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Grid-based many-objective optimiser for aircraft conceptual design with multiple aircraft configurations
... Effectively quantifying and resolving these conflicts not only aids businesses in enhancing product performance but also positions them advantageously in the competitive product market. Subsequently, many scholars have approached conceptual scheme design with designers as the primary focus, particularly in the realm of large-scale equipment design [10,11]. Several scholars have emphasized user-centric requirements analysis to construct design schemes for reference [12,13]. ...
In the context of rapid product iteration, design conflicts arise from discrepancies in designers’ understanding of user needs, influenced by subjective preferences, behavioural stances, and other factors. This paper proposes a product conceptual design approach based on the design conflict perspective. First, user comments and design documents are collected. Natural language processing (NLP) methods, including cleaning, filtering, lexical segmentation, feature clustering, and sentiment analysis, are employed to identify design themes. The intuitionistic fuzzy sets (IFSs) and term frequency–inverse document frequency (TF-IDF) algorithms are then utilized to obtain evaluation matrices for the products from both users and designers. Subsequently, design conflicts between users and designers are calculated, and an optimal configuration for product conceptual design is determined through regression analysis and planning methods. Finally, the proposed method is validated using a mobile phone as a product example, and suggestions for product improvement are presented. The results indicate that considering design conflicts as a factor in product design and synthesizing designer and user product concepts enhance the accuracy and reliability of product conceptual design generation. The findings of this study offer new insights into the conceptual design configuration for product iteration.
... In aeronautical engineering, design methodologies serve as one of the major advancements in aircraft component manufacturing (Arista et al. 2023;Romano et al. 2019). In the process of creating a design methodology and for the optimal outcome, it is proposed to combine engineering approaches with those based upon aerodynamical approaches, material sciences, and structural mechanics (Saporito et al. 2023;Champasak et al. 2023;Sun et al. 2023). When the experimental approach is extremely heavy in cost and structure, simulation-based analyses are advised to be conducted instead of real-life approaches. ...
Advancements in aircraft design and production necessitate exhaustive simulations of critical components, such as landing gear, to ensure optimal performance and safety. This paper presents a novel approach that combines finite element analysis (FEA) and machine learning (ML) for comprehensive simulations of aircraft landing gear during the landing phase, taking into account aircraft weight and wind force. In this paper, FEA-based simulations are performed accordingly where the deformation, stress, strain, and strain energy are all computed. A duration of 4 h and 42 min was the computed time for one run of ANSYS simulation for the landing touch methodology. Depending on the simulated deformation results, the values of stress, strain, and strain energy are forecasted based on the ML Naïve Bayes (NB) model. The proposed method is highly time effective as the forecasts would endure 1 s instead of around 5 h of calculation. To strengthen and present the model’s dependability, root mean square error (RMSE) and the coefficient of determination are called out, with values corresponding to 0.9% and 1.000, respectively. Overall, it has been evidenced that FEA–ML-based analyses are far more time effective and preferable over traditional FEA-based simulations. The proposed methodology is geometrical friendly as it might be applied over any part of an aircraft, hence advancing aircraft design capabilities.
... Effectively quantifying and resolving these con icts not only aids businesses in enhancing product performance but also positions them advantageously in the competitive product market. Subsequently, many scholars have approached conceptual scheme design with designers as the primary focus, particularly in the realm of large-scale equipment design 10,11 . Several scholars have emphasized user-centric requirements analysis to construct design schemes for reference 12,13 . ...
In the context of rapid product iteration, design conflicts arise from discrepancies in designers' understanding of user needs influenced by subjective preferences, behavioural stances, and other factors. This paper proposes a product conceptual design approach based on the design conflict perspective. First, user comments and design documents are collected. Natural language processing (NLP) methods, including cleaning, filtering, lexical segmentation, feature clustering, and sentiment analysis, are employed to identify design themes. The intuitionistic fuzzy sets (IFS) and term frequency-inverse document frequency (TF-IDF) algorithms are then utilized to obtain evaluation matrices for the products from both users and designers. Subsequently, design conflicts between users and designers are calculated, and an optimal configuration for product conceptual design is determined through regression analysis and planning methods. Finally, the proposed method is validated using a mobile phone as a product example, and suggestions for product improvement are presented. The results indicate that considering design conflicts as a factor in product design and synthesizing designer and user product concepts enhances the accuracy and reliability of product conceptual design generation. The findings of this study offer new insights into the conceptual design configuration for product iteration.
... FDM is one of the most known works 3D printing methods used nowadays to create polymeric parts [76]. The number of studies on FDM technology has risen recently, and many researchers have conducted several experiments to ascertain how printing parameters affect the mechanical characteristics of the parts produced [77][78][79][80][81]. Samykano et al. [64] focused on layer thickness, raster angle, and infill density while determining the mechanical characteristics of the ABS material generated by 3D printers. ...
In recent years, there has been a logarithmic interest in three-dimensional printing technologies. This technique has made it possible to make more intricately shaped parts of superior quality, allowing for use in a variety of industries, including aircraft, automobiles, and ships. This study characterized the materials and assessed the mechanical features of PLA, PETG, and ABS materials generated at various raster angles. The strength ratios of the various materials have been found to fluctuate when the raster angles change. The PLA parts created at a picture raster angle of 45° had the maximum tensile strength. ABS material created with a picture raster angle of 45° has been shown to have the best energy absorption, and PLA material made with a raster angle of 45° has the best performance compressive strength. When bending strength was evaluated, it was found that samples of ABS made with a raster angle of 0–90° had the greatest value. The SEM micrographs were obtained, and the tensile test was used to examine the fracture behavior of the materials. As a result, it has been found that materials created using various raster angles can have various strength values from one another.
Optimization techniques play a pivotal role in enhancing the performance of engineering components across various real-world applications. Traditional optimization methods are often augmented with exploitation-boosting techniques due to their inherent limitations. Recently, nature-inspired algorithms, known as metaheuristics (MHs), have emerged as efficient tools for solving complex optimization problems. However, these algorithms face challenges such as imbalance between exploration and exploitation phases, slow convergence, and local optima. Modifications incorporating oppositional techniques, hybridization, chaotic maps, and levy flights have been introduced to address these issues. This article explores the application of the recently developed crayfish optimization algorithm (COA), assisted by artificial neural networks (ANN), for engineering design optimization. The COA, inspired by crayfish foraging and migration behaviors, incorporates temperature-dependent strategies to balance exploration and exploitation phases. Additionally, ANN augmentation enhances the algorithm’s performance and accuracy. The COA method optimizes various engineering components, including cantilever beams, hydrostatic thrust bearings, three-bar trusses, diaphragm springs, and vehicle suspension systems. Results demonstrate the effectiveness of the COA in achieving superior optimization solutions compared to other algorithms, emphasizing its potential for diverse engineering applications.
