Combined CI-MD approach in formulation of engineering moduli of single layer graphene sheet

An evolutionary approach of multi-gene genetic programming (GP) is used to study the effects of aspect ratio, temperature, number of atomic planes and vacancy defects on the engineering moduli viz. tensile and shear modulus of single layer graphene sheet. MD simulation based on REBO potential is used to obtain the engineering moduli. This data is then fed into the paradigm of a GP cluster comprising of genetic programming, which was specifically designed to formulate the explicit relationship of engineering moduli of graphene sheets loaded in armchair and zigzag directions with respect to aspect ratio, temperature, number of atomic planes and vacancy defects. We find that our MGGP model is able to model the engineering moduli of armchair and zigzag oriented graphene sheets well in agreement with that of experimental results. We also conducted sensitivity and parametric analysis to find out specific influence and variation of each of the input system parameters on the engineering moduli of armchair and zigzag graphene sheets. It was found that the number of defects has the most dominating influence on the engineering moduli of graphene sheets.     

Stepwise approach for the evolution of generalized genetic programming model in prediction of surface finish of the turning process

Due to the complexity and uncertainty in the process, the soft computing methods such as regression analysis, neural networks (ANN), support vector regression (SVR), fuzzy logic and multi-gene genetic programming (MGGP) are preferred over physics-based models for predicting the process performance. The model participating in the evolutionary stage of the MGGP method is a linear weighted sum of several genes (model trees) regressed using the least squares method. In this combination mechanism, the occurrence of gene of lower performance in the MGGP model can degrade its performance. Therefore, this paper proposes a modified-MGGP (M-MGGP) method using a stepwise regression approach such that the genes of lower performance are eliminated and only the high performing genes are combined. In this work, the M-MGGP method is applied in modelling the surface roughness in the turning of hardened AISI H11 steel. The results show that the M-MGGP model produces better performance than those of MGGP, SVR and ANN. In addition, when compared to that of MGGP method, the models formed from the M-MGGP method are of smaller size. Further, the parametric and sensitivity analysis conducted validates the robustness of our proposed model and is proved to capture the dynamics of the turning phenomenon of AISI H11 steel by unveiling dominant input process parameters and the hidden non-linear relationships.     

A new multi-gene genetic programming approach to non-linear system modeling. Part II: geotechnical and earthquake engineering problems

Complexity of analysis of geotechnical behavior is due to multivariable dependencies of soil and rock responses. In order to cope with this complex behavior, traditional forms of engineering design solutions are reasonably simplified. Incorporating simplifying assumptions into the development of the traditional methods may lead to very large errors. This paper presents an endeavor to exploit a robust multi-gene genetic programming (MGGP) method for the analysis of geotechnical and earthquake engineering systems. MGGP is a modified genetic programming approach for model structure selection combined with a classical technique for parameter estimation. To justify the abilities of MGGP, it is systematically employed to formulate the complex geotechnical engineering problems. Different classes of the problems analyzed include the assessment of (i) undrained lateral load capacity of piles, (ii) undrained side resistance alpha factor for drilled shafts, (iii) settlement around tunnels, and (iv) soil liquefaction. The validity of the derived models is tested for a part of test results beyond the training data domain. Numerical examples show the superb accuracy, efficiency, and great potential of MGGP. Contrary to artificial neural networks and many other soft computing tools, MGGP provides constitutive prediction equations. The MGG-based solutions are particularly valuable for pre-design practices.     

SVM的几何方法—SK类思路的研究

支持向量机（Support Vector Machine,SVM）的几何方法是一种基于SVM计算过程中几何意义出发的求解方法。利用其几何特点,比较直观地对其基本算法的构建过程进行了分析。两凸包相对位置可以简要地归纳成5类,且在该类算法迭代过程最优点多在顶点和边界上,该类算法在第一次迭代就可能达到边界（最优点）;该类算法的手动单步模拟计结果揭示：很多情况下,该类算法迭代过程的投影并不成功,虽不影响解法的最终结果,但会影响迭代效率;基于几何的分析,给出软SK软算法的两种改进思路（Backward-SK和Forward-SK思路）,并进行了仿真比较计算。实验表明,该方法计算效果与原思路相似,但是计算过程理解更加直观。     

Prediction of quality characteristics of laser drilled holes using artificial intelligence techniques

