Typical Design Problems Research Articles

Intelligent design of multi-layered variable stiffness composite structure based on transfer learning

Variable stiffness composite structures offer more flexible design space than thin-walled metal structures and have greater potential for vibration-resistant design. When faced with multiple new types of design problems, the complex modelling and analysis procedures frequently prove to be both time-consuming and costly in terms of optimization. In this study, an innovative multi-layered variable stiffness (MVS) composite structure with high design flexibility is proposed, with images representation for curvilinearly stiffened paths, non-uniform layouts, and fiber and layup angles. Moreover, an intelligent optimization method based on transfer learning is proposed for addressing a variety of factors affecting dynamic design, including boundary types, structural features, and dynamic responses. The objective of the transfer learning model is to facilitate the inheritance and sharing of variable stiffness features, thereby enabling the efficient design of new problems with limited datasets. The validation of different examples shows that the transfer learning can effectively acquire the structural features from the existing source domain datasets, thereby significantly reducing the data for some new target domains by approximately 50 %. In comparison to the initial constant stiffness (CS) structures, the different optimized configurations indicate that the MVS composite structures are capable of effectively enhancing the dynamic responses by 10 %∼146 % for natural frequency and dynamic compliance. Furthermore, the MVS optimized configuration displays superior dynamic responses in some problems, when compared to the CS optimized configuration.

Analysis of Software Patterns to resolve Design Problems

A reproducible solution to a frequently occurring problem in software design was described as a software pattern, also known as a design pattern. It was a tested method for resolving kinds of software design issues that had been classified and identified by several years of experience and best practices. The key to making safe and accessible software was good software design. There were different types of design problems. Solving those problems was very important to produce good and efficient software. There were some tools, techniques, and patterns to solve these problems. Software design knowledge could be effectively captured and communicated using design patterns. Within a particular context of software design, a software design pattern was a standardized, repeatable solution to a frequently occurring problem. The patterns were effective for software design on several levels. In this paper, we identified the major design problems. Then we have analyzed those problems and present design patterns to develop an effective software design.

PSAO: An enhanced Aquila Optimizer with particle swarm mechanism for engineering design and UAV path planning problems

Metaheuristic algorithms have become increasingly significant in solving complex optimization problems. To address the limitations of the original Aquila Optimizer (AO), such as insufficient local exploitation ability, low optimization precision, and slow convergence rate, an enhanced Aquila Optimizer (PSAO) for global optimization has been proposed. PSAO uses a better ergodic good point set to initialize the Aquila population and modifies the search method by employing the golden sine operator and the mechanism of self-learning and social learning based on particle swarm, followed by designing a nonlinear balance factor γ as the switching condition of the algorithm. The simulation experiments on benchmark functions and CEC2017 functions have verified that the PSAO has better global optimization ability and stronger robustness compared with other intelligent algorithms. Meanwhile, the contribution of each component that belongs to PSAO has been validated by ablation experiments for the CEC2022 test functions. To further illustrate the practical application potential of PSAO, PSAO is successfully applied to four typical engineering design problems, three of which reach the best fitness values. In addition, utilizing PSAO to solve the UAV trajectory planning problem by considering the objectives of trajectory length, altitude and corner of the flight process, the total flight cost is reduced by 60.22 %, 27.94 %, and 22.41 % compared with AO, PSO and Gold-SA, respectively, which proves its applicability and superiority in solving the real optimization problems.

Exact approaches to solve the Transmission Expansion Planning Problem with Re-design

Expanding electrical transmission networks is a critical challenge faced by many developing economies. This process requires extensive investments, which must be strategically and methodically planned on a large scale, often encompassing regional or national considerations. Differing from typical Network Design Problems, efficiency in a transmission network can sometimes be enhanced by strategically deactivating certain transmission lines. Thanks to that, this paper focuses on a version of the transmission expansion planning problem that allows redesign during the network expansion. We propose using two exact methodologies as alternatives to the direct use of mixed integer programming formulations with commercial solvers. These methods are an application of Benders’ decomposition and a newly developed approach named Ring Space. Through comprehensive computational experiments, we assess and compare the efficacy of these methods against the conventional application of the mathematical formulation. Additionally, this study successfully obtained new high-quality solutions and proved their optimality for a subset of benchmark instances.