The material behavior under cyclic loading is more complex than under monotonic loading and the usage of the sophisticated constitutive models is required to accurately define the elastoplastic behaviors of the advanced high-strength steels and aluminum alloys. These models involve the numerous material parameters that are determined from cyclic tests and accurate calibration of the variables has a great influence on the description of the material response. Therefore, the development of a precise and robust identification method is needed to obtain reliable results. In this study, a systematic methodology depending upon the firefly algorithm (FA) with variable step size has been developed and Yoshida–Uemori combined hardening model parameters of a dual-phase steel (DP980) and an aluminum alloy (AA6XXX-T4) are determined. The identified parameters are verified based on comparisons between the finite element simulations of the cyclic uniaxial tension-compression tests and experimental data and also the search performance of the variable FA is evaluated by comparing it with the standard FA. It is seen from these comparisons that variable FA can easily find and rapidly converge to the global optimum solutions.
This study proposes a novel human-inspired metaheuristic search algorithm called marathon runner algorithm. This method mimics competitive behaviors observed in real marathon runners through mathematical modeling. Unlike classical elitist algorithms that prioritize position of the best agent, the marathon runner algorithm introduces a novel concept called vision point. This point considers the quality of the entire population, not just the leader. By guiding the population towards vision point, the risk of getting trapped in local optima is reduced. A two-part evaluation was conducted to thoroughly assess the search capabilities of the marathon runner algorithm. First, it is tested against a set of unconstrained benchmark mathematical functions and the algorithm’s quantitative attributes, such as complexity, accuracy, stability, diversity, sensitivity, and convergence rate are analyzed. Subsequently, the algorithm was applied to mechanical and structural optimization problems with both continuous and discrete variables. This application demonstrated the effectiveness of the algorithm in solving practical engineering challenges with constraints. The outcomes are compared with those obtained by six other well-established techniques. The obtained results indicate that the marathon runner algorithm yields promising and competitive solutions for both mathematical, mechanical, and structural problems.
In this article, a newly developed optimization approach based on a mathematics technique named the geometric mean optimization algorithm is employed to address the optimization challenge of the robot gripper, airplane bracket, and suspension arm of automobiles, followed by an additional three engineering problems. Accordingly, other challenges are the ten-bar truss, three-bar truss, tubular column, and spring systems. As a result, the algorithm demonstrates promising statistical outcomes when compared to other well-established algorithms. Additionally, it requires less iteration to achieve the global optimum solution. Furthermore, the algorithm exhibits minimal deviations in results, even when other techniques produce better or similar outcomes. This suggests that the proposed approach in this paper can be effectively utilized for a wide range of critical industrial and real-world engineering challenges.
In recent years, additive manufacturing (AM) technologies have been used in many industries, such as automotive, defense, space, and aviation. Depending on the development of this technology, the effect of the relationship between many parameters, such as raster angles, production speed, and melting temperature used during the production of materials, has been an important issue in the mechanical properties of materials. In this study, the effects of ±45°and 0-90°raster angles used during the production of 15 % short carbon fiber reinforced polyethylenetereflatate (CF15PET) and 30 % short glass fiber reinforced polypropylene (GF30PP) materials on the mechanical properties of the materials were investigated. As a result of the study, it was determined that different raster angles affect the mechanical properties of both materials.
Nowadays, the need for new technologies is increasing, especially to find solutions to the inadequacies in the production of complex structures. The additive manufacturing methods developed facilitate the production of complex parts and move the technology forward with factors such as cost and efficiency. With the optimization of new parts designed by additive manufacturing methods, it is possible to obtain the optimum product even in the most complex structures. At the end of the production process, the final product with the desired properties is obtained as a result of part size tolerance precision and optimizations. In this study, lattice optimization is applied to a passenger aircraft bracket. It is aimed to reduce the weight and, at the same time, increase the efficiency of the part by optimizing it with lattice structures. For this purpose, the Altair Inspire program was used, and the variation of mass, displacement, safety coefficient, and stress values of the part according to different lattice structures were investigated.
Nature-inspired metaheuristic optimization algorithms have many applications and are more often studied than conventional optimization techniques. This article uses the mountain gazelle optimizer, a recently created algorithm, and artificial neural network to optimize mechanical components in relation to vehicle component optimization. The family formation, territory-building, and food-finding strategies of mountain gazelles serve as the major inspirations for the algorithm. In order to optimize various engineering challenges, the base algorithm (MGO) is hybridized with the Nelder–Mead algorithm (HMGO-NM) in the current work. This considered algorithm was applied to solve four different categories, namely automobile, manufacturing, construction, and mechanical engineering optimization tasks. Moreover, the obtained results are compared in terms of statistics with well-known algorithms. The results and findings show the dominance of the studied algorithm over the rest of the optimizers. This being said the HMGO algorithm can be applied to a common range of applications in various industrial and real-world problems.
Nature-inspired metaheuristic algorithms are gaining popularity with their easy applicability and ability to avoid local optimum points, and they are spreading to wide application areas. Meta-heuristic optimization algorithms are used to achieve an optimum design in engineering problems aiming to obtain lightweight designs. In this article, structural optimization methods are used in the process of achieving the optimum design of a seat bracket. As a result of topology optimization, a new concept design of the bracket was created and used in shape optimization. In the shape optimization, the mass and stress values obtained depending on the variables, constraint, and objective functions were created by using artificial neural networks. The optimization problem based on mass minimization is solved by applying the dandelion optimization algorithm and verified by finite element analysis.
Composite materials have a wide range of applications in many industries due to their manufacturability, high strength values, and light filling. The sector where composite materials are mostly used is the aviation industry. Today, as a result of the development of aviation systems, drones have started to be actively used, and many studies have started to be carried out to mitigate them. In this study, the subcarrier part, which is part of the drone, was designed using glass and carbon fiber–reinforced composite materials. Using the data obtained at the end of the analysis, the stacking angle with the optimal displacement and stress value was determined by using the genetic algorithm (GA), gray wolf algorithm (GWO), and slime mold optimization (SMO) techniques in order to develop a carrier with a minimum displacement and stress value of more than 60 MPa. As a result of the optimization, it was determined that artificial intelligence algorithms could be used effectively in determining the stacking angle of composite materials, and the optimum values were determined in the slime mold algorithm.
In this article, a recently developed physics-based Fick’s law optimization algorithm is utilized to solve engineering optimization challenges. The performance of the algorithm is further improved by incorporating quasi-oppositional–based techniques at the programming level. The modified algorithm was applied to optimize the rolling element bearing system, robot gripper, planetary gear system, and hydrostatic thrust bearing, along with shape optimization of the vehicle bracket system. Accordingly, the algorithm realizes promising statistical results compared to the rest of the well-known algorithms. Furthermore, the required number of iterations was comparatively less required to attain the global optimum solution. Moreover, deviations in the results were the least even when other optimizers provided better or more competitive results. This being said that this optimization algorithm can be adopted for a critical and wide range of industrial and real-world challenges optimization.
Course timetabling is an ongoing challenge that universities face all around the world. This combinatorial optimization task involves allocating a set of events into finite time slots and rooms while attempting to satisfy a set of predefined constraints. Given the high number of constraints and the large solution space to be explored, the University Course Timetabling Problem (UCTP) is classified as an NP-hard problem. Meta-heuristic approaches have been commonly applied to this problem in the literature and have achieved high performance on benchmark datasets. This survey paper provides a comprehensive and systematic review of these approaches in the UCTP. It reviews, summarizes, and categorizes the approaches, and introduces a classification for hybrid meta-heuristic methods. Furthermore, it critically analyzes the benefits and limitations of the methods. It also presents challenges, gaps, and possible future work.
