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There are many investigations in design methodologies, but there are also divergences and convergences as there are so many points of view. This study aims to evaluate to corroborate and deepen other researchers’ findings, dissipate divergences and provide directing to future work on the subject from a methodological and convergent perspective.

This study analyzes the previous reviews (about 15 reviews) and based on the consensus and the classifications provided by these authors, a significant sample of research is analyzed in the design for additive manufacturing (DFAM) theme (approximately 80 articles until June of 2017 and approximately 280–300 articles until February of 2019) through descriptive statistics, to corroborate and deepen the findings of other researchers.

Throughout this work, this paper found statistics indicating that the main areas studied are: multiple objective optimizations, execution of the design, general DFAM and DFAM for functional performance. Among the main conclusions: there is a lack of innovation in the products developed with the methodologies, there is a lack of exhaustivity in the methodologies, there are few efforts to include environmental aspects in the methodologies, many of the methods include economic and cost evaluation, but are not very explicit and broad (sustainability evaluation), it is necessary to consider a greater variety of functions, among other conclusions

The novelty in this study is the methodology. It is very objective, comprehensive and quantitative. The starting point is not the case studies nor the qualitative criteria, but the figures and quantities of methodologies. The main contribution of this review article is to guide future work on the subject from a methodological and convergent perspective and this article provides a broad database with articles containing information on many issues to make decisions: design methodology; optimization; processes, selection of parts and materials; cost and product management; mechanical, electrical and thermal properties; health and environmental impact, etc.

The purpose of this sudy is to provide a complete optimization-based methodology to design waveguides with metamaterial walls. The present methodology is based on optimization. Indeed, the inverse problems of design are formulated as nonlinear black-box optimization problems with constraints. Two inequality black-box constraints are taken into account as penalized terms that are added to the objective function when the constraints are not satisfied. The numerical steps are done by using a finite element method solver (GetDP). Thus, different optimization software are tested to solve the nonlinear black-box optimization problems such as IPOPT, NLOPT and NOMAD from the Opti ToolBox in MatLab.

In this work, a methodology to design waveguides with metamaterial walls is proposed. The aim is to solve an inverse problem to find the best design where the electric field cartography is the closest to an imposed one.

The present methodology is applied to solve inverse problems of design and satisfactory results were provided by the three solvers IPOPT, NLOPT and NOMAD. Those numerical experiments show that NOMAD is the most efficient method to optimize the design of those cylindrical waveguide structures with metamaterial walls.

The model is set to find solutions using a specific pattern of metamaterials. This is promising to take those geometries as variables of the optimization problems. Moreover, in this exploratory work, no constraint on the fabrication limits has been taken into account.

The originality is to formulate design problems of waveguide with metamaterial walls into optimization problems. These optimization problems are difficult to solve because the objective function and two inequality constraints are computed via a numerical simulation code based on finite element methods. Thus, an original approach based on penalization is implemented and three optimization software are used. Hence, the authors propose an optimization-based methodology and apply to solve two inverse problems of design.

A method to solve shape and size optimisation problems with linear and non‐linear responses has been studied taking advantage of statistical methodologies. A nested optimisation procedure has been fixed. The global optimisation problem is decomposed in several subproblems where each non‐linear response is locally approximated with a first degree polynomial function identified by the definition and execution of an experimental plan. The approximating functions so obtained are used to evaluate the design sensitivity coefficients required by the optimisation procedure. The numerical results obtained during the optimisation process to verify exactly the value of the non‐linear responses are used to verify and to improve the approximating function accuracy. The non‐linear design sensitivity analysis method so defined has been used to solve a multidisciplinary shape optimisation problem involving a real 3D automotive structure.

This paper aims to present the results of a design optimization study for the impeller of a small mixed‐flow compressor. The objective of the optimization is to obtain an impeller geometry that could minimize a cost function based on the specific thrust and the thrust specific fuel consumption of a small turbojet engine.

The design methodology is based on an optimization process that uses a configurational database for various compressor geometries. The database is constructed using design of experiments and the compressor configurations are generated using one‐dimensional in‐house design codes, as well as various tools and programs of the Agile Engineering Design System^®, which is a commercially available turbomachinery design system developed at Concepts NREC. The cost function variations within the design space are represented through a neural network. The optimum configuration that minimizes the cost function is obtained using a direct search optimization procedure.

The optimization study generated a small 86 mm diameter mixed‐flow impeller with a 50° meridional exit angle. The optimized compressor, as well as the engine that it is designed for, were shown to have improved performance characteristics.

