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Thermomechanical Behavior of Brake Drums Under Extreme Braking Conditions

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Computers, Materials & Continua 2022年第8期72卷 2259-2273页

作者： T.khatir M.Bouchetara K.Derrar M.Djafri s.khatir M.Abdel Wahab Institute of science and Technology University Center Salhi Ahmed NaamaAlgeria Laboratory of Gas Combustion and Environment University of Sciences and Technology of OranL.P 1505 El-MenaouerOranUSTO 31000Algeria High school of Aeronautic Techniques Dar El Beida Algeria Ho Chi Minh City Open University Ho Chi Minh CityViet Nam Faculty of Engineering Van Lang UniversityHo Chi Minh CityVietnam soete Laboratory Faculty of Engineering and ArchitectureGhent UniversityTechnologieparkZwijnaarde 903B-9052 ZwijnaardeBelgium

Braking efficiency is characterized by reduced braking time and distance,and therefore passenger safety depends on the design of the braking *** the braking of a vehicle,the braking system must dissipate the kinetic energy by transforming it into heat energy.A too high temperature can lead to an almost total loss of braking *** excessive rise in brake temperature can also cause surface cracks extending to the outside edge of the drum friction *** transfer and temperature gradient,not to forget the vehicle’s travel environment(high speed,heavy load,and steeply sloping road conditions),must thus be the essential criteria for any brake system *** aim of the present investigation is to analyze the thermal behavior of different brake drum designs during the single emergency braking of a heavy-duty vehicle on a steeply sloping *** calculation of the temperature field is performed in transient mode using a three-dimensional finite element model assuming a constant coefficient of *** this study,the influence of geometrical brake drum configurations on the thermal behavior of brake drums with two different materials in grey cast iron FG200 and aluminum alloy 356.0 reinforced with silicon carbide(siC)particles is analyzed under extreme vehicle braking *** numerical simulation results obtained using FE software ANsYs are qualitatively compared with the results already published in the literature.

关键词： Drum brake finite element method braking energy distribution friction heat power friction heat flux transient temperature field

Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial neural network

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Frontiers of structural and Civil Engineering 2022年第8期16卷 976-989页

作者： Abdelwahhab khatir Roberto CAPOZUCCA samir khatir Erica MAGAGNINI structural section DICEA Polytechnic University of MarcheAncona 60131Italy Faculty of Civil Engineering Ho Chi Minh City Open UniversityHo Chi Minh City 700000Vietnam

Vibration-based damage detection methods have become widely used because of their advantages over traditional *** paper presents a new approach to identify the crack depth in steel beam structures based on vibration analysis using the Finite Element Method(FEM)and Artificial Neural Network(ANN)combined with Butterfly Optimization Algorithm(BOA).ANN is quite successful in such identification issues,but it has some limitations,such as reduction of error after system training is complete,which means the output does not provide optimal *** paper improves ANN training after introducing BOA as a hybrid model(BOA-ANN).Natural frequencies are used as input parameters and crack depth as *** data are collected from improved FEM using simulation tools(ABAQUs)based on different crack depths and locations as the first ***,data are collected from experimental analysis of cracked beams based on different crack depths and locations to test the reliability of the presented *** proposed approach,compared to other methods,can predict crack depth with improved accuracy.

关键词： damage prediction ANN BOA FEM experimental modal analysis

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An efficient improved Gradient Boosting for strain prediction in Near-surface Mounted fiber-reinforced polymer strengthened reinforced concrete beam

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Frontiers of structural and Civil Engineering 2024年第8期18卷 1148-1168页

作者： Abdelwahhab khatir Roberto CAPOZUCCA samir khatir Erica MAGAGNINI Brahim BENAIssA Thanh CUONG-LE structural section DICEA Polytechnic University of MarcheAncona 60131Italy Centre for Engineering Application&Technology solutions Ho Chi Minh City Open UniversityHo Chi Minh City 70000Vietnam Design Engineering Laboratory Toyota Technological InstituteNagoya 468-8511Japan

The Near-surface Mounted(NsM)strengthening technique has emerged as a promising alternative to traditional strengthening methods in recent *** the past two decades,researchers have extensively studied its potential,advantages,and applications,as well as related parameters,aiming at optimization of construction ***,there is still a need to explore further,both from a static perspective,which involves accounting for the nonconservation of the contact section resulting from the bond-slip effect between fiber-reinforced polymer(FRP)rods and resin and is typically neglected by existing analytical models,as well as from a dynamic standpoint,which involves studying the trends of vibration frequencies to understand the effects of various forms of damage and the efficiency of *** address this gap in knowledge,this research involves static and dynamic tests on simply supported reinforced concrete(RC)beams using rods of NsM carbon fiber reinforced polymer(CFRP)and glass fiber reinforced polymer(GFRP).The main objective is to examine the effects of various strengthening *** research conducts bending tests with loading cycles until failure,and it helps to define the behavior of beam specimens under various damage degrees,including concrete *** analysis by free vibration testing enables tracking of the effectiveness of the reinforcement at various damage levels at each stage of the loading *** addition,application of Particle swarm Optimization(PsO)and Genetic Algorithm(GA)is proposed to optimize Gradient Boosting(GB)training performance for concrete strain prediction in NsM-FRP *** GB using Particle swarm Optimization(GBPsO)and GB using Genetic Algorithm(GBGA)systems were trained using an experimental data set,where the input data was a static applied load and the output data was the consequent *** models of GBPsO and GBGA have been shown to provide highly accurate results for predicting *** model

