International Journal of Engineering
Volume:34 Issue: 1, Jan 2021

Nano bioactive glasses are known as suitable alternatives to repair the damaged bone tissues. In this research, novel sol-gel derived bioactive glass composites were synthesized through a reduction in the common weight percent of SiO2 substituted by 15 wt% of titanium dioxide nanotubes (TNTs) at two different steps by the synthetic procedure. The morphology, crystalline structure, and functional groups of the composites were evaluated through scanning electron microscopy (SEM), X-ray diffraction (XRD) and Fourier transform infrared (FTIR) analyses. Based on the SEM images, the step in which TNTs were added to the solution completely changed the morphology of the composite. Bioactivity tests were carried out by soaking the samples in the simulated body fluid (SBF) at the intervals of 14 and 28 days followed by the investigation of hydroxyapatite (HA) layer formation on the surface of the samples. According to XRD peaks at 2-theta angle of around 31 and 40 degrees, it was found that the presence of titanium dioxide nanotubes improved bioactivity after 14 days of immersion and both 58S-TNT composites were more bioactive than 58S bioglass, while 58S bioactive glass possessed more intense peaks of HA after 28 days of immersion in SBF. Furthermore, the drug loading characteristic of the prepared composites was examined and the results showed that the addition of nanotubes improved the drug loading performance of bioactive composites containing TNTs up to 70% compared to the 58S bioglass with 37% drug loading.

This experimental work is about the study of drying shrinkage followed by strength testing of lightweight foamed concrete (LFC) specimens with the confinement of woven fiberglass mesh (FGM) at three different densities. The LFC specimens were wrapped with 1-layer to 3-layer(s) of FGM for cube and cylinder specimens and in beam specimen, it was centrally spread along the longitudinal axis. The specimens were cured under air storage conditions and the drying shrinkage test was carried following ASTM C157/C 157M specification on three prism-shaped ‘75mmx75mmx285mm’ specimens. NORAITE PA-1 foaming agent was used to produce the desired density of LFC. All of 324 specimens were tested for mechanical properties of LFC. The cast specimens were put to test at 7days, 28days and 56 days. In compression strength test, cube dimensions of 100mm side following BS EN 12390-3:2009 were adopted. The flexural strength was conducted on  ‘100mmx100mmx500mm’ beam specimens following BS ISO 1920-8:2009. The specimens ‘100mm in diameter and 200mm in height’ were tested for split tensile strength considering ASTM C496/ C496M-04e1 specifications. The result showed that confinement with 160g/m2 (GSM) of FGM significantly restricts the drying shrinkage of LFC specimens compared to control specimens and it decreases with the increases in layer(s) from l-layer to 3-layer(s) and density of LFC. The testing of the mechanical properties of LFC showed a direct proportionality between strength and LFC density and confinement layer(s). The failure pattern observed in all specimens was either by debonding or splitting of fibers of  FGM. Thus,  LFC at 1600kg/m3 density confined/reinforced with 3-layers of  FGM conquers the good performance in drying shrinkage and strength properties while the poor performance was shown by the unconfined LFC at 600kg/m3 density.

The present study is to elucidate and efficacy of Ultra-fine slag and Calcium Chloride in improving the Engineering characteristics of expansive soil. An experimental program has evaluated the effects of Ultra-fine slag 3%, 6%, 9% and CaCl2 0.25%, 0.5%,  1.0%, Free swell index, swelling potential, swell pressure, plasticity, compaction, strength, hydraulic conductivity, Cation Exchange Capacity and microstructural XRD, SEM tests of expansive soil and also a statistical tool was used to predict the experimental values of unconfined compressive strength of the soil. Both admixtures were added independently and blended to the expansive soil. Mixing of Ultra-fine slag, CaCl2 and expansive soil results have shown that plasticity index, hydraulic conductivity, swelling properties of blends decreased and dry unit weight and unconfined compressive strength is increased in combination of soil +6% of Ultra-fine slag + 1% CaCl2. The unconfined compressive strength (UCS) of the samples is again found to decrease slightly beyond 6% Ultra-fine slag and 1% CaCl2. It was found that the optimum quantity of material for a favorable combination of soil +6% of Ultra-fine slag + 1% CaCl2 was taken for further study in view of its economy due to lower CaCl2 content.

