Optimization of FRP composites against impact induced failure using island model parallel genetic algorithm

Quadratic Assignment Problem (QAP) is one of the most difficult combinatorial problems in the literature and has a diverse field of applications. This paper presents the results of experiments on the impact of parallelization of a sequential GA using island model. Both of the genetic algorithms are applied to the QAP. For the island model parallel GA, we systematically change the number of islands and investigate the effects of dividing the same global population into a number of subpopulations. The number of islands is gradually increased to observe the effects on solution quality and speedup in total execution time using different problem instances. The results clearly indicate that, while parallelized version outperforms sequential counterpart in both solution quality and total execution time, an increasing number of subpopulations also positively effects the results until a critical point where every subpopulation has a certain number of individuals to be able to evolve independently. Beyond that point, the performance of the algorithm begins to decrease.

A method for optimally designing laminated plates subject to fatigue loads for minimum weight using a cumulative damage constraint

Lightweight composite sandwich structures are widely used in the automotive industry, particularly in vehicle body applications, due to their advantageous properties, e.g. low density, high stiffness and high strength-to-weight ratio. The goal of the research was the elaboration of an optimization method for a totally Fiber Reinforced Plastic (FRP) sandwich structure in order to construct a minimal weight structure. The all-composite sandwich panel consists of a hexagonal FRP honeycomb core and laminated FRP face sheets. The article investigates the optimization of the FRP composite layers of the cell wall of the honeycomb core and the face sheets simultaneously. The Classical Lamination Theory (CLT) with analytical expressions was adopted to calculate the stiffness and strength of the sandwich components. The minimization of the total weight of the sandwich structure was the main goal during the structural optimization. Therefore, the design variables were the following: the orientation of the FRP layer for the cell wall of the core; furthermore the number and orientation of the face sheets’ layers. During the optimization, 5 design constraints which related to structure strength criteria were considered. I-sight software was used in conjunction with Excel software to perform the optimization process. Some of the feasible design points were numerically modelled using Abaqus Cae software and showed good agreement with the optimization result. The main added value of the research is the elaboration of the single weight optimization method for a totally FRP sandwich structure.KeywordsComposite sandwich structureWeight optimizationFRP honeycomb coreLaminated FRP face sheets

Bellows is a component in piping systems which absorbs mechanical deformation with flexibility. Its geometry is an axial symmetric shell which consists of two toroidal shells and one annular plate or conical shell. In order to analyze bellows, this study presents the finite element analysis using a conical frustum shell element. A finite element analysis is developed to analyze various bellows. The validity of the developed program is verified by the experimental results for axial and lateral stiffness. The formula for calculating the natural frequency of bellows is made by the simple beam theory. The formula for fatigue life is also derived by experiments. The shape optimal design problem is formulated using multiple objective optimization. The multiple objective functions are transformed to a scalar function by weighting factors. The stiffness, strength and specified stiffness are considered as the multiple objective function. The formulation has inequality constraints imposed on the fatigue limit, the natural frequencies, and the manufacturing conditions. Geometric parameters of bellows are the design variables. The recursive quadratic programming algorithm is selected to solve the problem. The results are compared to existing bellows, and the characteristics of bellows is investigated through optimal design process. The optimized shape of bellows is expected to give quite a good guideline to practical design.

Purpose: To introduce a novel heuristic that increases the efficiency of IMRT fluence optimization, by employing sampled dose values in evaluation of the objective function. Methods: Pencil beam dose matrices were computed using CERR, for 1‐cm square beamlets. These matrices were then uniformly sampled at various rates (1–10%), and the resulting approximate dose‐volume values compared to the true values for various fractional volumes. By varying the beamlet weights for 10,000 random sets, we estimated the error in these approximations to be <1%. For IMRT optimization, a least squares objective function was used, based on clinical dose‐volume objectives. In the initial stage of optimization, the method utilizes approximate dose values, determined by 1% or 5% dose matrix sampling, in objective function evaluation. This results in a significant reduction in computation time, since the manipulation of large dose matrices is avoided. The optimized beamlet weights are subsequently used as the starting point in the final optimization. This method was evaluated on prostate bed and esophageal patients, by comparing the CPU time required with that of a standard optimization method. The downhill simplex and simulated annealing algorithms were used for the evaluations. Results: The prostate bed patient was optimized with both a 1% and 5% sampling rate resulting in patient treatment plans of equal score, and a time savings of 18% and 42%, respectively. The esophageal patient was optimized with a 5% sampling rate and provided a score improvement of 23%, with a time savings of 17%. Conclusion: We have developed a method to be used in conjunction with existing optimization algorithms, which dramatically decreases the number of standard objective function evaluations and computation time. This technique may be applied to any type of optimization algorithm and set of clinical variables requiring pre‐computed dose matrices and objective functions involving dose‐volume objectives (eg. multi‐criteria optimization).

