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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

New evaluation on angular distributions and energy spectra for neutron-induced charged-particle measurements

Recent development on neutron-induced charged particle reaction measurements can provide high-quality double differential cross sections at Los Alamos Neutron Science Center. With newly measured cross section data, we found lacking nuclear outputs for neutron-induced charged particle reactions in the latest ENDF/B-VIII.0 library, when compared with Monte Carlo simulations. Here, this paper presents how we improved the missing angular distributions and energy spectra for these reactions and the validation of our new evaluations with the measurements performed with the LENZ instrument at LANSCE. The impact of this additional evaluation is demonstrated by comparing the MCNP simulations with the LENZ experimental data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Ion Transport in 2D Nanostructured $\pi$-Conjugated Thieno[3,2- b ]thiophene-Based Liquid Crystal

Leveraging the self-assembling behavior of liquid crystals designed for controlling ion transport is of both fundamental and technological significance. Here, we have designed and prepared a liquid crystal that contains (2,5-bis(5-(2,5,8,11-tetraoxatridecan-13-yl)thiophen-2yl)thieno[3,2-b]thiophene (BTTT) as mesogenic core and conjugated segment and symmetric tetra(ethylene oxide) (EO4) as polar side chains for ionic conducting regions. Driven by the crystallization of the BTTT cores, BTTT/dEO4 exhibits well-ordered smectic phases below 71.5 °C as confirmed by differential scanning calorimetry, polarized optical microscope, temperature dependent wide-angle X-ray scattering and grazing incidence wide-angle X-ray scattering (GIWAXS). We adopted a combination of experimental GIWAXS and molecular dynamics (MD) simulations to better understand the molecular packing of BTTT/dEO4 films, particularly when loaded with the ion conducting salt, lithium bis(trifluoromethanesulfonyl)imide (LiTFSI). Ionic conduction of BTTT/dEO4 is realized by the addition of LiTFSI, with the material able to maintain smectic phases up to r = [Li + ]/[EO] = 0.1. In this work, The highest ionic conductivity of 8 × 10 -3 S/cm was attained at an intermedium salt concentration of r = 0.05. It was also found that ion conduction in BTTT/dEO4 is enhanced by forming a smectic layered structure with irregular interfaces between the BTTT and EO4 layers and by the lateral film expansion upon salt addition. This can be explained by the enhancement of the misalignment and configurational entropy of the side chains, which increase their local mobility and that of the solvated ions. Our molecular design thus illustrates how, beyond the favorable energetic interactions that drive the assembly and ion solvating domains, modulation of entropic effects can also be favorably harnessed to improve ion conduction.

thin film electrochemical impedance spectroscopy↗
DOE OSTI2025

Mass-Energy Compensation Effect of 3$\alpha$ Hamiltonian

The 3α phenomenological model describes the structure of the carbon-12 nucleus as a cluster of three alpha particles. This model includes a pairwise α–α interaction and a three-body force. To fit the three-body potential, the 12 C data are used, while ensuring that the pair potential reproduces the α–α scattering data. Alternatively, the mass-energy compensation (MEC) effect can be used to simulate the effect of the three-body potential by adjusting the mass of the α particle within the effective-mass approach. We demonstrate the MEC effect for the 3α ground state by numerically solving the differential Faddeev equation, in which the α–α interaction is described by the Ali-Bodmer potential. The effective masses of α particles are evaluated for the ground and excited 0 + and bound 2 + states. Here, we demonstrate a coupling between the ground and first excited 0 + states, indicated by an anti-crossing of these energy levels in the energy–mass coordinates. A correspondence between the effective mass and a three-body potential is demonstrated. We discuss the results of the 0$^{+}_{2}$ calculations for various models of the α–α interaction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Robust Optimal Experimental Design of Infinite-Dimensional Bayesian Nonlinear Inverse Problems

