Engineering Design Examples Research Articles

Second-order reliability analysis of an energy pile with CPT data

As pile foundations and ground source heat producers, energy piles are becoming more and more popular. Even after much research, engineers find their design to be challenging. An effective way for using the Second-Order Reliability method (SORM) in foundation engineering scenarios including explicit limit state functions is shown in this article. The suggested SORM process is simple to implement in a spreadsheet and is based on an approximate paraboloid fitted to the limit state surface close to the design point. Using well-established closed-form SORM formulae, the failure probability is automatically determined using the primary curvatures of the limit state surface and the reliability index. An energy pile foundation engineering design examples are analyzed using the proposed method and discussed. Comparison with FORM, SORM and Genetic Programming (GP)-based FORM and SORM are made. In the case of determination of group capacity of pile and thermal load the input random variables are qc0, qc1, qc2, fs and E. The reliability index and probability of failure value are found to be 9.34 for FORM, 9.55 for SORM, 9.24 for GP-based FORM, and 9.45 for GP-based SORM. The probability of failure value for FORM 4.60E-21, SORM 3.24E-21, GP-based FORM 1.20E-20, and GP-based SORM is 8.50E-21. The relative percentage error for the average second-order approximation corresponding to FORM is also calculated, and found that Pf2 is 29.48 % in the case of simple SORM and 29.15 % in the case of GP-based SORM. The Breitung provides the most accurate estimation, and the Cai_Elishakoff provides the least accurate estimate.

Hybrid beluga whale optimization algorithm with multi-strategy for functions and engineering optimization problems

Beluga Whale Optimization (BWO) is a new metaheuristic algorithm that simulates the social behaviors of beluga whales swimming, foraging, and whale falling. Compared with other optimization algorithms, BWO shows certain advantages in solving unimodal and multimodal optimization problems. However, the convergence speed and optimization performance of BWO still have some performance deficiencies when solving complex multidimensional problems. Therefore, this paper proposes a hybrid BWO method called HBWO combining Quasi-oppositional based learning (QOBL), adaptive and spiral predation strategy, and Nelder-Mead simplex search method (NM). Firstly, in the initialization phase, the QOBL strategy is introduced. This strategy reconstructs the initial spatial position of the population by pairwise comparisons to obtain a more prosperous and higher quality initial population. Subsequently, an adaptive and spiral predation strategy is designed in the exploration and exploitation phases. The strategy first learns the optimal individual positions in some dimensions through adaptive learning to avoid the loss of local optimality. At the same time, a spiral movement method motivated by a cosine factor is introduced to maintain some balance between exploration and exploitation. Finally, the NM simplex search method is added. It corrects individual positions through multiple scaling methods to improve the optimal search speed more accurately and efficiently. The performance of HBWO is verified utilizing the CEC2017 and CEC2019 test functions. Meanwhile, the superiority of HBWO is verified by utilizing six engineering design examples. The experimental results show that HBWO has higher feasibility and effectiveness in solving practical problems than BWO and other optimization methods.

Adaptive surrogate assisted multi-objective optimization approach for highly nonlinear and complex engineering design problems

Despite enormous advances in computer power, computationally costly models impede the use of traditional optimization approaches that must be invoked repeatedly during the optimization process in practical engineering applications. Surrogate models have been found to be a promising endeavor in multi-objective optimization problems involving expensive analysis and simulation processes such as multi-physics modeling and simulation, finite element analysis (FEA), and computational fluid dynamics (CFD. Developing an optimization algorithm that can easily identify the Pareto frontier of highly nonlinear multi-objective optimization problems with less computation cost is the aim of this work. In this paper, an Adaptive Multi-Objective Optimization approach based Surrogate models (AMOS) is developed to reduce computation cost of fitness evaluations and discover the Pareto optima for multi-objective optimization problems with comparable high accuracy. AMOS explores the design space by sampling using LHD to identify promising regions. Then, AMOS exploits the identified promising region by adaptively constructing the most suitable surrogate model, which could be response surface, radial basis, or Kriging surrogates, in the feasible design space based on root mean square error values (RMSE). AMOS stops iterating when a termination criterion is met, and a Pareto frontier is identified based on developed guidance and fitness functions. AMOS has successfully identified the pareto frontier of practical engineering optimization problems with expensive black box functions and significantly reduced the computation cost. The novel method was put to the test utilizing real-world challenges and engineering design examples such vehicle magnetorheological design and wind turbine airfoil geometry.

