Showing papers in "Archives of Computational Methods in Engineering in 2020"

A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning

Abstract: Nowadays, deep learning is a current and a stimulating field of machine learning. Deep learning is the most effective, supervised, time and cost efficient machine learning approach. Deep learning is not a restricted learning approach, but it abides various procedures and topographies which can be applied to an immense speculum of complicated problems. The technique learns the illustrative and differential features in a very stratified way. Deep learning methods have made a significant breakthrough with appreciable performance in a wide variety of applications with useful security tools. It is considered to be the best choice for discovering complex architecture in high-dimensional data by employing back propagation algorithm. As deep learning has made significant advancements and tremendous performance in numerous applications, the widely used domains of deep learning are business, science and government which further includes adaptive testing, biological image classification, computer vision, cancer detection, natural language processing, object detection, face recognition, handwriting recognition, speech recognition, stock market analysis, smart city and many more. This paper focuses on the concepts of deep learning, its basic and advanced architectures, techniques, motivational aspects, characteristics and the limitations. The paper also presents the major differences between the deep learning, classical machine learning and conventional learning approaches and the major challenges ahead. The main intention of this paper is to explore and present chronologically, a comprehensive survey of the major applications of deep learning covering variety of areas, study of the techniques and architectures used and further the contribution of that respective application in the real world. Finally, the paper ends with the conclusion and future aspects.

Modeling Strategies for the Computational Analysis of Unreinforced Masonry Structures: Review and Classification

Abstract: Masonry structures, although classically suitable to withstand gravitational loads, are sensibly vulnerable if subjected to extraordinary actions such as earthquakes, exhibiting cracks even for events of moderate intensity compared to other structural typologies like as reinforced concrete or steel buildings. In the last half-century, the scientific community devoted a consistent effort to the computational analysis of masonry structures in order to develop tools for the prediction (and the assessment) of their structural behavior. Given the complexity of the mechanics of masonry, different approaches and scales of representation of the mechanical behavior of masonry, as well as different strategies of analysis, have been proposed. In this paper, a comprehensive review of the existing modeling strategies for masonry structures, as well as a novel classification of these strategies are presented. Although a fully coherent collocation of all the modeling approaches is substantially impossible due to the peculiar features of each solution proposed, this classification attempts to make some order on the wide scientific production on this field. The modeling strategies are herein classified into four main categories: block-based models, continuum models, geometry-based models, and macroelement models. Each category is comprehensively reviewed. The future challenges of computational analysis of masonry structures are also discussed.

Fifty Shades of Brain: A Review on the Mechanical Testing and Modeling of Brain Tissue

Abstract: Brain tissue is not only one of the most important but also the most complex and compliant tissue in the human body. While long underestimated, increasing evidence confirms that mechanics plays a critical role in modulating brain function and dysfunction. Computational simulations–based on the field equations of nonlinear continuum mechanics–can provide important insights into the underlying mechanisms of brain injury and disease that go beyond the possibilities of traditional diagnostic tools. Realistic numerical predictions, however, require mechanical models that are capable of capturing the complex and unique characteristics of this ultrasoft, heterogeneous, and active tissue. In recent years, contradictory experimental results have caused confusion and hindered rapid progress. In this review, we carefully assess the challenges associated with brain tissue testing and modeling, and work out the most important characteristics of brain tissue behavior on different length and time scales. Depending on the application of interest, we propose appropriate mechanical modeling approaches that are as complex as necessary but as simple as possible. This comprehensive review will, on the one hand, stimulate the design of new experiments and, on the other hand, guide the selection of appropriate constitutive models for specific applications. Mechanical models that capture the complex behavior of nervous tissues and are accurately calibrated with reliable and comprehensive experimental data are key to performing reliable predictive simulations. Ultimately, mathematical modeling and computational simulations of the brain are useful for both biomedical and clinical communities, and cover a wide range of applications ranging from predicting disease progression and estimating injury risk to planning surgical procedures.