Fibre reinforced plastics (FRP) are being increasingly used for advanced applications where an appropriate mechanical performance should be achieved at minimum weight. A substantial increase of the FRP usage is expected across various industries e.g. in automotive sector in the nearest future. This leads to the mass manufacturing of FRP components. Reduction of manufacturing costs of FRP components is regarded as the main enabler for the usage of this material in mass production. Although FRP components are manufactured near-net-shape, they often have to be pierced or trimmed in one of the last manufacturing steps. With rising production numbers blanking is a potentially more cost efficient technology for trimming and piercing of FRP components compared to the conventionally performed abrasive water jet cutting or machining. The mechanisms of FRP separation in blanking have not yet been researched. In particular, the influence of the fibre orientation relative to the cutting line on the cutting force is not known. In the scope of this work an experimental study of blanking of a unidirectional carbon fibre reinforced plastic with a thermoset resin at different fibre orientations to the cutting line was performed. It was shown that the cutting force decreases from the perpendicular to the parallel fibre orientation to the cutting line. A possible mechanical explanation of this dependency was formulated.

This paper presents the first-ply failure analysis of laminated composite spherical shells by using the finite element method (FEM) in conjunction with Monte Carlo Simulation (MCS) approach. The material and geometric uncertainties caused by anisotropy and randomness inherent in the system configuration are predetermined in such structures and demand the stochastic analysis for realistic design. Finite element formulation is used to derive eight-noded iso-parametric quadratic elements. The input parameters include the ply orientation angle, assembly of ply, the number of layers, ply thickness and degree of orthotropy. The five failure criteria are considered, viz. maximum strain, maximum stress, Tsai-Hill, Tsai-Wu-Hahn and Tsai-Hill Hoffman theories. The variations in first-ply failure load are analysed. A deterministic study is carried out for the analyses of first-ply failure loads with respect to the mentioned failure criteria. The stochastic analysis is carried out firstly by the MCS approach. It is followed by implementation of the surrogate model. Radial basis function (RBF) is incorporated as the surrogate model. The current study predicts that the RBF surrogate model can be utilized to achieve similar computational efficiency with a significant reduction in computational time.

The research team proposed to develop innovative multi-scale models to predict corrosion and the resulting mechanical performances in aluminum-steel joints. The methods of joining considered are resistance spot welding, self-piercing riveting, and rivet-welding, all suitable for mass production applications. The multi-scale models integrate high throughput first-principle calculations based on density functional theory (DFT), high throughput calculation of phase diagrams (CALPHAD) modeling, and finite element method (FEM) simulations. These models are to be validated through laboratory experiments. Furthermore, the models are available as open source so as to enable scientists and engineers in the community to adapt and contribute to the development and application. The approaches rely on the research team’s extensive experience on the prediction of properties of individual phases at finite temperatures and variable compositions through DFT calculations, and our broad expertise on dissimilar material joining and their corrosion. The proposed computational framework enables high throughput computations for improved predictions of corrosion and the associated mechanical performance in dissimilar material joints, resulting in significant reduction in computational time needed by the current state-of-the-art methods. With the participation of researchers from three universities, an auto manufacturer, two manufacturing technology/equipment suppliers, and a software developer/vendor, the interdisciplinary research team applies the technical development on both phase-based modeling and laboratory experiments into the automobile body joining processes for validation and technology demonstration. The global cost of corrosion was estimated at about 3.4% of the global GDP in 2013. By using available corrosion control practices, it is estimated a saving between 15- 35% of the cost of corrosion. In the U.S., more than $276 billion is spent repairing corrosion damage. Prediction of the corrosion and its impact on performance of the dissimilar material joints is critical for reducing the massive number of the current corrosion-based recalls for automobiles. Thus, the project goal is to develop models to enable predictive maintenance and end-of-life planning of multi-metal joints with risk of corrosion under different conditions such as exposure to high temperatures in summer and salt solutions in winter, quantified through its pH. An academia-industry consortium led by the University of Michigan and including Pennsylvania State University, University of Illinois Urbana-Champaign, University of Georgia, General Motors Company, Livermore Software Technology Corporation, and Optimal Process Technologies, LLC. created multi-scale models for prediction of corrosion in aluminum-steel joint structures such of them used in vehicle subassemblies – chassis and transmission systems. Starting from the first principle calculations, the team developed mathematical and data-driven models to predict the metallic components, which are formed during joining of two metals, for example aluminum and steel - a lightweight multilateral system which is currently used in more than 60% car bodies. These models were used for simulating chemical reactions that are happening when the joining metallic components are exposed to high temperatures and different pH values. The team was able to predict how the corrosion installs on the metallic components and how they lead to a sudden failure of components in cars. Newly developed machine learning algorithms combining Science, Technology, Engineering and Math disciplines, advanced finite element simulation and experimental validations have been integrated in a platform for prediction of the corrosion evolution and prediction the failure of joints under mechanical loadings and fatigue. Moreover, based on machine learning and inverse analysis, the team proposed solutions for designing new metallic alloys less susceptible to corrosion when joining multi-material assembles. An average of 4% error compared with experiments was achieved for the most common joints that are used in vehicle subassemblies.

