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DOE OSTI2017

The influence of mechanical constraints introduced by β annealed microstructures on the yield strength and ductility of Ti-6Al-4V

Discussed is a computational study of the influence of the microstructure’s geometric morphology on the yield strength and ductility of Ti-6Al-4V. Uniaxial tension tests were conducted on physical specimens to determine the macroscopic yield strength and ductility of two microstructural variations (mill annealed and β annealed) to establish comparisons of macroscopic properties. A multi-experimental approach was utilized to gather two dimensional and three dimensional data, which were used to inform the construction of representative β annealed polycrystals. A highly parallelized crystal plasticity finite element framework was employed to model the deformation response of the generated polycrystals subjected to uniaxial tension. To gauge the macroscopic response’s sensitivity to the morphology of the geometry, the key geometrical features - namely the number of high temperature β phase grains, α phase colonies, and size of remnant secondary β phase lamellae - were altered systematically in a suite of simulations. Both single phase and dual phase aggregates were studied. Presented are the calculated yield strengths and ductilities, and the resulting trends as functions of geometric parameters are examined in light of the heterogeneity in deformation at the crystal scale.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Bayesian Conavigation: Dynamic Designing of the Material Digital Twins via Active Learning

Scientific advancement is universally based on the dynamic interplay between theoretical insights, modeling, and experimental discoveries. However, this feedback loop is often slow, including delayed community interactions and the gradual integration of experimental data into theoretical frameworks. This challenge is particularly exacerbated in domains dealing with high-dimensional object spaces, such as molecules and complex microstructures. Hence, the integration of theory within automated and autonomous experimental setups, or theory in the loop-automated experiment, is emerging as a crucial objective for accelerating scientific research. The critical aspect is to use not only theory but also on-the-fly theory updates during the experiment. Furthermore, we introduce a method for integrating theory into the loop through Bayesian conavigation of theoretical model space and experimentation. Our approach leverages the concurrent development of surrogate models for both simulation and experimental domains at the rates determined by latencies and costs of experiments and computation, alongside the adjustment of control parameters within theoretical models to minimize epistemic uncertainty over the experimental object spaces. This methodology facilitates the creation of digital twins of material structures, encompassing both the surrogate model of behavior that includes the correlative part and the theoretical model itself. While being demonstrated here within the context of functional responses in ferroelectric materials, our approach holds promise for broader applications, such as the exploration of optical properties in nanoclusters, microstructure-dependent properties in complex materials, and properties of molecular systems.

Microscopy↗

DOE OSTI2013

High-resolution neutron diffraction study of CuNCN: New evidence of structure anomalies at low temperature

Copper carbodiimide (CuNCN) is the nitrogen-containing analogue of cupric oxide. Based on high-resolution neutron-diffraction data, CuNCN's lattice parameters are derived as a function of the temperature. In accordance with a recent synchrotron study, a clear trend in the cell parameter a is observed accompanying the changing magnetic behavior. With decreasing temperature, a slowly decreases to a minimum at ∼100 K after which it rises again. The same trend—albeit more pronounced—is observed for the c lattice parameter at ∼35 K. The herein presented neutron powder-diffraction data also support the conjectured sequence of transitions from the high-temperature one-dimensional resonating valence-bond (RVB) state to a transient two-dimensional RVB state and eventually, at lowest temperatures, into another two-dimensional RVB state, presumably the ground state.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2014

Improving the efficiency of configurational-bias Monte Carlo: A density-guided method for generating bending angle trials for linear and branched molecules

A new method has been developed to generate bending angle trials to improve the acceptance rate and the speed of configurational-bias Monte Carlo. Whereas traditionally the trial geometries are generated from a uniform distribution, in this method we attempt to use the exact probability density function so that each geometry generated is likely to be accepted. In actual practice, due to the complexity of this probability density function, a numerical representation of this distribution function would be required. This numerical table can be generated a priori from the distribution function. This method has been tested on a united-atom model of alkanes including propane, 2-methylpropane, and 2,2-dimethylpropane, that are good representatives of both linear and branched molecules. It has been shown from these test cases that reasonable approximations can be made especially for the highly branched molecules to reduce drastically the dimensionality and correspondingly the amount of the tabulated data that is needed to be stored. Despite these approximations, the dependencies between the various geometrical variables can be still well considered, as evident from a nearly perfect acceptance rate achieved. For all cases, the bending angles were shown to be sampled correctly by this method with an acceptance rate of at least 96% for 2,2-dimethylpropane to more than 99% for propane. Since only one trial is required to be generated for each bending angle (instead of thousands of trials required by the conventional algorithm), this method can dramatically reduce the simulation time. The profiling results of our Monte Carlo simulation code show that trial generation, which used to be the most time consuming process, is no longer the time dominating component of the simulation.