Micro-drilling using lasers finds widespread industrial applications in aerospace, automobile, and bio-medical sectors for obtaining holes of precise geometric quality with crack-free surfaces. In order to achieve holes of desired quality on hard-to-machine materials in an economical manner, computational intelligence approaches are being used for accurate prediction of performance measures in drilling process. In the present study, pulsed millisecond Nd:YAG laser is used for micro drilling of titanium alloy and stainless steel under identical machining conditions by varying the process parameters such as current, pulse width, pulse frequency, and gas pressure at different levels. Artificial intelligence techniques such as adaptive neuro-fuzzy inference system (ANFIS) and multi gene genetic programming (MGGP) are used to predict the performance measures, e.g. circularity at entry and exit, heat affected zone, spatter area and taper. Seventy percent of the experimental data constitutes the training set whereas remaining thirty percent data is used as testing set. The results indicate that root mean square error (RMSE) for testing data set lies in the range of 8.17–24.17% and 4.04–18.34% for ANFIS model MGGP model, respectively, when drilling is carried out on titanium alloy work piece. Similarly, RMSE for testing data set lies in the range of 13.08–20.45% and 6.35–10.74% for ANFIS and MGGP model, respectively, for stainless steel work piece. Comparative analysis of both ANFIS and MGGP models suggests that MGGP predicts the performance measures in a superior manner in laser drilling operation and can be potentially applied for accurate prediction of machining output.

     

Temperature distribution in graphene doped with nitrogen and graphene with grain boundary

Graphene doped with nitrogen exhibits unique properties different than perfect graphene. The temperature distribution in nitrogen-doped graphene (N-graphene) and in the graphene with grain boundary is investigated using molecular dynamics simulations. The temperature distribution in nitrogen-doped graphene nanoribbon, containing two types of grain boundaries, was found to be sensitive to the number of dopants and grain boundary. We also found that there is a remarkable temperature gap in the temperature profile of N-graphene nanoribbon-containing a grain boundary. For any doping ratio N/C we found that the nitrogen atoms enhance roughness of N-graphene and decrease thermal conductivity.     

Thermal interactions in nanoscale fluid flow: molecular dynamics simulations with solid–liquid interfaces

Molecular dynamics (MD) simulations of nano-scale flows typically utilize fixed lattice crystal interactions between the fluid and stationary wall molecules. This approach cannot properly model interactions and thermal exchange at the wall–fluid interface. We present a new interactive thermal wall model that can properly simulate the flow and heat transfer in nano-scale channels. The new model utilizes fluid molecules freely interacting with the thermally oscillating wall molecules, which are connected to the lattice positions with “bonds”. Thermostats are applied separately to each layer of the walls to keep the wall temperature constant, while temperature of the fluid is sustained without the application of a thermostat. Two-dimensional MD simulation results for shear driven nano-channel flow shows parabolic temperature distribution within the domain, induced by viscous heating due to a constant shear rate. As a result of the Kapitza resistance, temperature profiles exhibit jumps at the fluid–wall interface. Time dependent simulation results for freezing of liquid argon in a nano-channel are also presented.     

An efficient numerical model for investigating the effects of anisotropy on the effective thermal conductivity of alumina/Al composites

The paper describes an efficient numerical model for better understanding the influence of the microstructure on the thermal conductivity of heterogeneous media. This is the extension of an approach recently proposed for simulating and evaluating effective thermal conductivities of alumina/Al composites. A C++ code called MultiCAMG, taking into account all steps of the proposed approach, has been implemented in order to satisfy requirements of efficiency, optimization and code unification. Thus, on the one hand, numerical tools such as the efficient Eyre–Milton scheme for computing the thermal response of composites have been implemented for reducing the calculation cost. On the other hand, statistical parameters such as the covariance and the distribution of contact angles between particles are now estimated for better analyzing the microstructure. In the present work we focus our investigations on the effects of anisotropy on the effective thermal conductivity of alumina/Al composites. First of all, an isotropic benchmark is set up for comparison purposes. Secondly, anisotropic configurations are studied in order to direct the heat flux. A transversally isotropic structure, taking benefit of wall effects, is finally proposed for controlling the orientation of contact angles. Its thermal capabilities are related to the current issue of heat dissipation in automotive engine blocks.     

