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Item type:Publication, Access status: with files , Semi-intrusive stiffener path optimisation for hybrid manufacturing(Springer, 2026-09-28); ; 29 5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Fail-safe topology optimization using artificial neural networksAccounting for fail-safety in topology optimization requires the consideration of a large number of failure scenarios. For each scenario, the structural responses must be evaluated, rendering explicit fail-safe topology optimization computationally extremely demanding. In recent years, artificial neural networks (ANNs) have gained increasing attention in topology optimization as a means to overcome existing limitations and advance the state of the art. To explore the potential and limitations of ANN-based fail-safe topology optimization, this work proposes a novel framework for generating fail-safe designs. Within this framework, fail-safe designs are derived from designs optimized for minimal compliance using an ANN. In addition, a post-processing step is introduced to address common problems associated with topology-optimized designs predicted by ANNs. Compared to the traditional approach, the proposed framework reduces the computing time required to generate fail-safe designs by 84.98%. However, due to the high initial cost of data generation and model training, achieving a net computational benefit would require very large number of applications. Compared with deterministic designs, the generated fail-safe designs achieve a median reduction in damage compliance of 96.02%. However, compared with the traditional fail-safe approach, the damage compliance is 85.32% higher. Moreover, the results indicate limited versatility, with performance degrading as problem formulations deviate increasingly from the training distribution.Publicationtype: Journal Article78 29 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Robust design optimization of structures with multimodal responses using Taylor expansions(Springer, 2025-11-15); ; ; ; This paper shows how a combination of multiple linear approximations can efficiently capture nonlinear and non-smooth response functions in the framework of robust design optimization. The basic idea is to split the domain of random parameters into multiple subdomains along discontinuities of the response. For each subdomain, a linear approximation is used to determine the stochastic moments of the response function due to the scatter in that subdomain. The total stochastic distribution of the structural response is considered to be a multimodal distribution, and its moments are composed of the approximations in the subdomains. The application to use cases shows that indeed the structural response is multimodal in these cases, and the stochastic moments are shown to be sufficiently accurate using the proposed method.Publicationtype: Journal Article65 46 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , On the application of the multigrid method to topology optimization with orthotropic material with varying orientationMultigrid methods are known to show good performance for most topology optimization problems, but suffer from anisotropies. The main problems are caused by poor performance of the smoothing algorithms. This paper analyzes different standard and advanced smoothing algorithms using the local Fourier analysis with regard to orthotropic materials in the linear elasticity problem. The results are generalized to real problems and contrast-rich topology designs using numeric experiments. Based on the analyses, recommendations are given and a new spatial Jacobi smoothing algorithm is presented. The results are finally approved by a topology optimization example and a material orientation and topology optimization example.Publicationtype: Journal Article72 30 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Adjoint-assisted robust shape optimization of an idealized arterial bypass graft using the FOSM methodThis paper presents the shape optimization of an idealized arterial bypass graft under uncertainties. The underlying blood flow problem is numerically solved using computational fluid dynamics (CFD) simulations, able to account for mechanical hemolysis and non-Newtonian viscosity properties of blood. The employed hemolysis and non-Newtonian models utilize a set of parameters that are considered uncertain in this work. To this end, the optimization problem is rendered robust and targets the minimization of the first two statistical moments of a hemolysis index. The propagation of the uncertainties to the hemolysis index is done through the first-order second-moment method (FOSM). The necessary derivatives are computed by the adjoint method and finite differences. Several steady-state robust shape optimization simulations of the idealized bypass graft are presented. Selected optimized shapes are further assessed by means of additional fluid–structure interaction (FSI) simulations under unsteady conditions.Publicationtype: Journal Article28 22 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Topology optimization for fatigue reserve factorsThis paper describes a topology optimization approach that applies the common fatigue analysis practices of rainflow cycle counting and critical plane searches to cover both proportional and non-proportional fatigue loading conditions of metals. The existing literature on topology optimization has so far mainly considered fatigue damage under proportional loading conditions and typically uses continuous damage models to avoid the discontinuous nature of fatigue rainflow cycle counting and critical plane searches. Furthermore, previous publications often introduced heuristic schemes to scale the fatigue damage and set the move limits for the design variables rather low to avoid oscillations in the design variables and damage responses during the optimization iterations, because fatigue damage is typically highly localized. Therefore, these approaches cause many optimization iterations. Contrarily, our present approach applies the fatigue reserve factor (FRF) directly in the optimization formulation instead of the fatigue damage where FRF is a fatigue reserve factor for infinite fatigue life. The inverse FRF scales nearly linearly with the