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DOE OSTI2026

Computing the Critical Temperature of the Affine-Transformed $D=3$ Ising Model Using Masked Autoregressive Flow

The simple Ising model provides a rich environment to build and study lattice field theories. As part of an ongoing project to construct a conformal field theory (CFT) on an arbitrarily curved manifold, in this work we develop methods to measure the critical temperature $β_c$ of the affine-transformed Ising model on the face-centered cubic (FCC) lattice. The main challenge in this endeavor is finding a computationally efficient and accurate method of interpolating and extrapolating Monte Carlo observables with respect to coupling coefficients and temperature. Herein, we compare two such methods. A traditional statistical approach uses the multiple histogram (MH) method, while a newer machine learning approach uses a masked autoregressive flow (MAF) to estimate the underlying probability density function of a set of observables. While the MH method is specifically designed to interpolate and extrapolate Monte Carlo observables, we find that MAF is a viable alternative for measuring $β_c$ with a computational cost that scales more favorably. Furthermore, we comment on additional advantages of MAF relevant to our work, such as extrapolating in system volume.

Svenson, Kai [Texas U.]↗

NASA NTRS2019

The operational environment and rotational acceleration of asteroid (101955) Bennu from OSIRIS-REx observations

During its approach to asteroid (101955) Bennu, NASA's Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer (OSIRIS-REx) spacecraft surveyed Bennu's immediate environment, photometric properties, and rotation state. Discovery of a dusty environment, a natural satellite, or unexpected asteroid characteristics would have had consequences for the mission's safety and observation strategy. Here we show that spacecraft observations during this period were highly sensitive to satellites (sub-meter scale) but reveal none, although later navigational images indicate that further investigation is needed. We constrain average dust production in September 2018 from Bennu's surface to an upper limit of 150g s −1 averaged over 34 min. Bennu's disk-integrated photometric phase function validates measurements from the pre-encounter astronomical campaign. We demonstrate that Bennu's rotation rate is accelerating continuously at 3.63+- 0.52 x10 −6 degrees A day -2 , likely due to the Yarkovsky-O'Keefe-Radzievskii-Paddack (YORP) effect, with evolutionary implications.

Carl William Hergenrother↗

NASA NTRS2025

On the Stability Analysis of Astrophysical Cooling Functions

To model the temperature evolution of optically thin astrophysical environments at MHD scales, radiative and collisional cooling rates are typically either pretabulated or fit into a functional form and then input into MHD codes as a radiative loss function. Thermal balance requires estimates of the analogous heating rates, which are harder to calculate, and due to uncertainties in the underlying dissipative heating processes these rates are often simply parameterized. The resulting net cooling function defines an equilibrium curve that varies with density and temperature. Such cooling functions can make the gas prone to thermal instability (TI), which will cause departures from equilibrium. There has been no systematic study of thermally unstable parameter space for nonequilibrium states. Motivated by our recent finding that there is a related linear instability, catastrophic cooling instability, that can dominate over TI, here we carry out such a study. We show that Balbus instability criteria for TI can be used to define a critical cooling rate, Λc, that permits a nonequilibrium analysis of cooling functions through the mapping of TI zones. We furthermore illustrate how thermal conduction modifies the shape of TI zones. Upon applying a Λc-based stability analysis to coronal loop simulations, we find that loops undergoing periodic episodes of coronal rain formation are linearly unstable to catastrophic cooling instability, while TI is stabilized by thermal conduction.

Amanda Stricklan↗

DOE OSTI2024

O Corona, where art thou? eROSITA’s view of UV-optical-IR variability-selected massive black holes in low-mass galaxies