The presence of fibers in concrete specimens has an effective role on how the specimens were failed. In this study, the effects of aluminium oxide nanoparticles on the workability, mechanical and, durability properties of SCCs containing glass fibers were investigated. Glass fibers contents of 0, 0.5, 1, and 1.5 % by volume of concrete and aluminium oxide nanoparticles contents of 0, 0.5, 1, 1.5, 2, and 3 % by weight of cement were used. The properties of fresh concrete were evaluated according to EFNARC consideartions. The mechanical properties were evaluated by compressive strength, splitting tensile strength, and ultrasonic pulse velocity tests. The durability of the specimens was also measured using water absorption tests, water penetration depth and, electrical resistivity. Combined use of 2% aluminium oxide nanoparticles and 1% glass fiber has increased the compressive and tensile strengths of SCCs by 59% and 119.2%, respectively. Aluminium nanoparticles have a very high specific surface area and their reactivity causes them to react rapidly with calcium hydroxide to produce silicate-hydrate gels. Therefore, calcium hydroxide crystals are reduced and the cavities in the cement gel are filled and the compressive strength is increased. The use of aluminium oxide nanoparticles along with glass fibers reduces the water absorption rate compared to the sample without these materials. This is one of the effective properties of aluminium oxide nanoparticles, which increases the resistance to adverse environmental factors by reducing water absorption.

Reinforced concrete hollow-core slab (HCS) is a new type of lightweight slabs in which the longitudinal voids provide the ability to reduce the concrete amount. Reducing the concrete amount causes a reduction of the dead loads which consequently leads to cost-saving, fast construction, and getting long-span. The experimental program includes constructing and testing slab species with dimensions 1700×435×125mm to investigate the effect of eliminating concrete ratio by changing the size of the longitudinal void and the number of longitudinal voids on the performance of HCS. The experimental results showed that elimination of the concrete with percentages 10.83, 17.20 and 24.37% from the hollow-core high strength slabs using three longitudinal voids of diameters 50, 63, and 75mm, respectively, resulted in saving the ultimate strength by 90.06, 87.84 and 85.07%, and increasing the ultimate deflection by 5.48, 10.80 and 17.44%. While, elimination of the concrete with percentages 16.25, 24.37 and 32.50% from the hollow-core high strength slabs using two, three, and four longitudinal voids of 75mm diameter resulted in saving the ultimate strength with percentages 89.29, 85.07 and 80.61%, and increasing the ultimate deflection with percentages 7.57, 17.44 and 22.81% respectively when compared with the reference solid slab.

The procedure of estimating the RC moment-resisting frames under blast loading using a multi-mode adaptive pushover (MADP) analysis is investigated in the current study. The main advantage of the proposed procedure is the combination of the multi-mode and adaptive pushover analysis approaches, which has not been done in the past for blast loadings. To investigate the efficiency of the proposed approach, several RC moment-resisting frames (RC-MRFs) of the 4-, 8-, and 20- storey are considered in the study. For a better comparison, the conventional modal pushover analysis (MPA), nonlinear response history analysis (NRHA), and the proposed approach are considered in the simulations. To this end, various influential parameters including the lateral force, floor displacement, storey drift, storey drift ratio, etc. are considered. For all models, the first three mode shapes were considered in the analysis procedure, while for the case of 20 storey RC-MRF, the torsional effect is included as well. The results indicated that the proposed MADP procedure has adequate accuracy and efficiency to estimate the blast loading demand on RC-MRFs.

The rapid growth of cloud environments has led to the expansion of resources that offer a variety of services. The opertions of the services are usually very simple and may not satisfy the  complex needs of the user, hence there is a need for a combination of these services that can fulfill the user's requirements. Most of the service composition methods in cloud environments assume that the involved services came from one cloud, and this is unrealistic because other clouds may provide more relevant services. The challenges in composition services distributed in multi-cloud environments include increased cost and a reduction in its speed due to the increasing number of services, providers, and clouds; so, in order to overcome these challenges, the number of providers and participating clouds must be reduced. This study used the Skyline service algorithm to compose services in multi-cloud environments, which examined all the clouds during the service composition process. The proposed method can provide an applicable composition service to the user with the lowest communication cost by considering the number of clouds and by using fewer providers. The Skyline algorithm involves two steps. In the first one, the best composition in a cloud environment is selected among all the possible providers by considering the number of providers and the communication time. In the second step, the Skyline algorithm is used to create all the possible compositions in a multi-cloud environment. Parameters such as fewer clouds and shorter communication times between the clouds are selected. The results show that the proposed method can find the composition with the least number of clouds, the lowest cost, and has the lowest calculation time. It can be said that the Skyline makes it possible to select a suitable composition of user-requested services in a multi-cloud environment.

