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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Effects of random forest modeling decisions on biogeochemical time series predictions

Abstract Random forests (RF) are an increasingly popular machine learning approach used to model biogeochemical processes in the Earth system. While RF models are robust to many assumptions that complicate deterministic models, there are several important parameterization decisions for appropriate use and optimal model fit. We explored the role that parameter decisions, including training/testing data splitting strategies, variable selection, and hyperparameters play on RF goodness‐of‐fit by constructing models using 1296 unique parameter combinations to predict concentrations of nitrate, a key nutrient for biogeochemical cycling in aquatic ecosystems. Models were built on long‐term, publicly available water quality and meteorology time series collected by the National Estuarine Research Reserve monitoring network for two contrasting ecosystems representing freshwater and brackish estuaries. We found that accounting for temporal dependence when splitting data into training and testing subsets was key for avoiding over‐estimation of model predictive power. In addition, variable selection, the ratio of training to testing data, and to a lesser degree, variables per split and number of trees, were significant parameters for optimizing RF goodness‐of‐fit. We also explored how model parameter decisions influenced interpretation of the relative importance of predictors to the model, and model predictor‐dependent variable relationships, with results suggesting that both data structure and model parameterization influence these factors. Because much of the current RF literature is written for the computational and statistical science communities, the primary goal of this study is to provide guidelines for aquatic scientists new to machine learning to apply RF techniques appropriately to aquatic biogeochemical datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) peak intensity normalization for complex mixture analyses

Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a preferred technique for analyzing complex organic mixtures. Currently, there is no consensus normalization approach, nor an objective method for selecting one, for quantitative analyses of FT-ICR-MS data. We investigate a method to evaluate and score the amount of bias various normalization approaches introduce into the data. We evaluate the ability of the Statistical Procedure for the Analysis of Normalization Strategies (SPANS) to guide selection of appropriate normalization approaches for two different FT-ICR-MS datasets. Further, we test the robustness of SPANS results to changes in SPANS parameter values and assess the impact of using various normalization approaches on downstream statistical analyses. The normalization approach identified by SPANS differed for the two datasets. Normalization methods impacted the statistical significance of peaks differently, underscoring the importance of carefully evaluating potential methods. More consistent SPANS scores resulted when at least 120 significant peaks are used, where larger sets of peaks were obtained by increasing the p-value threshold. Interestingly, we show that Total Sum Scaling and highest peak normalization, used in previous studies, underperformed relative to SPANS-recommended normalization approaches. While there is no single, best normalization method for all datasets, SPANS provides a mechanism to identify an appropriate normalization method for analyzing FT-ICR-MS data quantitatively. As a result, the number of peaks used in the background distributions of SPANS contributes more significantly to the reproducibility of results than the p-value thresholds used to obtain those peaks.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Use of a Lignin-Based Admixture for Tailoring the Rheological Properties of Mortars for 3D Printing: Preprint

Efforts toward decarbonizing construction materials and industrial processes related to cement and concrete can be aided via multifaceted approaches that target alternative admixtures as well as precision control of fabrication. Chemical admixtures for water reduction have played a crucial role in the development of advanced concrete mixtures. Newer biomass processing techniques developed for aviation fuel production from corn stover biomass produce a more reactive lignin byproduct that is suitable for chemical modifications to mimic the properties of polycarboxylate ether admixtures with a smaller carbon footprint. The present study examines the use of lignin-based water-reducing admixture in cement pastes and mortar mixtures for 3D printing. The experimental program explores the use of different dosages of lignin-based admixture to produce 3D-printed samples with appropriate extrudability and buildability. The rheological characterization was performed to determine the flow curve of various mixtures. Finally, the heat of hydration of cement pastes was monitored via isothermal calorimetry to assess the impact of lignin-based admixtures on the hydration process of cement. The results of this study indicate that the use of biomass by-products, such as lignin-based admixtures have great potential to effectively control the fresh-state properties of cement-based materials.

