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DOE OSTI2026

Neutrinos and Gamma Rays from Seyfert Galaxies Constrain the Properties of Coronal Turbulence

The TeV neutrino signal observed by IceCube from the active galactic nucleus NGC 1068 can probe its innermost coronal regions. If these neutrinos originate from hadrons accelerated within a magnetized turbulent corona, their intensity and spectrum depend on the turbulent magnetic field strength and turbulence coherence scale. The gamma rays accompanying neutrino production are absorbed in this optically thick environment, in a way that depends sensitively on the size of the corona. By a joint fit of the IceCube and Fermi-LAT observations, we translate the multimessenger signal from NGC 1068 and the tentative signal from NGC 7469 into quantitative constraints on coronal properties. NGC 1068, with a significant TeV neutrino excess, favors a compact, strongly magnetized corona with a large turbulence coherence length relative to the coronal size. NGC 7469, with two ∼100 TeV neutrino events, points instead to a somewhat larger corona with much smaller coherence length and high magnetization, but a very small fraction of energy in nonthermal protons. We obtain the diffuse flux from a population of Seyfert galaxies identical to either NGC 1068 or NGC 7469. Finally, we consider a third scenario, motivated by the spectral break observed in the diffuse neutrino flux at tens of TeV, with coronal properties intermediate between the two point-source-inspired models. To enable detailed comparisons with the IceCube and electromagnetic observations, we release our model predictions in a GitHub repository.

Testagrossa, Federico [Deutsches Elektronen-Synchr↗

DOE OSTI2021

Living on the edge: A continental-scale assessment of forest vulnerability to drought

Globally, forests are facing an increasing risk of mass tree mortality events associated with extreme droughts and higher temperatures. Hydraulic dysfunction is considered a key mechanism of drought-triggered dieback. By leveraging the climate breadth of the Australian landscape and a national network of research sites (Terrestrial Ecosystem Research Network), we conducted a continental-scale study of physiological and hydraulic traits of 33 native tree species from contrasting environments to disentangle the complexities of plant response to drought across communities. Here, we found strong relationships between key plant hydraulic traits and site aridity. Leaf turgor loss point and xylem embolism resistance were correlated with minimum water potential experienced by each species. Across the data set, there was a strong coordination between hydraulic traits, including those linked to hydraulic safety, stomatal regulation and the cost of carbon investment into woody tissue. These results illustrate that aridity has acted as a strong selective pressure, shaping hydraulic traits of tree species across the Australian landscape. Hydraulic safety margins were constrained across sites, with species from wetter sites tending to have smaller safety margin compared with species at drier sites, suggesting trees are operating close to their hydraulic thresholds and forest biomes across the spectrum may be susceptible to shifts in climate that result in the intensification of drought.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Stopping to reflect: Asymptotic static moving mirrors as quantum analogs of classical radiation

Radiation from an accelerating charge is a basic process that can serve as an intersection between classical and quantum physics. We present two exactly soluble electron trajectories that permit analysis of the radiation emitted, exploring its time evolution and spectrum by analogy with the moving mirror model of the dynamic Casimir effect. These classical solutions are finite energy, rectilinear (nonperiodic), asymptotically zero velocity worldlines with corresponding quantum analog beta Bogolyubov coefficients. One of them has an interesting connection to uniform acceleration and Leonardo da Vinci's water pitcher experiment.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

On the statistical theory of self-gravitating collisionless dark matter flow: Scale and redshift variation of velocity and density distributions

