Structural Health Monitoring (SHM) via data-driven techniques can be based upon vibrations acquired by sensor networks. However, technical and economic reasons may prevent the deployment of pervasive sensor networks over civil structures, thus limiting their reliability in terms of damage detection. Moreover, the effects of environmental (and operational) variability may lead to false alarms. To address these challenges, a multi-stage machine learning (ML) method is here proposed by exploiting autoregressive (AR) spectra as damage-sensitive features. The proposed method is framed as follows: (i) computing the distances between different sets of the AR spectra via the log-spectral distance (LSD), providing also the training and test datasets; (ii) removing the potential environmental variability by an auto-associative artificial neural network (AANN), to set normalized training and test datasets; (iii) running a statistical analysis via the Mahalanobis-squared distance (MSD) for early damage detection. The effectiveness of the proposed approach is assessed in the case of limited vibration data for the laboratory truss structure known as the Wooden Bridge. Comparative studies show that the AR spectrum is a reliable feature, sensitive to damage even in the presence of a limited number of sensors in the network; additionally, the multi-stage ML methodology succeeds in early detecting damage under environmental variability.
Multi terminal VSC-HVDC systems are a promising solution to the problem of connecting offshore wind farms to AC grids. Optimal power sharing and appropriate control of DC-link voltages are essential and must be maintained during the operation of VSC-MTDC systems, particularly in post-contingency conditions. The traditional droop control methods cannot satisfy these requirements, and accordingly, this paper proposes a novel centralized control strategy based on a look-up table to ensure optimal power sharing and minimum DC voltage deviation immediately during post-contingency conditions by considering converter limits. It also reduces destructive effects (e.g., frequency deviation) on onshore AC grids and guarantees the stable operation of the entire MTDC system. The proposed look-up table is an array of data that relates operating conditions to optimal droop coefficients and is determined according to N-1 contingency analysis and a linearized system model. Stability constraints and contingencies such as wind power changes, converter outage, and DC line disconnection are considered in its formation procedure. Simulations performed on a 4-terminal VSC-MTDC system in the MATLAB-Simulink environment validate the effectiveness and superiority of the proposed control strategy.
Asphaltene precipitation/deposition is one of the main problems in petroleum industry at various stages. In this study, various controlling factors on the adsorption of different types of asphaltene on the surface of dolomite of different size, as one of the most abundant minerals in the reservoir is investigated. First, asphaltenes and adsorbents (dolomite) properties were analyzed via several analytical techniques. Then, adsorption of four asphaltene samples of various origins and chemical composition on dolomite at micron and nano size were carried out in both static and dynamic modes and results were examined following the adsorption process. Based on the static tests, the adsorption amount of asphaltenes on nano- and micro-dolomite was measured 3.52–11 mg/m² and 0.82–2.74 mg/m², respectively. The results revealed that the most significant parameters that would affect the asphaltene adsorption on dolomite as an adsorbent are the total nitrogen and sulfur (N + S) content and the aromaticity of the asphaltenes. Moreover, as the flow rate was increased three times in the dynamic experiments, a decrease of 15–25% in asphaltene adsorption was observed, which was attributed to physisorption of asphaltene on dolomite or due to higher flow velocities. Collectively, the findings of this study can enable us to better understand the mechanisms of asphaltene adsorption/precipitation on dolomite and lead to better management of oil production from dolomitic reservoirs.
This study presented a novel liquid-cooled heat sink based on constructal theory. An experiment was conducted to investigate the influence of boundary conditions, such as the mass flow rate (ṁ), on heat transfer rate (Qin) and pressure drop. Five cylinder heater cartridges were used in the experiment for 11 different mass flow rates (0.008292 < ṁ (kg/s) < 0.03307). Through numerical simulation, the effects of changing the number of clusters on heat transfer and pressure drop were studied. The results showed that the optimal combination of pressure drop and Nusselt number occurs in four clusters. According to the results, increasing the number of clusters can increase the Nusselt number by up to 11.98% and 13.62% for the highest (ṁ = 0.03307 kg/s) and lowest (ṁ = 0.008292 kg/s) mass flow rates, respectively. This work may lay the foundation for creating the next generation of thermal management systems for compact heat sources, such as the CPU in a self-driving car, robots and high-performance computers (HPC).
