125 research outputs found
Incorporation of composite defects from ultrasonic NDE into CAD and FE models
No full textFiber-reinforced composites are widely used in aerospace industry due to their combined properties of high strength and low weight. However, owing to their complex structure, it is difficult to assess the impact of manufacturing defects and service damage on their residual life. While, ultrasonic testing (UT) is the preferred NDE method to identify the presence of defects in composites, there are no reasonable ways to model the damage and evaluate the structural integrity of composites. We have developed an automated framework to incorporate flaws and known composite damage automatically into a finite element analysis (FEA) model of composites, ultimately aiding in accessing the residual life of composites and make informed decisions regarding repairs. The framework can be used to generate a layer-by-layer 3D structural CAD model of the composite laminates replicating their manufacturing process. Outlines of structural defects, such as delaminations, are automatically detected from UT of the laminate and are incorporated into the CAD model between the appropriate layers. In addition, the framework allows for direct structural analysis of the resulting 3D CAD models with defects by automatically applying the appropriate boundary conditions. In this paper, we show a working proof-of-concept for the composite model builder with capabilities of incorporating delaminations between laminate layers and automatically preparing the CAD model for structural analysis using a FEA software.This proceeding may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This proceeding appeared in Bingol, Onur Rauf, Bryan Schiefelbein, Robert J. Grandin, Stephen D. Holland, and Adarsh Krishnamurthy. "Incorporation of composite defects from ultrasonic NDE into CAD and FE models." AIP Conference Proceedings 1806, no. 1, (2017): 150004. , and may be found at DOI: 10.1063/1.4974728. Posted with permission.</p
Automated Construction and Insertion of Layer-by-Layer Finite Element Sub-Models of Damaged Composites
No full textFinite element models of composite structures are generally shell-based and modeled at the laminate level. More detailed layer-by-layer lamina-level models are sometimes needed for representing joints or for modeling defect growth processes. We describe a method and toolkit for automating the creation and insertion of layerby-layer finite element sub-models of composite laminate. We focus in particular on representing damage captured from nondestructive evaluation (NDE) measurements. The method is based on scripting existing simulation and solid modeling tools (ABAQUS and ACIS). It works even on complicated, curved CAD models. The submodel location is identified by the intersection of a cylinder with the structure. We then execute a series of instructions to generate a new shell with the submodel region removed, generate the layer-by-layer submodel, and bond together the layers and models with desired boundary conditions and defects. The instructions represent the steps of lamination and bonding for creating the composite. The output of the method includes CAD models of the new shell and each lamina within the submodel, and a Python script for ABAQUS that will load the CAD models, bond them together, and apply the specified boundary conditions.This proceeding appeared in Holland, Stephen D., Adarsh Krishnamurthy, Onur Bingol, and Robert Grandin. "Automated Construction and Insertion of Layer-by-Layer Finite Element Sub-Models of Damaged Composites." In Proceedings of the American Society for Composites—Thirty-third Technical Conference (2018). Lancaster, PA: DEStech Publications, Inc. DOI: 10.12783/asc33/26002. Posted with permission.</p
A framework for 3D x-ray CT iterative reconstruction using GPU-accelerated ray casting
No full textX-ray Computed Tomography (CT) is a powerful nondestructive evaluation (NDE) tool to characterize internal defects and flaws, regardless of surface conditions and sample materials. After data acquisition from a series of X-ray 2D projection imaging, reconstruction methods play a key role to convert raw data (2D radiography) to 3D models. For the past 50 years, standard reconstruction have been performed using analytical methods based on filtered back-projection (FBP) concepts. Numerous iterative methods that have been developed have shown some improvements on certain aspects of the reconstruction quality, but have not been widely adopted due to their high computational requirements. With modern high performance computing (HPC) and graphics processing unit (GPU) technologies, the computing power barrier for iterative methods have been reduced. Iterative methods have more potential to incorporate physical models and a priori knowledge to correct artifacts generated from analytical methods. In this work, we