The statistics of velocity and density fields are crucial for cosmic structure formation and evolution. Here, this paper extends our previous work on the two-point second-order statistics for the velocity field [Phys. Fluids 35, 077105 (2023)] to one-point probability distributions for both density and velocity fields. The scale and redshift variation of density and velocity distributions are studied by a halo-based non-projection approach. First, all particles are divided into halo and out-of-halo particles so that the redshift variation can be studied via generalized kurtosis of distributions for halo and out-of-halo particles, respectively. Second, without projecting particle fields onto a structured grid, the scale variation is analyzed by identifying all particle pairs on different scales $r$. We demonstrate that: (i) Delaunay tessellation can be used to reconstruct the density field. The density correlation, spectrum, and dispersion functions were obtained, modeled, and compared with the N-body simulation; (ii) the velocity distributions are symmetric on both small and large scales and are non-symmetric with a negative skewness on intermediate scales due to the inverse energy cascade on small scales with a constant rate $\varepsilon_u$; (iii) On small scales, the even order moments of pairwise velocity $\Delta u_L$ follow a two-thirds law $\propto{(-\varepsilon_ur)}^{2/3}$, while the odd order moments follow a linear scaling $\langle(\Delta u_L)^{2n+1}\rangle=(2n+1)\langle(\Delta u_L)^{2n}\rangle\langle\Delta u_L\rangle\propto{r}$; (iv) The scale variation of the velocity distributions was studied for longitudinal velocities $u_L$ or $u_L^{'}$, pairwise velocity (velocity difference) $\Delta u_L$=$u_L^{'}$-$u_L$ and velocity sum $\Sigma u_L$=$u^{'}_L$+$u_L$. Fully developed velocity fields are never Gaussian on any scale, despite that they can initially be Gaussian; (v) On small scales, $u_L$ and $\Sigma u_L$ can be modeled by a $X$ distribution to maximize the entropy of the system. The distribution of $\Delta u_L$ can be different; (vi) On large scales, $\Delta u_L$ and $\Sigma u_L$ can be modeled by a logistic or a $X$ distribution, while $u_L$ has a different distribution; (vii) the redshift variation of the velocity distributions follows the evolution of the $X$ distribution involving a shape parameter $\alpha(z)$ decreasing with time.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Expanded Accident Scenarios and Frequency Characteristics for a Fast Spectrum Molten Salt Reactor in an Integrated Energy System

Integration of advanced nuclear reactors to industrial processes in an integrated energy system (IES) has many potential advantages. The coupling of nuclear power to varied industrial processes introduces additional accident scenarios unique to the specific systems included in the IES. Similarly, noise in one system of the IES could potentially create negative effects in the other systems. Because of the limited amount of comparable nuclear IES operating experience, there is added value in scoping the inherent behavior of these systems in response to these transients. Here, this paper investigates the safety-related behavior of an open-source IES dynamic model of a fast spectrum molten salt reactor (MSR) powering a regenerative Rankine cycle for electricity production and a hybrid sulfur (HyS) cycle for hydrogen production. The simulations include a loss of cooling water to the Rankine cycle, a loss of sulfur in the HyS cycle, and the frequency characteristics of the entire IES across six orders of magnitude of frequency. The results show safe behavior in response to the two accident scenarios, wherein a disruption in heat transfer leads to increased salt temperatures and a decrease in reactor power. The frequency analysis shows that at higher frequencies, temperature changes propagating through an IES are increasingly damped to the point of negligibility.

Dunkle, Nicholas [Univ. of Tennessee, Knoxville, T↗

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 OSTI2025

Directional Variations in Tidal Flow Multifractality and Intermittency

We explored distinct directional variations of multifractal and intermittent characteristics of ebb and flood flow velocities at nodule point, WA, tidal energy site, and complementary inspection on the East River, NY using scaling exponents of the structure function, distribution flatness, detrending moving average (DMA) analysis, multifractal detrended fluctuation analysis (MF-DFA), and high-order spectral moments. Our findings reveal that tidal flow presents higher long-range dependence (LRD) and intermittent levels for the ebb flow sections, whereas the flood flow sections exhibit a higher degree of multifractality and greater sensitivity to larger magnitude of turbulent fluctuations. We demonstrate that long-range dependence predominantly contributes to multifractal behavior in both ebb and flood flows, as evidenced by the significantly reduced multifractal spectrum width for temporally randomly permuted time series. Moreover, spectral kurtosis analysis uncovers a higher intermittent level across all frequency scales for ebb flow sections and reveals a distinct pattern of tidal flow intermittency differing from the monotonically increasing intermittent level observed in wall-bounded and grid turbulence. Finally, we demonstrate that DMA, MF-DFA, and high-order spectral moments provide more comprehensive insights than structure function scaling exponents and PDF flatness methods.

Cheng, Shyuan [Univ. of Illinois at Urbana-Champai↗

DOE OSTI2021

A New Sample of Gamma-Ray Emitting Jetted Active Galactic Nuclei—Preliminary Results

We are compiling a new list of gamma-ray jetted active galactic nuclei (AGN), starting from the fourth catalog of point sources of the Fermi Large Area Telescope (LAT). Our aim is to prepare a list of jetted AGN with known redshifts and classifications to be used to calibrate jet power. We searched in the available literature for all the published optical spectra and multiwavelength studies useful to characterize the sources. We found new, missed, or even forgotten information leading to a substantial change in the redshift values and classification of many sources. We present here the preliminary results of this analysis and some statistics based on the gamma-ray sources with right ascension within the interval 0 h --12 h (J2000). Although flat-spectrum radio quasars and BL Lac objects are still the dominant populations, there is a significant increase in the number of other objects, such as misaligned AGN, narrow-line Seyfert 1 galaxies, and Seyfert galaxies. We also introduced two new classes of objects: changing-look AGN and ambiguous sources. About one third of the sources remain unclassified.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Automotive fuel cell stack and system efficiency and fuel consumption based on vehicle testing on a chassis dynamometer at minus 18 °C to positive 35 °C temperatures