关键词： NsM technique fiber-reinforced polymer rods static and dynamic analysis GB PsO GA finite element analysis

Data-driven approach to solve vertical drain under time-dependent loading

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Frontiers of structural and Civil Engineering 2021年第3期15卷 696-711页

作者： Trong NGHIA-NGUYEN Mamoru KIKUMOTO samir khatir salisa CHAIYAPUT HNGUYEN-XUAN Thanh CUONG-LE Department of Civil Engineering Yokohama National UniversityYokohama 240-8501Japan Faculty of Civil Engineering Ho Chi Minh City Open UniversityHo Chi Minh City 70000Vietnam Department of Electrical Energy MetalsMechanical Constructions and SystemsFaculty of Engineering and ArchitectureGhent UniversityGhent 9000Belgium Department of Civil Engineering School of EngineeringKing Mongkut’s Institute of Technology LadkrabangBangkok 10520Thailand CIRTECH Institute Ho Chi Minh City University of Technology(HUTECH)Ho Chi Minh City 708300Vietnam

Currently,the vertical drain consolidation problem is solved by numerous analytical solutions,such as time-dependent solutions and linear or parabolic radial drainage in the smear zone,and no artificial intelligence(AI)approach has been ***,in this study,a new hybrid model based on deep neural networks(DNNs),particle swarm optimization(PsO),and genetic algorithms(GAs)is proposed to solve this *** DNN can effectively simulate any sophisticated equation,and the PsO and GA can optimize the selected DNN and improve the performance of the prediction *** the present study,analytical solutions to vertical drains in the literature are incorporated into the DNN–PsO and DNN–GA prediction models with three different radial drainage patterns in the smear zone under timedependent *** verification performed with analytical solutions and measurements from three full-scale embankment tests revealed promising applications of the proposed approach.

关键词： vertical drain artificial neural network time-dependent loading deep learning network genetic algorithm particle swarm optimization

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Machine learning based models for predicting compressive strength of geopolymer concrete

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Frontiers of structural and Civil Engineering 2024年第7期18卷 1028-1049页

作者： Quang-Huy LE Duy-Hung NGUYEN Thanh sANG-TO samir khatir Hoang LE-MINH Amir H.GANDOMI Thanh CUONG-LE Center for Engineering Application and Technology solutions Ho Chi Minh City Open UniversityHo Chi Minh 700000Vietnam Institute of Civil Engineering Ho Chi Minh City University of TransportHo Chi Minh 700000Vietnam Faculty of Engineering&Information Technology University of Technology SydneyUltimoNSW 2007Australia

Recently,great attention has been paid to geopolymer concrete due to its advantageous mechanical and environmentally friendly *** effort has been made in experimental studies to advance the understanding of geopolymer concrete,in which compressive strength is one of the most important *** facilitate engineering work on the material,an efficient predicting model is *** this study,three machine learning(ML)-based models,namely deep neural network(DNN),K-nearest neighbors(KNN),and support vector machines(sVM),are developed for forecasting the compressive strength of the geopolymer concrete.A total of 375 experimental samples are collected from the literature to build a database for the development of the predicting models.A careful procedure for data preprocessing is implemented,by which outliers are examined and removed from the database and input variables are standardized before feeding to the fitting *** standard K-fold cross-validation approach is applied for evaluating the performance of the models so that overfitting status is well managed,thus the generalizability of the models is *** effectiveness of the models is assessed via statistical metrics including root mean squared error(RMsE),mean absolute error(MAE),correlation coefficient(R),and the recently proposed performance index(PI).The basic mean square error(MsE)is used as the loss function to be minimized during the model fitting *** three ML-based models are successfully developed for estimating the compressive strength,for which good correlations between the predicted and the true values are obtained for DNN,KNN,and *** numerical results suggest that the DNN model generally outperforms the other two models.

