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Analysis of particle dispersion and cavity formation during bulk particle water entry using fully coupled CFD-DEM-VOF approach

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Particuology 2024年第7期90卷 558-580页

作者： Utkan Çalışkan Sanja Mišković Norman B.Keevil institute of Mining Engineering University of British Columbia2329 West MallVancouverBC V6T 1Z4Canada

This paper presents the development and validation of a fully coupled computational fluid dynamics—discrete element method—volume of fluid(CFD-DEM-VOF)model to simulate the complex behavior of particle-laden flows with free *** coupling between the fluid and particle phases is established through the implemented continuity,momentum,and alpha transport *** coupled particle forces such as drag,pressure gradient,dense virtual mass,viscous,and interface forces are also integrated,with drag and dense virtual mass forces being dependent on local *** integrated conservative alpha transport equation ensures phase volume conservation during interactions between particles and ***,we have implemented a trilinear interpolation method designed to operate on unstructured hexahedral *** method has been tested for its ability to properly resolve the coupling effects in the numerical simulations,particularly in cases with a relatively low cell-size *** model is validated through three distinct test cases:single particle water entry,dam break with particles,and water entry of a group of particles *** experimental setup is built to study the dynamics of the water entry of a group of particles,where three key flow features are analyzed:the evolution of average particle velocity,cavity shape,and particle dispersion cloud profiles in *** tests involve four different scenarios,including two different water levels(16.1 and 20.1 cm)and two different particle densities(2650 and 4000 kg/m3).High-speed videometry and particle tracking velocimetry(using imageJ/TrackMate)methods are employed for experimental data *** is demonstrated that numerical results are in excellent agreement with theoretical predictions and experimental *** study highlights the significance of vortices in cavity shaping and particle *** validated CFD-DEM-VOF model constitutes a robust tool for simulating particle-laden flows,cont

关键词： CFD-DEM-VOF Trilinear interpolation Particle-laden flow Free-surface Particle water entry Volume conservation

improved Dwarf Mongoose Optimization algorithm for Feature Selection:Application in Software Fault Prediction Datasets

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Journal of Bionic Engineering 2024年第4期21卷 2000-2033页

作者： Abdelaziz i.Hammouri Mohammed A.Awadallah Malik Sh.Braik Mohammed Azmi al-Betar Majdi Beseiso Department of Computer Science Al-Balqa Applied UniversityAl-Salt19117Jordan Department of Scientific Research and Graduate Studies University of Prince Mugrin42241MedinaSaudi Arabia Department of Computer Science Al-Aqsa UniversityGaza4051Palestine Artificial intelligence Research Center(AiRC) Ajman UniversityAjmanUnited Arab Emirates Department of information Technology Al-Huson University CollegeAl-Balqa Applied UniversityAl-HusonIrbid21110Jordan

Feature selection(FS)plays a crucial role in pre-processing machine learning datasets,as it eliminates redundant features to improve classification accuracy and reduce computational *** paper presents an enhanced approach to FS for software fault prediction,specifically by enhancing the binary dwarf mongoose optimization(BDMO)algorithm with a crossover mechanism and a modified positioning updating *** proposed approach,termed iBDMOcr,aims to fortify exploration capability,promote population diversity,and lastly improve the wrapper-based FS process for software fault prediction *** gained superb performance compared to other well-esteemed optimization methods across 17 benchmark *** ranked first in 11 out of 17 datasets in terms of average classification ***,iBDMOcr outperformed other methods in terms of average fitness values and number of selected features across all *** findings demonstrate the effectiveness of iBDMOcr in addressing FS problems in software fault prediction,leading to more accurate and efficient models.