propose a generalized framework for iterative reconstruction with GPU acceleration, which can be adapted for different physical and statistical models in the inner iteration during reconstruction. The forward projection algorithm is an important part of the framework, and is analogous to the ray casting depth map algorithm that was implemented in an earlier work [I] and accelerated using the GPU. Within this framework, different sub-models could be developed in future to deal with different artifacts, such as beam hardening effect and limited angle data problem.This proceeding may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This proceeding appeared in Zhang, Zhan, Sambit Ghadai, Onur Rauf Bingol, Adarsh Krishnamurthy, and Leonard J. Bond. "A framework for 3D x-ray CT iterative reconstruction using GPU-accelerated ray casting." AIP Conference Proceedings 2102, no. 1 (2019): 070002, and may be found at DOI: 10.1063/1.5099749. Posted with permission.</p
Automated Construction of Layer-by Layer Finite Element Sub-Models of Damaged Composites Based on NDE Data
No full textComposite laminate structures are usually modeled as a shell in finite element analysis tools for strength and stiffness determination. However, modeling for fatigue or degradation analysis often needs to be performed with layer-by-layer solid models, but building these models for nontrivial geometries can be extremely difficult, especially when trying to represent realistic defects. This paper discusses how the process of generating layer-by-layer solid finite element models, including insertion of defects, can be automated. We have developed a tool, Delamo, to automate the construction of such models. The tool provides an interface to a commercial solid modeling kernel (ACIS) and a commercial finite element analysis package (ABAQUS). It allows the solid model and finite element model to be built in parallel, layer by layer, starting with a mold, following the same assembly steps as the physical laminate. The bonding step determines the boundary conditions to be applied in the finite element model. Delaminations, determined from nondestructive evaluation (NDE) data can be inserted between layers as needed and are represented as unbonded regions. Potential delamination growth regions can be modeled with a cohesive layer or cohesive boundary condition. Fiber breakage in a layer will be represented by an internal boundary. Based on a mold and a sequence of layer construction and bonding instructions, the tool generates both a solid model and a Python script for ABAQUS that will generate a complete finite element model based on that solid model.This proceeding appeared in Holland, Stephen D., Adarsh Krishnamurthy, Onur Bingol, and Robert Grandin. Automated Construction of Layer-by-Layer Finite Element Sub-Models of Damaged Composites Based on NDE Data. In Proceedings of the American Society for Composites-Thirty-second Technical Conference (2017). Lancaster, PA: DEStech Publications, Inc. DOI: 10.12783/asc2017/15216. Posted with permission.</p
NURBS-based microstructure design for organic photovoltaics
No full textThe microstructure-spatial distribution of electron donor and acceptor domains-plays an important role in determining the photo current in thin film organic solar cells (OSCs). Optimizing the microstructure can lead to higher photo current generation, and is an active area of experimental research. There has been recent progress in framing OSC microstructure design as a computational design problem. However, most current approaches to microstructure optimization are based on volumetric distribution of material, which makes the design space very large. In contrast, we frame the microstructure design optimization problem in terms of designing the interface between the donor and acceptor regions, and thus pose it as a surface representation and optimization problem. This results in substantially reduced number of design variables, thus enabling use of standard optimization tools. In this work, we address the efficient design of OSC microstructure by using surface and curve modeling techniques to model the donor-acceptor interface, and use meta-heuristic, gradient-free optimization techniques to optimize the microstructure for maximum short circuit current generation. Our modeling framework consists of three major components: 1) geometric modeling of OSC microstructure that uses Non-Uniform Rational B-spline (NURBS) curves and surfaces to construct the free-form donor-acceptor interface, 2) photo-current generation modeling that uses a parallel, finite-element based exciton-drift-diffusion (XDD) model, and 3) optimization that utilizes genetic algorithms (GA) to optimize the OSCs microstructure via exploration of the NURBS representation. We apply these methods for the optimization of both 2D and 3D microstructures. Results show substantial improvement in current density compared to the bulk-heterojunction microstructures. These results provide promising microstructures for experimental groups to fabricate. The proposed surface representation approach seems to be a promising approach for interface design in engineered systems.This is a manuscript of an article published as Noruzi, Ramin, Sambit Ghadai, Onur Rauf Bingol, Adarsh Krishnamurthy, and Baskar Ganapathysubramanian. NURBS-based microstructure design for organic photovoltaics. Computer-Aided Design 118 (2019): 102771. DOI: 10.1016/j.cad.2019.102771. Posted with permission.</p
