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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

MSR Transient Simulation and MSRE Transient Benchmark with SAM and SPECTRA

In recent years, there has been renewed interest in molten salt reactors (MSRs) for their potential advantages compared to reactors that rely on solid fuel. In response to such interest, many methods and codes have been developed to capture the unique features of MSRs. Among them, SPECTRA and SAM are two system analysis codes that have been enhanced to include MSR-specific modeling capabilities, including delayed neutron precursor drift and modified point kinetics equations.This paper discusses the efforts taken to verify and validate these features. A standard MSR system test problem was developed to verify and demonstrate the capability of SPECTRA and SAM on the MSR transient simulation. Sixteen transients were simulated. Further, the results obtained from SPECTRA and SAM show good agreement. The Molten Salt Reactor Experiment transient experiments were reviewed and selected to validate the SPECTRA and SAM codes. The experiments included pump startup and coastdown tests at zero power, reactivity insertion tests at different power levels, frequency tests, and a natural convection test. The simulation results from SPECTRA and SAM show good agreement with the experimental data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Modeling the Electronic Absorption Spectra of the Indocarbocyanine Cy3

Accurate modeling of optical spectra requires careful treatment of the molecular structures and vibronic, environmental, and thermal contributions. The accuracy of the computational methods used to simulate absorption spectra is limited by their ability to account for all the factors that affect the spectral shapes and energetics. The ensemble-based approaches are widely used to model the absorption spectra of molecules in the condensed-phase, and their performance is system dependent. The Franck–Condon approach is suitable for simulating high resolution spectra of rigid systems, and its accuracy is limited mainly by the harmonic approximation. In this work, the absorption spectrum of the widely used cyanine Cy3 is simulated using the ensemble approach via classical and quantum sampling, as well as, the Franck–Condon approach. The factors limiting the ensemble approaches, including the sampling and force field effects, are tested, while the vertical and adiabatic harmonic approximations of the Franck–Condon approach are also systematically examined. Our results show that all the vertical methods, including the ensemble approach, are not suitable to model the absorption spectrum of Cy3, and recommend the adiabatic methods as suitable approaches for the modeling of spectra with strong vibronic contributions. We find that the thermal effects, the low frequency modes, and the simultaneous vibrational excitations have prominent contributions to the Cy3 spectrum. The inclusion of the solvent stabilizes the energetics significantly, while its negligible effect on the spectral shapes aligns well with the experimental observations.

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

A Significant Increase in Detection of High-resolution Emission Spectra Using a Three-dimensional Atmospheric Model of a Hot Jupiter

High-resolution spectroscopy has opened the way for new, detailed study of exoplanet atmospheres. There is evidence that this technique can be sensitive to the complex, three-dimensional (3D) atmospheric structure of these planets. In this work, we perform cross-correlation analysis of high-resolution (R ∼ 100,000) CRIRES/VLT emission spectra of the hot Jupiter HD 209458b. We generate template emission spectra from a 3D atmospheric circulation model of the planet, accounting for temperature structure and atmospheric motions—winds and planetary rotation—missed by spectra calculated from one-dimensional models. In this first-of-its-kind analysis, we find that using template spectra generated from a 3D model produces a more significant detection (6.9σ) of the planet’s signal than any of the hundreds of one-dimensional models we tested (maximum of 5.1σ). We recover the planet’s thermal emission, its orbital motion, and the presence of CO in its atmosphere at high significance. Additionally, we analyzed the relative influences of 3D temperature and chemical structures in this improved detection, including the contributions from CO and H{sub 2}O, as well as the role of atmospheric Doppler signatures from winds and rotation. This work shows that the hot Jupiter’s 3D atmospheric structure has a first-order influence on its emission spectra at high resolution and motivates the use of multidimensional atmospheric models in high-resolution spectral analysis.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Computational tools for the simulation and analysis of spin-polarized EPR spectra

