Finite element modeling of the glass sealing process for solid oxide cell stacks
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25 ENERGY STORAGE↗
SEARCH · Engineering Papers
Results for “finite element analysis”
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Anisotropic thermal diffusivity and conductivity in SiC/SiC tubes studied by infrared imaging and X-ray computed tomography
Traditional flash diffusivity evaluation of thermal diffusivity/conductivity of composite tubes require machining of specimens. For a thin-wall tube, this method can only be used to obtain through-thickness transport property. A novel method to evaluate anisotropic thermal diffusivity in a composite tube has been developed. Braided SiC/SiC composite tubes were subjected to a xenon flash heating pulse. A high-speed, high-sensitivity infrared camera was used to measure surface temperature changes as a function of time and nondestructively detect subsurface defects/damages, such as macroscopic pores. Standard reference material (Pyroceram 9606) and curved SiC/SiC composite tube specimens were used to validate thermal diffusivity obtained from infrared imaging. Unlike the traditional method, there is no need prepare special specimens, and thermal diffusivity values in three orientations are obtained after a single flash. A finite element analysis model based on x-ray computed tomography scans was developed to simulate the heat transfer. In conclusion, this technique is significant in assessing thermal conductivity and inspecting the health of ceramic tubes during and after service.
36 MATERIALS SCIENCE↗
3D simulations of spinlike flames in Co/Al multilayers with enhanced conduction losses
Reactive Co/Al multilayers are uniformly structured materials that may be ignited to produce rapid and localized heating. Prior studies varying the bilayer thickness (i.e., sum of two individual layers of Co and Al) have revealed different types of flame morphologies, including: (a) steady/planar, (b) wavy/periodic, and (c) transverse bands, originating in the flame front. These instabilities resemble the “spin waves” first observed in the early studies of solid combustion (i.e., Ti cylinder in a N 2 atmosphere), and are likewise thought to be due to the balance of heat released by reaction and heat conduction forward into the unreacted multilayer. However, the multilayer geometry and three-dimensional (3D) edge effects are relatively unexplored. In this work, a new diffusion-limited reaction model for Co/Al multilayers was implemented in large, novel 3D finite element analysis (FEA) simulations, in order to study the origins of these spinlike flames. This reaction model builds upon previous work by introducing three new phase-dependent property models for: (1) the diffusion coefficient, (2) anisotropic thermal conductivity tensor, and (3) bulk heat capacity, as well as one additional model for the bilayer-dependent heat of reaction. These novel 3D simulations are the first to predict both steady and unsteady flames in Co/Al multilayers. Moreover, two unsteady modes of flame propagation are identified, which depend on the enhanced conduction losses with slower flames, as well as flame propagation around notched edges. Finally, future work will consider the generality of the current modeling approach and also seek to define a more generalized set of stability criteria for additional multilayer systems.
42 ENGINEERING↗
Macro-level mechanical interlocking: A rapid joining approach for additively manufactured compression molded composite panels
Composite joining typically involves multiple steps, such as drilling and surface treatment, as part of the manufacturing process, which leads to low throughput and long cycle times. In the present study, we demonstrated a macro-level mechanical interlocking (MI) based, rapid joining technique to assemble additively manufactured compression molded (AMCM) panels, enabling the production of parts larger than the mold dimensions. Composite panels made of 20 wt% short carbon fiber reinforced acrylonitrile butadiene styrene (CF/ABS) were joined using MI features of various geometries, namely tree (TR), dovetail (Dov), rectangle 2 (Rect2), and rectangle 1 (Rect1), and their in-plane strength was evaluated. The resultant strength of the tested MI joints reached up to 74 % of the baseline tensile strength (i.e., the ‘no joint’ case). Observations from optical and scanning electron microscopy revealed inadequate polymer diffusion between the adherends, indicating that the joint strength was primarily derived from mechanical interlocking. Additionally, the fracture surfaces exhibited stress-whitening marks, which were characterized using differential scanning calorimetry (DSC). The increase in melting enthalpy suggested local stretching of polymer chains due to MI. Finite element analysis (FEA) indicated that the Rect1 MI feature, which generated the lowest stress concentration, outperformed the others in terms of joint strength, achieving 42 MPa. As a demonstration of the MI joining method, a battery box tray measuring 108 cm × 34 cm using a mold with an effective dimension of 36 cm × 34 cm successfully manufactured, resulting in a part with an area three times larger than the mold. In conclusion, this study presents a promising approach to improving composite joining techniques while minimizing production complexities.