bio-based admixtures↗
DOE OSTI2023

Scattering in black hole backgrounds and higher-spin amplitudes. Part II

We continue to investigate correspondences between, on the one hand, scattering amplitudes for massive higher-spin particles and gravitons in appropriate quantum-to-classical limits, and on the other hand, classical gravitational interactions of spinning black holes according to general relativity. We first construct an ansatz for a gravitational Compton amplitude, at tree level, constrained only by locality, crossing symmetry, unitarity and consistency with the linearized-Kerr 3-point amplitude, to all orders in the black hole’s spin. We then explore the extent to which a unique classical Compton amplitude can be identified by comparing with the results of the classical process of scattering long-wavelength gravitational waves off an exact Kerr black hole, determined by appropriate solutions of the Teukolsky equation. Up to fourth order in spin, we find complete agreement with a previously conjectured exponential form of the tree-level Compton amplitude. At higher orders, we extract tree-level contributions from the Teukolsky amplitude by an analytic continuation from a physical (a/GM < 1) to a particle-like (a/GM > 1) regime. Up to the sixth order in spin, we identify a unique conservative part of the amplitude which is insensitive both to the choice of boundary conditions at the black hole horizon and to branch choices in the analytic continuation. The remainder of the amplitude is determined modulo an overall sign from a branch choice, with the sign flipping under exchanging purely ingoing and purely outgoing boundary conditions at the horizon. Along the way, we make contact with novel applications of massive spinor-helicity variables pertaining to their relation to EFT operators and (spinning) partial amplitudes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

The Schwinger-Keldysh coset construction

The coset construction is a tool for systematically building low energy effective actions for Nambu-Goldstone modes. This technique is typically used to compute time-ordered correlators appropriate for S-matrix computations for systems in their ground state. In this paper, we extend this technique to the Schwinger-Keldysh formalism, which enables one to calculate a wider variety of correlators and applies also to systems in a mixed state. We focus our attention on internal symmetries and demonstrate that, after identifying the appropriate symmetry breaking pattern, Schwinger-Keldysh effective actions for Nambu-Goldstone modes can be constructed using the standard rules of the coset construction. Particular emphasis is placed on the thermal state and ensuring that correlators satisfy the KMS relation. We also discuss explicitly the power counting scheme underlying our effective actions. We comment on the similarities and differences between our approach and others that have previously appeared in the literature. In particular, our prescription does not require the introduction of additional “diffusive” symmetries and retains the full non-linear structure generated by the coset construction. We conclude with a series of explicit examples, including a computation of the finite-temperature two-point functions of conserved spin currents in non-relativistic paramagnets, antiferromagnets, and ferromagnets. Along the way, we also clarify the discrete symmetries that set antiferromagnets apart from ferromagnets, and point out that the dynamical KMS symmetry must be implemented in different ways in these two systems.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Applications of a dual-column technique in actinide separations

Selective separation of an individual actinide of interest from other actinides and accompanying lanthanides is a challenging task in radiochemistry and radioanalytical chemistry. This paper illustrates a dual column technique (i.e., stacked columns of two appropriate resins) to separate an individual actinide of interest, where the selection of two resins and a common effluent running through the stacked columns are key parameters in design of an appropriate dual column. This paper describes the dual column design and practice with two examples, one for a heavy actinide 249Bk and the other for a light actinide 230U. In the former case, stacked columns of anion exchange (AX) resin and LN resin replaced the traditional CX-AHIB method used at Oak Ridge National Laboratory for 60 years. In the latter case, an elution process with an AX column followed with a stacked column of AX resin and a DGA resin was applied, instead of traditional methods, e.g., PUREX or U-TEVA methods. Application results of the dual column method to the two example actinides are displayed, while the considerations in method design and the required conditions are discussed.

Du, Miting↗
DOE OSTI2021

Creep Behavior of a High-$\gamma^{\prime}$ Ni-Based Superalloy Fabricated via Electron Beam Melting

Additive manufacturing enables the fabrication of complex engineering components previously inaccessible through traditional processes. Nickel-base superalloys with large γ' volume fraction are typically considered non-weldable and therefore exhibit a propensity for cracking during the fusion process. These crack-prone materials, however, are of great importance in gas turbine engines due to their excellent high temperature creep resistance. In this study we investigate the creep behavior of IN738LC produced by the electron beam melting process. Here, we find that with appropriate post-build heat treatment the creep response of material oriented in the build direction exhibits deformation and rupture behavior comparable to that of conventionally cast IN738 & IN738LC. In the transverse direction properties fall below the expected cast behavior, however, we argue this is likely due to differences in grain scale and crystallographic texture. It may be possible to coarsen the grain morphology with appropriate process-parameter optimization in order to reduce the severity of intergranular fracture in the transverse direction. These results illustrate that high temperature properties exhibited by additively manufactured IN738LC are suitable for the hot section of gas turbine engines.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Microwave synthesis of single-phase nanoparticles made of multi-principal element alloys