关键词： Dwarf mongoose optimization algorithm Optimization Feature selection Classification

Thermal heat flux distribution prediction in an electrical vehicle battery cell using finite element analysis and neural network

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Green Energy and intelligent Transportation 2024年第3期3卷 54-62页

作者： Luttfi A.al-Haddad Latif ibraheem Ahmed i.EL-Seesy alaa Abdulhady Jaber Sinan A.al-Haddad Reza Khosrozadeh Training and Workshops Center University of TechnologyBaghdadIraq Mechanical Engineering Department Memorial University of NewfoundlandNewfoundland and LabradorCanada Mechanical Engineering Department Benha Faculty of EngineeringBenha UniversityBenhaEgypt Mechanical Engineering Department University of TechnologyBaghdadIraq Civil Engineering Department University of TechnologyBaghdadIraq

in terms of battery design and evaluation,Electric Vehicles(EVs)are receiving a great deal of attention as a modern,eco-friendly,sustainable transportation *** this paper,a novel battery pack is designed to maintain a uniform temperature distribution,allowing the battery to operate within its optimal temperature *** proposed battery design is part of a main channel where a portion of cool air will pass from an inlet then exit from an outlet where a uniform temperature distribution is ***,a 3-D model of a battery cell was created,followed by thermal simulation for 15C,25C,and 35C ambient *** simulation results reveal that the temperature distribution is nearly uniform,with slightly higher values in the middle portion of the cell ***,using finite element analysis(FEA),it was determined that the heat flux per unit area is nearly uniform with a slight increase at the ***,a machine learning model is proposed by utilizing a neural network(NN).Lastly,the heat flux values were predicted using the NN model that was *** model was assessed based on statistical measures where a root mean square error(RMSE)value of 0.87%was *** NN outperformed FEA in terms of time consumption with a high prediction accuracy,leveraging the potential of adopting machine learning over FEA in related operational assessments.

关键词： EV battery Thermal distribution Finite element analysis Neural network Heat flux

Comparative analysis of jet and underlying event properties across various models as a function of charged particle multiplicity at 7 TeV

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Chinese Physics C 2024年第9期48卷 118-125页

作者： Maryam Waqar Haifa i.alrebdi Muhammad Waqas K.S.al-mugren Muhammad Ajaz Department of Physics Abdul Wali Khan University MardanMardan 23200Pakistan Department of Physics College of SciencePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia School of Nuclear Science and Technology University of Chinese Academy of SciencesBeijing 100049China

in this study,a comprehensive analysis of jets and underlying events as a function of charged particle multiplicity in proton-proton(pp)collisions at a center-of-mass energy of √s=7 TeV is *** Monte Carlo(MC)event generators,including Pythia8.308,EPOS 1.99,EPOSLHC,EPOS4_(Hydro),and EPOS4_(noHydro),are employed to predict particle *** predictions from these models are compared with experimental data from the CMS *** charged particles are categorized into those associated with underlying events and those linked to jets,and the analysis is restricted to charged particles with|η|0.25 GeV/*** comparing the MC predictions with CMS data,we find that EPOS4_(Hydro),EPOSLHC,and Pythia8 consistently reproduce the experimental results for all charged particles,underlying events,intrajets,and leading charged *** charged jet rates with p_T^(***)>5 GeV/c,EPOS4_(Hydro)and Pythia8 perform exceptionally *** the case of charged jet rates with p_T^(***)→30 GeV/c,EPOSLHC reproduces satisfactorily good results,whereas EPOS4 Hydro exhibits good agreement with the data at higher charged particle multiplicities compared to the other *** can be attributed to the conversion of energy into flow when"Hydro=on"leading to an increase in *** EPOSLHC model describes the data better owing to the new collective flow effects,correlated flow treatment,and parameterization compared to EPOS ***,the examination of the jet p_T spectrum and normalized charged p_T density reveals that EPOS4_(Hydro),EPOS4_(noHydro),and EPOSLHC exhibit good agreement with the experimental results,whereas Pythia8 and EPOS 1.99 do not perform as well owing to the lack of correlated flow treatment.