Abstract. We consider robust optimal experimental design (ROED) for nonlinear Bayesian inverse problems governed by partial differential equations (PDEs). An optimal design is one that maximizes some utility quantifying the quality of the solution of an inverse problem. However, the optimal design is dependent on elements of the inverse problem such as the simulation model, the prior, or the measurement error model. ROED aims to produce an optimal design that is aware of the additional uncertainties encoded in the inverse problem and remains optimal even after variations in them. We follow a worst-case scenario approach to develop a new framework for robust optimal design of nonlinear Bayesian inverse problems. The proposed framework (a) is scalable and designed for infinite-dimensional Bayesian nonlinear inverse problems constrained by PDEs; (b) develops efficient approximations of the utility, namely the expected information gain; (c) employs eigenvalue sensitivity techniques to develop analytical forms and efficient evaluation methods of the gradient of the utility with respect to the uncertainties against which we wish to be robust; and (d) employs a probabilistic optimization paradigm that properly defines and efficiently solves the resulting combinatorial max-min optimization problem. The effectiveness of the proposed approach is illustrated for optimal sensor placement problem in an inverse problem governed by an elliptic PDE.

Chowdhary, Abhijit↗
DOE OSTI2025

Application of physics-informed neural networks (PINNs) solution to coupled thermal and hydraulic processes in silty sands

Abstract The accurate modeling of water and heat transport in soils is crucial for both geo-environmental and geothermal engineering. Traditional modeling methods are problematic because they require well-defined boundaries and initial conditions. Recently, physics-informed neural networks (PINNs), which incorporate partial differential equations (PDEs) to solve forward and inverse problems, have attracted increasing attention in machine learning research. In this study, we applied PINNs to tackle hydraulic and thermal transport coupling forward problems in silty sands. A fully connected deep neural network was utilized for training. This neural network model leverages automatic differentiation to apply the governing equations as constraints, based on the mathematical approximations established by the neural network itself. We conducted forward problems and compared the solutions derived from PINNs with those from Finite Element Method (FEM) simulations. The forward problem results demonstrate the PINNs model’s capability in predicting hydraulic transport, heat transport, and thermal–hydraulic coupling in silty sands under various boundary conditions. The PINNs exhibited great performance in simulating the thermal–hydraulic coupling problem. The accuracy of the PINNs solutions shows its potential for simulation in geotechnical engineering.

Feng, Yuan↗
DOE OSTI2022

UAE6 - Wind Tunnel Tests Data - UAE6 - Sequence 4 - Raw Data

Sequence 4: Static Pressure Calibration (P) This test sequence used a downwind, teetered turbine with a 3.4° cone angle. The wind speed ranged from 5 m/s to 25 m/s. Yaw angles of 0° to 30° were achieved. The blade tip pitch was 3°. The rotor rotated at 72 RPM. Blade and probe pressure measurements were collected. A static pitot probe was installed on a boom 2.3 m upwind of the nacelle at hub height and connected to the reference side of the Mensor digital differential pressure transducer through the pneumatic rotary coupling. This provided the reference pressure for all blade-mounted pressure transducers, as opposed to the NASA atmospheric pressure reference that was used for all other test sequences. This simulates the pressure system connections used in field measurements where the static pressure was unknown.

17 WIND ENERGY↗
DOE OSTI2023

Neural network surrogate models for equations of state

Equation of state (EOS) data provide necessary information for accurate multiphysics modeling, which is necessary for fields such as inertial confinement fusion. Here, we suggest a neural network surrogate model of energy and entropy and use thermodynamic relationships to derive other necessary thermodynamic EOS quantities. We incorporate phase information into the model by training a phase classifier and using phase-specific regression models, which improves the modal prediction accuracy. Our model predicts energy values to 1% relative error and entropy to 3.5% relative error in a log-transformed space. Although sound speed predictions require further improvement, the derived pressure values are accurate within 10% relative error. Our results suggest that neural network models can effectively model EOS for inertial confinement fusion simulation applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Reconstruction of beam parameters and betatron radiation spectra measured with a Compton spectrometer

