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Algorithms
Special Issues
Simulation-Based Optimization: Methods and Applications in Engineering Design
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Simulation-Based Optimization: Methods and Applications in Engineering Design

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (1 July 2022) | Viewed by 23831

Special Issue Editors

Dr. Andrea Serani

E-Mail Website
Guest Editor
CNR-INM, National Research Council-Institute of Marine Engineering, Via di Vallerano 139, 00128 Rome, Italy
Interests: simulation-based design optimization; machine learning; dimensionality reduction; surrogate-modelling; multi-fidelity methods; optimization algorithms; uncertainty quantification; application of computational fluid dynamics
Dr. ‪Riccardo Pellegrini

E-Mail Website
Guest Editor
Institute of Marine Engineering, Italian National Research Council, 00128 Rome, Italy
Interests: simulation-based design; optimization; surrogate models; variable fidelity; fluid-structure interaction

Special Issue Information

Dear Colleagues,

In the engineering design context, the demand for efficient products is constantly increasing. These products must respond to an ever-increasing number of specific and complex requirements. Over the last thirty years, engineering design has radically transformed thanks to the exponential development of IT and digital resources. This has allowed the transformation of the classical approach of design, built, and test toward a more efficient simulation-based design optimization (SBDO) process, integrating numerical solvers, design modification methods, optimization algorithms, and also uncertainty quantification methods. The results obtained through the SBDO process are often a compromise between its efficiency (speed in achieving the optimum) and effectiveness (accurate simulations, requiring high-fidelity/computationally expensive solvers). Despite the advancement of computational resources, the challenge is to improve the SBDO framework (as a whole or its single components) in order to efficiently achieve accurate optimal solutions in solving complex engineering design problems.

The aim of this Special Issue is to collect state-of-the-art research on simulation-based optimization methods and their applications to complex engineering design problems. Relevant topics, methods, and applications are included in (but not limited to) the list below

  • Single- and multiobjective optimization algorithms;
  • Multidisciplinary optimization;
  • Metamodeling and machine learning in SBDO;
  • Multi-fidelity methods;
  • Dimensionality reduction;
  • Optimization under uncertainty;
  • Design modification methods;
  • Engineering design of aeronautical, aerospace, electrical, mechanical, naval applications.

Dr. Andrea Serani
Dr. ‪Riccardo Pellegrini
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (8 papers)

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Editorial

Jump to: Research

2 pages, 157 KiB  
Editorial
Overview on the Special Issue on “Simulation-Based Optimization: Methods and Applications in Engineering Design”
by Riccardo Pellegrini and Andrea Serani
Algorithms 2023, 16(4), 191; https://doi.org/10.3390/a16040191 - 30 Mar 2023
Viewed by 1258
Abstract
The simulation-based design optimization (SBDO) paradigm is a well-known approach that has assisted, assists, and will continue to assist designers to develop ever-improving systems [...] Full article
(This article belongs to the Special Issue Simulation-Based Optimization: Methods and Applications in Engineering Design)

