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Results for “Complex Loading”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Predicting Deformation Limits of Dual-Phase Steels Under Complex Loading Paths

The deformation limits of various DP980 steels are examined in this study with deformation instability theory. Under uniaxial tension, overall stress-strain curves of the material are estimated based on simple rule of mixture (ROM) with both iso-strain and iso-stress assumptions. Under complex loading paths, actual microstructure-based finite element (FE) method is used to explicitly resolve the deformation incompatibilities between the soft ferrite and hard martensite phases. The results show that, for uniaxial tension, the deformation instability theory with iso-strain-based ROM can be used to provide the lower bound estimate of the uniform elongation (UE) for the various DP980 considered. Under complex loading paths, the deformation instability theory with microstructure-based FE method can be used in examining the effects of various microstructural features on the deformation limits of DP980 steels.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Fifth-degree elastic energy for predictive continuum stress–strain relations and elastic instabilities under large strain and complex loading in silicon

Materials under complex loading develop large strains and often phase transformation via an elastic instability, as observed in both simple and complex systems. Here, we represent a material (exemplified for Si I) under large Lagrangian strains within a continuum description by a 5th-order elastic energy found by minimizing error relative to density functional theory (DFT) results. The Cauchy stress—Lagrangian strain curves for arbitrary complex loadings are in excellent correspondence with DFT results, including the elastic instability driving the Si I → II phase transformation (PT) and the shear instabilities. PT conditions for Si I → II under action of cubic axial stresses are linear in Cauchy stresses in agreement with DFT predictions. Such continuum elastic energy permits study of elastic instabilities and orientational dependence leading to different PTs, slip, twinning, or fracture, providing a fundamental basis for continuum physics simulations of crystal behavior under extreme loading.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Fifth-degree elastic energy for predictive continuum stress-strain relations and elastic instabilities under large strain and complex loading in silicon

Materials under complex loading develop large strains and often phase transformation via an elastic instability, as observed in both simple and complex systems. Here, we represent a material (exemplified for Si I) under large Lagrangian strains within a continuum description by a 5th-order elastic energy found by minimizing error relative to density functional theory (DFT) results. The Cauchy stress—Lagrangian strain curves for arbitrary complex loadings are in excellent correspondence with DFT results, including the elastic instability driving the Si I → II phase transformation (PT) and the shear instabilities. PT conditions for Si I → II under action of cubic axial stresses are linear in Cauchy stresses in agreement with DFT predictions. Such continuum elastic energy permits study of elastic instabilities and orientational dependence leading to different PTs, slip, twinning, or fracture, providing a fundamental basis for continuum physics simulations of crystal behavior under extreme loading.

Levitas, Valery↗
DOE OSTI2025

Mechanical response of HFIR-irradiated M5FRAMATOME cladding under simple and complex loading conditions

This study investigates the mechanical behavior of High Flux Isotope Reactor (HFIR) irradiated M5FRAMATOME cladding under simple and complex loading conditions through axial tensile and reversible cyclic bending. Axial tension specimens were pre-machined prior to HFIR irradiation while cyclic bend specimens were inserted as intact tubes. Tests articles were neutron-irradiated to 4 and 16 dpa, and specimens were tested at ORNL's hotcell facilities. The axial tension tests were conducted under constant displacement control, and the reversible cyclic bend tests were performed using ORNL's Cyclic Integrated Reversible-Bending Fatigue Tester (CIRFT) apparatus. Results showed that mechanical response of Cr-coated and uncoated M5FRAMATOME cladding were similar and independent of irradiation dose for axial tension tests, while Cr-coated specimens’ reversible cyclic bend behavior differed from uncoated counterparts. For all tests, irradiation temperature showed a significant impact on the mechanical behavior. Below 280°C, all axial tensile specimens whether coated or not behaved similarly. Above 280°C, YS and UTS showed decrease with increasing irradiation temperature. A similar behavior was also observed in cyclic bend tests as well. The mechanical damage during cyclic bend tests was linked to damage accumulation in unirradiated Cr-coated zircaloy-4 specimens, and the effect of irradiation temperature was related to changing characteristics of defect mobility during high temperature irradiation.

