Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería / International Journal of Numerical Methods for Calculation and Design in Engineering

RIMNI - An International Journal of Numerical Methods for Calculation and Design in Engineering (Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería) is a peer-reviewed Open Access journal, founded in 1985. RIMNI contributes to the spread of theoretical advances and practical applications of numerical methods in engineering and other applied sciences. RIMNI publishes articles written in Spanish ... show more

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Optimizing Convolutional Neural Network-Long Short-Term Memory Architecture with Additive Attention Mechanism for Stock Price Prediction
B. Wu, Y. Qi, G. Fu

Rev. int. métodos numér. cálc. diseño ing. (2024). Vol. Online First, 47

Abstract
Stock price fluctuations reflect market expectations for the economic situation and company profits. Accurately predicting stock prices has become a hot topic in academia. With the rapid development of artificial intelligence, many researchers are starting to use machine learning algorithms to predict stock prices. In this paper, a new time series prediction model, the combination of the convolutional neural network and long shortterm memory neural network with additive attention mechanism (CNNLSTM-AAM), is proposed for stock price prediction. It can combine the advantages of the convolutional neural network (CNN), the long shortterm memory (LSTM) neural network, and the additive attention mechanism (AAM), and better capture nonlinear features of time series data. In the simulation analysis, we select sample data of three stocks (Vanke A, Shanghai International Port Group, and China Merchants Bank) and three stock price indexes (China Securities 500 Index, Shanghai Stock Exchange 50 Index, and Growth Enterprise Index) in the Chinese stock market for comparative analysis, and use mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) as the evaluation indexes. The CNN-LSTM-AAM model has the best prediction ability relative to the CNN and CNN-LSTM models. In addition, we also find that the prediction ability of the CNN-LSTM-AAM model for different stock data sets is different under the existing parameter conditions. For a specific dataset, the parameter conditions of the CNN-LSTM-AAM model need to be further adjusted to achieve the best prediction effect. Based on the above findings, the CNN-LSTM-AAM model has better performance and higher accuracy, and can provide credible decision-making basis and research methods for investors, financial institutions, and regulators.
Abstract
Stock price fluctuations reflect market expectations for the economic situation and company profits. Accurately predicting stock prices has become a hot topic in academia. [...]
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A Numerical Study of the Impact of Bending and Torsional Stiffness on the Static Aeroelastic Characteristics of a Large Aspect Ratio Composite Wing
S. qiao, F. Wang, P. Wang