Meta heuristics is an optimization approach that works as an intelligent technique to solve optimization problems. Evolutionary algorithms, human-based algorithms, physics-based algorithms and swarm intelligence are categorized under meta-heuristic algorithms. This study presents a critical review of meta-heuristic algorithms for future reference, including concepts, applications, advantages and disadvantages, before focusing on one specific meta-heuristic algorithm, namely, Emperor Penguin Optimizer (EPO). It is an intelligent algorithm developed after observing the behaviour of emperor penguins during cold winters. This technique was introduced by Dhiman in 2018 and adopted to solve optimization problems. The study reviews the algorithm variants starting from its invention in 2018 until 2022. The literature is comprehensively reviewed to reflect on the progress of the algorithm’s adoption, highlighting a new area for improvement. The most significant result is that the proposed algorithm has been proven an effective technique. The merits and demerits of the algorithm are explored to provide valuable perspectives for future research. This study answers the question regarding meta-heuristic algorithms’ effectiveness, especially EPO. Both beginners and experts of EPO research can use the findings of this study as guidelines for enhancing current concepts and applications of state-of-the-art algorithms for future development works.
The optimal yawing angle of sun-tracking solar aircraft is tightly related to the solar azimuth angle, which results in a large arc flight path to dynamically track the sun position. However, the limited detection range of payload usually requires solar aircraft to loiter over areas of interest for persistent surveillance missions. The large arc sun-tracking flight may cause the target area on the ground to be outside the maximum coverage area of payload. The present study therefore develops an optimal flight control approach for planning the flight path of sun-tracking solar aircraft within a mission region. The proposed method enables sun-tracking solar aircraft to maintain the optimal yawing angle most of the time during daylight flight, except when the aircraft reverses its direction by turning flight. For a circular region with a mission radius of 50 km, the optimal flight trajectory and controls of an example Λ-shaped sun-tracking solar aircraft are investigated theoretically. Results demonstrate the effectiveness of the proposed approach to optimize the flight path of the sun-tracking aircraft under the given circular region while maximizing the battery input power. Furthermore, the effects of varying the mission radius on energy performance are explored numerically. It has been proved that both net energy and energy balance remain nearly constant as the radius constraint varies, which enables the solar aircraft to achieve perpetual flight at almost the same latitude as the large arc flight. The method and results presented in this paper can provide reference for the persistent operation of sun-tracking solar aircraft within specific mission areas.
This paper develops a novel optimization method oriented to the resilience of multiple Unmanned Aerial Vehicle (multi-UAV) formations to achieve rapid and accurate reconfiguration under random attacks. First, a resilience metric is applied to reflect the effect and rapidity of multi-UAV formation resisting random attacks. Second, an optimization model based on a parameter optimization problem to maximize the system resilience is established. Third, an Adaptive Learning-based Pigeon-Inspired Optimization (ALPIO) algorithm is designed to optimize the resilience value. Finally, typical formation topologies with six UAVs are investigated as a case study to verify the proposed approach. The experimental results indicate that the proposed scheme can achieve resilience optimization for a multi-UAV formation reconfiguration by increasing the system resilience values to 97.53% and 81.4% after random attacks.
Hubs act as intermediate points for the transfer of materials in the transportation system. In this study, a novel p-mobile hub location-allocation problem is developed. Hub facilities can be transferred to other hubs for the next period. Implementation of mobile hubs can reduce the costs of opening and closing the hubs, particularly in an environment with rapidly changing demands. On the other hand, the movement of facilities reduces lifespan and adds relevant costs. The depreciation cost and lifespan of hub facilities must be considered and the number of movements of the hub’s facilities must be assumed to be limited. Three objective functions are considered to minimize costs, noise pollutions, and the harassment caused by the establishment of a hub for people, a new objective that locates hubs in less populated areas. A multi-objective mixed-integer non-linear programming (MINLP) model is developed. To solve the proposed model, four meta-heuristic algorithms, namely multi-objective particle swarm optimization (MOPSO), a non-dominated sorting genetic algorithm (NSGA-II), a hybrid of k-medoids as a famous clustering algorithm and NSGA-II (KNSGA-II), and a hybrid of K-medoids and MOPSO (KMOPSO) are implemented. The results indicate that KNSGA-II is superior to other algorithms. Also, a case study in Iran is implemented and the related results are analyzed.
The paper overviews the state-of-art of aircraft powered by hybrid electric propulsion systems. The research status of the design and energy management of hybrid aircraft and hybrid propulsion systems are further reviewed. The first contribution of the review is to demonstrate that, in the context of relatively underdeveloped electrical storage technologies, the study of mid-scale hybrid aircraft can contribute the most to both theoretical and practical knowledge. Meanwhile, the profits and potential drawbacks of applying hybrid propulsion to mid-scale hybrid airplanes have not been thoroughly illustrated. Secondly, as summed in the overview of design methodologies, the multi-objective optimization transcends the single-objective one. The potential of the hybrid propulsion system can be thoroughly evaluated in only one optimization run, if several objectives optimized simultaneously. Yet there are few researches covering the conceptual design of hybrid aircraft using multi-objective optimization. The review of the most popular energy management strategies discloses the third research gap—current methodologies favoured in hybrid ground vehicles do not consider the aircraft safety. Additionally, both non-causal and causal energy management are needed for performing a complicated flight mission with several sub-tasks.
This paper proposes a new self-adaptive meta-heuristic (MH) algorithm for multiobjective optimisation. The adaptation is accomplished by means of estimation of distribution. The differential evolution reproduction strategy is modified and used in this dominance-based multiobjective optimiser whereas population-based incremental learning is used to estimate the control parameters. The new method is employed to solve aeroelastic multiobjective optimisation of an aircraft wing which optimises structural weight and flutter speed. Design variables in the aeroelastic design problem include thicknesses of ribs, spars and composite layers. Also, the ply orientation of the upper and lower composite skins are assigned as the design variables. Additional benchmark test problems are also use to validate the search performance of the proposed algorithm. The performance validation reveals that the proposed optimiser is among the state-of-the-art multiobjective meta-heuristics. The concept of using estimation of distribution algorithm for tuning meta-heuristic control parameters is efficient and effective and becomes a new direction for improving MH performance.
In Wireless Sensor Network (WSN), the formation of the load-balanced clusters of the sensor nodes is very burning research issue. Most of the existing node clustering schemes suffer from energy-hole and non-uniform load assignment problems. These problems affect the network lifetime of the WSN severely. To solve these problems, this paper proposed an an improved Memetic algorithm-based cluster head selection scheme and also a load-balanced cluster formation scheme. The performance of the proposed scheme is compared with two well-known node clustering schemes in terms of energy consumption, residual energy, and network lifetime. Result analysis of the proposed scheme validates its superior performance over the existing node clustering schemes under different network configurations.
