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Browsing by Subject "structural optimization"
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Item Design for Crashworthiness of Categorical Multimaterial Structures Using Cluster Analysis and Bayesian Optimization(ASME, 2019-12) Liu, Kai; Wu, Tong; Detwiler, Duane; Panchal, Jitesh; Tovar, Andres; Mechanical and Energy Engineering, School of Engineering and TechnologyThis work introduces a cluster-based structural optimization (CBSO) method for the design of categorical multimaterial structures subjected to crushing, dynamic loading. The proposed method consists of three steps: conceptual design generation, design clustering, and Bayesian optimization. In the first step, a conceptual design is generated using the hybrid cellular automaton (HCA) algorithm. In the second step, threshold-based cluster analysis yields a lower-dimensional design. Here, a cluster validity index for structural optimization is introduced in order to qualitatively evaluate the clustered design. In the third step, the optimal design is obtained through Bayesian optimization, minimizing a constrained expected improvement function. This function allows to impose soft constraints by properly redefining the expected improvement based on the maximum constraint violation. The Bayesian optimization algorithm implemented in this work has the ability to search over (i) a real design space for sizing optimization, (ii) a categorical design space for material selection, or (iii) a mixed design space for concurrent sizing optimization and material selection. With the proposed method, materials are optimally selected based on multiple attributes and multiple objectives without the need for material ranking. The effectiveness of this approach is demonstrated with the design for crashworthiness of multimaterial plates and thin-walled structures.Item Development of ABAQUS-MATLAB Interface for Design Optimization using Hybrid Cellular Automata and Comparison with Bidirectional Evolutionary Structural Optimization(2021-12) Antony, Alen; Tovar, Andres; Nematollahi, Khosrow; Du, XiaopingTopology Optimization is an optimization technique used to synthesize models without any preconceived shape. These structures are synthesized keeping in mind the minimum compliance problems. With the rapid improvement in advanced manufacturing technology and increased need for lightweight high strength designs topology optimization is being used more than ever. There exist a number of commercially available software's that can be used for optimizing a product. These software have a robust Finite Element Solver and can produce good results. However, these software offers little to no choice to the user when it comes to selecting the type of optimization method used. It is possible to use a programming language like MATLAB to develop algorithms that use a specific type of optimization method but the user himself will be responsible for writing the FEA algorithms too. This leads to a situation where the flexibility over the optimization method is achieved but the robust FEA of the commercial FEA tool is lost. There have been works done in the past that links ABAQUS with MATLAB but they are primarily used as a tool for finite element post-processing. Through this thesis, the aim is to develop an interface that can be used for solving optimization problems using different methods like hard-kill as well as the material penalization (SIMP) method. By doing so it's possible to harness the potential of a commercial FEA software and gives the user the requires flexibility to write or modify the codes to have an optimization method of his or her choice. Also, by implementing this interface, it can also be potentially used to unlock the capabilities of other Dassault Systèmes software's as the firm is implementing a tighter integration between all its products using the 3DExperience platform. This thesis as described uses this interface to implement BESO and HCA based topology optimization. Since hybrid cellular atomata is the only other method other than equivalent static load method that can be used for crashworthiness optimization this work suits well for the role when extended into a non-linear region.Item Metamodel-Based Global Optimization of Vehicle Structures for Crashworthiness Supported by Clustering Methods(Springer, 2018) Liu, Kai; Detwiler, Duane; Tovar, Andres; Mechanical Engineering, School of Engineering and TechnologyThis work introduces a metamodel-based global optimization method for crashworthiness with the ability to synthesize continuum structures with an optimal distribution of material phases or gauges. The proposed optimization method makes use of fully nonlinear, dynamic crash simulations and consists of three main elements: (1) the generation of a conceptual design from the structures crash response, (2) the optimal clustering of the conceptual design to define the location of the material phases or gauges, (3) the metamodel-based global optimization, which aims to find the optimal settings for each cluster. The conceptual design can be generated from extracting finite element analysis information or by using structural optimization. The conceptual design is then clustered using clustering analysis to reduce the dimension of the design space. The global optimization problem aims to find the optimal material distribution on the reduced design space using metamodels. The metamodels are built using sampling and cross-validation, and sequentially updated using an expected improvement function until convergence. The proposed methodology is demonstrated using examples from multi-objective crashworthiness design examples.Item Shape optimization of lightweight structures under blast loading(2013-05) Israel, Joshua James; Tovar, Andres; Wasfy, Tamer; El-Mounayri, HazimStructural optimization of vehicle components for blast