2-2-DIMETHYLPROPANE↗

DOE OSTI2022

Time-series forecasting using manifold learning, radial basis function interpolation, and geometric harmonics

We address a three-tier numerical framework based on nonlinear manifold learning for the forecasting of high-dimensional time series, relaxing the “curse of dimensionality” related to the training phase of surrogate/machine learning models. At the first step, we embed the high-dimensional time series into a reduced low-dimensional space using nonlinear manifold learning (local linear embedding and parsimonious diffusion maps). Then, we construct reduced-order surrogate models on the manifold (here, for our illustrations, we used multivariate autoregressive and Gaussian process regression models) to forecast the embedded dynamics. Finally, we solve the pre-image problem, thus lifting the embedded time series back to the original high-dimensional space using radial basis function interpolation and geometric harmonics. The proposed numerical data-driven scheme can also be applied as a reduced-order model procedure for the numerical solution/propagation of the (transient) dynamics of partial differential equations (PDEs). In conclusion, we assess the performance of the proposed scheme via three different families of problems: (a) the forecasting of synthetic time series generated by three simplistic linear and weakly nonlinear stochastic models resembling electroencephalography signals, (b) the prediction/propagation of the solution profiles of a linear parabolic PDE and the Brusselator model (a set of two nonlinear parabolic PDEs), and (c) the forecasting of a real-world data set containing daily time series of ten key foreign exchange rates spanning the time period 3 September 2001–29 October 2020.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Transfer learning nonlinear plasma dynamic transitions in low dimensional embeddings via deep neural networks

Deep learning algorithms provide a new paradigm to study high-dimensional dynamical behaviors, such as those in fusion plasma systems. Development of novel, data-driven model reduction methods, coupled with detection of abnormal modes with plasma physics, opens a unique opportunity to identify plasma instabilities through automated construction of parsimonious models that can be tuned to balance accuracy and cost. Our fusion transfer learning (FTL) model demonstrates success in rapidly reconstructing nonlinear kink mode structures by learning from a limited amount of nonlinear simulation data. The knowledge transfer process leverages a pre-trained neural encoder–decoder network, initially trained on linear simulations, to effectively capture nonlinear dynamics. The low-dimensional embeddings extract the coherent structures of interest, while preserving the inherent dynamics of the complex system. Experimental results highlight FTL’s capacity to capture transitional behaviors and dynamical features in plasma dynamics—a task often challenging for conventional methods. The model developed in this study is generalizable and can be extended broadly through transfer learning to address various magnetohydrodynamics modes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

A Constrained Data Assimilation Algorithm Based on GSI Hybrid 3D-EnVar and Its Application

Abstract Data assimilation (DA) at mesoscales is important for severe weather forecasts, yet the techniques of data assimilation at this scale remain a challenge. This study introduces dynamical constraints in the Gridpoint Statistical Interpolation (GSI) three-dimensional ensemble variational (3D-EnVar) data assimilation algorithm to enable the use of high-resolution surface observations of precipitation to improve atmospheric analysis at mesoscales. The constraints use the conservations of mass and moisture. Mass constraint suppresses the unphysical high-frequency oscillation, while moisture conservation constrains the atmospheric states to conform with the observed high-resolution precipitation. We show that the constrained data assimilation (CDA) algorithm significantly reduced the spurious residuals of the mass and moisture budgets compared to the original data assimilation (ODA). A case study is presented for a squall line over the Southern Great Plains on 20 May 2011 during Midlatitude Continental Convective Clouds Experiment (MC3E) of the Atmospheric Radiation Measurement (ARM) program by using ODA or CDA analysis as initial condition of forecasts. The state variables, and the location and intensity of the squall line are better simulated in the CDA experiment. Results show how surface observation of precipitation can be used to improve atmospheric analysis through data assimilation by using the dynamical constraints of mass and moisture conservations.