A novel hybrid many-objective evolutionary algorithm called Reference Vector Guided Evolutionary Algorithm based on hypervolume indicator (H-RVEA) is proposed in this paper. The reference vectors are used in a number of sub-problems to decompose the optimization problem. An adaptation strategy is used in the proposed algorithm to adjust the reference vector distribution. The proposed algorithm is compared over well-known benchmark test functions with five state-of-the-art evolutionary algorithms. The results show H-RVEA’s superior performance in terms of the inverted generational distance and hypervolume performance measures than the competitor algorithms. The suggested algorithm’s computational complexity is also analysed. The statistical tests are carried out to demonstrate the statistical significance of the proposed algorithm. In order to demonstrate its efficiency, H-RVEA is also applied to solve two real-life constrained many-objective optimization problems. The experimental results indicate that the proposed algorithm can solve the many-objective real-life problems. Note that the source codes of the proposed technique are available at http://dhimangaurav.com/.
Every day, the immature sunflowers generate heliotropic movements. Two growth mechanisms are controlled on the heliotropic movements of immature sunflowers. The first mechanism is the sun-tracking phenomenon, which is caused by a growth hormone called Auxin.While the second mechanism is the biological clock which is responsible for the nocturnal reorientation of the immature sunflowers at the night. By clarifying and idealizing the growth mechanisms of the immature sunflowers in nature, a new type of nature-inspired optimization algorithm, called Smart Flower Optimization Algorithm (SFOA), is proposed in this paper. The proposed algorithm has been presented in two modes: sunny and cloudy or rainy modes depending on the weather. The SFOA is benchmarked on three parts. First, a set of 15 benchmark functions on CEC 2015 is used to test the efficiency of the SFOA using statistical analysis and Wilcoxon’s test. Second, the SFOA is utilized for designing an adaptive IIR system to appropriate the unknown system. Finally, four different engineering design problems (three-bar truss, tension/compression spring, speed reducer, and welded beam) are solved by SFOA. In the first part, the SFOA is compared with well-known algorithms. The results confirm the capabilities and efficiency of the proposed algorithm in finding the optimum. The promising solutions obtained for the IIR system identification and the engineering design problems demonstrate that the SFOA significantly outperforms other algorithms and show the applicability of the proposed algorithm in solving the real-world problems with unknown search spaces as well.
Load balancing of tasks on the cloud environment is an important aspect of distributing resources from a data centre. Due to the dynamic computing through the internet; cloud computing agonizes from overloading of requests. Load balancing has to be carried out in such a manner that all virtual machines (VM) should have balanced load to achieve optimal utilization of its capabilities. This paper proposes a novel methodology of dynamic balancing of load among the virtual machines using hybridization of modified Particle swarm optimization (MPSO) and improved Q-learning algorithm named as QMPSO. The hybridization process is carried out to adjust the velocity of the MPSO through the gbest and pbest based on the best action generated through the improved Q-learning. The aim of hybridization is to enhance the performance of the machine by balancing the load among the VMs, maximize the throughput of VMs and maintain the balance between priorities of tasks by optimizing the waiting time of tasks. The robustness of the algorithm has been validated by comparing the results of the QMPSO obtained from the simulation process with the existing load balancing and scheduling algorithm. The comparison of the simulation and real platform result shows our proposed algorithm is outperforming its competitor.
Performing the design of a truss including topological, shape and sizing (TSS) variables simultaneously is a challenging but important task for a designer. It is even more interesting when the design problem involves random parameters. In this paper, a novel hybrid meta-heuristic based on the whale optimisation algorithm (WOA) and success history–based adaptive differential evolution (SHADE) is developed to solve the multi-objective reliability optimisation of a truss. The reliability design problem is assigned as a bi-objective truss optimisation with mass and reliability index being the objectives. Two novel algorithms called success history–based adaptive multi-objective differential evolution (SHAMODE) and success history–based adaptive multi-objective differential evolution with whale optimisation (SHAMODE-WO) are developed in this paper. The proposed algorithms are used to solve the test problems for TSS truss reliability optimisation along with some established optimisers. Comparative results show that they are among the top algorithms in solving such design problems. Moreover, SHAMODE-WO shows obvious improvement compared to SHAMODE.
This paper presents a Fuzzy Preference Function-based Robust Multidisciplinary Design Optimization (FPF-RMDO) methodology. This method is an effective approach to multidisciplinary systems, which can be used to designer experiences during the design optimization process by fuzzy preference functions. In this study, two optimizations are done for Predator MQ-1 Unmanned Aerial Vehicle (UAV): (A) deterministic optimization and (B) robust optimization. In both problems, minimization of takeoff weight and drag is considered as objective functions, which have been optimized using Non-dominated Sorting Genetic Algorithm (NSGA). In the robust design optimization, cruise altitude and velocity are considered as uncertainties that are modeled by the Monte Carlo Simulation (MCS) method. Aerodynamics, stability and control, mass properties, performance, and center of gravity are used for multidisciplinary analysis. Robust design optimization results show 46% and 42% robustness improvement for takeoff weight and cruise drag relative to optimal design respectively.
This work proposes a new multi-objective algorithm inspired from the navigation of grass hopper swarms in nature. A mathematical model is first employed to model the interaction of individuals in the swam including attraction force, repulsion force, and comfort zone. A mechanism is then proposed to use the model in approximating the global optimum in a single-objective search space. Afterwards, an archive and target selection technique are integrated to the algorithm to estimate the Pareto optimal front for multi-objective problems. To benchmark the performance of the algorithm proposed, a set of diverse standard multi-objective test problems is utilized. The results are compared with the most well-regarded and recent algorithms in the literature of evolutionary multi-objective optimization using three performance indicators quantitatively and graphs qualitatively. The results show that the proposed algorithm is able to provide very competitive results in terms of accuracy of obtained Pareto optimal solutions and their distribution.
Over the last three decades, a large number of evolutionary algorithms have been developed for solving multiobjective optimization problems. However, there lacks an up-to-date and comprehensive software platform for researchers to properly benchmark existing algorithms and for practitioners to apply selected algorithms to solve their real-world problems. The demand of such a common tool becomes even more urgent, when the source code of many proposed algorithms has not been made publicly available. To address these issues, we have developed a MATLAB platform for evolutionary multi-objective optimization in this paper, called PlatEMO, which includes more than 50 multi-objective evolutionary algorithms and more than 100 multi-objective test problems, along with several widely used performance indicators. With a user-friendly graphical user interface, PlatEMO enables users to easily compare several evolutionary algorithms at one time and collect statistical results in Excel or LaTeX files. More importantly, PlatEMO is completely open source, such that users are able to develop new algorithms on the basis of it. This paper introduces the main features of PlatEMO and illustrates how to use it for performing comparative experiments, embedding new algorithms, creating new test problems, and developing performance indicators. Source code of PlatEMO is now available at: http://bimk.ahu.edu.cn/index.php?s=/Index/Software/index.html.
