Real-life Engineering Problems Research Articles

Modified Archimedes optimization algorithm for global optimization problems: a comparative study

Archimedes Optimization Algorithm (AOA) is a recent optimization algorithm inspired by Archimedes’ Principle. In this study, a Modified Archimedes Optimization Algorithm (MDAOA) is proposed. The goal of the modification is to avoid early convergence and improve balance between exploration and exploitation. Modification is implemented by a two phase mechanism: optimizing the candidate positions of objects using the dimension learning-based (DL) strategy and recalculating predetermined five parameters used in the original AOA. DL strategy along with problem specific parameters lead to improvements in the balance between exploration and exploitation. The performance of the proposed MDAOA algorithm is tested on 13 standard benchmark functions, 29 CEC 2017 benchmark functions, optimal placement of electric vehicle charging stations (EVCSs) on the IEEE-33 distribution system, and five real-life engineering problems. In addition, results of the proposed modified algorithm are compared with modern and competitive algorithms such as Honey Badger Algorithm, Sine Cosine Algorithm, Butterfly Optimization Algorithm, Particle Swarm Optimization Butterfly Optimization Algorithm, Golden Jackal Optimization, Whale Optimization Algorithm, Ant Lion Optimizer, Salp Swarm Algorithm, and Atomic Orbital Search. Experimental results suggest that MDAOA outperforms other algorithms in the majority of the cases with consistently low standard deviation values. MDAOA returned best results in all of 13 standard benchmarks, 26 of 29 CEC 2017 benchmarks (89.65%), optimal placement of EVCSs problem and all of five real-life engineering problems. Overall success rate is 45 out of 48 problems (93.75%). Results are statistically analyzed by Friedman test with Wilcoxon rank-sum as post hoc test for pairwise comparisons.

Optimization of numerical and engineering problems using altered differential evolution algorithm

In this study, an altered differential evolution (ADE) is presented for numerical and engineering problem optimization. It incorporates innovative mutation strategy with new control parameters using the perception of particle swarm optimization (PSO) process, to enhance exploration and exploitation activities extra profusely and increase the global search capacity. Also, a new crossover rate is employed in ADE, to attain higher convergence accuracy and quality optimal solutions. Finally, a novel selection strategy is introduced in ADE, to facilitate information sharing as well as for escaping local minima and keeps progressing. To investigate the suggested ADE performance, a collection of 13 classical benchmark functions, CEC2014 and CEC2017 benchmark suite are solved. Furthermore, the superiority and applicability of the ADE algorithm are further demonstrated through experimentation on six famous real-life engineering problems. The experimental and statistical test outcomes, collectively indicate that compared to other modern optimization algorithms, overall ADE exhibits superior performance. Also, comparison results show that ADE has powerful exploration and exploitation capabilities, excellent convergence performance, and strong ability for gaining high quality solution.

A resolved SPH-DEM coupling method for analysing the interaction of polyhedral granular materials with fluid

Hybrid fluid–particle systems are prevalent in nature and engineering practices, but accurately simulating the dynamic behaviour is challenging due to their inherent strong non-linearity. This study proposes a three-dimensional resolved numerical framework for analysing complex shape polyhedron–fluid interaction. The weakly compressible smoothed particle hydrodynamics (SPH) is employed to describe the complex hydrodynamic behaviour, and the discrete element method (DEM) is applied to solve the motion of irregular polyhedral granular materials. An accurate contact collision algorithm is implemented in the coupling model to address the dynamic response of polyhedral particles. The high-fidelity modelling of the complex morphology of granular materials is realised. Hence, the coupling code is developed based on two high-performance open-source platforms, and an efficient two-way coupling scheme is designed to manage the fluid–particle interaction under a unified time framework. The reliability and the applicability of the SPH-DEM solver are demonstrated through extensive validations based on existing experimental and numerical data involving violent free-surface flow impacting multiple rigid bodies and cube deposition. Furthermore, a more complex case of wave-flow impact on rock piles is conducted, exhibiting great potential to employ this model in real-life engineering problems.

A conservative finite volume method for incompressible two-phase flows on unstructured meshes

A robust finite volume framework for solving incompressible two-phase flow problems based on an explicit dual-time stepping based artificial compressibility formulation has been developed by Parameswaran and Mandal (2019) for structured meshes. This artificial compressibility framework is extended to unstructured meshes in the present paper. In order to further enhance the applicability to real life engineering problems, the surface tension effect is also modeled here. To deal with arbitrary unstructured mesh topology, least square and Green-Gauss integral based methods are used. The efficacy of the proposed method is demonstrated using a set of scalar advection based test problems and a set of standard incompressible two-phase flow test problems involving gravitational, viscous and surface tension forces. The numerical results show improvement in accuracy compared to the results of the structured mesh formulation.

