Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “FUNCTIONAL MODELS”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

EvoProtGrad (Directed Evolution for Proteins with Gradients) [SWR-23-48]

A Python package for directed evolution on a protein sequence with gradient-based discrete Markov chain monte carlo (MCMC). Users are able to compose custom models that map sequence to function with pretrained models, including protein language models (PLMs), to guide and constrain search. Our package natively integrates with the HuggingFace platform and supports PLMs from transformers. Our MCMC sampler identifies promising amino acids to mutate via model gradients taken with respect to the input (i.e., sensitivity analysis). We allow users to compose their own custom target function for MCMC by leveraging the Product of Experts MCMC paradigm. Each model is an "expert" that contributes its own knowledge about the protein's fitness landscape to the overall target function. The sampler is designed to be more efficient and effective than brute force and random search while maintaining most of the generality and flexibility. Additional information can be found in the related publication: https://iopscience.iop.org/article/10.1088/2632-2153/accacd

Emami, Patrick↗
DOE OSTI2021

Verification Testing For Solid-Element Material Models 11-19 in DYNA3D/ParaDyn

This technical report documents the creation and implementation of verification tests for solid-element material models 11 through 19 available in DYNA3D/ParaDyn. The verification tests covered all aspects of each material model, except for the Weibull distribution functionality in material models 15 and 19. General test cases were created to verify the elastic and plastic behavior of the material models. Other additional tests were developed to examine the intricacies of each material model. Each test involved the use a kinematic load case and specification of material parameters necessary to activate corresponding features of the material model. The load cases prescribed the full time history of the kinematic motion for the solid elements, and these loads are independent of the material model or element formulation. When possible, closed form solutions were then derived for each verification test in a continuum setting. The DYNA3D simulations for each test were carried out over a unit time interval, t ϵ [0, 1], and the as implemented DYNA3D response was compared to the closed form solutions evaluated at discrete points in time. A relative error measure was determined for each test to justify the proper implementation of the material model. The relative errors comparing the DYNA3D solution to the analytical solution were, in general, on the order of machine precision except where noted. This signifies the proper implementation of solid-element material models 11-19. In the development of these verification tests, six bugs were found and fixed in the source code. Additionally, this work generated eighteen DYNA3D input decks and answer extraction scripts in the DYNA3D/ParaDyn Software Quality Assurance test suite, which are comprised of a total of 285 solid-element tests. Testing for each material model utilizes two input decks and answer extraction scripts, where one focuses on the linear elastic response and the other examines the inelastic and remaining functionalities of the material model. In total, this work added 285 individual verification test problems in the DYNA3D/ParaDyn test suite.

42 ENGINEERING↗
DOE OSTI2023

Prediction of nonlinear specific heat during single crystal HMX phase transition

Here, we develop a thermodynamically consistent chemo-thermo-mechanical model for the β → δ phase transition of energetic HMX crystals. In contrast to previous models, which either considered specific heat to be a constant or utilized a calibrated function, this model provides novel expressions for the specific heats at constant volume and constant elastic strains derived directly from continuum mechanics. In addition, the model provides a novel prediction for the critical temperature at which the chemical heating rate achieves its extremum for Arrhenius kinetics. The numerical solution predicts highly nonlinear specific heat behavior including order of magnitude changes.

42 ENGINEERING↗
DOE OSTI2011

SU(3) chiral symmetry for baryons

Three-quark nucleon interpolating fields in QCD have well-defined SU{sub L}(3)xSU{sub R}(3) and U{sub A}(1) chiral transformation properties, viz. [(6,3)+(3,6)], [(3,3-bar)+(3-bar,3)], [(8,1)+(1,8)] and their 'mirror' images. It has been shown (phenomenologically) in Ref. [2] that mixing of the [(6,3)+(3,6)] chiral multiplet with one ordinary ('naive') and one 'mirror' field belonging to the [(3,3-bar)+(3-bar,3)], [(8,1)+(1,8)] multiplets can be used to fit the values of the isovector (g{sub A}{sup (3)}) and the flavor-singlet (isoscalar) axial coupling (g{sub A}{sup (0)}) of the nucleon and then predict the axial F and D coefficients, or vice versa, in reasonable agreement with experiment. In an attempt to derive such mixing from an effective Lagrangian, we construct all SU{sub L}(3)xSU{sub R}(3) chirally invariant non-derivative one-meson-baryon interactions and then calculate the mixing angles in terms of baryons' masses. It turns out that there are (strong) selection rules: for example, there is only one non-derivative chirally symmetric interaction between J 1/2 fields belonging to the [(6,3)+(3,6)] and the [(3,3-bar)+(3-bar,3)] chiral multiplets, that is also U{sub A}(1) symmetric. We also study the chiral interactions of the [(3,3-bar)+(3-bar,3)] and [(8,1)+(1,8)] nucleon fields. Again, there are selection rules that allow only one off-diagonal non-derivative chiral SU{sub L}(3)xSU{sub R}(3) interaction of this type, that also explicitly breaks the U{sub A}(1) symmetry. We use this interaction to calculate the corresponding mixing angles in terms of baryon masses and fit two lowest lying observed nucleon (resonance) masses, thus predicting the third (J = 1/2, I = 3/2){Delta} resonance, as well as one or two flavor-singlet {Lambda} hyperon(s), depending on the type of mixing. The effective chiral Lagrangians derived here may be applied to high density matter calculations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