In this work we present an in-depth laboratory technology assessment of a 2016 Toyota Mirai Fuel Cell (FC) vehicle based on chassis dynamometer testing. The 114.6 kW FC stack has a high dynamic response, which makes this powertrain a FC-dominant hybrid electric vehicle. The measured peak efficiency is 66.0% FC stack and 63.7% FC system with an idle hydrogen flow rate of 4.39 g/hr. The high FC system efficiencies at low loads match typical vehicle power spectrums, resulting in a high average vehicle efficiency of 62% compared to 45% and 23% for a hybrid electric vehicle and a conventional vehicle, respectively. An energy breakdown accounts for the FC stack losses, FC system losses, air compressor loads, and heater loads for different drive cycles and different thermal conditions. The cold-start North American city drive cycle (UDDS) energy consumption values are, respectively, 758, 581, 226, and 321 Wh/km at ambient conditions of -18 degrees C, -7 degrees C, -25 degrees C and 35 degrees C with 850 W/m 2 of solar loading. The FC system shutdown and startup processes at temperatures below the freezing point contribute to the increased hydrogen consumption. Additionally, the raw test data files are available for download, thus providing the research community with a public reference data on a modern production automotive FC system.

08 HYDROGEN↗

DOE OSTI2020

Global X-Ray Properties of the Vela and Puppis A Supernova Remnants

The Vela and Puppis A supernova remnants (SNRs) comprise a large emission region of ∼8° diameter in the soft X-ray sky. The HaloSat CubeSat mission provides the first soft X-ray (0.4–7 keV) observation of the entire Vela SNR and Puppis A SNR region with a single pointing and moderate spectral resolution. HaloSat observations of the Vela SNR are best fit with a two-temperature thermal plasma model consisting of a cooler component with kT{sub 1}=0.19{sub −0.01}{sup +0.01} keV in collisional ionization equilibrium and a hotter component with kT{sub 2}=1.06{sub −0.27}{sup +0.45} keV in nonequilibrium ionization. Observations of the Puppis A SNR are best fit with a single-component plane-parallel shocked plasma model with kT=0.86{sub −0.05}{sup +0.06} keV in nonequilibrium ionization. For the first time, we find the total X-ray luminosities of both components of the Vela SNR spectrum in the 0.5–7 keV energy band to be L{sub X}=4.4{sub −1.4}{sup +1.4}×10{sup 34} erg s{sup −1} for the cooler component and L{sub X}=4.1{sub −1.5}{sup +1.8}×10{sup 34} erg s{sup −1} for the hotter component. We find the total X-ray luminosities of the Vela and Puppis A SNRs to be L{sub X}=8.4×10{sup 34} erg s{sup −1} and L{sub X}=6.7{sub −0.9}{sup +1.1}×10{sup 36} erg s{sup −1}.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Bayesian inference in band excitation scanning probe microscopy for optimal dynamic model selection in imaging

The universal tendency in scanning probe microscopy (SPM) over the last two decades is to transition from simple 2D imaging to complex detection and spectroscopic imaging modes. The emergence of complex SPM engines brings forth the challenge of reliable data interpretation, i.e., conversion from detected signals to descriptors specific to tip–surface interactions and subsequently to material’s properties. In this work, we implemented a Bayesian inference approach for the analysis of the image formation mechanisms in band excitation SPM. Compared to the point estimates in classical functional fit approaches, Bayesian inference allows for the incorporation of extant knowledge of materials and probe behavior in the form of corresponding prior distribution and return the information on the material functionality in the form of readily interpretable posterior distributions. We explore the nonlinear mechanical behaviors spatially in a classical ferroelectric material, PbTiO 3 . We observe the non-trivial evolution of the Duffing stiffness term and the nonlinearity of the sample surface, determine spatial clustering of the nonlinear response, and perform a Landau analysis on predicting the nonlinear coefficient, which indicates that ferroelectric behavior can be a cause of the observed results. These observations suggest that the spectrum of anomalous behaviors at the ferroelectric domain walls may be broader than previously believed and can extend to non-conventional mechanical properties in addition to static and microwave conductance.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Solving quasi-free and quadratic Lindblad master equations for open fermionic and bosonic systems

