Particle swarm optimization with sequential niche technique for dynamic finite element model updating

Peer reviewedPostprin

Design and optimization of self-deployable damage tolerant composite structures: A review

Composite deployable structures are becoming increasingly important for the space industry, emerging as an alternative to conventional metallic mechanical systems in space applications. In most cases, the life-cycle of these structures includes a single deployment sequence, once the spacecraft is in orbit. So long as reliability is ensured, this fact opens the possibility of using the materials past their elastic regime and, possibly, beyond the initiation of damage, increasing the efficiency and applicability of the developed designs. This review explores this possibility, surveying the design of deployable structures, as well as the state of the art on the design and damage tolerance in composites. An overview of the developments performed on the topology optimization of composite structures is included for its novelty and potential application in the design of deployable structures. Finally, the possibility of combining these topics into a single efficient design approach is discussed

Adaptiver Suchansatz zur multidisziplinären Optimierung von Leichtbaustrukturen unter Verwendung hybrider Metaheuristik

Within the last few years environmental regulations, safety requirements and market competitions forced the automotive industry to open up a wide range of new technologies. Lightweight design is considered as one of the most innovative concepts to fulfil environmental, safety and many other objectives at competitive prices. Choosing the best design and production process in the development period is the most significant link in the automobile production chain. A wide range of design and process parameters needs to be evaluated to achieve numerous goals of production. These goals often stand in conflict with each other. In addition to the variation of the concepts and following the objectives, some limitations such as manufacturing restrictions, financial limits, and deadlines influence the choice of the best combination of variables. This study introduces a structural optimization tool for assemblies made of sheet metal, e.g. the automobile body, based on parametrization and evaluation of concepts in CAD and CAE. This methodology focuses on those concepts, which leads to the use of the right amount of light and strong material in the right place, instead of substituting the whole structure with the new material. An adaptive hybrid metaheuristic algorithm is designed to eliminate all factors that would lead to a local minimum instead of global optimum. Finding the global optimum is granted by using some explorative and exploitative search heuristics, which are intelligently organized by a central controller. Reliability, accuracy and the speed of the proposed algorithm are validated via a comparative study with similar algorithms for an academic optimization problem, which shows valuable results. Since structures might be subject to a wide range of load cases, e.g. static, cyclic, dynamic, temperature-dependent etc., these requirements need to be addressed by a multidisciplinary optimization algorithm. To handle the nonlinear response of objectives and to tackle the time-consuming FEM analyses in crash situations, a surrogate model is implemented in the optimization tool. The ability of such tool to present the optimum results in multi-objective problems is improved by using some user-selected fitness functions. Finally, an exemplary sub-assembly made of sheet metal parts from a car body is optimized to enhance both, static load case and crashworthiness.Die Automobilindustrie hat in den letzten Jahren unter dem Druck von Umweltvorschriften, Sicherheitsanforderungen und wettbewerbsfähigem Markt neue Wege auf dem Gebiet der Technologien eröffnet. Leichtbau gilt als eine der innovativsten und offenkundigsten Lösungen, um Umwelt- und Sicherheitsziele zu wettbewerbsfähigen Preisen zu erreichen. Die Wahl des besten Designs und Verfahrens für Produktionen in der Entwicklungsphase ist der wichtigste Ring der Automobilproduktionskette. Um unzählige Produktionsziele zu erreichen, müssen zahlreiche Design- und Prozessparameter bewertet werden. Die Anzahl und Variation der Lösungen und Ziele sowie einige Einschränkungen wie Fertigungsbeschränkungen, finanzielle Grenzen und Fristen beeinflussen die Auswahl einer guten Kombination von Variablen. In dieser Studie werden strukturelle Optimierungswerkzeuge für aus Blech gefertigte Baugruppen, z. Karosserie, basierend auf Parametrisierung und Bewertung von Lösungen in CAD bzw. CAE. Diese Methodik konzentriert sich auf die Lösungen, die dazu führen, dass die richtige Menge an leichtem / festem Material an der richtigen Stelle der Struktur verwendet wird, anstatt vollständig ersetzt zu werden. Eine adaptive Hybrid-Metaheuristik soll verhindern, dass alle Faktoren, die Bedrohungsoptimierungstools in einem lokalen Minimum konvergieren, anstelle eines globalen Optimums. Das Auffinden des globalen Optimums wird durch einige explorative und ausbeuterische Such Heuristiken gewährleistet. Die Zuverlässigkeit, Genauigkeit und Geschwindigkeit des vorgeschlagenen Algorithmus wird mit ähnlichen Algorithmen in akademischen Optimierungsproblemen validiert und führt zu respektablen Ergebnissen. Da Strukturen möglicherweise einem weiten Bereich von Lastfällen unterliegen, z. statische, zyklische, dynamische, Temperatur usw. Möglichkeit der multidisziplinären Optimierung wurde in Optimierungswerkzeugen bereitgestellt. Um die nichtlineare Reaktion von Zielen zu überwinden und um den hohen Zeitverbrauch von FEM-Analysen in Absturzereignissen zu bewältigen, könnte ein Ersatzmodell vom Benutzer verwendet werden. Die Fähigkeit von Optimierungswerkzeugen, optimale Ergebnisse bei Problemen mit mehreren Zielsetzungen zu präsentieren, wird durch die Verwendung einiger vom Benutzer ausgewählten Fitnessfunktionen verbessert. Eine Unterbaugruppe aus Blechteilen, die zur Automobilkarosserie gehören, ist optimiert, um beide zu verbessern; statischer Lastfall und Crashsicherheit