The photon flux resulting from high-energy electron beam interactions with high-field systems, such as those found in the upcoming FACET-II experiments at the SLAC National Accelerator Laboratory, yields deep insight into the electron beam’s underlying dynamics during the interaction. However, extracting this information is an intricate process. To demonstrate how to approach this challenge using modern methods, this paper utilizes simulated data that models plasma wakefield acceleration-derived betatron radiation in experiments to determine reliable methods of reconstructing key beam and beam-plasma interaction properties. For betatron radiation measurements, translating the observed 200⁢ keV to 30⁢ MeV photon double-differential energy-angle spectra obtained from an advanced Compton spectrometer requires testing multiple methods to optimize the pipeline from its response to incident electron beam information. The paper compares maximum likelihood estimation and machine learning to refine the translation of photon spectra into precise electron beam metrics, such as spot size, energy, and emittance, enhancing the understanding of beam behavior within these dense, high-field environments. We also introduce machine learning and the expected maximization algorithm to reconstruct the primary photon spectrum, employing a multilayer neural network for regression analysis of the energy and angle spectra. With appropriate modifications, the advanced methods reproduce relevant incident beam parameters with high accuracy, even for beam sizes in the <10 μ⁢m range. This capacity is critical to understanding intense beam propagation and its optimization in plasma.

Beam code development & simulation techniques↗
DOE OSTI2023

Minor Elements and Solidification Cracking During Laser Powder-Bed Fusion of a High $\gamma ^{\prime }$ CoNi-Base Superalloy

Here, the cracking behavior of a high γ' volume fraction CoNi-base superalloy fabricated via laser powder bed fusion (LPBF) is studied in relation to the content of carbon and boron. Severe cracking occurred with the increase in boron content from 0.08 to 0.16 at. pct (0.015 to 0.029 wt pct), while compositions with 0.1 to 0.36 at. pct C (0.02 to 0.076 wt pct) and 0.08 at. pct B exhibited minimal cracking. Assessment of cracks in the high-boron composition shows a variation in crack density with printing parameters, and alignment of the cracks with the build direction. Scanning electron microscopy (SEM) of the crack surfaces shows evidence of a solidification cracking mode. Differential thermal analysis (DTA) reveals a decreased incipient melting temperature for the high-boron composition, and atom probe tomography (APT) is used to measure the enrichment at grain boundaries, revealing distinct boron segregation. Scheil-Gulliver solidification simulations for the different C and B levels are consistent with the incipient melting behavior observed with DTA. Evaluation of the solidification cracking susceptibility from the simulations allow for comparison of the CoNi alloy behavior to Ni-base superalloys studied for LPBF fabrication and displays how such metrics may aid in the design of new precipitation-strengthened superalloys for additive manufacturing (AM).

36 MATERIALS SCIENCE↗
DOE OSTI2023

Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets

Abstract Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Single-cell technologies such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine cell-type specific gene regulation at unprecedented detail. However, current approaches to infer cell type-specific GRNs are limited in their ability to integrate scRNA-seq and scATAC-seq measurements and to model network dynamics on a cell lineage. To address this challenge, we have developed single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer the GRN for each cell type on a lineage from scRNA-seq and scATAC-seq data. Using simulated and real datasets, we show that scMTNI is a broadly applicable framework for linear and branching lineages that accurately infers GRN dynamics and identifies key regulators of fate transitions for diverse processes such as cellular reprogramming and differentiation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Sparsified Time-dependent PDEs FNO (STFNO) v1.0.0

STFNO (Sparsified Time-dependent PDEs FNO code) is an extension of the popular Fourier Neural Operator (FNO) architecture to the solution of coupled systems of time-dependent partial differential equations. STFNO leverages the sparsified dependencies on the field quantities based on the semi-discretiezed form of the PDEs, enabling significant reduction in the number of model parameters. STFNO has been extensively tested on two fusion simulation codes, NIMROD and GTC, and can be easily tailored to other systems of PDEs.

Rahman, Mustafa [Lawrence Berkeley National Labora↗
DOE OSTI2022

Autoregressive Neural Network for Simulating Open Quantum Systems via a Probabilistic Formulation