We consider the effect of segregation on opinion formation in social networks with and without influential leaders in scale-free random networks, which is found in many social and natural phenomena. We have used agent-based modeling and simulation, focusing on the social impact model of opinion formation. Two simulation scenarios of this opinion formation model have been considered: (1) the original scenario which randomly assigns persuasion strengths to the agents, and (2) a centrality-based scenario, which assigns persuasion strengths proportional to the agents’ centralities. In the latter scenario, hubs are considered more influential leaders who are more connected to others and have higher persuasion strengths than others. The simulation results show a correlation between segregation and change of population opinion in the original model, but no correlation between both variables in the centrality-based scenario. The results lead us to conclude that with strong influential leaders in society, the effect of segregation in opinion formation is neglectable.

Epilepsy is one of the common neurological disorders which can cause unprovoked seizures. Currently, diagnosis and evaluation are carried out using electroencephalogram (EEG) signal analysis, which is performed visually by clinicians. Since EEG signals tend to be random and non-stationary, the visual inspection often provides misrepresentation of results. Numerous studies have been proposed computer-based analysis for epileptic EEG classification; however, there is still a gap to improve detection accuracy with a small number of features. Therefore, in this study, we proposed an automatic detection protocol for epileptic EEG classification. The proposed methods are relative wavelet energy and wavelet entropy for feature extraction and combined with the classifier method for automatic detection. In this study, three classes of EEG consisted of pre-ictal, ictal, and interictal were used as test data and also evaluate the proposed method. EEG signals were decomposed using wavelet transform into five conventional sub-bands, including gamma, beta, alpha, theta, and delta. The relative energy and entropy were then calculated in each of these bands as a feature set. These methods are chosen with consider of low-cost computing. We tested the performance of our feature extraction method using Support Vector Machine (SVM), both linear and non-linear kernels. From the simulation, the highest accuracy was 80-96.7% for ictal vs. pre-ictal, ictal vs. inter-ictal, pre-ictal vs. inter-ictal, and ictal vs. non-ictal. Finally, this work was expected to help clinicians in the detection of epilepsy onset based on EEG signals.

This research work is concerned with the predictability of ensemble and singular tree-based machine learning algorithms during the recession and prosperity of the two companies listed in the Tehran Stock Exchange in the context of big data. In this regard, the main issue is that economic managers and the academic community require predicting models with more accuracy and reduced execution time; moreover, the prediction of the companies recession in the stock market is highly significant. Machine learning algorithms must be able to appropriately predict the stock return sign during the market downturn and boom days. Addressing the stated challenge will upgrade the quality of stock purchases and, subsequently, will increase profitability. In this article, the proposed solution relies on the utilization of tree-based machine learning algorithms in the context of big data. The proposed solution exploits the decision tree algorithm, which is a traditional and singular tree-based learning algorithm. Furthermore, two modern and ensemble tree-based learning algorithms, random forest and gradient boosted tree, has been utilized for predicting the stock return sign during recession and prosperity. The mentioned cases were implemented by applying the machine learning tools in python programming language and PYSPARK library that is used explicitly for the big data context. The utilized research data of the current study are the shares information of two companies of the Tehran Stock Exchange. The obtained results reveal that the applied ensemble learning algorithms have performed better than the singular learning algorithms. Additionally, adding 23 technical features to the initial data and subsequent applying of the PCA feature reduction method have demonstrated the best performance among other modes. In the meantime, it has been concluded that the initial data do not possess the proper resolution or generalizability, either during prosperity or recession.

Random Telegraph Noise (RTN) is a stochastic phenomenon which leads to characteristic variations in electronic devices. Finding features of this signal may result in its modeling and eventually removing the noise in the device. Measuring this signal is accompanied by some noise and therefore we require a method to improve the Signal to Noise Ratio (SNR). As a result, the extraction of an accurate RTN is a remarkable challenge. Empirical Mode Decomposition (EMD) as a fully adaptive and signal dependent method, with no dependency to the specific function, can be an appropriate solution. In this paper, we evaluate the most recent methods and compare them with our proposed approach for the artificial and actual RTN signals. The results show the higher accuracy and efficiency by about 54%, 61% and 39% improvement in SNR, Mean Square Error (MSE) and Percent Root mean square Difference (PRD) respectively for the optimized wited method. Finally, an indicator to evaluate the reliability in digital circuits is introduced.

