Hysteresis is a natural phenomenon that widely exists in structural and mechanical systems. The characteristics of structural hysteretic behaviors are complicated. Therefore, numerous methods have been developed to describe hysteresis. In this paper, a review of the available hysteretic modeling methods is carried out. Such methods are divided into: a) model-driven and b) data-driven methods. The model-driven method uses parameter identification to determine parameters. Three types of parametric models are introduced including polynomial models, differential based models, and operator based models. Four algorithms as least mean square error algorithm, Kalman filter algorithm, metaheuristic algorithms, and Bayesian estimation are presented to realize parameter identification. The data-driven method utilizes universal mathematical models to describe hysteretic behavior. Regression model, artificial neural network, least square support vector machine, and deep learning are introduced in turn as the classical data-driven methods. Model-data driven hybrid methods are also discussed to make up for the shortcomings of the two methods. Based on a multi-dimensional evaluation, the existing problems and open challenges of different hysteresis modeling methods are discussed. Some possible research directions about hysteresis description are given in the final section.
Under thermo-mechanical stress via a bulge test (BT), composite circular diaphragms (CCD) exhibit temperature-dependent mechanical behavior, including changes in Young’s modulus, yield strength, and residual stress. The application of a differential pressure and temperature causes the membrane to deform, allowing researchers to characterize composite material properties, particularly for materials used in microelectromechanical sensors (MEMS) operating in harsh environments. This paper aims to explore how CCD made from basalt fiber reinforced polymer (BFRP) behaves under thermal and mechanical stress, particularly in various engineering and bioengineering sensor applications, using a technique known as the BT. To start, the diaphragm is pre-stressed and clamped between two plates. When applying differential pressure, it causes the diaphragm to deform. An analytical approach is developed for utilizing the BT to describe the thermo-mechanical properties of these diaphragms. This method is well-suited for examining how diaphragms behave mechanically in both elastic and plastic states. A finite element model (FEM) is extended to analyze the BT outcomes and look into how pre-stress influences the pressure testing, comparing results from the FEM with those derived from analytical calculations. The variations in thickness and material type are also taken into account to better understand how they affect the diaphragm’s mechanical behavior under stress. Additionally, this work considers how temperature impacts the material properties of the diaphragm, which is crucial for analyzing its thermo-mechanical response. The relative () for maximum deflection the analytical and numerical results is less than 0.3%. The simulations are done using ANSYS, MATLAB and its PDE toolbox to get the results.
Intelligent and resilient infrastructure and smart cities make up a rapidly emerging field that is redefining the future of urban development and ways of preserving the existing infrastructure against natural hazards...
As the study of internal corrosion of pipeline need a large number of experiments as well as long time, so there is a need for new computational technique to expand the spectrum of the results and to save time. The present work represents a new non-destructive evaluation (NDE) technique for detecting the internal corrosion inside pipeline by evaluating the dielectric properties of steel pipe at room temperature by using electrical capacitance sensor (ECS), then predict the effect of pipeline environment temperature (<TEX>${\theta}$</TEX>) on the corrosion rates by designing an efficient artificial neural network (ANN) architecture. ECS consists of number of electrodes mounted on the outer surface of pipeline, the sensor shape, electrode configuration, and the number of electrodes that comprise three key elements of two dimensional capacitance sensors are illustrated. The variation in the dielectric signatures was employed to design electrical capacitance sensor (ECS) with high sensitivity to detect such defects. The rules of 24-electrode sensor parameters such as capacitance, capacitance change, and change rate of capacitance are discussed by ANSYS and MATLAB, which are combined to simulate sensor characteristic. A feed-forward neural network (FFNN) structure are applied, trained and tested to predict the finite element (FE) results of corrosion rates under room temperature, and then used the trained FFNN to predict corrosion rates at different temperature using MATLAB neural network toolbox. The FE results are in excellent agreement with an FFNN results, thus validating the accuracy and reliability of the proposed technique and leads to better understanding of the corrosion mechanism under different pipeline environmental temperature.
In various civil engineering applications (CEAs), most vibration responses vary with time and space and are characterized by nonlinearities and uncertainties that are not accounted for during data acquisition. Furthermore, these responses may be contaminated by various sources, which may affect the damage identification process. The main challenge is how to denoise these data in order to acquire a sensitive feature for damage identification that is insensitive to noise and environmental effects. Wavelet Transform (WT) has been proven to be useful for denoising in the field of structural health monitoring (SHM). However, its efficiency is affected by the selection of wavelet parameters. The questions related to the best approach for utilizing the most suitable parameters have not been adequately answered. This study attempts to address this issue by proposing a new denoising algorithm based on the Discrete Wavelet Transform (DWT) technique. The proposed technique provides a strategy to choose the right decomposition levels for denoising and selecting proper mother wavelets. The proposed algorithm uses separate noise thresholds for negative and positive coefficients at each level and applies denoising to detail and approximation components. Datasets from actual civil structures have been analyzed. According to the presented experimental results, the proposed technique exhibits promising results for signal denoising using "db3" compared with traditional techniques. In addition, "db3" and "sym3" are shown to be the best choices for the mother wavelet.