关键词： Monte Carlo models underlying event jets charged particle multiplicity

A Hybrid Classification and identification of Pneumonia Using African Buffalo Optimization and CNN from Chest X-Ray images

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2497-2517页

作者： Nasser alalwan Ahmed i.Taloba Amr Abozeid Ahmed ibrahim alzahrani ali H.al-Bayatti Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia information System Department Faculty of Computers and InformationAssiut UniversityAssiutEgypt Mathematics Department Faculty of ScienceAl-Azhar UniversityCairoEgypt Cyber Technology institute(CTi) De Montfort UniversityLeicesterUK

An illness known as pneumonia causes inflammation in the *** there is so much information available fromvarious X-ray images,diagnosing pneumonia has typically proven *** improve image quality and speed up the diagnosis of pneumonia,numerous approaches have been *** date,several methods have been employed to identify *** Convolutional Neural Network(CNN)has achieved outstanding success in identifying and diagnosing diseases in the fields of medicine and ***,these methods are complex,inefficient,and imprecise to analyze a big number of *** this paper,a new hybrid method for the automatic classification and identification of Pneumonia from chest X-ray images is *** proposed method(ABOCNN)utilized theAfrican BuffaloOptimization(ABO)algorithmto enhanceCNNperformance and *** Weinmed filter is employed for pre-processing to eliminate unwanted noises from chest X-ray images,followed by feature extraction using the Grey Level Co-Occurrence Matrix(GLCM)*** features are then selected from the dataset using the ABO algorithm,and ultimately,high-performance deep learning using the CNN approach is introduced for the classification and identification of *** results on various datasets showed that,when contrasted to other approaches,the ABO-CNN outperforms them all for the classification *** proposed method exhibits superior values like 96.95%,88%,86%,and 86%for accuracy,precision,recall,and F1-score,respectively.

关键词： African buffalo optimization convolutional neural network pneumonia X-ray

Single image super-resolution:a comprehensive review and recent insight

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Frontiers of Computer Science 2024年第1期18卷 139-156页

作者： Hanadi al-MEKHLAFi Shiguang LiU College of intelligence and Computing Tianjin UniversityTianjin 300350China

Super-resolution(SR)is a long-standing problem in image processing and computer vision and has attracted great attention from researchers over the *** main concept of SR is to reconstruct images from low-resolution(LR)to high-resolution(HR).it is an ongoing process in image technology,through up-sampling,de-blurring,and *** neural network(CNN)has been widely used to enhance the resolution of images in recent *** alternative methods use deep learning to improve the progress of image super-resolution based on ***,we review the recent findings of single image super-resolution using deep learning with an emphasis on distillation knowledge used to enhance image super-resolution.,it is also to highlight the potential applications of image super-resolution in security monitoring,medical diagnosis,microscopy image processing,satellite remote sensing,communication transmission,the digital multimedia industry and video ***,we present the challenges and assess future trends in super-resolution based on deep learning.

关键词： super-resolution deep learning single-image interpolation-based learning-based reconstruction-based

RoGRUT: A Hybrid Deep Learning Model for Detecting Power Trapping in Smart Grids

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Computers, Materials & Continua 2024年第5期79卷 3175-3192页

作者： Farah Mohammad Saad al-Ahmadi Jalal al-Muhtadi Center of Excellence in information Assurance(CoEiA) King Saud UniversityRiyadh11543Saudi Arabia College of Computer&information Sciences King Saud UniversityRiyadh11543Saudi Arabia

Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity *** hinders the economic growth of utility companies,poses electrical risks,and impacts the high energy costs borne by *** development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data,including information on client consumption,which may be used to identify electricity theft using machine learning and deep learning ***,there also exist different solutions such as hardware-based solutions to detect electricity theft that may require human resources and expensive ***-based solutions are presented in the literature to identify electricity theft but due to the dimensionality curse,class imbalance issue and improper hyper-parameter tuning of such models lead to poor *** this research,a hybrid deep learning model abbreviated as RoGRUT is proposed to detect electricity theft as amalicious and non-malicious *** key steps of the RoGRUT are data preprocessing that covers the problem of class imbalance,feature extraction and final theft *** advanced-level models like RoBERTa is used to address the curse of dimensionality issue,the near miss for class imbalance,and transfer learning for *** effectiveness of the RoGRUTis evaluated using the dataset fromactual smartmeters.A significant number of simulations demonstrate that,when compared to its competitors,the RoGRUT achieves the best classification *** performance evaluation of the proposed model revealed exemplary results across *** accuracy achieved was 88%,with precision at an impressive 86%and recall reaching 84%.The F1-Score,a measure of overall performance,stood at 85%.Furthermore,themodel exhibited a noteworthyMatthew correlation coefficient of 78%and excelled with an area under the curve of 91%.