The dynamics of Markovian open quantum systems are described by Lindblad master equations. For fermionic and bosonic systems that are quasi-free, i.e. with Hamiltonians that are quadratic in the ladder operators and Lindblad operators that are linear in the ladder operators, we derive the equation of motion for the covariance matrix. This determines the evolution of Gaussian initial states and the steady states, which are also Gaussian. Using ladder super-operators (a.k.a. third quantization), here we show how the Liouvillian can be transformed to a many-body Jordan normal form which also reveals the full many-body spectrum. Extending previous work by Prosen and Seligman, we treat fermionic and bosonic systems on equal footing with Majorana operators, shorten and complete some derivations, also address the odd-parity sector for fermions, give a criterion for the existence of bosonic steady states, cover non-diagonalizable Liouvillians also for bosons, and include quadratic systems. In extension of the quasi-free open systems, quadratic open systems comprise additional Hermitian Lindblad operators that are quadratic in the ladder operators. While Gaussian states may then evolve into non-Gaussian states, the Liouvillian can still be transformed to a useful block-triangular form, and the equations of motion for k-point Green’s functions form a closed hierarchy. Based on this formalism, results on criticality and dissipative phase transitions in such models are discussed in a companion paper.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Validation and Verification of Python based Neutron Spectrum Unfolding Software

To validate and verify the python-based code (PySL), designed to replicate the programs used by STAYSL for Beam Correction Factor (BCF) and Self-Shielding Factor (SHIELD), a series of tests were performed. To test BCF a python script was written to generate a random flux history file and both versions of the code processed the data. The test verified matching values up to at least one decimal place, approximately 10,000 tests where run and each one passed. Isotopes began to fail the tests once neutron saturation was reached. To verify this the total time of exposure was varied the isotopes that failed were compared to a list of their half-lives. The test process for SHIELD was very similar but, in this case, the code began by producing an input file with varying thickness and device type/environment for the SHIELD input. The failure condition for this test was if any of the data points for an isotope had a difference above 3%. Approximately 40 of these tests were run and there were only 3 isotopes that had reoccurring failures but only 2% of their points were above the 3% difference. A visual comparison was conducted by plotting the results from both programs. Although the test failed, the differences between their values were minuscule, and the self-shielding factor’s shape was preserved when plotted. Next steps for this project will be validating and verifying the python-based SigPhi code and then reproducing and testing the least squares unfolding performed by STAYSL.

73 - NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Development of Masitinib Derivatives with Enhanced Mpro Ligand Efficiency and Reduced Cytotoxicity

Recently, a high-throughput screen of 1900 clinically used drugs identified masitinib, an orally bioavailable tyrosine kinase inhibitor, as a potential treatment for COVID-19. Masitinib acts as a broad-spectrum inhibitor for human coronaviruses, including SARS-CoV-2 and several of its variants. In this work, we rely on atomistic molecular dynamics simulations with advanced sampling methods to develop a deeper understanding of masitinib’s mechanism of M pro inhibition. To improve the inhibitory efficiency and to increase the ligand selectivity for the viral target, we determined the minimal portion of the molecule (fragment) that is responsible for most of the interactions that arise within the masitinib-Mpro complex. We found that masitinib forms highly stable and specific H-bond interactions with Mpro through its pyridine and aminothiazole rings. Importantly, the interaction with His 163 is a key anchoring point of the inhibitor, and its perturbation leads to ligand unbinding within nanoseconds. Based on these observations, a small library of rationally designed masitinib derivatives (M1–M5) was proposed. Our results show increased inhibitory efficiency and highly reduced cytotoxicity for the M3 and M4 derivatives compared to masitinib.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Radial oscillations of viscous neutron stars: Zero diffusion case

In this article, the spectrum of radial oscillations of neutron stars is systematically studied within two frameworks of viscous relativistic hydrodynamics: the relativistic Navier-Stokes and Israel-Stewart theories. A correspondence is established between the discrete stellar eigenmodes and the continuous dispersion relation of perturbations around a homogeneous fluid, providing a basis for interpreting our numerical results. We analyze the Newtonian limit and assess the impact of relativistic corrections, such as the gravitational redshifting of microscopic relaxation timescales. We show that bulk viscosity can significantly affect the behavior of both hydrodynamic and nonhydrodynamic fundamental modes, and that, depending on the magnitude of the viscous effects, it is the nonhydrodynamic mode that becomes unstable beyond the turning point in a sequence of equilibrium configurations. These results provide a useful step toward systematic studies of neutron star quasinormal modes in the presence of viscosity.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Slow ferromagnetic fluctuations in the kagome metal Sc 3 ⁢ Mn 3 ⁢Al 7 ⁢ Si 5 revealed by 27 Al NMR