In-plane joining↗
Delamination-informed lifecycle decisions: A dielectric and machine learning framework for composite sorting and recycling
Composite materials are widely used in aerospace, marine, and automotive sectors due to their high strength-to-weight ratio and durability. However, their long-term reliability can be compromised by damage accumulation. Specifically, delamination initiation serves as a precursor to structural failure, which is often difficult to detect during damage inspection. Identifying and sorting delamination initiation in samples not only increases operational safety while providing critical information for end-of-life decisions, which influences both the service life extension value and the efficiency of fiber extraction during recycling. This research addresses two challenges: (1) developing a nondestructive, ex-situ framework to sort composite materials based on damage severity, particularly delamination, and (2) understanding how damage in composites influences resin removal during pyrolysis. Both experimental work and finite element analysis were performed to predict critical stress levels that are associated with delamination onset. Based on these results, three loading levels 50 %, 75 %, and 90 % of maximum stress, were selected for controlled experiments, generating composite samples with varying extents of damage for machine learning model training. Microscopic imaging of these samples confirmed the damage progression from matrix cracking to delamination, validating the computational predictions. We explored supervised machine learning using dielectric measurements to classify damage states. Preliminary results show an artificial neural network can identify early delamination which is a potential precursor to failure, with 94.44 % accuracy on our dataset. A parallel investigation into the effect of damage severity on pyrolysis recycling showed that heavily delaminated samples required significantly less energy for comparable matrix removal than undamaged samples.
dielectric variables↗
A deep learning and finite element approach for exploration of inverse structure–property designs of lightweight hybrid composites
Hybrid composites have important applications, such as high-performance and lightweight materials in aerospace and automotive industries. Hybrid composites utilize the synergy of diverse fillers to achieve desired material properties, but usually have more complicated microstructures. While topology optimization can optimize a particular property, designing hybrid composites for customized mechanical performances, e.g. full-range stress–strain curve, remains challenging. Here, a computational framework that integrated finite element analysis (FEA) and artificial intelligence (AI) methods of Conditional Generative Adversarial Networks (cGAN) deep learning and transfer learning was developed to establish inverse structure–property relationships and design tailor-made hybrid composites. Based on FEA-generated datasets of hybrid fiber-particle–matrix microstructures and their corresponding full-range stress–strain curves, a cGAN architecture was trained to generate tailored microstructures and establish structure–property relationships. Similarity in microstructural features and well-matched stress–strain curves based on the AI-generated composites were achieved. In conclusion, transfer learning was used to expand the pre-trained model for designing different materials systems.
Hybrid composites↗
In situ synchrotron X-ray tomography of 304 stainless steels undergoing chlorine-induced stress corrosion cracking
To understand the mechanisms behind chlorine-induced stress corrosion cracking (CISCC) in 304 stainless steel, synchrotron high energy x-rays were used to perform in-situ x-ray microtomography on a pre-cracked sample under load in a corrosive simulated marine environment. The tomography scans showed the crack morphology evolving into a branching crack around the surface of the sample. Finite element analysis and stress intensity analysis are conducted to explain the observed crack branching.