Melanoma, despite its relatively low incidence compared to other types of skin cancer, accounts for a significant proportion of skin cancer-related deaths. Early detection of melanoma is crucial for improving patient survival rates. Deep learning algorithms, which heavily rely on data, are widely used in melanoma detection. However, the performance of these algorithms is greatly influenced by the distribution of the dataset, particularly class imbalance. In this manuscript, the authors present a novel method based on the Kemeny–Young rule for optimal rank aggregation to address the class imbalance problem in melanoma detection. The proposed approach aims to reduce class bias and enhance overall classification accuracy. Furthermore, a cost-sensitive learning approach is introduced to improve the classifier’s ability to handle class imbalance effectively. This novel cost-sensitive learning method utilizes Self-Adaptive Differential Evolution Optimization to determine optimal weights for each class. Our approach differs from traditional methods that assign weights based on predefined criteria. To evaluate the effectiveness of the proposed methods, extensive experiments and ablation studies are conducted on the highly imbalanced ISIC 2020 dataset, which is widely used in melanoma detection research. The Kemeny–Young rule-based majority voting achieves an overall error rate of 2.44%, while the cost-sensitive learning based on the Self-Adaptive Differential Evolution approach achieves an even lower error rate of 1.99%. Moreover, the proposed method achieves a sensitivity of 87.93% and a specificity of 98.19%. These experimental results demonstrate the competitiveness and effectiveness of the proposed methods in addressing the challenges posed by class imbalance and improving the accuracy of melanoma detection. By effectively mitigating class imbalance, these methods improve the accuracy and reliability of melanoma detection, thus offering valuable insights for developing advanced computer-aided diagnosis systems in dermatology. The relevant codes for our proposed approach are publicly available at: https://github.com/ctrl-gaurav/Handling-Imbalanced-Class-in-Melanoma.
Steel gable frames, or pitched roof portal frames, have been widely used for industrial building applications due to their ease of erection and fabrication. The roof slope is one of the most important geometric characteristics of gable frames. The roof slope affects wind loads, snow loads, rainwater drainage, and the optimal weight of the frame. The purpose of this study is to find the optimal roof slope of gable frames with tapered members for different spans. A novel meta-heuristic optimization algorithm called the Enhanced Crystal Structure Algorithm (ECryStAl) is utilized to optimize the steel frames. In this broad study, all practical load types, load combinations, and design constraints are considered for the structural optimization process. The results of this study show that up to 39.50% of the project material cost can be reduced by the proper choice of the roof slope depending on the span length and column height as the optimal roof slope depends mainly on them. In addition, mathematical models, which can be used in practical design situations, are developed to determine the optimal roof slope for different span lengths and column heights.
physical parameters. After sensing, data is processed and sent to the base station through a given route.
Sensing and transmitting nodes consume a lot of energy; hence nodes die quickly; therefore, hot spot problems
occur. Henceforth, data transmission is done by a single route; thus, WSNs experience network overhead
problems. Nowadays, the enhancement of the energy of WSNs remains a challenging issue. Alternatively,
efficient processes such as routing or clustering may be improved. Dynamic cluster head selection can be
considered an important decision approach for optimal path selection and saving energy. This paper proposes
a Meta-heuristic Optimized Cluster head selection-based Routing algorithm for WSNs (MOCRAW) to minimize
node’s energy consumption and fast data transmission. MOCRAW removes isolated nodes or hot-spot problems
and provides loop-free routing with the help of the Dragonfly Algorithm (DA), wherein the decision is based
on Local Search Optimization (LSO) and Global Search Optimization (GSO). This protocol exploits two subprocesses:
the optimal Cluster Head Selection Algorithm (CHSA) and Route Search Algorithm (RSA). CHSA uses
Energy Level Matrix (ELM). ELM is based on node density, residual energy, the distance between Cluster Head
(CH) and Base Station (BS), and inter-cluster formation. The inter-cluster discovers the optimum path between
source to destination in RSA by levy distribution. MOCRAW performance is compared with other clustering
and routing protocols on parameters such as the number of alive nodes, delay, packet delivery ratio, and
average energy consumption. Simulation-based findings exhibit that the proposed methodology surpasses its
peers and competitors in terms of energy efficiency.
The rapid and accurate acquisition of model parameters of photovoltaic (PV) modules is of great significance for the efficient operation and maintenance of photovoltaic power plants under the background of the development of new power systems. To solve the problems of poor accuracy and slow velocity of identification of traditional PV modules model parameters, this paper proposed an identification of parameter method based on fuzzy adaptive differential evolution algorithm (FADE). In the proposed method, based on the I–V output characteristics of PV modules, a DE/current-to-SP-best/1 variation strategy is constructed to increase the local search capability of module model parameter identification; In addition, fuzzy selection strategy and an adaptive parameter adjustment strategy are introduced to effectively control the crossover probability and mutation factors to avoid the discrimination into local optimum while improving the convergence of the algorithm. The performance of the proposed method has been verified by extracting classical polycrystalline and monocrystalline modules parameters, The solution results of the polycrystalline module Photowatt-PWP201 (2.42507E−3), STP6-120/36 (1.66006E−2) and monocrystalline module STM6-40/36 (1.72981E−3) comprehensively show that FADE has better accuracy and robustness compared with other algorithms.
The reliability optimisation methodology is developed to solve a conceptual design problem of a fixed-wing Unmanned Aerial Vehicle (UAV). The reliability quantification is based on the most probable point (MPP) concept, leading to a double-loop optimisation problem. The design problem is formulated as an outer-loop multiobjective optimisation for the main design problem and an inner-loop optimisation for estimating a reliability index (β) of a design solution. The goal of the outer-loop optimisation is to minimise the aircraft take-off weight, and simultaneously maximise β. The aerodynamic and stability properties of an aircraft solution are calculated by a vortex lattice method (VLM), while various types of empirical weight methods are obtained. The design problem is set up to have uncertainties in calculating aircraft empty weight and aerodynamic coefficients and derivatives. For the inner loop optimisation, the MPP is used for approximating the reliability index and probability of failure (pf). Multiobjective Meta-heuristic with Iterative Parameter Distribution Estimation (MMIPDE) and Success-History based Adaptive Differential Evolution (SHADE) are used for solving the outer- and inner-loop optimisations respectively. Four parameter setting strategies for running metaheuristics are proposed for use with the proposed metaheuristic-based reliability optimisation. The comparative results reveal that the best dynamic parameter setting from this study can reduce runtime by 22.5% compared to the traditional metaheuristic run while maintaining competitive results.
The paper offers a novel technique for resolving the multi-robot cooperation for stick carrying applications. The problem addresses the computation of a collision-free optimal path from a predefined initial position to target position during transportation of stick or object cooperatively by robot pair in the multi-robot environment. The stick carrying application has been resolved by embedding the modified Q-learning into the hybrid process of an improved version of particle swarm optimization and intelligent water drop algorithm. In the present context, modified Q-learning generates the best solution for particle swarm optimization and particle swarm optimization is upgraded through the perception of cubic spline and generate the optimal position in the successive iteration using intelligent water drop algorithm and also enhances the intensification and diversification capability of particle swarm optimization. The proposed hybrid algorithm computes the collision-free subsequent position for each robot pair by avoiding the obstacles in its path, avoiding the trapping at the local optima, improving the convergence speed, optimizing the path distance for every pair of robots, energy usage and path smoothness both in the static and dynamic environment’s. The validation of the proposed hybrid algorithm has been verified and checked the robustness of the algorithm through computer simulation and real robots through Webots simulator. Further, the efficiency of the proposed algorithm has been verified by comparing the result obtained proposed algorithm and its competitor algorithms and comparing the result of the proposed algorithm with the existing state-of arts. The comparison result shows that the proposed algorithm is superior to tis competitor algorithms and state of arts for different matrices.
