Archives of Computational Methods in Engineering最新文献

The principle of electrohydrodynamics (EHD) processes relies on manipulating fluids using electric forces. The advantage of EHD over other fluid manipulation approaches, such as thermal or acoustic-based processes, consists in its excellent controllability, versatility (in terms of the range of suitable fluids), and relatively low cost. The importance of modeling and simulation of EHD processes, particularly for the microsystems, has been growing over the past decade, replacing on many occasions trial-and-error approaches. The present paper is devoted to the advances in the numerical modeling and simulation of electrohydrodynamic (EHD) problems. Physical phenomena playing an essential role in EHD are explained and governing equations are formulated. Major challenges faced when modeling EHD problems are highlighted and different classes of numerical approaches used for handling them are outlined. Benefits and disadvantages as well as open issues in different numerical approaches are also discussed. Finally, the paper provides an overview of numerical studies of EHD in multi-phase micro-systems emphasizing some key findings for three classes of problems, namely droplets, jets and planar films exposed to external electric fields.

In the operation of power grid, worldwide, non-technical losses (NTLs) occur in a massive amount of proportion which is observed up to 40% of the total electric transmission and distribution losses. These dominant losses severely affect to adverse the performance of all the private and public distribution sectors. By rectifying these NTLs, the necessity of establishing new power plants will automatically be cut down. Hence, NTLs have become a critical challenge to do research in this emerging area for researchers of power systems due to the limitations of the current methodologies to detect and fix up these prominent type of losses. The existing survey so for basically contains the detail of identification of non-technical losses by machine and deep learning methods while this paper is a complete trouble shooting to resolve this issue by systematic approach. To address this, causes of NTLs along with its impact on economies and types of NTLs are elaborated in various countries. In addition, we have also prepared a comparative analysis based on several essential parameters. Further, implementation process of detection of NTLs or electricity theft based on Machine Learning or Deep Learning has also been demonstrated. Moreover, major challenges of detection of NTLs or electricity theft based on ML and Deep Learning, and its possible solutions are also described. Hence, definitely this comprehensive survey will help to the leading researchers to reach a new height in this thrust area.

As researchers sought for new methods to decrease noxious emissions and improve engine performance, they discovered biodiesel as a promising biofuel. However, traditional study methodologies were deemed inadequate, prompting the need for computational methods to offer numerical solutions. This approach was seen as a creative and practical solution to the problem at hand. In response to the limitations of conventional modeling approaches, researchers turned towards the innovative solution of using machine-learning techniques as data processing systems. This creative approach has proven effective in addressing a broad variety of technical and scientific concerns, particularly in fields where traditional modeling approaches have fallen short of expectations. This review discusses using machine learning algorithms for predicting biodiesel performance and emissions with nanoparticles. Researchers have solved these problems with the application of machine learning to anticipate engine efficiency and emissions. The machine-learning algorithm predicts engine performance very precisely, proving its efficacy. Nanotechnology and biodiesel engine technologies are quickly advancing, making this review vital. Previous studies have examined nanoparticles' influence on engine performance and emissions. This review uniquely focuses on the application of machine learning techniques. Through the utilization of machine-learning algorithms, it is possible for gaining deeper understanding of intricate connections existing between the properties of nanoparticles and the behavior of engines. This methodology provides extensive comprehension of an impact of nanoparticles upon performance and emissions of biodiesel engines, hence enabling a development of more effectual and sustainable engine designs.

Building resilient and sustainable urban infrastructures is imperative to prepare future generations against new pandemics and climate change uncertainties. In general, modelling of urban infrastructures requires modelers to carefully consider their initial design phase, subsequent life-span management, and long-term resilience development. With the continual development of machine learning (ML) and artificial intelligence (AI) approaches, significant opportunities are available to civil engineers to improve the existing computing systems of urban infrastructures to contribute to their overall design, management, and resilience-development. Often, an important requirement for the successful adoption of ML/AI techniques is to ensure sufficient field data for training effective predictive models for the above objectives. However, this requirement may be difficult to achieve for all infrastructure engineering applications in the practical field context due to sensor constraints (e.g., limited sensor deployment), coupled with other computational challenges. To address the multiple challenges, this review paper evaluates the important and relevant physics informed machine learning (PIML) publications from 1992 to 2022 for various critical infrastructure engineering applications, namely: (1) PIML for Infrastructures Design and Analysis, (2) PIML for Infrastructure Built-Environment Modelling, (3) PIML for Infrastructures Health Monitoring, and (4) PIML for Infrastructures Resilience Management/Development. In each application, we discuss on the key modelling objectives involved for the specific infrastructure systems, and their associated advantages and/or likely limitations obtained from the PIML implementation. Finally, we then summarize the key research trends and their associated challenges to continue leveraging on PIML techniques to better benefit the overall design, management, and resilience-development of urban infrastructures.

Recently, the mechanical performance of various mechanical, electrical, and civil structures, including static and dynamic analysis, has been widely studied. Due to the neuroma's advanced technology in various engineering fields and applications, developing small-size structures has become highly demanded for several structural geometries. One of the most important is the nano/micro-plate structure. However, the essential nature of highly lightweight material with extraordinary mechanical, electrical, physical, and material characterizations makes researchers more interested in developing composite/laminated-composite-plate structures. To comprehend the dynamical behavior, precisely the linear/nonlinear-free vibrational responses, and to represent the enhancement of several parameters such as nonlocal, geometry, boundary condition parameters, etc., on the free vibrational performance at nano/micro scale size, it is revealed that to employ all various parameters into various mathematical equations and to solve the defined governing equations by analytical, numerical, high order, and mixed solutions. Thus, the presented literature review is considered the first work focused on investigating the linear/nonlinear free vibrational behavior of plates on a small scale and the impact of various parameters on both dimensional/dimensionless natural/fundamental frequency and Eigen-value. The literature is classified based on solution type and with/without considering the size dependency effect. As a key finding, most research in the literature implemented analytical or numerical solutions. The drawback of classical plate theory can be overcome by utilizing and developing the elasticity theories. The nonlocality, weight fraction of porosity, or the reinforcements, and its distribution type of elastic foundation significantly influence the frequencies.