Polycyclic aromatic hydrocarbons (PAHs) are dangerous environmental compounds that are sometimes found in food. The objective of present study was to measure the level of 16 PAHs in bottled water samples (non-carbonated or drinking, mineral, carbonated and carbonated flavored water) in Tehran by using magnetic solid-phase extraction and gas chromatography–mass spectrometry (MSPE/GC–MS) method. The limit of detections (LOD), limit of quantifications (LOQ) and recovery of PAH compounds were 0.010–0.210, 0.03–0.700 μg/L and 92.5–103.4%, respectively. The results showed that the mean of total PAHs in samples was 2.98 ± 1.63 µg/L and the mean of Benzo[a]pyrene (BaP) was 0.08 ± 0.03 µg/L, which were lower than standard level of the US-EPA (0.2 µg/L, BaP in drinking water). Also, our results showed that carbonated flavored water had maximum mean of total PAHs (4.95 ± 0.8 µg/L) and mineral water had minimum mean of total PAHs (1.24 ± 0.8 µg/L). The Monte Carlo method was applied to calculate the Estimated Daily Intake (EDI) and Incremental Life Cancer Risk (ILCR) indexes. In all samples, the rank order of the estimated CDI values based on the 95 percent percentile was F > B(a)A > Ace > Fl > Na > Ph > B(b)F > B(k)F > B(a)P > P > Ac > A. The cancer risk and uncertainty analysis of 95th Percentile for bottled waters studied gave values lower permissible limit of 10−6, indicating not pose a serious concern to humans.
Gas injection has emerged over the recent decades as a promising technology to enhance oil recovery in various fields worldwide. The efficiency and success of a gas injection operation can be assessed through a number of vital experimental studies. Interfacial Tension (IFT) between the injected gas and the displacing fluid is a key parameter playing an eminent role in the foregoing studies. The main scope of this work is making a progress in modeling the IFTs between diverse n-alkanes and Methane (CH4), Carbon Dioxide (CO2), and Nitrogen (N2) natural gases. For this purpose, two smart AI-based approaches of Cascaded Feedforward Neural Network (CFNN) and Decision Tree Learning (DT) were used to simultaneously model the IFTs between foregoing immiscible binary systems as a function of pressure, temperature, the gases properties, and the properties of the liquid. Several statistical measures and graphical descriptions were employed to aid the accuracy analysis of the proposed models. Both developed CFNN and DT networks represented desirable close-to-reality predictions in all binary systems. Besides, CFNN established itself as the most robust model for all studies binary systems with RMSE values of 0.5924, 0.5649, and 0.5870 mN/m, and R² values of 0.9902, 0.9910, and 0.9904 for the train, test, and overall data, respectively.
This paper proposes a fast and reliable hybrid islanding detection method (IDM) for mini-hydro-based distributed generations (DGs) with zero non-detection zone (NDZ). The proposed IDM aims to tackle the islanding events caused by a self-excited induction generator (SEIG) when it is driven by a mini-hydro turbine system utilising the transient dynamic response of the governor for the first time. To achieve such a goal, it takes advantage of a two-stage process in which both passive and active techniques are combined. Thus, if the rate of change of frequency (ROCOF)-based threshold of the first stage is exceeded, the power reference of the mini-hydro unit is modified, implying a change in the turbine governor gate position. The mechanical torque applied to the prime mover is accordingly shifted to a new state so that both frequency and its derivative will exceed the established thresholds in the second stage in the islanding mode. Conversely, the effect of imposed disturbance is eminently negligible in the grid-connected mode since the frequency is strictly dictated by the main grid. The proposed IDM has been evaluated through numerous islanding and non-islanding case studies considering both single and multi-DG scenarios in MATLAB/Simulink. The outcomes highlight the outstanding performance of the proposed algorithm with zero NDZ and 473 ms average detection time, indicating the capability of the governor system as a reliable tool to identify islanding operations. The proposed technique does not degrade the power quality (PQ) of the grid, requires a low level of computational complexity and provides a high degree of reliability. Therefore, it is a robust and cost-effective solution for future microgrids with great penetration of mini-hydro units. © 2017 Elsevier Inc. All rights reserved.
Welding processes often produce high levels of tensile residual stress. Low transformation temperature (LTT) welding wires utilise phase transformation strains to overcome the thermal contraction of a cooling weld. In this paper, the residual stress within each weld was quantified using the milling/strain gauge method, being the strain change measured as the weldment was milled away. The fatigue tests were conducted under uniaxial loading considering two types of LTT materials. The results show that the crack propagation of all samples was similar in cycles although both LTT materials extended the crack initiation, and, therefore, the overall life of the part. It was found that both LTT materials reduced the residual tensile stresses, increased the residual compressive stresses, leading to increase in fatigue life about 30%.
