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• A three-dimensional CFD model aiming to improve SOFC's performance was developed. • Nineteen artificial intelligence algorithms were examined and used to train the database. • Genetic algorithm was employed to optimize the power output of SOFC. Solid oxide fuel cell (SOFC) is the electrochemical device that directly convert the chemical energy of fuels into electrical energy, which are considered one of the promising methods for achieving high power generation efficiency. However, the commercialization of SOFC encounters the challenge due to its high manufacturing and operating cost. This study aims to present a framework and methodology for improving SOFC' performance assisted by computational fluid dynamic (CFD) modeling, artificial neural network (ANN), and genetic algorithm (GA). Firstly, a three-dimensional computational fluid dynamic (CFD) model, referring to three types of parameters, e.g. geometry parameters, microscopic parameters and operating conditions, was developed and then the databases were obtained. Then 19 widely used intelligence algorithms, e.g. Artificial Neural Network (ANN), Boltzmann Machines (BMs), Support Vector Machines (SVMs), etc., were employed to train the databases. Next, the developed ANN surrogate model was used to replace the complicated and time-consuming CFD model and to predict SOFC's performance and optimize the power density output of SOFC. Finally, the system optimization was performed by using genetic algorithm (GA) to maximize the power density. The results showed that artificial neural network (ANN) achieved the best accuracy (R2 = 0.99889) in terms of predictions of SOFC performance. Besides, it was found that the optimal SOFC had a better gas concentration distribution which can enhance the mass transfer in the electrode, and thus the SOFC performance was improved. The combination of CFD modeling, ANN and GA can provide a promising solution for the performance prediction, improvement and optimization of SOFC accurately and rapidly. [ABSTRACT FROM AUTHOR]

Abstract: Empirical mode decomposition (EMD), a new self-adaptive signal processing method, has been recently developed for nonlinear and non-stationary time series analysis. In this paper, EMD method is described and employed for signal denoising. Aiming at the problems of intrinsic mode function (IMF) criterion in the EMD method, neural network (NN) prediction model and wavelet packet transform (WPT) technology are simultaneously introduced into the EMD method to improve the border effect and to enhance the ability of signal denoising, and thus a type of hybrid EMD-based model is proposed and applied in drift denoising for a dynamically tuned gyroscope (DTG). Experimental results on real drift data from the long-term measurement system of a certain DTG indicate that the proposed hybrid model is feasible and more effective in drift denoising compared with the wavelet and single EMD denoising methods. [Copyright &y& Elsevier]

In this paper, support vector machine (SVM) is described and applied in the temperature drift modeling and compensation to reduce the influence of temperature variation on the output of dynamically tuned gyroscope (DTG) and to enhance its precision. To improve the modeling capability, empirical mode decomposition (EMD) is introduced into the SVM model to eliminate any impactive noises. The real temperature drift data set from the long-term measurement system of a certain DTG is employed to validate the effectiveness of the proposed combination model. The modeling and compensation results indicate that the proposed EMD-SVM model outperforms the neural network (NN) and single SVM models, and is feasible and effective in temperature drift modeling and compensation of the DTG. [Copyright &y& Elsevier]

This study proposes a systematic methodology for improving PEMFC's performance combining computational fluid dynamic (CFD), artificial neural network (ANN), and intelligent optimization algorithms. Firstly, a three-dimensional (3-D) multiphase PEMFC CFD model with 3-D fine-mesh flow field is developed. Then the key structural features of the fine-mesh flow field are extracted as optimization decision variables, and the sampling points are selected by using the Latin hypercube sampling (LHS) experimental method. The power density and oxygen uniformity index of sampling points are calculated by CFD modeling to form the database, which is used to train the artificial neural network (ANN) surrogate model. Finally, the single-objective optimization (SOO) and multi-objective optimization (MOO) are implemented by using genetic algorithm (GA) and non-dominated sorting genetic algorithm (NSGA-II), respectively. It was found that using trained ANN surrogate models can get a high prediction precision. The maximum power density of SOO is increased by 7.546% than that of base case and is 0.562% larger than that of MOO case. However, the overall pressure drop in cathode flow field of SOO case is greater than that of MOO case and the base case. Furthermore, the oxygen concentration, the oxygen uniformity index and the water removal capacity of MOO case are better than that of SOO case. It is recommended that the improved flow field structure optimized by MOO is more beneficial to improve the overall performance of PEMFC. [Display omitted] • A novel three-dimensional fine-mesh flow field of PEMFC is designed. • Proposing a co-simulation model for improving the novel PEMFC's performance. • The single-objective and multi-objective optimizations are performed using genetic algorithm. [ABSTRACT FROM AUTHOR]