Research

Jump to: Editorial

18 pages, 6796 KiB  
Article
Research of Flexible Assembly of Miniature Circuit Breakers Based on Robot Trajectory Optimization
by Yan Han, Liang Shu, Ziran Wu, Xuan Chen, Gaoyan Zhang and Zili Cai
Algorithms 2022, 15(8), 269; https://doi.org/10.3390/a15080269 - 31 Jul 2022
Cited by 5 | Viewed by 2428
Abstract
This paper is dedicated to achieving flexible automatic assembly of miniature circuit breakers (MCBs) to resolve the high rigidity issue of existing MCB assembly by proposing a flexible automatic assembly process and method with industrial robots. To optimize the working performance of the [...] Read more.
This paper is dedicated to achieving flexible automatic assembly of miniature circuit breakers (MCBs) to resolve the high rigidity issue of existing MCB assembly by proposing a flexible automatic assembly process and method with industrial robots. To optimize the working performance of the robot, a time-optimal trajectory planning method of the improved Particle Swarm Optimization (PSO) with a multi-optimization mechanism is proposed. The solution uses a fitness switch function for particle sifting to improve the stability of the acceleration and jerk of the robot motion as well as to increase the computational efficiency. The experimental results show that the proposed method achieves flexible assembly for multi-type MCB parts of varying postures. Compared with other optimization algorithms, the proposed improved PSO is significantly superior in both computational efficiency and optimization accuracy. Compared with the standard PSO, the proposed trajectory planning method shortens the assembly time by 6.9 s and raises the assembly efficiency by 16.7%. The improved PSO is implemented on the experimental assembly platform and achieves smooth and stable operations, which proves the high significance and practicality for MCB fabrication. Full article
(This article belongs to the Special Issue Simulation-Based Optimization: Methods and Applications in Engineering Design)
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26 pages, 2207 KiB  
Article
Multi-Fidelity Low-Rank Approximations for Uncertainty Quantification of a Supersonic Aircraft Design
by Sihmehmet Yildiz, Hayriye Pehlivan Solak and Melike Nikbay
Algorithms 2022, 15(7), 250; https://doi.org/10.3390/a15070250 - 19 Jul 2022
Cited by 3 | Viewed by 2855
Abstract
Uncertainty quantification has proven to be an indispensable study for enhancing reliability and robustness of engineering systems in the early design phase. Single and multi-fidelity surrogate modelling methods have been used to replace the expensive high fidelity analyses which must be repeated many [...] Read more.
Uncertainty quantification has proven to be an indispensable study for enhancing reliability and robustness of engineering systems in the early design phase. Single and multi-fidelity surrogate modelling methods have been used to replace the expensive high fidelity analyses which must be repeated many times for uncertainty quantification. However, since the number of analyses required to build an accurate surrogate model increases exponentially with the number of random input variables, most surrogate modelling methods suffer from the curse of dimensionality. As an alternative approach, the Low-Rank Approximation method can be applied to high-dimensional uncertainty quantification studies with a low computational cost, where the number of coefficients for building the surrogate model increases only linearly with the number of random input variables. In this study, the Low-Rank Approximation method is implemented for multi-fidelity applications with additive and multiplicative correction approaches to make the high-dimensional uncertainty quantification analysis more efficient and accurate. The developed uncertainty quantification methodology is tested on supersonic aircraft design problems and its predictions are compared with the results of single- and multi-fidelity Polynomial Chaos Expansion and Monte Carlo methods. For the same computational cost, the Low-Rank Approximation method outperformed both in surrogate modeling and uncertainty quantification cases for all the benchmarks and real-world engineering problems addressed in the present study. Full article
(This article belongs to the Special Issue Simulation-Based Optimization: Methods and Applications in Engineering Design)
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35 pages, 6328 KiB  
Article
Multi-Fidelity Gradient-Based Optimization for High-Dimensional Aeroelastic Configurations
by Andrew S. Thelen, Dean E. Bryson, Bret K. Stanford and Philip S. Beran
Algorithms 2022, 15(4), 131; https://doi.org/10.3390/a15040131 - 16 Apr 2022
Cited by 12 | Viewed by 3796
Abstract
The simultaneous optimization of aircraft shape and internal structural size for transonic flight is excessively costly. The analysis of the governing physics is expensive, in particular for highly flexible aircraft, and the search for optima using analysis samples can scale poorly with design [...] Read more.