The EPR spectra of paramagnetic species induced by photoexcitation typically exhibit enhanced absorptive and emissive features resulting from sublevel populations that differ from thermal equilibrium. The populations and the resulting spin polarization of the spectra are dictated by the selectivity of the photophysical process generating the observed state. Simulation of the spin-polarized EPR spectra is crucial in the characterization of both the dynamics of formation of the photoexcited state as well as its electronic and structural properties. EasySpin, the simulation toolbox for EPR spectroscopy, now includes extended support for the simulation of the EPR spectra of spin-polarized states of arbitrary spin multiplicity and formed by a variety of different mechanisms, including photoexcited triplet states populated by intersystem crossing, charge recombination or spin polarization transfer, spin-correlated radical pairs created by photoinduced electron transfer, triplet pairs formed by singlet fission and multiplet states arising from photoexcitation in systems containing chromophores and stable radicals. In this paper, we highlight EasySpin’s capabilities for the simulation of spin-polarized EPR spectra on the basis of illustrative examples from the literature in a variety of fields ranging across chemistry, biology, material science and quantum information science.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Beta-delayed gamma spectra compilation and analysis following the thermal-neutron induced fission of 235 U, 239,241 Pu

The integral gamma and electron spectra emitted by fission products, also known as delayed gamma and electron spectra, were measured at Oak Ridge National Laboratory in the 1970s for the thermal-neutron induced fission of 235 U and 239,241 Pu. Scintillator detectors were used to measure these spectra, data used later on to obtain decay heat values - that is, the spectra mean values per unit time as function of time - work that was published in the Nuclear Science and Technology journal; the spectral data, however, was only published in laboratory reports. Here, in this work, we analyze the gamma spectra data using modern methods and nuclear databases to reveal the signature of individual fission products as well as to gauge the performance of the ENDF/B-VIII.0 decay data sub-library, concluding about possible future enhancements in predictive capabilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Machine learning framework for predicting uranium enrichments from M400 CZT gamma spectra

A machine learning framework was developed for predicting uranium enrichments from M400 CZT gamma spectra. This framework leverages the availability of a large amount of measured M400 gamma spectra and uses a recently updated version of Gamma Detector Response and Analysis Software (GADRAS) for gamma spectrum analysis and generation. It also leverages the existing machine learning modules in Python for gamma spectrum data processing, curation, model training, benchmarking, and optimization of the deep machine learning models. The framework is used to develop a deep learning model to analyze gamma spectra from a set of U 3 O 8 samples with enrichments ranging from 0.31 to 93.17% and UF 6 cylinders with enrichments ranging from 0.2 to 4.95%, and the model performance is tested using a set of measured spectra and the respective declared enrichment values. Results show that the model can correctly classify 99.35% of the U 3 O 8 sample enrichments, and can predict the samples’ enrichments within an average absolute error of 0.099% (in percentage points of enrichment). For the UF 6 cylinders, the average absolute error was approximately 0.03%, with an accuracy of 98% in classifying discrete enrichment values of UF 6 samples. Finally, the results also show that the model has performed significantly better in terms of predicting enrichments in UF 6 cylinders based on measured gamma spectra than the GEM code, with a standard deviation (of the relative errors) of 2.23% (compared with the 11.51% value for the GEM code) based on results from a set of test data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

A practical method for modeling temporally-averaged ocean wave frequency-directional spectra for characterizing wave energy climates

Wave energy resource characterization investigations have been hindered because frequency-directional wave spectra are not broadly available for many coastal regions due to the insufficient spatial coverage of buoy observation networks and spectral wave model hindcast outputs. We address this problem by developing and validating a practical method for approximating temporally-averaged frequency-directional wave spectra averaged over periods of months or years from bulk wave parameters, which are available at a much greater coverage and resolution than frequency-directional wave spectra. While the temporally averaged frequency-directional wave spectrum over these periods cannot be used for analyzing a single sea state, it aggregates multiple sea states, identifies dominant wave energy systems representing wave climate, and resolves their spectral characteristics. Therefore, modelling temporally averaged frequency-directional wave spectra is of great value for planning and designing wave energy projects, e.g., resource characterization, site assessment, and conceptual design of wave energy converters. Temporally-averaged frequency-directional wave spectra and related important wave energy parameters approximated using this method are found to be more accurate than commonly used parametric wave spectrum models. This method can be applied to a wide range of wave climates given its universality and high accuracy. Also, the availability of bulk wave parameters from multi-decade high-resolution wave model hindcasts is increasing.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Quantifying Temperature Dependence of Pu(IV) Absorbance Spectra for Advanced Online Monitoring of Nuclear Processes

