Abstract
The flow behavior of polymer melts within a slit die is an important consideration when designing a die geometry. The quality of the extruded polymer product can be determined through an evaluation of the flow homogeneity, wall shear rate and pressure drop across the central height of the die. However, mathematical formulations cannot fully determine the behavior of the flow due to the complex nature of fluid dynamics and the nonlinear physical properties of the polymer melts. This paper examines two slit die geometries in terms of outlet velocity uniformity, shear rate uniformity at the walls and pressure drop by using the licensed computational fluid dynamics package, Ansys POLYFLOW, based on the finite element method. The Carreau-Yasuda viscosity model was used for the rheological properties of the polypropylene. Comparative analysis of the simulation results will conclude that the modified die design performs better in all three aspects providing uniform exit velocity, uniform wall shear rates, and lower pressure drop.
Address
Dastan Igali and Asma Perveen: Department of Mechanical & Aerospace Engineering, School of Engineering & Digital Sciences, Nazarbayev University, Nur-Sultan city 010000, Republic of Kazakhstan
Dongming Wei: Mathematics Department, School of Sciences and Humanities, Nazarbayev University, Nur-Sultan city 010000, Republic of Kazakhstan
Dichuan Zhang: Department of Civil & Environmental Engineering, School of Engineering & Digital Sciences, Nazarbayev University, Nur-Sultan city 010000, Republic of Kazakhstan; Nur-Sultan city 010000, Republic of Kazakhstan
Abstract
Building Information Modeling (BIM) is increasingly used throughout the facility's life cycle for various applications, such as design, construction, facility management, and maintenance. For existing buildings, the geometry of as-built BIM is often constructed using dense, three dimensional (3D) point clouds data obtained with laser scanners. Traditionally, as-built BIM systems do not contain the material and textural information of the buildings' elements. This paper presents a semi-automatic method for generation of material and texture rich as-built BIM. The method captures and integrates material and textural information of building elements into as-built BIM using thermal infrared sensing (TIS). The proposed method uses TIS to capture thermal images of the interior walls of an existing building. These images are then processed to extract the interior walls using a segmentation algorithm. The digital numbers in the resulted images are then transformed into radiance values that represent the emitted thermal infrared radiation. Machine learning techniques are then applied to build a correlation between the radiance values and the material type in each image. The radiance values were used to extract textural information from the images. The extracted textural and material information are then robustly integrated into the as-built BIM providing the data needed for the assessment of building conditions in general including energy efficiency, among others
Key Words
As-Built BIM; building materials; thermal infrared imaging; thermography; texture extraction; feature technology; information visualization; machine learning
Address
Asem Zabin, Tarig Ali, Jamal A. Abdalla: Department of Civil Engineering, American University of Sharjah, Sharjah, UAE
Baha Khalil: Department of Mathematics and Statistics, American University of Sharjah, Sharjah, UAE
Ahmed Elaksher: New Mexico State University, Las Cruces, New Mexico, United States of America
Abstract
To achieve appropriate stresses, two new rectangular elements are presented in this study. For reaching this aim, a complementary energy functional is used within an element for the analysis of plane problems. In this energy form, the Airy stress function will be used as a functional variable. Besides, some basic analytical solutions are found for the stress functions. These trial functions are matched with each element number of degrees of freedom, which leads to a number of equations with the anonymous constants. Subsequently, according to the principle of minimum complementary energy, the unknown constants can be expressed in terms of displacements. This system can be rewritten in terms of the nodal displacement. In this way, two new hybrid-rectangular triangular elements are formulated, which have 16 and 40 degrees of freedom. To validate the outcomes, extensive numerical studies are performed. All findings clearly demonstrate accuracies of structural displacements, as well as, stresses.
Key Words
rectangular element; hybrid element; plane problem; finite element method; airy stress function
Address
Department of Civil Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Abstract
In the present research, dynamic analysis of functionally graded (FG) graphene-reinforced beams under thermal loading has been carried out based on finite element approach. The presented formulation is based on a higher order refined beam element accounting for shear deformations. The graphene-reinforced beam is exposed to transverse periodic mechanical loading. Graphene platelets have three types of dispersion within the structure including uniform-type, linear-type and nonlinear-type. Convergences and validation studies of derived results from finite element approach are also presented. This research shows that the resonance behavior of a nanocomposite beam can be controlled by the GPL content and dispersions. Therefore, it is showed that the dynamical deflections are notably influenced by GPL weight fractions, types of GPL distributions, temperature changes, elastic foundation and harmonic load excitation frequency.
Key Words
finite element method; thermal load; refined beam element; graphene platelet; dynamic loading
Abstract
The present study focuses on the application of artificial neural network (ANN) and Multiple linear Regression (MLR) analysis for developing a model to predict the unconfined compressive strength (UCS) and split tensile strength (STS) of the fiber reinforced clay stabilized with grass ash, fly ash and lime. Unconfined compressive strength and Split tensile strength are the nonlinear functions and becomes difficult for developing a predicting model. Artificial neural networks are the efficient tools for predicting models possessing non linearity and are used in the present study along with regression analysis for predicting both UCS and STS. The data required for the model was obtained by systematic experiments performed on only Kaolin clay, clay mixed with varying percentages of fly ash, grass ash, polypropylene fibers and lime as between 10-20%, 1-4%, 0-1.5% and 0-8% respectively. Further, the optimum values of the various stabilizing materials were determined from the experiments. The effect of stabilization is observed by performing compaction tests, split tensile tests and unconfined compression tests. ANN models are trained using the inputs and targets obtained from the experiments. Performance of ANN and Regression analysis is checked with statistical error of correlation coefficient (R) and both the methods predict the UCS and STS values quite well; but it is observed that ANN can predict both the values of UCS as well as STS simultaneously whereas MLR predicts the values separately. It is also observed that only STS values can be predicted efficiently by MLR.
Key Words
Kaolin clay; grass ash; unconfined compression test; split tensile test; artificial neural network; regression model
Address
Department of Civil Engineering, Dr. B R Ambedkar National Institute of Technology Jalandhar, India