A robust and generalized effective DAE framework encompassing different methods, algorithms, and model order reduction for linear and nonlinear second order dynamical systems

Finite element methods have been applied to evaluate in vivo joint behavior, new devices, and surgical techniques but have typically been applied to a small or single subject cohort. Anatomic variability necessitates the use of many subject-specific models or probabilistic methods in order to adequately evaluate a device or procedure for a population. However, a fully deformable finite element model can be computationally expensive, prohibiting large multisubject or probabilistic analyses. The aim of this study was to develop a group of subject-specific models of the patellofemoral joint and evaluate trade-offs in analysis time and accuracy with fully deformable and rigid body articular cartilage representations. Finite element models of eight subjects were used to tune a pressure-overclosure relationship during a simulated deep flexion cycle. Patellofemoral kinematics and contact mechanics were evaluated and compared between a fully deformable and a rigid body analysis. Additional eight subjects were used to determine the validity of the rigid body pressure-overclosure relationship as a subject-independent parameter. There was good agreement in predicted kinematics and contact mechanics between deformable and rigid analyses for both the tuned and test groups. Root mean square differences in kinematics were less than 0.5 deg and 0.2 mm for both groups throughout flexion. Differences in contact area and peak and average contact pressures averaged 5.4%, 9.6%, and 3.8%, respectively, for the tuned group and 6.9%, 13.1%, and 6.4%, respectively, for the test group, with no significant differences between the two groups. There was a 95% reduction in computational time with the rigid body analysis as compared with the deformable analysis. The tuned pressure-overclosure relationship derived from the patellofemoral analysis was also applied to tibiofemoral (TF) articular cartilage in a group of eight subjects. Differences in contact area and peak and average contact pressures averaged 8.3%, 11.2%, and 5.7% between rigid and deformable analyses in the tibiofemoral joint. As statistical, probabilistic, and optimization techniques can require hundreds to thousands of analyses, a viable platform is crucial to component evaluation or clinical applications. The computationally efficient rigid body platform described in this study may be integrated with statistical and probabilistic methods and has potential clinical application in understanding in vivo joint mechanics on a subject-specific or population basis.

The finite element method (FEM) hybridized with a boundary integral (BI) formulation is used to solve for the radiation characteristics of finite arrays of cavity-backed slot (CBS) antennas. The global matrix system representing the finite array is formed by judiciously decomposing the domain into sub-domains thus allowing us to save on memory due to repetitive geometrical features. Further memory savings and reduction in computational time are achieved by introducing singular value decomposition (SVD) on the so-called interaction matrices. The final global block matrix system is solved iteratively using stationary iteration techniques. Coupling parameters among radiating elements are computed effectively and fast as compared to the direct application of the FE-BI on the entire array. (6 pages)

Optimal sizing of PV/wind/diesel hybrid microgrid system using multi-objective self-adaptive differential evolution algorithm

Shape optimization in laminated composite plates

The sandwich composites fuselages appear to be a promising choice for the future aircrafts because of their structural efficiency and functional integration advantages. However, the design of sandwich composites is more complex than other structures because of many involved variables. In this paper, the fuselage is designed as a sandwich composites cylinder, and its structural optimization using the finite element method (FEM) is outlined to obtain the minimum weight. The constraints include structural stability and the composites failure criteria. In order to get a verification baseline for the FEM analysis, the stability of sandwich structures is studied and the optimal design is performed based on the analytical formulae. Then, the predicted buckling loads and the optimization results obtained from a FEM model are compared with that from the analytical formulas, and a good agreement is achieved. A detailed parametric optimal design for the sandwich composites cylinder is conducted. The optimization method used here includes two steps: the minimization of the layer thickness followed by tailoring of the fiber orientation. The factors comprise layer number, fiber orientation, core thickness, frame dimension and spacing. Results show that the two-step optimization is an effective method for the sandwich composites and the foam sandwich cylinder with core thickness of 5 mm and frame pitch of 0.5 m exhibits the minimum weight.

In this paper, the transient electrical-thermal co-simulation is implemented with the finite-element method. The capability of the co-simulation is extended to solve large-scale problems by incorporating a domain decomposition scheme called the finite element tearing and interconnecting (FETI). With the high parallel efficiency of FETI for computing with multiple processors, a significant reduction in computation time is achieved. The transient electrical-thermal behaviors of largescale power distribution networks (PDNs), including on-chip power grids, solder bump arrays, and TSV-based PDN are simulated and analyzed with the proposed method. The impacts on the design of the PDNs from different types of input pulses, power maps, and via pitches are also investigated.