The scalar-scalar-tensor inflationary three-point function in the axion monodromy model

The axion monodromy model involves a canonical scalar field that is governed by a linear potential with superimposed modulations. The modulations in the potential are responsible for a resonant behavior which gives rise to persisting oscillations in the scalar and, to a smaller extent, in the tensor power spectra. Interestingly, such spectra have been shown to lead to an improved fit to the cosmological data than the more conventional, nearly scale invariant, primordial power spectra. The scalar bi-spectrum in the model too exhibits continued modulations and the resonance is known to boost the amplitude of the scalar non-Gaussianity parameter to rather large values. An analytical expression for the scalar bi-spectrum had been arrived at earlier which, in fact, has been used to compare the model with the cosmic microwave background anisotropies at the level of three-point functions involving scalars. In this work, with future applications in mind, we arrive at a similar analytical template for the scalar-scalar-tensor cross-correlation. We also analytically establish the consistency relation (in the squeezed limit) for this three-point function. We conclude with a summary of the main results obtained.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Bayesian estimation of the $S$ factor and thermonuclear reaction rate for 16 O(p, γ) 17 F

The 16 O(p, γ) 17 F reaction is the slowest hydrogen-burning process in the CNO mass region. Its thermonuclear rate sensitively impacts predictions of oxygen isotopic ratios in a number of astrophysical sites, including AGB stars. The reaction has been measured several times at low bombarding energies using a variety of techniques. The most recent evaluated experimental rates have a reported uncertainty of about 7.5% below 1 GK. However, the previous rate estimate represents a best guess only and was not based on rigorous statistical methods. We apply a Bayesian model to fit all reliable 16 O(p, γ) 17 F cross section data, and take into account independent contributions of statistical and systematic uncertainties. The nuclear reaction model employed is a single-particle potential model involving a Woods-Saxon potential for generating the radial bound state wave function. The model has three physical parameters, the radius and diffuseness of the Woods-Saxon potential, and the asymptotic normalization coefficients (ANCs) of the final bound state in 17 F. Here, we find that performing the Bayesian S -factor fit using ANCs as scaling parameters has a distinct advantage over adopting spectroscopic factors instead. Based on these results, we present the first statistically rigorous estimation of experimental 16 O(p, γ) 17 F reaction rates, with uncertainties (±4.2%) of about half the previously reported values.

6 ≤ A ≤ 19↗
DOE OSTI2023

RouteE-Powertrain [SWR-19-19]

RouteE-Powertrain is a tool for predicting energy usage over a set of road links. RouteE-Powertrain is a Python package that allows users to work with a set of pre-trained mesoscopic vehicle energy prediction models for a varity of vehicle types. Additionally, users can train their own models if "ground truth" energy consumption and driving data are available. RouteE-Powertrain models predict vehicle energy consumption over links in a road network, so the features considered for prediction often include traffic speeds, road grade, turns, etc. The typical user will utilize RouteE's catalog of pre-trained models. Currently, the catalog consists of light-duty vehicle models, including conventional gasoline, diesel, hybrid electric (HEV), and battery electric (BEV). These models can be applied to link-level driving data (in the form of pandas dataframes) to output energy consumption predictions. Users that wish to train new RouteE models can do so. The model training function of RouteE enables users to use their own drive-cycle data, powertrain modeling system, and road network data to train custom models. https://pypi.org/project/nrel.routee.powertrain/ pip install nrel.routee.powertrain