Static and dynamical magnetic and electronic properties of the kagome metal Sc 3 ⁢Mn 3 ⁢ Al 7⁢ Si 5 have been investigated by 27 Al nuclear magnetic resonance (NMR) measurements. Two distinct 27 Al -NMR signals with two different values of quadrupolar frequencies of 𝜈 Q = 1.55(2) and 1.07(2) MHz are observed, which are assigned to Al(1) and Al(2), respectively. From the detailed NMR spectrum measurements under three different magnetic field directions and the density functional theory calculations, the principal axes of the electric field gradient for each Al site have been determined. The temperature dependence of Knight shift (𝐾) shows a similar temperature dependence of the DC magnetic susceptibility 𝜒 except for the low-temperature region below ∼50 K where 𝐾 is almost constant while 𝜒 keeps increasing, which suggests that the increase in 𝜒 at low temperatures is not intrinsic. 27 Al spin-lattice relaxation rate divided by temperature (1/𝑇 1 ⁢𝑇) is found to be constant, confirming the metallic state of Sc 3 ⁢Mn 3 ⁢ Al 7⁢ Si 5 from a microscopic point of view. Based on a Korringa ratio analysis using the 𝑇 1 and 𝐾 data, ferromagnetic fluctuations are found to dominate in Sc 3 ⁢Mn 3 ⁢ Al 7⁢ Si 5 . In conclusion, these fluctuations are suggested to be very slow with frequencies on the order of kilohertz or lower.

Ding, Qing-Ping [Ames Laboratory (AMES), Ames, IA ↗

DOE OSTI2025

Constrained or unconstrained? Neural-network-based equation discovery from data

Throughout many fields, practitioners often rely on differential equations to model systems. Yet, for many applications, the theoretical derivation of such equations and/or the accurate resolution of their solutions may be intractable. Instead, recently developed methods, including those based on parameter estimation, operator subset selection, and neural networks, allow for the data-driven discovery of both ordinary and partial differential equations (PDEs), on a spectrum of interpretability. The success of these strategies is often contingent upon the correct identification of representative equations from noisy observations of state variables and, as importantly and intertwined with that, the mathematical strategies utilized to enforce those equations. Specifically, the latter has been commonly addressed via unconstrained optimization strategies. Representing the PDE as a neural network, we propose to discover the PDE (or the associated operator) by solving a constrained optimization problem and using an intermediate state representation similar to a physics-informed neural network (PINN). The objective function of this constrained optimization problem promotes matching the data, while the constraints require that the discovered PDE is satisfied at a number of spatial collocation points. We present a penalty method and a widely used trust-region barrier method to solve this constrained optimization problem, and we compare these methods on numerical examples. Our results on several example problems demonstrate that the latter constrained method outperforms the penalty method, particularly for higher noise levels or fewer collocation points. This work motivates further exploration into using sophisticated constrained optimization methods in scientific machine learning, as opposed to their commonly used, penalty-method or unconstrained counterparts. For both of these methods, we solve these discovered neural network PDEs with classical methods, such as finite difference methods, as opposed to PINNs-type methods relying on automatic differentiation. Here, we briefly highlight how simultaneously fitting the data while discovering the PDE improves the robustness to noise and other small, yet crucial, implementation details.

Data-driven discovery↗

DOE OSTI2022

Primer for OTERR Core Reloading Optimization

OTERR (Optimization of Test Reactor Reloading) is a software tool which assists in finding optimal fuel assembly reloading patterns for nuclear reactors. It was created specifically to support the Versatile Test Reactor (VTR) program, but its functionality is general enough to be applied to most fast spectrum reactors which use hexagonal prismatic fuel elements. This document is meant to be a primer for new users of OTERR to walk through example workflows for core reloading optimization cases. Simple cases are followed step-by-step to discuss what needs to be done to complete a reloading optimization sequence. Note that the intent of this document is to provide practical examples for users to follow along with so they can quickly start using OTERR and then make changes to fit their own modeling needs. Detail is limited in terms of addressing additional features not used in these examples and especially lacking in discussion of theory used in the code. To better address these points, users are highly encouraged to refer to the OTERR User Manual and the OTERR Theory Manual.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