The following Material Point Method (MPM) simulation of sloshing fluid goes “haywire” at the end, just when things are starting to settle down:

(if the animated gif isn’t visible, please wait for it to load)

The following Material Point Method (MPM) simulation of sloshing fluid goes “haywire” at the end, just when things are starting to settle down:

(if the animated gif isn’t visible, please wait for it to load)

This post has the following aims:

- Provide documentation and source code for a spherically symmetric wave propagation in a linear-elastic medium.
- Tell a story illustrating how this simple verification problem helped to validate a complicated rate-dependent and history-dependent geomechanics model.
- Warn against believing previously reported material parameters, since they might have been the result of constitutive parameter tweaking to compensate for unrelated errors in the host code. Continue reading

Illustrated below is the solution to an idealized problem of a linear elastic annulus (blue) subjected to twisting motion caused by rotating the T-bar an angle . The motion is presumed to be applied slowly enough that equilibrium is satisfied.

This simple problem is taken to be governed by the equations of equilibrium , along with the plane strain version of Hooke’s law in which Cauchy stress is taken to be linear with respect to the small strain tensor (symmetric part of the displacement gradient). If this system of governing equations is implemented in a code, the code will give you an answer, but it is up to you to decide if that answer is a reasonable approximation to reality. This observation helps to illustrate the distinction between *verification *(i.e., evidence that the equations are solved correctly) and *validation *(evidence that physically applicable and physically appropriate equations are being solved). The governing equations always have a correct answer (verification), but that answer might not be very predictive of reality (validation).

Constitutive modeling refers to the development of equations describing the way that materials respond to various stimuli. In classical deformable body mechanics, a simple constitutive model might predict the stress required to induce a given strain; the canonical example is Hooke’s law of isotropic linear elasticity. More broadly, a constitutive model predicts increments in some macroscale state variables of interest (such as stress, entropy, polarization, etc.) that arise from changes in other macroscale state variables (strain, temperature, electric field, etc.).

Constitutive equations are ultimately implemented into a finite element code to close the set of equations required to solve problems of practical interest. This course describes a few common constitutive equations, explaining what features you would see in experimental data or structural behavior that would prompt you to select one constitutive model over another, how to use them in a code, how to test your understanding of the model, how to check if the code is applying the model as advertised in its user’s manual, and how to quantitatively assess the mathematical and physical believability of the solution.

R.M. Brannon, J.M. Wells, and O.E. Strack

Validating simulated predictions of internal damage within armor ceramics is preferable to simply assessing a models ability to predict penetration depth, especially if one hopes to perform subsequent ‘‘second strike’’ analyses. We present the results of a study in which crack networks are seeded by using a statistically perturbed strength, the median of which is inherited from a deterministic ‘‘smeared damage’’ model, with adjustments to reﬂect experimentally established size eﬀects. This minor alteration of an otherwise conventional damage model noticeably mitigates mesh dependencies and, at virtually no computational cost, produces far more realistic cracking patterns that are well suited for validation against X-ray computed tomography (XCT) images of internal damage patterns. For Brazilian, spall, and indentation tests, simulations share qualitative features with externally visible damage. However, the need for more stringent quantitative validation, software quality testing, and subsurface XCT validation, is emphasized.

