benefits of performing CFD simulation before experimentation to select rib parameters that will give the highest magnitude of the rate of heat transfer with the least magnitude of friction penalty. Therefore, considering the large scope of research for a thermal and fluid flow simulation of ribbed surfaces this review article is presented. Numerical studies performed by the researchers on rib geometrical parameters, and different rib arrangements affecting thermal performance have been reviewed and explored. The details of flow governing equations, turbulence model, discretisation scheme, grid size; the type used for CFD simulation have been discussed. This review article will certainly provide a vision to the researchers for fluid flow modification and thermal performance intensification using ribs in thermal systems.
    Keywords: CFD; discretisation scheme; grid; geometry of rib; model of turbulence; rib arrangements; thermo- hydraulic performance; simulation.
    DOI: 10.1504/IJESMS.2022.10051123
     
  • Seismic performance analysis of auxiliary pier of long-span cable-stayed bridge under seismic excitation   Order a copy of this article
    by Mengqiang Cao 
    Abstract: In order to overcome the problems of low accuracy and long-time, the seismic performance evaluation method of auxiliary pier of long-span cable-stayed bridge based on structural dynamics theory is designed considering the seismic excitation effect. The seismic performance evaluation standard is set and the structure and dynamic characteristics of the auxiliary pier are analysed by using OpenSees finite element analysis software and structural dynamics theory, and the natural vibration frequency and mode characteristics of the auxiliary pier are obtained. The seismic excitation is simulated to determine the damage degree corresponding to different earthquake damage levels, and the response of auxiliary pier is analysed. The evaluation index of seismic performance is set, and the evaluation result is obtained by comparing with the evaluation standard. The experimental results show that the proposed method has high evaluation accuracy and short evaluation time, which shows that this method has high practical application value.
    Keywords: seismic excitation; long span bridge; cable stayed bridge; auxiliary pier; seismic performance evaluation.
    DOI: 10.1504/IJESMS.2022.10051355
     
  • A stacking ensemble for credit card fraud detection using SMOTE technique   Order a copy of this article
    by Kaithekuzhical Leena Kurien, Ajeet A. Chikkamannur 
    Abstract: In present times online shopping is on rise as more people stay at home and credit card do the walking resulting in increase in credit card fraud. COVID-19 outbreak pushed digital payments exponentially, making gateway for online frauds even more common to this dependence on digital payment platforms. Empirical evidence on ensemble techniques of machine learning algorithms for fraud prediction has exhibited superior performance in identifying fraud patterns in usage of credit card data through technique of stacking and voting. The ensemble techniques like AdaBoost, gradient boost and XGBoost are applied along with stacking classifier on the credit card dataset. The stacking classifier using heterogeneous classifiers of random forest, K-nearest neighbours and logistic regression to learn the fraud patterns effectively in credit card data. The proposed methods are analysed using F1-score and recall metrics due to skewness of data.
    Keywords: ensemble methods; random forest; fraud detection; XGBoost; AdaBoost; anomaly detection.
    DOI: 10.1504/IJESMS.2023.10053233
     
  • Modified robust droop control based on control strategy of artificial neural systems for proportional load sharing between parallel operated inverters   Order a copy of this article
    by Shraddha Gajbhiye 
    Abstract: In distributed generation (DG) unit operation, the inverter plays a vital role in combine energy sources. Effective combine energy can successfully be accomplished by operating inverters with effective control techniques. Most researchers have worked for the control of inverters in a microgrid. This research discusses the methods for inverter control for proper control of frequency, power sharing and voltage used in an isolated microgrid. This research introduces a control strategy made of the virtual impedance robust droop control with artificial neural systems as a primary controller, and the current controller is used as a secondary controller in single phase microgrid. The work is implemented for linear and non-linear load, connected with two parallelly connected voltage source inverters (VSIs) which are sharing power in 1:2 ratio. Comprehensive simulations have been performed to approve the proposed control strategy’s capability in terms of balance of frequency, voltage, and power proportionately among the micro sources in the isolated microgrid.
    Keywords: microgrid; robust droop control; current controller; inverter; artificial neural system; ANS.
    DOI: 10.1504/IJESMS.2023.10053940
     
  • Lubrication characteristics of MRF ship stern tube bearing based on nitrile rubber   Order a copy of this article
    by Shengdong Zhang 
    Abstract: A mathematical calculation model of the magnetorheological fluid stern tube bearing based on nitrile rubber (MRF-STB-NR), was proposed, and established in this study, considering the coupling influence of eccentricity, temperature, and magnetic field strength. The lubricating film in both the circumferential and axial directions was evenly divided using the finite difference method, and the pressure distribution of the lubricating film was calculated using the successive over relaxation (SOR) iteration method. The study investigates the influence of eccentricity, temperature, and magnetic field strength on the total force, friction coefficient, lubricating film pressure, and lubricating film thickness of the MRF-STB-NR. The results show that the bearing capacity of MRF-STB-NR can be improved, and the friction coefficient is reduced by increasing temperature, magnetic field strength, and eccentricity.
    Keywords: stern tube bearing; STB; magnetorheological fluids; MRF; eccentricity; lubrication characteristics; successive over relaxation; SOR.
    DOI: 10.1504/IJESMS.2023.10058255
     