This article presents a systematic study of Pu(IV) absorbance spectral features as a function of temperature to develop an understanding of this parameter’s effect on chemometric models that can be used as online monitoring tools to support nuclear processing. The descriptive and predictive models that provide real-time feedback of these processes are usually constructed with data collected in conditions typical of a laboratory environment, which can differ drastically from a processing environment. To assess the impact of temperature on Pu(IV) absorbance spectra, 11 samples of Pu(IV) were synthesized with varying HNO 3 concentrations ranging from 0.6 to 9.5 M and heated between 15 and 45 °C. Ultraviolet (UV)–visible (vis)–near-infrared (NIR) absorption spectra collected at different HNO 3 concentrations and temperatures revealed that features associated with Pu(IV) are sensitive to temperature at all HNO 3 concentrations and that changes in features depend on HNO 3 concentration. The contributions of temperature and HNO 3 concentration to variation in Pu(IV) spectral features were evaluated using the principal component analysis of spectra that were baseline-corrected with an asymmetric least-squares method. Furthermore, predictive modeling for HNO 3 concentration with partial least-squares regression of UV–vis–NIR spectra highlighted the importance of accounting for temperature in the calibration set to optimize model performance. This methodology constitutes a new, systematic approach to account for the effect of temperature on the absorption spectra of metal ions and is useful for process monitoring applications in many industries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Machine Learning Models for Predicting Molecular UV–Vis Spectra with Quantum Mechanical Properties

Accurate understanding of Ultraviolet–visible (UV–Vis) spectra is critical for highthroughput design of compounds for drug discovery. Experimentally determining UV–Vis spectra can become expensive when dealing with a large quantity of novel molecules. This provides us an opportunity to drive computational advances in molecular property predictions using quantum mechanics and machine learning. In this work, we use both Quantum Mechanically (QM) predicted and measured UV–Vis spectra as input to modify four different machine learning architectures: UVvis-SchNet, UVvis- DTNN, UVvis-Transformer, and UVvis-MPNN. Here we find that the UVvis-MPNN model outperforms the other models when using optimized 3D coordinates and QM predicted spectra as input features. This model has the highest performance for predicting UVVisible spectra with a training RMSE of 0.06 and validation RMSE of 0.08.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Peak2Patch: High-Fidelity Functional Group Identification through Attention-Based Fusion of Infrared and Mass Spectra

Identifying molecular structure based on spectroscopic readings is a key task in a variety of chemical and biological applications. Common spectroscopy techniques, such as Infrared (IR) Spectroscopy and Mass Spectrometry (MS), provide detailed information on the structure of molecular compounds but nonetheless require expert-level knowledge to decode. Machine learning has emerged as a potential solution for automating structure prediction from chemical spectra; however, current approaches generally focus on single sensor modalities, neglecting to leverage the complementary information contained within differing spectra. In this paper, we introduce Peak2Patch, a novel approach to fusion-enhanced prediction of functional groups from IR and mass spectra. First, we perform a detailed comparison of backbone networks for encoding both sparse mass spectra and dense IR spectra and demonstrate the superior performance of transformer neural networks over current state-of-the-art convolutional neural networks. Second, we evaluate three broad categories of fusion: early (raw feature), middle (deep feature), and late (decision) fusion, demonstrating the potential of a deep feature fusion-based approach. Lastly, we present Peak2Patch, our attention-based fusion scheme, which leverages cross-attention to mix features between encoded tokens of the two modalities. We validate our approach on a publicly available multimodal spectroscopic data set of 790k simulated molecules, demonstrating a large improvement in functional group prediction over both the previous state-of-the-art and our own strong single-modal baselines.

Jacobson, Philip [Sandia National Laboratories (SN↗
DOE OSTI2024

First Time-Resolved Leader Spectra Associated With a Downward Terrestrial Gamma-Ray Flash Detected at the Telescope Array Surface Detector