mitigation seeks to counteract the damaging effects of an impulsive threat on occupants and critical components. The strong and urgent need for improved protection from blast events has made blast mitigating component design an active research subject. Standard up-armoring of ground vehicles can significantly increase the mass of the vehicle. Without concurrent modifications to the power train, suspension, braking and steering components, the up-armored vehicles suffer from degraded stability and mobility. For these reasons, there is a critical need for effective methods to generate lightweight components for blast mitigation. The overall objective of this research is to make advances in structural design methods for the optimization of lightweight blast-mitigating systems. This thesis investigates the automated design process of isotropic plates to mitigate the effects of blast loading by addressing the design of blast-protective structures from a design optimization perspective. The general design problem is stated as finding the optimum shape of a protective shell of minimum mass satisfying deformation and envelops constraints. This research was conducted in terms of three primary research projects. The first project was to investigate the design of lightweight structures under deterministic loading conditions and subject to the same objective function and constraints, in order to compare feasible design methodologies through the expansion of the problem dimension in order to reach the limits of performance. The second research project involved the investigation of recently developed uncertainty quantification methods, the univariate dimensional reduction method and the performance moment integration method, to structures under stochastic loading conditions. The third research project involved application of these uncertainty quantification methods to problems of design optimization under uncertainty, in order to develop a methodology for the generation of lightweight reliable structures. This research has resulted in the construction of a computational framework, incorporating uncertainty quantification methods and various optimization techniques, which can be used for the generation of lightweight structures for blast mitigation under uncertainty. Applied to practical structural design problems, the results demonstrate that the methodologies provide a practical tool to aid the design engineer in generating design concepts for blast-mitigating structures. These methods can be used to advance research into the generation of reliable structures under uncertain loading conditions inherent to blast events.Item Structural Optimization of Thin-Walled Tubular Structures for Progressive Collapse Using Hybrid Cellular Automaton with a Prescribed Response Field(SAE, 2019) Valladares, Homero; Najmon, Joel; Tovar, Andres; Mechanical and Energy Engineering, School of Engineering and TechnologyThe design optimization of thin-walled tubular structures is of relevance in the automotive industry due to their low cost, ease of manufacturing and installation, and high-energy absorption efficiency. This study presents a methodology to design thin-walled tubular structures for crashworthiness applications. During an impact, thin-walled tubular structures may exhibit progressive collapse/buckling, global collapse/buckling, or mixed collapse/buckling. From a crashworthiness standpoint, the most desirable collapse mode is progressive collapse due to its high-energy absorption efficiency, stable deformation, and low peak crush force (PCF). In the automotive industry, thin-walled components have complex structural geometries. These complexities and the several loading conditions present in a crash reduce the possibility of progressive collapse. The Hybrid Cellular Automata (HCA) method has shown to be an efficient continuum-based approach in crashworthiness design. All the current implementations of the HCA method use a scalar set point to design structures with a uniform distribution of a field variable, e.g., stress, strain, internal energy density (IED), mutual potential energy. For example, using IED and mutual potential energy as the field variable result in high stiffness and progressive collapsing structures, respectively. This paper presents a modified version of the HCA method to design thin-walled structures that collapse progressively. In this methodology, the set point has two components, a prescribed response field, which promotes progressive collapse, and a variable offset value, which satisfies the mass constraint. The numerical examples show that this modified HCA method is capable of finding material distributions that exhibit progressive collapse, resulting in significant improvement in specific energy absorption (SEA) with relatively little change in the PCF.Item Topology Optimization of Plastic Parts for Injection Molding(ASME, 2020-01) Oliver, Kathryn; Anwar, Sohel; Tova, Andres; Mechanical and Energy Engineering, School of Engineering and TechnologyTopology optimization is broadly recognized as a design approach to generate high-performance conceptual designs suitable for freeform fabrication, e.g., additive manufacturing. When other fabrication methods are considered, topology optimization must integrate manufacturing constraints. The integration of constraints for extrusion and casting has been addressed in the past by a few researcher groups. In this work, extrusion and casting constraints are revisited and extended to include plastic injection. The proposed method relies on the use of intersection planes and the definition of a parting line within the planes. The resulting topologies can be injected in a two-plate mold without the use of inserts. The implementation and results of the proposed approach are demonstrated in classic three-dimensional problems that include a cantilevered beam with different load conditions.