Integration of fatigue life assessment into structural optimization of a railway vehicle bogie frame: development and application of a new methodology

Rolling stock manufacturers are finding innovative structural solutions to improve the quality and reliability of railway vehicles components. Structural optimization processes represent an effective strategy for reducing manufacturing costs, resulting in geometries easier to design and produce. In this framework, the present paper proposes a new methodology to design a railway metro bogie frame, combining structural–topological optimization methods and sensitivity analysis. In addition, manufacturing constraints were included to make the component design suitable for production through sand-casting. A robust sensitivity analysis has highlighted the most critical load conditions acting on the bogie frame. Its effectiveness was verified by carrying out two different structural optimizations based on different loadings. Two equivalent designs were obtained. Computational times were positively reduced by about 57%. The maximum value of stress was reduced about 23%. This new methodology has shown encouraging results to streamline the design process of this complex mechanical system, allowing researchers to also include manufacturing requirements.

Structural optimization is one of the important aspects of the overall ship design process. A number of advanced optimization tools are available. As these tools require large computational resources, they are not suitable for use in the preliminary design stages, when many important aspects of the design, such as overall dimensions and hull configurations, are determined. This article describes the development of an efficient structural weight optimization method for a trimaran hull form within the governing rules and regulations of the classification society. The method is particularly useful in the early design stage because of its simplicity. Two trimaran hull forms (HSTT-180 and TriFerry) are selected and optimized using the multi-island genetic algorithm optimization process. The weight optimization process achieved a weight reduction for HSTT-180 and TriFerry of about 32.85% and 8.95%, respectively. Furthermore, in both cases, optimum structural weights were obtained with smaller values of main-hull and side-hull stiffener spacing but higher values of main-hull frame spacing. Results obtained indicate that the structural weight of trimaran designs, such as those referenced here, can be substantially reduced by advanced optimization methods. 1. Introduction High-speed ships are of increasing interest for diverse applications, including military and commercial purposes. Hydrodynamic requirements for many high-speed concepts can only realistically be addressed by multihull designs since multihull ships provide better performance than monohull ships at high speeds. Trimaran hull form vessels, in particular, can offer several favorable characteristics over comparable monohulls, including fuel efficiency and the possibility of superior seakeeping characteristics (Fuentes et al. 2015). Trimarans are designed as Ro-Ro and passenger ships, ferries, containers, and military vessels. Multidisciplinary design and optimization (MDO) of trimarans, particularly at the synthesis design level, is an active area of research (Hefazi et al. 2010, 2011). A major element of the MDO process is the development of a structural optimization subsystem to investigate the impact of variations in vessel configurations on structural design and weight. A number of advanced analysis tools for calculation of structural weight are available. However, these tools require large computational resources and time and as such are not suitable to use in the preliminary design stages. Ironically, that is when many important aspects of the design, such as overall dimensions and hull configurations, are often determined. Therefore, an efficient method for calculation of structural weight, and its integration in an automated optimization process, is highly desirable and is the focus of this article. This approach allows structural weight optimization to be included in determining important principal characteristics of the ship at the early stage of the design.

Structural optimization was not prioritized in offshore structural design for various reasons. The environmental conditions have many uncertainties. The large-scale offshore structures have complicated systems and shapes. The design process is multidisciplinary. The structural design often only converges towards the end of the project thus very little adjustment will be possible. As a result, the objectives of structural optimization, reducing the cost and enhancing the performance by minimizing structural weight, are often not strong enough to motivate optimization in offshore structures design process. Despite all these factors, there were applications of structural optimization at local level and early design stage where the design space is relatively simple. In recent time, there are growing concerns about the cost and environmental impact of offshore structures. The optimization techniques in computer-aided engineering designs have been proven effective and robust by numerous applications in many industries. Under these circumstances, offshore structures are expected to implement more optimization process in the design. This paper presents a multi-level structural optimization approach in the topsides deck truss design of a floating offshore platform. First, in the concept study phase, fundamental design parameters including the numbers of decks and major truss rows are determined by parametric study. Next, the preliminary design uses topology optimization technique to find optimal material distribution under simplified load conditions and generate basic truss structure configurations. After the initial deck truss structural layout is set up, the individual member sizes are optimized based on Finite Element Analysis and code check results. Last, in the local deck joint detailed analysis, optimal shapes and sizes are selected for local reinforcement structures. The optimized offshore platform topsides deck truss structure demonstrates advantages of lower structural weight, lower manufacturing cost, higher performance, and quantifiable environmental benefits. The practical multi-level structural optimization approach presented in this study utilizes mathematical optimization algorithms, first principle design analysis, and design experiences. The design optimization approach has been effectively applied to the complex offshore platform deck truss system which takes a multi-year project to complete and often requires assumptions and approximated design data at early phase. The optimization approach can be applied to other marine and offshore systems like the floating offshore wind turbine concept development.

