Deformation behavior of a stiffened panel subjected to underwater shock loading using the non-linear finite element method

Given the superior strength-to-weight ratio, stiffened panels have been used extensively in the main structure of ships and underwater vehicles. The loads acting on a stiffened panel in a ship is in-plane compression or tension, resulting from the overall hull-girder bending moment or torsion, shear force resulting from the hull-girder shear force, and lateral pressure resulting from the external wave or shock loading. This work addresses the transient responses of a panel structure reinforced by ribs of different sizes to underwater shock loads using non-linear finite element code-ABAQUS. Verification of the reliability was made between the Ramajeyathilagam’s experiments results [Ramajeyathilagam K, Vendhan CP, Rao VB. Non-linear transient dynamic response of rectangular plates under shock loading. Int J Impact Eng 2000;24:999–1015, Ramajeyathilagam K, Vendhan CP. Deformation and rupture of thin rectangular plates subjected to underwater shock. Int J Impact Eng 2004;30:699–719] at several different locations on the plates. The shock factor is adopted to describe the shock severity. Additionally, the displacement–time histories under different shock loadings are presented which will be used in designing stiffened panels so as to enhance resistance to underwater shock damage.     

Inverse modelling of multi-objective thermodynamically optimized turbojet engines using GMDH-type neural networks and evolutionary algorithms

A novel approach is presented in this article for obtaining inverse mapping of thermodynamically Pareto-optimized ideal turbojet engines using group method of data handling (GMDH)-type neural networks and evolutionary algorithms (EAs). EAs are used in two different aspects. Firstly, multi-objective EAs (non–dominated sorting genetic algorithm-II) with a new diversity preserving mechanism are used for Pareto-based optimization of the thermodynamic cycle of ideal turbojet engines considering four important conflicting thermodynamic objectives, namely, specific thrust ({ST}), specific fuel consumption ({SFC}), propulsive efficiency (η_p), and thermal efficiency (η_t). The best obtained Pareto front, as a result, is a data table representing data pairs of non-dominated vectors of design variables, which are Mach number and pressure ratio, and the corresponding four objective functions. Secondly, EAs and singular value decomposition are deployed simultaneously for optimal design of both connectivity configuration and the values of coefficients, respectively, involved in GMDH-type neural networks which are used for the inverse modelling of the input–output data table obtained as the best Pareto front. Therefore, two different polynomial relations among the four thermo-mechanical objectives and both Mach number and pressure ratio are searched using that Pareto front. The results obtained in this paper are very promising and show that such important relationships may exist and could be discovered using both multi-objective EAs and evolutionarily designed GMDH-type neural networks.     

Damage monitoring of CFRP stiffened panels under compressive load using FBG sensors

FBG sensors were embedded in each of two CFRP stiffened panels fabricated by VaRTM. Low-velocity impacts were applied to one of the panels in order to compare the methods of monitoring impact events using FBG sensors. The main impact damage was an interlaminar delamination inside the skin, which could be observed by an ultrasonic C-scan. A monitoring method using the full spectral signals was more effective in evaluating the impact damages in detail than that using the center wavelength. Following the impact tests, buckling behaviors were investigated under compressive loading using FBG sensors and surface-attached strain gauges. The FBG sensors could evaluate strain changes resulting from buckling behaviors under relatively low compressive loading. They could also evaluate damage growth until the final failure and difference of buckling behaviors between panels with and without impact damages.     

Experimental and theoretical investigations on stiffened and unstiffened composite panels under uniform transverse loading

An experimental investigation on fibre-reinforced stiffened and unstiffened panels under transverse uniform pressure has been carried out. The deflections and strains measured inside the laminate are compared with a finite element analysis. The effect of one or two stiffeners within the panel is small since the stiffener fail at small loads. In contrary to this, the effect of a different lay-up is big, because the angle-ply panels [+45°/−45°]₂ carried higher loads than the cross-ply panels [0°/90°]₂. The failure of the panels near the centre of the long edge at the clamping is correctly predicted by the FEA. In addition, calculated and predicted stresses are close to each other.     

Analyzing Fe–Zn system using molecular dynamics, evolutionary neural nets and multi-objective genetic algorithms

Failure behavior of Zn coated Fe is simulated through molecular dynamics (MD) and the energy absorbed at the onset of failure along with the corresponding strain of the Zn lattice are computed for different levels of applied shear rate, temperature and thickness. Data-driven models are constructed by feeding the MD results to an evolutionary neural network. The outputs of these neural networks are utilized to carry out a multi-objective optimization through genetic algorithms, where the best possible tradeoffs between two conflicting requirements, minimum deformation and maximum energy absorption at the onset of failure, are determined by constructing a Pareto frontier.     

