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Results for “Yield function”

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 OSTI2019

A method for smoothing multiple yield functions

A method is presented that produces a single C 2 differentiable and convex yield function from a plastic model that contains multiple yield surfaces that are individually C 2 differentiable and convex. The method is suitable for implementation in numerical computer codes. Here, the error incurred through the smoothing procedure is quantified. The method is illustrated through a number of examples. Two simple finite-element models are solved to compare this method with existing approaches.

42 ENGINEERING↗
DOE OSTI2023

Multiaxial Plastic Deformation of Zircaloy-4 Nuclear Fuel Cladding Tubes

Here, this work is motivated by the desire to devise an internal pressure test that can mimic a displacement-controlled loading scenario and demonstrate how to apply the multiaxial stress and strain data from the test to develop an elastic/plastic constitutive model for a thin-walled tubular component. This is achieved by conducting simultaneous measurements of tangential and axial strain during the pressure test and integrating these strain measures into a feedback loop with the pressure controller. It is shown how data from such a test can be used to develop a large mechanical property data set relevant to biaxial loading conditions. The data obtained have high confidence evidenced by their low variability and alignment with other literature studies. Additionally, data from these internal pressure tests combined with full-tube axial tensile tests allow for the derivation of the Hill anisotropic yield function. The developed Hill yield function is validated by comparing the plastic strain ratios from the full-tube tension tests and by comparing the predicted yield stress in the tangential direction with measured values from ring tension tests in a previous study.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Complementing a continuum thermodynamic approach to constitutive modeling with symbolic regression

Here, an interpretable machine learning method, physics-informed genetic programming-based symbolic regression (P-GPSR), is integrated into a continuum thermodynamic approach to developing constitutive models. The proposed strategy for combining a thermodynamic analysis with P-GPSR is demonstrated by generating a yield function for an idealized material with voids, i.e., the Gurson yield function. First, a thermodynamic-based analysis is used to derive model requirements that are exploited in a custom P-GPSR implementation as fitness criteria or are strongly enforced in the solution. The P-GPSR implementation improved accuracy, generalizability, and training time compared to the same GPSR code without physics-informed fitness criteria. The yield function generated through the P-GPSR framework is in the form of a composite function that describes a class of materials and is characteristically more interpretable than GPSR-derived equations. The physical significance of the input functions learned by P-GPSR within the composite function is acquired from the thermodynamic analysis. Fundamental explanations of why the implemented P-GPSR capabilities improve results over a conventional GPSR algorithm are provided.

42 ENGINEERING↗
DOE OSTI2022

Anisotropic yield models for lattice unit cell structures exploiting orthotropic symmetry

We report numerical homogenization enables efficient computational analysis and design of multiscale structures made of micro-architected materials including lattice unit cells. However, predicting yield is nontrivial because it requires accurate and efficient predictions of the maximum stress inside the homogenized unit cells. To address this challenge, we develop a macroscale anisotropic yield function for micro-architected materials. The yield function depends on the three-dimensional macroscale stress state and the parameters describing a family of micro-architectures, such as the radii of the struts in a lattice unit cell. To ensure accuracy, we determine the maximum stress using three-dimensional continuum finite-element analysis. To ensure efficiency, we construct surrogate models from the aforementioned high-fidelity results for yield prediction. To reduce simulation costs and surrogate modeling complexity, we leverage orthotropic symmetry commonly found in lattice unit cells. In this paper, we provide a thorough presentation of group representations and the systematic procedure to exploit orthotropic domain symmetry in homogenization and surrogate modeling. We illustrate the use of linear homogenization results to predict yield in specific unit cells without further simulations. We furthermore show that surrogate modeling presents a viable option for anisotropic yield prediction in a continuously parametrized family of micro-architectures. More specifically, despite the dimensionality and degree of nonlinearity in the maximum micro von Mises stress within the unit cell, the surrogate models can predict it with less than 5% error at least 90% of the time. Moreover, the largest under-prediction error, which is more critical than the over-prediction error, is typically less than 10%.

