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Ductile failure of structural metals is relevant to a wide range of engineering scenarios. Computational methods are employed to anticipate the critical conditions of failure, yet they sometimes provide inaccurate and misleading predictions. Challenge scenarios, such as the one presented in the current work, provide an opportunity to assess the blind, quantitative predictive ability of simulation methods against a previously unseen failure problem. Rather than evaluate the predictions of a single simulation approach, the Sandia Fracture Challenge relies on numerous volunteer teams with expertise in computational mechanics to apply a broad range of computational methods, numerical algorithms, and constitutive models to the challenge. This exercise is intended to evaluate the state of health of technologies available for failure prediction. In the first Sandia Fracture Challenge, a wide range of issues were raised in ductile failure modeling, including a lack of consistency in failure models, the importance of shear calibration data, and difficulties in quantifying the uncertainty of prediction [see Boyce et al. (Int J Fract 186:5-68, 2014) for details of these observations]. This second Sandia Fracture Challenge investigated the ductile rupture of a Ti-6Al-4V sheet under both quasi-static and modest-rate dynamic loading (failure in 0.1 s). Like the previous challenge, the sheet had an unusual arrangement of notches and holes that added geometric complexity and fostered a competition between tensile- and shear-dominated failure modes. The teams were asked to predict the fracture path and quantitative far-field failure metrics such as the peak force and displacement to cause crack initiation. Fourteen teams contributed blind predictions, and the experimental outcomes were quantified in three independent test labs. Additional shortcomings were revealed in this second challenge such as inconsistency in the application of appropriate boundary conditions, need for a thermomechanical treatment of the heat generation in the dynamic loading condition, and further difficulties in model calibration based on limited real-world engineering data. As with the prior challenge, this work not only documents the 'state-of-the-art' in computational failure prediction of ductile tearing scenarios, but also provides a detailed dataset for non-blind assessment of alternative methods.
The focus of this paper is the simulation of the thermal-mechanical fatigue behavior (TMF) of two single crystalline Nickel-based superalloys in a temperature range between 400 degrees C and 980 degrees C. The newly developed rhenium-free alloy Astra-3OptW and the rhenium-free alloy CMSX-6 are analyzed concerning the basic deformation mechanisms, i.e. elasticity, time-independent and time-dependent plasticity contributing to hardening. In detail, the relevant parameters for high temperature deformation are identified from isothermal creep experiments and used in a numerical model to simulate the deformation behavior under instationary thermal and mechanical loading. Special attention is focused on the determination of the hardening by the second phase (gamma'-precipitates) and their influence on time-dependent deformation and relaxation mechanisms. Therefore, the parameters describing the stress and temperature dependence of the creep rate (i.e. stress exponent n and activation energy Q) are interpreted in terms of a threshold stress taking into account the hardening contribution of the gamma'-phase. Thus, only a reduced effective stress is active for plastic deformation. Particular attention is focused on the accurate determination of the threshold stress as a function of temperature and applied stress from the Langeborg-Bergmann-plot. The comparison of the simulated TMF-deformation to the experimental TMF-data clearly indicates the accuracy of the model in predicting the resulting stresses induced by instationary thermal and mechanical loading. (C) 2016 Elsevier Ltd. All rights reserved.
This work studies the performance and dry mass of the under development LOX/Ethanol L75 liquid rocket engine. To this end, an object-oriented program written in C++ was developed. The program is intended to be versatile and easily extensible in order to analyze different configurations of liquid rocket engines. The UML (Unified Modeling Language) tool is used to model the architecture of the codes. UML diagrams help to visualize the code structure and the communication between objects, enabling a high degree of abstraction. The cryogenics Vulcain and HM7B engines power cycles along with the staged-combustion SSME engine perform the verification of the codes. Finally, the influence of changes in design parameters on the performance and dry mass of the L75 rocket engine is analyzed.
The Sandia Fracture Challenges provide a forum for the mechanics community to assess its ability to predict ductile fracture through a blind, round-robin format where mechanicians are challenged to predict the deformation and failure of an arbitrary geometry given experimental calibration data. The Third Challenge (SFC3) required participants to predict fracture in an additively manufactured (AM) 316L stainless steel bar containing through holes and internal cavities that could not have been conventionally machined. The volunteer participants were provided extensive data including tension and notched tensions tests of 316L specimens built on the same build-plate as the Challenge geometry, micro-CT scans of the Challenge specimens and geometric measurements of the feature based on the scans, electron backscatter diffraction (EBSD) information on grain texture, and post-test fractography of the calibration specimens. Surprisingly, the global behavior of the SFC3 geometry specimens had modest variability despite being made of AM metal, with all of the SFC3 geometry specimens failing under the same failure mode. This is attributed to the large stress concentrations from the holes overwhelming the stochastic local influence of the AM voids and surface roughness. The teams were asked to predict a number of quantities of interest in the response based on global and local measures that were compared to experimental data, based partly on Digital Image Correlation (DIC) measurements of surface displacements and strains, including predictions of variability in the resulting fracture response, as the basis for assessment of the predictive capabilities of the modeling and simulation strategies. Twenty-one teams submitted predictions obtained from a variety of methods: the finite element method (FEM) or the mesh-free, peridynamic method; solvers with explicit time integration, implicit time integration, or quasi-statics; fracture methods including element deletion, peridynamics with bond damage, XFEM, damage (stiffness degradation), and adaptive remeshing. These predictions utilized many different material models: plasticity models including J2 plasticity or Hill yield with isotropic hardening, mixed Swift-Voce hardening, kinematic hardening, or custom hardening curves; fracture criteria including GTN model, Hosford-Coulomb, triaxiality-dependent strain, critical fracture energy, damage-based model, critical void volume fraction, and Johnson-Cook model; and damage evolution models including damage accumulation and evolution, crack band model, fracture energy, displacement value threshold, incremental stress triaxiality, Cocks-Ashby void growth, and void nucleation, growth, and coalescence. Teams used various combinations of calibration data from tensile specimens, the notched tensile specimens, and literature data. A detailed comparison of results based of these different methods is presented in this paper to suggest a set of best practices for modeling ductile fracture in situations like the SFC3 AM-material problem. All blind predictions identified the nominal crack path and initiation location correctly. The SFC3 participants generally fared better in their global predictions of deformation and failure than the participants in the previous Challenges, suggesting the relative maturity of the models used and adoption of best practices from previous Challenges. This paper provides detailed analyses of the results, including discussion of the utility of the provided data, challenges of the experimental-numerical comparison, defects in the AM material, and human factors.