The theory of open quantum systems lays the foundation for a substantial part of modern research in quantum science and engineering. Rooted in the dimensionality of their extended Hilbert spaces, the high computational complexity of simulating open quantum systems calls for the development of strategies to approximate their dynamics. In this Letter, we present an approach for tackling open quantum system dynamics. Using an exact probabilistic formulation of quantum physics based on positive operator-valued measure, we compactly represent quantum states with autoregressive neural networks; such networks bring significant algorithmic flexibility due to efficient exact sampling and tractable density. We further introduce the concept of string states to partially restore the symmetry of the autoregressive neural network and improve the description of local correlations. Efficient algorithms have been developed to simulate the dynamics of the Liouvillian superoperator using a forward-backward trapezoid method and find the steady state via a variational formulation. Our approach is benchmarked on prototypical one-dimensional and two-dimensional systems, finding results which closely track the exact solution and achieve higher accuracy than alternative approaches based on using Markov chain Monte Carlo method to sample restricted Boltzmann machines. Our Letter provides general methods for understanding quantum dynamics in various contexts, as well as techniques for solving high-dimensional probabilistic differential equations in classical setups.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Dynamic calibration of differential equations using machine learning, with application to turbulence models

We present a methodology for calibration of parametric ordinary and partial differential equation models, using off-the-shelf software for back-propagation in Neural Networks (NN). As a prototypical example, we consider calibration of a Reynolds-averaged Navier-Stokes (RANS) turbulence closure model, against ground truth data from direct numerical simulations (DNS) of two different turbulent flows. Numerical time integration is represented as a custom NN, where only the RANS model parameters are trainable. A loss function is defined to quantify the mismatch between the NN prediction and the ground truth over a predefined, finite time integration window. This loss function is then minimized using a gradient descent method utilizing the back-propagation algorithm. Furthermore, this dynamic approach to training is to be contrasted with a static approach, wherein a least square regression estimate for parameters is obtained in the limit of an infinitesimal time integration window. In a first test of static and dynamic approaches against ground truth data generated by the model, the former proves to be significantly faster and more accurate than the latter at recovering the parameters. When both calibration approaches are tested against DNS data, for which it is known that the model cannot achieve a perfect fit, the static approach yields a good prediction only for short times, while the dynamic approach results in physical and stable predictions over the entire integration window. After optimization of the dynamic approach for time step, spatial resolution, stability, and physics-based constraints, we obtain a 50% improvement of outcomes over those obtained from the existing, manually calibrated set of parameters, demonstrating the merits of this systematic and automated procedure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Which nuclear data can be validated with LLNL pulsed-sphere experiments?

It is shown here that 14-MeV D+T LLNL pulsed-sphere experiments bring complementary information into the process of validating nuclear data compared to experiments that are traditionally used for this purpose—such as critical assemblies. To be more specific, the 14-MeV D+T LLNL pulsed-sphere neutron-leakage spectra enable to validate scattering and fission nuclear data up to 15 MeV (compared to approximately up to 5 MeV when using criticality experiments) and employ to this end simple compound targets containing only few isotopes. In this work, sensitivity profiles of the spectra to nuclear data are calculated in order to understand in detail which isotopes, observables, and energy ranges of nuclear data contribute significantly to their simulation. These profiles are presented for a few selected spheres containing 16 O, 12 C, 56 Fe, and 239 Pu. It is shown that the neutron-leakage spectra of spheres containing light elements are mostly sensitive to elastic- and inelastic-scattering cross sections on discrete levels and corresponding angular distributions. Spheres of structural materials are sensitive to elastic- and inelastic-scattering cross sections, including scattering on discrete levels and the continuum, and double-differential cross sections. Actinide spheres are also strongly sensitive to the fission observables, in particular to the total-fission neutron spectrum. Thin spheres (in which neutrons experience on average less than one scatter) are mostly sensitive to data near the elastic peak, in the energy range from 12–15 MeV, while thicker ones can be sensitive to data at lower incident-neutron energies due to multiple-scattering effects. This information is brought together with simulations of 71 pulsed-sphere neutron-leakage spectra using the ENDF/B-VII.1 and ENDF/B-VIII.0 nuclear-data libraries. This analysis highlights ENDF/B-VIII.0 data that could be further investigated for potential shortcomings ( 6 Li, 12 C, 16 O, 24-26 Mg, 27 Al, 48 Ti, 56 Fe, and 208 Pb) or are likely reliable ( 1,2 H, 7 Li, 9 Be, 14 N, 235,238 U, and 239 Pu) as indicated by validating with LLNL pulsed-sphere experiments.