The effects of foliar application of 5 mM arginine (Arg) on the growth and control of salinity-induced osmotic and oxidative stresses (0, 200, 400 and 600 mM NaCl) in Salicornia europaea seedlings were investigated. Despite higher levels of lipid peroxidation, lower membrane stability index (MSI), decreased pigment content and phenolic compounds, and reduced activity of antioxidant enzymes observed under salinity, seedling growth indices, including plant height and biomass, increased significantly, and some protective and antioxidant molecules such as proline and flavonoids accumulated. Soluble protein level increased at the low salt concentration (200 mM) but decreased at other doses. Exogenous Arg treatment alone had less or no effect on plant biomass and other metabolites, but in combination with salt, further enhanced growth parameters, MSI and accumulation of soluble protein, phenolic compounds and proline. Arg-induced changes under salinity were associated with decreased lipid peroxidation, flavonoids content and antioxidant enzymes activity. These results show that S. europaea seedlings are well tolerant to applied salt doses. The treatment with exogenous Arg alone affects plant growth slightly, but in combination with salt, synergistically increases growth and salt tolerance of these plants by enhancing the accumulation of proline and antioxidant molecules instead of enzymatic antioxidant.
Vibration characteristics analysis of various types of plates is of great significance in engineering applications. In this paper, an effective analytical method is developed to investigate the vibration properties of irregular plates with a lumped mass. The assumed mode method is extended to establish the equations of motion of irregular plates with a lumped mass, and vibration properties of the structural system are studied. The accuracy of this method is verified by the finite element method (FEM) and the vibration experiments. The natural frequencies and mode shapes of a typical irregular plate, i.e. cantilever trapezoidal plate, with a lumped mass obtained by the present method are in good agreement with the FEM and the vibration experiments. The influences of some parameters such as the additional lumped mass and the position of the additional mass on the vibration characteristics of cantilever trapezoidal plates are analyzed. The results could provide a reference for the vibration characteristics analysis of various irregular plates.
In this paper, a new analytical approach to the nonlinear analysis of functionally graded graphene platelet reinforced composite (FG-GPLRC) laminated cylindrical shells under external pressure and thermal environment is presented for the first time. The analytical approach is based on the higher-order shear deformation theory (HSDT), which is enriched by quasi-3D assumed natural strain (ANS) cover functions. The thermomechanical properties of composite laminated shells are considered to be temperature-dependent, and are evaluated using the modified Halpin–Tsai model and the rule of mixture. The governing equations for the GPLRC laminated cylindrical shells are established by using the enriched HSDT and the principle of virtual work. A higher-order quasi-3D strain field is proposed for the assumed kinematic field. The trigonometric series and the Laplace transform are used to establish the nonlinear buckling and post-buckling relations. The proposed analytical method is compared with different equivalent single-layer models. Moreover, two nonlinear parametric studies of GPLRC laminated cylindrical shells with different geometrical dimensions, temperature gradients, foundation stiffnesses and distribution patterns are presented. Finally, a stress analysis of GPLRC cylindrical shells under the thermal environment is carried out.
The air-side performance of a novel alternating cross-section flattened (ACF) tube heat exchanger was experimentally investigated. Three heat exchangers with an in-line tube arrangement and two tube rows were used as the test sections. The tube configuration is the highlight of the research work. ACF tubes with hydraulic diameters of 4.16, 4.75, and 5.20 mm were assembled as the heat exchanger. The experimental results showed that the ACF tube heat exchangers exhibited a prominent heat transfer characteristic with a reasonable pressure loss penalty. Performance evaluation criteria, as a nondimensional parameter, were employed to assess the heat exchanger performance. Finally, the j and f of the ACF tube heat exchanger were compared with those obtained from various shapes of noncircular tube and fin tube heat exchangers. All the details are examined and discussed in this study.
In this paper, the photo-thermoelastic wave propagation analysis in a semiconductor nanorod resonator under laser excitation employing the strain-gradient Moore–Gibson–Thompson (MGT) and Love–Bishop theories is presented, which is for the first time to the authors’ knowledge. The governing equations of the photo-thermoelastic wave propagation are derived using the proposed novel size-dependent MGT heat conduction model, strain-gradient and Love–Bishop theories. The size-dependent MGT coupled photo-thermoelasticity theory is developed by taking into account of five nano-scale parameters for the first time, which is employed to derive the governing equations. The governing equations are converted into the Laplace-transformed domain and then analytical solution is obtained for a semiconductor Love–Bishop nanorod resonator subjected to plasma and thermal shock loadings. To obtain the transient field variables in the time-domain, the Talbot Laplace-inversion technique is employed. The size-effects on the propagation of the photo-thermoelastic waves are studied in detail. The transient behaviors of the displacement, temperature and carrier density (plasma density) fields are also investigated with considering the nano-scale effects by performing the parametric studies. The effects of the carrier density, the relaxation time in the size-dependent MGT theory and the nano-scale parameters on the plasma-affected photo-thermoelastic wave propagation are revealed. Numerical examples demonstrate that the derived governing equations and also the proposed analytical solutions can be employed to determine the realistic behaviors of the field variables in a semiconductor Love–Bishop nanorod resonator under laser shock loading with considering the nano-scale effects.