The applications of wind turbines are consistently increasing across the globe. Competent and sustainable wind energy harnessing inherently requires the implementation of optimal design and advanced materials. To minimize all the risks associated with severe environmental loadings, reduced cost, and improved performance, advanced computational methodologies should be utilized as a part of the analysis process. The recently introduced non-local theory called Peridynamic (PD) theory crafted by Silling has interesting advantages over the conventional computational method such as the finite element method (FEM) and finite volume method (FVM). PD theory is a computational and theoretical framework where partial differential equations (PDEs) of classic continuum theory are replaced by integral equations. Unlike the local continuum theory, the integro-differential equations of PD theory are without derivatives of displacement function, hence suitable to capture discontinuities. Therefore, the present paper reviews the structural and aerodynamics of wind turbines, the existing computational challenges that are related to the modeling and analysis of wind turbines, and finally examines the potential use of Peridynamic theory concerning wind turbines.

The prevalence of skin cancer has been increasing for the last few decades. Abnormal growth of cells forms skin lesions, which if not treated at the earliest, may turn into cancer. With the advancement in technology, computer-aided or remote diagnosis is possible, but a lot of efforts are required. An exclusive survey of the work done is required to consolidate the information regarding the various methods adopted to date and to ascertain future opportunities. In this paper, we have reviewed major works that have been proposed to automate the diagnosis of melanoma using dermoscopic images.

The mobility applications of laminated composites are constantly expanding due to their improved mechanical properties and superior strength-to-weight ratio. Such advancements directly contribute to a significant reduction in energy consumption in mobile applications. However, the orthotropic nature of these materials results in complex failure modes that require advanced damage detection techniques to prevent catastrophic failures. Therefore, various non-destructive evaluation techniques for structural health monitoring (SHM) of laminated composites are constantly being developed. Moreover, due to the latest advancements in intelligent computational methods, such as machine learning and deep learning, more reliable inspections can be performed. This review discusses current advances in SHM of composite laminates for safety–critical mobility applications such as aerospace, automobile, and marine. A comprehensive overview of the steps involved in SHM of mobility composite structures, such as sensing systems and intelligent computational methods, is presented. Additionally, the review discusses the procedure for developing these intelligent computational methods. The article also describes various public-domain datasets that readers can utilize to create novel, intelligent computational methods. Finally, potential research directions are highlighted that will enable researchers and practitioners to develop more accurate and efficient damage monitoring systems for mobility composite structures.

Ovarian cancer is a significant health concern because of its high mortality rates and potential to cause glomerular injury, which can obstruct the urinary tract. It is very crucial to diagnose and treat these diseases accurately as well as timely. In the era of artificial intelligence, deep learning models have emerged as powerful tools in analysing medical images as they showcase exceptional capabilities to detect diseases. In this study, an innovative approach has been proposed that uses deep transfer learning classifiers for the detection as well as classification of ovarian cancer, sclerosed glomeruli, and normal glomeruli in histopathological images. To gather relevant data, two different repositories have been explored which contain images of ovarian cancer, sclerosed glomeruli, and normal glomeruli. These images are thoroughly pre-processed by converting them into grayscale. Afterwards, advanced segmentation techniques are applied such as image equalization, thresholding, image inversion, and morphological opening which effectively highlight the affected areas using contour features, and various measurements such as area, mean intensity, height, width, and epsilon are calculated. Our study employed a range of deep learning techniques such as AlexNet2, InceptionV3, EfficientNetB0, EfficientNetB5, DenseNet121, Xception, MobileNetV2, and InceptionResNetV2 along with the two optimization techniques: Adam and RMSprop optimizer. Remarkably, during experimentation, AlexNet2 demonstrated exceptional accuracy by achieving 99.74%, with a low loss of 0.0018 and a root mean square error of 0.042426 when incorporating the Adam optimizer. Similarly, using the RMSprop optimizer, Xception delivered outstanding results with an accuracy of 99.74%, a minimal loss of 0.0027, and a root mean square error of 0.051962. This pioneering research significantly contributes to the field of medical diagnostics by harnessing deep learning technology to enhance the precision and efficiency of ovarian cancer and sclerosed glomeruli detection.

In the last few decades, metaheuristic algorithms that use the laws of nature have been used dramatically in numerous and complex optimization problems. The artificial hummingbird algorithm (AHA) is one of the metaheuristic algorithms that was invented in 2022 based on the foraging and migration behavior of the hummingbird for modeling and solving optimization problems. The algorithm initially starts with an initial random population of solutions. It then uses iterative processes and hummingbird position updates to balance exploration and exploitation toward the most optimal solutions. This paper has a detailed and extensive review of the AHA algorithm considering the aspects of hybrid, improved, binary, multi-objective, and optimization problems. In addition, a wide range of applications of AHA in various fields such as feature selection, image processing, scheduling, Internet of Things, classification, clustering, financial and economic issues, forecasting, wireless sensor networks, and many engineering challenges are explored. The statistical and numerical results showed that the AHA algorithm with deep learning methods, Levy flight, and opposition-based learning had the best performance. Also, the AHA algorithm is most widely used in solving multimodal optimization problems and continuous functions.