Turbine blade temperature calculation and life estimation-a sensitivity analysis

The overall operating cost of the modern gas turbines is greatly influenced by thedurability of hot section components operating at high temperatures.In turbine operatingconditions,some defects may occur which can decrease hot section life.In the present paper,methods used for calculating blade temperature and life are demonstrated and validated.Usingthese methods,a set of sensitivity analyses on the parameters affecting temperature and life ofa high pressure,high temperature turbine first stage blade is carried out.Investigateduncertainties are:(1)blade coating thickness,(2)coolant inlet pressure and temperature(asa result of secondary air system),and(3)gas turbine load variation.Results show thatincreasing thermal bamier coating thickness by 3 times,leads to rise in the blade life by 9times.In addition,considering inlet cooling temperature and pressure,deviation in temperaturehas greater effect on blade life.One of the interesting points that can be realized from theresults is that 300 hours operation at 70%load can be equal to one hour operation atbase load.

VIBRATION ANALYSIS OF A BEAM WITH EMBEDDED SHAPE MEMORY ALLOY WIRES

In this study,analytical relations for evaluating the exact solution of natural frequency and mode shape of beams with embedded shape memory alloy(SMA)wires are presented.Beams are modeled according to Euler-Bernoulli,Timoshenko and third order beam(Reddy)theories.A relation is obtained for determining the efect of axial load generated by the recovery action of pre-strained SMA wires.By defining some dimensionless quantities,the efect of diferent mechanical properties on the frequencies and mode shapes of the system are carefully examined.The efect of axial load generated by SMA wires with buckling load and frequency jump is accurately studied.

Regularized machine learning through constraint swarm and evolutionary computation applied to regression problems

Purpose–The purpose of this paper is to demonstrate the applicability of swarm and evolutionary techniques for regularized machine learning.Generally,by defining a proper penalty function,regularization laws are embedded into the structure of common least square solutions to increase the numerical stability,sparsity,accuracy and robustness of regression weights.Several regularization techniques have been proposed so far which have their own advantages and disadvantages.Several efforts have been made to find fast and accurate deterministic solvers to handle those regularization techniques.However,the proposed numerical and deterministic approaches need certain knowledge of mathematical programming,and also do not guarantee the global optimality of the obtained solution.In this research,the authors propose the use of constraint swarm and evolutionary techniques to cope with demanding requirements of regularized extreme learning machine(ELM).Design/methodology/approach–To implement the required tools for comparative numerical study,three steps are taken.The considered algorithms contain both classical and swarm and evolutionary approaches.For the classical regularization techniques,Lasso regularization,Tikhonov regularization,cascade Lasso-Tikhonov regularization,and elastic net are considered.For swarm and evolutionary-based regularization,an efficient constraint handling technique known as self-adaptive penalty function constraint handling is considered,and its algorithmic structure is modified so that it can efficiently perform the regularized learning.Several well-known metaheuristics are considered to check the generalization capability of the proposed scheme.To test the efficacy of the proposed constraint evolutionary-based regularization technique,a wide range of regression problems are used.Besides,the proposed framework is applied to a real-life identification problem,i.e.identifying the dominant factors affecting the hydrocarbon emissions of an automotive engine,for further assurance on the performance of the proposed scheme.Findings–Through extensive numerical study,it is observed that the proposed scheme can be easily used for regularized machine learning.It is indicated that by defining a proper objective function and considering an appropriate penalty function,near global optimum values of regressors can be easily obtained.The results attest the high potentials of swarm and evolutionary techniques for fast,accurate and robust regularized machine learning.Originality/value–The originality of the research paper lies behind the use of a novel constraint metaheuristic computing scheme which can be used for effective regularized optimally pruned extreme learning machine(OP-ELM).The self-adaption of the proposed method alleviates the user from the knowledge of the underlying system,and also increases the degree of the automation of OP-ELM.Besides,by using different types of metaheuristics,it is demonstrated that the proposed methodology is a general flexible scheme,and can be combined with different types of swarm and evolutionary-based optimization techniques to form a regularized machine learning approach.