In this work, metal nanoparticles of multi-principal element alloys (MPEA) with a single crystalline phase have been synthesized by flash heating/cooling of nanosized metals encapsulated in micelle vesicles dispersed in an oil phase (e.g., cyclohexane). Flash heating is realized by selective absorption of a microwave pulse in metals to rapidly heat metals into uniform melts. The oil phase barely absorbs microwave and maintains the low temperature, which can rapidly quench the high-temperature metal melts to enable the flash cooling process. The precursor ions of four metals, including Au, Pt, Pd, and Cu, can be simultaneously reduced by hydrazine in the aqueous solution encapsulated in the micelle vesicles. The resulting metals efficiently absorb microwave energy to locally reach a temperature high enough to melt themselves into a uniform mixture. The duration of microwave pulse is crucial to ensure the reduced metals mix uniformly, while the temperature of oil phase is still low to rapidly quench the metals and freeze the single-phase crystalline lattices in alloy nanoparticles. The microwave-enabled flash heating/cooling provides a new method to synthesize single-phase MPEA nanoparticles of many metal combinations when the appropriate water-in-oil micelle systems and the appropriate reduction reactions of metal precursors are available.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Noise-aware optimization in nominally identical manufacturing and measuring systems for high-throughput parallel workflows

Device-to-device variability in experimental noise critically impacts reproducibility, especially in automated, high-throughput systems like additive manufacturing farms. While manageable in small labs, such variability can escalate into serious risks at larger scales, such as architectural 3D printing, where noise may cause structural or economic failures. This contribution presents a noise-aware decision-making algorithm that quantifies and models device-specific noise profiles to manage variability adaptively. It uses distributional analysis and pairwise divergence metrics with clustering to choose between single-device and robust multi-device Bayesian optimization strategies. Unlike conventional methods that assume homogeneous devices or enforce generic robustness, the proposed framework explicitly determines whether shared optimization across devices is appropriate based on the degree of inter-device noise heterogeneity. This enables improved performance, reproducibility, and efficiency. An experimental case study involving three nominally identical 3D printers (same brand, model, and close serial numbers) demonstrates reduced redundancy, lower resource usage, and improved reliability, along with improved convergence stability and solution quality through the selection of the appropriate optimization strategy based on the degree of inter-device noise heterogeneity. Overall, this framework establishes a general approach for precision- and resource-aware optimization in scalable, automated experimental platforms, demonstrated here on a representative multi-device 3D printing case study.

Schenk, Christina↗
DOE OSTI2020

Three-dimensional traction microscopy accounting for cell-induced matrix degradation

Tractions exerted by cells on the extracellular matrix (ECM) are critical in many important physiological and pathological processes such as embryonic morphogenesis, wound healing, and cancer metastasis. Three-dimensional Traction Microscopy (3DTM) is a tool to quantify cellular tractions by first measuring the displacement field in the ECM in response to these tractions, and then using this measurement to infer tractions. Most applications of 3DTM have assumed that the ECM has spatially-uniform mechanical properties, but cells secrete enzymes that can locally degrade the ECM. In this work, a novel computational method is developed to quantify both cellular tractions and ECM degradation. In particular, the ECM is modeled as a hyperelastic, Neo-Hookean solid, whose material parameters are corrupted by a single degradation parameter. The feasibility of determining both the traction and the degradation parameter is first demonstrated by showing the existence and uniqueness of the solution. An inverse problem is then formulated to determine the nodal values of the traction vector and the degradation parameter, with the objective of minimizing the difference between a predicted and measured displacement field, under the constraint that the predicted displacement field satisfies the equation of equilibrium. The inverse problem is solved by means of a gradient-based optimization approach, and the gradient is computed efficiently using appropriately derived adjoint fields. The computational method is validated in-silico using a geometrically realistic neuronal cell model and synthetic traction and degradation fields. It is found that the method accurately recovers both the traction and degradation fields. Moreover, it is found that neglecting ECM degradation can yield significant errors in traction measurements. Finally, our method can extend the range of context where tractions can be appropriately measured.