Cinbiz, Nedim [ORNL] (ORCID:0000000346268515)↗
DOE OSTI2017

The Kinetochore Receptor for the Cohesin Loading Complex

The ring-shaped cohesin complex brings together distant DNA domains to maintain, express, and segregate the genome. Establishing specific chromosomal linkages depends on cohesin recruitment to defined loci. One such locus is the budding yeast centromere, which is a paradigm for targeted cohesin loading. The kinetochore, a multiprotein complex that connects centromeres to microtubules, drives the recruitment of high levels of cohesin to link sister chromatids together. We have exploited this system to determine the mechanism of specific cohesin recruitment. We show that phosphorylation of the Ctf19 kinetochore protein by a conserved kinase, DDK, provides a binding site for the Scc2/4 cohesin loading complex, thereby directing cohesin loading to centromeres. A similar mechanism targets cohesin to chromosomes in vertebrates. These findings represent a complete molecular description of targeted cohesin loading, a phenomenon with wide-ranging importance in chromosome segregation and, in multicellular organisms, transcription regulation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Barrier experiment: Shock initiation under complex loading

The barrier experiments are a variant of the gap test; a detonation wave in a donor HE impacts a barrier and drives a shock wave into an acceptor HE. The question we ask is: What is the trade-off between the barrier material and threshold barrier thickness to prevent the acceptor from detonating. This can be viewed from the perspective of shock initiation of the acceptor subject to a complex pressure drive condition. Here we consider key factors which affect whether or not the acceptor undergoes a shock-to-detonation transition. These include the following: shock impedance matches for the donor detonation wave into the barrier and then the barrier shock into the acceptor, the pressure gradient behind the donor detonation wave, and the curvature of detonation front in the donor. Numerical simulations are used to illustrate how these factors affect the reaction in the acceptor.

36 MATERIALS SCIENCE↗
DOE OSTI2020

(U) PRAD0697 & PRAD0698: Complex Loading of CeO₂ Powder

Cerium(IV) oxide (CeO₂) powder is shock compressed using the Precision High Energy-density Liner Implosion eXperiment (PHELIX) platform. Experimental results are compared against several modeling approaches. Compaction behavior is best captured with a P-∝ model, which calculates CeO₂ powder bulk densities within 80-99% of experimental values but overpredicts densi cation at the cylindrical target's outer radius and center by up to 20%. Preliminary calculations suggest that accuracy could be increased with the inclusion of a coupled strength model. Several common computational modeling approaches for the shock compression response of granular materials and the magnetohydrodynamic (MHD) force upon the impactor/liner in pulsed power compression experiments are investigated and analyzed for their validity. The Bi-linear Ramp, P-∝ PACXP, and P-∝ Menikoff-Kober continuum compaction models are calibrated to planar impact Hugoniot data for CeO₂ powder and used to predict the powder's shock compaction response under non-planar shock wave compression. MHD calculations of the PHELIX pulsed power driver are performed using an idealized resistor-inductor-capacitor (RLC) circuit calibrated to previous experiments. All simulations are performed using the LANL code FLAG. Two validation experiments are computationally designed using the calibrated compaction and circuit models, executed using the PHELIX platform on CeO₂ targets with initial porous densities of 3.95 and 4.03 g/cm³, measured with proton radiography, and analyzed against the model predictions. The two P-∝ models more accurately describe CeO₂ powder densi cation than the Bi-linear Ramp model. However, the two P-∝ models overpredict bulk density of the shock compressed CeO₂ powder by up to 20% when the appropriate impact velocities are applied. MHD calculations for both validation experiments underpredict liner impact velocities by 4-11% when using the idealized RLC circuit model calibrated to previous experiments. Compensating underpredictions of impact velocity and overpredictions of powder densication lead to a false accuracy in pre-shot calculations compared to experimental data. To improve correlation between simulations and experiments, the following improvements are suggested: 1. A coupled strength model for CeO₂ powder that updates strength as a function of porosity and applied stress. 2. An improved MHD circuit model that more accurately captures the PHELIX machine.