Particle swarm optimization has been a popular and common met heuristic algorithm from its genesis time. However, some problems such as premature convergence, weak exploration ability and great number of iterations have been accompanied with the nature of this algorithm. Therefore, in this paper we proposed a novel classification for particles to organize them in a different way. This new method which is inspired from president election is called President Election Particle Swarm Optimization (PEPSO). This algorithm is trying to choose useful particles and omit functionless ones at initial steps of algorithm besides considering the effects of all generated particles to get a directed and fast convergence. Some preparations are also done to escape from premature convergence. To validate the applicability of our proposed PEPSO, it is compared with the other met heuristic algorithm including GAPSO, Logistic PSO, Tent PSO, and PSO to estimate the parameters of the controller for a hybrid power system. Results verify that PEPSO has a better reaction in worst conditions in finding parameters of the controller.

This paper investigates the effect of tail capacitance on phase noise of an LC-VCO (LC voltage-controlled-oscillator). First, the analytical relations of the phase noise for different values of tail capacitor (CT) are derived and then for verifying them, simulation and calculated results are compared. For simplicity, three scenarios such as small, medium and large values of CT are considered. In a case study an LC-VCO is designed in a standard 0.18µm CMOS technology, and simulation and numerical results have been presented for different values of CT. In this case study, numerical analysis shows that for CT =200fF (medium CT) and CT =10pF (large CT), the phase noise at 1MHz offset from the 5.2GHz is -96dBc/Hz and -118dBc/Hz, respectively. According to the results, the ISF (Impulse sensitivity function) is improved by increasing the amount of CT. Numerical values also demonstrate that excessive increase of CT has no effect on the phase noise. While choosing bigger CT can effectively reduce the noise contribution of the tail by bypassing the noise of tail transistor, but low impedance path generated by CT may degrade the phase noise by reducing tank quality factor.

Multi-area economic load dispatch (MAELD) decides the measure of power that can be fiscally generated in one area and transfered to another area. The goal of MAELD is to determine the most prudent production arrangement that could deliver the nearby power requirement without violating tie-line limits. This study presents a new swarm algorithm called as squirrel search optimization (SSO) to solve the MAELD problems. The impacts of transmission losses, prohibited operating zones, valve point loading and multi-fuel alternatives are additionally contemplated. SSO impersonates the searching conduct of flying squirrels which depends on the dynamic bouncing and skimming procedures. To demonstrate the potency of the suggested approach, it is examined on three different test systems for solving the MAELD problems. Comparative examinations are performed to analyze the adequacy of the suggested SSO approach with exchange market algorithm and different strategies revealed in the literature. The experimental results show that the proposed SSO approach is equipped for acquiring preferred quality solutions over the other existing strategies.

In some countries, regional authorities may attempt to rebalance the allocation of national facilities in benefit of their own region which, in turn, may cause disturbances in the central government’s decision-making proces. Regarding the hierarchical nature of these types of decisions, classical optimization models are not effective in decision-making and the use of multi-level programming can increase the efficiency of planning. Our paper aims to address the issue of a bi-level programming model to conduct the location analysis of emergency warehouses. A three-echelon relief supply chain is considered in which the relief network involves national and regional warehouses and demand cities. The upper-level model decides on the location of national warehouses, allocating them to regional warehouses. The lower-level model determines the location of regional warehouses and allocates them to demand points. The structure of both levels is based on the median location-allocation problem. Three solution approaches are presented based on the full enumeration and two types of nested evolutionarymethods (genetic and heuristic local search algorithms). For the model to be used in Iran, the efficiency of algorithms is analyzed for two sizes of problems. The obtained results show the proper functioning of the solution approaches.