Optical emissions associated with Terrestrial Gamma ray Flashes (TGFs) have recently become important subjects in space-based and ground-based observations as they can help us understand how TGFs are produced during thunderstorms. In this paper, we present the first time-resolved leader spectra of the optical component associated with a downward TGF. The TGF was observed by the Telescope Array Surface Detector (TASD) simultaneously with other lightning detectors, including a Lightning Mapping Array (LMA), an INTerFerometer (INTF), a Fast Antenna (FA), and a spectroscopic system. The spectroscopic system recorded leader spectra at 29,900 frames per second (33.44 μs time resolution), covering a spectral range from 400 to 900 nm, with 2.1 nm per pixel. The recordings of the leader spectra began 11.7 ms before the -18 kA return stroke and at a height of 2.37 km above the ground. These spectra reveal that optical emissions of singly ionized nitrogen and oxygen occur between 167 μs before and 267 μs after the TGF detection, while optical emissions of neutrals (H I, 656 nm; N I, 744 nm, and O I, 777 nm) occur right at the moment of the detection. The time-dependent spectra reveal differences in the optical emissions of lightning leaders with and without downward TGFs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Rapid water dynamics structures the OH-stretching spectra of solitary water in ionic liquids and dipolar solvents

In a recent study [J. Phys. Chem. B 126, 4584–4598 (2022)], we have used infrared spectroscopy to investigate the solvation and dynamics of solitary water in ionic liquids and dipolar solvents. Complex shapes observed for water OH-stretching bands, common to all high-polarity solvents, were assigned to water in several solvation states. Here, in the present study, classical molecular dynamics simulations of a single water molecule in four ionic liquids and three dipolar solvents were used to test and refine this interpretation. Consistent with past assignments, simulations show solitary water usually donates two hydrogen bonds to distinct solvent molecules. Such symmetrically solvated water produces the primary pair of peaks identified in the OH spectra of water in nearly all solvents. We had further proposed that additional features flanking this main peak are due to asymmetric solvation states, states in which only one OH group makes a hydrogen bond to solvent. Such states were found in significant concentrations in all of the systems simulated. Simulations of the OH stretching spectra using a semiclassical description and the vibrational map developed by Auer and Skinner [J. Chem. Phys. 128, 224511–224512 (2008)] provided semi-quantitative agreement with experiment. Analysis of species-specific spectra confirmed assignment of the additional features in the experimental spectra to asymmetrically solvated water. The simulations also showed that rapid water motions cause a marked motional narrowing compared with the inhomogeneous limit. This narrowing is largely responsible for making the additional features due to minority solvation states manifest in the spectra.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Radiation transfer in the spectra of short-pulse laser-heated targets

The conditions in laser-produced plasmas are frequently determined with x-ray spectroscopy by comparing calculated to measured spectra. Line spectra from K-shell transitions of low- to mid-atomic number elements are most often used since the important physical processes are well understood and reliable spectra can be readily measured and calculated. Radiation transfer effects due to large optical depths of strong lines can influence the spectra. In this work, the effects of radiation transfer on the emission spectra of short-laser pulse-heated targets are studied. The possible errors made in inferring electron temperature by not including radiation transfer are quantified. The inclusion of radiative transfer in spectral calculations improves the accuracy of typical temperature diagnostics and allows the use of strong lines for diagnostics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

The Atacama Cosmology Telescope: delensed power spectra and parameters

We present ΛCDM cosmological parameter constraints obtained from delensed microwave background power spectra. Lensing maps from a subset of DR4 data from the Atacama Cosmology Telescope (ACT) are used to undo the lensing effect in ACT spectra observed at 150 and 98 GHz. At 150 GHz, we remove the lensing distortion with an effective efficiency of 30% (TT), 30% (EE), 26% (TE) and 20% (BB); this results in detections of the delensing effect at 8.7σ (TT), 5.1σ (EE), 2.6σ (TE), and 2.4σ (BB) significance. The combination of 150 and 98 GHz TT, EE, and TE delensed spectra is well fit by a standard ΛCDM model. We also measure the shift in best-fit parameters when fitting delensed versus lensed spectra; while this shift does not inform our ability to measure cosmological parameters, it does provide a three-way consistency check among the lensing inferred from the best-fit parameters, the lensing in the CMB power spectrum, and the reconstructed lensing map. This shift is predicted to be zero when fitting with the correct model since both lensed and delensed spectra originate from the same region of sky. Furthermore, fitting with a ΛCDM model and marginalizing over foregrounds, we find that the shift in cosmological parameters is consistent with zero. Our results show that gravitational lensing of the microwave background is internally consistent within the framework of the standard cosmological model.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Weak lensing trispectrum and Kurt-spectra