Design and Fabrication of Components with Optimized Lattice Microstructures

The design and fabrication of components with optimized lattice microstructures is a new approach to creating lightweight high-performance objects. This paper introduces a unique and complete integration of design and fabrication leading to the creation of structural components with complex composite microstructures. Rather than a solid cast component with optimized outer shape this new approach leads to a component with an inner skeleton or microstructure maximizing one or more properties such as the stiffness-to-weight ratio. Three dimensional gradient materials are a natural outcome of this approach. An introduction to the design optimization and hybrid fabrication approach will be provided in addition to research progress and challenges through Spring 2004.Mechanical Engineerin

Gnowee: A Hybrid Metaheuristic Optimization Algorithm for Constrained, Black Box, Combinatorial Mixed-Integer Design

This paper introduces Gnowee, a modular, Python-based, open-source hybrid metaheuristic optimization algorithm (Available from https://github.com/SlaybaughLab/Gnowee). Gnowee is designed for rapid convergence to nearly globally optimum solutions for complex, constrained nuclear engineering problems with mixed-integer and combinatorial design vectors and high-cost, noisy, discontinuous, black box objective function evaluations. Gnowee's hybrid metaheuristic framework is a new combination of a set of diverse, robust heuristics that appropriately balance diversification and intensification strategies across a wide range of optimization problems. This novel algorithm was specifically developed to optimize complex nuclear design problems; the motivating research problem was the design of material stack-ups to modify neutron energy spectra to specific targeted spectra for applications in nuclear medicine, technical nuclear forensics, nuclear physics, etc. However, there are a wider range of potential applications for this algorithm both within the nuclear community and beyond. To demonstrate Gnowee's behavior for a variety of problem types, comparisons between Gnowee and several well-established metaheuristic algorithms are made for a set of eighteen continuous, mixed-integer, and combinatorial benchmarks. These results demonstrate Gnoweee to have superior flexibility and convergence characteristics over a wide range of design spaces. We anticipate this wide range of applicability will make this algorithm desirable for many complex engineering applications.Comment: 43 pages, 7 tables, 6 figure

A Bayesian Approach to Computer Model Calibration and Model-Assisted Design

Computer models of phenomena that are difficult or impossible to study directly are critical for enabling research and assisting design in many areas. In order to be effective, computer models must be calibrated so that they accurately represent the modeled phenomena. There exists a rich variety of methods for computer model calibration that have been developed in recent decades. Among the desiderata of such methods is a means of quantifying remaining uncertainty after calibration regarding both the values of the calibrated model inputs and the model outputs. Bayesian approaches to calibration have met this need in recent decades. However, limitations remain. Whereas in model calibration one finds point estimates or distributions of calibration inputs in order to induce the model to reflect reality accurately, interest in a computer model often centers primarily on its use for model-assisted design, in which the goal is to find values for design inputs to induce the modeled system to approximate some target outcome. Existing Bayesian approaches are limited to the first of these two tasks. The present work develops an approach adapting Bayesian methods for model calibration for application in model-assisted design. The approach retains the benefits of Bayesian calibration in accounting for and quantifying all sources of uncertainty. It is capable of generating a comprehensive assessment of the Pareto optimal inputs for a multi-objective optimization problem. The present work shows that this approach can apply as a method for model-assisted design using a previously calibrated system, and can also serve as a method for model-assisted design using a model that still requires calibration, accomplishing both ends simultaneously

Manufacturing-constrained multi-objective optimization of local patch reinforcements for discontinuous fiber reinforced composite parts

In this work, contributes to the optimization of local continuous fiber reinforcement patches, under consideration of manufacturing constraints. This approach requires specific optimization strategies. Therefore, an multi-objective optimization strategy for the placement of local reinforcement patches, under consideration of manufacturing constraints, has been developed. During the multi objective optimization, structural and process related objectives are considered