This paper discusses the problems of short-term forecasting of cryptocurrency time series using a supervised machine learning (ML) approach. For this goal, we applied two of the most powerful ensemble methods including Random Forests (RF) and Stochastic Gradient Boosting Machine (SGBM). As the dataset was collected from daily close prices of three of the most capitalized coins: Bitcoin (BTC), Ethereum (ETH) and Ripple (XRP), and as features we used  past price information and technical indicators (moving average). To check the effectiveness of these models we made an out-of-sample forecast for selected time series by using the one step ahead technique. The accuracy rate of the forecasted prices by using RF and GBM were calculated. The results verify the applicability of the ML ensembles approach for the forecasting of cryptocurrency prices. The out of sample accuracy of short-term prediction daily close prices obtained by the SGBM and RF in terms of Mean Absolut Percentage Error (MAPE) for the three most capitalized cryptocurrencies (BTC, ETH, and XRP) were within 0.92-2.61 %.

Given the complexities of the electricity market, various factors, such as uncertainties, the ways upon which the markets are set, how the debts are settled, the market structure and regulations, production prices, constraints governing the units and networks, etc. are influential in determining the optimal pricing strategies. Various methods and models have been presented to resolve the pricing issue in the competitive electricity industry. The most prominent of which include pricing methods are based on the prediction of competitors’ behavior; also pricing methods based on the forecasts of market price, methods based on the game theory and lastly, pricing methods based on the intelligent algorithms. Therefore, this study was conducted to provide an optimal strategy in order to forecast the electricity market price set in the competitive Iranian electricity market (based on the data collected). In this paper, the proposed method uses a compound network based on the neural networks. The analyzed data include the amount of the consumed energy as well as temperature (if applicable) and the price set for the past days and weeks. The self-organizing map (SOM) network was used for the input clustering based on the similar days. A number of multilayer perceptron (MLP) neural networks were used to combine the extracted data consisting of the energy levels, the price set, and temperature (if possible). The results showed improvements in the performance of the smart systems based on the neural networks in predicting the electricity prices.

Most production environments face random, unexpected events such as machine failure, uncertain processing times, the arrival of new jobs, and cancellation of jobs. For the reduction of the undesirable side effects of an unexpected disruption, the initial schedule needs to be reformed partially or entirely. In this paper, a mathematical model is presented to address the integrated cell formation and cellular rescheduling problems in a cellular manufacturing system. As a reactive model, the model is developed to handle the arrival of a new job as a disturbance to the system. Based on the principle of resistance to change, the reactive model seeks a new solution with the minimum difference from the initial solution. This is realized through a simultaneous minimization of the total completion time and the number of displaced machines. For the investigation of the performance of the proposed model, some numerical examples are solved using the GAMS software. The results demonstrate the ability of the reactive model to obtain solutions resistant to unexpected changes

The features of the organization information support for business processes on the typical virtual machine-building enterprise (VME) with a multi-nomenclature nature of production in the framework of Industrial 4.0 were considered. It has been established that in accordance with the concept of Industry 4.0, namely with the individualization of production and consumption, a modern virtual machine-building enterprise must adapt to the production of goods in small batches, moreover in a large assortment and with frequent change of nomenclature in a wide range. It is shown that under these conditions, the effectiveness of the implementation of production processes directly depends on the effectiveness of information support at all stages of the product life cycle (PLC). A methodology is proposed for evaluating the effectiveness of information support for PLC processes in the conditions of multi-nomenclature production of a virtual engineering enterprise. The methodological basic concepts are web-mining and multi-agent technology. A comparative analysis of the activities of a typical VME was carried out, which showed that the introduction of information support tools increased the efficiency of VME business processes.

In this study, at first step nanopowder particles of  α-Fe2O3 (Hematite) and  Co3O4 were synthesized separately thorough simple chemical method from an aqueous solution of iron (III) nitrate nonahydrate (Fe(NO3)3.9H2O) and cobalt (II) nitrate hexahydrate (Co(NO3)2.6H2O) as  precursors.  After that, three composites from synthesized nanopowders of Fe2O3 with 8, 16 and 24 wt.% of Co3O4 were prepared. Graphite nanopowder was added to one composition of samples in weight percentages of 1.17 and 2.35. The composition and morphology of the composites were investigated by XRD and FE-SEM, respectively. FE-SEM analysis showed that the morphology of the powders and composites were all spherical in nanoscale. The photocatalytic activity of the composites was examined by measuring the photo-degradation of the aqueous solution of methylene blue under simulated solar light. To determine the photo catalytic activity, the degradation of methylene blue (MB) in the absence of light (dark test) was taken as well. Results showed that addition of Co3O4 to Fe2O3 decrease the activity of photo-catalytic process while nano-graphite enhanced photo-catalytic process by upward of ~2 % with respect to the composite without graphite nanoparticles. Stoichiometric calculations showed that the amount of hydrogen produced by water by the composite of Fe2O3-16% Co3O4- 2.35% Graphite nanoparticles was 27 μmol H2/h.g under solar light irradiation.