The simultaneous optimization of aircraft shape and internal structural size for transonic flight is excessively costly. The analysis of the governing physics is expensive, in particular for highly flexible aircraft, and the search for optima using analysis samples can scale poorly with design space size. This paper has a two-fold purpose targeting the scalable reduction of analysis sampling. First, a new algorithm is explored for computing design derivatives by analytically linking objective definition, geometry differentiation, mesh construction, and analysis. The analytic computation of design derivatives enables the accurate use of more efficient gradient-based optimization methods. Second, the scalability of a multi-fidelity algorithm is assessed for optimization in high dimensions. This method leverages a multi-fidelity model during the optimization line search for further reduction of sampling costs. The multi-fidelity optimization is demonstrated for cases of aerodynamic and aeroelastic design considering both shape and structural sizing separately and in combination with design spaces ranging from 17 to 321 variables, which would be infeasible using typical, surrogate-based methods. The multi-fidelity optimization consistently led to a reduction in high-fidelity evaluations compared to single-fidelity optimization for the aerodynamic shape problems, but frequently resulted in a cost penalty for cases involving structural sizing. While the multi-fidelity optimizer was successfully applied to problems with hundreds of variables, the results underscore the importance of accurately computing gradients and motivate the extension of the approach to constrained optimization methods. Full article
(This article belongs to the Special Issue Simulation-Based Optimization: Methods and Applications in Engineering Design)
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26 pages, 622 KiB  
Article
Combinatorial Integral Approximation Decompositions for Mixed-Integer Optimal Control
by Clemens Zeile, Tobias Weber and Sebastian Sager
Algorithms 2022, 15(4), 121; https://doi.org/10.3390/a15040121 - 31 Mar 2022
Cited by 3 | Viewed by 2680
Abstract
Solving mixed-integer nonlinear programs (MINLPs) is hard from both a theoretical and practical perspective. Decomposing the nonlinear and the integer part is promising from a computational point of view. In general, however, no bounds on the objective value gap can be established and [...] Read more.
Solving mixed-integer nonlinear programs (MINLPs) is hard from both a theoretical and practical perspective. Decomposing the nonlinear and the integer part is promising from a computational point of view. In general, however, no bounds on the objective value gap can be established and iterative procedures with potentially many subproblems are necessary. The situation is different for mixed-integer optimal control problems with binary variables that switch over time. Here, a priori bounds were derived for a decomposition into one continuous nonlinear control problem and one mixed-integer linear program, the combinatorial integral approximation (CIA) problem. In this article, we generalize and extend the decomposition idea. First, we derive different decompositions and analyze the implied a priori bounds. Second, we propose several strategies to recombine promising candidate solutions for the binary control functions in the original problem. We present the extensions for ordinary differential equations-constrained problems. These extensions are transferable in a straightforward way, though, to recently suggested variants for certain partial differential equations, for algebraic equations, for additional combinatorial constraints, and for discrete time problems. We implemented all algorithms and subproblems in AMPL for a proof-of-concept study. Numerical results show the improvement compared to the standard CIA decomposition with respect to objective function value and compared to general-purpose MINLP solvers with respect to runtime. Full article
(This article belongs to the Special Issue Simulation-Based Optimization: Methods and Applications in Engineering Design)
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13 pages, 3712 KiB  
Article
Dynamic Line Scan Thermography Parameter Design via Gaussian Process Emulation
by Simon Verspeek, Ivan De Boi, Xavier Maldague, Rudi Penne and Gunther Steenackers
Algorithms 2022, 15(4), 102; https://doi.org/10.3390/a15040102 - 22 Mar 2022
Cited by 3 | Viewed by 2480
Abstract
We address the challenge of determining a valid set of parameters for a dynamic line scan thermography setup. Traditionally, this optimization process is labor- and time-intensive work, even for an expert skilled in the art. Nowadays, simulations in software can reduce some of [...] Read more.