304 Stainless Steel↗
The 3D printing and modeling of functionally graded Kelvin foams for controlling crushing performance
Mechanical impact protection is an important consideration in many applications, ranging from product transportation to sports. Cellular materials are typically used due to their desirable energy absorption properties and light weight. However, their large deformation and rate dependent responses (especially of polymer foams) are challenging to consider in design. Additionally, the use of foams with uniform properties, such as uniform density and uniform stiffness, often restricts the designed foams to only be suitable for a narrow range of mechanical impact conditions whereas real applications commonly face unpredictable situations. 3D printing offers fabrication flexibility and thus opens the door to create foams with tailored properties. In this work, we investigate the feasibility of using 3D printing for functionally graded foams (FGFs) that are optimal over a broad range of mechanical environments. The foams are fabricated by the recently developed grayscale digital light processing (g-DLP) method which can print parts with locally designed properties. These foams are tested under drop test conditions and with slower displacement control. We also model the large deformation behavior of FGFs using finite element analysis in which we account for the different viscoelastic behaviors of the distinct grayscale regions. We then use the model to examine the impact mitigation capabilities of FGFs in different loading scenarios. Finally, we show how FGFs can be used to satisfy real-world design goals using the case study of a motorcycle helmet. In contrast to prior work, we investigate continuous, functionally graded foams of a single density that differ in their viscoelastic responses. This work provides further insight into the benefits of viscoelastic properties and modulus graded foams and presents a manufacturing approach that can be used to produce the next generation of flexible lattice foams as mechanical absorbers.
3D printing↗
On the roles of welding residual stresses in determination of fracture toughness in austenitic stainless steel SUS 304 pipeline girth welds
Welding residual stresses especially the high tensile stresses are proved to have negative impacts on the fatigue and fracture behaviors of welded structures. In this study, a virtual fabrication of test specimens from welding process to specimen preparation was carried out by numerical simulation. An austenitic stainless steel multi-pass pipe welding was simulated by transient thermal–mechanical finite element analysis, the residual stresses were then mapped into the test specimen to evaluate fracture toughness. The findings in this study confirmed that, residual stress can be high in a sub-sized compact tensile specimen, which may accelerate or hinder the crack propagation during actual fatigue and fracture tests as reported in recent years. The influence of the cutting location and orientation of the specimen on fracture performance was investigated systematically to provide a fundamental understanding of welding residual stress and necessary insights into the specimen preparation procedure. Considering the limitation of measuring techniques and the complexity of the stress distribution, the developed numerical model can be a very useful tool to elucidate the stress evolution and quantify the effect of remaining welding stress on fracture toughness.
Fracture behavior↗
Large scale polymer toughening of two-dimensional materials revealed by in situ TEM fracture tests and multiscale simulations
Two-dimensional (2D) materials offer significant potential for applications in energy-harvesting devices, batteries, sensors, and transistors. However, their intrinsic brittleness makes them prone to mechanical failure, limiting their practical use. In this work, we perform in situ transmission electron microscopy (TEM) fracture tests on monolayer MoSe2 and uncover an extrinsic toughening effect induced by an ultrathin adsorbed polystyrene adlayer. This adlayer substantially enhances the fracture resistance of the 2D flakes. Through a combination of molecular dynamics simulations and finite element analysis, we elucidate the molecular mechanism behind this toughening effect. Further, it arises from the active crack-bridging behavior of entangled polymer chains and the formation of a fracture process zone that stabilizes crack propagation and increases the energy required for crack extension. The proposed toughening mechanism offers a pathway to improving the mechanical reliability of 2D material-based devices by mitigating the risk of sudden failure.
2D materials↗
Regularizing the linearly extrapolated BDF2 scheme for incompressible flows with time relaxation
This paper presents a highly-efficient finite element scheme for the time relaxation model (TRM). The efficiency is achieved through the second-order BDF2 time-stepping scheme with linear extrapolation (BDF2LE). The accuracy of the scheme is also greatly enhanced through the use of the divergence-free Scott-Vogeulis finite elements, and van Cittert approximate deconvolution. A complete finite element analysis is provided, which includes rigorous proofs for the stability, well-possessedness, and convergence of both velocity and pressure solutions. Furthermore, we also demonstrate that the inclusion of the linear time relaxation term preserves the long-time stability of the unregularized BDF2LE scheme. Finally, numerical experiments are presented that demonstrate the added stability and accuracy that time relaxation can provide.