Preliminary performance and flow analysis of the optimized impeller show shock structures and possible shock‐boundary layer interactions within the blade passages indicating further geometrical fine tuning may be required based on more detailed computational studies or experimental tests.

A further study including the effect of diffuser is required to carry the results to a more practical level.

The originality and the value of the paper comes mainly from two different aspects: combining various in‐house and commercial turbomachinery design codes in one robust methodology to obtain an optimum mixed‐flow compressor impeller that will maximize the performance requirements of a small unmanned air vehicle (UAV) turbojet engine under restricted size and power conditions; and investigation of the design optimization and analysis of a mixed‐flow compressor that could have potential applications in small jet engines to be used in high‐performance UAV applications. Design optimization studies on this type of compressor are very limited in the open literature. For many years, these compressors have been disregarded because of their bulky design in large‐scale engines. However, as mentioned above, they present a great potential for small‐scale jet engines by supplying enough pressure rise, as well as high mass flow rate compared to their centrifugal counterparts.

In this article, a practical design methodology is proposed for discrete sizing optimization of high-rise concrete buildings with a focus on large-scale and real-life structures.

This framework relies on a computationally efficient approximation of the constraint and objective functions using a radial basis function model with a linear tail, also called the combined response surface methodology (RSM) in this article. Considering both the code-stipulated constraints and other construction requirements, three sub-optimization problems were constructed based on the relaxation model of the original problem, and then the structural weight could be automatically minimized under multiple constraints and loading scenarios. After modulization, the obtained results could meet the discretization requirements. By integrating the commercially available ETABS, a dedicated optimization software program with an independent interface was developed and details for practical software development were also presented in this paper.

The proposed framework was used to optimize different high-rise concrete buildings, and case studies showed that material usage could be saved by up to 12.8% compared to the conventional design, and the over-limit constraints could be adjusted, which proved the feasibility and effectiveness.

This methodology can therefore be applied by engineers to explore the optimal distribution of dimensions for high-rise buildings and to reduce material usage for a more sustainable design.

The paper aims to reduce the aerodynamic drag of a rotorcraft stabilizer in forward flight by taking into account downwash effects from the main rotor wake (power-on conditions).

A shape design methodology based on numerical optimization, CAD-in-the-loop (CAD: computer-aided design) approach and high-fidelity Computational Fluid Dynamics (CFD) tools was set-up and applied to modify the horizontal empennage of a rotorcraft configuration. This included the integration of both commercial and in-house computer-aided engineering tools for parametric geometry handling, adaptive mesh generation, CFD solution and evolutionary optimization within a robust evaluation chain for the aerodynamic simulation of the different design candidates generated during the automatic design loop. Geometrical modifications addressed both the stabilizer planform and sections, together with its setting angle in cruise configuration, accounting for impacts on the equilibrium, stability and control characteristics of the empennage.

An overall improvement of 11.1 per cent over the rotorcraft drag was estimated at the design condition (cruise flight; power-on) for the stabilizer configuration with optimized planform shape, which is increased to 11.4 per cent when combined with the redesigned airfoil to generate the stabilizer surface.

Critical design considerations are introduced with regard to structural and systems integration issues, and a design candidate alternative is identified and proposed as a compromise solution, achieving 8.3 per cent reduction of the rotorcraft configuration drag in cruise conditions with limited increase in the empennage aspect ratio and leading edge sweep angle when compared to the pure aerodynamic optimal design obtained from genetic algorithm evolution.

The proposed methodology faces the empennage design problem by explicitly taking into account the effects of main rotor wake impinging the stabilizer surface in forward flight conditions and using an automated optimization approach which directly incorporates professional CAD tools in the design loop.

In furthering numerical optimization techniques for the light-weighting of components, it is paramount to produce algorithms that closely mimic the physical behavior of the specific manufacturing method under which they are created. The continual development in topology optimization (TO) has reduced the difference in the optimized geometry from what can be physically realized. As the reinterpretation stage inevitably deviates from the optimal geometry, each progression in the optimization code that renders the final solution more realistic is beneficial. Despite the efficacy of material extrusion (MEx) in producing complex geometries, select manufacturing constraints are still required. Thus, the purpose of this paper is to develop a TO code which demonstrates the incorporation of MEx specific manufacturing constraints into a numerical optimization algorithm.