Bone char (BC) is one of the most common adsorbent with extensive applications in the removal of pollutions. The adsorption capability of BC is proportional to the crystalline index, i.e, the atomic ratio of Ca/P. This study is an attempt to model the crystalline index of BC that by thermal decomposition of natural bone using artificial neural network (ANN) and genetic expression programming (GEP). In this regard, 100 various experimental data used to construct the ANN and GEP models, separately. Through the data collection step, heating rate, the type of precursor, calcination temperature, and residence time selected as the inputs for the preset output as Ca/P ratio. The results reveal that the minimum amount of Ca/P ratio are at the heating rate 10 °C/min, HNO3 1.6 M as activation agent, calcination temperature 1000 °C, and residence time 2 h. R squared indices is used to compare the performance of extracted models. Finally, the best ANN uses to investigate the effect of each practical variable by sensitivity analysis and revealed that the residence time is the most effective parameter on the crystalline index while acid activation is of secondary importance.

This paper is concerned with the dynamic stability study of a gliding parachute-payload system along its gliding path. To scrutinize the respective dynamic response characteristics after releasing from high altitude, a modified multi-body model is developed. In the stability analysis procedure, the yawing motion of the payload is considered in system dynamics, which in turn creates a state-dependent matrix in the stability analysis and makes the linearization algorithm more cumbersome. To solve the problem, a unified Jacobian-based symbolic differentiation algorithm is implemented and the dynamics is linearized about various operating points along gliding segment of a typical planned trajectory. Based on results, the system has short period and phugoid modes in longitudinal channel just like an aircraft. In addition to dutch roll mode, the system has a low frequency coupled roll-spiral mode in lateral-directional channel which is a result of effective canopy anhedral angle. It is shown, the coupled mode can be decomposed into two distinct roll and spiral modes for small anhedral angles. Based on results, as the parachute descends, both the period and damping ratio for the short period mode were increased by 18 and 30%, respectively. For the phugoid mode the period of oscillations is decreased by 20% and the damping ratio, almost remains constant. For the lateral-directional channel,. As the parachute descends, the dutch roll mode is destabilized whereas the other modes are stabilized. Furthermore, from a practical point of view, lengthening the suspension lines stabilizes the coupled roll-spiral mode whereas destabilizes the other modes.

The present article is an attempt at utilizing a feedback control system based on cylinder rotary oscillations in order to attenuate the two-degree-of-freedom vibrations of an elastically-supported square-section cylinder in presence of flow. The control system benefits from the cylinder rotational oscillations about its axis that acts according to lift coefficient feedback signal of the cylinder. Based on the performed numerical simulations, it becomes clear that the active control system has successfully mitigated the two-degree-of-freedom vibrations of square cylinder both in the lock-in region and galloping zone. For a Reynolds number of Re = 90 located in the lock-in region, the active rotary oscillating (ARO) controller has achieved a 98% reduction in the cylinder transverse vibration amplitude, while the corresponding value for the in-line vibration is 88%. Moreover, for a Reynolds number of Re = 250 in the galloping zone, the ARO controller has successfully attenuated the cylinder transverse vibration amplitude by 72%, while the same value for the in-line vibration is 70%. One also observes that the ARO controller decreases the amplitude of lift and drag coefficients in the lock-in region by, respectively, 95% and 94%. In contrast, the corresponding percentages for the cylinder in the galloping zone are 24% and 39%, respectively.