We introduce two kurt-spectra to probe fourth-order statistics of weak lensing convergence maps. Using state-of-the-art numerical simulations, we study the shapes of these kurt-spectra as a function of source redshifts and smoothing angular scales. We employ a pseudo-Cℓ approach to estimate the spectra from realistic convergence maps in the presence of an observational mask and noise for stage-IV large-scale structure surveys. We compare these results against theoretical predictions calculated using the FFTLog formalism, and find that a simple nonlinear clustering model — the hierarchical ansatz — can reproduce the numerical trends for the kurt-spectra in the nonlinear regime. In addition, we provide estimators for beyond fourth-order spectra where no definitive analytical results are available, and present corresponding results from numerical simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Using active learning to improve quasar identification for the DESI spectra processing pipeline

The Dark Energy Spectroscopic Instrument (DESI) survey uses an automatic spectral classification pipeline to classify spectra. QuasarNET is a convolutional neural network used as part of this pipeline originally trained using data from the Baryon Oscillation Spectroscopic Survey (BOSS). In this paper we implement an active learning algorithm to optimally select spectra to use for training a new version of the QuasarNET weights file using only DESI data, with the goal of improving classification accuracy. This active learning algorithm includes a novel outlier rejection step using a Self-Organizing Map to ensure we label spectra representative of the larger quasar sample observed in DESI. We perform two iterations of the active learning pipeline, assembling a final dataset of 5600 labeled spectra, a small subset of the approximately 1.3 million quasar targets in DESI's Data Release 1. When splitting the spectra into training and validation subsets we achieve similar performance to the previously trained weights file in completeness and purity calculated on the validation dataset but do so with less than one tenth of the amount of training data. The new weights also more consistently classify objects in the same way when used on unlabeled data compared to the old weights file. In the process of improving QuasarNET's classification accuracy we discovered a systemic error in QuasarNET's redshift estimation and used our findings to improve our understanding of QuasarNET's redshifts.

Machine learning↗
DOE OSTI2023

Direct prediction of inelastic neutron scattering spectra from the crystal structure*

Abstract Inelastic neutron scattering (INS) is a powerful technique to study vibrational dynamics of materials with several unique advantages. However, analysis and interpretation of INS spectra often require advanced modeling that needs specialized computing resources and relevant expertise. This difficulty is compounded by the limited experimental resources available to perform INS measurements. In this work, we develop a machine-learning based predictive framework which is capable of directly predicting both one-dimensional INS spectra and two-dimensional INS spectra with additional momentum resolution. By integrating symmetry-aware neural networks with autoencoders, and using a large scale synthetic INS database, high-dimensional spectral data are compressed into a latent-space representation, and a high-quality spectra prediction is achieved by using only atomic coordinates as input. Our work offers an efficient approach to predict complex multi-dimensional neutron spectra directly from simple input; it allows for improved efficiency in using the limited INS measurement resources, and sheds light on building structure-property relationships in a variety of on-the-fly experimental data analysis scenarios.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Influence of Disorder and State Filling on Charge-Transfer-State Absorption and Emission Spectra

We conduct comprehensive temperature-dependent measurements of the charge-transfer-(CT) state photocurrent and emission spectra for two organic small molecule donor:fullerene (C 60 ) acceptor bulk heterojunction solar cells. We reveal that the CT spectral width and position are affected by static energetic disorder in the blend, especially evident at low temperatures. The relative contributions of the static and dynamic disorder broadening in the CT spectra are effectively extracted through consideration of a Gaussian CT energetic distribution. However, electroluminescence (EL) spectra can only be interpreted when injected carriers reach thermal equilibrium sites within the disordered density of states and emission occurs from the lowest possible CT energy. For the blend with the smaller energetic disorder, this is the case near room temperature; for the other blend with larger static disorder, carriers fail to reach thermal equilibrium sites even at room temperature and EL spectra need to be interpreted with care. For example, in the latter case, the effect of energetic disorder might not be apparent from EL spectra because the lowest energy sites are not participating. Nonetheless, these states contribute to the photocurrent generation-recombination and energy-loss processes and thus demand accurate characterization, which we show is feasible through temperature-dependent external quantum-efficiency measurements.

14 SOLAR ENERGY↗