14-MeV D+T LLNL pulsed-sphere neutron-leakage spec↗
DOE OSTI2020

Technology Summary DNATRAX

DNATrax (DNA Tagged Reagents for Aerosol eXperiments) was developed to provide a safe simulant for understanding the transport and dispersion of pathogens. Specifically, it was developed as a safe surrogate for the Bacillus anthrasis spore bioagent, since most naturally occurring spores are not safe for public release. As DNA-tagged sugar particles, DNATrax aerosols are safe for public release. Scientists can add unique DNA barcode sequences to the sugar particles to produce a wide variety of test particles that can be easily differentiated, making it possible to release and test them simultaneously. The unique barcodes also eliminate the need to conduct decontamination between tests, dramatically reducing the cost and time to run experiments. Additionally, the surrogate offers a high-sensitivity detection method to understand key public health issues. We conducted both lab-based and largescale field studies using DNATrax to validate its capabilities for use as a pathogen surrogate. We have demonstrated the ability to produce DNATrax in a range of sizes, adjusting the microparticles to simulate different sized pathogens with ability to create unique sequences of DNA bases, enabling them to create a nearly unlimited variety of test particles.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Search for jet quenching effects in high-multiplicity pp collisions at $\sqrt{s}$ = 13 TeV via di-jet acoplanarity

The ALICE Collaboration reports a search for jet quenching effects in high-multiplicity (HM) proton-proton collisions at $\sqrt{s}$ = 13 TeV, using the semi-inclusive azimuthal-difference distribution Δφ of charged-particle jets recoiling from a high transverse momentum (high-p T,trig ) trigger hadron. Jet quenching may broaden the Δφ distribution measured in HM events compared to that in minimum bias (MB) events. The measurement employs a p T,trig -differential observable for data-driven suppression of the contribution of multiple partonic interactions, which is the dominant background. While azimuthal broadening is indeed observed in HM compared to MB events, similar broadening for HM events is observed for simulations based on the PYTHIA 8 Monte Carlo generator, which does not incorporate jet quenching. Detailed analysis of these data and simulations show that the azimuthal broadening is due to bias of the HM selection towards events with multiple jets in the final state. The identification of this bias has implications for all jet quenching searches where selection is made on the event activity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Monte Carlo characterization of the cosmic ray muon flux in shallow subsurface geological repositories intended for disposal of radioactive materials

Recent challenges in monitoring subsurface geological repositories intended for disposal of radioactive materials such as spent nuclear fuel call for new, innovative concepts that are facility independent, cost-effective, passive, and reliable. Once nuclear material is in place at these facilities, reverifying the inventory may no longer be feasible if continuity of knowledge is lost or unavailable to the inspectors. Using cosmic ray muons may present several potential advantages over conventional photon/neutron signatures, and their use in safeguards applications have only received attention in the past decade. However, there have been limited efforts to explore the integration of cosmic ray muons into repository safeguards and study potential gains, risks, and costs. This paper presents a Monte Carlo-based methodology to characterize the cosmic ray muon flux, including muon angular and energy differential distributions at depths representative of subsurface geological repositories. Since there have been limited measurements at these sites and a measurement made in one site is not always transferable to another site, the objective is to develop an efficient simulation method and useful parametrizations to provide a convenient tool for enabling muon simulations at any geological repository site. It is expected these results will provide a better understanding of how muons can be integrated into an existing geological repository safeguards framework.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Constructing the Mechanism of Dinoflagellate Luciferin Bioluminescence Using Computation

Dinoflagellate luciferin bioluminescence is unique since it does not rely on decarboxylation but is poorly understood compared to that of firefly, bacteria, and coelenterata luciferins. Here, in this work, we computationally investigate possible protonation states, stereoisomers, a chemical mechanism, and the dynamics of the bioluminescence intermediate that is responsible for chemiexcitation. Using semiempirical dynamics, time-dependent density functional theory static calculations, and a correlation diagram, we find that the intermediate’s functional group that is likely responsible for chemiexcitation is a 4-member ring, a dioxetanol, that undergoes [2π + 2π] cycloreversion and the biolumiphore is the cleaved structure. The simulated emission spectra and luciferase-dependent absorbance spectra agree with the experimental data, giving support to our proposed mechanism and biolumiphore. We also compute circular dichroism spectra of the intermediate’s four stereoisomers to guide future experiments in differentiating them.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