Figure - available from: Advances in Materials Science and Engineering
This content is subject to copyright. Terms and conditions apply.
Source publication
Two-dimensional FE models of CGI with different pearlite contents for thermal conductivity analysis were established according to the real metallographic images obtained by Pro/E and ANSYS. Meanwhile, thermal conductivity of CGI with different pearlite contents was tested through the laser flash method. It is indicated that the thermal conductivity...
Citations
... Compared with the traditional diesel engine, the volume and weight of HPD are reduced by more than 50%, but the rotation speed of the crankshaft and explosion pressure increase by 100% and 200%, respectively [1][2][3]. As an essential hightemperature component, the cylinder head made of compacted graphite cast iron (CGI) alloys is usually experiencing 400°C-500°C and 20-30 MPa explosion pressure [4][5][6]. When the engine is running, the service life of CGI alloy is greatly affected by irregular mechanical load changes and high temperature, often referred to as the start-operate-stop cycle. ...
The tension-tension fatigue test of the compacted graphite cast iron (CGI) alloy was carried out at 500℃ and 550℃, respectively. Because of the time-dependent deformation at elevated temperatures, the stress-strain curve presents hysteresis loops, and the area of the hysteresis loop increases gradually with continuous cyclic loading and sustained loading times. Intergranular and transgranular cracks in the microstructure accelerate the CGI alloy fracture failure. The fatigue life is sensitive to the short loading time and decreases with the sustained loading time exponentially under the tension-tension fatigue condition. The short holding time has a great influence on the fatigue life of CGI. The fatigue behavior of CGI alloys and the influence of holding time on the fatigue life can be characterized by y=aexp(bx). In addition, the fatigue life of CGI alloy can be predicted by the ductility depletion method. But the equivalent stress amplitude needs to be modified to eliminate the effects of oxidation damage.
... The numerical simulation method can realize the control of a single microstructure variable. Previous studies, combining with experiments and 2-D or 3-D finite element analysis, reported the effects of vermicularity [18], pearlite content [19], interfacial contact thermal conductivity (ICTC) [20] and spatial connectivity [21,22] on the thermal conductivity of VGI, respectively. Nevertheless, finite element modeling of vermicular graphite is difficult considering graphite anisotropy. ...
... Therefore, if several simplified graphite models with topological equivalence [31,32] are established according to the 2-D and 3-D morphological characteristics of three types of graphite and the ratio of the a-axis and c-axis, it is believed that the real vermicular graphite will be included. Finally, the effect of interface between graphite and matrix is not also negligible on thermal conductivity of VGI [19,20]. ...
To provide the basis for thermal conductivity regulation of vermicular graphite cast iron (VGI), a new theoretical method consisting of shape interpolation, unit cell model and numerical calculation was proposed. Considering the influence of the graphite anisotropy and interfacial contact thermal conductivity (ICTC), the effective thermal conductivity of a series of unit cell models was calculated by numerical calculation based on finite difference. The effects of micro-structure on effective thermal conductivity of VGI were studied by shape interpolation. The experimental results were in good agreement with the calculated ones. The effective thermal conductivity of VGI increases in power function with the decrease in graphite shape parameter, and increases linearly with the increase in graphite volume fraction and thermal conductivity of matrix. When the graphite volume fraction increases by 1%, the thermal conductivity of nodular cast iron increases by about 0.18 W/(m·K), while that of gray cast iron increases by about 3 W/(m·K). The thermal conductivity of cast iron has the same sensitivity to the thermal conductivity of matrix regardless of the graphite shape parameter. The thermal conductivity of matrix increased by 15 W/(m·K) and the thermal conductivity of cast iron increased by about 12 W/(m·K). Moreover, the more the graphite shape deviates from the sphere, the greater the enhancement effect of graphite anisotropy on thermal conductivity than the hindrance effect of interface between graphite and matrix. This work can provide guidance for the development of high thermal conductivity VGI and the study of thermal conductivity of composites containing anisotropic dispersed phase particles with complex shapes.
... Quantitative models for microstructure and thermal conductivity of vermicular graphite cast iron cylinder block based on cooling rate nonhomogeneous temperature distributions in service. Many researchers [8][9][10] have focused on the effects of microstructure including graphite morphology, graphite amount and pearlite fraction on thermal conductivity of cast iron. Matsushita et al. [8] studied the influence of the nodularity on the thermal conductivity and corrected the traditional thermal conductivity model for ductile cast iron by changing the thickness of cuboid castings. ...