Shaft keys and couplings quickly became the new standard in the field of shaft connections. This technology allows users to transmit high torque between two shafts. Using only hand tools, it can be installed and removed quickly and easily, and thermal work and hydraulic pressure are not required. This chapter aims to study the types of shaft keys and couplings in mechanical applications; it covers the full design of several types of keys and couplings, along with examples and solved/unsolved problems. The chapter analyzes forces and stresses on keys, including rectangular and square keys, gib-head keys, feather keys, and woodruff keys, and couplings such as clamp or compression couplings and flange couplings. This chapter can be used as teaching material for courses in machine design for mechanical and industrial engineering majors in colleges and universities, and can also be used as a reference for scientific and technical personnel engaged in scientific and engineering calculations.
Over the last two decades, several experimental and numerical studies have been performed in order to investigate the acoustic behavior of different muffler materials. However, there is a problem in which it is necessary to perform large, important, time-consuming calculations particularly if the muffler was made from advanced materials such as composite materials. Therefore, this work focused on developing the concept of the indirect dual-chamber muffler made from a basalt fiber reinforced polymer (BFRP) laminated composite, which is a monitoring system that uses a deep learning algorithm to predict the acoustic behavior of the muffler material in order to save effort and time on muffler design optimization. Two types of deep neural networks (DNNs) architectures are developed in Python. The first DNN is called a recurrent neural network with long short-term memory blocks (RNN-LSTM), where the other is called a convolutional neural network (CNN). First, a dual-chamber laminated composite muffler (DCLCM) model is developed in MATLAB to provide the acoustic behavior datasets of mufflers such as acoustic transmission loss (TL) and the power transmission coefficient (PTC). The model training parameters are optimized by using Bayesian genetic algorithms (BGA) optimization. The acoustic results from the proposed method are compared with available experimental results in literature, thus validating the accuracy and reliability of the proposed technique. The results indicate that the present approach is efficient and significantly reduced the time and effort to select the muffler material and optimal design, where both models CNN and RNN-LSTM achieved accuracy above 90% on the test and validation dataset. This work will reinforce the mufflers' industrials, and its design may one day be equipped with deep learning based algorithms.
In this research, a new approach for fatigue damage monitoring of composite pipelines based on checking the stability of electrical capacitance sensor (ECS) system measurements is established. The study pipeline is made of basalt fiber-reinforced polymer (BFRP) and is subjected to fatigue and thermal loading. The ECS electrodes are installed peripherally outside of pipeline. First, the capacitance between the sensor electrode pairs due to transient excitations is measured numerically using ANSYS before and after damage. Then, the capacitance data between electrode pairs was analyzed by plotting the transfer function (TF) maps, considering that the pipeline system is an “open loop system” to indicate the damage growth. To evaluate the proposed technique's reliability and applicability, a comparison between the present and experimental results available in the literature is validated. The current results are convergent with experimental results, which shows the effectiveness of the current method and the significant potential for different applications in engineering.
The importance of energy harvesting is considered when harvesting the neglected ambient energy that graduated from different systems and dissipates around us, such as electromagnetic waves, heat, vibration, etc [...]
Energy harvesting from piezoelectric materials is quite common and has been studied for the past few decades, but, recently, there have been a lot of new advancements in harnessing electrical energy via piezoelectric materials. In this regard, several studies were carried out in electrochemistry and fluid flow. Furthermore, consideration of productive and valuable resources is important to meet the needs of power generation. For this purpose, energy harvesting from fluids such as wind and water is significant and must be implemented on a large scale. So, developing self-powering devices can resolve the problem like that, and piezoelectric materials are gaining interest day by day because these materials help in energy generation. This review paper discusses different techniques for harnessing energy from fluid flows using piezoelectric materials. In addition, various vibration-based energy-harvesting mechanisms for improving the efficiency of piezoelectric energy harvesters have also been investigated and their opportunities and challenges identified.
The composite pipeline is a relatively new and viable alternative pipeline to the more commonly used traditional one due to its good mechanical and fatigue properties and lower production cost. For this purpose, it is critical to assess the mechanical and fatigue performance of composite pipeline material under various working conditions, particularly for monitoring long-term creep thermo-mechanical fatigue behavior. In this paper, a long-term creep thermo-mechanical fatigue behavior in a basalt fiber reinforced polymer laminated composite pipeline is detected through an integrated expert system consisting of the electrical capacitance sensors and a deep learning algorithm. First, a multi-physics finite element model is established for the simulation of a long-term creep thermo-mechanical fatigue behavior in basalt fiber reinforced polymer composite pipelines subjected to long-term fatigue loading of internal pressure and thermal effect. Second, theoretical model results of long-term creep thermo-mechanical fatigue compliance ( S f ( t ) ) over the time of creep are analyzed in pipeline material using the modulus degradation approach. Finally, an electrical potential change between electrical capacitance sensors electrodes corresponding to S f ( t ) over the time of creep for some levels of long-term creep thermo-mechanical fatigue ( R f % ) is recorded and then used in these datasets for training of the novel deep neural network based on one of the most widely used of the deep neural network families is the convolutional neural network, to predict S f ( t ) in pipeline for various R f % not included in the previous finite element model evaluation (i.e. electrical capacitance sensors technique). In this paper first is detected the long-term creep thermo-mechanical fatigue behavior for R f % = 25 % , 50 % , and 75 % from a finite element model and modulus degradation approach, and then is predicted the long-term creep thermo-mechanical fatigue behavior for R f % = 15 % , 35 % , 60 % , and 85 % respectively via deep neural network. The proposed method results are in good agreement with the experimental results available in the literature, thus verifying the accuracy and reliability of the proposed technique and its applicability to other different composite structures.