平衡态分子动力学 Green-Kubo 方法计算氮化硼 单层结构热导率的模型尺寸效应研究

分子动力学模拟可以直接表征体系原子的行为,因此成为研究氮化硼相关材料微观导热机理的重要工具,但目前尚没有关于氮化硼材料模型尺寸对其热传导相关性质影响规律的研究。该文采用平衡态分子动力学并结合 Green-Kubo 方法,研究了纯净氮化硼单层结构热导率、声子色散关系以及态密度随模拟尺寸的变化规律,并解释了其内部机理。实验发现,氮化硼单层材料热导率随着模拟尺寸的增大而减小,并在单层面积约 4.1 nm×4.1 nm 时收敛于(349±22)W/(m?K),此收敛值远小于平衡态分子动力学计算中石墨烯热导率的收敛尺寸(10 nm×10 nm),这说明氮化硼单层中声子之间的散射大于石墨烯。此外,不同于热导率,氮化硼单层结构的声子色散曲线、态密度几乎不受模拟尺寸的影响。该研究结果可为采用平衡态分子动力学研究氮化硼相关材料的微观导热机理提供重要参考。     

A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems

This paper presents a new approach for behavioral modeling of structural engineering systems using a promising variant of genetic programming (GP), namely multi-gene genetic programming (MGGP). MGGP effectively combines the model structure selection ability of the standard GP with the parameter estimation power of classical regression to capture the nonlinear interactions. The capabilities of MGGP are illustrated by applying it to the formulation of various complex structural engineering problems. The problems analyzed herein include estimation of: (1) compressive strength of high-performance concrete (2) ultimate pure bending of steel circular tubes, (3) surface roughness in end-milling, and (4) failure modes of beams subjected to patch loads. The derived straightforward equations are linear combinations of nonlinear transformations of the predictor variables. The validity of MGGP is confirmed by applying the derived models to the parts of the experimental results that are not included in the analyses. The MGGP-based equations can reliably be employed for pre-design purposes. The results of MSGP are found to be more accurate than those of solutions presented in the literature. MGGP does not require simplifying assumptions in developing the models.     

BN、SiC、Ge纳米管热学特性的分子动力学研究

采用分子动力学方法,分别分析了单壁BN、SiC及Ge纳米管的导热系数与熔化特性;进而根据模拟结果,讨论了直径、温度等因素对几种纳米管导热性的影响,以及几种纳米管之间导热性及熔化特性的差异。研究结果表明,各单壁纳米管的导热系数均随温度的升高以及直径的增大而降低;温度相同时,BN管的导热系数最大,而SiC和Ge管的导热系数相当;BN及SiC纳米管的熔点、比热容以及熔化热均远高于Ge管,但系统能量却要比Ge管低得多。     

Transient flow and heat transfer mechanism for Williamson-nanomaterials caused by a stretching cylinder with variable thermal conductivity

The utilization of nanometre-sized solid particles in working fluids has been seriously recommended due to their enhanced thermal characteristics. This suspension of solid particles in base fluids can significantly enhance the physical properties, such as, viscosity and thermal conductivity. They are widely used in several engineering processes, like, heat exchangers, cooling of electronic equipment, etc. In this exploration, we attempt to deliver a numerical study to simulate the nanofluids flow past a circular cylinder with convective heat transfer in the framework of Buongiorno’s model. A non-Newtonian Williamson rheological model is used to describe the behavior of nanofluid with variable properties (i.e., temperature dependent thermal conductivity). The leading flow equations for nanofluid transport are mathematical modelled with the assistance of Boussinesq approximation. Numerical simulation for the system of leading non-linear differential equations has been performed by employing versatile, extensively validated, Runge–Kutta Fehlberg scheme with Cash–Karp coefficients. Impacts of active physical parameters on fluid velocity, temperature and nanoparticle concentration is studied and displayed graphically. It is worth to mention that the temperature of non-Newtonian nanofluids is significantly enhanced by higher variable thermal conductivity parameter. One major outcome of this study is that the nanoparticle concentration is raised considerably by an increasing values of thermophoresis parameter.

     

弹药温度场仿真结果的影响因素分析

温度是影响弹药命中精度的关键因素之一,所以一些弹药试验中存在弹药的保温问题.弹药保温过程中弹药的温度场变化可以通过仿真的手段得到.本文通过对仿真过程分析,找出影响弹药温度场仿真结果的几个因素,结合各因素特点分别设计了其对结果影响大小的检测方法,并分别对不同网格大小、有无接触热阻、不同材料参数值和不同几何结构的模型进行了...     

氮化铝陶瓷薄膜热导率的分子动力学模拟

采用平衡分子动力学方法(EMD)研究了平衡温度为300K的氮化铝薄膜的法向热导率.利用Stillinger-Weber势函数以及Green-Kubo线性响应理论计算热导率.计算结果表明,氮化铝薄膜的热导率值显著小于对应大体积材料的实验值,具有明显的尺寸效应.在氮化铝薄膜为1.47～5.39 nm的范围内,氮化铝薄膜的热导率随着薄膜厚度的增加而近似的线性增加.     