Comparison of the Performance of New and Traditional Numerical Methods for Long-Term Simulations of Heat Transfer in Walls with Thermal Bridges

Several previous experiments showed that the leapfrog–hopscotch and the adapted Dufort–Frankel methods are the most efficient among the explicit and stable numerical methods to solve heat transfer problems in building walls. In this paper, we extensively measure the running times of the most successful methods and compare them to the performance of other available solvers, for example, ANSYS transient thermal analysis and the built-in routines of MATLAB, where three different mesh resolutions are used. We show that the running time of our methods changes linearly with mesh size, unlike in the case of other methods. After that, we make a long-term simulation (one full winter month) of two-dimensional space systems to test the two best versions of the methods. The real-life engineering problem we solve is the examination of thermal bridges with different shapes in buildings to increase energy efficiency.

Fast random opposition-based learning Golden Jackal Optimization algorithm

Nowadays, optimization techniques are required in various engineering domains in order to find optimal solutions for complex problems. As a result, there is a growing tendency among scientists to enhance existing nature-inspired algorithms using various evolutionary strategies and to develop new nature-inspired optimization methods that can properly explore the feature space. The recently designed nature-inspired metaheuristic, named the Golden Jackal Optimization​ (GJO) algorithm, was inspired by the collaborative hunting actions of the golden jackal in nature to solve various challenging problems. However, like other approaches, the GJO has the limitations of poor exploitation ability, ease to get stuck in a local optimal region, and an improper balancing of exploration and exploitation. To overcome these limitations, this paper proposes a novel contribution to GJO based on a new technique, namely the fast random opposition-based learning Golden Jackal Optimization algorithm (FROBL-GJO). The FROBL technique is mainly inspired by opposition-based learning (OBL) and random opposition-based learning (ROBL) techniques to enhance the optimization precision and convergence speed of the GJO algorithm. Furthermore, two other models, such as OBL-GJO and ROBL-GJO, are also proposed for comparison purposes. To examine the proficiency of the newly proposed FROBL-GJO algorithm, it has been examined with several well-known existing meta-heuristic algorithms while solving the CEC-2005 and CEC-2019 benchmark test functions and real-life engineering problems. The experimental outcomes and statistical tests reveal the superior performance of the proposed FROBL-GJO in solving both global optimization and engineering design problems. Hence, the findings of benchmark functions and engineering problems endorse that the proposed FROBL-GJO algorithm can be considered a promising method for solving complex optimization problems.

Application of metaheuristic optimization based support vector machine for milling cutter health monitoring

With the arrival of Industry 4.0, intelligent condition-based maintenance has become a must, if not a need, for industries with significant capital investments in rotating machineries. Tool Condition Monitoring (TCM) is one of the strategic research domains in condition-based maintenance. Lately, supervised algorithms based on Machine Learning (ML) techniques assist classification of the cutting tool's condition in operation. One such algorithm is the Support Vector Machine (SVM) popularly used for training the data however, choosing optimal hyper-parameters for an SVM is essential in making the model robust. Owing to intermittent cutting in a milling operation, the modeling of tool conditions based on vibrations evolved during machining needs to be handled wisely. Consequently, there exists a need for meta-heuristic optimization algorithms to drive SVM for evaluating the robustness of the model and to increase accuracy, thereby minimizing the risk of false classification of tool bits. Over the past decade, meta-heuristic algorithms have found immense use in optimizing ML models and solving real–life engineering problems. This research paper aims to optimize hyperparameters of SVM – ‘C’ and ‘gamma’ using metaheuristic algorithms in the context of TCM. Further, the paper evaluates popular metaheuristic algorithms. It compares their respective efficacies, enabling researchers in the field of TCM to choose the appropriate algorithm for their optimization problem statement to get higher performance predictions from their SVM models.

An immersive virtual reality learning environment with CFD simulations: Unveiling the Virtual Garage concept.