Holden, Jacob↗
DOE OSTI2011

A turbulent transport network model in MULTIFLUX coupled with TOUGH2

A new numerical method is described for the fully iterated, conjugate solution of two discrete submodels, involving (a) a transport network model for heat, moisture, and airflows in a high-permeability, air-filled cavity; and (b) a variably saturated fractured porous medium. The transport network submodel is an integrated-parameter, computational fluid dynamics solver, describing the thermal-hydrologic transport processes in the flow channel system of the cavity with laminar or turbulent flow and convective heat and mass transport, using MULTIFLUX. The porous medium submodel, using TOUGH2, is a solver for the heat and mass transport in the fractured rock mass. The new model solution extends the application fields of TOUGH2 by integrating it with turbulent flow and transport in a discrete flow network system. We present demonstrational results for a nuclear waste repository application at Yucca Mountain with the most realistic model assumptions and input parameters including the geometrical layout of the nuclear spent fuel and waste with variable heat load for the individual containers. The MULTIFLUX and TOUGH2 model elements are fully iterated, applying a programmed reprocessing of the Numerical Transport Code Functionalization model-element in an automated Outside Balance Iteration loop. The natural, convective airflow field and the heat and mass transport in a representative emplacement drift during postclosure are explicitly solved in the new model. The results demonstrate that the direction and magnitude of the air circulation patterns and all transport modes are strongly affected by the heat and moisture transport processes in the surrounding rock, justifying the need for a coupled, fully iterated model solution such as the one presented in the paper.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

A MIMO AAA Agorithm for Frequency Dependent Line Modeling

Modeling of power distribution system components that are valid for a wide range of frequencies are crucial for highly accurate modeling of electromagnetic transient (EMT) events. This has recently become of interest due to the improvements needed for the resilient operation of distribution systems. Vector fitting (VF) is a very popular and commonly used algorithm for wide band representations of power system components in EMT simulations. In this research, we present a new multi-input rational approximation algorithm (MIAAA) and illustrate its advantages with respect to VF using examples of approximations of admittance matrices discussed in the literature. We show that MIAAA not only outperforms VF in terms of achieving better accuracy using lesser number of poles, but also has no numerical issues achieving convergence. In contrast to VF, MIAAA is not sensitive to the location of input sample points and it does not require good estimates for the location of the desired approximation poles. The novelty of this research work is the use of recent mathematical results to solve existing challenges in distribution system modeling and to develop rational approximations for power system models that intend to be optimal in terms of accuracy and performance.

AAA algorithm↗
DOE OSTI2021

Prediction of electron density and pressure profile shapes on NSTX-U using neural networks

A new model for prediction of electron density and pressure profile shapes on NSTX and NSTX-U has been developed using neural networks. The model has been trained and tested on measured profiles from experimental discharges during the first operational campaign of NSTX-U. By projecting profiles onto empirically derived basis functions, the model is able to efficiently and accurately reproduce profile shapes. In order to project the performance of the model to upcoming NSTX-U operations, a large database of profiles from the operation of NSTX is used to test performance as a function of available data. The rapid execution time of the model is well suited to the planned applications, including optimization during scenario development activities, and real-time plasma control. Finally, a potential application of the model to real-time profile estimation is demonstrated.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Prediction of electron density and pressure profile shapes on NSTX-U using neural networks

A new model for prediction of electron density and pressure profile shapes on NSTX and NSTX-U has been developed using neural networks. The model has been trained and tested on measured profiles from experimental discharges during the first operational campaign of NSTX-U. By projecting profiles onto empirically derived basis functions, the model is able to efficiently and accurately reproduce profile shapes. In order to project the performance of the model to upcoming NSTX-U operations, a large database of profiles from the operation of NSTX is used to test performance as a function of available data. The rapid execution time of the model is well suited to the planned applications, including optimization during scenario development activities, and real-time plasma control. A potential application of the model to real-time profile estimation is demonstrated.

Boyer, Mark↗
DOE OSTI2024

Collapse of neutrino wave functions under Penrose gravitational reduction

Models of spontaneous wave function collapse have been postulated to address the measurement problem in quantum mechanics. Their primary function is to convert coherent quantum superpositions into incoherent ones, with the result that macroscopic objects cannot be placed into widely separated superpositions for observably prolonged times. Many of these processes will also lead to loss of coherence in neutrino oscillations, producing observable signatures in the flavor profile of neutrinos at long travel distances. The majority of studies of neutrino oscillation coherence to date have focused on variants of the continuous state localization model, whereby an effective decoherence strength parameter is used to model the rate of coherence loss with an assumed energy dependence. Another class of collapse models that have been proposed posit connections to the configuration of gravitational field accompanying the mass distribution associated with each wave function that is in the superposition. A particularly interesting and prescriptive model is Penrose’s description of gravitational collapse which proposes a decoherence time τ determined through E g τ ∼ ℏ , where E g is a calculable function of the Newtonian gravitational potential. Here we explore application of the Penrose collapse model to neutrino oscillations, reinterpreting previous experimental limits on neutrino decoherence in terms of this model. We identify effects associated with both spatial collapse and momentum diffusion, finding that the latter is ruled out in data from the IceCube South Pole Neutrino Observatory so long as the neutrino wave packet width at production is σ ν , x ≤ 2 × 10 − 12 m . Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2010