With the development of humanoid robots, lightweight construction and energy efficiency play an important role. In state-of-the-art processes and methods concerning structural optimization it is assumed that there exists a set of external loads or load functions acting on the part. But humanoid robots are very complex mechatronic systems. The fact that the system's dynamic properties and its overall behavior may change due to geometric modifications of a part caused by an optimization process is typically neglected. In order to take into account the interaction between the part, dynamic system, control system and the changing mechanical behavior with all its consequences for the optimization process, a simulation of the complete mechatronic system is integrated into the optimization process within the research work presented in this paper. A hybrid multibody system (MBS) simulation, that is, a MBS containing flexible bodies, in conjunction with a co-simulation of the control system represented by tools of the Computer Aided Control Engineering (CACE) is integrated into the optimization process. By an inner optimization loop the controller parameter are adopted new in each iteration of the topology optimization in order to provide realistic load cases. The research work presented in this paper is a contribution towards the integration of existing CAE methods into a continuous process for structural optimization. The benefits will be illustrated by an optimization of parts of the humanoid robot ARMAR of the collaborative research centre for ¿Humanoid Robots¿. The new process allows an efficient optimization of structures ¿within¿ their surrounding mechatronic system.

A CAD/CAE integrated framework for structural design optimization using sequential approximation optimization

I T IS a key subject in aircraft design to have the maximum lift and the minimum drag. Computational fluid dynamics (CFD)-based optimization design has been widely used to optimize rigid configurations in aircraft engineering [1,2]. As is well known, for a highaspect-ratio wing, structural deformation can reduce its cruise aerodynamic performance. Because the aeroelastic characteristic has not been considered in the original optimum design, the real flight performances will deviate from the rigid-design results. Therefore, an optimum design that contains the aeroelastic effect needs to be developed. However, the static aeroelastic optimization is usually coupled with the aerodynamicmodel, structural model, optimization algorithms, and even such related issues as response surface method (RSM), fluid–structure interface, and moving grid. Therefore, it is muchmore difficult and time-consuming than the CFD aerodynamic optimization. High-fidelity CFD tools are now available to aircraft designers and are commonly used in the design of aerodynamic configuration. CFD codes are capable of accurately predicting flowfields about complex aircraft configurations. Compared with linear aerodynamics, CFD tools are characterized by large computational costs due to complex geometrical modeling and grid generation. They require a higher level of proficiency from the users in defining run parameters and interpreting results. Endeavors to exploit CFD solvers for aeroelastic analyses and aircraft structural design are relatively advanced, due to the main hindrance of large computational cost. They are typically characterized by iterations between CFD and a structure solver, which aggravate the problem of computational cost. Bennett and Edwards [3] reviewed the current state of computational aeroelasticity (CA), where CA is defined as the numerical analyses of coupled CFD and structural dynamics. They listed the main efforts needed in the development of CA: that is, the reduction of elapsed run times, improvement of the credibility of CFD tools, and simplification of methods for further applications. For the interface between fluid and structure, Smith et al. [4] provided a comprehensive review of spline methods, their mathematical formulation, and practical applications, which contain the infinite plate spline (IPS) for plate configuration and the beam spline for fuselage configuration. A large amount of literature exists on the subject of grid deformation, which mainly contains the algebraic transfinite interpolation (TFI) [5], the springnetwork analogy by Batina [6] and Farhat et al. [7], and the boundary element method by Chen and Hill [8]. Even fluid–structuremultidisciplinary optimizationmay cut down the design cycles and reduce the reliance on wind-tunnel and flight tests; the drawbacks are the repeated fluid–structure coupling analyses during the optimization process. At present, there are very few studies that consider the adaptation of CFD-based aeroelastic analyses in structural-design optimization. Applications of CFDbased structural and multidisciplinary design optimization were reviewed by Guruswamy and Obayashi [9]. Martins et al. [10–12] presented a CFD-based methodology for aerodynamic-structural optimization. Cross sensitivity is computed by the adjoint method, which was shown to be vastly more computationally efficient for a supersonic business jet optimization involving a larger number of design variables than ever. Reuther et al. [13] presented a structural optimization in which the aerodynamic and structural analyses were performed separately. CFD analyses were used to generate a response surface, and the response surface was incorporated in the structural optimization to improve the roll maneuverability, in which only the generation of a response surface required an amount of CFD-based analyses. In this Note, a quadratic polynomial response surface model is put forward based on the CFD-based static aeroelastic calculations. The structure is represented by a finite element model and holds fixed in the optimization process. The genetic algorithm (GA) optimization method is used for the improvement of static aeroelastic performance through the optimization of the spanwise sectional airfoil shapes. To reduce the amount of CFD-based static aeroelastic analyses for the generation of RSM, only two sectional airfoils are chosen for the optimizations. Either for singleor multi-objectives optimizations, the study shows that the optimization of aeroelastic performances can be greatly improved compared with the original static aeroelastic results.