... Ma et al. [9] found that when the vermicularity (40%-90%) increased, the thermal conductivity of VGI increased, according to the numerical simulation by finite element software ANSYS. Wu et al. [10] found that the thermal conductivity of VGI decreased with increasing pearlite content (10%-80%) by finite element numerical simulation. These studies provide a reference for studying the quantitative effect of microstructure on thermal conductivity of VGI cylinder blocks and heads. ...
The relationships of cooling rate with microstructure and thermal conductivity of vermicular graphite cast iron (VGI) cylinder block were studied, which are important for design and optimization of the casting process of VGI cylinder blocks. Cooling rates at different positions in the cylinder block were calculated based on the cooling curves recorded with a solidification simulation software. The metallographic structure and thermal conductivity were observed and measured using optical microscopy (OM), scanning electrical microscopy (SEM) and laser flash diffusivity apparatus, respectively. The effects of the cooling rate on the vermicularity, total and average areas of all graphite particles, and the pearlite fraction in the VGI cylinder block were investigated. It is found that the vermicularity changes in parabola trend with the increase of cooling rate. The total area of graphite particles and the cooling rate at eutectoid stage can be used to predict pearlite fraction well. Moreover, it is found that the thermal conductivity at room temperature is determined by the average area of graphite particles and pearlite fraction when the range of vermicularity is from 80% to 93%. Finally, the quantitative models are established to calculate the vermicularity, pearlite fraction, and thermal conductivity of the VGI cylinder block.
In this paper, an efficient image-based simulation toolkit for material characterization is presented, which is scalable to work from personal computers to workstations. The effective thermal conductivity, elasticity, and permeability are evaluated employing a computational homogenization framework based on the Finite Element Method (FEM). Two complementary open-source packages are presented: one developed in Python, which can convert digital images into voxel meshes (pyTomoviewer); the other developed in Julia, that can run numerical simulations to compute effective material properties (chpack). Also, a CUDA C version of chpack is provided (chfem_gpu). They were designed to deal with large multi-phase models, so strategies were devised to minimize their memory footprint, while avoiding a high toll on execution time. The voxel-based approach significantly simplifies the FEM meshes and allows efficient matrix-free implementations. In that sense, to handle large linear systems of equations, the element-by-element (EBE) technique is adopted, in conjunction with a low-memory implementation of the Preconditioned Conjugate Gradient (PCG) method. The code was thoroughly tested on an artificial geometry made of a square array of cylinders, for which analytical solutions exist, as well as on a real micro-tomographic reconstruction of FiberFormTM, a carbon preform commonly used in thermal protection systems.
Image-based physics simulations in heterogeneous media at a microscopic scale are a growing trend in various fields within and around Scientific Computing. We consider the scope of Numerical Homogenization, where micro-computed tomography is used to obtain digital models of physical samples, naturally leading to pixel and voxel-based solutions. In this context, the Finite Element Method (FEM) is commonly employed to solve the governing differential equations, via a system of algebraic equations. As image dimensions increase, the memory allocation due to the matrix associated with the FEM quickly becomes unfeasible, even in sparse format. Assembly-free strategies are adopted to reduce memory usage, with the caveat of increased computational cost. The Preconditioned Conjugate Gradient (PCG) method is widely employed to solve this sort of large-scale sparse linear systems , and is fitting to be adapted for assembly-free implementations. This work focuses on a massively parallel PCG solver applied to finite element analyses of heat conduction and linear elasticity on image-based models. Memory-efficiency is one of our main concerns, in an attempt to make feasible the employment of personal-use GPUs for large-scale simulations. The resulting solver is validated with an analytical benchmark, and by comparing the obtained results for a microtomographic model of a cast iron sample against experimental values found in the literature. Time and memory metrics are presented and discussed. It is shown that the developed program allows for homogenization studies of nearly 500 million degrees-of-freedom to be conducted in personal computers equipped with CUDA-enabled devices of 8 GB RAM, taking seconds or a few minutes per system solution with the PCG method. Up to 400× speed-up was observed in comparison to an analogous solver running in a 16-thread CPU. Our GPU implementation makes it possible to conduct, in a matter of minutes, homogenization studies that would take hours, or even days, in personal CPUs.