Wang, Jia↗

DOE OSTI2021

Multi-fidelity modeling to predict the rheological properties of fiber suspensions

Unveiling the rheological properties of fiber suspensions is of paramount interest to many industrial applications like biofuel production. The 3D numerical simulations of the suspension of fibers are often computationally expensive and time-consuming. Machine learning methods such as neural networks can simplify the prediction of rheological behavior; however, they require a relatively large training data set. Multi -fidelity models, which combine high-fidelity data from numerical simulations and less expensive lower fidelity data from resources such as simplified physical equations, can lead to optimized predictions. Here, we focus on a neural network with two levels of fidelity, i.e., high and low fidelity networks. To produce high-fidelity data, we perform direct numerical simulations to model the fibers as one-dimensional inextensible slender bodies that obey the Euler- Bernoulli beam equation. The Navier-Stokes equations govern the suspended fluid, and an immersed boundary method is used to couple the fluid and solid motion. The low-fidelity data is produced by using constitutive equations. Noticeable improvements have been observed in the accuracy of predicting the rheological behavior when a multi-fidelity network is used compared to the single-fidelity network.

Boodaghidizaji, Miad↗

DOE OSTI2015

A sparse reconstruction method for the estimation of multi-resolution emission fields via atmospheric inversion

Atmospheric inversions are frequently used to estimate fluxes of atmospheric greenhouse gases (e.g., biospheric CO 2 flux fields) at Earth's surface. These inversions typically assume that flux departures from a prior model are spatially smoothly varying, which are then modeled using a multi-variate Gaussian. When the field being estimated is spatially rough, multi-variate Gaussian models are difficult to construct and a wavelet-based field model may be more suitable. Unfortunately, such models are very high dimensional and are most conveniently used when the estimation method can simultaneously perform data-driven model simplification (removal of model parameters that cannot be reliably estimated) and fitting. Such sparse reconstruction methods are typically not used in atmospheric inversions. In this work, we devise a sparse reconstruction method, and illustrate it in an idealized atmospheric inversion problem for the estimation of fossil fuel CO 2 (ffCO 2 ) emissions in the lower 48 states of the USA. Our new method is based on stagewise orthogonal matching pursuit (StOMP), a method used to reconstruct compressively sensed images. Our adaptations bestow three properties to the sparse reconstruction procedure which are useful in atmospheric inversions. We have modified StOMP to incorporate prior information on the emission field being estimated and to enforce non-negativity on the estimated field. Finally, though based on wavelets, our method allows for the estimation of fields in non-rectangular geometries, e.g., emission fields inside geographical and political boundaries. Our idealized inversions use a recently developed multi-resolution (i.e., wavelet-based) random field model developed for ffCO 2 emissions and synthetic observations of ffCO 2 concentrations from a limited set of measurement sites. We find that our method for limiting the estimated field within an irregularly shaped region is about a factor of 10 faster than conventional approaches. It also reduces the overall computational cost by a factor of 2. Further, the sparse reconstruction scheme imposes non-negativity without introducing strong nonlinearities, such as those introduced by employing log-transformed fields, and thus reaps the benefits of simplicity and computational speed that are characteristic of linear inverse problems.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1994

An Aerodynamic Analysis of a Mixed Flow Turbine

The aerodynamic performance of a high-work Mixed Flow Turbine (MFT) is computed and compared with experimental data. A three dimensional (3-D) viscous analysis is applied to the single stage MFT geometry with a relatively long upstream transition duct. Predicted vane surface static pressures and circumferentially averaged spanwise quantities at stator and rotor exits agree favorably with data. Compared to the results of axisymmetric flow analysis from design intent, the 3-D computation agrees much better especially in the endwall regions where throughflow prediction fails to assess the loss mechanism properly. Potential sources of performance loss such as tip leakage and secondary flows are also properly captured by the analysis.

Kim, Chan M.↗

NASA NTRS2003

Numerical Simulations of Silverpit Crater Collapse

The Silverpit crater is a recently discovered, 60-65 Myr old complex crater, which lies buried beneath the North Sea, about 150 km east of Britain. High-resolution images of Silverpit's subsurface structure, provided by three-dimensional seismic reflection data, reveal an inner-crater morphology similar to that expected for a 5-8 km diameter terrestrial crater. The crater walls show evidence of terrace-style slumping and there is a distinct central uplift, which may have produced a central peak in the pristine crater morphology. However, Silverpit is not a typical 5-km diameter terrestrial crater, because it exhibits multiple, concentric rings outside the main cavity. External concentric rings are normally associated with much larger impact structures, for example Chicxulub on Earth, or Orientale on the Moon. Furthermore, external rings associated with large impacts on the terrestrial planets and moons are widely-spaced, predominantly inwardly-facing, asymmetric scarps. However, the seismic data show that the external rings at Silverpit represent closely-spaced, concentric faultbound graben, with both inwardly and outwardly facing fault-scarps. This type of multi-ring structure directly analogous to the Valhalla-type multi-ring basins found on the icy satellites. Thus, the presence and style of the multiple rings at Silverpit is surprising given both the size of the crater and its planetary setting. A further curiosity of the Silverpit structure is that the external concentric rings appear to be extensional features on the West side of the crater and compressional features on the East side. The crater also lies in a local depression, thought to be created by postimpact movement of a salt layer buried beneath the crater.