Machine Learning (ML) based condition monitoring and fault detection of industrial equipment is the current scenario for maintenance in the era of Industry-4.0. The application of ML techniques for automatic fault detection minimizes the unexpected breakdown of the system. However, these techniques heavily rely on the historical data of equipment for its training which limits its widespread application in industry. As the historical data is not available for each industrial machine and generating the data experimentally for each fault condition is not viable. Therefore, this challenge is addressed for gear application with tooth defect. In this paper, ML algorithms are trained using simulated vibration data of the gearbox and tested with the experimental data. Simulated data is generated for the gearbox with different operating and fault conditions. A gearbox dynamic model is utilized to generate simulated vibration data for normal and faulty gear condition. A pink noise is added to simulated data to improve the exactness to the actual field data.  Further, these simulated-data are processed using Empirical Mode Decomposition and Discrete Wavelet Transform, and features are extracted. These features are then fed to the training of different well-established ML techniques such as Support Vector Machine, Random Forest and Multi-Layer Perceptron. To validate this approach, trained ML algorithms are tested using experimental data. The results show more than 87% accuracy with all three algorithms. The performance of the trained model is evaluated using precision, recall and ROC curve. These metric show the affirmative results for the applicability of this approach in gear fault detection.

Heat transfer analysis in channels and enclosures has significant attention nowadays. In the present work, fluid flow and heat transfer of a vertical channel consisting of a rotating cylinder utilizing nanofluid have been studied, numerically. Uniform magnetic field has been applied to the fluid field. Different cylinder rotation directions, Hartmann number and rotational velocity of cylinder configurations have been considered. The results indicate that by increasing the Hartmann number, for low values of non-dimensional angular velocity the average Nusselt number increases. In addition, in higher Hartmann numbers, the average Nusselt number does not change remarkably with non-dimensional angular velocity. Furthermore, studying lift and drag coefficients demonstrate that in a constant Hartmann number, the highest drag coefficient takes place in maximum cylinder angular velocity. Additionally, almost uniform distribution of drag coefficient can be seen in higher Hartmann numbers. The numerical results have been compared with the previously reported results. This comparison illustrates excellent agreement between them.

An innovative design of a supersonic rotor pressure-exchange ejector is introduced in this paper. In this design, momentum is exchanged between supersonic primary flow and secondary flow using an idle rotor. A CFD code developed to model the 3-D compressible, viscous and turbulent flow of air inside the new design of ejector. Roe approach and Spallart-Allmaras methods used to analyze flow inside the ejector. The flow inside the ejector was modeled by using a structured grid and air was employed as the working fluid in both primary and secondary streams. The Mach number of the motive flow was set at 2. Momentum exchanged between the primary and secondary flows because of direct contact between those. In addition to that, rotation of idle rotor and mechanical blades entrained the secondary flow to the ejector. Enthalpy, entrained mass flow rate and created vacuum presented for the flow inside the ejector for different configurations of the rotor and ejector until an optimum case was achieved. Also, uniformity of the flow at discharge section compared between ejectors. For the optimum case with the presented geometry, the ultimate rotor speed of 50000 rpm was obtained and an increase of 47% in entrainment ratio achieved with respect to the stationary blades. To study the flow field in more details, the contours of the Mach number and stagnation pressure were compared according to the different sections of computational domain.

Perforated composite panels are widely used in many engineering applications as subcomponents of complex structures including aircraft, ships, and other transport vehicles. In many of these applications, the primary objective of using the panel is to resist buckling. In this present study, a finite element analysis is performed adopting popular commercial software code Ansys on the buckling behavior of a simply supported quasi-isotropic symmetric composite panel with central circular cutouts, reinforced with stiffeners on both sides of the cutouts under uniaxial, biaxial and combined loading conditions. The main objective is to achieve the elastic buckling response of the perforated composite panels considering some important aspects of the stiffener as follows: (1) effect of the presence of reinforcement, (2) effect of stiffener area, (3) effect of stiffener thickness, (4) effect of stiffener material and (5) effect of fiber orientation angle. It is observed that reinforcement can significantly improve the critical buckling load of a panel, which is already reduced due to cutouts. Then, increasing the area of the stiffener does not have a major impact on the buckling stability of the panels. However, increasing the thickness can play a crucial role to strengthen the buckling stability. Finally, it is found that in comparison to aluminum and titanium alloys, epoxy-carbon is more practical as a stiffener material with correct fiber orientation angle (90°), considering the low weight increment and higher buckling achievability.