The finite element method (FEM) is commonly employed to solve the governing equations of various physics problems in the context of numerical homogenization. Imaging techniques, such as micro-computed tomography, are used to obtain digital models of the microscale of real samples, which generates a demand for pixel and voxel-based solutions. As image resolution and/or dimension increases, the memory allocation due to the characteristic finite element global matrix, even in sparse format, quickly becomes unfeasible, making it harder to fully explore state-of-the-art imaging resources. Assembly-free strategies are based on the premise of never storing the global matrix, working with local element matrices instead, which considerably reduces memory usage, but increases computational cost. Hence, optimized implementation approaches are sought out to reduce runtime. This paper presents a memory-efficient assembly-free FEM solver for the numerical homogenization of thermal conductivity and elasticity, of 2D and 3D image-based models, implemented entirely in MATLAB, exploring its vectorization paradigm. The proposed vectorized solver performs significantly better than a sequential element-by-element implementation. The preconditioned conjugate gradient (PCG) method is used to solve the linear systems of algebraic equations. Concise script code to perform key steps of the vectorized homogenization is exposed. The resulting program, called vhifem, is validated with an analytical benchmark and is compared with an educational program. Performance metrics are presented, making evident the trade-off between time and allocated memory. At last, a model of a cast iron sample with up to 81 million degrees-of-freedom is analyzed with a personal computer, allocating about 4 GB. This would be impossible without the assembly-free strategy.
To steadily produce high-quality vermicular graphite cast iron (VGI), it is essential to have a deep understanding of the growth mechanism of vermicular graphite. The morphology and microstructure of graphite in VGI were studied using various techniques, including both experimental and theoretical methods. An abnormal graphite structure with an anomalous layer distance of 6.60 Å was observed by transmission electron microscopy (TEM) imaging, which is approximately two times larger than the normal structure (3.40 Å). The theoretical simulations of possible trace elements added (approximately 34 different elements) to the graphite basal planes were studied and analyzed individually to understand the observed phenomenon and their effects on graphite growth. The theoretical calculations (6.56 Å) show good agreement with the experimental results (6.60 Å), which proves that the lattice anomaly is caused by Mg. The abnormal graphite was believed to be the remains of the transition state during the growth of graphite. Based on the investigation, a mechanism is proposed to describe the influence of Mg during the growth process of vermicular graphite.
The article is devoted to the problem of implementation new cast irons into mechanical engineering, namely, cast iron with vermicular graphite (CGI) for the manufacture of parts for engines operating under thermomechanical stress. The article presents the results of the analysis of the CGI modern situation. It is shown that the serial production of engineering products in the world began relatively recently due to the development of reliable technology of cast iron modifying for vermicular graphite (VG) only by the end of the XX century. The use of CGI in mechanical engineering in Russia is extremely limited mainly because of outdated inaccurate and even erroneous information about its properties. The data of own research of microstructure, thermal conductivity and strength of cast iron obtained with the use of modern methods and techniques of control are presented. It is shown that the thermal conductivity of CGI may significantly differ within the grades regulated by the obsolete domestic standard GOST 28394-89 which, inter alia, requires reworking for this reason. All investigated microstructures of CGI have thermal conductivity in the temperature interval from 22ºC to 300ºC varying within no more than 5 %. At high and irregular share of sphere graphite (SG) with temperature growth thermal conductivity can grow a little, with decrease of SG share its growth slows down, and since ~200 ºС it starts to fall. At a high share of VG more than 90% the thermal conductivity starts to fall from ~100 °С, but by 300 °С the fall is no more than 10%, which is much less than the fall in the same temperature range of the thermal conductivity of lamellar graphite iron (GI) more than 30%. The difference in thermal conductivities between the perlitic gray cast iron and CGI with a low and medium share of SG already at temperatures higher than 200 °C becomes commensurate with the error of measurement, and at 300 °C practically disappears. At the same time, in the presence of a high SG share in the structure of CGI its thermal conductivity at 300ºС remains considerably lower than that of the perlitic gray cast iron, the difference being ~30%.