Analysis for buckling and vibrations of composite stiffened shells and plates

This paper deals with development of triangular finite element for buckling and vibration analysis of laminated composite stiffened shells. For the laminated shell, an equivalent layer shell theory is employed. The first-order shear deformation theory including extension of the normal line is used. In order to take into account a non-homogeneous distribution of the transverse shear stresses a correction of transverse shear stiffness is employed. Based on the equivalent layer theory with six degrees of freedom (three displacements and three rotations), a finite element that ensures C⁰ continuity of the displacement and rotation fields across inter-element boundaries has been developed. Numerical examples are presented to show the accuracy and convergence characteristics of the element. Results of vibration and buckling analysis of stiffened plates and shells are discussed.     

A variational asymptotic approach for hygrothermal analysis of composite laminates

A hygrothermal model for analyzing composite laminates under both mechanical and hygrothermal loadings is constructed by the variational asymptotic method (VAM). The original 3-D nonlinear, one-way coupled, hygrothermoelasticity problem is formulated based on a set of intrinsic variables defined on the reference plane and for arbitrary deformation of the normal line. Then the VAM is used to rigorously split the 3-D problem into two problems: a nonlinear, 2-D, plate analysis over the reference plane to obtain the global deformation and a linear analysis through the thickness to provide the 2-D generalized constitutive law and the recovering relations to approximate the original 3-D results. The nonuniqueness of asymptotic theory correct up to a certain order is used to cast the obtained asymptotically correct second-order free energy into a Reissner–Mindlin type model to account for transverse shear deformation. The present theory is implemented into the computer program, Variational Asymptotic Plate and Shell Analysis (VAPAS). Results from VAPAS for several cases have been compared with the exact hygrothermal solutions, classical lamination theory and first-order shear-deformation theory to demonstrate the accuracy and power of the proposed theory and use of VAPAS. The proposed theory can achieve an accuracy comparable to high-order layerwise theories with many more degrees of freedom at the cost of a first-order shear-deformation theory.     

The use of a genetic algorithm to improve the postbuckling strength of stiffened composite panels susceptible to secondary instabilities

The use of genetic algorithms (GAs) for structural optimisation is well established but little work has been reported on the inclusion of damage variables within an optimisation framework. This approach is particularly useful in the optimisation of composite structures which are prone to delamination damage. In this paper a challenging design problem is presented where the objective was to delay the catastrophic failure of a postbuckling secondary-bonded stiffened composite panel susceptible to secondary instabilities. It has been conjectured for some time that the sudden energy release associated with secondary instabilities may initiate structural failure, but this has proved difficult to observe experimentally. The optimisation methodology confirmed this indirectly by evolving a panel displaying a delayed secondary instability whilst meeting all other design requirements. This has important implication in the design of thin-skinned lightweight aerostructures which may exhibit this phenomenon.     

Optimal shape control of functionally graded smart plates using genetic algorithms

This paper deals with optimal shape control of functionally graded smart plate containing patches of piezoelectric sensors and actuators. The genetic algorithm (GA) is designed to search for optimal actuator voltage and displacement control gains for the shape control of the functionally graded material (FGM) plates. The work extends the earlier finite element formulations of the two leading authors, so that it can be readily treated using genetic algorithms. Numerical results have been obtained to study the effect of the shape control of the FGM plates under a temperature gradient by optimising (i) the voltage distribution for the open loop control, and (ii) the displacement control gain values for the closed loop feedback control. The effect of the constituent volume fractions of zirconia, through varying the volume fraction exponent n, on the optimal voltages and gain values has also been examined.     

量子神经计算和量子遗传算法的理论分析和应用

经过比较研究发现,在量子计算与神经网络和遗传算法之间,不论在计算思想上还是模型表达上,都存在着许多相似之处,这些相似性启发人们去研究基于量子理论的神经网络和遗传算法模型,一方面探索神经网络和遗传算法在量子系统上的实现方法,另一方面研究量子理论启发下的新的神经网络与遗传算法模型。本文总结了本课题组近年来在量子计算与神经网络和遗传算法相结合领域的研究工作,包括量子系统实现神经计算的理论分析,量子神经网络物理模型的研究,基于量子概率表达的量子遗传算法及其应用研究等,并对今后的发展提出了展望。     

A calibration framework for discrete element model parameters using genetic algorithms