97 MATHEMATICS AND COMPUTING↗
On the failure mechanisms of Cr-coated 316 stainless steel in bending fatigue tests
Here, fully-reversed cyclic bending tests were conducted on uncoated and Cr-coated 316 stainless steel specimens. At high stresses, the uncoated specimens outperformed the Cr-coated ones, while the trend reversed at low stresses. For Cr-coated specimens subjected to high stress amplitude loading, experimental observations showed crack initiation at the Cr/substrate interface due to shear steps formed from dislocation activities in the substrate—a conclusion supported by accompanying finite element analysis. Interfacial crack initiation was not observed under low stress amplitudes. This difference in fatigue crack initiation mechanisms induced an opposite trend in fatigue life between high and low stress amplitudes.
36 MATERIALS SCIENCE↗
A machine-learning-aided data recovery approach for predicting multi-material thermal behaviors in advanced test reactor capsules
Instrumented experiments conducted at test reactors are essential to the deployment of new advanced reactor systems. Designing new experiments and generating data on specific reactor conditions require significant investments in terms of both time and cost. Finite element analysis software can be used to create high-fidelity models of experiment environments in order to support the actual experiments, but computation time remains a concern in terms of applying outcomes to real-time usage of data (e.g., a digital twin [DT]). Here, the present research proposes a machine-learning (ML) aided approach to making temperature and displacement predictions based on the thickness of the outer gas gap on the experimental capsule used for in-pile demonstration of a novel new thermal conductivity probe in the Advanced Test Reactor (ATR). This capsule consisted of U10Zr fuel, a rodlet, sodium, and inner and outer capsules. Gas gaps existed between the fuel and the rodlet, and between the inner and the outer capsule. The learning data pertained to an experimental capsule's radial distributions of temperature and displacement, as obtained based on Abaqus and the physical features. For the first step of ML sequence, the temperature was predicted using three positional parameters. Next, the displacement was predicted using seven additional parameters. Each physical feature was normalized in order to be both nondimensional and standardized. The temperature and displacement predictions showed good agreement with the simulation results in all cases involving interpolation and extrapolation. Furthermore, data similarity enhancement increased the similarity between the training and the target data, thereby increasing the predictive accuracy of the ML models. In certain extrapolation cases involving limited original ML model accuracy, data similarity enhancement and data recovery was able to somewhat improve this accuracy.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Assessment of reverse gun taylor cylinder experimental configuration
Experimental efforts for Taylor-anvil impact tests have often been limited to near room temperature. The ‘Reverse Gun’ method proposed by Gust in 1982 allows for the Taylor impact specimen to be uniformly heated without temperature losses before impact. Through the use of finite element analysis, we explore two topics in this work. First, we examine whether the reverse gun experimental configuration is comparable to the traditional Taylor-anvil setup. Second, we assess the accuracy of several commonly employed flow strength models in terms of their ability to predict the reverse gun experimental results which involve dynamic loading conditions and complex thermo-mechanical coupling. The reverse gun simulations are performed for tantalum targets at initial temperatures in the range 295 K to 1295 K and velocities from 135 m/s to 242 m/s. We show that with suitable care in the modeling of the preheated reverse gun experiments one can make valuable assessments of flow strength models. Given the conditions explored, these observations probe the thermal softening, strain hardening, and strain rate sensitivity of the material.