42 ENGINEERING↗
DOE OSTI2021

Fourier Neural Networks as Function Approximators and Differential Equation Solvers

We present a Fourier neural network (FNN) that can be mapped directly to the Fourier decomposition. The choice of activation and loss function yields results that replicate a Fourier series expansion closely while preserving a straightforward architecture with a single hidden layer. The simplicity of this network architecture facilitates the integration with any other higher-complexity networks, at a data pre- or postprocessing stage. We validate this FNN on naturally periodic smooth functions and on piecewise continuous periodic functions. We showcase the use of this FNN for modeling or solving partial differential equations with periodic boundary conditions. The main advantages of the current approach are the validity of the solution outside the training region, interpretability of the trained model, and simplicity of use.

Fourier decomposition↗
DOE OSTI2020

Taming the fixed-node error in diffusion Monte Carlo via range separation

By combining density-functional theory (DFT) and wave function theory via the range separation (RS) of the interelectronic Coulomb operator, we obtain accurate fixed-node diffusion Monte Carlo (FN-DMC) energies with compact multi-determinant trial wave functions. In particular, we combine here short-range exchange-correlation functionals with a flavor of selected configuration interaction known as configuration interaction using a perturbative selection made iteratively (CIPSI), a scheme that we label RS-DFT-CIPSI. One of the take-home messages of the present study is that RS-DFT-CIPSI trial wave functions yield lower fixed-node energies with more compact multi-determinant expansions than CIPSI, especially for small basis sets. Indeed, as the CIPSI component of RS-DFT-CIPSI is relieved from describing the short-range part of the correlation hole around the electron-electron coalescence points, the number of determinants in the trial wave function required to reach a given accuracy is significantly reduced as compared to a conventional CIPSI calculation. Importantly, by performing various numerical experiments, we evidence that the RS-DFT scheme essentially plays the role of a simple Jastrow factor by mimicking short-range correlation effects, hence avoiding the burden of performing a stochastic optimization. Considering the 55 atomization energies of the Gaussian-1 benchmark set of molecules, we show that using a fixed value of mu = 0.5 bohr(-1) provides effective error cancellations as well as compact trial wave functions, making the present method a good candidate for the accurate description of large chemical systems.

Scemama, Anthony↗
DOE OSTI2022

Theoretical and Experimental Investigation of Functionalized Cyanopyridines Yield an Anolyte with an Extremely Low Reduction Potential for Nonaqueous Redox Flow Batteries

Abstract Cyanopyridines and cyanophenylpyridines were investigated as anolytes for nonaqueous redox flow batteries (RFBs). The three isomers of cyanopyridine are reduced at potentials of −2.2 V or lower vs. ferrocene +/0 (Fc +/0 ), but the 3‐CNPy⋅ − radical anion forms a sigma‐dimer that is re‐oxidized at E ≈−1.1 V, which would lead to poor voltaic efficiency in a RFB. Bulk electrochemical charge‐discharge cycling of the cyanopyridines in acetonitrile and 0.50 M [NBu 4 ][PF 6 ] shows that 2‐CNPy and 4‐CNPy lose capacity quickly under these conditions, due to irreversible chemical reaction/decomposition of the radical anions. Density‐functional theory (DFT) calculations indicated that adding a phenyl group to the cyanopyridines would, for some isomers, limit dimerization and improve the stability of the radical anions, while shifting their E 1/2 only about +0.10 V relative to the parent cyanopyridines. Among the cyanophenylpyridines, 3‐CN‐6‐PhPy and 3‐CN‐4‐PhPy are the most promising as anolytes. They exhibit reversible reductions at E 1/2 =−2.19 and −2.22 V vs. ferrocene +/0 , respectively, and retain about half of their capacity after 30 bulk charge‐discharge cycles. An improved version of 3‐CN‐6‐PhPy with three methyl groups (3‐cyano‐4‐methyl‐6‐(3,5‐dimethylphenyl)pyridine) has an extremely low reduction potential of −2.50 V vs. Fc +/0 (the lowest reported for a nonaqueous RFB anolyte) and loses only 0.21 % of capacity per cycle during charge‐discharge cycling in acetonitrile.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Reinforcement Learning Configuration Interaction