The paper presents the weight optimization process of the main steel truss that supports the Slatina Sport Hall roof. The structure was loaded with self-weight, dead loads, live loads, snow, wind and temperature, grouped in eleven load cases. The optimization of the structure was made using genetic algorithms implemented in a Matlab code. A total number of four different cases were taken into consideration when trying to determine the lowest weight of the structure, depending on the types of connections with the concrete structure ( types of supports, bearing modes), and the possibility of the lower truss chord nodes to change their vertical position. A number of restrictions for tension, maximum displacement and buckling were enforced on the elements, and the cross sections are chosen by the program from a user data base. The results in each of the four cases were analyzed in terms of weight, element tension, element section and displacement. The paper presents the optimization process and the conclusions drawn.

PurposeThis paper aims to summarize design rules based on the process characteristics of selective laser melting (SLM) and structural optimization and apply the design rules in the lightweight design of an aluminum alloy antenna bracket. The design goal is to reduce 30 per cent of the weight while maintaining the stress levels in the original part.Design/methodology/approachTo reduce weight as much as possible, the titanium alloy with higher specific strength was selected during the process of optimization. The material distribution of the bracket was improved by the topology optimization design. The redesign for SLM was used to obtain an optimization model, which was more suitable for SLM. The component performance was improved by shape optimization. The modal analysis data of the structural optimization model were compared with those of the stochastic lightweight model to verify the structural optimization model. The scanning data were compared with those of the original model to verify whether the model was suitable for SLM.FindingsStructural optimization design for antenna bracket realized the mass decrease of 30.43 per cent and the fundamental frequency increase of 50.18 per cent. The modal analysis data of the stochastic lightweight model and the structural optimization model indicated that the optimization performance of structural optimization method was better than that of the stochastic lightweight method. The comparison results between the scanning data of the forming part and the original data confirmed that the structural optimization design for SLM lightweight component could achieve the desired forming accuracy.Originality/valueThis paper summarizes geometric constraints in SLM and derives design rules of structural optimization based on the process characteristics of SLM. SLM design rules make structural optimization design more reasonable. The combination of structural optimization design and SLM can improve the performance of lightweight antenna bracket significantly.

The paper presents a practical methodology for design and structural verification of the locomotive bogie frames using a modern software package for design, structural verification and validation through combined, analytical and experimental methods. In the initial stage, the bogie geometry is imported from a CAD program into a finite element analysis program, such as Ansys. The analytical model validation is done by experimental modal analysis carried out on a finished bogie frame. The bogie frame own frequencies and own modes by both experimental and analytic methods are determined and the correlation analysis of the two types of models is performed. If the results are unsatisfactory, the structural optimization should be performed. If the results are satisfactory, the qualification procedures follow by static and fatigue tests carried out in a laboratory with international accreditation in the field. This paper presents an application made on bogie frames for the LEMA electric locomotive of 6000 kW.

This paper describes the structural optimization technique with FEM topography optimization technology, the design shape of engine components have been optimized based on the sensitivity analysis, and the purpose is to minimize the vibration of engine block. The process of structural optimization display that the vibration on FEM models of engine block as boundary conditions for subsequent topography optimization were used to be an output from dynamic response analysis, and the loads exerted on the FEM models are acquired from the multibody dynamics system, and topography optimization is performed to detect the effective design parameters of engine block shape to structural strength, to reduce the vibration velocity intensity. This paper presents today’s computer design capabilities in the simulation of the dynamic and vibration behaviour of engine and focuses on the relative merits of modification and full-scale structural optimization of engine, together with the creation of new low-vibration designs. The results verify the analysis, assessment and vibration optimization of the engine.

In this paper, we introduce a new topology of fuzzy polynomial neural networks (FPNNs) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs). The study offers a comprehensive design methodology involving mechanisms of genetic optimization, especially those exploiting genetic algorithms (GAs). Let us recall that the design of the FPNNs uses an extended group method of data handling (GMDH) and uses a fixed scheme of fuzzy inference (such as simplified, linear, and regression polynomial fuzzy inference) in each FPN of the network. It also considers a fixed number of input nodes (as being selected in advance by a network designer) at FPNs (or nodes) located in each layer. However such design process does not guarantee that the resulting FPNs will always result in an optimal networks architecture. Here, the development of the FPNN gives rise to a structurally optimized topology and comes with a substantial level of flexibility which becomes apparent when contrasted with the one we encounter in the conventional FPNNs. The design of each layer of the FPNN deals with its structural optimization involving a selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial forming a consequent part of fuzzy rules and a collection of the specific subset of input variables) and addresses detailed aspects of parametric optimization. Along this line, two general optimization mechanisms are explored. The structural optimization is realized via GAs. In case of the parametric optimization we proceed with a standard least square method-based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network becomes generated in a dynamic fashion. To evaluate the performance of the genetically optimized FPNN (gFPNN), we experimented with two time series data (gas furnace and chaotic time series) as well as some synthetic data. A comparative analysis reveals that the proposed FPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