42 ENGINEERING↗
DOE OSTI2022

Learning high-dimensional parametric maps via reduced basis adaptive residual networks

We propose a scalable framework for the learning of high-dimensional parametric maps via adaptively constructed residual network (ResNet) maps between reduced bases of the inputs and outputs. When just few training data are available, it is beneficial to have a compact parametrization in order to ameliorate the ill-posedness of the neural network training problem. By linearly restricting high-dimensional maps to informed reduced bases of the inputs, one can compress high-dimensional maps in a constructive way that can be used to detect appropriate basis ranks, equipped with rigorous error estimates. A scalable neural network learning framework is thus to learn the nonlinear compressed reduced basis mapping. Unlike the reduced basis construction, however, neural network constructions are not guaranteed to reduce errors by adding representation power, making it difficult to achieve good practical performance. Inspired by recent approximation theory that connects ResNets to sequential minimizing flows, we present an adaptive ResNet construction algorithm. This algorithm allows for depth-wise enrichment of the neural network approximation, in a manner that can achieve good practical performance by first training a shallow network and then adapting. We prove universal approximation of the associated neural network class for $L^2_v$ functions on compact sets. Our overall framework allows for constructive means to detect appropriate breadth and depth, and related compact parametrizations of neural networks, significantly reducing the need for architectural hyperparameter tuning. Numerical experiments for parametric PDE problems and a 3D CFD wing design optimization parametric map demonstrate that the proposed methodology can achieve remarkably high accuracy for limited training data, and outperformed other neural network strategies we compared against.

42 ENGINEERING↗
DOE OSTI2025

A resolution independent neural operator

The Deep operator network (DeepONet) is a powerful yet simple neural operator architecture that utilizes two deep neural networks to learn mappings between infinite-dimensional function spaces. This architecture is highly flexible, allowing the evaluation of the solution field at any location within the desired domain. However, it imposes a strict constraint on the input space, requiring all input functions to be discretized at the same locations; this limits its practical applications. Here, in this work, we introduce a general framework for operator learning from input–output data with arbitrary number and locations of sensors. This begins by introducing a resolution-independent DeepONet (RI-DeepONet), enabling it to handle input functions that are arbitrarily, but sufficiently finely, discretized. To this end, we propose two dictionary learning algorithms to adaptively learn a set of appropriate continuous basis functions, parameterized as implicit neural representations (INRs), from correlated signals defined on arbitrary point cloud data. These basis functions are then used to project arbitrary input function data as a point cloud onto an embedding space (i.e., a vector space of finite dimensions) with dimensionality equal to the dictionary size, which can be directly used by DeepONet without any architectural changes. In particular, we utilize sinusoidal representation networks (SIRENs) as trainable INR basis functions. The introduced dictionary learning algorithms are then used in a similar way to learn an appropriate dictionary of basis functions for the output function data, which defines a new neural operator architecture referred to as the R esolution I ndependent N eural O perator (RINO). In the RINO, the operator learning task simplifies to learning a mapping from the coefficients of input basis functions to the coefficients of output basis functions. We demonstrate the robustness and applicability of RINO in handling arbitrarily (but sufficiently richly) sampled input and output functions during both training and inference through several numerical examples.

Deep operator network (DeepONet)↗
DOE OSTI2020

The effects of introducing elasticity using different interpolation schemes to the grand potential phase field model

Introducing elastic energy in the phase field method has been shown to influence interfacial energy, depending on the elastic interpolation scheme. This study investigates the impact of the elastic energy when using a grand potential-based phase field method, comparing the result of Khachaturyan’s strain interpolation scheme (KHS) and Voight-Taylor’s elastic energy interpolation scheme (VTS). The KHS model leads to a decrease in the interfacial energy, while the VTS model leads to an increase. The change in interfacial energy is greater with the VTS model than the KHS model, which suggests that the KHS model is more appropriate to limit the artificial impact of the elastic energy on the interfacial energy. When the contribution at the interface is not negligible, it is shown that both the microstructure evolution kinetics and the equilibrium microstructure can be influenced by the choice of the elastic scheme being used. In addition, this paper shows that the grand potential model might not be appropriate when the system requires the introduction of a composition-dependent term in the elastic energy contribution. This limitation is due to the need for an explicit and invertible relation between the total potential and the composition.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Electronic Structures and Magnetism of Zr-, Th-, and U-based Metal-Organic Frameworks (MOFs) by Density Functional Theory

Metal-organic frameworks (MOFs) have recently gained wide interest as candidate materials for nuclear waste immobilization. While the fundamental thermodynamic properties, such as the substitution energies determine the favorability of radionuclide sequestration by utilization of a MOF matrix, the studies of MOF electronic structure reveal the role of d-, and/or f-electrons on changes in physical properties of actinide-containing materials. We use density functional theory (DFT) calculations to investigate the electronic structures of Zr-, Th-, and U-MOFs, including their electronic band structures and, where appropriate, their magnetic properties. We employ various DFT methods including DFT+U, collinear spin-polarization, spin-orbit coupling, and different flavors of exchange-correlation functionals to assess the robustness to the specific exchange-correlation functional. Unlike the Zr-, and Th-MOFs, the U-MOF is found to be sensitive to electron localization and spin; hence we explore the magnetic structure of the U-MOF in further detail.