stresses. Therefore, the present approach needs no heuristic scaling for the fatigue topology optimization. The numerical implementation applies the semi-analytic adjoint sensitivity method for multiple load cases. Numerically, FRF shows more stable optimization convergence using less optimization iterations. Different FRF topology-optimized designs for a variety of fatigue damage types are validated and compared. Additionally, the optimized FRF designs are compared to both strictly stiffness optimized designs and stress strength optimized designs.Publicationtype: Journal Article29 56 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Robust design optimization using a non-intrusive second-order approximation of stochastic momentsThis paper presents a new formulation of the second-order fourth-moment method (sometimes referred to as second-order perturbation method or second-order method of moments). The method allows to efficiently predict the stochastic moments of a response function and is therefore often used within robust design optimization. The new approach allows a non-intrusive implementation at the same cost as existing, highly intrusive formulations. Therefore, the new approach can be applied to any objective function without significant implementation effort. It is based on a few finite difference steps into special directions and hence is dependent on the corresponding step sizes. An automatic step size procedure is supplied beside a detailed convergence analysis. The advantages of the new formulation are demonstrated by robust design optimizations of a 2D and a 3D example using the geometrically nonlinear finite element method.Publicationtype: Journal Article29 49 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Robust contact-constrained topology optimization considering uncertainty at the contact supportIn this paper, the general framework for contact-constrained topology optimization of Strömberg and Klarbring (2010) is extended to robust topology optimization. In doing so, a linear elastic design domain is considered and the augmented Lagrangian approach is used to model the unilateral contact. For topology optimization, the design space is parametrized with the SIMP-approach and the Sigmund’s filter is applied. Additionally, the robust framework considers uncertainties at the contact support such as deviations of the geometry of the contact surface and the friction coefficient. Both uncertainties are described by the first-order second-moment method which leads to minimal additional costs. In fact, only two additional linear equations must be solved to obtain the robust objective and its gradient with respect to the design variables. Having both the objective and the gradient, the design update is computed with the method of moving asymptotes. The robust framework is applied to 2D and 3D examples to prove its scalability for real-world applications.Publicationtype: Journal Article85 162 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A fully coupled level set-based topology optimization of flexible components in multibody systems(2023-07); ; A fully coupled level set-based topology optimization of flexible components in multibody systems is considered. Thereby, using the floating frame of reference approach, the flexible components are efficiently modeled and incorporated in multibody systems. An adjoint sensitivity analysis is utilized to obtain the gradient of the objective function with respect to a set of density-like design variables assigned to elements included in the underlying finite element model. The utilized adjoint sensitivity analysis provides a gradient, which is within numerical limits exact. In this process, the parametrization of material properties of finite elements has a significant influence on the calculated gradient, in particular for poorly filled elements. These influences are studied in detail. As an application example, the compliance minimization problem of a flexible piston rod in a transient slider-crank mechanism is considered. For this model, the influence of different parametrization methods on the obtained gradient is discussed, and a gradient strategy is proposed to overcome numerical issues included in different parametrization laws. Using this gradient strategy within a level set-based algorithm, a topology optimization of the flexible piston rod is performed. The corresponding results are then compared with optimization results provided by the method of moving asymptotes (MMA). Moreover, the computational effort of the sensitivity analysis is high and scales with the number of design variables. In this work, a gradient approximation is introduced using radial basis functions (RBFs). This helps to develop an appropriate gradient for a level set-based topology optimization of the flexible components in multibody systems, where the RBF-based design space reduction decreases the computational effort of the utilized sensitivity analysis. Finally, the efficiency gain obtained by the introduced design space reduction is demonstrated by optimization examples.Publicationtype: Journal ArticleScopus© Citations 3 51 210 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A generalized approach for robust topology optimization using the first-order second-moment method for arbitrary response functions(2023-04-13); ; The paper presents a rigorous formulation of adjoint systems to be solved for a robust design optimization using the first-order second-moment method. This formulation allows to apply the method for any objective function, which is demonstrated by considering deformation at certain point and maximum stress as objectives subjected to random material stiffness and geometry, respectively. The presented approach requires the solution of at most three additional adjoint systems per uncertain system response, when compared to the deterministic case. Hence, the number of adjoint systems to be solved is independent of the number of random variables. This comes at the expense of accuracy, since the objective functions are assumed to be linear with respect to random parameters. However, the application to two standard cases and the validation with Monte Carlo simulations show that the approach is still able to find robust designs.Publicationtype: Journal ArticleScopus© Citations 5 90 123