Virtual reality has become a significant asset to diversify the existing toolkit supporting engineering education and training. The cognitive and behavioral advantages of virtual reality (VR) can help lecturers reduce entry barriers to concepts that students struggle with. Computational fluid dynamics (CFD) simulations are imperative tools intensively utilized in the design and analysis of chemical engineering problems. Although CFD simulation tools can be directly applied in engineering education, they bring several challenges in the implementation and operation for both students and lecturers. In this study, we develop the "Virtual Garage" as a task-centered educational VR application with CFD simulations to tackle these challenges. The Virtual Garage is composed of a holistic immersive virtual reality experience to educate students with a real-life engineering problem solved by CFD simulation data. The prototype is tested by graduate students (n = 24) assessing usability, user experience, task load and simulator sickness via standardized questionnaires together with self-reported metrics and a semi-structured interview. Results show that the Virtual Garage is well-received by participants. We identify features that can further leverage the quality of the VR experience with CFD simulations. Implications are incorporated throughout the study to provide practical guidance for developers and practitioners.

Wing jig shape optimisation with gradient-assisted metamodel building in a trust-region optimisation framework

Significant computational resources are required to obtain an optimised wing jig shape by solving a high-fidelity large-scale aero-structural design optimisation problem. Gradient-based methods are efficient; however, some of the features of real-life engineering problems including numerical noise that pollutes the function values and occurrences of failed evaluations in the optimisation may limit their performance. To address these issues, this paper presents the latest developments in the multipoint approximation method (MAM) based on a gradient-assisted metamodel assembly technique within a trust-region optimisation framework. The proposed method is tested by a benchmark case first, and then, an aircraft wing jig shape optimisation problem is offered to demonstrate its performance. The gradient-based optimisation is used as a benchmark case, and the metamodel-based optimisation utilises the latest developments in MAM to solve the same problem. The results show that the proposed method can achieve the same design goal as the gradient-based method but with enhanced robustness and efficient performance. In the wing jig shape optimisation, the difference in the design objective, the global equivalent drag coefficient, between the two aforementioned optimisation approaches is 0.20 counts, whose relative difference is approximately 0.10%. Three approximate sub-optimisations have been conducted in every iteration of the metamodel-based optimisation to reduce the possibility of local optimality, while the overall elapsed time of the metamodel-based optimisation is approximately 1.98 times that of one gradient-based optimisation, which confirms the competitiveness of the proposed method bearing in mind the added safeguards for numerical noise, failed evaluations and possible local optimality.

Dealing with multi-criteria decision analysis in time-evolving approach using a probabilistic prediction method

Multi-criteria Decision Analysis (MCDA) is a methodology that has been classically used to rank alternatives according to a set of decision criteria. The MCDA techniques have been shown to be an efficient tool in a number of real-life engineering problems. Nevertheless, most of the proposed approaches in the field do not consider the temporal characteristic of the criteria values, which can be an interesting information to be explored in order to predict future rankings. The present work proposes a novel MCDA methodology in which the past data of the criteria are considered to predict their future values. Our approach is based on a tensorial formulation, together with the use of the recursive least mean squares method in the prediction step. In addition, we consider a probabilistic prediction and use the Stochastic Multi-criteria Acceptability Analysis, which allows the decision maker to observe the degree of uncertainty in the ranking. Numerical experiments with synthetic and actual data attest to the proposal’s relevance in scenarios in which the criteria values change over time.

Cε-LDE: A lightweight variant of differential evolution algorithm with combined ε constrained method and Lévy flight for constrained optimization problems

Differential evolution (DE) algorithm is popular to tackle real-world optimization problems. The standard DE, however, is not able to solve constrained optimization problems (COPs) for their complicated linear or nonlinear constraints. The transformation of constraints, which is realized via constraint handling technique, has a great impact on dealing with constrained algorithms. In this paper, a lightweight and efficient variant of DE named Cε-LDE is proposed to solve constrained single-objective optimization problems. This study firstly introduces a combined constraint handling method, which is designed based on the difference and relationship between two different ε constrained methods and balances infeasible solution and feasible solution with rules from the perspective of probability allocation. In addition, an extra control parameter is redefined in terms of different initial ε level in consideration of the complexity and diversity of COPs. On the other hand, DE evolves by opposition-based learning initialization and modifications on mutation operator and crossover rate selection, respectively. Mutation strategies frequently named rand/1 and best/1 are adopted and Lévy flight is added to the mutation strategy as a multiplier with scale factor F for a long jump. The crossover rate CR tends to be a larger or smaller stochastic value according to the pros and cons of the selected vector. A set of 28 problems under different dimension settings (D = 10, 30, 50 and 100) used for the CEC 2017 Competition is employed to evaluate the performance of the proposed Cε-LDE. The computational results demonstrate the effectiveness and superiority of the proposed combined ε constrained method. A comparative study of Cε-LDE with state-of-art algorithms is conducted on the obtained experimental results under the rules of CEC 2017 Competition. The ranking outcomes reveal that Cε-LDE is equipped with high competitiveness. Furthermore, the applications in several standard real-life engineering problems verify the effectiveness and practicability of the proposed scheme.