Efficient Electromagnetic Compatibility Optimization Design Based on the Stochastic Collocation Method

Nowadays, in the field of electromagnetic compatibility (EMC), numerical methods such as finite element analysis are often used for simulation analysis. These numerical methods take a long time to solve some complex simulation problems, which is not conducive to the optimal design of EMC. In particular, the intelligent optimization algorithm that needs continuous iterative calculation will not be realized because of the long optimization time. This paper realizes the innovative application of the uncertainty analysis method (Stochastic Collocation Method) in EMC optimization design. Two typical EMC optimization design problems, namely, the prediction of cable crosstalk and the design of shielding performance of metal boxes, are proposed to verify the effectiveness of the optimization algorithm. Meanwhile, its performance is compared with the classical intelligent optimization algorithms such as genetic algorithms and immune algorithms.

Three-dimensional freeform reflector design with a scattering surface.

We introduce an approach to calculating three-dimensional freeform reflectors with a scattering surface. Our method is based on optimal transport and utilizes a Fredholm integral equation to express scattering. By solving this integral equation through a process analogous to deconvolution, we can recover a typical specular design problem. Consequently, we consider freeform reflector design with a scattering surface as a two-step process wherein the target distribution is first altered to account for scattering, and then the resulting specular problem is solved. We verify our approach using a custom raytracer that implements the surface scattering model we used to derive the Fredholm integral.

Sine cosine algorithm with communication and quality enhancement: Performance design for engineering problems

Abstract In recent years, the sine cosine algorithm (SCA) has become one of the popular swarm intelligence algorithms due to its simple and convenient structure. However, the standard SCA tends to fall into the local optimum when solving complex multimodal tasks, leading to unsatisfactory results. Therefore, this study presents the SCA with communication and quality enhancement, called CCEQSCA. The proposed algorithm includes two enhancement strategies: the communication and collaboration strategy (CC) and the quality enhancement strategy (EQ). In the proposed algorithm, CC strengthens the connection of SCA populations by guiding the search agents closer to the range of optimal solutions. EQ improves the quality of candidate solutions to enhance the exploitation of the algorithm. Furthermore, EQ can explore potential candidate solutions in other scopes, thus strengthening the ability of the algorithm to prevent trapping in the local optimum. To verify the capability of CCEQSCA, 30 functions from the IEEE CEC2017 are analyzed. The proposed algorithm is compared with 5 advanced original algorithms and 10 advanced variants. The outcomes indicate that it is dominant over other comparison algorithms in global optimization tasks. The work in this paper is also utilized to tackle three typical engineering design problems with excellent optimization capabilities. It has been experimentally demonstrated that CCEQSCA works as an effective tool to tackle real issues with constraints and complex search space.

Creativity of Neural Networks: Risks and Opportunities for Modern Designers

In connection with the large-scale development of artificial intelligence in all spheres of human activity, the article discusses the problems and prospects for the use of neural networks in the work of professional designers. The aim of the study is to identify risks and find optimal directions for the use of neural networks in graphic and fashion design based on the advantages of artificial intelligence. The objectives of the study include the analysis of the work and types of artificial neural networks, the search and selection of neural networks for this study, the development of queries for neural networks in accordance with the goal, the analysis and formulation of strengths and weaknesses in the work of neural networks for their application in the field of design. The basis of the functioning of neural networks is the principle of constructing a unique image based on the selection and generation of a large number of ready-made images uploaded to the database, created by professional designers from around the world. Since the neural network is an adapted biological model of the neural network, it is capable of remembering its own errors in the process of work and their subsequent correction, that is capable of learning. The neural network is trained using special machine learning methods. The following conclusions can be formulated as prospects for using neural networks for design. The lack of the ability of neural networks to cultivate their own author’s style and the impossibility of developing innovations in design does not allow to completely replace human labor. Work with the use of neural networks will be more efficient due to the intensification of solving typical design problems and freeing up time for the designer to create and improve new ideas.