Selected configuration interaction (sCI) methods exploit the sparsity of the full configuration interaction (FCI) wave function, yielding significant computational savings and wave function compression without sacrificing the accuracy. Despite recent advances in sCI methods, the selection of important determinants remains an open problem. Furthermore, we explore the possibility of utilizing reinforcement learning approaches to solve the sCI problem. By mapping the configuration interaction problem onto a sequential decision-making process, the agent learns on-the-fly which determinants to include and which to ignore, yielding a compressed wave function at near-FCI accuracy. This method, which we call reinforcement-learned configuration interaction, adds another weapon to the sCI arsenal and highlights how reinforcement learning approaches can potentially help solve challenging problems in electronic structure theory.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Assessing cathode property prediction via exchange-correlation functionals with and without long-range dispersion corrections

In this work, we benchmark calculated interlayer spacings, average topotactic voltages, thermodynamic stabilities, and band gaps in layered lithium transition-metal oxides (TMOs) and their de-lithiated counterparts, which are used in lithium-ion batteries as positive electrode materials, against available experimental data. Specifically, we examine the accuracy of properties calculated within density functional theory (DFT) using eight different treatments of electron exchange-correlation: the strongly constrained and appropriately normed (SCAN) and Perdew–Burke–Ernzerhof (PBE) density functionals, Hubbard-U-corrected SCAN and PBE (i.e., SCAN+U and PBE+U), and SCAN(+U) and PBE(+U) with added long-range dispersion (D) interactions (i.e., DFT(+U)+D). van der Waals interactions are included respectively via the revised Vydrov-Van Voorhis (rVV10) for SCAN(+U) and the DFT-D3 for PBE(+U). We find that SCAN-based functionals predict larger voltages due to an underestimation of stability of the MO2 systems, while also predicting smaller interlayer spacings compared to their PBE-based counterparts. Furthermore, adding dispersion corrections to PBE has a greater effect on voltage predictions and interlayer spacings than with SCAN, indicating that DFT-SCAN – despite being a ground-state theory – fortuitously captures some short and medium-range dispersion interactions better than PBE. While SCAN-based and PBE-based functionals yield qualitatively similar band gap predictions, there is no significant quantitative improvement of SCAN-based functionals over the corresponding PBE-based versions. Finally, we expect SCAN-based functionals to yield more accurate property predictions than the respective PBE-based functionals for most TMOs, given SCAN's stronger theoretical underpinning and better predictions of systematic trends in interlayer spacings, intercalation voltages, and band gaps obtained in this work.

25 ENERGY STORAGE↗
DOE OSTI2022

Geometric prior of multi-resolution yielding manifolds and the local closest point projection for nearly non-smooth plasticity

Elastoplasticity models often introduce a scalar-valued yield function to implicitly represent the boundary between elastic and plastic material states. Here this paper introduces a new alternative where the yield envelope is represented by a manifold of which the topology and the geometry are learned from a set of data points in a parametric space (e.g. principal stress space, π-plane). Here, deep geometric learning enables us to reconstruct a highly complex yield envelope by breaking it down into multiple coordinate charts. The global atlas that consists of these coordinate charts in return allows us to represent the yield surface via multiple overlapping patches, each with a specific local parametrization. This setup provides several advantages over the classical implicit function representation approach. For instance, the availability of coordinate charts enables us to introduce an alternative stress integration algorithm where the trial stress may project directly on a local patch and hence circumvent the issues related to non-smoothness and the lack of convexity of yield surfaces. Meanwhile, the local parametric approach also enables us to predict hardening/softening locally in the parametric space, even without complete knowledge of the yield surface. Comparisons between the classical yield function approach on the non-smooth plasticity and anisotropic cam-clay plasticity model are provided to demonstrate the capacity of the models for highly precise yield surface and the feasibility of the implementation of the learned model in the local stress integration algorithm.