This study presents a cellular lane-changing model where the traffic lanes are discretized into cells and formulates the lane-changing process as a multi-player non-zero-sum non-cooperative game in a connected environment where the real-time surrounding traffic data is shared. In a congested traffic scenario where a large quantity of mandatory lane-changing maneuvers are required simultaneously, the size of the game is extended dynamically (i.e., 2–5 players in this case, and it can be extended more if necessary) based on the traffic situation. Discretionary lane-changing maneuvers are also considered in the decision-making process to maximize the use of the capacity of each traffic lane, i.e., distributing the vehicles to each traffic lane as uniform as possible. Moreover, the competing vehicle on the target lane has more flexible actions (i.e., changing to other lanes) except for accelerating and decelerating actions. Thus, its temporary benefit is sometimes sacrificed by taking these actions to pursue the global benefit so that other vehicles could complete mandatory lane-changing maneuvers. It is a known fact that the space complexity expands exponentially as the game size increases, so a novel decomposition algorithm based on game theory is proposed to reduce the complexity and improve computational efficiency. Finally, a rule-based approach, a classic Nash equilibrium approach and the proposed decomposition algorithm are compared by the critical indicators such as the number of lane-changing vehicles, the maximum incoming queues during the process, the mean of computational time per iteration, etc. The performance shows no significant difference in the efficacy of lane-changing maneuvers among these approaches under the uncongested traffic condition. At the same time, the decomposition algorithm is more efficient in computing time than the classic Nash equilibrium approach. As the traffic gets congested, the game theory-based approaches prove more effective in lane-changing behaviours than the rule-based approach. Meanwhile, the decomposition algorithm outperforms the classic Nash equilibrium approach more significantly in terms of computational time.
With the advancement in electric propulsion systems, aircraft designers and manufacturers are no longer constrained to established configurations. Developments in Vertical Take-off and Landing (VTOL) aircraft have been seen in recent times through the design of modern tiltrotor aircraft, tiltwing concepts and multi-rotor designs. The combination of these developments allowed engineers to propose designs which utilise the vertical take-off and landing capabilities of a tiltrotor aircraft with electrically driven propulsion systems, deemed eVTOL (Electrically driven Vertical Take-off and Landing). This investigation aims to develop an understanding of the aeroacoustic emissions associated with the non-linear interaction resulting from multi-rotor integrated propellers and a tiltwing eVTOL airframe. Acoustics is one of the key requirements of any future eVTOL aircraft certification, hence, an investigation was conducted into the baseline design, followed by an optimisation study aiming to reduce the amount of noise generated.
Particle swarm optimization (PSO) is one of the most popular stochastic swarm-based metaheuristic algorithms. Kalman filter principle is introduced to predict the global optimum more accurately to enhance convergence. However, the evolution of particles in current Kalman PSO merely depends on the adjustment based on observation. In this paper, a modified Kalman particle swarm optimization (MKPSO) algorithm is proposed. The population is extended with the estimated optimum based on Kalman filtering, in which the prediction model is formulated as the weighted central optimum. Benchmark functions in the CEC14 test suite are adopted to verify the effectiveness of MKPSO. Numerical results show that MKPSO is more effective in mining capability for high-dimensional problems. Besides, the superiority of MKPSO lies in solving hybrid optimization problems. At last, MKPSO is applied to maximize the attainable moments subset of very flexible aircraft (VFA) on account for redundancy of control surfaces. Simulation results reveal that there is a trade-off between flight and control performance for VFA.
This paper presents an efficient inverse global optimization approach for damage identification of plate-like structures. In this approach, the damage identification process is performed by minimizing an objective function based on modal parameters of CFRP laminated structures. The identification process entails two steps: i) the direct problem is modeled using the finite element method. Damage is induced into the two different situations, first as a variation in physical properties, i.e., delamination, as a variation in stiffness and also as a variation in the grommet properties, for example small circular holes; ii) For solving the optimization problem, an enhanced SunFlower Optimization (SFO) algorithm is applied in the inverse problem methodology. The SFO metaheuristic algorithm has its biological operators optimized by mixture design method. The efficiency of the proposed identification is investigated through two numerical examples for laminated composite plates where Genetic Algorithm, SFO and an improved SFO algorithm are compared. The obtained results indicate that the proposed Structural Health Monitoring method can successfully identify the location and the severity of small induced damage cases in the laminated composite plate. In addition, the improved algorithm was shown to be more efficient and accurate than the widely known and applied Genetic Algorithm.
A key challenge in the design of hybrid-electric propulsion systems (HEPSs) for aircraft is the complexity involved in handling efficient sizing of the components as well as control synergy between multiple power sources. To handle this challenge effectively, combined optimal design and control (codesign) methods that enable the integration of energy management optimization along with vehicle sizing are required. Even though some studies have explored such methods, they have done so in a computationally intensive nested formulation with limited depth on the design and control modeling aspect. This paper addresses these issues by posing the system codesign problem using a simultaneous formulation of multidisciplinary dynamic system design optimization (MDSDO). The simultaneous formulation generally facilitates superior computational performance while the MDSDO method solves codesign problems from a more balanced perspective between design- and control-related variables. This method is applied in this paper to aircraft HEPS design with an objective of determining the optimal propulsion component designs and supervisory control strategy that minimizes total energy consumption. The hybrid configuration is compared with its conventional counterpart on the basis of system efficiency. In addition to that, a parametric study on the battery energy density is presented to explore the near-term viability of HEPS for aircraft.
Although Cuckoo Search (CS) is a quite new nature-inspired metaheuristic optimization algorithm, it has been extensively used in engineering applications, since it has been proven very efficient in solving complex nonlinear problems. In this paper, efficient modifications have been made to the original CS algorithm to enhance its efficiency and robustness. More specifically, constant parameters of the algorithm, such as the probability of the alien egg being discovered by the host bird and the step size of Levy flights have been dynamically tuned. In addition, static and dynamic penalty functions are introduced within the optimization formulation. Finally, a hybrid optimization approach is developed to combine the advantages of CS with those of Bird Swarm Algorithm (BSA). Benchmark problems, widely used in relevant studies, have been solved and the obtained solutions are compared with those previously reported using the standard CS algorithm and other popular evolutionary optimization techniques (i.e., Genetic Algorithms, Particle Swarm Optimization, etc.).