In this research, a universal framework for automated calibration of microscopic properties of modeled granular materials is proposed. The proposed framework aims at industrial scale applications, where optimization of the computational time step is important. It can be generally applied to all types of DEM simulation setups. It consists of three phases: data base generation, parameter optimization, and verification. In the first phase, DEM simulations are carried out on a multi-dimensional grid of sampled input parameter values to generate a database of macroscopic material responses. The database and experimental data are then used to interpolate the objective functions with respect to an arbitrary set of parameters. In the second phase, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to solve the calibration multi-objective optimization problem. In the third phase, the DEM simulations using the results of the calibrated input parameters are carried out to calculate the macroscopic responses that are then compared with experimental measurements for verification and validation.The proposed calibration framework has been successfully demonstrated by a case study with two-objective optimization for the model accuracy and the simulation time. Based on the concept of Pareto dominance, the trade-off between these two conflicting objectives becomes apparent. Through verification and validation steps, the approach has proven to be successful for accurate calibration of material parameters with the optimal simulation time.     

Design of artificial neural networks using a genetic algorithm to predict collection efficiency in venturi scrubbers

In this study, a new approach for the auto-design of neural networks, based on a genetic algorithm (GA), has been used to predict collection efficiency in venturi scrubbers. The experimental input data, including particle diameter, throat gas velocity, liquid to gas flow rate ratio, throat hydraulic diameter, pressure drop across the venturi scrubber and collection efficiency as an output, have been used to create a GA-artificial neural network (ANN) model. The testing results from the model are in good agreement with the experimental data. Comparison of the results of the GA optimized ANN model with the results from the trial-and-error calibrated ANN model indicates that the GA-ANN model is more efficient. Finally, the effects of operating parameters such as liquid to gas flow rate ratio, throat gas velocity, and particle diameter on collection efficiency were determined.     

Redundancy allocation for multi-state systems using physical programming and genetic algorithms

This paper proposes a multi-objective optimization model for redundancy allocation for multi-state series–parallel systems. This model seeks to maximize system performance utility while minimizing system cost and system weight simultaneously. We use physical programming as an effective approach to optimize the system structure within this multi-objective optimization framework. The physical programming approach offers a flexible and effective way to address the conflicting nature of these different objectives. Genetic algorithm (GA) is used to solve the proposed physical programming-based optimization model due to the following three reasons: (1) the design variables, the number of components of each subsystems, are integer variables; (2) the objective functions in the physical programming-based optimization model do not have nice mathematical properties, and thus traditional optimization approaches are not suitable in this case; (3) GA has good global optimization performance. An example is used to illustrate the flexibility and effectiveness of the proposed physical programming approach over the single-objective method and the fuzzy optimization method.     

Multiobjective spare part allocation by means of genetic algorithms and Monte Carlo simulation

The management of spare parts is a major concern for several industrial organizations, due to the significant amount of resources invested every year for holding spares inventories.In this paper, we explore the possibility of using genetic algorithms for the task of optimizing the number of spare parts required by a multi-component system. To address the question of how many spares should be kept in inventory for each component kind, the analyst is required to define objective functions with respect to which the optimization is sought. In our work we will look at multiple objectives such as, for example, the maximization of system revenues and the minimization of the total spares volume. The modeling of the system failure, repair and replacement stochastic processes is done by means of Monte Carlo simulation, whose flexibility allows a closer adherence to reality.     

Combining neural networks and genetic algorithms for optimising the parameter design of the inter-metal dielectric process

An inter-metal dielectric (IMD) is deposited between metal layers to provide isolation capability to a device and separate the different metal layers that are unnecessary in the conduction of electricity. Owing to the complicated input/response relationships of the IMD process, the void problem results in electric leakage and causes wafer scraping. In the current study, we combined neural networks, genetic algorithms (GAs) and the desirability function in order to optimise the parameter settings of the IMD process. Initially, a backpropagation (BP) neural network was developed to map the complex non-linear relationship between the process parameters and the corresponding responses. Moreover, the desirability function and GAs were employed to obtain the optimum operating parameters in respect to each response. The implementation of the proposed approach was carried out in a semiconductor manufacturing company in Taiwan, and the results illustrate the practicability of the proposed approach.     

Flow curve prediction of an Al-MMC under hot working conditions using neural networks

The plastic flow behaviour of a particle-reinforced aluminium alloy matrix composite (AA2618 + Al₂O_3p) was studied by analysing the results of hot compression tests carried out in extended ranges of temperature and strain rate, typical of hot working operations. In general, for a given temperature and strain rate, the flow curves exhibit a peak, at relatively low strains, followed by flow softening; for a constant strain, the flow stress increases with increasing strain rate and decreasing temperature. The experimental data were used as an input for training artificial neural networks in order to predict the flow curves of the composite investigated. The comparison of the predicted stress–strain curves with the ones obtained by experimental testing, under conditions different from those used for the training stage, has proven the prediction generalisation capability of the artificial neural network-based models.     