Finding massive black holes (MBHs,M BH ≈ 10 4 –10 7 M ⊙ ) in the nuclei of low-mass galaxies $\left( {{M_*}\mathop {\mathop < \limits_ }\limits_ {{10}^{10}}{M_ \odot }} \right)$ is crucial to constrain seeding and growth of black holes over cosmic time, but it is particularly challenging due to their low accretion luminosities. Variability selection via long-term photometric ultraviolet, optical, or infrared (UVOIR) light curves has proved effective and identifies lower-Eddington ratios compared to broad and narrow optical spectral lines searches. In the inefficient accretion regime, X-ray and radio searches are effective, but they have been limited to small samples. Therefore, differences between selection techniques have remained uncertain. Here, we present the first large systematic investigation of the X-ray properties of a sample of known MBH candidates in dwarf galaxies. We extracted X-ray photometry and spectra of a sample of ~200 UVOIR variability-selected MBHs and significantly detected 17 of them in the deepest available SRG/eROSITA image, of which four are newly discovered X-ray sources and two are new secure MBHs. This implies that tens to hundreds of LSST MBHs will have SRG/eROSITA counterparts, depending on the seeding model adopted. Surprisingly, the stacked X-ray images of the many non-detected MBHs are incompatible with standard disk-corona relations, typical of active galactic nuclei, inferred from both the optical and radio fluxes. They are instead compatible with the X-ray emission predicted for normal galaxies. After careful consideration of potential biases, we identified that this X-ray weakness needs a physical origin. A possibility is that a canonical X-ray corona might be lacking in the majority of this population of UVOIR-variability selected low-mass galaxies or that unusual accretion modes and spectral energy distributions are in place for MBHs in dwarf galaxies. This result reveals the potential for severe biases in occupation fractions derived from data from only one waveband combined with SEDs and scaling relations of more massive black holes and galaxies.

Astronomy & Astrophysics↗

NASA NTRS1995

Space Shuttle Main Engine Propellant Path Leak Detection Using Sequential Image Processing

Initial research in this study using theoretical radiation transport models established that the occurrence of a leak is accompanies by a sudden but sustained change in intensity in a given region of an image. In this phase, temporal processing of video images on a frame-by-frame basis was used to detect leaks within a given field of view. The leak detection algorithm developed in this study consists of a digital highpass filter cascaded with a moving average filter. The absolute value of the resulting discrete sequence is then taken and compared to a threshold value to produce the binary leak/no leak decision at each point in the image. Alternatively, averaging over the full frame of the output image produces a single time-varying mean value estimate that is indicative of the intensity and extent of a leak. Laboratory experiments were conducted in which artificially created leaks on a simulated SSME background were produced and recorded from a visible wavelength video camera. This data was processed frame-by-frame over the time interval of interest using an image processor implementation of the leak detection algorithm. In addition, a 20 second video sequence of an actual SSME failure was analyzed using this technique. The resulting output image sequences and plots of the full frame mean value versus time verify the effectiveness of the system.

L Montgomery Smith↗

NASA NTRSDate not supplied

Li-ion Battery Aging with Hybrid Physics-Informed Neural Networks and Fleet-wide Data

In this work, we propose a hybrid model for Li-ion battery discharge and aging prediction that leverages fleet-wide data to predict future capacity drops.The model is built upon an hybrid approach merging physics-based and empirical equations, as well as neural network models in a recurrent neural network cell. The hybrid physics-informed neural network can predict voltage discharge cycles given the loading profile, and estimate the used capacity of the battery under random-loading conditions by tracking aging parameters connected to the residual capacity of the battery. By merging information on the battery aging parameters with existing fleet-wide aging data, the model can predict the future residual capacity of the battery that is being monitored, and therefore enable predictions of voltage discharge curves far ahead in the battery life cycle. We validated the approach using the NASA Prognostics Data Repository Battery data-set, which contains experimental data on Li-ion batteries discharged at random loading conditions in a controlled environment. The approach also allows the identification of discrepancies between the battery aging trend and the trend observed at the fleet level, so that batteries behaving differently from the rest of the fleet can be subject to closer monitoring and further testing to refine predictions.

PINN↗

NASA NTRSDate not supplied

A Prognostics Framework for Battery Health Monitoring Integrated with Thermal Modeling

Urban Air Mobility (UAM) promises to revolutionize transportation in major cities, offering passenger travel, cargo delivery, and emergency medical services through a network of electric vertical takeoff and landing (eVTOL) aircraft. However, the limited range of current eVTOLs, due to the low specific energy of lithium-ion batteries along with a possibility of thermal runaway conditions poses significant safety concerns, leading to potentially compromising operational safety. To address this critical challenge, researchers are actively evaluating the impact of flight and environmental conditions on onboard lithium-ion battery health. This involves carefully assessing the performance of battery packs under laboratory and operational conditions for developing models to estimate future health using prognostics framework. This study examines the effectiveness of evaluating battery degradation leading to catastrophic failures under varying operational conditions in laboratory. These are captured using physics based models of underlying phenomenons and integrated into the prognostics framework. A fully charged battery undergoes controlled discharge cycles at varying C-rates based on the simulated power draw profile, with current and voltage, temperature data recorded throughout the experiment. The observed data provides valuable insights into how different operating conditions and mission profiles affect battery performance. This information is crucial for developing strategies to optimize battery systems, enhance range, and ultimately ensure the safe and reliable operation of UAM vehicles.