Metal-Organic Framework, MOF, Actinides, DFT↗
DOE OSTI2020

Scalable implementation of polynomial filtering for density functional theory calculation in PARSEC

In this work, we present an efficient implementation of polynomial filtering methods in PARSEC, a real-space pseudopotential based Kohn–Sham density functional theory solver. The implementation described here improves upon a Chebyshev-filtered subspace iteration algorithm used in the previous version of PARSEC. We present a hybrid polynomial filtering scheme that combines Chebyshev-filtered subspace iteration and a spectrum slicing method that partitions the spectrum into several spectral slices and uses bandpass-filtered subspace iteration to compute approximate eigenpairs within each interior slice simultaneously. We describe a procedure to partition a spectrum and construct polynomial filters. We also discuss a number of practical issues such as the use of appropriate data layouts for carrying out the computation on a two-dimensional process grid and how to achieve good load balance by allocating an appropriate number of process groups to each spectral slice. Numerical examples are presented to demonstrate the effectiveness of the hybrid polynomial filtering method as well as the superior parallel scalability of spectrum slicing in comparison to that of Chebyshev-filtered subspace iteration.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Q-POP-Thermo: A general-purpose thermodynamics solver for ferroelectric materials

We report that Q-POP-Thermo is a program designed to compute thermodynamic monodomain equilibrium states and their properties for ferroelectric single crystals and thin films based on the Landau-Ginzburg-Devonshire (LGD) Theory. Utilizing symbolic manipulation with the SymPy Library, the governing equations along with appropriate boundary conditions are solved for speedy minimization of the free energy of a crystal. Utilizing the popular Differential Evolution algorithm, with appropriate hybridization, multiple phase diagrams, such as the pressure-temperature phase diagram for bulk single crystals and the common strain-temperature phase diagram for monodomain thin-film systems can be readily generated. Furthermore, a variety of material properties of stable ferroelectric phases, including dielectric, piezoelectric, and electrocaloric properties, can simultaneously be calculated. Validation studies are presented for both thin-film and single crystal systems to test the effectiveness and capability of the open-source program.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

An OpenMP GPU-offload implementation of a non-equilibrium solidification cellular automata model for additive manufacturing

Here, in this paper, performance strategies on GPU-based HPC platforms of a cellular automata (CA) simulation code for non-equilibrium solidification, including nucleation, grain growth, solute partitioning and transport for the metal additive manufacturing (AM) process are investigated using OpenMP 4.5. To accurately report the speed-up for multicore CPUs and GPUs, a rigorous performance analysis employed optimizations appropriate for both CPU-only code (baseline) and GPU offload codes for an isothermal test problem. The performance results on Summit at the Oak Ridge Leadership Computing Facility indicate that using a precomputed list of interface cells significantly decreased the wall-clock time on GPUs. The speedup due to GPU acceleration was evaluated for a full Summit node and measured to be 1.8X when comparing a 6 MPI tasks run with 6 GPUs versus 36 MPI tasks on the CPU only. That speed-up was found to be 7.9X when comparing 6 MPI tasks with 6 GPUs versus the 6 MPI tasks running on the CPU only. Performance measurements showed that system total time is almost constant for runs with more than 96 MPI tasks (or GPUs), indicating that the GPU-accelerated code showed an excellent weak scaling performance. Finally, a rapid directional solidification problem was considered to demonstrate the CA code capability on Summit. It was found that a mesh size of at least 0.05 μm is recommended for the AM-like simulations in order to obtain accurate elongated grain microstructure and elongated subgrain features, which are in qualitative good agreement with experimental data. The results presented in this study indicate that the performance strategies on GPU-based HPC platforms for the CA code are appropriate for novel HPC exascale platforms.

36 MATERIALS SCIENCE↗