Development of a model efficiency improvement for the designing of feedwater heaters network in thermal power plants

Thermal power plants play a significant role in generating power, electricity, and energy consumption in the world, especially in developing countries. Therefore, the energy analysis of these power plants is very useful to increase the efficiency of systems and reduce energy consumption. One of the components of power plants that play a great role in energy consumption and recovery is the feedwater heater. In this study, a design method-based pinch technology for feedwater heaters of a coal power plant is presented. This method is used to reduce the irreversibility of heat transfer in feedwater heaters in this power plant. This study is performed on six feedwater heaters, which are similar in pairs. The results of this method show that this method is feasible for this system, and the results also show that the implementation of this method with a Pinch range of 10 °C indicated a deficit hot utility of about 48.54 MW. Also, the amount of power plant efficiency improvement is 12.12%, and according to the Pinch method, the energy price of the power plant can be reduced by about 125,489 $/year.https://asmedigitalcollection.asme.org/energyresourceshj2023Mechanical and Aeronautical Engineerin

Improvements in stable inversion of NARX models by using Mann iteration

The use of Mann iteration in the stable inversion of NARX models that have been converted to state space form is investigated to either recover the convergence or improve the accuracy of the best approximate solution under conditions when Picard iteration fails to converge. Attention is given to the use of filtering and time-varying iteration gains. The results are potentially of use in response reconstruction for fatigue testing purposes where the inverse of a NARX model, obtained by system identification, may be used to achieve the reconstruction.http://www.tandfonline.com/loi/gipe202016-06-30hb201

Flow stress identification of tubular materials using the progressive inverse identification method

PURPOSE : Propose a progressive inverse identification algorithm to characterize flow stress of tubular materials from the material response, independent of choosing an a priori hardening constitutive model. DESIGN /METHODOLOGY / APPROACH : In contrast to the conventional forward flow stress identification methods, the flow stress is characterized by a multi-linear curve rather than a limited number of hardening model parameters. The proposed algorithm optimizes the slopes and lengths of the curve increments simultaneously. The objective of the optimization is that the finite element simulation response of the test estimates the material response within a predefined accuracy. FINDINGS : We employ the algorithm to identify flow stress of a 304 stainless steel tube in a tube bulge test as an example to illustrate application of the algorithm. Comparing response of the finite element simulation using the obtained flow stress with the material response shows that the method can accurately determine the flow stress of the tube. PRACTICAL IMPLICATIONS : The obtained flow stress can be employed for more accurate finite element simulation of the metal forming processes as the material behaviour can be characterized in a similar state of stress as the target metal forming process. Moreover, since there is no need for a priori choosing the hardening model, there is no risk for choosing an improper hardening model, which in turn facilitates solving the inverse problem. ORIGINALITY / VALUE : The proposed algorithm is more efficient than the conventional inverse flow stress identification methods. In the latter, each attempt to select a more accurate hardening model, if it is available, result in constructing an entirely new inverse problem. However, this problem is avoided in the proposed algorithm.http://www.emeraldinsight.com/loi/echb2016Mechanical and Aeronautical Engineerin

A comparative study of finite element methodologies for the prediction of torsional response of bladed rotors

The prevention of torsional vibration-induced fatigue damage to turbo-generators requires determining natural frequencies by either field testing or mathematical modelling. Torsional excitation methods, measurement techniques and mathematical modelling are active fields of research. However, these aspects are mostly considered in isolation and often without experimental verification. The objective of this work is to compare one dimensional (1D), full three dimensional (3D) and 3D cyclic symmetric (3DCS) finite element (FE) methodologies for torsional vibration response. Results are compared to experimental results for a small-scale test rotor. It is concluded that 3D approaches are feasible given the current computing technology and require less simplification with potentially increased accuracy. Accuracy of 1D models is reduced due to simplifications but faster solution times are obtained. For high levels of accuracy model updating using field test results is recommended.The Eskom Power Plant Engineering Institute (EPPEI) as well as the NRF Technology and Human Resources Programme (THRIP).http://link.springer.com/journal/122062017-09-30hb2016Mechanical and Aeronautical Engineerin

An overview of numerical methodologies for durability assessment of vehicle and transport structures

Numerical methodologies for assessing the durability of vehicle and transport structures are reviewed. These methodologies are mapped in terms of a framework that emphasizes the relationships among them. Load inputs are obtained from either measurements or simulation. These loads are used as inputs into stress analyses, which may be either quasi-static or dynamic, and either in the time domain or in the frequency domain. The outputs of these analyses can then be used in fatigue analyses. The advantages and disadvantages of each method are analysed. A case study is described to demonstrate the insights gained from the mapping framework.https://inderscience.metapress.com/ai201

Thermo-structural fatigue and lifetime analysis of a heat exchanger as a feedwater heater in power plant