36 MATERIALS SCIENCE↗
DOE OSTI2018

(U) Pre-shot Report: Complex Loading of CeO 2 Powder

A cylindrically converging shock compaction experiment is designed utilizing the Precision High Energy-density Liner Implosion eXperiment (PHELIX) platform to dynamically load CeO 2 powder. Parametric analyses investigating the effect of PHELIX cassette geometry, PHELIX total capacitor charge, and powder compaction model on the predicted shocked density profile of the CeO 2 powder are performed using the LANL codes FLAG and RAVEN. One PHELIX cassette geometry is designed to be used in two compaction experiments using PHELIX capacitor charges of 85 and 90 kV. Predictions of the shocked density profile in the CeO 2 powder are presented for each experimental configuration using both the Ramp and P-α compaction models. The two compaction models predict shocked density profiles in the CeO 2 powder that are significantly different and should be distinguishable using proton radiography as a diagnostic.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Complex Loading Project, FY 2015 Final Report

Report describes project to design experiments to develop diagnostic methods, search for, and measure quasi-elastic yielding in tin, with a goal of examining both the β and BCT tin phases to see whether their elastic yield strengths differ after being shocked or shocked and reshocked, learning more about quasi-elastic yielding, and developing diagnostics to permit similar experiments on other metals.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The Role of Stratigraphy and Loading History in Generating Complex Compaction Bands in Idealized Field-Scale Settings

The Buckskin Gulch locality in Utah is a landmark example of compaction localization. The outcrop of this locality involves distinct stratigraphic heterogeneity and was exposed to complex loading history. It features multiple sets of deformation bands with different kinematics and orientation. Similar formations were seen in the Valley of Fire, Nevada, and the Orange quarry, France, among other localities. The formation of such complex structures, their propagation mechanisms, and frequency is affected by numerous local and ambient factors whose impacts are not yet fully understood. The simulation of the above-mentioned localities is not feasible because of the limited amount of available information. This work, instead, investigates from a geomechanics standpoint how the interplay among material nonlinearity, outcrop stratigraphy, and loading history interconnects with specific spatiotemporal patterns of compaction band propagation. Our study shows that the system stratigraphy can be responsible for the emergence of coexisting compaction bands with different inclination and kinematics. Specifically, we show that stiffness contrasts induce nonlocal stress changes which may favor the initiation of secondary structures with different compaction localization characteristics. Furthermore, systems of inclined compaction bands induced by burial increase display secondary, noncontemporaneous sets of vertical compaction bands under the effects of postburial shortening. Our results indicate that stages of intermediate burial decrease prior to tectonic shortening can promote the formation of such complex systems. Despite the simplifications involved in our analyses, these findings show how geomechanics computations complement field observations and could provide a mechanics-based validation of site-specific reconstruction hypothesis.

58 GEOSCIENCES↗
DOE OSTI2023

Robust deep learning framework for constitutive relations modeling

Modeling the full-range deformation behaviors of materials under complex loading and materials conditions is a significant challenge for constitutive relations (CRs) modeling. Here, we propose a general encoder-decoder deep learning framework that can model high-dimensional stress-strain data and complex loading histories with robustness and universal capability. The framework employs an encoder to project high-dimensional input information (e.g., loading history, loading conditions, and materials information) to a lower-dimensional hidden space and a decoder to map the hidden representation to the stress of interest. We evaluated various encoder architectures, including gated recurrent unit (GRU), GRU with attention, temporal convolutional network (TCN), and the Transformer encoder, on two complex stress-strain datasets that were designed to include a wide range of complex loading histories and loading conditions. All architectures achieved excellent test results with an root-mean-square error (RMSE) below 1 MPa. Additionally, we analyzed the capability of the different architectures to make predictions on out-of-domain applications, with an uncertainty estimation based on deep ensembles. The proposed approach provides a robust alternative to empirical/semi-empirical models for CRs modeling, offering the potential for more accurate and efficient materials design and optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A generalizable machine learning-assisted fast Fourier transform algorithm to simulate the large strain phenomena in polycrystalline materials