Many-objective optimisation is a design problem, having more than 3 objective functions, which is found to be difficult to solve. Implementation of such optimisation on aircraft conceptual design will greatly benefit a design team, as a great number of trade-off design solutions are provided for further decision making. In this paper, a many-objective optimisation problem for an unmanned aerial vehicle (UAV) is posed with 6 objective functions: take-off gross weight, drag coefficient, take off distance, power required, lift coefficient and endurance subject to aircraft performance and stability constraints. Aerodynamic analysis is carried out using a vortex lattice method, while aircraft component weights are estimated empirically. A new self-adaptive meta-heuristic based on decomposition is specifically developed for this design problem. The new algorithm along with nine established and recently developed multi-objective and many-objective meta-heuristics are employed to solve the problem, while comparative performance is made based upon a hypervolume indicator. The results reveal that the proposed optimiser is the best performer for this design task.
While vertical takeoff and landing aircraft have shown promise for urban air transport, distributed electric propulsion on existing aircraft may offer immediately implementable alternatives. Distributed electric propulsion could potentially decrease takeoff distances enough to enable thousands of potential intercity runways. This conceptual study explores the effects of a retrofit of open-bladed electric propulsion units. To model and explore the design space, blade element momentum method, vortex lattice method, linear-beam finite element analysis, classical laminate theory, composite failure, empirically based blade noise modeling, motor and motor-controller mass models, and gradient-based optimization are used. With liftoff time of seconds and the safe total field length for this aircraft type undefined, this paper focused on the minimum conceptual takeoff distance. It was found that 16 propellers could reduce the takeoff distance by over 50% compared with the optimal 2-propeller case. This resulted in a conceptual minimum takeoff distance of 20.5 m to clear a 50 ft (15.24 m) obstacle. It was also found that, when decreasing the allowable noise by approximately 10 dBa, the 8-propeller case performed the best with a 43% reduction in takeoff distance compared with the optimal 2-propeller case. This resulted in a noise-restricted conceptual minimum takeoff distance of 95 m.
Purpose
The purpose of this paper is to mount Gurney flaps at the trailing edges of the canards and investigate their influence on aerodynamic characteristics of a simplified canard-configuration aircraft model.
Design/methodology/approach
A force measurement experiment was conducted in a low-speed wind tunnel. Hence, the height and shape effects of the Gurney flaps on the canards were investigated.
Findings
Gurney flaps can increase the lift and pitching-up moment for the aircraft model tested, thereby increasing the lift when trimming the aircraft. The dominant parameter to influence aerodynamic characteristics is the height of Gurney flaps. When the flap heights are the same, the aerodynamic efficiency of the triangular Gurney flaps is higher than that of the rectangular ones. Moreover, the canard deflection efficiency will be reduced with Gurney flaps equipped, but the total aerodynamic increment is considerable.
Practical implications
This paper helps to solve the key technical problem of increasing take-off and landing lift coefficients, thus improving the aerodynamic performance of the canard-configuration aircraft.
Originality/value
This paper recommends to adopt triangular Gurney flaps with the height of 3 per cent chord length of the canard root ( c ) for engineering application.
Multiple objective structural optimization is well known as a highly nonlinear and challenging problem in which suitable optimization methods are needed to find optimal solutions. Therefore, to answer such problems effectively, a multi-objective modified adaptive symbiotic organisms search (MOMASOS) with two modified phases is proposed for solving structural optimization problems. The proposed algorithm consists of two separate improved phases including adaptive mutualism and modified parasitism phases. The probabilistic nature of mutualism phase of MOSOS lets design variables to have higher exploration and higher exploitation of the search space. As search advances, a good balance between global exploration and local exploitation has a significant effect on the results. Therefore, adaptive mutualism phase is now added to the propose MOASOS. Also, the parasitism phase of MOSOS offers over exploration which is a major issue of this phase. The over exploration results in higher computational cost as majority of the new solutions gets rejected due to inferior objective functional values. In consideration of this issue, the parasitism phase is upgraded to a modified parasitism phase to increase the possibility of getting improved solutions. In addition, the proposed changes are comparatively simple and do not need an extra parameter setting for MOSOS.
For the truss problems, mass minimization and maximization of nodal deflection are considered as objective functions whereas elemental stress and discrete elemental sections are considered to be behavior and side constraints respectively. The effectiveness of the proposed algorithms to solve complex engineering problems is validated by five truss optimization problems. The results confirmed that the proposed adaptive mutualism phase and modified parasitism phase provide better and competitive solutions than the previous studies.
The paper introduces artificial intelligence (AI) approach for the optimization study of a hydrogen concentrations in syngas via CaO Sorption. The performed model allows estimating the hydrogen content in the syngas produced from biomass in different types of facilities. Bubbling fluidized bed (FB) and circulating fluidized bed (CFB) are taken into account in the study. Comparing with the previous FB gasification results, CFB gasification was capable of producing syngas with higher hydrogen concentration.
The model considers and covers a broad range of conditions, influencing the hydrogen rich-gas production. The presented non-iterative approach gives quick and accurate results as an answer to the input data sets. The H2 concentration in the gas, estimated using the developed model, is in good agreement with the experimental data. Maximum relative error between measured and calculated data is lower than ±8%.
The model allows also studying the influence of operating parameters on the hydrogen concentration in the gas. The method constitutes an easy to employ and useful complementary technique in relation to the other ways of data handling, including experimental procedures. The model can be used by scientists and engineers for optimizations purposes and can be applied as a submodel or a separate module in engineering calculations, capable to predict the H2 concentration in the syngas from biomass via the CaO sorption both in FB and CFB gasifiers.
Due to increased search complexity in multi-objective optimization, premature convergence becomes a problem. Complex engineering problems poses high number of variables with many constraints. Hence, more difficult benchmark problems must be utilized to validate new algorithms performance. A well-known optimizer, Multi-Objective Particle Swarm Optimizer (MOPSO), has a few weakness that needs to be addressed, specifically its convergence in high dimensional problems and its constraints handling capability. For these reasons, we propose a modified MOPSO (M-MOPSO) to improve upon these aspects. M-MOPSO is compared with four other algorithms namely, MOPSO, Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Evolutionary Algorithm based on Decompositions (MOEA/D) and Multi-Objective Differential Evolution (MODE). M-MOPSO emerged as the best algorithm in eight out of the ten constrained benchmark problems. It also shows promising results in bioprocess application problems and tumor treatment problems. In overall, M-MOPSO was able to solve multi-objective problems with good convergence and is suitable to be used in real world problem.
Box-wing aircraft designs have the potential to achieve significant reductions in fuel consumption. Closed non-planar wing designs have been shown to reduce induced drag and the statically indeterminate wing structure can lead to reduced wing weight. In addition, the streamwise separation of the two main wings can provide the moments necessary for static stability and control, eliminating the weight and aerodynamic drag of a horizontal tail. Proper assessment of the disciplinary interactions in box-wing designs is essential to determine any realistic performance benefits arising from the use of such a configuration. This study analyzes both box-wing and conventional aircraft designed for representative regional-jet missions. A preliminary parametric investigation shows a lift-to-drag ratio advantage for box-wing designs, while a more detailed multidisciplinary study indicates that the requirement to carry the mission fuel in the wings leads to an increase of between 5% and 1% in total fuel burn compared to conventional designs. However, the multidisciplinary study identified operating conditions where the box-wing can have superior performance to conventional aircraft despite the fuel volume constraint.