From Topology Optimization Design to Additive Manufacturing: Today’s Success and Tomorrow’s Roadmap

Abstract: This work grew out of rapid developments of topology optimization approaches and emerging industry trends of “3D printing” techniques, the latter bridging to a large extent the gap between innovative design and advanced manufacturing. In the present work, we first make an application-oriented review of topology optimization approaches in an attempt to illustrate their efficacy in the design of high-performance structures. Subsequently, a broad panorama of additive manufacturing is provided with a particular interest in its application in the automotive and the aerospace sectors. Taking an aerospace bracket as an example, we further go through an entire procedure from topology optimization design to additive manufacturing, then to performance verification. In the interest of cultivating a long-term partnership upon this combination, we finally examine, in face of present and near future, limitations of additive manufacturing in the loss of geometric accuracy and performance deterioration, and provide a roadmap for future work.

A Comprehensive Review on Image Encryption Techniques

Abstract: Image encryption techniques play a significant role in multimedia applications to secure and authenticate digital images. This paper presents a comprehensive study of various image encryption techniques. This paper covers the most significant developments in meta-heuristic based image encryption techniques. The various attacks and performance measures related to image encryption techniques have also been studied. The existing techniques are analyzed with respect to differential, statistical, and key analyses. The main goal of this paper is to give a broad perspective on characteristics of image encryption techniques. The paper concludes by discussing significant advancements in the field of image encryption and highlighting future challenges.

Virtual, Digital and Hybrid Twins: A New Paradigm in Data-Based Engineering and Engineered Data

Abstract: Engineering is evolving in the same way than society is doing. Nowadays, data is acquiring a prominence never imagined. In the past, in the domain of materials, processes and structures, testing machines allowed extract data that served in turn to calibrate state-of-the-art models. Some calibration procedures were even integrated within these testing machines. Thus, once the model had been calibrated, computer simulation takes place. However, data can offer much more than a simple state-of-the-art model calibration, and not only from its simple statistical analysis, but from the modeling and simulation viewpoints. This gives rise to the the family of so-called twins: the virtual, the digital and the hybrid twins. Moreover, as discussed in the present paper, not only data serve to enrich physically-based models. These could allow us to perform a tremendous leap forward, by replacing big-data-based habits by the incipient smart-data paradigm.

A Comparative Study of Recent Non-traditional Methods for Mechanical Design Optimization

Abstract: Solving practical mechanical problems is considered as a real challenge for evaluating the efficiency of newly developed algorithms. The present article introduces a comparative study on the application of ten recent meta-heuristic approaches to optimize the design of six mechanical engineering optimization problems. The algorithms are: the artificial bee colony (ABC), particle swarm optimization (PSO) algorithm, moth-flame optimization (MFO), ant lion optimizer (ALO), water cycle algorithm (WCA), evaporation rate WCA (ER-WCA), grey wolf optimizer (GWO), mine blast algorithm (MBA), whale optimization algorithm (WOA) and salp swarm algorithm (SSA). The performances of the algorithms are tested quantitatively and qualitatively using convergence speed, solution quality, and the robustness. The experimental results on the six mechanical problems demonstrate the efficiency and the ability of the algorithms used in this article.

Recent Advances and Future Directions on Underwater Wireless Communications

Abstract: More than 75% of the Earth surface is covered by water in the form of oceans. The oceans are unexplored and very far-fetched to investigate due to distinct phenomenal activities in the underwater environment. Underwater wireless communication (UWC) plays a significant role in observation of marine life, water pollution, oil and gas rig exploration, surveillance of natural disasters, naval tactical operations for coastal securities and to observe the changes in the underwater environment. In this regard, the widespread adoption of UWC has become a vital field of study to envisage various military and commercial applications that have been growing interest to explore the underwater environment for numerous applications. Acoustic, Optical and RF wireless carriers have been chosen to be used for data transmission in an underwater environment. The internet of underwater things (IoUT) and next-generation (5G) networks have a great impact on UWC as they support the improvement of the data rate, connectivity, and energy efficiency. In addition to the potential emerging UWC techniques, assisted by 5G network and improve existing work is also focusing in this study. This survey presents a comprehensive overview of existing UWC techniques, with possible future directions and recommendations to enable the next generation wireless networking systems in the underwater environment. The current project schemes, applications and deployment of latest amended UWC techniques are also discussed. The main initiatives and contributions of current wireless communication schemes in underwater for improving quality of service and quality of energy of the system over long distances are also mentioned.