Collins, G. S.↗

NASA NTRS2003

Numerical Simulations of Silverpit Crater Collapse

The Silverpit crater is a recently discovered, 60-65 Myr old complex crater, which lies buried beneath the North Sea, about 150 km east of Britain. High-resolution images of Silverpit's subsurface structure, provided by three-dimensional seismic reflection data, reveal an inner-crater morphology similar to that expected for a 5-8 km diameter terrestrial crater. The crater walls show evidence of terracestyle slumping and there is a distinct central uplift, which may have produced a central peak in the pristine crater morphology. However, Silverpit is not a typical 5-km diameter terrestrial crater, because it exhibits multiple, concentric rings outside the main cavity. External concentric rings are normally associated with much larger impact structures, for example Chicxulub on Earth, or Orientale on the Moon. Furthermore, external rings associated with large impacts on the terrestrial planets and moons are widely-spaced, predominantly inwardly-facing, asymmetric scarps. However, the seismic data show that the external rings at Silverpit represent closely-spaced, concentric fault-bound graben, with both inwardly and outwardly facing faults-carps. This type of multi-ring structure is directly analogous to the Valhalla-type multi-ring basins found on the icy satellites. Thus, the presence and style of the multiple rings at Silverpit is surprising given both the size of the crater and its planetary setting.

Collins, G. S.↗

NASA NTRS2001

Radiation dosimetry using three-dimensional optical random access memories

Three-dimensional optical random access memories (3D ORAMs) are a new generation of high-density data storage devices. Binary information is stored and retrieved via a light induced reversible transformation of an ensemble of bistable photochromic molecules embedded in a polymer matrix. This paper describes the application of 3D ORAM materials to radiation dosimetry. It is shown both theoretically and experimentally, that ionizing radiation in the form of heavy charged particles is capable of changing the information originally stored on the ORAM material. The magnitude and spatial distribution of these changes are used as a measure of the absorbed dose, particle type and energy. The effects of exposure on 3D ORAM materials have been investigated for a variety of particle types and energies, including protons, alpha particles and 12C ions. The exposed materials are observed to fluoresce when exposed to laser light. The intensity and the depth of the fluorescence is dependent on the type and energy of the particle to which the materials were exposed. It is shown that these effects can be modeled using Monte Carlo calculations. The model provides a better understanding of the properties of these materials. which should prove useful for developing systems for charged particle and neutron dosimetry/detector applications. c2001 Published by Elsevier Science B.V.

Non-NASA Center↗

DOE OSTI2026

Strong Lensing Cosmology with Population-level Calibrated Neural Ratio Estimation

Strong gravitational lensing contains key information about cosmic acceleration. Modern and next-generation galaxy imaging surveys are expected to provide high-quality data on $\mathcal{O}(10^5)$ galaxy-galaxy lensing systems. The plethora and complexity of the data are likely to present computational challenges for parameter inference methods for fitting high-dimensional likelihoods, which are often analytically intractable. Neural Ratio Estimation (NRE) efficiently computes individual likelihood ratios that can be combined into population-level posteriors. We use simulations to study the capacity of NRE to jointly predict the dark energy equation-of-state parameter $w$ and the total matter density $Ω_{m}$ from lensing images and companion spectroscopic information. We also introduce a post hoc posterior coverage calibration procedure that mitigates the model overconfidence that is typically found in neural density estimation applications. Our experiments show that the errors on both parameters decrease with increasing inference population sizes. In particular, for 100 lenses in a standard $Λ$CDM Universe, our calibrated NRE model achieves median fractional uncertainty of $22.8\%$ in $w$ and $2.9\%$ in $Ω_{m}$. This proof of concept demonstrates a potentially scalable approach for efficient cosmological parameter inference with large populations of galaxy-scale lenses observed in future surveys.