Optimal design of an Autonomous Underwater Vehicle (AUV) consists of various subsystems and disciplines such as guidance and control, payload, hydrodynamics, power and propulsion, sizing, structure, trajectory and performance. The designed vehicle is also employed in an operational environment with tactical parameters such as distance to target, uncertainty in estimation of target position and target velocity. Multidisciplinary Design Optimization (MDO) is the best way for finding both optimum and feasible designs. In this paper, a new optimization design framework is proposed in which Multidisciplinary Feasible (MDF) as MDO framework and Particle Swarm Optimization (PSO) as optimizer were combined together for optimal and feasible conceptual design of an AUV. Initially, we found an optimal system design by using MDF-PSO methodology in engineering space for any single tactical situation (locally tactical parameters). Then the optimal off-design AUVs in tactical subspaces were found by minimizing the difference between the locally optimized objective function and sub-optimal objective function. In this framework, we have shown that not only is the tactical situation affected by AUV design parameters, but an optimal AUV for each tactical regions are also found.
The current work is an aerodynamic design study of a Blended Wing Body (BWB) Medium-Altitude-Long-Endurance (MALE) Unmanned-Aerial-Vehicle (UAV). Using a combined approach of presizing tools and computational simulations, a step-by-step layout design study was conducted to define the key layout characteristics and select the optimal airframe-engine combination. Trade studies were also carried out to optimize the aerodynamic performance and stability. The traditional sizing and aerodynamic estimation methods were adopted to incorporate the characteristics of the BWB platform, whereas CFD computations were employed in order to calculate the aerodynamic and stability coefficients, during the layout comparison and trade studies. Drawings and tables are provided to show the progression of the design study at each stage. The performance specifications are also compared with a conventional UAV platform to point out the main advantages and disadvantages of the BWB for MALE UAV applications.
This study investigates the potential of unconventional aircraft transports through numerical optimization. Three distinct configurations are investigated: a box wing, a C-tip blended wing-body, and a braced wing. Each transport is sized for the same regional mission and is subjected to the same optimization strategy based on the Euler equations. The figure of merit is inviscid pressure drag at transonic speed; the nonlinear constraints are lift, pitching moment, and internal volume. The design variables include the section shape and twist distribution of the main lifting surfaces. It is found that the box-wing, C-tip blended-wing-body, and braced-wing configurations investigated here are, respectively, 34.1, 36.2, and 40.3% more efficient than a similarly optimized conventional tube-and-wing configuration. Each optimization revealed, in one way or another, the importance of accounting for flow nonlinearity during the early stages of unconventional aircraft design. For the blended wing-body, the C tip does not appear to provide a drag benefit over a purely vertical winglet, presumably as a result of the compressibility effects prevalent in the C opening. For the braced wing, compressibility effects also lead to a curious result, where the supporting strut finds itself carrying negative lift at the optimum.
In the present study the aerodynamic design procedure of a Medium-Altitude-Long-Endurance (MALE) Unmanned-Aerial-Vehicle (UAV) is presented. The procedure is broken down into the conceptual and preliminary design phases. For the conceptual design, four groups worked with a common roadmap and developed four presizing tools and four different configurations, based on the same mission requirements. Following an evaluation procedure and merging process, a single design concept was eventually developed, which served as the basis for the preliminary design phase. Considering the preliminary design phase, emphasis was given on the aerodynamic aspects of the study. Namely, the fuselage design, wing design, stability and control study, empennage design, and the winglet design optimization technique, as well as the inlets sizing and cooling study, are all included in this work. The analytical calculations and methods are presented at each step of the study, whereas the CFD supportive computations are also shown in detail. The UAV final concept, at the end of the aerodynamic study, together with the main geometric, aerodynamic, stability and performance parameters, is presented and discussed.
A comprehensive approach to the air vehicle design process using the principles of systems engineering Due to the high cost and the risks associated with development, complex aircraft systems have become a prime candidate for the adoption of systems engineering methodologies. This book presents the entire process of aircraft design based on a systems engineering approach from conceptual design phase, through to preliminary design phase and to detail design phase. Presenting in one volume the methodologies behind aircraft design, this book covers the components and the issues affected by design procedures. The basic topics that are essential to the process, such as aerodynamics, flight stability and control, aero-structure, and aircraft performance are reviewed in various chapters where required. Based on these fundamentals and design requirements, the author explains the design process in a holistic manner to emphasise the integration of the individual components into the overall design. Throughout the book the various design options are considered and weighed against each other, to give readers a practical understanding of the process overall. Readers with knowledge of the fundamental concepts of aerodynamics, propulsion, aero-structure, and flight dynamics will find this book ideal to progress towards the next stage in their understanding of the topic. Furthermore, the broad variety of design techniques covered ensures that readers have the freedom and flexibility to satisfy the design requirements when approaching real-world projects. Key features: Provides full coverage of the design aspects of an air vehicle including: aeronautical concepts, design techniques and design flowcharts Features end of chapter problems to reinforce the learning process as well as fully solved design examples at component level Includes fundamental explanations for aeronautical engineering students and practicing engineers.
Introduction Primary Functions of Aircraft Components Aircraft Configuration Alternatives Aircraft Classification and Design Constraints Configuration Selection Process and Trade-Off Analysis Conceptual Design Optimization Problems References
Due to the novelty of the Grey Wolf Optimizer (GWO), there is no study in the literature to design a multi-objective version of this algorithm. This paper proposes a Multi-Objective Grey Wolf Optimizer (MOGWO) in order to optimize problems with multiple objectives for the first time. A fixed-sized external archive is integrated to the GWO for saving and retrieving the Pareto optimal solutions. This archive is then employed to define the social hierarchy and simulate the hunting behavior of grey wolves in multi-objective search spaces. The proposed method is tested on 10 multi-objective benchmark problems and compared with two well-known meta-heuristics: Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) and Multi-Objective Particle Swarm Optimization (MOPSO). The qualitative and quantitative results show that the proposed algorithm is able to provide very competitive results and outperforms other algorithms. Note that the source codes of MOGWO are publicly available at http://www.alimirjalili.com/GWO.html.
Having developed multiobjective optimization algorithms using evolutionary optimization methods and demonstrated their niche on various practical problems involving mostly two and three objectives, there is now a growing need for developing evolutionary multiobjective optimization (EMO) algorithms for handling many-objective (having four or more objectives) optimization problems. In this paper, we recognize a few recent efforts and discuss a number of viable directions for developing a potential EMO algorithm for solving many-objective optimization problems. Thereafter, we suggest a reference-point-based many-objective evolutionary algorithm following NSGA-II framework (we call it NSGA-III) that emphasizes population members that are nondominated, yet close to a set of supplied reference points. The proposed NSGA-III is applied to a number of many-objective test problems with three to 15 objectives and compared with two versions of a recently suggested EMO algorithm (MOEA/D). While each of the two MOEA/D methods works well on different classes of problems, the proposed NSGA-III is found to produce satisfactory results on all problems considered in this paper. This paper presents results on unconstrained problems, and the sequel paper considers constrained and other specialties in handling many-objective optimization problems.