Nucleon-Nucleon Interactions from the Quark Model

We report on investigations of the applicability of non-relativistic constituent quark models to the low-energy nucleon-nucleon (NN) interaction. The major innovations of a resulting NN potential are the use of the {sup 3}P{sub 0} decay model and quark model wave functions to derive nucleon-nucleon-meson form-factors, and the use of a colored spin-spin contact hyperfine interaction to model the repulsive core rather than the phenomenological treatment common in other NN potentials. We present the results of the model for experimental free NN scattering phase shifts, S-wave scattering lengths and effective ranges and deuteron properties. Plans for future study are discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Revealing the Origin and Nature of the Buried Metal‐Substrate Interface Layer in Ta/Sapphire Superconducting Films

Abstract Despite constituting a smaller fraction of the qubit's electromagnetic mode, surfaces and interfaces can exert significant influence as sources of high‐loss tangents, which brings forward the need to reveal properties of these extended defects and identify routes to their control. Here, we examine the structure and composition of the metal‐substrate interfacial layer that exists in Ta/sapphire‐based superconducting films. Synchrotron‐based X‐ray reflectivity measurements of Ta films, commonly used in these qubits, reveal an unexplored interface layer at the metal‐substrate interface. Scanning transmission electron microscopy and core‐level electron energy loss spectroscopy identified an intermixing layer (≈0.65 ± 0.05 nm) at the metal‐substrate interface containing Al, O, and Ta atoms. Density functional theory modeling reveals that the structure and properties of the Ta/sapphire heterojunctions are determined by the oxygen content on the sapphire surface prior to Ta deposition for two atomic terminations of sapphire. Using a multimodal approach, we gained deeper insights into the interface layer between the metal and substrate, which suggests that the orientation of deposited Ta films depend on the surface termination of sapphire. The observed elemental intermixing at the metal‐substrate interface influences the thermodynamic stability and electronic behavior of the film, which may also affect qubit performance.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Hydrogen Dissociation Controls 1-Hexyne Selective Hydrogenation on Dilute Pd-in-Au Catalysts

Increasing the selectivity of the catalytic hydrogenation of alkynes to alkenes is of major importance for the processing of petrochemicals and the production of fine chemicals. Achieving high selectivity for alkene formation at high conversions, however, remains a long-standing challenge in heterogeneous catalysis. Here, the mechanism and origin of the high selectivity of dilute Pd-in-Au catalysts has been studied by a combination of first-principles calculations, microkinetic simulations, and isotopic exchange hydrogenation experiments using Pd 0.04 Au 0.96 nanoparticles embedded in raspberry colloid-templated silica. The Pd is predominantly in the form of isolated atoms, only surrounded by Au atoms, based on prior studies. The simulations indicate that the rate-limiting process for 1-hexyne hydrogenation on Pd monomers in Au(111) is H 2 dissociation, which has a large free energy barrier of 0.86 eV at 363 K and 0.2 bar of H 2 . The C–H bond formation steps, on the other hand, proceed with lower barriers, which contrasts with previous studies of extended Pd catalysts. The microkinetic simulations identify the sizable H 2 dissociation barrier and the small barrier for the hydrogenation of 1-hexyne as key factors that lead to a high selectivity for the production of 1-hexene from 1-hexyne, even at high conversion. The unconventional H 2 dissociation limiting process in combination with the low coverage of weakly bound hydrocarbon intermediates explains the near-zero order of 1-hexyne found experimentally. Furthermore, the partial hydrogenation of 1-hexyne to form 1-hexene is shown to be an irreversible process from our isotopic exchange hydrogenation experiments and is explained by the strongly exothermic nature of the reaction. Diluting active species, like Pd, in a less active host metal, like Au, hence appears promising as a means of tuning the binding energy of reactants and altering reaction profiles, leading to distinct kinetic behavior for an optimal catalytic activity and selectivity. Furthermore, the combination of microkinetic modeling, density functional theory calculations, and isotopic exchange experiments is thus demonstrated to be an effective approach to modeling important catalytic phenomena.