Recent Trends in the Modeling and Quantification of Non-probabilistic Uncertainty

Abstract: This paper gives an overview of recent advances in the field of non-probabilistic uncertainty quantification. Both techniques for the forward propagation and inverse quantification of interval and fuzzy uncertainty are discussed. Also the modeling of spatial uncertainty in an interval and fuzzy context is discussed. An in depth discussion of a recently introduced method for the inverse quantification of spatial interval uncertainty is provided and its performance is illustrated using a case studies taken from literature. It is shown that the method enables an accurate quantification of spatial uncertainty under very low data availability and with a very limited amount of assumptions on the underlying uncertainty. Finally, also a conceptual comparison with the class of Bayesian methods for uncertainty quantification is provided.

State-of-the-Art Review of Metallic Dampers: Testing, Development and Implementation

Abstract: Structural control systems have gained popularity for the ability to reduce the structural vibration response of civil structures subjected to different types of dynamic loads. Passive, semi-active, active and hybrid control systems have been widely utilized in various types of structures. This article presents one of the most economical and yet the most effective approaches used in structural vibration control. Herein, a comprehensive state-of-the-art review of the development and application of metallic dampers is discussed. The dampers are classified into five categories: steel, aluminum, lead, copper and shaped-memory alloy dampers. In addition, the details of various computational methods used in the analysis of metallic dampers are briefly explained. This article reveals that the use of metallic dampers is being advanced broadly owing to their low manufacturing costs, stable hysteresis behavior, resistance to ambient temperature, reliability and high energy dissipation capability. It is also concluded that mild steel is the most popular material among metallic dampers.

A Tutorial on Bayesian Inference to Identify Material Parameters in Solid Mechanics

Abstract: The aim of this contribution is to explain in a straightforward manner how Bayesian inference can be used to identify material parameters of material models for solids. Bayesian approaches have already been used for this purpose, but most of the literature is not necessarily easy to understand for those new to the field. The reason for this is that most literature focuses either on complex statistical and machine learning concepts and/or on relatively complex mechanical models. In order to introduce the approach as gently as possible, we only focus on stress–strain measurements coming from uniaxial tensile tests and we only treat elastic and elastoplastic material models. Furthermore, the stress–strain measurements are created artificially in order to allow a one-to-one comparison between the true parameter values and the identified parameter distributions.

Computational Methods of Acquisition and Processing of 3D Point Cloud Data for Construction Applications

Abstract: 3D point cloud data from sensing technologies such as 3D laser scanning and photogrammetry are able to capture the 3D surface geometries of target objects in an accurate and efficient manner. Due to these advantages, the construction industry has been capturing 3D point cloud data of construction sites, construction works, and construction equipment to enable better decision making in construction project management. The captured point cloud data are utilized to reconstruct 3D building models, check construction quality, monitor construction progress, improve construction safety etc. throughout the project lifecycle from design to construction and facilities management phase. This paper aims to review the state-of-the-art methods to acquire and process 3D point cloud data for construction applications. The different approaches to 3D point cloud data acquisition are reviewed and compared including 3D laser scanning, photogrammetry, videogrammetry, RGB-D camera, and stereo camera. Furthermore, the processing methods of 3D point cloud data are reviewed according to the four common processing procedures including (1) data cleansing, (2) data registration, (3) data segmentation, and (4) object recognition. For each processing procedure, the different processing methods and algorithms are compared and discussed in detail, which provides a useful guidance to both researchers and industry practitioners for adopting point cloud data in the construction industry.