Resolution of thermal energy transfer by diffusion and by convection / Resolução da transferência de energia térmica por difusão e por convecção

Transfer of thermal energy is present all around us, it can be used to analyse systems such as our body, a launching rocket, a heating pan, refrigeration systems, engines and so on. A solid comprehension of how the thermal energy convection works in a simpler body is very important to guarantee a correct understanding and development of more complex real life engineering problems. The main subject of this paper is to study the transfer of thermal energy by diffusion and by convection and create an algorithm that can realize the simulation of this transfer of thermal energy in a bidimensional domain. To reach this goal, firstly was studied the physical model of the transfer of thermal energy. After that, the finite-difference numerical technique to spatial discretization was used to create the mesh of the body and two different methods - explicit and implicit - were used to discretize in time. To accomplish the numerical analysis, a Fortran90 algorithm was developed for each method in order to simulate the thermal energy transfer by diffusion and by convection given the boundaries conditions. Lastly, the results were quantitative validated by the manufactured method solution to ensure the code is functional. As a conclusion, the code showed up to be consistent, returning quantitative and qualitative validated results.

Optimal values range of interval polynomial programming problems

Uncertainty exists in many real-life engineering and mechanical problems. Here, we assume that uncertainties are caused by intervals of real numbers. In this paper, we consider the interval nonlinear programming (INLP) problems where the objective function and constraints include interval coefficients. So that the variables are deterministic and sign-restricted. Additionally, the constraints are considered in the form of inequalities. A basic task in INLP is calculating the optimal values range of objective function, which may be computationally very expensive. However, if the boundary functions are available, the problems become much easier to solve. By making these assumptions, an efficient method is proposed to compute the optimal values range using two classic nonlinear problems. Then, the optimal values range are obtained by direct inspection for a special kind of interval polynomial programming (IPP) problems. Two numerical examples are given to verify the effectiveness of the proposed method.

Outcome Based Education (OBE): Defining the Process and Practice for Engineering Education

Outcome Based Education aka OBE is one of the de-facto standards for modern educational system. An outcome is a culminating demonstration of learning that the students should be able to do at the end of a course, and in process at end of the degree program. Therefore, OBE is an approach to education in which decisions about the curriculum are driven by the exit learning outcomes that the students should perform in their professional life. The curriculum thus defines all the learning outcomes clearly and unambiguously, along with the contents, teaching/learning methods, assessment strategies and academic quality control process to ensure all-round development of the students. An engineer is a unique combination of different skill sets that must be mastered to resolve nontrivial real-life engineering problems. Consequently, the adoption of OBE in engineering education is the compelling necessity. This study offers a comprehensive, ready to adopt OBE framework for tertiary level engineering programs complying with the benchmark mandates of the OBE and the guidelines of Washington Accord. Additionally, the framework is successfully deployed in the department of CSE, IUB for the design and implementation of the undergraduate CSE program, a transcript of which is also documented. This will assist the concerned institutions to design their program in OBE model to gain international academic equivalency and accreditation.

Some Real-Life Applications of a Newly Designed Algorithm for Nonlinear Equations and Its Dynamics via Computer Tools

In this article, we design a novel fourth-order and derivative free root-finding algorithm. We construct this algorithm by applying the finite difference scheme on the well-known Ostrowski’s method. The convergence analysis shows that the newly designed algorithm possesses fourth-order convergence. To demonstrate the applicability of the designed algorithm, we consider five real-life engineering problems in the form of nonlinear scalar functions and then solve them via computer tools. The numerical results show that the new algorithm outperforms the other fourth-order comparable algorithms in the literature in terms of performance, applicability, and efficiency. Finally, we present the dynamics of the designed algorithm via computer tools by examining certain complex polynomials that depict the convergence and other graphical features of the designed algorithm.

Blood Coagulation Algorithm: A Novel Bio-Inspired Meta-Heuristic Algorithm for Global Optimization

This paper introduces a novel population-based bio-inspired meta-heuristic optimization algorithm, called Blood Coagulation Algorithm (BCA). BCA derives inspiration from the process of blood coagulation in the human body. The underlying concepts and ideas behind the proposed algorithm are the cooperative behavior of thrombocytes and their intelligent strategy of clot formation. These behaviors are modeled and utilized to underscore intensification and diversification in a given search space. A comparison with various state-of-the-art meta-heuristic algorithms over a test suite of 23 renowned benchmark functions reflects the efficiency of BCA. An extensive investigation is conducted to analyze the performance, convergence behavior and computational complexity of BCA. The comparative study and statistical test analysis demonstrate that BCA offers very competitive and statistically significant results compared to other eminent meta-heuristic algorithms. Experimental results also show the consistent performance of BCA in high dimensional search spaces. Furthermore, we demonstrate the applicability of BCA on real-world applications by solving several real-life engineering problems.