Rev. int. métodos numér. cálc. diseño ing. (2024). Vol. Online First, 46

Abstract
The bending and torsional stiffness influence structural deformation to varying degrees. Utilizing the coupled numerical method of Computational Fluid Dynamics and Computational Structural Dynamics (CFD/CSD), a comprehensive study was conducted on the impact of bending and torsional stiffness on the static aeroelastic behavior of a high-aspect-ratio wing, employing FLUENT software across diverse flight conditions. The study summarized this influence by comparing the computational outcomes. The results indicate that as the angle of attack rises, the lift increment diminishes gradually until the angle reaches 14 degrees, rendering the strategy of enhancing bending and torsional stiffness to gain more lift ineffective. Between 2 and 14 degrees of angle of attack, the lift difference between the scenarios decreases from 23.98% to 7.31%. At higher Mach numbers and lower angles of attack, augmenting wing stiffness significantly boosts wing lift. Optimal lift-to-drag characteristics are achieved at approximately 6 degrees of angle of attack. By averagely increasing the wing’s bending and torsional stiffness by 8.28% and 5.22%, respectively, the lift-to-drag characteristics can be enhanced by 5.27% at a low angle of attack of 0.75 Mach. The disparity in maximum deflection between the two stiffness wings is most pronounced at higher flight speeds and smaller angles of attack, with the opposite trend observed for the difference in maximum torsion angle. The key findings presented in this paper can expedite the integrated design of stiffness for this type of wing structure by providing vital technical insights.OPEN ACCESS Received: 24/07/2024 Accepted: 22/10/2024
Abstract
The bending and torsional stiffness influence structural deformation to varying degrees. Utilizing the coupled numerical method of Computational Fluid Dynamics and Computational [...]
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EDITORIAL BOARD
CARLOS AGELET DE SARACIBAR E.T.S. Ing. Caminos, Canales y Puertos Univ. Politéc. Cataluña, ESPAÑA	PILAR ARIZA E.T.S. de Ing. Industriales Univ. de Sevilla, ESPAÑA	ALEX H. BARBAT E.T.S. de Ing. de Caminos, Canales y Puertos Univ. Politéc. de Cataluña, Barcelona, ESPAÑA
JAVIER BONET School of Engineering Swansea University, UK	SALVADOR BOTELLO Facultad de Ingeniería Civil Univ. de Guanajuato, MÉXICO	GABRIEL BUGEDA E.T.S. Ing. Caminos, Canales y Puertos Univ. Politéc. Cataluña, ESPAÑA
NING CAI School of Intelligence Engineering and Automation Beijing University of Posts and Telecommunications, China	BEGOÑA CALVO Centro Politécnico Superior Univ. de Zaragoza, ESPAÑA	MIGUEL CERVERA Centre Internacional de Mètodes Numèrics en Enginyeria(CIMNE), Barcelona, Spain
FRANCISCO CHINESTA Ecole Nationale Supérieure d'Arts et Métiers \| ENSAM, Paris, FRANCE	IGNASI COLOMINAS Civil Engineering School, University of A Coruna, Spain	ALVARO COUTINHO Programa de Engenharia Civil, COPPE/ Univ. Federal do Rio de Janeiro, BRASIL
MARCELA CRUCHAGA Dpto. de Ing. Mecánica, Univ. de Santiago de Chile, CHILE	ELÍAS CUETO Centro Politécnico Superior Univ. de Zaragoza, ESPAÑA	MANUEL DOBLARÉ Centro Politécnico Superior Univ. de Zaragoza, ESPAÑA
ESTEVAM B. DE LAS CASAS Dept. Eng. Estruturas Univ. Federal Minas Gerais, Belo Horizonte, BRASIL	GUILLERMO ETSE Laboratorio de Estructuras Univ. Nac. de Tucumán, ARGENTINA	CARLOS A. FELIPPA Dept. of Aerospace Eng. Sciences Univ. of Colorado at Boulder, USA
LUIS A. GODOY Dpto. de Estructuras Univ. Nacional de Córdoba, ARGENTINA	JOSÉ Mª GOICOLEA E.T.S. Ing. de Caminos, Canales y Puertos Univ. Politécnica de Madrid, Madrid, ESPAÑA	RAINALD LÖHNER Dept. of Comp. and Data Sciences George Mason Univ., Fairfax, VA, USA
PAULO LYRA Univ. Federal de Pernambuco Recife, BRASIL	SERGIO OLLER E.T.S. Ing. Caminos, Canales y Puertos Univ. Politéc. Cataluña, ESPAÑA	MANUEL PASTOR E.T.S. Ing. Caminos Univ. Politéc. de Madrid, ESPAÑA
FRANCO PERAZZO Dpto. Mecánica Univ. Téc. Federico Santa María, Valparaíso, CHILE	IGNACIO ROMERO E.T.S. Ingenieros Industriales Universidad Politécnica de Madrid, ESPAÑA	VITORIANO RUAS Laboratoire de Modélisation en Mécanique Université Pierre et Marie Curie Paris, FRANCIA
ADELIA SEQUEIRA Instituto Superior Técnico Universidade Técnica de Lisboa, PORTUGAL	MARIO STORTI CIMEC - INTEC Universidad Nacional del Litoral Santa Fe, ARGENTINA	ZHANG YIMING School of Construction Engineering, Zhejiang Sci-Tech University, China
BURAK TAŞÇI Firat University Vocational School of Technical Sciences, 23119, Elazig TURKEY

EDITOR IN CHIEF
EUGENIO OÑATE E.T.S. de Ingenieros de Caminos Canales y Puertos Centro Internacional de Métodos Numéricos en ingenieria (CIMNE), Univ. Politécnica de Cataluña, Barcelona, ESPAÑA

ASSOCIATE EDITOR
Francisco Zárate International Centre for Numerical Methods in Engineering (CIMNE), Technical University of Catalonia (UPC), Barcelona, SPAIN

HONORARY EDITOR
SERGIO R. IDELSOHN Univ. Nacional del Litoral, Santa Fe, ARGENITNA ICREA en el Centro Internacional de Métodos Numéricos en ingeniería (CIMNE), Barcelona, ESPAÑA