The Grey Wolf Optimizer for Antenna Optimization Designs: Continuous, binary, single-objective, and multiobjective implementations

The grey wolf optimizer (GWO) is a newly invented metaheuristic that simulates the hunting process of grey wolves in nature. As a robust optimization technique, the GWO engine has the capacity of handling antenna optimization problems with both continuous and binary variables and single and multiple objectives. In this article, the GWO and its binary (BGWO) version are introduced first. Their multiobjective versions, i.e., (MOGWO) and (MOBGWO), respectively, follow. To show the versatility of the GWO engine, some typical antenna optimization design problems are considered. In particular, a low-sidelobe sparse linear array and a high-directivity Yagi–Uda antenna are optimized by continuous GWO (CGWO); a thinned planar array is designed by a BGWO for sidelobe suppression in the two principal planes. To evaluate the performance of the GWO engine, comparative studies of the GWO with two popular optimization algorithms, i.e., a genetic algorithm (GA) and particle swarm optimization (PSO), are presented. It turns out that the GWO can, in most cases, outperform a GA and PSO. Further, these examples are expanded to consider more than one objective, and multiobjective versions of CGWO and BGWO, respectively, are employed to obtain the Pareto fronts, which clearly show the best tradeoffs that can be made.

Improved Slime Mold Algorithm with Dynamic Quantum Rotation Gate and Opposition-Based Learning for Global Optimization and Engineering Design Problems

The slime mold algorithm (SMA) is a swarm-based metaheuristic algorithm inspired by the natural oscillatory patterns of slime molds. Compared with other algorithms, the SMA is competitive but still suffers from unbalanced development and exploration and the tendency to fall into local optima. To overcome these drawbacks, an improved SMA with a dynamic quantum rotation gate and opposition-based learning (DQOBLSMA) is proposed in this paper. Specifically, for the first time, two mechanisms are used simultaneously to improve the robustness of the original SMA: the dynamic quantum rotation gate and opposition-based learning. The dynamic quantum rotation gate proposes an adaptive parameter control strategy based on the fitness to achieve a balance between exploitation and exploration compared to the original quantum rotation gate. The opposition-based learning strategy enhances population diversity and avoids falling into the local optima. Twenty-three benchmark test functions verify the superiority of the DQOBLSMA. Three typical engineering design problems demonstrate the ability of the DQOBLSMA to solve practical problems. Experimental results show that the proposed algorithm outperforms other comparative algorithms in convergence speed, convergence accuracy, and reliability.

A Hybrid Level Set Method for the Topology Optimization of Functionally Graded Structures

This paper presents a hybrid level set method (HLSM) to design novelty functionally graded structures (FGSs) with complex macroscopic graded patterns. The hybrid level set function (HLSF) is constructed to parametrically model the macro unit cells by introducing the affine concept of convex optimization theory. The global weight coefficients on macro unit cell nodes and the local weight coefficients within the macro unit cell are defined as master and slave design variables, respectively. The local design variables are interpolated by the global design variables to guarantee the C0 continuity of neighboring unit cells. A HLSM-based topology optimization model for the FGSs is established to maximize structural stiffness. The optimization model is solved by the optimality criteria (OC) algorithm. Two typical FGSs design problems are investigated, including thin-walled stiffened structures (TWSSs) and functionally graded cellular structures (FGCSs). In addition, additively manufactured FGCSs with different core layers are tested for bending performance. Numerical examples show that the HLSM is effective for designing FGSs like TWSSs and FGCSs. The bending tests prove that FGSs designed using HLSM are have a high performance.

EVALUATING THE INFLUENCE OF REGIONAL FACTORS ON THE DESIGN QUALITY OF TRUSSED STEEL TOWERS

This paper researches on typical design problem of steel tower truss structure. Assess the advantages and disadvantages of the typical steel tower truss design, which has been widely used in Vietnam. Use specialized software to calculate and evaluate the safety and usability of a typical design. From there, proceed to adjust the design parameters in the direction of saving materials, easy processing and erection. Thereby, evaluate the effectiveness of the typical design adjustment plan. Give comment and recommendations for similar structures.

A boosted chimp optimizer for numerical and engineering design optimization challenges.