We address the challenge of determining a valid set of parameters for a dynamic line scan thermography setup. Traditionally, this optimization process is labor- and time-intensive work, even for an expert skilled in the art. Nowadays, simulations in software can reduce some of that burden. However, when faced with many parameters to optimize, all of which cover a large range of values, this is still a time-consuming endeavor. A large number of simulations are needed to adequately capture the underlying physical reality. We propose to emulate the simulator by means of a Gaussian process. This statistical model serves as a surrogate for the simulations. To some extent, this can be thought of as a “model of the model”. Once trained on a relative low amount of data points, this surrogate model can be queried to answer various engineering design questions. Moreover, the underlying model, a Gaussian process, is stochastic in nature. This allows for uncertainty quantification in the outcomes of the queried model, which plays an important role in decision making or risk assessment. We provide several real-world examples that demonstrate the usefulness of this method. Full article
(This article belongs to the Special Issue Simulation-Based Optimization: Methods and Applications in Engineering Design)
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19 pages, 1080 KiB  
Article
Multi-Fidelity Sparse Polynomial Chaos and Kriging Surrogate Models Applied to Analytical Benchmark Problems
by Markus P. Rumpfkeil, Dean Bryson and Phil Beran
Algorithms 2022, 15(3), 101; https://doi.org/10.3390/a15030101 - 21 Mar 2022
Cited by 5 | Viewed by 2973
Abstract
In this article, multi-fidelity kriging and sparse polynomial chaos expansion (SPCE) surrogate models are constructed. In addition, a novel combination of the two surrogate approaches into a multi-fidelity SPCE-Kriging model will be presented. Accurate surrogate models, once obtained, can be employed for evaluating [...] Read more.
In this article, multi-fidelity kriging and sparse polynomial chaos expansion (SPCE) surrogate models are constructed. In addition, a novel combination of the two surrogate approaches into a multi-fidelity SPCE-Kriging model will be presented. Accurate surrogate models, once obtained, can be employed for evaluating a large number of designs for uncertainty quantification, optimization, or design space exploration. Analytical benchmark problems are used to show that accurate multi-fidelity surrogate models can be obtained at lower computational cost than high-fidelity models. The benchmarks include non-polynomial and polynomial functions of various input dimensions, lower dimensional heterogeneous non-polynomial functions, as well as a coupled spring-mass-system. Overall, multi-fidelity models are more accurate than high-fidelity ones for the same cost, especially when only a few high-fidelity training points are employed. Full-order PCEs tend to be a factor of two or so worse than SPCES in terms of overall accuracy. The combination of the two approaches into the SPCE-Kriging model leads to a more accurate and flexible method overall. Full article
(This article belongs to the Special Issue Simulation-Based Optimization: Methods and Applications in Engineering Design)
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16 pages, 4336 KiB  
Article
Design of Selective Laser Melting (SLM) Structures: Consideration of Different Material Properties in Multiple Surface Layers Resulting from the Manufacturing in a Topology Optimization
by Jan Holoch, Sven Lenhardt, Sven Revfi and Albert Albers
Algorithms 2022, 15(3), 99; https://doi.org/10.3390/a15030099 - 19 Mar 2022
Cited by 4 | Viewed by 3395
Abstract
Topology optimization offers a possibility to derive load-compliant structures. These structures tend to be complex, and conventional manufacturing offers only limited possibilities for their production. Additive manufacturing provides a remedy due to its high design freedom. However, this type of manufacturing can cause [...] Read more.
Topology optimization offers a possibility to derive load-compliant structures. These structures tend to be complex, and conventional manufacturing offers only limited possibilities for their production. Additive manufacturing provides a remedy due to its high design freedom. However, this type of manufacturing can cause areas of different material properties in the final part. For example, in selective laser melting, three areas of different porosity can occur depending on the process parameters, the geometry of the part and the print direction, resulting in a direct interrelation between manufacturing and design. In order to address this interrelation in design finding, this contribution presents an optimization method in which the three porous areas are identified and the associated material properties are considered iteratively in a topology optimization. For this purpose, the topology optimization is interrupted in each iteration. Afterwards, the three areas as well as the material properties are determined and transferred back to the topology optimization, whereby those properties are used for the calculation of the next iteration. By using the optimization method, a design with increased volume-specific stiffness compared to a design of a standard topology optimization can be created and will be used in the future as a basis for the extension by a global strength constraint to maintain the maximum permissible stress and the minimum wall thickness. Full article
(This article belongs to the Special Issue Simulation-Based Optimization: Methods and Applications in Engineering Design)
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