This paper examines the variability of predicted responses when multiple stress-strain curves (reflecting variability from replicate material tests) are propagated through a finite element model of a ductile steel can being slowly crushed. Over 140 response quantities of interest (including displacements, stresses, strains, and calculated measures of material damage) are tracked in the simulations. Each response quantity’s behavior varies according to the particular stress-strain curves used for the materials in the model. We desire to estimate response variability when only a few stress-strain curve samples are available from material testing. Propagation of just a few samples will usually result in significantly underestimated response uncertainty relative to propagation of a much larger population that adequately samples the presiding random-function source. A simple classical statistical method, Tolerance Intervals, is tested for effectively treating sparse stress-strain curve data. The method is found to perform well on the highly nonlinear input-to-output response mappings and non-standard response distributions in the can-crush problem. The results and discussion in this paper support a proposition that the method will apply similarly well for other sparsely sampled random variable or function data, whether from experiments or models. Finally, the simple Tolerance Interval method is also demonstrated to be very economical.
This work examines the variability of predicted responses when multiple stress-strain curves (reflecting variability from replicate material tests) are propagated through a transient dynamics finite element model of a ductile steel can being slowly crushed. An elastic-plastic constitutive model is employed in the large-deformation simulations. The present work assigns the same material to all the can parts: lids, walls, and weld. Time histories of 18 response quantities of interest (including displacements, stresses, strains, and calculated measures of material damage) at several locations on the can and various points in time are monitored in the simulations. Each response quantity's behavior varies according to the particular stressstrain curves used for the materials in the model. We estimate response variability due to variability of the input material curves. When only a few stress-strain curves are available from material testing, response variance will usually be significantly underestimated. This is undesirable for many engineering purposes. This paper describes the can-crush model and simulations used to evaluate a simple classical statistical method, Tolerance Intervals (TIs), for effectively compensating for sparse stress-strain curve data in the can-crush problem. Using the simulation results presented here, the accuracy and reliability of the TI method are being evaluated on the highly nonlinear inputto- output response mappings and non-standard response distributions in the can-crush UQ problem.
This report demonstrates versatile and practical model validation and uncertainty quantification techniques applied to the accuracy assessment of a computational model of heated steel pipes pressurized to failure. The Real Space validation methodology segregates aleatory and epistemic uncertainties to form straightforward model validation metrics especially suited for assessing models to be used in the analysis of performance and safety margins. The methodology handles difficulties associated with representing and propagating interval and/or probabilistic uncertainties from multiple correlated and uncorrelated sources in the experiments and simulations including: material variability characterized by non-parametric random functions (discrete temperature dependent stress-strain curves); very limited (sparse) experimental data at the coupon testing level for material characterization and at the pipe-test validation level; boundary condition reconstruction uncertainties from spatially sparse sensor data; normalization of pipe experimental responses for measured input-condition differences among tests and for random and systematic uncertainties in measurement/processing/inference of experimental inputs and outputs; numerical solution uncertainty from model discretization and solver effects.
ASME 2012 Heat Transfer Summer Conf. Collocated with the ASME 2012 Fluids Engineering Div. Summer Meeting and the ASME 2012 10th Int. Conf. on Nanochannels, Microchannels and Minichannels, HT 2012
The increased demand for Liquefied Natural Gas (LNG) as a fuel source in the U.S. has prompted a study to improve our capability to predict cascading damage to LNG tankers from cryogenic spills and subsequent fire. To support this large modeling and simulation effort, a suite of experiments were conducted on two tanker steels, ABS Grade A steel and ABS Grade EH steel. A thorough and complete understanding of the mechanical behavior of the tanker steels was developed that was heretofore unavailable for the span of temperatures of interest encompassing cryogenic to fire temperatures. This was accomplished by conducting several types of experiments, including tension, notched tension and Charpy impact tests at fourteen temperatures over the range of -191 C to 800 C. Several custom fixtures and special techniques were developed for testing at the various temperatures. The experimental techniques developed and the resulting data will be presented, along with a complete description of the material behavior over the temperature span.
Instrumented, fully coupled thermal-mechanical experiments were conducted to provide validation data for finite element simulations of failure in pressurized, high temperature systems. The design and implementation of the experimental methodology is described in another paper of this conference. Experimental coupling was accomplished on tubular 304L stainless steel specimens by mechanical loading imparted by internal pressurization and thermal loading by side radiant heating. Experimental parameters, including temperature and pressurization ramp rates, maximum temperature and pressure, phasing of the thermal and mechanical loading and specimen geometry details were studied. Experiments were conducted to increasing degrees of deformation, up to and including failure. Mechanical characterization experiments of the 304L stainless steel tube material was also completed for development of a thermal elastic-plastic material constitutive model used in the finite element simulations of the validation experiments. The material was characterized in tension at a strain rate of 0.001/s from room temperature to 800 C. The tensile behavior of the tube material was found to differ substantially from 304L bar stock material, with the plasticity characteristics and strain to failure differing at every test temperature.
Coupled thermal-mechanical experiments with well-defined, controlled boundary conditions were designed through an iterative process involving a team of experimentalists, material modelers and computational analysts. First the basic experimental premise was selected: an axisymmetric tubular specimen mechanically loaded by internal pressurization and thermally loaded asymmetrically by side radiant heating. Then several integrated experimental-analytical steps were taken to determine the experimental details. The boundary conditions were mostly thermally driven and were chosen so they could be modeled accurately; the experimental fixtures were designed to ensure that the boundary conditions were met. Preliminary, uncoupled analyses were used to size the specimen diameter, height and thickness with experimental consideration of maximum pressure loads and fixture design. Iterations of analyses and experiments were used to efficiently determine heating parameters including lamp and heating shroud design, set off distance between the lamps and shroud and between the shroud and specimen, obtainable ramp rates, and the number and spatial placement of thermocouples. The design process and the experimental implementation of the final coupled thermomechanical failure experiment design will be presented.
An interdisciplinary team of scientists and engineers having broad expertise in materials processing and properties, materials characterization, and computational mechanics was assembled to develop science-based modeling/simulation technology to design and reproducibly manufacture high performance and reliable, complex microelectronics and microsystems. The team's efforts focused on defining and developing a science-based infrastructure to enable predictive compaction, sintering, stress, and thermomechanical modeling in ''real systems'', including: (1) developing techniques to and determining materials properties and constitutive behavior required for modeling; (2) developing new, improved/updated models and modeling capabilities, (3) ensuring that models are representative of the physical phenomena being simulated; and (4) assessing existing modeling capabilities to identify advances necessary to facilitate the practical application of Sandia's predictive modeling technology.