Nature-Inspired Optimization Algorithms and Their Application in Multi-Thresholding Image Segmentation

Abstract: In the field of image processing, there are several problems where an efficient search of the solutions has to be performed within a complex search domain to find an optimal solution. Multi-thresholding which is a very important image segmentation technique is one of them. The multi-thresholding problem is simply an exponential combinatorial optimization process which traditionally is formulated based on complex objective function criterion which can be solved using only nondeterministic methods. Under such circumstances, there is also no unique measurement which quantitatively judges the quality of a given segmented image. Therefore, researchers are solving those issues by using Nature-Inspired Optimization Algorithms (NIOAs) as alternative methodologies for the multi-thresholding problem. This study presents an up-to-date review on all most important NIOAs employed in multi-thresholding based image segmentation domain. The key issues which are involved during the formulation of NIOAs based image multi-thresholding models are also discussed here.

A State of the Art Review of the Particle Finite Element Method (PFEM)

Abstract: The particle finite element method (PFEM) is a powerful and robust numerical tool for the simulation of multi-physics problems in evolving domains. The PFEM exploits the Lagrangian framework to automatically identify and follow interfaces between different materials (e.g. fluid–fluid, fluid–solid or free surfaces). The method solves the governing equations with the standard finite element method and overcomes mesh distortion issues using a fast and efficient remeshing procedure. The flexibility and robustness of the method together with its capability for dealing with large topological variations of the computational domains, explain its success for solving a wide range of industrial and engineering problems. This paper provides an extended overview of the theory and applications of the method, giving the tools required to understand the PFEM from its basic ideas to the more advanced applications. Moreover, this work aims to confirm the flexibility and robustness of the PFEM for a broad range of engineering applications. Furthermore, presenting the advantages and disadvantages of the method, this overview can be the starting point for improvements of PFEM technology and for widening its application fields.

Applications of Computer Vision in Plant Pathology: A Survey

Abstract: The real-time decision support system can enhance the crop or plant growth, therefore, increasing their productivity, quality, and economic value. This also helps us in serving the nature by supervising the plant growth in balancing the environment. Computer vision techniques have proven to play an important role in the number of applications like medical, defense, agriculture, remote sensing, business analysis, etc. The use of digital image processing methods for simulating the visual capability of the human being has proven to be a dynamic feature in smart or precision agriculture. This concept has provided with the automatic preventing and monitoring of plants, cultivation, disease management, water management etc. to increase the crop productivity and quality. In this paper, we have surveyed the number of articles that adopt the concept of computer vision and soft computing methods for the identification and classification of diseases from the leaf of the plant. Our aim is to present the state of the art of the concepts, applications, and theories associated with the digital image processing and soft computing methodologies. The various outcomes have been discussed separately.

Review of Scene Text Detection and Recognition

Abstract: Scene texts contain rich semantic information which may be used in many vision-based applications, and consequently detecting and recognizing scene texts have received increasing attention in recent years. In this paper, we first introduce the history and progress of scene text detection and recognition, and classify conventional methods in detail and point out their advantages as well as disadvantages. After that, we study these methods and illustrate the corresponding key issues and techniques, including loss function, multi-orientation, language model and sequence labeling. Finally, we describe commonly used benchmark datasets and evaluation protocols, based on which the performance of representative scene text detection and recognition methods are analyzed and compared.

A Survey on the Combined Use of Optimization Methods and Game Theory

Abstract: Game theory is a field of applied mathematics that studies strategic behavior of rational factors. In other words, game theory is a collection of analytical tools that can be used to make optimal choices in interactional and decision making problems. Optimization in mathematics and computer science is the choice of the best member of an existing collection for a specific purpose. Several optimization methods have been used in many problems to minimize costs or maximize profits. From a particular point of view, it can be said that the game theory is in fact a kind of optimization. In this paper, a combined use of game theory and optimization algorithms has been reviewed and a new categorization is presented for researches which have been conducted in this area. In some of these combinations, game theory has been used to improve the performance of optimization algorithms, and in some others, optimizations methods help to solve game theory problems. Game theory and optimization algorithms are also used together to solve some other problems.