Chimp optimization algorithm (ChoA) has a wholesome attitude roused by chimp’s amazing thinking and hunting ability with a sensual movement for finding the optimal solution in the global search space. Classical Chimps optimizer algorithm has poor convergence and has problem to stuck into local minima for high-dimensional problems. This research focuses on the improved variants of the chimp optimizer algorithm and named as Boosted chimp optimizer algorithms. In one of the proposed variants, the existing chimp optimizer algorithm has been combined with SHO algorithm to improve the exploration phase of the existing chimp optimizer and named as IChoA-SHO and other variant is proposed to improve the exploitation search capability of the existing ChoA. The testing and validation of the proposed optimizer has been done for various standard benchmarks and Non-convex, Non-linear, and typical engineering design problems. The proposed variants have been evaluated for seven standard uni-modal benchmark functions, six standard multi-modal benchmark functions, ten standard fixed-dimension benchmark functions, and 11 types of multidisciplinary engineering design problems. The outcomes of this method have been compared with other existing optimization methods considering convergence speed as well as for searching local and global optimal solutions. The testing results show the better performance of the proposed methods excel than the other existing optimization methods.

Adaptive active subspace-based efficient multifidelity materials design

Materials design calls for an optimal exploration and exploitation of the process-structure-property (PSP) relationships to produce materials with targeted properties. Recently, we developed and deployed a closed-loop multi-information source fusion (multi-fidelity) Bayesian Optimization (BO) framework to optimize the mechanical performance of a dual-phase material by adjusting the material composition and processing parameters. While promising, BO frameworks tend to underperform as the dimensionality of the problem increases. Herein, we employ an adaptive active subspace method to efficiently handle the large dimensionality of the design space of a typical PSP-based material design problem within our multi-fidelity BO framework. Our adaptive active subspace method significantly accelerates the design process by prioritizing searches in the important regions of the high-dimensional design space. A detailed discussion of the various components and demonstration of three approaches to implementing the adaptive active subspace method within the multi-fidelity BO framework is presented.

Integrating simulation into design: an experiment in pedagogical environments

With the growing complexity of design technology and the emergence of intelligent design assistance, architectural studio classes are facing a new pedagogical paradigm. The digital literacy of younger generations and availability of scientific simulation have the potential to transform the traditional master–apprentice model. In our experiment, we had students perform a museum layout task and observed their behaviors from three perspectives: (1) how students utilize an assistance tool and whether we can group their behavior, (2) how the new simulation-aided design process is different from a traditional one, particularly in terms of the evolution of solution over iteration, and (3) whether students' behavior is affected by the type of design problem given. Protocol analysis on design processes and interviews revealed that individual’s characteristic design processes in terms of iteration ranged from distinct iterations guided by simulation to monotonous progress with little simulation. When comparing between an instructor and software, it was not the given environment but the designer’s subjectivity that determined their attitudes toward either type of feedback. Lastly, the challenge of integrating design concepts with performance requirements stemmed from a misalignment between their true evaluative measures. We propose that a versatile design platform implement real-time, non-intrusive mechanisms for performance reporting and solution branching, and include social and psychological measures as well as physical ones in order to expand designers’ concept choices.

A study on correlations between architectural smells and design patterns

Design patterns are recommended solutions for typical software design problems, with an extensively studied and documented impact on various quality factors. Flaws in design at a higher levels of abstraction are manifested in architectural smells. Some of those smells, similarly to code smells, can reduce the expected advantages of design patterns or even prevent their proper implementation. In this paper we study if and how design patterns and architectural smells are related, and how this knowledge could be exploited in practice. We present an empirical study with an analysis of 16 design patterns and 3 architectural smells in 60 open source Java systems. We analyze their diffuseness and correlation, and we extract association rules that describe their presence and dependencies. We demonstrate that there exist relationships between architectural smells and design patterns, both at the class and package levels. Some smells appear falsely positive, as they result from conscious decisions made by programmers, while the application of some patterns can be a cause of certain smells. Our results provide evidence that design patterns and architectural smells are related and affect each other. With knowledge about the relationships, programmers can avoid the side effects of applying some design patterns.