In recent years, the synthesis of Au-Rh nanoparticles with different chemical arrangements have been reported. Studies indicated that the Au-Rh nanoparticles show the highly effective catalytic activity in the hydrogenation reactions at 300 K. Therefore, in this work, the stability of different chemical arrangements of Au-Rh nanoparticles, including Aucore@Rhshell, Rhcore@Aushell, Auball-Rhcup, Rhball-Aucup, Rh|Au Janus, and Au-Rh disordered-alloy, was investigated at room temperature by molecular dynamics simulation. The different parameters such as excess energy and strain were employed for comparison of stability of Au-Rh nanoparticles. Results show that the Au atoms always show the highest strain values regardless of their core or shell position. Therefore, the placement of Au atoms in the core of the nanoparticle leads to the most strain in the core position and the instability of Au-Rh nanoparticles. While, the placement of Rh atoms in the core of the nanoparticle and the placement of Au atoms in the shell of the nanoparticle lead to the reduction of the strain in the core and reduction of the surface energy in the shell, respectively, and consequently increase the stability of the Au-Rh nanoparticle. Hence, among the Au-Rh nanoparticles with different chemical arrangements, the group of Rhball-Aucup, Rh|Au Janus, and Rhcore@Aushell are known as nanoparticles with the most stability and the group of Aucore@Rhshell, and Auball-Rhcup are known as nanoparticles with the lowest stability. Meanwhile, the disordered-alloy nanoparticle shows the intermediate stability between two groups. Generally, in the Au-Rh nanoparticles with different chemical arrangements, it can be concluded that the stability of nanoparticles decreases by increasing the number of Au atoms in the positions close to the core.
Aggregation of flexible loads and power generation of solar photo-voltaic (PV) systems is considered as a valuable power resource in residential demand response (DR). Despite the rapid growth of smart appliances, there are few practical solutions for exploiting their potentials in DR load aggregation. In this paper, we present a practical multi-prosumer framework to enable the aggregator reach a minimum bidding power and participate in the wholesale market. This is attainable through directly rescheduling a large number of smart appliances and utilizing the surplus power generation of residential PVs. An optimization model is designed which maximizes the aggregator’s profit while respecting customers convenience. Fairness is a significant component of this model ensuring fair selection of appliances for shifting by the aggregator and not biased toward customers availability. We investigate the model as a hard instance of the 0–1 Knapsack problem and devise a heuristic algorithm to cope with its time complexity and to improve its scalability. The simulation results of two large-scale case studies are presented and discussed. It is demonstrated that the proposed framework is beneficial to both the aggregator and its customers, leading to a greener environment.
In this paper, the problem of predicting times to failure of units censored in multiple stages of generalized progressively hybrid censoring from exponential and Weibull distributions is discussed. Different classical point predictors, namely, the best unbiased, the maximum likelihood and the conditional median predictors are all derived. Moreover, the problem of interval prediction is investigated. Numerical example as well as two real data sets are used to illustrate the proposed prediction methods. Using a Monte-Carlo simulation algorithm, the performance of the point predictors is investigated in terms of the bias and mean squared prediction error criteria. Also, the width and the coverage rate of the obtained prediction intervals are studied by simulations.
Volatility estimation is an important issue in certain aspects of the financial community, such as risk management and asset pricing. It is known that stock returns often exhibit volatility clustering and the tails of the distributions of these series are fatter than the normal distribution. As a response to the need of these issues, the high unconditional volatility of assets encourages the users to predict their price in an ever changing market environment. Our main focus in this paper is to study the behavior of returns and volatility dynamics of some general stochastic economic models. First, we apply the local polynomial kernel smoothing method based on nonparametric regression to estimate the mean and the variance of the returns. We then implement and develop an empirical likelihood procedure in terms of conditional variance on daily log returns for inference on the nonparametric stochastic volatility as well as to construct a confidence interval for the volatility function. It appears that the proposed algorithm is applicable to some popular financial models and represents a good fit for the behavior observed in the stock and cryptocurrency markets. Some numerical results in connection to real data on the S&P 500 index and highly volatile Bitcoin dataset are also illustrated.
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