42 ENGINEERING↗
Shock compression behavior of stainless steel 316L octet-truss lattice structures
Lattice structures offer desirable mechanical properties for applications of energy absorption and impact mitigation but limited research has been carried out on their shock compression behavior. In this work, the shock compression behavior of stainless steel 316L (SS316L) octet-truss lattice structures was investigated through experimental techniques and numerical simulations. Plate impact experiments with high-speed imaging were conducted at impact velocities of 270 – 390 m/s on lattice specimens with 5x5x10 unit cell geometries additively manufactured (AM) using direct metal laser sintering. High-speed imaging together with digital image correlation was used to extract full-field measurements and define a two-wave structure consisting of an elastic wave and planar compaction (shock) wave which propagated along the impact direction. A linear shock velocity versus particle velocity relation was found to approximate the measurements with a unit slope and a linear fit constant equal to the crushing speed. Furthermore, the shock velocity versus particle velocity relation, full-field measurements, and elastic limit together with the Eulerian form of the Rankine-Hugoniot jump conditions were used to find relations for the stress and internal energy behind the shock. Stress behind the shock increased with relative density and particle velocity, and specific internal energy converged to a single curve similar to that of bulk AM SS316L. Explicit finite element analysis using the Johnson-Cook constitutive model demonstrated similar shock behavior observed in experiments and a linear shock velocity versus particle velocity relation and corresponding Hugoniot calculations were found to be in agreement with experimental results. Numerical simulations confirmed negligible effects of exterior versus interior measurements and further validated the application of one-dimensional shock theory.
42 ENGINEERING↗
Structural modeling and validation of laminated stacks in magnetic gearing applications
Laminated structures are commonly employed in magnetic gears based on their ability to direct magnetic flux, while inhibiting eddy currents. Unfortunately, the stiffness of the laminated structure is much lower than a solid part, which can lead to unacceptable deflections, given the large magnetic forces and small air gaps in magnetic gears. Finite element analysis of laminations to accurately predict these deflections can be computationally expensive due to the small elements needed to model the thin lamination dimensions. These analyses also have some uncertainty due to the assignment of bonding stiffness. This paper presents a means of accurately modelling the deflection of a laminated stack with reduced complexity and computational time, which is compared to experimental results. Here, the resulting methods are then used to assess the magnetic and mechanical advantages of different designs for the modulating pieces of a magnetic gear.
42 ENGINEERING↗
Exact solution of burst pressure for thick-walled pipes using the flow theory of plasticity
This paper develops an exact solution of burst pressure for defect-free, thick-walled pipes using the flow theory of plasticity in terms of the average shear stress yield criterion (or Zhu-Leis yield criterion). The pipe steel is assumed to obey the power-law strain hardening rule, and large plastic deformation is described by the finite strain theory. On this basis, internal pressure is obtained as a power series function of the effective strains on the inside and outside surfaces of the thick-walled pipe with use of the second Bernoulli numbers. At burst failure, the Zhu-Leis flow solution of burst pressure is determined as a power series solution, and the burst effective strains, the burst effective stresses, and the burst pressure are functions of the diameter ratio (D o / D i ), strain hardening exponent (n), and ultimate tensile strength (UTS). Similarly, the Tresca and von Mises flow solutions of burst pressure are also determined as a power series solution in terms of the Tresca and von Mises yield criteria. A general closed-from exact solution of burst pressure is then proposed for the three yield criteria, and the results showed that the proposed exact solution matches well with the power series solution for each yield criterion. Moreover, the von Mises flow solution is an upper bound prediction, the Tresca flow solution is a lower bound prediction, and the Zhu-Leis flow solution is an intermediate prediction that agrees well with the finite element analysis results of burst pressure for thick-walled pipes. Furthermore, two datasets of full-scale burst tests are then utilized to evaluate and validate the proposed flow solutions of burst pressure for both thin and thick-walled pipes.
42 ENGINEERING↗
Numerical Simulation and Experimental Validation of Joint Performance in Aluminum-Steel Lap Welds Formed by Friction Stir Dovetailing
Friction stir dovetailing (FSD) is a new dissimilar material joining process that simultaneously forms a mechanical interlock and metallurgical bond at the dissimilar material interface. This work presents development of a modeling and simulation approach to predict mechanical performance of FSD thick section aluminum to steel joints. A finite element analysis (FEA) was carried out in order to predict the load-carrying capacity and failure location for aluminum thicknesses between 12.7 mm and 50.8 mm with different numbers of dovetails. The numerical results and corresponding experimental investigation are in agreement and suggest that the developed methods can be used effectively for design and analysis of FSD joints using standard FEA tools.
Friction stir dovetailing, Friction stir welding, ↗