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Shells are widely used structural systems in engineering practice. These structures have been used in the civil, automobile and aerospace industries. Many shells are designed using the finite element analysis through the conventional and costly trial and error scheme. As a more efficient alternative, optimization procedures can be used to design economic and safe structures.

This paper presents developments, integration and applications of reliable and efficient computational tools for the structural optimization of variable thickness plates and free‐form shells. Topology, sizing and shape optimization procedures are considered here. They are applied first as isolated subjects. Then these tools are combined to form a robust and reliable fully integrated design optimization tool to obtain optimum designs. The unique feature is the application of a flexible integrally stiffened plate and shell formulation to the design of stiffened plates and shells.

This work showed the use of different optimization strategies to obtain an optimal design for plates and shells. Both topology optimization (TO) and structural shape optimization procedures were considered. These two optimization applications, as separate procedures produce new designs with a great improvement when compared to the initial designs. However, the combination of stiffening TO and sizing optimization using integrally stiffened shells appears as a more attractive tool to be used. This was illustrated with several examples.

This work represents a novel approach to the design of optimally stiffened shells and overcomes the drawbacks of both topology optimization and structural shape optimization procedures when applied individually. Furthermore, the unique use of integrally stiffened shell elements for optimization, unlike conventional shell‐stiffening optimization techniques, provided a general and extremely flexible tool.

The purpose of this paper is to propose physically based varying fidelity surrogates to be used in structural design optimization of space trusses. The main aim is to demonstrate its efficiency in reducing the number of high fidelity (HF) runs in the optimization process.

In this work, surrogate models are built for space truss structures. This study uses functional as well as physical surrogates. In the latter, a grid analogy of the space truss is used thereby reducing drastically the analysis cost. Global and local approaches are considered. The latter will require a globalization scheme (sequential approximate optimization (SAO)) to ensure convergence.

Physically based surrogates were proposed. Classical techniques, namely Taylor series and kriging, are also implemented for comparison purposes. A parameter study in kriging is necessary to select the best kriging model to be used as surrogate. A test case was considered for optimization and several surrogates were built. The CPU time is reduced when compared with the HF solution, for all surrogate‐based optimization performed. The best result was achieved combining the proposed physical model with additive corrections in a SAO strategy in which C1 continuity was imposed at each trust region center. Some guidance for other engineering applications was given.

This is the first time that physical‐based surrogates for optimum design of space truss systems are used in the SAO framework. Physical surrogates typically exhibit better generalization properties than other surrogates forms, produce faster solutions, and do not suffer from dimensionality curse when used in approximate optimization strategies.

Topology optimization and conventional structural sizing optimization procedures are used together to obtain optimum designs for plate structures. A three‐layer, Mindlin‐Reissner plate model is first used with topology optimization to determine optimal stiffening zones. The central layer represents the unstiffened plate and the symmetrically located upper and lower layers are potential stiffening zones. A stiffening volume is specified and the objective is to minimize the strain energy. From these stiffening zones, a set of centre lines of equivalent stiffening Timoshenko beam elements is selected. A sizing optimization procedure is then used to optimize the stiffener dimensions. The objective of the design in the final sizing optimization stage is to minimise the strain energy keeping the total stiffened plate volume constant. The efficiency and accuracy of the proposed strategy is illustrated through several applications.

Optimization under a deterministic approach generally leads to a final design in which the performance may degrade significantly and/or constraints can be violated because of perturbations arising from uncertainties. The purpose of this paper is to obtain a better strategy that would obtain an optimum design which is less sensitive to changes in uncertain parameters. The process of finding these optima is referred to as robust design optimization (RDO), in which improvement of the performance and reduction of its variability are sought, while maintaining the feasibility of the solution. This overall process is very time consuming, requiring a robust tool to conduct this optimum search efficiently.

In this paper, the authors propose an integrated tool to efficiently obtain RDO solutions. The tool encompasses suitable multiobjective optimization (MO) techniques (encompassing: Normal-Boundary Intersection, Normalized Normal-Constraint, weighted sum method and min-max methods), a surrogate model using reduced order method for cheap function evaluations and adequate procedure for uncertainties quantification (Probabilistic Collocation Method).

To illustrate the application of the proposed tool, 2D structural problems are considered. The integrated tool prove to be very effective reducing the computational time by up to five orders of magnitude, when compared to the solutions obtained via classical standard approaches.

The proposed combination of methodologies described in the paper, leads to a very powerful tool for structural optimum designs, considering uncertainty parameters, that can be extended to deal with other class of applications.

This paper gives a bibliographical review of the finite element and boundary element parallel processing techniques from the theoretical and application points of view. Topics include: theory – domain decomposition/partitioning, load balancing, parallel solvers/algorithms, parallel mesh generation, adaptive methods, and visualization/graphics; applications – structural mechanics problems, dynamic problems, material/geometrical non‐linear problems, contact problems, fracture mechanics, field problems, coupled problems, sensitivity and optimization, and other problems; hardware and software environments – hardware environments, programming techniques, and software development and presentations. The bibliography at the end of this paper contains 850 references to papers, conference proceedings and theses/dissertations dealing with presented subjects that were published between 1996 and 2002.