Game Theory Approaches for the Solution of Power System Problems: A Comprehensive Review

Abstract: Deregulation and competition appearance in electric power systems and fundamental changes in control and operation structures of such systems require a strong tool for handling such issues. Game theory approach, which is defined as an analytical concept for dealing with the decision-making process in a variety of sciences, is vastly employed in power system problems. This paper provides a comprehensive review of the application of game theory approach to the solution of electric power system problems. The basic foundation of game theory approach and the basic concepts of such concept will be introduced to make the readers familiar with principals of game theory. Moreover, the introduction and a brief definition of main classifications of game theory including cooperative game, dynamic game, evolutionary game theory and strategic game will be studied. In addition, the implementation of different types of game theory approach to accomplish decision making process in power system problems will be reviewed. The main contributions of recent researches in the area of employment of game theory to power system problems are studied and discussed in details.

Computational Methods for Optimal Planning of Hybrid Renewable Microgrids: A Comprehensive Review and Challenges

Abstract: This manuscript aims to present a comprehensive literature reviews of various aspects for hybrid microgrids (HMGs) comprising mathe modeling, different optimization techniques, and common adapted objective functions along with their equality and inequality constraints and so on. Classical and modern optimization methodologies are recognized with their inherent features. Special care of renewable energy sources expressly wind and solar including energy storage systems are in order. In addition, uncertainties in solar and wind energy resources are highlighted, and the applications of forecasting models are presented. Comparisons among various HMGs planning and design methods are summarized and criticized for the sake of concluding their merits and demerits. Finally, technical advices for giving good insights for HMGs designers and future researches in this regard are emphasized.

A Comprehensive Survey on Travel Recommender Systems

Abstract: Travelling is a combination of journey, transportation, travel-time, accommodation, weather, events, and other aspects which are likely to be experienced by most of the people at some point in their life. To enhance such experience, we generally look for assistance in planning a tour. Today, the information available on tourism-related aspects on the Internet is boundless and exploring suitable travel package/product/service may be time-consuming. A recommender system (RS) can assist for various tour-related queries such as top destinations for summer vacation, preferable climate conditions for tracking, the fastest way to transport, or photography assistance for specific destinations. In this survey, we have presented a pervasive review on travel and associated factors such as hotels, restaurants, tourism package and planning, and attractions; we have also tailored recommendations on a tourist’s diverse requirements such as food, transportation, photography, outfits, safety, and seasonal preferences. We have classified travel-based RSs and presented selection criteria, features, and technical aspects with datasets, methods, and results. We have briefly supplemented research articles from diverse facets; various frameworks for a travel-based RS are discussed. We believe our survey would introduce a state-of-the-art travel RS; it may be utilized to solve the existing limitations and extend its applicability.

An Innovative Framework of 5D BIM Solutions for Construction Cost Management: A Systematic Review

Abstract: Cost management represents an important part of management in construction projects. Developments in the fifth dimension (5D) of building information modelling (BIM) has fostered new and potential improvements in terms of efficiency, quality and precision during cost management processes. The research aims to develop an innovative framework of 5D BIM solutions for construction cost management. A systematic review approach was adopted for the evaluation of the 5D BIM solutions. Interviews were conducted with three industry experts to validate and comment on the research findings. Eighteen software or web solutions were found and analysed against five main areas of the cost management practices. The research contributes to creating new awareness and knowledge from the available 5D BIM solutions towards the current cost management requirements as well as the future need in the digitalised working environment.

Numerical Methods for the Modelling of Chip Formation

Abstract: The modeling of metal cutting has proved to be particularly complex due to the diversity of physical phenomena involved, including thermo-mechanical coupling, contact/friction and material failure. During the last few decades, there has been significant progress in the development of numerical methods for modeling machining operations. Furthermore, the most relevant techniques have been implemented in the relevant commercial codes creating tools for the engineers working in the design of processes and cutting devices. This paper presents a review on the numerical modeling methods and techniques used for the simulation of machining processes. The main purpose is to identify the strengths and weaknesses of each method and strategy developed up-to-now. Moreover the review covers the classical Finite Element Method covering mesh-less methods, particle-based methods and different possibilities of Eulerian and Lagrangian approaches.