42 ENGINEERING↗
DOE OSTI2025

A phase-field fracture formulation for generalized standard materials: The interplay between thermomechanics and damage

Accurately modeling fracture of ductile materials poses open challenges in the field of computational mechanics due to the multiphysics nature of their failure processes. Integrating the interplay between thermodynamics and damage into ductile fracture models is vital for predicting critical failure modes. Here, in this paper, we develop a versatile phase-field (PF) framework for modeling ductile fracture, taking into account finite-strain elasto-plasticity. The framework stems from a variational formulation of constitutive relations for generalized standard materials (GSMs), whose response is described by a Helmholtz free energy and a dissipation pseudo-potential. Its variational structure is based on a minimum principle for a functional that expresses the sum of power densities for reversible and irreversible processes. By minimizing this functional with a constraint on a von Mises yield function, we derive the evolution equation for the equivalent plastic strain and an associative flow rule. This constrained optimization problem is analytically solved for a wide class of thermo-viscoplasticity models. The key innovations of the current work include (i) a cubic plastic degradation function that accounts for a non-vanishing damage-dependent yield stress, (ii) closed-form expressions of the Helmholtz free energy and dissipation pseudo-potential for three thermo-viscoplasticity models, (iii) an extended Johnson–Cook plasticity model with a nonlinear hardening law, and (iv) a plastic work heat source that depends on the plastic degradation function and a variable Taylor–Quinney (TQ) coefficient. The capabilities of the proposed framework are tested with the aid of four ductile fracture problems, including the Sandia Fracture Challenge. In each of these problems, we examine the evolution of relevant field variables such as the PF order parameter, the equivalent plastic strain, the temperature, and the internal power dissipation density, in addition to the overall structural response quantified by the force–displacement curve. These numerical studies demonstrate that the proposed framework effectively represents ductile fracture, yielding computational results that exhibit good agreement with experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Designing 1D multiheme peptide amphiphile assemblies reminiscent of natural systems

We discuss protein assemblies that bind and organize ordered arrays of cofactors yield function structures. Multiheme assemblies found in nature yield electronically conductivity 1D nanoscale fibers and are employed in anaerobic respiration. To understand the fundamental characteristics of these organized arrays, the design of peptide amphiphiles that assemble into 1D nanostructures and yield metalloporphyrin binding sites is presented. One challenge with this class of peptide amphiphiles is identifying the correct sequence composition for high affinity binding with high heme density. Here, the peptide c16-AH(Kx)n-CO 2 H is explored to identify the impact of sequence length (n) and amino acid identity (x = L, I, or F) on binding affinity and midpoint potential. When n = 2, the peptide assembly yields the greatest affinity. The resulting nanoscale assemblies yield ordered arrays of the redox active molecule heme and have potential utility in the development of supramolecular bioelectronic materials useful in sensing as well as the development of enzymatic materials.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Thiol–ene Click Chemistry Incorporates Hydroxyl Functionality on Polycyclooctene to Tune Properties

Polyolefins compose the majority of plastic waste, but conventional mechanical recycling degrades their properties, thereby reducing their value. We report the functionalization of a model for dehydrogenated polyethylene, polycyclooctene (PCOE), with thiol–ene click chemistry to install pendant hydroxyl ethyl thioethers. Functionalization of PCOE using mercaptoethanol via thiol–ene click chemistry yielded functionalization between 1.4 and 22.9% based on ethylene monomeric units. Reactions were well-controlled by varying the reagent stoichiometry and reaction time. Crystallinity and melting temperature decreased, and glass transition temperature increased with greater functionalization. Contact angle measurements reveal an increase in surface polarity with functionalization. Here, comparisons with poly(ethylene-co-vinyl alcohol) (EVOH) show comparable surface polarity at similar levels of alcohol functionalization. At 12% functionalization, the ultimate shear stress (USS) of functionalized PCOE in an adhesive configuration is 4.10 ± 0.48 MPa, comparable to EVOH. At >12% functionalization, the failure mode changed from adhesive to mixed adhesive–cohesive, and the USS decreased.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Finite-volume pionless effective field theory for few-nucleon systems with differentiable programming