Hybrid chameleon swarm algorithm with multi-strategy: A case study of degree reduction for disk Wang–Ball curves

In this paper, an enhanced hybrid chameleon swarm algorithm (CSA) is proposed and applied to the degree reduction problem of disk Wang–Ball (DWB) curve. CSA is a novel population-based algorithm inspired by the hunting behavior of chameleons, its simplicity and easy implementation make it applied to different fields. However, it suffers from premature convergence and easy to fall into local optimum, especially in the face of complex optimization problems. Therefore, this paper proposes an enhanced hybrid CSA (CCECSA, for short). Compared with the classic CSA, the proposed CCECSA mainly introduces three improvements: (1) The crisscross optimization algorithm is mixed to avoid premature convergence, in which the horizontal and vertical crossover can generate moderation solutions to increase the diversity of the population. (2) Elite guidance mechanism is introduced to speed up the convergence. (3) Competitive substitution mechanism is added to replace the worst individual, and an interference strategy is set to prevent the algorithm from falling into a local optimum. The efficiency and robustness of the proposed CCECSA are demonstrated by the comparison results with some advanced meta-heuristic algorithms on CEC2014, CEC2017, and 4 engineering design examples. In addition, for the degree reduction problem of DWB curves, the multi-degree reduction optimization models of its center curve and radius function are established respectively. At the same time, the optimal center curve and radius function of the approximating DWB curves of lower degree are obtained by the proposed CCECSA. The experimental results show that the proposed CCECSA achieves the optimal solution with better convergence and robustness. The source code of CCECSA is publicly available in the supplementary material related to this article.

GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty

Abstract Deep generative models have demonstrated effectiveness in learning compact and expressive design representations that significantly improve geometric design optimization. However, these models do not consider the uncertainty introduced by manufacturing or fabrication. The past work that quantifies such uncertainty often makes simplifying assumptions on geometric variations, while the “real-world,” “free-form” uncertainty and its impact on design performance are difficult to quantify due to the high dimensionality. To address this issue, we propose a generative adversarial network-based design under uncertainty framework (GAN-DUF), which contains a deep generative model that simultaneously learns a compact representation of nominal (ideal) designs and the conditional distribution of fabricated designs given any nominal design. This opens up new possibilities of (1) building a universal uncertainty quantification model compatible with both shape and topological designs, (2) modeling free-form geometric uncertainties without the need to make any assumptions on the distribution of geometric variability, and (3) allowing fast prediction of uncertainties for new nominal designs. We can combine the proposed deep generative model with robust design optimization or reliability-based design optimization for design under uncertainty. We demonstrated the framework on two real-world engineering design examples and showed its capability of finding the solution that possesses better performance after fabrication.

High-Fidelity Surrogate Based Multi-Objective Optimization Algorithm

The employment of conventional optimization procedures that must be repeatedly invoked during the optimization process in real-world engineering applications is hindered despite significant gains in computing power by computationally expensive models. As a result, surrogate models that require far less time and resources to analyze are used in place of these time-consuming analyses. In multi-objective optimization (MOO) problems involving pricey analysis and simulation techniques such as multi-physics modeling and simulation, finite element analysis (FEA), and computational fluid dynamics (CFD), surrogate models are found to be a promising endeavor, particularly for the optimization of complex engineering design problems involving black box functions. In order to reduce the expense of fitness function evaluations and locate the Pareto frontier for MOO problems, the automated multiobjective surrogate based Pareto finder MOO algorithm (AMSP) is proposed. Utilizing data samples taken from the feasible design region, the algorithm creates three surrogate models. The algorithm repeats the process of sampling and updating the Pareto set, by assigning weighting factors to those surrogates in accordance with the values of the root mean squared error, until a Pareto frontier is discovered. AMSP was successfully employed to identify the Pareto set and the Pareto border. Utilizing multi-objective benchmark test functions and engineering design examples such airfoil shape geometry of wind turbine, the unique approach was put to the test. The cost of computing the Pareto optima for test functions and real engineering design problem is reduced, and promising results were obtained.