Jarugula, Sreevani [Fermilab] (ORCID:0000000253867↗

NASA NTRS2003

Comparisons of Gas-phase Temperature Measurements in a Flame Using Thin-Filament Pyrometry and Thermocouples

Less-intrusive, fast-responding, and full-field temperature measurements have long been a desired tool for the research community. Recently, the emission of a silicon-carbide (SiC) fiber placed in a flowing hot (or reacting) gas has been used to measure the temperature profile along the length of the fiber. The relationship between the gas and fiber temperature comes from an energy balance on the fiber. In the present work, we compared single point flame temperature measurements using thin-filament pyrometry (TFP) and thermocouples. The data was from vertically traversing a thermocouple and a SiC fiber through a methanol/air diffusion flame of a porous-metal wick burner. The results showed that the gas temperature using the TFP technique agreed with the thermocouple measurements (25.4 m diameter wire) within 3.5% for temperatures above 1200 K. Additionally, we imaged the entire SiC fiber (with a spatial resolution of 0.14 mm) while it was in the flame using a high resolution CCD camera. The intensity level along the fiber length is a function of the temperature. This results in a one-dimensional temperature profiles at various heights above the burner wick. This temperature measurement technique, while having a precision of less than 1 K, showed data scatter as high as 38 K. Finally, we discuss the major sources of uncertainty in gas temperature measurement using TFP.

Struk, Peter↗

DOE OSTI2014

Correlating electroluminescence characterization and physics-based models of InGaN/GaN LEDs: Pitfalls and open issues

Electroluminescence (EL) characterization of InGaN/GaN light-emitting diodes (LEDs), coupled with numerical device models of different sophistication, is routinely adopted not only to establish correlations between device efficiency and structural features, but also to make inferences about the loss mechanisms responsible for LED efficiency droop at high driving currents. The limits of this investigative approach are discussed here in a case study based on a comprehensive set of current- and temperature-dependent EL data from blue LEDs with low and high densities of threading dislocations (TDs). First, the effects limiting the applicability of simpler (closed-form and/or one-dimensional) classes of models are addressed, like lateral current crowding, vertical carrier distribution nonuniformity, and interband transition broadening. Then, the major sources of uncertainty affecting state-of-the-art numerical device simulation are reviewed and discussed, including (i) the approximations in the transport description through the multi-quantum-well active region, (ii) the alternative valence band parametrizations proposed to calculate the spontaneous emission rate, (iii) the difficulties in defining the Auger coefficients due to inadequacies in the microscopic quantum well description and the possible presence of extra, non-Auger high-current-density recombination mechanisms and/or Auger-induced leakage. In the case of the present LED structures, the application of three-dimensional numerical-simulation-based analysis to the EL data leads to an explanation of efficiency droop in terms of TD-related and Auger-like nonradiative losses, with a C coefficient in the 10{sup −30} cm{sup 6}/s range at room temperature, close to the larger theoretical calculations reported so far. However, a study of the combined effects of structural and model uncertainties suggests that the C values thus determined could be overestimated by about an order of magnitude. This preliminary attempt at uncertainty quantification confirms, beyond the present case, the need for an improved description of carrier transport and microscopic radiative and nonradiative recombination mechanisms in device-level LED numerical models.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference

Operator inference learns low-dimensional dynamical-system models with polynomial nonlinear terms from trajectories of high-dimensional physical systems (non-intrusive model reduction). Here, this work focuses on the large class of physical systems that can be well described by models with quadratic and cubic nonlinear terms and proposes a regularizer for operator inference that induces a stability bias onto learned models. The proposed regularizer is physics informed in the sense that it penalizes higher-order terms with large norms and so explicitly leverages the polynomial model form that is given by the underlying physics. This means that the proposed approach judiciously learns from data and physical insights combined, rather than from either data or physics alone. Additionally, a formulation of operator inference is proposed that enforces model constraints for preserving structure such as symmetry and definiteness in linear terms. Numerical results demonstrate that models learned with operator inference and the proposed regularizer and structure preservation are accurate and stable even in cases where using no regularization and Tikhonov regularization leads to models that are unstable.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1999

Blade Heat Transfer Measurements and Prediction in a Transonic Turbine Cascade

Detailed heat transfer measurements and predictions are given for a turbine rotor with 136 deg of turning and an axial chord of 12.7 cm. Data were obtained for inlet Reynolds numbers of 0.5 and 1.0 x 10(exp 6), for isentropic exit Mach numbers of 1.0 and 1.3, and for inlet turbulence intensities of 0.25% and 7.0%. Measurements were made in a linear cascade having a highly three-dimensional flow field resulting from thick inlet boundary layers. The purpose of the work is to provide benchmark quality data for three-dimensional CFD code and model verification. Data were obtained by a steady-state technique using a heated, isothermal blade. Heat fluxes were determined from a calibrated resistance layer in conjunction with a surface temperature measured by calibrated liquid crystals. The results show the effects of strong secondary vortical flows, laminar-to-turbulent transition, shock impingement, and increased inlet turbulence on the surface heat transfer.

Giel, P. W.↗