A support index is derived for each element of the finite element mesh that is used to penalize elements, which are insufficiently supported, discouraging their existence. The support index captures the self-supporting angle and maximum allowable bridging distance for a given MEx component. The incorporation of the support index into a TO code is used to demonstrate the efficacy of the method on multiple academic examples.

The case studies presented demonstrate the methodology is successful in generating a resulting topology that is self-supporting given the manufacturing parameters specified in the code. Comparative to a general TO problem formulation, the optimal material distribution results in a minimally penalized design on a compliance normalization metric while fully adhering to the MEx specific parameters. The methodology, thus, proves useful in generating an infill geometry is fully enclosed regions, where support material extraction is not a possibility.

The work presented is the first paper to produce a novel methodology that incorporates the manufacturing-specific constraint of bridging distance for MEx into TO code. The results generated allow for the creation of printed components with hollow inclusions that do not require any additional support material beyond the intended structure. Given the advancement, the numerical optimization technique has progressed to a more realistic representation of the physical manufacturing method.

The purpose of this paper is to present an application of a multidisciplinary multi-level design optimization methodology for the optimal design of a complex device from the field of electrical engineering throughout discipline-based decomposition. The considered benchmark is a single-phase low voltage safety isolation transformer.

The multidisciplinary optimization of a safety isolation transformer is addressed within this paper. The bi-level collaborative optimization (CO) strategy is employed to coordinate the optimization of the different disciplinary analytical models of the transformer (no-load and full-load electromagnetic models and thermal model). The results represent the joint decision of the three distinct disciplinary optimizers involved in the design process, under the coordination of the CO's master optimizer. In order to validate the proposed approach, the results are compared to those obtained using a classical single-level optimization method – sequential quadratic programming – carried out using a multidisciplinary feasible formulation for handling the evaluation of the coupling model of the transformer.

Results show a good convergence of the CO process with the analytical modeling of the transformer, with a reduced number of coordination iterations. However, a relatively important number of disciplinary models evaluations were required by the local optimizers.

The CO multi-level methodology represents a new approach in the field of electrical engineering. The advantage of this approach consists in that it integrates decisions from different teams of specialists within the optimal design process of complex systems and all exchanges are managed within a unique coordination process.

This paper, by taking randomness and uncertainty of structural systems into account aims to implement a combined reliability‐based robust design optimization (RRDO) formulation. The random variables to be considered include the cross section dimensions, modulus of elasticity, yield stress, and applied loading. The RRDO problem is to be formulated as a multi‐objective optimization problem where the construction cost and the standard deviation of the structural response are the objectives to be minimized.

The solution of the optimization problem is performed with the non‐dominant cascade evolutionary algorithm with the weighted Tchebycheff metric, while the probabilistic analysis required is carried out with the Monte Carlo simulation method. Despite the computational advances, the solution of a RRDO problem for real‐world structures is extremely computationally demanding and for this reason neurocomputing estimations are implemented.

The obtained estimates with the neural network predictions are shown to be very satisfactory in terms of accuracy for performing this type of computation. Furthermore, the present numerical results manage to achieve a reduction in computational time up to four orders of magnitude, for low probabilities of violation, compared to the conventional procedure making thus feasible the reliability‐robust design optimization of realistic structures under probabilistic constraints.

The novel parts of the present work include the implementation of neurocomputing strategies in RRDO problems for reducing the computational cost and the comparison of the results given by RRDO and robust design optimization formulations, where the significance of taking into account probabilistic constraints is emphasized.

Among various efforts pursued to produce fuel efficient vehicles, light weight engineering (i.e. the use of low‐density structurally‐efficient materials, the application of advanced manufacturing and joining technologies and the design of highly‐integrated, multi‐functional components/sub‐assemblies) plays a prominent role. In the present work, a multi‐disciplinary design optimization methodology has been presented and subsequently applied to the development of a light composite vehicle door (more specifically, to an inner door panel). The door design has been optimized with respect to its weight while meeting the requirements /constraints pertaining to the structural and NVH performances, crashworthiness, durability and manufacturability. In the optimization procedure, the number and orientation of the composite plies, the local laminate thickness and the shape of different door panel segments (each characterized by a given composite‐lay‐up architecture and uniform ply thicknesses) are used as design variables. The methodology developed in the present work is subsequently used to carry out weight optimization of the front door on Ford Taurus, model year 2001. The emphasis in the present work is placed on highlighting the scientific and engineering issues accompanying multidisciplinary design optimization and less on the outcome of the optimization analysis and the computational resources/architecture needed to support such activity.