Discussion on Construction Technology of Deep Foundation Pit Support in ConstructionEngineering——An Example of Geotechnical Engineering Design in Beijing's Western Region

Discussion on Construction Technology of Deep Foundation Pit Support in ConstructionEngineering——An Example of Geotechnical Engineering Design in Beijing's Western Region

A Highly Efficient Approach for Bi-Level Programming Problems Based on Dominance Determination

Abstract Bi-level programming, where one objective is nested within the other, is widely used in engineering design, e.g., structural optimization and electronic system design. One major issue of current solvers for these bi-level problems is their low computational efficiency, especially for complex nonlinear problems. The existing methods usually applied time-consuming nested computational structure, which requires an amount of function evaluations (FEs) since a lower-level (LL) optimization needs to be constructed for each upper-level (UL) solution. To solve this issue, a new method based on bi-level grey wolf optimizer (BLGWO) is proposed in this paper. The basic idea is to drop the conventional nested computational structure and instead use a simultaneous computational structure. The simultaneous structure is built on top of a dominance determination process for the grey wolf optimizer, so that the upper-level and lower-level problems can be optimized simultaneously, which greatly improves the efficiency of solving the bi-level problems. The effectiveness of this new method has been validated with ten benchmark functions and two engineering design examples, as well as comparisons with three important existing methods in the bi-level programming domain.

Three-dimensional topology optimization of a fluid–structure system using body-fitted mesh adaption based on the level-set method

We propose a new framework for the two- and three-dimensional topology optimization (TO) of the weakly-coupled fluid–structure system. The proposed design methodology uses a reaction–diffusion equation (RDE) for updating the level-set function based on the topological sensitivity. From the numerical point of view, two key ingredients are highlighted: (i) the body-fitted adaptive mesh strategy allows the disjoint reunion of a global mesh that contains several (fluid/solid) subdomains, whose interfaces can be described by an implicitly defined surface (zero level-set); (ii) our framework uses FreeFEM for finite element analysis (FEA) and PETSc for distributed linear algebra. Efficient preconditioner techniques are utilized to solve the large-scale finite element systems. From the engineering stand point, we propose a complete product development workflow including the pre-processing, TO, B-Rep conversion, and the numerical experiment. The performance of our methodology is demonstrated by solving three different optimization problems: compliance, power dissipation, and fluid–structure interaction (FSI). For comparison and for assessing our various techniques, we benchmark our designs against state-of-the-art works followed by showcasing a variety of practical engineering design examples.

A stochastic process discretization method combing active learning Kriging model for efficient time-variant reliability analysis

Time-variant reliability analysis (TRA) has attracted tremendous interest for evaluating product reliability in full life cycle. Discretization of stochastic process is considered one of the simplest ways to transform a time-variant problem into a time-invariant problem that becomes easier to handle. Its adoption in time-variant problem, nevertheless, requires overcoming two main issues on (1) the low efficiency of small discrete time interval, and (2) the low accuracy of large discrete time interval. To tackle these two challenges, we propose a Kriging-assisted time-variant reliability analysis method based upon stochastic process discretization (namely, K-TRPD for short). First, a complex time-variant reliability problem is converted into conventional time-invariant problem through discretization of stochastic process. Second, the most probable point (MPP) trajectory is approximated through a Kriging model over the entire time period concerned, whose input is identified from the discrete time points by an active learning approach; and the output is obtained by the first order reliability method (FORM) at the identified time points. Finally, the constructed Kriging model is utilized for time-invariant reliability analysis at each discrete time point, and the time-variant reliability is obtained by using the time-invariant reliability analysis results for analyzing the multivariate normal distribution function. In this study, three numerical analysis examples and one engineering design example are presented to demonstrate the effectiveness of the proposed method.

Precision Engineering Design Process for Optimal Design Based on Engineering Sciences

Concepts of precision engineering design process for optimal design where engineering sciences contribute in the successful good design are elaborated in this paper. Scientific theory and practicality are discussed in this paper. Factors necessary for a complete product or systems design are detailed and application of mathematical design optimization in producing a good design are shown. Many applicable engineering design examples are itemized to show relevancy of the optimal design theory to engineering design. Future trends of optimal design with respect to the 4th industrial revolution of digitization are presented. Paper sets to elaborate that most of the engineering and scientific design problems can be optimized to a good design based on many new/advanced optimization techniques.