A New Methodology for Solving Multiobjective Chance-Constrained Problems: An Application on IoT Systems

This study presents a newly developed methodology to transform the chance-constrained problem into a deterministic problem and then solving this multiobjective deterministic problem with the proposed method. Chance-constrained problem contains independent gamma random variables that are denoted as a i j . Two methods are proposed to obtain the deterministic equivalent of chance-constrained problem. The first of the methods is directly based on using the distribution, and the second consists of normalizing probabilistic constraints using Lyapunov’s central limit theorem. An algorithm which uses the Global Criterion Method is developed to solve the multiobjective deterministic equivalent of chance-constrained problem. The methodology is applied to a real-life engineering problem that consists of an IoT device and its data sending process. Using Lyapunov’s central limit theorem for large numbers of random variables is found to be more appropriate.

The Real-Life Application of Differential Evolution with a Distance-Based Mutation-Selection

This paper proposes the real-world application of the Differential Evolution (DE) algorithm using, distance-based mutation-selection, population size adaptation, and an archive for solutions (DEDMNA). This simple framework uses three widely-used mutation types with the application of binomial crossover. For each solution, the most proper position prior to evaluation is selected using the Euclidean distances of three newly generated positions. Moreover, an efficient linear population-size reduction mechanism is employed. Furthermore, an archive of older efficient solutions is used. The DEDMNA algorithm is applied to three real-life engineering problems and 13 constrained problems. Seven well-known state-of-the-art DE algorithms are used to compare the efficiency of DEDMNA. The performance of DEDMNA and other algorithms are comparatively assessed using statistical methods. The results obtained show that DEDMNA is a very comparable optimiser compared to the best performing DE variants. The simple idea of measuring the distance of the mutant solutions increases the performance of DE significantly.

EDITORIAL

The editorial of Thermal Engineering of this issue continues the discussion on scientific research needs in vital areas in which thermal engineering has important participation. The main goal is to motivate the readers, within their specialties, to identify possible subjects for their future research. Mathematical modeling is a powerful tool used in engineering when one wants to design and optimize and equipment and/or processes. The main idea behind a mathematical model is to be able to simulate, as accurately as possible, the behavior of any physical system, real or virtual. However, the task of creating a mathematical model is far from simple. For the sake of comparison, disciplines of physics also propose mathematical models, but these models need to be universal. In engineering, this degree of universality is practically impossible. In reality, a good mathematical model for engineering is the one that is a good tool, assisting the design and optimization of equipment and systems. But which model should we choose when we find ourselves in real life engineering problems? Unfortunately, mathematical models for engineering are quite subjective. It takes into account what each author of the model saw and what problem he/she wanted to solve. That is why for the same system, for example, a shell and tube heat exchangers, there are multitude mathematical models proposed with different features, from highly complex models that use computational fluid dynamics to the simplest models used in undergraduate courses. This makes creativity and analytical skills very important when designing and optimizing a real system in view of the fact that design failures can generate monetary and human life losses. Choosing the right mathematical model is not a simple task, especially in unconsolidated areas, which are generally those where there is a great interest in applied research. Due to the very large number of existing mathematical models, engineers generally choose to propose their own mathematical model. A classification system for mathematical models would be very useful to find what mathematical models are most useful for a given situation.The mission of Thermal Engineering is to document the scientific progress in areas related to thermal engineering (e.g., energy, oil and renewable fuels). We are confident that we will continue to receive articles’ submissions that contribute to the progress of science.

A fast boundary-finite element approach for estimating anchor losses in Micro-Electro-Mechanical System resonators

This paper is focused on a fast and reliable estimate of anchor energy losses from a Micro-Electro-Mechanical System resonator, working at pressure in the order of the microbar, due to the scattering of elastic waves from the resonator into the substrate to which it is attached. The proposed numerical method is based on the fundamentals of the analytical procedure employed to estimate quality factors of resonators in the literature and consists in the modeling of the resonator by Finite Elements and of the radiated waves dissipation into the substrate by collocation Boundary Element Method. In order to simulate as quickly as possible an accurate response of the substrate in real time, we further consider H-matrix/vector product in the Boundary Element Method. The proposed numerical approach results in a very small elapsed computational time and it will therefore allow applications to real life engineering problems, targeted as future investigations.