关键词： Electricity theft smart grid RoBERTa GRU transfer learning

Developing a Model for Parkinson’s Disease Detection Using Machine Learning algorithms

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Computers, Materials & Continua 2024年第6期79卷 4945-4962页

作者： Naif al Mudawi Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia

Parkinson’s disease(PD)is a chronic neurological condition that progresses over *** start to have trouble speaking,writing,walking,or performing other basic skills as dopamine-generating neurons in some brain regions are injured or *** patient’s symptoms become more severe due to the worsening of their signs over *** this study,we applied state-of-the-art machine learning algorithms to diagnose Parkinson’s disease and identify related risk *** research worked on the publicly available dataset on PD,and the dataset consists of a set of significant characteristics of *** aim to apply soft computing techniques and provide an effective solution for medical professionals to diagnose PD *** research methodology involves developing a model using a machine learning *** the model selection,eight different machine learning techniques were adopted:Namely,Random Forest(RF),Decision Tree(DT),Support Vector Machine(SVM),Naïve Bayes(NB),Light Gradient Boosting Machine(LightGBM),K-Nearest Neighbours(KNN),Extreme Gradient Boosting(XGBoost),and Logistic Regression(LR).Subsequently,the concentrated models were validated through 10-fold Cross-Validation and Receiver Operating Characteristic(ROC)—Area Under the Curve(AUC).in addition,GridSearchCV was utilised to measure each algorithm’s best parameter;eventually,the models were trained through the hyperparameter tuning *** 98%accuracy,LightGBM had the highest accuracy in this ***,KNN,and SVM came in second with 96%***,the performance scores of NB and LR were recorded to be 76%and 83%,*** is to be mentioned that after applying 10-fold cross-validation,the average performance score of LightGBM accounted for 93%.At the same time,the percentage of ROC-AUC appeared at 0.92,which indicates that this LightGBM model reached a satisfactory ***,we extracted meaningful insights and figured out potential gaps on top of *** extracting meaningful in

关键词： Light GBM cross-validation ROC-AUC Parkinson’s disease(PD) SVM and XGBoost

The optimal semi-analytical modeling for the infinite-conductivity horizontal well performance under rectangular bounded reservoir based on a new instantaneous source function

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Petroleum 2024年第1期10卷 68-84页

作者： Firas A.A.al-Kabbawi independent scholar LivingstonScotlandUNITED KINGDOM

The main objective of this study is to develop the optimal semi-analytical modeling for the infiniteconductivity horizontal well performance under rectangular bounded reservoir based on a new instantaneous source *** available semi-analytical infinite-conductivity models(iCMs)for horizontal well under rectangular bounded reservoir in literature were developed by applying superposition of pressures in space(SPS).A new instantaneous source function(i.e.,instantaneous uniform-flux segmentary source function under bounded reservoir)is derived to be used instead of SPS to develop the optimal semi-analytical *** new semi-analytical iCM is verified with iCM of Schlumberger[1]and with previous semi-analytical iCMs in terms of bottom hole pressure(BHP)profile and inflow rate distribution along the *** model is also validated with real horizontal wells in terms of inflow rate distribution along the *** results show that the developed model gives the optimal semi-analytical modeling for the infinite-conductivity horizontal well performance under rectangular bounded *** that,high computationalefficiency and high-resolution of wellbore discretization have been achieved(i.e.,wellbore segment number could be tens of hundreds depending on solution requirement).The results also show that at pseudosteady state(PSS)flow regime,inflow rate distribution along the wellbore by previous semi-analytical iCMs is stabilized U-shaped as performance of inflow rate distribution at late radial flow ***,the previous semi-analytical iCMs are incorrectly modeling inflow rate distribution at PSS flow regime due to the negative influence of applying *** optimal semi-analytical iCM is in a general form and real time domain,and can be applicable for 3D horizontal well and 2D vertical fracture well under infinite and rectangular bounded reservoirs,of uniform-flux and infinite-conductivity wellbore conditions at any time of well life.

关键词： infinite-conductivity model Horizontal well performance Semi-analytical modeling Rectangular bounded reservoir instantaneous source functions Flow regimes