Quantum-inspired satin bowerbird algorithm with Bloch spherical search for constrained structural optimization

<p style='text-indent:20px;'>To enhance the optimization ability of the satin bowerbird optimization (SBO) algorithm, in this paper, a novel quantum-inspired SBO with Bloch spherical search is proposed. In this algorithm, satin bowerbirds are encoded using qubits described on the Bloch sphere, each satin bowerbird occupies three locations in the search space and each location represents an optimization solution. Using the search method of general SBO to adjust the two parameters of the qubit, qubit rotation is performed on the Bloch sphere, which simultaneously updates the three locations occupied by a qubit and quickly approaches the global optimal solution. Finally, the experimental results of five examples of structural engineering design show that the proposed algorithm is superior to other state-of-the-art metaheuristic algorithms in terms of the performance measures.</p>

Design space exploration and optimization using self-organizing maps

Identifying regions of interest (RoI) in the design space is extremely useful while building metamodels with limited computational budget. Self-organizing maps (SOM) are used as a visualization technique for design space exploration that permits identifying RoI. Conventional implementation of SOM is susceptible to folds or intersections that hinder visualizing the design space. This work proposes a modified SOM algorithm whose maps are interpretable and that does not fold and allows smoother input and performance space visualization. The modified algorithm enables identification of RoI and additional sampling in the identified RoI allows building accurate Kriging metamodel, which is then used for optimization. The proposed approach is demonstrated on benchmark nonlinear analytical examples and two practical engineering design examples. Results show that the proposed approach is highly efficient in identifying the RoI and in obtaining the optima with less samples.

Smell Bees Optimization algorithm for continuous engineering problem

Nowadays, optimization meta-heuristic algorithms are used in different fields of science, including a computer, mechanics and civil engineering. The algorithms are inspired by the laws governing nature, such as the principle of physics, an association of animals or finding and hunting food by animals. Using the smell sense of a bee insect, called Smell Bees Optimization (SBO), the present paper proposes an optimization algorithm that is meta-heuristic and inspired by nature. To verify and validate, the proposed algorithm, benchmark functions and engineering design examples were applied, which were previously optimized using different algorithms. In doing so, such as a cantilever beam, pressure vessel, three-bar truss, tension/compression spring and a welded beam were applied, which were previously optimized using different algorithms. In order to run programming, MATLAB was used. The results obtained by SBO are compared to the previous algorithms, optimized solutions of engineering examples are improved, and the target global minimums of the standard benchmark functions are almost obtained.

Repressive coping as a confounding factor for sleep measurement

Reuse of designers’ knowledge and experience of solving problems during the engineering design process holds the key to increase efficiency of decision making in future projects. An important part of this useful knowledge and experience is the interpretation of data and information about design objects and processes as well as the generation of new information for decision-making. However, previous studies on knowledge representation models have mainly focused on developing a structure to describe the knowledge about design objects and design processes while a systematic method that can effectively integrate knowledge about design objects and knowledge about problem-solving strategies is still missing. To fill this gap, a RFBSE knowledge representation model for capturing useful design knowledge and experience for future reuse is developed and evaluated in this study. This paper describes the key elements of this model, explains the rationale of using particular elements, and discusses the evaluation of the model using an engineering design example.

Automatic Design of Hydraulic Manifold Block Based on 3D Printing

Aiming at the problem of structural and internal quality optimization bottlenecks caused by the limitation of traditional machining capacity, the automatic design method of hydraulic manifold blocks (HMB) based on 3D printing is designed. According to the processing capability of complex parts and the production economy of small batch production, the typical hole structure of HMB is optimized with the support of 3D printing and evaluating of fluent simulation and experiment. A set of hole design rules was put forward and the automatic design of HMB based on the intelligent optimization algorithm was realized. The design method is verified by engineering design examples, which provides a reference for the further optimization of the structure and internal quality of HMB.