Technology enhanced learning of quantitative critical thinking

A Quantitative Critical Thinking (QCT) software tool was developed in this study to facilitate students’ learning of quantitative critical thinking via repeated practice by chemical engineering students reading a core module called fluid-solid systems. The software tool generated detailed calculation steps to typical engineering design problems encountered in this module that contained weaknesses, flaws or even errors. Students utilized the software tool to practice identifying these weaknesses, flaws or errors in the design solutions and then present a better or correct design by applying the concepts and knowledge acquired in the module. Since the QCT software tool was built upon an existing design software tool that was able to generate the correct, detailed design calculation steps to design problems, students were able to check their own design calculations against those presented by the software tool during this second learning step, thereby engaging in and learning quantitative critical thinking via a repeated practice approach. The software tool was successful in enhancing the performance of second-year undergraduate students in solving a question that required quantitative critical thinking in the final examination of the module. The average percentage scores achieved by students for the question who reported higher frequencies of usage of the software were generally higher than those who reported lower frequencies of usage or did not utilize the software tool throughout the semester.

Method for optimizing the number of glass-fiber reinforced plastic rebars in concrete structures

Innovations in the construction industry have led to a change in the technological structure, the use of end-to-end technologies, innovative materials, information management and the need for new technical, technological and managerial solutions. The research problem is the optimal placement of glass-fiber reinforced plastic bars in concrete structures. It took several steps to solve the research problem: 1) development of an algorithm for solving the design problem in TEKLA Structures; 2) performing of calculations based on the finite element method in LIRA-SAPR, which were further reproduced in Rhinoceros and Grasshopper (based on interoperability in a single BIM environment); 3) implementation of the optimization algorithm using the parametric optimization method; 4) A practical calculation of determining the optimal values of the loads of glass-fiber reinforcement in the slab on the example of a typical design problem of a structural element of a construction facility; 5) calculation of positive economic effect. The conclusion is that the algorithm can be used in the practice of building design.

Smooth Design of 3D Self-Supporting Topologies Using Additive Manufacturing Filter and SEMDOT

The smooth design of self-supporting topologies has attracted great attention in the design for additive manufacturing (DfAM) field as it cannot only enhance the manufacturability of optimized designs but can obtain light-weight designs that satisfy specific performance requirements. This paper integrates Langelaar’s AM filter into the Smooth-Edged Material Distribution for Optimizing Topology (SEMDOT) algorithm—a new element-based topology optimization method capable of forming smooth boundaries—to obtain print-ready designs without introducing post-processing methods for smoothing boundaries before fabrication and adding extra support structures during fabrication. The effects of different build orientations and critical overhang angles on self-supporting topologies are demonstrated by solving several compliance minimization (stiffness maximization) problems. In addition, a typical compliant mechanism design problem—the force inverter design—is solved to further demonstrate the effectiveness of the combination between SEMDOT and Langelaar’s AM filter.

Optimizing Topology and Fiber Orientations With Minimum Length Scale Control in Laminated Composites

Abstract Discrete material optimization (DMO) has proven to be an effective framework for optimizing the orientation of orthotropic laminate composite panels across a structural design domain. The typical design problem is one of maximizing stiffness by assigning a fiber orientation to each subdomain, where the orientation must be selected from a set of discrete magnitudes (e.g., 0 deg, ±45 deg, 90 deg). The DMO approach converts this discrete problem into a continuous formulation where a design variable is introduced for each candidate orientation. Local constraints and penalization are then used to ensure that each subdomain is assigned a single orientation in the final solution. The subdomain over which orientation is constant is most simply defined as a finite element, ultimately leading to complex orientation layouts that may be difficult to manufacture. Recent literature has introduced threshold projections commonly used in density-based topology optimization into the DMO approach in order to influence the manufacturability of solutions. This work takes this idea one step further and utilizes the Heaviside projection method within DMO to provide formal control over the minimum length scale of structural features, holes, and patches of uniform orientation. The proposed approach is demonstrated on benchmark maximum stiffness design problems, and numerical results are near discrete with strict length scale control, providing a direct avenue to controlling the complexity of orientation layouts. This ultimately suggests that projection-based methods can play an important role in controlling the manufacturability of optimized material orientations.