陶瓷材料微波烧结腔体温度软测量研究

软测量是近年来检测和过程控制领域涌现出的一种新技术,基本原理是利用易测变量来建立模型计算待测变量。针对微波烧结中温度难以测量的问题,在研究微波温度场的基础上,提出了微波烧结材料仿真软测量的理论方法,通过对非微波加热区温度的测量,根据热传导及界面热阻的理论推导出烧结材料的实际温度。并通过试验,用微波炉模拟微波烧结腔体测得温度数据,建立模型。结果证明:根据试验数据建立的模型与假设的指数神经网络模型很吻合,证明了方法的可行性。     

Interfacing geometric model data with rapid prototyping systems

The direct slicing of CAD models created in CADDS V to generate geometric data for rapid prototyping using fused feposition modeling technique (FDM) is presented in this paper. The report file from an explicit model is accessed for obtaining model data. Algorithms have been developed for determining the volumes of model material as well as support materials. New algorithms have been developed for filling the sheet solid. A simulation module has been developed to verify whether the filling is correctly done. Example of a model is manufactured using this approach is also presented in this paper.     

Algorithm optimization in molecular dynamics simulation

Establishing the neighbor list to efficiently calculate the inter-atomic forces consumes the majority of computation time in molecular dynamics (MD) simulation. Several algorithms have been proposed to improve the computation efficiency for short-range interaction in recent years, although an optimized numerical algorithm has not been provided. Based on a rigorous definition of Verlet radius with respect to temperature and list-updating interval in MD simulation, this paper has successfully developed an estimation formula of the computation time for each MD algorithm calculation so as to find an optimized performance for each algorithm. With the formula proposed here, the best algorithm can be chosen based on different total number of atoms, system average density and system average temperature for the MD simulation. It has been shown that the Verlet Cell-linked List (VCL) algorithm is better than other algorithms for a system with a large number of atoms. Furthermore, a generalized VCL algorithm optimized with a list-updating interval and cell-dividing number is analyzed and has been verified to reduce the computation time by 30∼60% in a MD simulation for a two-dimensional lattice system. Due to similarity, the analysis in this study can be extended to other many-particle systems.     

Nanofluids thermal conductivity prediction applying a novel hybrid data-driven model validated using Monte Carlo-based sensitivity analysis

Accurate estimation of the thermal conductivity of nanofluids plays a key role in industrial heat transfer applications. Currently available experimental and empirical relationships can be used to estimate thermal conductivity. However, since the environmental conditions and properties of the nanofluids constituents are not considered these models cannot provide the expected accuracy and reliability for researchers. In this research, a robust hybrid artificial intelligence model was developed to accurately predict wide variety of relative thermal conductivity of nanofluids. In the new approach, the improved simulated annealing (ISA) was used to optimize the parameters of the least-squares support vector machine (LSSVM-ISA). The predictive model was developed using a data bank, consist of 1800 experimental data points for nanofluids from 32 references. The volume fraction, average size and thermal conductivity of nanoparticles, temperature and thermal conductivity of base fluid were selected as influent parameters and relative thermal conductivity was chosen as the output variable. In addition, the obtained results from the LSSVM-ISA were compared with the results of the radial basis function neural network (RBF-NN), K-nearest neighbors (KNN), and various existing experimental correlations models. The statistical analysis shows that the performance of the proposed hybrid predictor model for testing stage (R = 0.993, RMSE = 0.0207) is more reliable and efficient than those of the RBF-NN (R = 0.970, RMSE = 0.0416 W/m K), KNN (R = 0.931, RMSE = 0.068 W/m K) and all of the existing empirical correlations for estimating thermal conductivity of wide variety types of nanofluids. Finally, robustness and convergence analysis were conducted to evaluate the model reliability. A comprehensive sensitivity analysis using Monte Carlo simulation was carried out to identify the most significant variables of the developed models affecting the thermal conductivity predictions of nanofluids.

     

平面内运动热执行器的节点模型

本文提出了一种平面运动热执行器的热-电-机械耦合节点模型,此模型不仅可以仿真热执行器中随温度变化的热导率、电阻率和热膨胀率效应,而且可以仿真热执行器机械域的几何非线性效应,从而使用于仿真热执行器的行为的节点法模型实用化.ANSYS有限元仿真的结果证明此模型的不但精度较高,而且计算效率很高.