Thermal Modeling↗

NASA NTRSDate not supplied

A Prognostics Framework for Battery Health Monitoring Integrated with Thermal Modeling

Thermal Modeling↗

DOE OSTI2026

The critical role of intrinsic defects and many-body interactions on the stability of MnBi2Te4

Intrinsic antisite defects pose a major challenge to understanding and predicting the exotic properties of the layered topological magnetic insulator MnBi2Te4 (MBT). In this work, we study the origin of the abundance of intrinsic defects in MBT, including many-body defect–defect interactions and many-body electronic correlations. Until now, ab initio methods have struggled to explain thermodynamic stability and properties influenced by defect behavior in MBT. We model native Mn–Bi antisite defects in MBT at finite temperatures using a cluster expansion that includes defect–defect interactions. To overcome the limitations of conventional density functional theory (DFT), we introduce a hybrid approach that incorporates high-accuracy quantum Monte Carlo (QMC) calculations, introducing missing correlations. This strategy allows for accurate estimation of defect energetics and finite-temperature properties. We compute the configurational free energy, defect concentration, and configurational heat capacity, revealing a second-order order–disorder phase transition near the experimental synthesis temperature. Our study provides the first theoretical insight into the thermodynamics of intrinsic defects in MBT. The negative free energy relative to pristine MBT at synthesis temperatures indicates that Mn–Bi antisite formation is thermodynamically spontaneous. We also present a broadly applicable general framework for correcting low-level theoretical theories using highly accurate many-body corrections from QMC.

Ghaffar, Abdul [ORNL] (ORCID:0000000241190168)↗

DOE OSTI2026

Weak Lensing Mass Calibration of the ACT DR5 Galaxy Clusters with the DES Year 3 Weak Lensing Data

We use weak gravitational lensing measurements from Year 3 Dark Energy Survey data to calibrate the masses of 443 galaxy clusters selected via the Sunyaev-Zel'dovich effect from Atacama Cosmology Telescope Data Release 5 maps of the cosmic microwave background. We incorporate redshift and SZ measurements for individual clusters into a hierarchical model for the stacked lensing signals and perform Bayesian analyses to constrain the hydrostatic mass bias of the clusters. Our treatment of systematic uncertainties includes a prescription for measuring and accounting for the weak lensing boost factor, consideration of a miscentering effect, as well as marginalization over uncertainties in the source galaxy photometric redshift distributions and shear calibration. The resultant constraints on the normalization of the mass-observable relation have a precision of approximately 7%, with the mean WL halo mass of M $_{500c}$ = 5.4 × 10$^{14}$ M $_{⊙}$. We measure the bias between the true cluster mass and the mass estimated from the SZ signal based on an X-ray-calibrated scaling relation assuming hydrostatic equilibrium, to be 1 - b = 0.74$^{+0.06}$ $_{-0.05}$ over the full sample. When splitting the clusters into high (z = 0.43-0.70) and low (z = 0.15-0.43) redshift bins, we measure 1 - b = 0.58$^{+0.06}$ $_{-0.05}$ and 0.81$^{+0.08}$ $_{-0.06}$, respectively. When introducing additional freedom in redshift and mass to the hydrostatic bias model, we find that 1 - b decreases with redshift (with the power law of -1.8$^{+0.5}$ $_{-0.6}$, 99.95% confidence), consistent with findings from other recent studies, while we do not find any significant trend in mass. We also demonstrate that our result is robust against various systematics such as a scale cut, priors on baryonic and miscentering parameters, and degree of scatter in mass-observable relation. The weak-lensing mass calibration presented in this study will be a useful tool for using the ACT clusters as probes of astrophysics, and as a step towards using their abundance as a cosmological probe.