A Review on Application of Soft Computing Techniques in Machining of Particle Reinforcement Metal Matrix Composites

Abstract: In this paper, a wide literature review of soft computing methods in conventional machining processes of metal matrix composites is carried out. The tool wear, cutting force along with surface quality are presented in the different types of machining processes and examined thoroughly. Summary of the different particular soft computing approaches in machining such as turning, milling, drilling and grinding operations are thoroughly discussed. Furthermore, this work put emphases on the optimization and modeling of the machining process. The study will emphasis on the most general methods used by researchers in literature for developing the statistical and mathematical modeling using soft computing approaches including, genetic algorithm, response surface methodology, fuzzy logic, artificial neural network, Taguchi method and particle swarm optimization. In last section the comprehensive open issues and conclusion are presented for application of soft computing techniques in machining of metal matrix composite performance prediction and optimization.

State of the Art in the Optimisation of Wind Turbine Performance Using CFD

Abstract: Wind energy has received increasing attention in recent years due to its sustainability and geographically wide availability. The efficiency of wind energy utilisation highly depends on the performance of wind turbines, which convert the kinetic energy in wind into electrical energy. In order to optimise wind turbine performance and reduce the cost of next-generation wind turbines, it is crucial to have a view of the state of the art in the key aspects on the performance optimisation of wind turbines using Computational Fluid Dynamics (CFD), which has attracted enormous interest in the development of next-generation wind turbines in recent years. This paper presents a comprehensive review of the state-of-the-art progress on optimisation of wind turbine performance using CFD, reviewing the objective functions to judge the performance of wind turbine, CFD approaches applied in the simulation of wind turbines and optimisation algorithms for wind turbine performance. This paper has been written for both researchers new to this research area by summarising underlying theory whilst presenting a comprehensive review on the up-to-date studies, and experts in the field of study by collecting a comprehensive list of related references where the details of computational methods that have been employed lately can be obtained.

Steady and Transient State Analyses on Conjugate Laminar Forced Convection Heat Transfer

Abstract: The term ‘conjugate heat transfer’ refers to a heat transfer process involving an interaction of heat conduction within a solid body with either of the free, forced, and mixed convection from its surface to a fluid flowing over it. It finds application in numerous fields starting from thermal interaction between surrounding air and fins to thermal interaction between flowing fluid and turbine blades. In this article, a systematic literature review of studies pertinent to laminar conjugate conduction-forced convection heat transfer analysis subjected to internal and external flow conditions is performed. The review reports both steady and unsteady state analyses related to experimental, analytical and numerical investigations, in both rectangular and cylindrical geometries with an exemption to micro and mini channel related studies. The studies are categorically put forth initially and an overview of these studies is presented in tabular and graphical form for a swift glance later under each section. This paper is concluded highlighting the salient features of the review, with respect to physical and mathematical models, methodology and applications. The challenges and scope for future study reported at the end of this paper gives the reader an insight into the gaps in the area of conjugate heat transfer analysis of steady and transient state under laminar forced convection flow regimes.

Review of Vehicle Detection Systems in Advanced Driver Assistant Systems

Abstract: Driverless cars and autonomous vehicles have significantly changed the face of transportation those days. Efficient use of vision system in the recent development of advanced driver assistance systems since last two decades have equipped cars and light vehicles to reduce accidents, congestion, crashes and pollution. The robust performance of the driver assistance systems absolutely depend on the flawless detection of the vehicles from the images. Developments of vigorous computer vision techniques based on various Image level features have enabled intelligent Transportation systems to solve some of the core challenges in vehicle detection. A detailed study of the vehicle detection in dynamic conditions is presented in this paper. The complexity of the vehicle detection in variable on-road driving conditions is evident from the diverse challenges illustrated in this paper. Dynamic vehicle detection mechanism has obviously attracted numerous approaches like feature based techniques and model based techniques. Different set of visual information representation as edge, shadow, light are used to detect the vehicles. Out of all low level features shape representation for vehicle detection is observed more efficient. The need of handling massive visual data for processing is addressed using novel feature representation like object proposal methods is discussed in more detail. The efficacy of ongoing research in Autonomous vehicles is validated using deep learning techniques on aerial image analysis.