Finite-volume pionless effective field theory provides an efficient framework for the extrapolation of nuclear spectra and matrix elements calculated at finite volume in lattice QCD to infinite volume, and to nuclei with larger atomic number. In this work, it is demonstrated how this framework may be implemented via a set of correlated Gaussian wave functions optimized using differentiable programming and via solution of a generalized eigenvalue problem. This approach is shown to be significantly more efficient than a stochastic implementation of the variational method based on the same form of correlated Gaussian wave functions, yielding comparably accurate representations of the ground-state wave functions with an order of magnitude fewer terms. The efficiency of representation allows such calculations to be extended to larger systems than in previous work. Further, the method is demonstrated through calculations of the binding energies of nuclei with atomic number A ϵ {2,3,4} in finite volume, matched to lattice QCD calculations at quark masses corresponding to m π = 806 MeV, and infinite-volume effective field theory calculations of A ϵ {2,3,4,5,6} systems based on this matching.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Mobile sensing of point-source gas emissions using Bayesian inference: An empirical examination of the likelihood function

This paper evaluates likelihood function forms for Bayesian inference of point-source gas emissions using a mobile sensor. Whereas Bayesian inference has been successfully used to estimate emission rates from time-averaged concentration data measured by stationary sensors, data collected by mobile sensors do not represent ensemble or time-averaged conditions. To examine the potential impact of this contrast, controlled release experiments were conducted with a mobile sensor measuring concentrations repeatedly along transverse cross sections of the downwind plumes. Experiments were conducted with measurements made at different downwind distances, different sensor heights, and with different obstacle states. An examination is made between two commonly-used likelihood functions, the Gaussian and the log-normal. For experiments conducted in the absence of obstacles, the Bayesian estimates using the log-normal likelihood function yield a much smaller bias than those based on the Gaussian likelihood function. This finding is consistent with the non-Gaussian nature of concentration fluctuations near a point-source. For experiments conducted in the presence of obstacles, the Bayesian inference based on the Gaussian likelihood function exhibits a better performance. This can be explained by the enhanced turbulent mixing due to the obstacle-introduced wake eddies. Overall, we find that the selection of the likelihood function can be physically related to the underlying conditions, and the proper selection is critical to ensure the performance of the Bayesian inference for source characterization using mobile sensing data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Dynamic thermal behavior of polycrystalline LaB 6 hollow cathodes

Lanthanum hexaboride (LaB6) hollow cathodes have demonstrated a capability for long life operation, which is critical to many space exploration missions. Thermal characterization of LaB6 hollow cathodes has revealed lower than expected electron emitter temperatures when the cathode reaches a steady state. This phenomenon is observed at discharge currents ranging from 5 to 35 A and xenon mass flow rates of 5–25 SCCM in cathodes with three different orifice diameters. Thus, the currently accepted value of the work function for polycrystalline LaB 6 , 2.67 eV, does not describe well the emission characteristics of LaB6 hollow cathodes operating with internal gas discharges at a steady state. We use empirically measured temperatures combined with a model of the hollow cathode emitter and xenon discharge to estimate the value of the work function, yielding a value ranging from 2.1 to 2.44 eV. This lower work function value implies that LaB6 hollow cathodes are expected to have even longer lifetimes than previously anticipated, further establishing them as a more suited alternative to other conventional cathode technologies for the task of long duration travel. Direct measurements of the work function as a function of depth on a hollow cathode emitter using x-ray photoelectron spectroscopy and ion beam milling indicate that the work function decreases with depth. We postulate several mechanisms that could explain the observed work function enhancement. Altogether, our results have important implications to the design, study approach, and operation of LaB 6 cathodes and potentially other cathodes with hollow configuration. Finally, our work opens the question of why the work function is reduced upon interaction with Xe plasma.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