Shin, T. [Carnegie Mellon U.] (ORCID:0000000263895↗

NASA NTRS1993

Development of global/chemistry model for jet-fuel thermal stability based on observations from static and flowing experiments

Two global-chemistry models for oxidative deposition of jet fuels are evaluated by integrating them into a Computational Fluid Dynamics with Chemistry (CFDC) code. A previously developed two-step global-chemistry model was found to be insufficient to describe the thermal-oxidation and -deposition rates associated with a Jet-A fuel. A new global-chemistry model has been developed systematically based on observations from flowing and static experiments. The global-autoxidation reaction is modified such that the reaction rate becomes zeroth-order with respect to the dissolved oxygen concentration. The generation of deposit-forming precursor is coupled with the autoxidation reaction by introducing a radical species ROO. A formulation for the sticking probability has also been developed. Deposition profiles are well represented by this new model under a variety of temperature and flow conditions. The model correctly predicts the changes in magnitude and spatial location of the deposition peak due to changes in flow. The CFDC model, which is designed for flowing systems, has been extended to static experiments. The model incorporates a non-depleting species F(sub s) representing all non-oxygen compounds responsible for deposition. Static experiments were found to provide a useful and inexpensive method for estimating the concentration of F(sub s) in the fuel.

V. R. Katta↗

DOE OSTI2025

Multireference diffusion Monte Carlo reaches 2D materials

Abstract Quantum confinement in 2D materials strongly enhances electronic correlation effects. Therefore, predicting the properties of these unique materials, with both a high level of accuracy and computational efficiency, without relying on adjustable parameters or functionals, remains an outstanding theoretical challenge. The majority of theoretical studies are based on the approximations of density functional theory (DFT). The reliability of DFT predictions are heavily dependent on the choice of an approximated exchange-correlation functional. Here, we estimate the magnitude of impact of correlation on the total energy for the quintessential 2D material, graphene, by performing and comparing state-of-the-art selected CI and quantum Monte Carlo extrapolated calculations for a single unit cell at the$$\Gamma$$point. We demonstrate that Self-Healing Diffusion Monte Carlo (SHDMC) obtains a very compact, but high-quality wavefunction for this system that lacks the strong basis set dependence displayed by state of the art quantum chemistry methods. The SHDMC wavefunction is of higher quality compared to that obtained from sCI, in the same orbital basis, while being$$\sim$$ 1000 times smaller in terms of determinant count compared to sCI. We also demonstrate that extrapolating SHDMC results to the infinite determinant limit compares extremely well with complete basis set extrapolated sCI. Our work paves the way for future validation of SHDMC applied to challenging 2D materials.

Science & Technology - Other Topics↗

DOE OSTI2026

Dark Energy Survey year 6 results: Magnification modeling and its impact on galaxy clustering and galaxy-galaxy lensing cosmology

Gravitational lensing magnification alters the observed spatial distribution of galaxies and must be accounted for to prevent biases in cosmological probes of the large-scale structure. We investigate its effects on the Dark Energy Survey Year 6 galaxy clustering and galaxy-galaxy lensing analyses using the fiducial lens (position tracer) sample M ag L im++. Magnification bias is parameterized by a coefficient that describes the response of the number of selected objects per unlensed area element to a change in the lensing convergence. We quantify this coefficient using the BALROG synthetic source injection catalog to account for the complexity of the selection function, and compare these results with simplified estimates. The resulting values of the magnification coefficients for each redshift bin are [3.16 ± 0.08, 2.76 ± 0.21, 4.09 ± 0.15, 4.42 ± 0.16, 4.90 ± 0.29, 4.83 ± 0.25]. Relative to Year 3, this analysis provides more precise and accurate magnification bias estimates through a larger BALROG area and reweighting to better match the data properties. Here, the cosmological results are robust when tested against various magnification parameter prior choices and also when adding cross-clustering between lens redshift bins. Neglecting magnification, however, introduces significant systematic shifts: relative to the fiducial analysis with Gaussian priors centered on the BALROG -derived estimates, we observe shifts of 1.37σ in S 8 and -0.84σ in Ω m (with cosmic shear included: -0.61σ in S 8 and -0.71σ in Ω m ), in agreement with findings from simulated data, demonstrating that magnification must be modeled to avoid biases. Freeing the magnification bias in lens bin 2 leads to unphysical negative values, further justifying its exclusion from the fiducial Year 6 analysis.