  • A generalised approach to the dynamic modelling of a water tube boiler   Order a copy of this article
    by R.S. Jha, Mandar M. Lele 
    Abstract: The present work aims to develop a generalised model to study the boiler dynamics in fluctuating load conditions. The model has been developed for a water tube boiler, but it can be extended to a hybrid boiler configuration as well. It generates a system of equations including mass and energy conservation equations for natural circulation circuits, drum below water level and drum above water level. It uses a transient momentum conservation equation for the natural circulation circuit and a smaller time step is used for convergence. This also presents the boiler dynamics with a feedback control system for load and water level management.
    Keywords: circulation; riser; downcomer; drum; dryness fraction; steam volume fraction; condensation enthalpy; void fraction; rate of condensation; drift velocity.
    DOI: 10.1504/IJESMS.2023.10059726
     
  • Modelling and structural analysis of coconut tree climbing mechanism   Order a copy of this article
    by Pradip Solanki, Ravi Kumar Mandava 
    Abstract: Due to the shortage of professional climbers for cutting the coconuts, many farmers are looking for coconut tree climbing mechanisms. This article aims to develop the mechanism model and examine the various types of stresses, that is, Von-misses stress and maximum shear stress, and deformation of the various mechanical components of the climbing mechanism. The mechanical parts are required to run the mechanisms are modelled individually in CREO. Further, the analysis has been conducted using ANSYS 2021. The result shows that the stresses acting on each mechanism components are less than the permissible limit of the estimated loading conditions. Therefore, it can be observed that the designed components of the mechanism can effectively withstand the load during operating conditions.
    Keywords: climbing mechanism; mechanical components; coconut tree; CREO; ANSYS.
    DOI: 10.1504/IJESMS.2023.10061187
     
  • Influence of geometric properties on the natural frequency of bending vibration of polymethacrylamide sandwich composite   Order a copy of this article
    by Rilwan Kayode Apalowo 
    Abstract: The influences of fibre orientation and thicknesses of the skin and the core on the natural frequencies of cantilever polymethacrylamide rohacell sandwich composites (PMRCs) were numerically studied in this work, using the FEA approach. The study established that maximum natural frequency is obtained for the sandwich when the fibre orientation of its GFRP skin is either lateral, (i.e., 0) or longitudinal (90) due to the comparative higher bending stiffness of the skin at these angles. It was also found that the natural frequency increases with increasing core thickness for all bending modes, increases with increasing skin thickness for the fundamental bending mode, and decreases with increasing skin thickness for higher bending modes. It was further found that higher frequencies of bending vibration are obtained in the PMRC sandwich when the PMRF core has a superior thickness ratio of the entire sandwich thickness compared to the GFRP skin.
    Keywords: vibration analysis; finite element analysis; FEA; natural frequency; sandwich laminate; honeycomb structure.

  • Research on numerical simulation of wind load on high-rise buildings along the street based on BIM model   Order a copy of this article
    by Lianguang Mo, Wenying Lu 
    Abstract: In order to overcome the problems of poor accuracy and low efficiency in the existing numerical simulation methods of building surface wind load, a new numerical simulation method of high-rise street building surface wind load based on BIM model is proposed. This method obtains the data of high-rise buildings along the street based on BIM model, and selects Realisable k-? model as the turbulence model. The non-equilibrium wall function method is used to deal with the turbulence state on the building surface, the boundary conditions are set, and the turbulence model is calculated and solved by a separate solver to realise the numerical simulation of the surface wind load of high-rise street buildings. The experimental results show that the average error of the wind pressure coefficient of the proposed numerical simulation method is less than 0.4, which fully shows that the proposed numerical simulation method has good performance.
    Keywords: BIM model; high-rise buildings along the street; building surface; wind load; numerical simulation.

  • Aerodynamic properties comparison between natural feather, nylon, and synthetic shuttlecocks   Order a copy of this article
    by Dangsuria Ab Rasid, Muhammad Fairuz Remeli, Baljit Singh, Hazim Moria 
    Abstract: This study compared the aerodynamic properties between the natural feather, nylon, and foam shuttlecocks under various pitch angles, using computational fluid dynamics (CFD) during steady-state flight conditions. The velocity varied within 10-60 m/s and =10, 20,30. The drag coefficient for feather/foam was approximately constant at 0.56. Other aerodynamic properties including the lift and moment coefficients were also investigated. The lift coefficient obtained for nylon and feather/foam were 0.38 and 0.30 at = 30 (30 m/s). All models had shown a negative sign for the moment coefficient, which indicates the aerodynamic centre is always behind the centre of gravity. Therefore, it will give stability to the shuttlecock during the flight. The nylon shuttlecock showed a higher drag coefficient compared to others due to its larger gap area and an increased wake behind the shuttlecock.
    Keywords: aerodynamic properties comparison; natural feather shuttlecocks; nylon shuttlecocks; synthetics shuttlecocks; computational fluids dynamics; CFD; drag coefficients; lift coefficients; steady-state flight.