Usually mechanical laws are applied to determine the structural response, for example deflections and stress state, while loads, boundary conditions, and geometry of the structure, i.e. the topology and the shape, are given. However, the mechanical principles can also be used to determine topology and shape of a structure for a prescribed structural response. This inverse method is called structural optimization. Since structural optimization deals in general with nonlinear and implicit functionals, only numerical methods have a chance to solve application-orientated problems in engineering design. Structural optimization can be distinguished into material, shape, and topology optimization depending on what is varied in the optimization process. The most challenging task is to determine the basic geometrical layout by topology optimization. In particular, recently the so-called material topology optimization of continuous structures has gained substantial interest both by mathematicians as well as engineers. The present contribution tries to consolidate these developments from an engineering point of view. In order to overcome problems of conventional numerical modeling techniques, material topology optimization is extended to geometrically adaptive methods. The adaptive discretization of the geometrical model in topology optimization turns out to be an appropriate way to obtain reliable results and simultaneously to reduce the numerical effort. This is verified by topology optimization problems with stress constraints and considering elastoplastic material behavior. The optimization based on either a linear or a nonlinear structural response leads to completely different results and shows the relevance of an appropriate mechanical model in the optimization process.

The composite materials, which have a high strength-to-weight ratio, are preferred for designing complex and light structures in various industries. Especially in the railway sector, the composites with lightweight property and flexibility in designs cause an increment in fuel efficiency and cost-effectiveness on overall rail vehicle developments. The fiber-reinforced polymer (FRP) composites meet the above requirements and are used in the production of rail components, such as bogie frames, floor materials, sleepers, switches, crossings, and other internal parts. These FRPs are good replacements for existing metal-based components interns of durability, high stiffness, and corrosion resistance. This review gives an overview of polymer composite's role in the railway sector for various applications and outlines the previous research works done on railway vehicles using FRP composites. This review will be beneficial for researchers to overcome all challenges in the railway sector and promotes new developments.

Since tremendous resources are consumed in the architecture, engineering, and construction (AEC) industry, the sustainability and efficiency in this field have received increasing concern in the past few decades. With the advent and development of computational tools and information technologies, structural optimization based on mathematical computation has become one of the most commonly used methods for the sustainable and efficient design in the field of civil engineering. However, despite the wide attention of researchers, there has not been a critical review of the recent research progresses on structural optimization yet. Therefore, the main objective of this paper is to comprehensively review the previous research on structural optimization, provide a thorough analysis on the optimization objectives and their temporal and spatial trends, optimization process, and summarize the current research limitations and recommendations of future work. The paper first introduces the significance of sustainability and efficiency in the AEC industry as well as the background of this review work. Then, relevant articles are retrieved and selected, followed by a statistical analysis of the selected articles. Thereafter, the selected articles are analyzed regarding the optimization objectives and their temporal and spatial trends. The four major steps in the structural optimization process, including structural analysis and modelling, formulation of optimization problems, optimization techniques, and computational tools and design platforms, are also reviewed and discussed in detail based on the collected articles. Finally, research gaps of the current works and potential directions of future works are proposed. This paper critically reviews the achievements and limitations of the current research on structural optimization, which provide guidelines for future research on structural optimization in the field of civil engineering.

Investigations on optimization of structures working in high-cycle load conditions were carried out and are presented in this paper. The development of a simple in application optimization algorithm for such structures was the main object of the authors. The work was concentrated on three principle areas: fatigue of material (with special regard to multiaxial criteria of high-cycle fatigue), parametric optimization of structures, and application of the finite element method. The investigations and numerical implementation of several high-cycle criteria were made and the most convenient one for optimization was selected. The main process of fatigue optimization was preceded by the testing of methods of structural optimization and the preparing the tools for improving the efficiency of the optimization algorithm. This stage includes preparation of software tools based on evolutionary algorithms. In addition, the decision variables were preselected through an investigation of the sensitivity of the objective function on small increments of these variables. The work was illustrated by examples of optimization of mechanical structures working in high-cycle load conditions. As observed in the computational examples, the proposed methodology of optimization allowed effectively lowering the mass of the studied structure while maintaining its durability on an established level. The tools and fatigue optimization methodology presented in this paper have universal character and can be applied to any case of a structure subjected to high-cycle loads.