Design Optimization of Heat Exchangers with Advanced Optimization Techniques: A Review

Abstract: This literature review presents the extensive literature survey of various heat exchangers (HEs) for the design optimization using advanced optimization techniques concerning with various aspects. The chief objective of this work is to focus on the parametric design optimization of different types of HEs using advanced optimization algorithms and therefore only the research works associated with advanced optimization techniques are considered. This is the first paper which exclusively summaries the research works concerning with the parameter optimization of HEs using advanced optimization techniques. Various types of HEs considered in this review paper are shell-and-tube HEs, plate-fin HEs, fin-tube HEs and various configurations of HE networks etc. The parametric design optimization of HEs is associated with number of structural and physical parameters having highly complexity. Trial and error method is used in the general design approaches and this becomes tediously and time consuming and not having the guarantee of getting an optimum design. Therefore, for the design of HEs advanced optimization techniques are preferred. The review work on parametric design optimization was not attempted previously by taking into consideration various types of HEs therefore this review paper may turn into the complete information at one place and it may be very useful to the industrial design and successive researchers to choose the direction of their research work in the field of parameter optimization of HEs using advanced optimization algorithm.

Frictional Heating in Railway Brakes: A Review of Numerical Models

Abstract: In the design process of a railway vehicle it is crucial to determine the operating temperature of the brake friction elements. The thermal load related to braking depends on both the design of the vehicle and its braking system but also on the operating conditions in service. It is therefore justified to model frictional heating of the friction elements in the design phase. As railway braking differs substantially from automotive or aircraft braking, current review article is focused on the published literature concerning specifically modelling of railway tread and disc brakes. However, to present the complete picture of the state-of-the-art, studies undertaking related topics concerning frictional heating are also discussed. It is concluded that the existing models should be further developed to account for mutual coupling of operation conditions and the coefficient of friction.

A Systematic Literature Review on Empirical Analysis of the Relationship Between Code Smells and Software Quality Attributes

Abstract: Code smells indicate problems in design or code which makes software hard to change and maintain. It has become a sign of software systems that cause complications in maintaining software quality. The detection of harmful code smells which deteriorate the software quality has resulted in a favourable shift in interest among researchers. Therefore, a significant research towards analysing the impact of code smells on software quality has been conducted over the last few years. This study aims at reporting a systematic literature review of such existing empirical studies investigate the impact of code smells on software quality attributes. The results indicate that the impact of code smells on software quality is not uniform as different code smells have the opposite effect on different software quality attributes. The findings of this review will provide the awareness to the researchers and a practitioner regarding the impact of code smells on software quality. It would be more advantageous to conduct further studies that consider less explored code smells, least or not investigated quality attributes, involve industry researchers and use large commercial software systems.

Numerical Simulation of Supercooled Large Droplet Icing Phenomenon: A Review

Abstract: The impingement and ice accretion of supercooled large droplets (SLD) on the aircraft surface is one of the crucial factors threatening flight safety. The movement and impingement of SLD have many unique characteristics that conventional small droplets do not own. Therefore, a large number of experimental and numerical studies about SLD have been carried out to explore its physical properties and simulation method. The distribution and motion characteristics of supercooled large droplet during the process of approaching to the aircraft are first reviewed in this paper. Then the governing equations of SLD under the framework of Lagrangian and Eulerian methods are analyzed and established. The unique phenomena of SLD such as water droplet deformation and breakup, droplet–wall interaction and re-impingement in the literature are analyzed. The research development and results of the droplet–wall interaction phenomenon have been discussed particularly, which is summarized and classified from three aspects: droplet splashing threshold, splashing model and the method of modification of governing equation. Finally, the establishment process and the corresponding modification of the icing model in SLD condition is given, and the related calculation results are exhibited to validate the numerical simulation methods of SLD. Some shortcomings in current research are presented and the aspects needed to be developed further in future studies for the acquisition of more accurate simulated results are also recommended.