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Modular Linear Thermoviscoelastic Model

Lester, Brian T.; Long, Kevin N.

Time-dependent, viscoelastic responses of materials like polymers and glasses have long been studied. As such, a variety of models have been put forth to describe the behavior including simple rheological models (e.g. Maxwell, Kelvin), linear "fading memory" theories, and hereditary integral based linear thermal viscoelastic approaches as well as more recent nonlinear theories that are either integral, fictive temperature, or differential internal state variable based. The current work details a new LINEAR_THERMOVISCOELASTIC model that has been added to LAME. This formulation represents a viscoelastic theory that neglects some of the phenomenological details of the PEC/SPEC models in favor of efficiency and simplicity. Furthermore, this new model is a first step towards developing "modular" viscoelastic capabilities akin to those available with hardening descriptions for plasticity models in LAME. Specifically, multiple different (including user-defined) shift-factor forms are implemented with each being easily selected via parameter specification rather than requiring distinct material models.

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Library of Advanced Materials for Engineering (LAME) 4.54

Lester, Brian T.; Scherzinger, William M.

Accurate and e ffi cient constitutive modeling remains a cornerstone issue fo r solid mechanics analysis. Over the years, the LAME advanced material model l ibrary has grown to address this challenge by implementing models capable of describing mat erial systems spanning soft polymers to sti ff ceramics including both isotropic and anisotropic respons es. Inelastic behaviors including (visco)plasticity, damage, and fracture have al l incorporated for use in various analyses. This multitude of options and flexibility, however, comes at the cost of many capabilities, features, and responses and the ensuing complexity in the re sulting implementation. Therefore, to enhance confidence and enable the utilization of the LAME lib rary in application, this e ff ort seeks to document and verify the various models in the LAME library . Specifically, the broader strategy, organization, and interface of the library itsel f is first presented. The physical theory, numerical implementation, and user guide for a large set of m odels is then discussed. Importantly, a number of verification tests are performed with each model t o not only have confidence in the model itself but also highlight some important response cha racteristics and features that may be of interest to end-users. Finally, in looking ahead to the fu ture, approaches to add material models to this library and further expand the capabilities are pres ented.

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Library of Advanced Materials for Engineering (LAME) 4.52

Lester, Brian T.; Scherzinger, William M.

Accurate and efficient constitutive modeling remains a cornerstone issue for solid mechanics analysis. Over the years, the LAME advanced material model library has grown to address this challenge by implementing models capable of describing material systems spanning soft polymers to stiff ceramics including both isotropic and anisotropic responses. Inelastic behaviors including (visco)plasticity, damage, and fracture have all incorporated for use in various analyses. This multitude of options and flexibility, however, comes at the cost of many capabilities, features, and responses and the ensuing complexity in the resulting implementation. Therefore, to enhance confidence and enable the utilization of the LAME library in application, this effort seeks to document and verify the various models in the LAME library. Specifically, the broader strategy, organization, and interface of the library itself is first presented. The physical theory, numerical implementation, and user guide for a large set of models is then discussed. Importantly, a number of verification tests are performed with each model to not only have confidence in the model itself but also highlight some important response characteristics and features that may be of interest to end-users. Finally, in looking ahead to the future, approaches to add material models to this library and further expand the capabilities are presented.

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Library of Advanced Materials for Engineering (LAME) 4.50

Merewether, Mark T.; Crane, Nathan K.; Plews, Julia A.; de Frias, Gabriel J.; Le, San L.; Littlewood, David J.; Mosby, Matthew D.; Pierson, Kendall H.; Porter, V.L.; Shelton, Timothy S.; Thomas, Jesse D.; Tupek, Michael R.; Veilleux, Michael V.; Xavier, Patrick G.; Scherzinger, William M.; Lester, Brian T.

Accurate and efficient constitutive modeling remains a cornerstone issue for solid mechanics analysis. Over the years, the LAME advanced material model library has grown to address this challenge by implement- ing models capable of describing material systems spanning soft polymers to stiff ceramics including both isotropic and anisotropic responses. Inelastic behaviors including (visco)plasticity, damage, and fracture have all incorporated for use in various analyses. This multitude of options and flexibility, however, comes at the cost of many capabilities, features, and responses and the ensuing complexity in the resulting imple- mentation. Therefore, to enhance confidence and enable the utilization of the LAME library in application, this effort seeks to document and verify the various models in the LAME library. Specifically, the broader strategy, organization, and interface of the library itself is first presented. The physical theory, numerical implementation, and user guide for a large set of models is then discussed. Importantly, a number of verifi- cation tests are performed with each model to not only have confidence in the model itself but also highlight some important response characteristics and features that may be of interest to end-users. Finally, in looking ahead to the future, approaches to add material models to this library and further expand the capabilities are presented.

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Born Qualified Grand Challenge LDRD Final Report

Roach, R.A.; Argibay, Nicolas A.; Allen, Kyle M.; Balch, Dorian K.; Beghini, Lauren L.; Bishop, Joseph E.; Boyce, Brad B.; Brown, Judith A.; Burchard, Ross L.; Chandross, M.; Cook, Adam W.; DiAntonio, Christopher D.; Dressler, Amber D.; Forrest, Eric C.; Ford, Kurtis R.; Ivanoff, Thomas I.; Jared, Bradley H.; Johnson, Kyle J.; Kammler, Daniel K.; Koepke, Joshua R.; Kustas, Andrew K.; Lavin, Judith M.; Leathe, Nicholas L.; Lester, Brian T.; Madison, Jonathan D.; Mani, Seethambal S.; Martinez, Mario J.; Moser, Daniel M.; Rodgers, Theron R.; Seidl, Daniel T.; Brown-Shaklee, Harlan J.; Stanford, Joshua S.; Stender, Michael S.; Sugar, Joshua D.; Swiler, Laura P.; Taylor, Samantha T.; Trembacki, Bradley T.

This SAND report fulfills the final report requirement for the Born Qualified Grand Challenge LDRD. Born Qualified was funded from FY16-FY18 with a total budget of ~$13M over the 3 years of funding. Overall 70+ staff, Post Docs, and students supported this project over its lifetime. The driver for Born Qualified was using Additive Manufacturing (AM) to change the qualification paradigm for low volume, high value, high consequence, complex parts that are common in high-risk industries such as ND, defense, energy, aerospace, and medical. AM offers the opportunity to transform design, manufacturing, and qualification with its unique capabilities. AM is a disruptive technology, allowing the capability to simultaneously create part and material while tightly controlling and monitoring the manufacturing process at the voxel level, with the inherent flexibility and agility in printing layer-by-layer. AM enables the possibility of measuring critical material and part parameters during manufacturing, thus changing the way we collect data, assess performance, and accept or qualify parts. It provides an opportunity to shift from the current iterative design-build-test qualification paradigm using traditional manufacturing processes to design-by-predictivity where requirements are addressed concurrently and rapidly. The new qualification paradigm driven by AM provides the opportunity to predict performance probabilistically, to optimally control the manufacturing process, and to implement accelerated cycles of learning. Exploiting these capabilities to realize a new uncertainty quantification-driven qualification that is rapid, flexible, and practical is the focus of this effort.

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Results 26–50 of 67
Results 26–50 of 67