An integrated framework for solid modeling and structural analysis of layered composites with defects
No full textLaminated fiber-reinforced polymer (FRP) composites are widely used in aerospace and automotive industries due to their combined properties of high strength and low weight. However, owing to their complex structure, it is difficult to assess the impact of manufacturing defects and service damage on their residual life. Non-destructive evaluation (NDE) of composites using ultrasonic testing (UT) can identify the presence of defects. However, manually incorporating the damage in a CAD model of a multi-layered composite structure and evaluating its structural integrity is a tedious process. We have developed an automated framework to create a layered 3D CAD model of a composite structure and automatically preprocess it for structural finite element (FE) analysis. In addition, we can incorporate flaws and known composite damage automatically into this CAD model. The framework generates a layer-by-layer 3D structural CAD model of the composite laminate, replicating its manufacturing process. The framework can create non-trivial composite structures such as those that include stiffeners. Outlines of structural defects, such as delaminations detected using UT of the laminate, are incorporated into the CAD model between the appropriate layers. The framework is also capable of incorporating fiber/matrix cracking, another common defect observed in fiber-reinforced composites. Finally, the framework can preprocess the resulting 3D CAD models with defects for direct structural analysis by automatically applying the appropriate boundary conditions. In this paper, we show a working proof-of-concept of the framework with capabilities of creating composite structures with stiffeners, incorporating delaminations between the composite layers, and automatically preprocessing the CAD model for finite element structural analysis. The framework will ultimately aid in accurately assessing the residual life of the composite and making informed decisions regarding repairs.This is a manuscript of an article published as Bingol, Onur Rauf, Bryan Schiefelbein, Robert J. Grandin, Stephen D. Holland, and Adarsh Krishnamurthy. "An integrated framework for solid modeling and structural analysis of layered composites with defects." Computer-Aided Design 106 (2018). DOI: 10.1016/j.cad.2018.07.006. Posted with permission.</p
ACIS-Python3 v0.1.6
No full text<p>This release combines all implemented functionality under <em>Modeler</em> module and removes all other modules to eliminate confusion. The code base is also updated as well as the Python examples.</p>
The Diving Beetle Fauna of Diyarbakir and Bingol Provinces, Turkey (Coleoptera: Dytiscidae) with a New Record
No full textThe present study was carried out from September 2013 to August 2015 in Bingol and Diyarbakir provinces in the Eastern and South-eastern Anatolian regions of Turkey. A total of 3,990 dysticid specimens were collected from 118 different localities and identified. In total, 40 species in 17 genera of family Dytiscidae were recorded, of which 37 were new to the study area. Eretes sticticus (Linnaeus, 1767) is the second record for Turkish fauna. Furthermore, Hydroporus inscitus Sharp, 1882 is new record for the Turkish fauna.Research Fund of Dicle University (DUBAP) [14-DUBTAM-81]The author especially thanks to Dr. Hans Fery (Berlin, Germany) for confirming the determination of a part of the material and providing insightful comments to improve the manuscript. This study was partly supported by the Research Fund of Dicle University (DUBAP), project no 14-DUBTAM-81. A part of this study was presented orally in Symposium on Euroasian Biodiversity (SEAB), May 23-27, 2016, Antalya, Turkey
NURBS-Python (geomdl) v4.4.1
No full text<p>NURBS-Python (geomdl) v4.4.1 has been released on November 15th, 2018 with the following updates:</p>
<ul>
<li>Updated Cython-compiled module location for more convenient binary package generation</li>
<li>Major documentation update with more examples and API descriptions</li>
<li>Added DockerFiles</li>
</ul>
<p>Please see the <a href="https://nurbs-python.readthedocs.io/en/latest/install.html">documentation</a> for installation and upgrade information.</p>
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