Today, the use of shell and tube heat exchangers has become widespread and they are used in various industries under very diverse operating conditions. Specific operating conditions make it possible to consider and simulate the operating terms and failure conditions of these converters. In this study, the design of a shell-and-tube counter-flow heat exchanger in AutoCAD software is first considered, and the system is then meshed and simulated in ANSYS 2019 software. Simulation results of temperature, pressure, heat flux and fluid velocity within the system are reported in order to understand the system performance. Failure conditions are evaluated according to the ASME VIII Boiler and Pressure Vessel Code, and the results of equivalent thermal stress analysis and system lifetime under two extreme loading conditions are reported. The highest equivalent thermal stresses under these extreme load conditions occur at the joints of the tubes and tubes sheet and is equal to 641 and 931 MPa, respectively. Also, the lifetimes of tubes and tube sheets are 105 and 104 cycles respectively for the valley and peak load conditions.http://www.elsevier.com/locate/engfailanal2021-07-01hj2020Mechanical and Aeronautical Engineerin

Application of an ANN-based methodology for road surface condition identification on mining vehicles and roads

An artificial neural networks-based methodology for the identification of road surface condition was applied to two different vehicles in their normal operating environments at two mining sites. An ultra-heavy haul truck used for hauling operations in surface mining and a small utility underground mine vehicle were utilised in the current investigation. Unlike previous studies where numerical models were available and road surfaces were accurately profiled with profilometers, in this study, that was not the case in order to replicate the real mine road management situation. The results show that the methodology performed very well in reconstructing discrete faults such as bumps, depressions or potholes but, owing to the inevitable randomness of the testing conditions, these conditions could not fit the fine undulations present on the arbitrary random rough surface. These are better represented by the spectral displacement densities of the road surfaces. Accordingly, the proposed methodology can be applied to road condition identification in two ways: firstly, by detecting, locating and quantifying any existing discrete road faults/features, and secondly, by identifying the general level of the road’s surface roughness.http://www.elsevier.com/locate/jterrahb201

Low speed rolling bearing diagnostics using acoustic emission and higher order statistics techniques

Diagnostics in low speed rolling element bearings is difficult. Not only are normal frequency domain diagnostics methods not appropriate for this application, but the bearing response signals are usually immersed in background noise which make it difficult to detect these faults. Higher order statistics (HOS) techniques have been available for decades but have not been widely applied to machine condition monitoring with the exceptions of skewness and kurtosis. There is however reason to believe that these HOS techniques could play an important role in acoustic emission (AE) based condition monitoring of rolling element bearings at low speeds provided appropriate care is taken. To explore this hypothesis, AE signals at low bearing rotational speeds of 70, 80, 90 and 100 rpm respectively were used in this work for the monitoring of tapered roller bearings. In addition to the well-established statistical parameters such as mean, standard deviation, skewness and kurtosis, higher moments such as hyper flatness are considered in this study. A novel diagnostic method is proposed for fault extraction based on hyperflatness, combined with Kullback-Leibler divergence, and an indicator formula derived with the use of Lempel-Ziv Complexity is given. The Kullback-Leibler divergence is used together with the skewness and hyperflatness to obtain the Kullback-Leibler information Wave (KLW) with which the analysis is performed, and better results obtained as compared to conventional frequency domain analysis.https://jmerd.org.myam2019Mechanical and Aeronautical Engineerin

In-belt vibration monitoring of conveyor belt idler bearings by using wavelet package decomposition and artificial intelligence

Visual and acoustic methods are commonly used to identify faulty or failing idler bearings but these methods can become tedious and time consuming in practice. While vibration monitoring might look like an obvious choice to explore, the instrumentation of individual idler bearings would be prohibitively expensive. The potential for using an accelerometer that moves with the belt while tracking the condition of all bearings encountered along the way is therefore potentially interesting. This possibility is explored in this work on a laboratory scale test rig. Wavelet package decomposition is used to extract the bearing features and present it to an artificial neural network and support vector machine to identify and classify faulty idler bearings. The system could not only identify faulty bearings but also classify the faults accurately.http://www.inderscience.com/jhome.php?jcode=IJMME2022-05-06hj2021Mechanical and Aeronautical Engineerin

Fault classification of low-speed bearings based on support vector machine for regression and genetic algorithms using acoustic emission

PURPOSE : This work under consideration makes use of support vector machines (SVM) for regression and genetic algorithms (GA) which may be referred to as SVMGA, to classify faults in low-speed bearings over a specified speed range, with sinusoidal loads applied to the bearing along the radial and axial directions. METHODS : GA is used as a heuristic tool in finding profound solution to the difficult problem of solving the highly non-linear situation through the application of the principles of evolution by optimizing the statistical features selected for the SVM for regression training solution. It is used to determine the training parameters of SVM for regression which can optimize the model and hence without the forehand knowledge of the probabilistic distribution can form new features from the original dataset. Using SVM for regression, the non-linear regression and fault recognition are achieved. Classification is performed for three classes. In this work, the GA is used to first optimize the statistical features for the best performance before they are used to train the SVM for regression. Experimental studies using acoustic emission caused by bearing faults showed that SVMGA with a Gaussian kernel function better achieves classification on the bearings operated at low speed, regardless of the load type and, under different fault conditions, compared to the exponential kernel function and the other many kernel functions which also can be used for the same conditions. RESULTS : This study accomplished the effective classification of different bearing fault patterns especially at low speeds and at varying load conditions using support vector machines (SVM) for regression and genetic algorithms (GA) referred to as SVMGA.https://www.springer.com/journal/424172020-06-12hj2019Mechanical and Aeronautical Engineerin