离位增韧复合材料加筋板三点弯曲试验研究与数值分析

为研究离位增韧对复合材料加筋板结构承载性能的影响,首先,分别对未离位增韧和离位增韧复合材料帽型长桁加筋板进行了三点弯曲试验,比较了2种加筋板的初始分层载荷、极限载荷以及试验现象;然后,利用ABAQUS建立了三维渐进失效模型,考虑长桁-蒙皮界面和复合材料层合板的失效,对界面的分层机制和蒙皮的失效过程进行了分析。结果表明:有限元结果与试验结果吻合较好,离位增韧仅能略微提高界面的初始分层载荷,但对极限载荷有较大提升;在分层起始阶段I型拉伸模式占主要作用,随着分层不断扩展,II型剪切模式占比持续增加,而剪切模式的高韧性正是离位增韧界面具有良好性能的重要原因。所得结论表明离位增韧界面有良好的抗剥离性能,且在剪切方向上的表现尤为突出。     

Optimization of principal-component-analysis-applied in situ spectroscopy data using neural networks and genetic algorithms

A new model of multidimensional in situ diagnostic data is presented. This was accomplished by combining a back-propagation neural network (BPNN), principal component analysis (PCA), and a genetic algorithm (GA). The PCA was used to reduce input dimensionality. The GA was applied to search for a set of optimized training factors involved in BPNN training. The presented technique was evaluated with optical emission spectroscopy (OES) data measured during the etching of oxide thin films in a CHF(3)-CF(4) inductively coupled plasma. For a systematic modeling, the etching process was characterized by a face-centered Box Wilson experiment. The etch responses to be modeled include oxide etch rate, oxide profile angle, and oxide etch rate non-uniformity. In PCA, three types of data variances were employed and the reduced input dimensionality corresponding to 100, 99, and 98% are 16, 8, and 5. The BPNN training factors to be optimized include the training tolerance, number of hidden neurons, magnitude of initial weight distribution, gradient of bipolar sigmoid function, and gradient of linear function. The prediction errors of GA-BPNN models are 249 A/min, 2.64 degrees, and 0.439% for the etch rate, profile angle, and etch rate non-uniformity, respectively. Compared to the conventional and previous full OES models, the presented models demonstrated a significantly improved prediction for all etch responses.     

Prediction of inter-laminar stresses in composite honeycomb sandwich panels under mechanical loading using Variational Asymptotic Method

This work focuses on the formulation of an asymptotically correct theory for symmetric composite honeycomb sandwich plate structures. In these panels, transverse stresses tremendously influence design. The conventional 2-D finite elements cannot predict the thickness-wise distributions of transverse shear or normal stresses and 3-D displacements. Unfortunately, the use of the more accurate three-dimensional finite elements is computationally prohibitive. The development of the present theory is based on the Variational Asymptotic Method (VAM). Its unique features are the identification and utilization of additional small parameters associated with the anisotropy and non-homogeneity of composite sandwich plate structures. These parameters are ratios of smallness of the thickness of both facial layers to that of the core and smallness of 3-D stiffness coefficients of the core to that of the face sheets. Finally, anisotropy in the core and face sheets is addressed by the small parameters within the 3-D stiffness matrices. Numerical results are illustrated for several sample problems. The 3-D responses recovered using VAM-based model are obtained in a much more computationally efficient manner than, and are in agreement with, those of available 3-D elasticity solutions and 3-D FE solutions of MSC NASTRAN.     

Optimization of composite structures under multiple load cases using a discrete approach based on lamination parameters

This paper presents a new optimization technique applicable to optimization of composite structures subjected to multiple objectives. The composite structures may be composed of an arbitrary number of laminates. The technique is especially suited for the case where the layers of the laminates may assume a discrete number of orientations. However, given the efficiency of the technique, it is readily extendable to situations where the ply orientations vary quasi‐continuously, for instance, by one degree in one degree. The high efficiency is obtained through application of lamination parameters, which, in the case of symmetric laminates, consist of only 10 parameters per laminate. Three traditional structures, a rectangular composite plate, a cantilever composite beam, and a stiffened composite panel, are optimized against buckling when subjected to multiple load cases. Copyright © 2015 John Wiley & Sons, Ltd.