Analysis of pollution control on the construction of garden green space in petrochemical enterprises

According to the production characteristics of petrochemical enterprises, this paper puts forward the control ability and the reduction ability of the environmental pollution of the plant area. It is the important goal of the construction of the garden green space for the petrochemical enterprise. It is also the key content of the research on the construction plan of the petrochemical enterprise garden green space. The latest plant ecological restoration theory and related technology are the direction for the aspects of pollution control, water pollution control, soil pollution control and air pollution and noise pollution control. The use of plants combined with the engineering design examples.

DNA vaccine with discontinuous T‐cell epitope insertions into HSP65 scaffold as a potential means to improve immunogenicity of multi‐epitope Mycobacterium tuberculosis vaccine

DNA-based vaccine is a promising candidate for immunization and induction of a T-cell-focused protective immune response against infectious pathogens such as Mycobacterium tuberculosis (M. tb). To induce multi-functional T response against multi-TB antigens, a multi-epitope DNA vaccine and a 'protein backbone grafting' design method is adopted to graft five discontinuous T-cell epitopes into HSP65 scaffold protein of M. tb for enhancement of epitope processing and immune presentation. A DNA plasmid with five T-cell epitopes derived from ESAT-6, Ag85B, MTB10.4, PPE25 and PE19 proteins of H37Rv strain of M. tb genetically inserted into HSP65 backbone was constructed and designated as pPES. After confirmation of its in vitro expression efficiency, pPES DNA was i.m. injected into C57BL/6 mice with four doses of 50 µg DNA followed by mycobacterial challenge 4 weeks after the final immunization. It was found that pPES DNA injection maintained the ability of HSP65 backbone to induce specific serum IgG. ELISPOT assay demonstrated that pPES epitope-scaffold construct was significantly more potent to induce IFN-γ(+) T response to five T-cell epitope proteins than other DNA constructs (with epitopes alone or with epitope series connected to HSP65), especially in multi-functional-CD4(+) T response. It also enhanced granzyme B(+) CTL and IL-2(+) CD8(+) T response. Furthermore, significantly improved protection against Mycobacterium bovis BCG challenge was achieved by pPES injection compared to other DNA constructs. Taken together, HSP65 scaffold grafting strategy for multi-epitope DNA vaccine represents a successful example of rational protein backbone engineering design and could prove useful in TB vaccine design.

A RFBSE model for capturing engineers’ useful knowledge and experience during the design process

Reuse of designers' knowledge and experience of solving problems during the engineering design process holds the key to increase efficiency of decision making in future projects. An important part of this useful knowledge and experience is the interpretation of data and information about design objects and processes as well as the generation of new information for decision-making. However, previous studies on knowledge representation models have mainly focused on developing a structure to describe the knowledge about design objects and design processes while a systematic method that can effectively integrate knowledge about design objects and knowledge about problem-solving strategies is still missing. To fill this gap, a RFBSE knowledge representation model for capturing useful design knowledge and experience for future reuse is developed and evaluated in this study. This paper describes the key elements of this model, explains the rationale of using particular elements, and discusses the evaluation of the model using an engineering design example. Capture and represent tacit knowledge in engineers' minds in a structure way.Integrate informal design knowledge with formal design knowledge.Integrate design knowledge on both design objects and design processes.Provide a platform to track design evolution and capture the useful knowledge within this process.

Some considerations regarding the use of multi-fidelity Kriging in the construction of surrogate models

Surrogate models or metamodels are commonly used to exploit expensive computational simulations within a design optimization framework. The application of multi-fidelity surrogate modeling approaches has recently been gaining ground due to the potential for further reductions in simulation effort over single fidelity approaches. However, given a black box problem when exactly should a designer select a multi-fidelity approach over a single fidelity approach and vice versa? Using a series of analytical test functions and engineering design examples from the literature, the following paper illustrates the potential pitfalls of choosing one technique over the other without a careful consideration of the optimization problem at hand. These examples are then used to define and validate a set of guidelines for the creation of a multi-fidelity Kriging model. The resulting guidelines state that the different fidelity functions should be well correlated, that the amount of low fidelity data in the model should be greater than the amount of high fidelity data and that more than 10 % and less than 80 % of the total simulation budget should be spent on low fidelity simulations in order for the resulting multi-fidelity model to perform better than the equivalent costing high fidelity model.