Machine learning methods have shown initial promise in constitutive modeling for single crystals or homogenized polycrystals, delivering notable computational efficiency. However, existing machine learning-based constitutive models often lack generalizability, limiting their application across diverse boundary value problems. This study introduces a thermodynamics-informed artificial neural network model to accelerate rate-tangent crystal plasticity fast Fourier transform simulations for cross-scale deformation behaviors of polycrystals under complex loading. Our model integrates microstructural variability and local interactions effectively. To address local effects in each grain, we employ K-means clustering to group Gauss points within the microstructure into clusters assumed to be in similar mechanical states. This approach, based on self-clustering analysis, extends model scope from macroscopic stress response to the granular level, capturing mechanical responses and orientation evolution across grains. This reduces the number of nonlinear problems to solve, with cluster responses propagated throughout each group. The thermodynamics-based artificial neural network-extracted features are further processed using local material state clusters to account for history-dependent deformation and evolving microstructures. Additionally, representative volume element simulations with rate-tangent crystal plasticity fast Fourier transform provide reliable datasets for model training. The proposed model demonstrates high efficiency, accuracy, self-consistency, and enhanced generalizability in predicting strain–stress responses and orientation evolution at both individual grain and aggregate scales under complex loading conditions, such as biaxial tension and arbitrary loading scenarios.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Occupancy-Driven Stochastic Decision Framework for Ranking Commercial Building Loads

For effective integration of building operations into the evolving demand response programs of the power grid, real-time decisions concerning the use of building appliances for grid services must excel on multiple criteria, ranging from the added value to occupants' comfort to the quality of the grid services. In this paper, we present a data-driven stochastic decision-support framework to dynamically rank load control alternatives in a commercial building, addressing the needs of multiple decision criteria (e.g. occupant comfort, grid service quality) under uncertainties in occupancy patterns. We adopt a stochastic multi-criteria decision algorithm recently applied to prioritize residential on/off loads, and extend it to i) consider complex load control decisions (e.g. dimming of lights, changing zone temperature set-points) in a commercial building; and ii) systematically integrate zonal occupancy patterns to better identify short-term (and time-varying) opportunities for grid service participation. We evaluate the performance of the proposed framework for curtailment of air-conditioning, lighting, and plug-loads in a multi-zone commercial office building for a range of design choices. With the help of a prototype system that integrates an interactive \textit{Data Analytics and Visualization} frontend we demonstrate a way for the building operators to monitor and change in real-time the available flexibility in energy consumption and to develop trust in the decision recommendations by interpreting the rationale behind the ranking.

Jain, Milan↗
DOE OSTI2020

Thermodynamic Consistent Neural Networks for Learning Material Interfacial Mechanics

For multilayer materials in thin substrate systems, interfacial failure is one of the most challenges. The traction-separation relations (TSR) quantitatively describe the mechanical behavior of a material interface undergoing openings, which is critical to understand and predict interfacial failures under complex loadings. However, existing theoretical models have limitations on enough complexity and flexibility to well learn the real-world TSR from experimental observations. A neural network can fit well along with the loading paths but often fails to obey the laws of physics, due to a lack of experimental data and understanding of the hidden physical mechanism. In this paper, we propose a thermodynamic consistent neural network (TCNN) approach to build a data-driven model of the TSR with sparse experimental data. The TCNN leverages recent advances in physics-informed neural networks (PINN) that encode prior physical information into the loss function and efficiently train the neural networks using automatic differentiation. We investigate three thermodynamic consistent principles, i.e., positive energy dissipation, steepest energy dissipation gradient, and energy conservative loading path. All of them are mathematically formulated and embedded into a neural network model with a novel defined loss function. A real-world experiment demonstrates the superior performance of TCNN, and we find that TCNN provides an accurate prediction of the whole TSR surface and significantly reduces the violated prediction against the laws of physics.

Zhang, Jiaxin↗
DOE OSTI2024

Three-dimensional continuum point cloud method for large deformation and its verification

This study presents a strong form based meshfree collocation method, which is named Continuum Point Cloud Method, to solve nonlinear field equations derived from classical mechanics for deformed bodies in three-dimensional Euclidean space. The method and its implementation are benchmarked against a nonlinear vector field using manufactured solutions. The analysis of mechanical fields firstly focuses on the study of St. Venant Kirchhoff and compressible neo-Hookean materials. Results for various initial boundary value problems are presented, including benchmark cases involving unidirectional tension and simple shear. Subsequently, the study concludes with an analysis of a displacement-controlled simulation of a compressible neo-Hookean material, specifically a bar that is pulled to 50% of its original length and rotated 90°. The pure tension case yields a 1.5% error in displacement between computed and expected values and a combined tension and torsion loading case provides further insight into material behavior under complex loading conditions. The resulting normal axial and transverse stress-strain curves are also presented. Lastly, the consistency and robustness of the proposed nonlinear numerical schemes are successfully demonstrated through various numerical experiments.

Compressible neo-Hookean materials↗