The optimization of variable thickness plates and shells is studied. In particular, three types of shell are considered: hyperbolic paraboloid, conoid and cylindrical shell. The main objective is to investigate the optimal thickness distributions as the geometric form of the structure changes from a plate to a deep shell. The optimal thickness distribution is found by use of a structural optimization algorithm which integrates the Coons patch technique for thickness definition, structural analysis using 9‐node Huang‐Hinton shell elements, sensitivity evaluation using the global finite difference method and the sequential quadratic programming method. The composition of the strain energy is monitored during the optimization process to obtain insight into the energy distribution for the optimum structures. Several benchmark examples are considered illustrating optimal thickness variations under different loading, boundary and design variable linking conditions.

The purpose of this paper is to develop robust metamodels, which allow propagating parametric uncertainties, in the presence of localized nonlinearities, with reduced cost and without significant loss of accuracy.

The proposed metamodels combine the generalized polynomial chaos expansion (gPCE) for the uncertainty propagation and reduced order models (ROMs). Based on the computation of deterministic responses, the gPCE requires prohibitive computational time for large-size finite element models, large number of uncertain parameters and presence of nonlinearities. To overcome this issue, a first metamodel is created by combining the gPCE and a ROM based on the enrichment of the truncated Ritz basis using static residuals taking into account the stochastic and nonlinear effects. The extension to the Craig–Bampton approach leads to a second metamodel.

Implementing the metamodels to approximate the time responses of a frame and a coupled micro-beams structure containing localized nonlinearities and stochastic parameters permits to significantly reduce computation cost with acceptable loss of accuracy, with respect to the reference Latin Hypercube Sampling method.

The proposed combination of the gPCE and the ROMs leads to a computationally efficient and accurate tool for robust design in the presence of parametric uncertainties and localized nonlinearities.

In multidisciplinary design optimization (MDO), if the relationships between design variables and some output parameters, which are important performance constraints, are complex implicit problems, plenty of time should be spent on computationally expensive simulations to identify whether the implicit constraints are satisfied with the given design variables during the optimization iteration process. The purpose of this paper is to propose an ensemble of surrogates-based analytical target cascading (ESATC) method to tackle such MDO engineering design problems with reduced computational cost and high optimization accuracy.

Different surrogate models are constructed based on the sample point sets obtained by Latin hypercube sampling (LHS) method. Then, according to the error metric of each surrogate model, the repeated ensemble of surrogates is constructed to approximate the implicit objective functions and constraints. Under the framework of analytical target cascading (ATC), the MDO problem is decomposed into several optimization subproblems and the function of analysis module of each subproblem is simulated by repeated ensemble of surrogates, working together to find the optimum solution.

The proposed method shows better modeling accuracy and robustness than other individual surrogate model-based ATC method. A numerical benchmark problem and an industrial case study of the structural design of a super heavy vertical lathe machine tool are utilized to demonstrate the accuracy and efficiency of the proposed method.

This paper integrates a repeated ensemble method with ATC strategy to construct the ESATC framework which is an effective method to solve MDO problems with implicit constraints and black-box objectives.

Metamodels are widely used to replace simulation models in engineering design optimization to reduce the computational cost. The purpose of this paper is to develop a novel sequential sampling strategy (weighted accumulative error sampling, WAES) to obtain accurate metamodels and apply it to improve the quality of global optimization.

A sequential single objective formulation is constructed to adaptively select new sample points. In this formulation, the optimization objective is to select a sample point with the maximum weighted accumulative predicted error obtained by analyzing data from previous iterations, and a space-filling criterion is introduced and treated as a constraint to avoid generating clustered sample points. Based on the proposed sequential sampling strategy, a two-step global optimization approach is developed.

The proposed WAES approach and the global optimization approach are tested in several cases. A comparison has been made between the proposed approach and other existing approaches. Results illustrate that WAES approach performs the best in improving metamodel accuracy and the two-step global optimization approach has a great ability to avoid local optimum.

The proposed WAES approach overcomes the shortcomings of some existing approaches. Besides, the two-step global optimization approach can be used for improving the optimization results.

The relationship between the local option sales tax (LOST) and property taxes and own source revenue is not well documented in the literature. This may be due in part to the aggregated nature of the data, which fails to capture different motivations for adoption of LOSTs. Using county-level data from 35 states, this study finds that LOSTs increase own source revenue and in some circumstances decrease property tax burdens. The primary contribution of this research is that it uses a policy variable, the LOST rate, to distinguish between the two types of counties that use their LOST revenues differently. This research represents the first step in bridging the gap between the LOST literature and the tax mix choice literature.