T.J. Fuller, R.M. Brannon, O.E. Strack, J.E. Bishop

A persistent challenge in simulating damage of natural geological materials, as well as rock-like engineered materials, is the development of efficient and accurate constitutive models.The common feature for these brittle and quasi-brittle materials are the presence of flaws such as porosity and network of microcracks. The desired models need to be able to predict the material responses over a wide range of porosities and strain rate. Kayenta [1] (formerly called the Sandia GeoModel) is a unifi ed general-purpose constitutive model that strikes a balance between rst-principles micromechanics and phenomenological or semi-empirical modeling strategies. However, despite its sophistication and ability to reduce to several classical plasticity theories, Kayenta is incapable of modeling deformation of ductile materials in which deformation is dominated by dislocation generation and movement which can lead to signi cant heating. This stems from Kayenta’s roots as a geological model, where heating due to inelastic deformation is often neglected or presumed to be incorporated implicitly through the elastic moduli.The sophistication of Kayenta and its large set of extensive features, however, make Kayenta an attractive candidate model to which thermal eff ects can be added. This report outlines the initial work in doing just that, extending the capabilities of Kayenta to include deformation of ductile materials, for which thermal e ffects cannot be neglected. Thermal e ffects are included based on an assumption of adiabatic loading by computing the bulk and thermal responses of the material with the Kerley Mie-Gruneisen equation of state and adjusting the yield surface according to the updated thermal state. This new version of Kayenta, referred to as Thermo-Kayenta throughout this report, is capable of reducing to classical Johnson-Cook plasticity in special case single element simulations and has been used to obtain reasonable results in more complicated Taylor impact simulations in LS-Dyna. Despite these successes, however, Thermo-Kayenta requires additional re nement for it to be consistent in the thermodynamic sense and for it to be considered superior to other, more mature thermoplastic models. The initial thermal development, results, and required refinements are all detailed in the following report.

Available Online:

http://www.mech.utah.edu/~brannon/pubs/7-2010FullerBrannonStrackBishopThermodynamicsInKayenta.pdf

J.M. Wells and R.M. Brannon

With the relatively recent introduction of quantitative and volumetric X-ray computedtomography (XCT) applied to ballistic impact damage diagnostics, signiﬁcant inroads have beenmade in expanding our knowledge base of the morphological variants of physical impactdamage. Yet, the current state of the art in computational and simulation modeling of terminalballistic performance remains predominantly focused on the penetration phenomenon, withoutdetailed consideration of the physical characteristics of actual impact damage. Similarly, armorceramic material improvements appear more focused on penetration resistance than on improved intrinsic damage tolerance and damage resistance. Basically, these approaches minimizeour understanding of the potential inﬂuence that impact damage may play in the mitigation orprevention of ballistic penetration. Examples of current capabilities of XCT characterization,quantiﬁcation, and visualization of complex impact damage variants are demonstrated anddiscussed for impacted ceramic and metallic terminal ballistic target materials. Potential beneﬁtsof incorporating such impact damage diagnostics in future ballistic computational modeling arealso brieﬂy discussed.

Available Online:

http://dx.doi.org/10.1007/s11661-007-9304-5

http://www.mech.utah.edu/~brannon/pubs/7-2007WellsBrannonAdvancesInXrayComputedTomographyDiagnosticsOfBallisticDamage.pdf

Analysis and computations have been performed by the Utah CSM group to support experimental investigations of unvalidated assumptions in plasticity theory. The primary untested assumption is that of a regular flow rule in which it is often assumed that the direction of the inelastic strain increment is unaffected by the total strain increment itself. To support laboratory testing of this hypothesis, the general equations of classical plasticity theory were simplified for the case of axisymmetric loading to provide experimentalists with two-parameter control of the axial and lateral stress increments corresponding to a specified loading trajectory in stress space. Loading programs involving changes in loading directions were designed. New methods for analyzing the data via a moving least squares fit to tensor-valued input-output data were used to quantitatively infer the apparent plastic tangent modulus matrix and thereby detect violations of the regular flow rule. Loading programs were designed for validating isotropic cap hardening models by directly measuring the effect of shear loading on the hydrostatic elastic limit.

**UofU Contributors/collaborators:**

Michael Braginski (postdoc, Mech. Engr., UofU)

Jeff Burghardt (PhD student, Mech. Engr., UofU)

**External collaborators/mentors:**

Stephen Bauer (Manager, Sandia National Labs geomechanics testing lab)

David Bronowski (Sandia geomechanics lab technician)

Erik Strack (Manager, Sandia Labs Computational Physics)

MMS stands for “Method of Manufactured Solutions,” which is a rather sleazy sounding name for what is actually a respected and rigorous method of verifying that a finite element (or other) code is correctly solving the governing equations.

A simple introduction to MMS may be found on page 11 of The ASME guide for verification and validation in solid mechanics. The basic idea is to analytically determine forcing functions that would lead to a specific, presumably nontrivial, solution (of your choice) for the dependent variable of a differential equation. Then you would verify a numerical solver for that differential equation by running it using your analytically determined forcing function. The difference between the code’s prediction and your selected manufactured solution provides a quantitative measure of error.