关键词： geopolymer concrete compressive strength prediction machine-learning based model deep neural network K-nearest neighbor support vector machines

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葫芦巴种子多糖对糖尿病大鼠血糖的动态影响

南京大学学报（自然科学版） 1999年第2期35卷 146-150页

作者：黄诚朴春艳 khatir 南京大学生物科学与技术系无锡轻工大学食品科学院

用四氧嘧啶（ａｌｏｘａｎ）引发Ｓ．Ｄ大鼠糖尿病模型，饲喂不同剂量葫芦巴种子提取物（ＦＳＰ１），测定饲喂后不同时间（ｔ）的空腹血糖浓度（Ｇ），建立Ｇ－ｔ函数关系，结合各组死亡率，降糖速率及统计检验，得出如下结论：４．５... 详细信息

用四氧嘧啶（ａｌｏｘａｎ）引发Ｓ．Ｄ大鼠糖尿病模型，饲喂不同剂量葫芦巴种子提取物（ＦＳＰ１），测定饲喂后不同时间（ｔ）的空腹血糖浓度（Ｇ），建立Ｇ－ｔ函数关系，结合各组死亡率，降糖速率及统计检验，得出如下结论：４．５ｇ／ｋｇ体重剂量有极明显的降糖作用，对正常鼠无致毒性，其血糖下降速率随时间变小，表明可能存在受血糖浓度制约的ＦＳＰ１降糖效果的负反馈调节；０．９ｇ／ｋｇ剂量也有较明显的降糖作用，但理论时效较长；０．

关键词：葫芦巴种子多糖糖尿病降血糖作用

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反复短路运作下IGBT器件失效的研究

电力电子 2007年第1期5卷 35-38,7页

作者： Z.khatir s.Lefebvre 寇茜

本文描述600V穿通型、600V非穿通型和1200V非穿通型的IGBT,在其器件寿命期限内,对它施加不同能量来进行反复短路试验的结果。文中指出,一种和试验条件有关的临界能量,可以区分两种失效模式:具有累积退化效应的第一种失效模式,需要进行大... 详细信息

本文描述600V穿通型、600V非穿通型和1200V非穿通型的IGBT,在其器件寿命期限内,对它施加不同能量来进行反复短路试验的结果。文中指出,一种和试验条件有关的临界能量,可以区分两种失效模式:具有累积退化效应的第一种失效模式,需要进行大约104次短路才损坏,而具有热击穿效应的第二种失效模式,在第一次短路时就损坏。基于试验和仿真的结果,详细描述了短路时的"极限能量"和"延缓"模式。事实上,临界能量对应阻挡结附近区域约650℃的临界温度,这点似乎和测试条件无关。尤其是,分析了短路后的漏泄电流,说明存在将两种失效模式明显区分开来的临界温度。

关键词：短路试验 IGBT 器件失效失效模式运作非穿通型临界能量临界温度

Exploring Trees Diversity and Ecological Value of Mountain Forests in the Blue Nile Region of sudan

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Open Journal of Ecology 2024年第3期14卷 245-256页

作者： Dafa-Alla M. Dafa-Alla Haytham H. Gibreel Ahmed A. H. siddig Yousif E. Yagoub Hashim A. Abdelkarim khatir H.T. saeed Ahmed I. Abuelbashar Faculty of Forestry-shambat University of Khartoum Khartoum Sudan Harvard Forest Harvard University Petersham MA USA Faculty of Forest science and Wood Technology University of Gezira Wad-Medani Sudan Department of Forestry and Range sciences Faculty of Natural Resources and Environmental Studies Alsalam University Alfula Sudan Forest Research and Gum Arabic Centre Agricultural Research Corporation Khartoum-Soba Sudan

We carried out this research at Abu-Gaddaf Natural Forest Reserve (ANFR) which is located east of Blue Nile River, in Blue Nile state, sudan. It aims at exploring tree composition assessing their diversity indices, and ecological importance values. For survey of types and estimation of density of tree species in the forest, we randomly distributed 97 circular sample plots (0.1 hectare (ha)). In each sample plot we identified all trees to the species level, recorded their frequencies and computed species diversity and importance value indices (IVI). A total of 13 tree species, which belong to 12 genera and nine families, were identified in the tree layer of the forest. Mean tree density in ANFR was 116 trees/ha, composed mainly of Boswellia papyrifera (Del.) Hochst. (48), followed by Combretum hartmannianu (19) and Lannea fruticosa (18). Fabaceae was the most common family followed by Combretaceae and Malvaceae. B. papyrifera Delile Hochst was the most abundant while Acacia seyal was the least abundant species. species richness (R = 1.71), evenness (E = 0.69), dominance (0.24) indices and simpson’s Index of Diversity (D' = 0.76) suggest a moderate diversity, moderate numbers of individuals in each species and a moderate community stability. The research provides empirical results on diversity and ecological importance value of species, signifies the urging need to safeguard threatened species and to prioritize them for conservation, as well as the need to promote management of abundant species to provide multiple forest ecosystem services.

关键词： Tree species Diversity Dry-Land Forest sudan