Brian Granzow

Sandia National Laboratories California

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Daniel Seidl

Sandia National Laboratories

Thomas E. Voth

Sandia National Laboratories California

Stephen Bond

Sandia National Laboratories California

Daniel Ibanez

Sandia National Laboratories California

Alan Stagg

Sandia National Laboratories

Glen Hansen

Colorado State University

Mark S. Shephard

Rensselaer Polytechnic Institute

Steven W. Bova

Sandia National Laboratories California

Alejandro Mota

Sandia National Laboratories California

Irina Tezaur

Sandia National Laboratories California

Cooperative Institutions

Sandia National Laboratories

Sandia National Laboratories California

Office of Scientific and Technical Information

National Technical Information Service

Los Alamos National Laboratory

University of Michigan–Ann Arbor

Rensselaer Polytechnic Institute

The University of Texas at Austin

Stanford University

University of Colorado Boulder

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An automated approach for parallel adjoint-based error estimation and mesh adaptation

Engineering With Computers (2019)

Brian Granzow Assad A. Oberai Mark S. Shephard

Adaptive Mesh Refinement

10.1007/s00366-019-00890-2

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Citations (1)

FLEXO: Development Activities for Z-Next Simulation.

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (2019)

Kristian Beckwith Stephen Bond Brian Granzow Nathaniel Donald Hamlin Glen Hansen

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Component-based workflows for parallel thermomechanical analysis of arrayed geometries

Engineering With Computers (2016)

Max O. Bloomfield Zhen Li Brian Granzow Daniel Ibanez Assad A. Oberai

Microelectronics

Component (thermodynamics)

Surface-mount technology

10.1007/s00366-016-0487-5

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Citations (2)

Calibration of Elastoplastic Constitutive Model Parameters from Full-Field Data with Automatic Differentiation-based Sensitivities.

Daniel Seidl Brian Granzow

than its calculation from finite difference approximations. Through the use of AD, we need only to write the constraints in terms of AD objects, where all of the derivatives required for the forward and inverse problems are obtained by appropriately seeding and evaluating these quantities. We present three numerical examples that verify the correctness of the gradient, demonstrate the AD approach's parallel computation capabilities via application to a large-scale FE model, and highlight the formulation's ease of extensibility to other classes of constitutive models.

Automatic differentiation

Extensibility

Numerical differentiation

10.2172/1884132

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FLEXO Development Activities.

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (2020)

Thomas E. Voth Kristian Beckwith Stephen Bond Brian Granzow Nathaniel Donald Hamlin

Source

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FLEXO: A Portably Performant Code for Pulsed Power Target Physics

M. Adams Stephen Bond Steven W. Bova Griffin Cearley Kyle Cochrane

Pulsed Power

Code (set theory)

10.2172/2462910

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Supporting end-to-end workflows for the design and analyst communities through an intuitive frontend and extensible backend grounded in user experience.

James W. Foulk Andrew Ackerman Ashley Fate Terri Galpin Brian Granzow

an adapted version of UCD that we refer to as Scientist-Centered Design (SCD) to engage with users in the design and development of Tigres, a workflow application programming interface. Tigres provides a simple set of programming templates (e.g., sequence, parallel, split, merge) that can be can used to compose and execute computational and data transformation pipelines. In this paper, we describe Tigres and discuss our experiences with the use of UCD for the initial development of Tigres. Our experience-to-date is that the UCD process not only resulted in better requirements gathering but also heavily influenced the architecture design and implementation details. User engagement during the development of tools such as Tigres is critical to ensure usability and increase adoption.

Merge (version control)

End-user development

Application programming interface

10.2172/2003324

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In‐memory integration of existing software components for parallel adaptive unstructured mesh workflows

Concurrency and Computation Practice and Experience (2018)

Cameron Smith Brian Granzow Gerrett Diamond Daniel Ibanez Onkar Sahni

Summary Reliable mesh‐based simulations are needed to solve complex engineering problems. Mesh adaptivity can increase reliability by reducing discretization errors but requires multiple software components to exchange information. Often, components exchange information by reading and writing a common file format. This file‐based approach becomes a problem on massively parallel computers where filesystem bandwidth is a critical performance bottleneck. Our approach using data streams and component interfaces avoids the filesystem bottleneck. In this paper, we present these techniques and their use for coupling mesh adaptivity to the PHASTA computational fluid dynamics solver, the Albany multi‐physics framework, and the Omega3P linear accelerator frequency analysis applications. Performance results are reported on up to 16,384 cores of an Intel Knights Landing‐based system.

Adaptive Mesh Refinement

Component (thermodynamics)

10.1002/cpe.4510

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Citations (7)

Linearization errors in discrete goal-oriented error estimation

Computer Methods in Applied Mechanics and Engineering (2023)

Brian Granzow Daniel Seidl Stephen Bond

Linearization

10.1016/j.cma.2023.116364

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Citations (1)