Extensive recent researches have been underway to model the fracture mechanics degradation based on continuum damage mechanics (CDM) technique. CDM theory is a powerful tool for solving problems such as large plastic deformations that the fracture mechanics is unable to solve. This model is derived by means of the thermodynamics internal variable theory and based on the experimental results on material properties. In this paper, the reliability of rectangular plates containing a central circular hole under static tensile load using the CDM approach for ductile fracture has been studied. To investigate the initiation and evolution of damages, anisotropic damage expressed by second order damage tensor is used to derive constitutive equations. Then, these relationships together with material constants are implemented with subroutine in ABAQUS software. The reliability assessment has been investigated using first order reliability method (FORM) and second order reliability method (SORM).  Based on the FORM and SORM, the limit state functions and random variables have been obtained according to the energy density release rate. The probability of failure of each plate with different hole sizes is estimated based on the anisotropic damage theory, and the results are compared with the isotropic damage model. Finally, the sensitivity analysis of the coefficient of variation is performed.

In thermal protection of healthy tissues during hyperthermia with the phase-change micro/nano-materials, the impossibility of performing a similar experiment with the theoretical parameters is inevitable because of different errors such as modeling, measuring, particle deposition area, etc. These errors may affect the practical thermal protection from damaging the healthy tissue or not destroying the tumor tissue. To perform a numerical procedure, the electrical potential is obtained solving the Laplace equation and the Pennes Biothermal equation is used to find the temperature distribution in the tissue using the finite difference method. The Pennes equation is transiently resolved by considering intracellular conductance, blood perfusion, and metabolic heating. Consequently, the deviation and the uncertainty of each parameters in the thermal protection including the concentration of the phase change material, the radius of microcapsules, the latent heat, the melting point, the temperature range of phase change of micro/nanoparticles, and the concentration and the radius of the superparamagnetic materials are investigated. According to the results of the uncertainty analysis, the radius of the superparamagnetic materials is the most important parameter so that a 20% deviation from the numerical value changes the temperature of the tissue up to 4 °C.

Fused Deposition Modelling (FDM) is an additive manufacturing process to build 3D objects on a horizontal plane from bottom to top. In the conventional FDM process, the printing of curved objects causes the staircase effect and results in poor surface finish. In this work, the FDM process integrated with a 6-DOF Industrial robot is used to print the curved objects by generating non-linear tool paths to avoid the staircase effect. A standard NACA 0015 aircraft wing having curved surfaces is printed without staircase effect at a uniform deposition rate using an industrial robot. The wing is sliced into concentric curved layers either in the form of convex or a concave shape. A new methodology is developed by combining the non-linear toolpaths with the change in extruder orientation to print curved objects at a uniform deposition without any staircase effect. ABB Robotstudio simulation software is used for simulating the printing process and simulation results are validated by printing the portion of the wing using the Industrial robot with an FDM extruder as an end effector. The experimental results showed that the aircraft wing is printed successfully with uniform deposition at constant velocity without any staircase effect.

The Shendong mining area where is located in the northwest of China has the highest fully mechanized working face in the world. The purpose of this paper is to understand the causes of abnormal mine ground pressure appearance (MGPA) on the fully mechanized mining working face with super-large height in Shendong mining area. Field monitoring, physical similarity material test and UDEC numerical simulation were used to investigate the influencing factors of abnormal MGPA on the super-large mining height working face. The results show that the simultaneous breaking movement of multiple key strata will transfer more load than that of a single key stratum, which intensifies the MGPA in working face. The distance between primary key stratum and coal seam determines MGPA, but this distance is limited to 115 m above the coal seam. The results of this study are of guiding significance to control the MGPA in Shendong mining area.

Abrupt termination of the coal open pit operation is complicated in many cases by endogenous fires that not only cause significant damage to property but also worsen sanitation and hygienic conditions for human life in nearby settlements, as well as those for fauna and flora. Therefore, the purpose of the research was to prevent endogenous fires at Korkinsk brown coal open pit. In this connection, we used an analytical method to process the statistical data obtained from observations. The laboratory method included the study of coal samples for moisture exchange intensity by creating conditions with relative humidity up to 97 %, temperature up to plus 50°С and air speed up to 0.5 l/minute. Dependencies were determined for fire occurrence frequency, relative humidity, and average temperature of the air upon the time of day, the intensity of moisture evaporation and initial coal humidity on equilibrium coal humidity as well as the intensity of moisture absorption and equilibrium coal humidity upon initial humidity in different temperature periods. An environmentally friendly solution was created that featured good adhesion to the materials being coated, fire and explosion resistance. The results obtained make it possible to understand the causes and methods of fighting endogenous fires.