Archives of Computational Methods in Engineering

The objective of this paper is to develop a computational model and corresponding solution algorithm to enable rapid simulation of laser processing and subsequent targeted zonal heating of materials composed of packed, discrete, particles. Because of the complex microstructure, containing gaps and interfaces, this type of system is extremely difficult to simulate using continuum-based methods, such as the Finite Difference Time Domain Method or the Finite Element Method. The computationally-amenable model that is developed captures the primary physical events, such as reflection and absorption of optical energy, conversion into heat, thermal conduction through the microstructure and possible phase transformations. Specifically, the features of the computational model are (1) a discretization of a concentrated laser beam into rays, (2) a discrete element representation of the particulate material microstructure and (3) a discrete element transient heat transfer model that accounts for optical (laser) energy propagation (reflection and absorption), its conversion into heat, the subsequent conduction of heat and phase transformations involving possible melting and vaporization. A discrete ray-tracking algorithm is developed, along with an embedded, staggered, iterative solution scheme, which is needed to calculate the optical-to-thermal conversion, particle-to-particle conduction and phase-transformations, implicitly. Numerical examples are given, focusing on concentrated laser beams and the effects of surrounding material conductivity, which draws heat away from the laser contact zone, thus affecting the targeted material state.

This article presents a survey of the Core-Congruential Formulation (CCF) for geometrically nonlinear mechanical finite elements based on the Total Lagrangian (TL) kinematic description. Although the key ideas behind the CCF can be traced back to Rajasekaran and Murray in 1973, it has not subsequently received serious attention. The CCF is distinguished by a two-phase development of the finite element stiffness equations. The initial phase develop equations for individual particles. These equations are expressed in terms of displacement gradients as degrees of freedom. The second phase involves congruential-type transformations that eventually binds the element particles of an individual element in terms of its node-displacement degrees of freedom. Two versions of the CCF, labeled Direct and Generalized, are distinguished. The Direct CCF (DCCF) is first described in general form and then applied to the derivation of geometrically nonlinear bar, and plane stress elements using the Green-Lagrange strain measure. The more complex Generalized CCF (GCCF) is described and applied to the derivation of 2D and 3D Timoshenko beam elements. Several advantages of the CCF, notably the physically clean separation of material and geometric stiffnesses, and its independence with respect to the ultimate choice of shape functions and element degrees of freedom, are noted. Application examples involving very large motions solved with the 3D beam element display the range of applicability of this formulation, which transcends the kinematic limitations commonly attributed to the TL description.

Energy crisis and climate change are the major concerns which has led to a significant growth in the renewable energy resources which includes mainly the solar and wind power generation. In smart grid, there is a increase in the penetration level of solar PV and wind power generation. The solar radiation received at the earth surface is greatly dependent on various atmospheric parameters. Forecasting of solar radiation and photovoltaic power is a major concern in terms of efficient integration of solar PV plants in the power grid. There are significant challenges in smart grid energy management due to the variability of large-scale renewable energy generation. Renewable energy forecasting is critical to reduce the uncertainty related to renewable energy generation for a wide range of planning, investment and decision-making purposes. As renewable energy sources are highly intermittent and variable, all the forecasting models available in the literature contain errors. This paper presents an overview of current and new development of weather forecasting such as solar and wind forecasting techniques for renewable energy system in smart grid. Many forecasting models such as physical models, statistical models, artificial intelligence based models, machine learning and deep learning based models were discussed. It is observed that, despite having no clear understanding on atmospheric physics, the artificial intelligence based methods such as machine learning and deep learning method produces reasonable weather forecasting results.

Stay cables are one of the key elements of cable-stayed bridges and are characterized by lightweight, low inherent damping, and high flexibility. They are continuously subjected to small-to large-amplitude vibrations due to various types of dynamic loads that may, in the long term, cause fatigue and fracture problems for the cable system, and may eventually compromise the safety of cable-stayed bridges. Thus, several countermeasures including surface profiling, cross-ties, and structural vibrational control systems have been used to improve the dynamic performance of stay cables. This article presents a comprehensive state-of-the-art and state-of-the-practice review of structural vibration control systems specifically designed and used for the cables in cable-stayed bridges. Generally, the stay cable dampers are classified as internal and external dampers. Consequently, important aspects of each control strategy are highlighted and various types of devices and their designs are discussed to find the best control solution for suppressing the cable vibrations.

Soft computing (SC), due to its high abilities to solve the complex problems with uncertainty and multiple parameters, has been widely investigated and used, especially in structural engineering. They have successfully estimated the capacity of structural reinforced concrete (RC) members and determined the properties of concrete. There are so many articles in literature that applied SC methods for the above goals. However, there is no work to present the capability of such approaches by providing an overview on the available and existing studies. The lack of state-of-the-art review on the subject is the main motivation to present a comprehensive review on the latest trends between 2010 and 2020 in predicting the behavior of concrete elements using soft computing methods. The considered RC structural elements are beams, columns, joints, slabs, frames, concrete filled tube sections and strengthened elements with fibre reinforced polymer. The purpose of the investigated works was predicting the concrete characteristics such as crack, bond, shrinkage, or the strength of the elements. The review showed that SC methods are powerful tools which could provide flexible computational techniques with high level of accuracy for civil engineering problems. However, most of the published works neglected to present the required details and mathematical framework.

Quantum Machine Learning (QML) has emerged as a unique computing area. The utilization of quantum technology in machine learning can solve complex problems (unsolvable using classical computational methodologies). The revolutionary paradigms potential has spurred scientific research and progress. Therefore, a highly essential exploration is needed to extract scientific breakthrough paths. The proposed work supports the concept by providing a scientometric analysis of QML scientific literature for the period 2003–2023, gathered from the Web of Science database. The study explores the powerful machine learning techniques in the quantum realm. The scientometric implication of the article provides deep insights into the publication and citation pattern, geographical distribution analysis, document co-citation, and keyword co-occurrence network analysis. The research findings highlight the predominant use of algorithms such as quantum support vector machines, quantum neural networks, and Q-learning. Notably active research hotspots in this field include drug design and discovery, quantum control, optimization, error-correction, and quantum state tomography. Additionally, collaborative efforts are evident in the domains of quantum unsupervised and reinforcement machine learning. The overall inference of QML literature portrays insightful recommendations and research directions for the academic community.

Fluid film bearings are well suited for high-speed industrial machineries like turbine generators and marine propulsion systems. Several numerical methods are employed to theoretically investigate the flow phenomenon and performance parameters of journal bearings, operating under various conditions. This paper reviews various Computational Fluid Dynamics (CFD) and Fluid–Structure Interaction (FSI) techniques that are adopted to solve the flow equations pertaining to fluid film bearings. This review attempts to highlight the importance of cavitation, temperature, and their effect on bearing performance using CFD and FSI techniques. A comparison of various performance parameters in the form of load-carrying capacity, oil flow rate, friction as well as deformation and stresses developed in the bearing element due to oil film pressure using CFD and FSI, are also presented. Observations from the reviewed literature are presented which aims to benefit the researchers working in the field of both CFD and FSI analysis on hydrodynamic journal bearing. This review article emphasized the significance of cavitation and temperature effects on journal bearing performance using CFD and FSI techniques.