Simultaneous knowledge-based identification and optimization of PHEV fuel economy using hyper-level Pareto-based chaotic Lamarckian immune algorithm, MSBA and fuzzy programming

Purpose–The purpose of this paper is to probe the potentials of computational intelligence(CI)and bio-inspired computational tools for designing a hybrid framework which can simultaneously design an identifier to capture the underlying knowledge regarding a given plug-in hybrid electric vehicle’s(PHEVs)fuel cost and optimize its fuel consumption rate.Besides,the current investigation aims at elaborating the effectiveness of Pareto-based multiobjective programming for coping with the difficulties associated with such a tedious automotive engineering problem.Design/methodology/approach–The hybrid intelligent tool is implemented in two different levels.The hyper-level algorithm is a Pareto-based memetic algorithm,known as the chaos-enhanced Lamarckian immune algorithm(CLIA),with three different objective functions.As a hyper-level supervisor,CLIA tries to design a fast and accurate identifier which,at the same time,can handle the effects of uncertainty as well as use this identifier to find the optimum design parameters of PHEV for improving the fuel economy.Findings–Based on the conducted numerical simulations,a set of interesting points are inferred.First,it is observed that CI techniques provide us with a comprehensive tool capable of simultaneous identification/optimization of the PHEV operating features.It is concluded that considering fuzzy polynomial programming enables us to not only design a proper identifier but also helps us capturing the undesired effects of uncertainty and measurement noises associated with the collected database.Originality/value–To the best knowledge of the authors,this is the first attempt at implementing a comprehensive hybrid intelligent tool which can use a set of experimental data representing the behavior of PHEVs as the input and yields the optimized values of PHEV design parameters as the output.

Auto-regressive multiple-valued logic neurons with sequential Chua’s oscillator back-propagation learning for online prediction and synchronization of chaotic trajectories

Purpose–The purpose of this paper is to examine the structural and computational potentials of a powerful class of neural networks(NNs),called multiple-valued logic neural networks(MVLNN),for predicting the behavior of phenomenological systems with highly nonlinear dynamics.MVLNNs are constructed based on the integration of a number of neurons working based on the principle of multiple-valued logics.MVLNNs possess some particular features,namely complex-valued weights,input,and outputs coded by kth roots of unity,and a continuous activation as a mean for transferring numbers from complex spaces to trigonometric spaces,which distinguish them from most of the existing NNs.Design/methodology/approach–The presented study can be categorized into three sections.At the first part,the authors attempt at providing the mathematical formulations required for the implementation of ARX-based MVLNN(AMVLNN).In this context,it is indicated that how the concept of ARX can be used to revise the structure of MVLNN for online applications.Besides,the stepwise formulation for the simulation of Chua’s oscillatory map and multiple-valued logic-based BP are given.Through an analysis,some interesting characteristics of the Chua’s map,including a number of possible attractors of the state and sequences generated as a function of time,are given.Findings–Based on a throughout simulation as well as a comprehensive numerical comparative study,some important features of AMVLNN are demonstrated.The simulation results indicate that AMVLNN can be employed as a tool for the online identification of highly nonlinear dynamic systems.Furthermore,the results show the compatibility of the Chua’s oscillatory system with BP for an effective tuning of the synaptic weights.The results also unveil the potentials of AMVLNN as a fast,robust,and efficient control-oriented model at the heart of NMPC control schemes.Originality/value–This study presents two innovative propositions.First,the structure of MVLNN is modified based on the concept of ARX system identification programming to suit the base structure for coping with chaotic and highly nonlinear systems.Second,the authors share the findings about the learning characteristics of MVLNNs.Through an exhaustive comparative study and considering different rival methodologies,a novel and efficient double-stage learning strategy is proposed which remarkably improves the performance of MVLNNs.Finally,the authors describe the outline of a novel formulation which prepares the proposed AMVLNN for applications in NMPC controllers for dynamic systems.