1-hexyne↗
DOE OSTI2016

Molecular Dynamics Modeling of PPTA Crystals in Aramid Fibers

In this work, molecular dynamics modeling is used to study the mechanical properties of PPTA crystallites, which are the fundamental microstructural building blocks of polymer aramid bers such as Kevlar. Particular focus is given to constant strain rate axial loading simulations of PPTA crystallites, which is motivated by the rate-dependent mechanical properties observed in some experiments with aramid bers. In order to accommodate the covalent bond rupture that occurs in loading a crystallite to failure, the reactive bond order force eld ReaxFF is employed to conduct the simulations. Two major topics are addressed: The rst is the general behavior of PPTA crystallites under strain rate loading. Constant strain rate loading simulations of crystalline PPTA reveal that the crystal failure strain increases with increasing strain rate, while the modulus is not a ected by the strain rate. Increasing temperature lowers both the modulus and the failure strain. The simulations also identify the C N bond connecting the aromatic rings as weakest primary bond along the backbone of the PPTA chain. The e ect of chain-end defects on PPTA micromechanics is explored, and it is found that the presence of a chain-end defect transfers load to the adjacent chains in the hydrogen-bonded sheet in which the defect resides, but does not in uence the behavior of any other chains in the crystal. Chain-end defects are found to lower the strength of the crystal when clustered together, inducing bond failure via stress concentrations arising from the load transfer to bonds in adjacent chains near the defect site. The second topic addressed is the nature of primary and secondary bond failure in crystalline PPTA. Failure of both types of bonds is found to be stochastic in nature and driven by thermal uctuations of the bonds within the crystal. A model is proposed which uses reliability theory to model bonds under constant strain rate loading as components with time-dependent failure rate functions. The model is shown to work well for predicting the onset of primary backbone bond failure, as well as the onset of secondary bond failure via chain slippage for the case of isolated non-interacting chain-end defects.

36 MATERIALS SCIENCE↗
DOE OSTI2014

MO-E-17A-02: Incorporation of Contrast Medium Dynamics in Anthropomorphic Phantoms: The Advent of 5D XCAT Models

Purpose: To develop a unique method to incorporate the dynamics of contrast-medium propagation into the anthropomorphic phantom, to generate a five-dimensional (5D) patient model for multimodality imaging studies. Methods: A compartmental model of blood circulation network within the body was embodied into an extended cardiac-torso (4D-XCAT) patient model. To do so, a computational physiologic model of the human cardiovascular system was developed which includes a series of compartments representing heart, vessels, and organs. Patient-specific cardiac output and blood volume were used as inputs influenced by the weight, height, age, and gender of the patient's model. For a given injection protocol and given XCAT model, the contrast-medium transmission within the body was described by a series of mass balance differential equations, the solutions to which provided the contrast enhancement-time curves for each organ; thereby defining the tissue materials including the contrastmedium within the XCAT model. A library of time-dependent organ materials was then defined. Each organ in each voxelized 4D-XCAT phantom was assigned to a corresponding time-varying material to create the 5D-XCAT phantom in which the fifth dimension is blood/contrast-medium within the temporal domain. Results: The model effectively predicts the time-varying concentration behavior of various contrast-medium administration in each organ for different patient models as function of patient size (weight/height) and different injection protocol factors (injection rate and pattern, iodine concentration or volume). The contrast enhanced XCAT patient models was developed based on the concentration of iodine as a function of time after injection. Conclusion: Majority of medical imaging systems take advantage of contrast-medium administration in terms of better image quality, the effect of which was ignored in previous optimization studies. The study enables a comprehensive optimization of contrast administration both in terms of image quality and radiation dose, and can be used in different modalities such as CT, MRI, and ultrasound.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Machine learning of Kondo physics using variational autoencoders and symbolic regression

In this work, we employ variational autoencoders to extract physical insight from a dataset of one-particle Anderson impurity model spectral functions. Autoencoders are trained to find a low-dimensional, latent space representation that faithfully characterizes each element of the training set, as measured by a reconstruction error. Variational autoencoders, a probabilistic generalization of standard autoencoders, further condition the learned latent space to promote highly interpretable features. In our study, we find that the learned latent variables strongly correlate with well known, but nontrivial, parameters that characterize emergent behaviors in the Anderson impurity model. In particular, one latent variable correlates with particle-hole asymmetry, while another is in near one-to-one correspondence with the Kondo temperature, a dynamically generated low-energy scale in the impurity model. Using symbolic regression, we model this variable as a function of the known bare physical input parameters and "rediscover" the nonperturbative formula for the Kondo temperature. The machine learning pipeline we develop suggests a general purpose approach, which opens opportunities to discover new domain knowledge in other physical systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