Identification of a dynamic model for shape memory alloy actuator using Hammerstein-Wiener gray box and mutable smart bee algorithm

Purpose–The purpose of the current investigation is to design a robust and reliable computational framework to effectively identify the nonlinear behavior of shape memory alloy(SMA)actuators,as one of the most applicable types of actuators in engineering and industry.The motivation of proposing such an intelligent paradigm emanates in the pursuit of fulfilling the necessity of devising a simple yet effective identification system capable of modeling the hysteric dynamical respond of SMA actuators.Design/methodology/approach–To address the requirements of designing a pragmatic identification system,the authors integrate a set of fast yet reliable intelligent methodologies and provide a predictive tool capable of realizing the nonlinear hysteric behavior of SMA actuators in a computationally efficient fashion.First,the authors utilize the governing equations to design a gray box Hammerstein-Wiener identifier model.At the next step,they adopt a computationally efficient metaheuristic algorithm to elicit the optimum operating parameters of the gray box identifier.Findings–Applying the proposed hybrid identifier framework allows the authors to find out its advantages in modeling the behavior of SMA actuator.Through different experiments,the authors conclude that the proposed identifier can be used for identification of highly nonlinear dynamic behavior of SMA actuators.Furthermore,by extending the conclusions and expounding the obtained results,one can easily infer that such a hybrid method may be conveniently applied to model other engineering phenomena that possess dynamic nonlinear reactions.Based on the exerted experiments and implementing the method,the authors come to the conclusion that integrating the power of metaheuristic exploration/exploitation with gray box identifier results a predictive paradigm that much more computationally efficient as compared with black box identifiers such as neural networks.Additionally,the derived gray box method has a higher degree of preference over the black box identifiers,as it allows a manipulated expert to extract the knowledge of the system at hand.Originality/value–The originality of the research paper is twofold.From the practical(engineering)point of view,the authors built a prototype biased-spring SMA actuator and carried out several experiments to ascertain and validate the parameters of the model.From the computational point of view,the authors seek for designing a novel identifier that overcomes the main flaws associated with the performance of black-box identifiers that are the lack of a mean for extracting the governing knowledge of the system at hand,and high computational expense pertinent to the structure of black-box identifiers.

An evolvable self-organizing neuro-fuzzy multilayered classifier with group method data handling and grammar-based bio-inspired supervisors for fault diagnosis of hydraulic systems

Purpose–The purpose of this paper is to apply a hybrid neuro-fuzzy paradigm called self-organizing neuro-fuzzy multilayered classifier(SONeFMUC)to classify the operating faults of a hydraulic system.The main motivation behind the use of SONeFMUC is to attest the capabilities of neuro-fuzzy classifier for handling the difficulties associated with fault diagnosis of hydraulic circuits.Design/methodology/approach–In the proposed methodology,first,the neuro-fuzzy nodes at each layer of the SONeFMUC are trained separately using two well-known bio-inspired algorithms,i.e.a semi deterministic method with random walks called co-variance matrix adaptation evolutionary strategy(CMA-ES)and a swarm-based explorer with adaptive fuzzified parameters(SBEAFP).Thereafter,a revised version of the group method data handling(GMDH)policy that uses the Darwinian concepts such as truncation selection and elitism is engaged to connect the nodes of different layers in an effective manner.Findings–Based on comparative numerical experiments,the authors conclude that integration of neuro-fuzzy method and bio-inspired supervisor results in a really powerful classification tool beneficial for uncertain environments.It is proved that the method outperforms some well-known classifiers such as support vector machine(SVM)and particle swarm optimization-based SVM(PSOSVM).Besides,it is indicated that an efficient bio-inspired method can effectively adjust the constructive parameters of the multi-layered neuro-fuzzy classifier.For the case,it is observed that designing a fuzzy controller for PSO predisposes it to effectively balance the exploration/exploitation capabilities,and consequently optimize the structure of SONeFMUC.Originality/value–The originality of the paper can be considered from both numerical and practical points of view.The signals obtained through the data acquisition possess six different features in order for the hydraulic system to undergo four types of faults,i.e.cylinder fault,pump fault,valve leakage fault and rupture of the piping system.Besides,to elaborate on the authenticity and efficacy of the proposed method,its performance is compared with well-known rival techniques.