Cosmological parameters↗

NASA NTRS2025

High-density Boron Nitride Nanotube Composites via Surfactant-stabilized Lyotropic Liquid Crystals for Enhanced Space Radiation Shielding

Despite significant technological advancements in space exploration, human space travel and colonization remain limited by the health risks associated with space radiation. Boron nitride nanotubes (BNNTs) have been proposed as an advanced material for space applications due to their high specific strength and efficient radiation shielding capabilities. However, the practical implementation of BNNTs has been slow, primarily due to technological challenges in fabricating structural materials incorporating BNNTs. In this study, a method is presented for fabricating high-density BNNT films that are mechanically robust, exhibit high thermal conductivity, and effectively attenuate space radiation. The key advancement enabling high-density BNNT films is the successful preparation of BNNT liquid crystals (LCs), achieved through the strategic use of a commercial dodecylbenzenesulfonic acid surfactant. This surfactant ensures exceptional BNNT stability in aqueous dispersion, even at concentrations exceeding the LC phase transition threshold. Simulations, estimating the equivalent radiation dose to the human body in space, indicate that a high-density BNNT film with a surface density of 50 g cm−2 reduces the total dose equivalent rate by 56% compared to zero shielding. This enhancement would allow astronauts to extend their mission duration on the lunar surface by a factor of two.

Young-Kyeong Kim↗

DOE OSTI2026

Influence of controlled disorder on the dipolar spin-ice state of Ho-based pyrochlores

Pyrochlore magnets of the form 𝑅 2 ⁢𝐵 2 ⁢O 7 , in which rare-earth ions on the 𝑅 site form a three-dimensional network of corner-sharing tetrahedra, provide a canonical setting for geometrical frustration. Ho-based pyrochlores host a dipolar spin-ice ground state, characterized by Ising moments constrained by the ice rules and elementary excitations analogous to magnetic monopoles. Here, in this work, we examine how controlled chemical disorder influences this state by introducing site mixing on the nonmagnetic 𝐵 site in two compounds. Ho 2⁢ GaSbO 7 contains only Ga 3+ /Sb 5+ charge disorder, whereas Ho 2 ⁢ScSbO 7 exhibits both charge and substantial size disorder arising from the large ionic-radius mismatch between Sc 3+ and Sb 5+ . Although both materials retain the pyrochlore structure, neutron-scattering measurements reveal a reduced correlation length for the 𝑅/𝐵-site cation ordering and enhanced local structural distortions in Ho 2 ⁢ScSbO 7 . Despite these structural differences, bulk thermodynamic measurements and magnetic diffuse scattering demonstrate that both systems exhibit the defining signatures of a dipolar spin-ice state. Low-energy inelastic neutron spectroscopy further uncovers broad magnetic excitations that develop within the dipolar spin-ice regime, a feature absent in pristine Ho pyrochlores and indicative of disorder-induced splitting of the non-Kramers ground-state doublet. Together, these results show that controlled disorder generates tunable transverse-field-driven quantum fluctuations in Ho-based pyrochlores, although the dipolar spin-ice state is remarkably robust to this disorder.

magnetic anisotropy↗

NASA NTRS1984

Electrodynamic Tethers. 1: Power Generator in LEO. 2: Thrust for Propulsion and Power Storage

An electrodynamic tether consists of a long insulated wire in space whose orbital motion cuts across lines of magnetic flux to produce an induce voltage that in typical low orbits averages about 200 v/km. Such a system should be capable of generating substantial electrical power, at the expense of IXB drag acting on its orbital energy. If a reverse current is driven against the induced voltage, the system should act as a motor producing IXB thrust. A reference system was designed, capable of generating 20 KW of power into an electrical load located anywhere along the wire at the expense of 2.6N (20,000 J/sec) drag on the wire. In an ideal system, the conversion between mechanical and electrical energy would reach 100% efficiency. In the actual system part of the 20 KW is lost to internal resistance of the wire, plasma and ionosphere, while the drag force is increased by residual air drag. The 20 KW PMG system as designed is estimated to provide 18.7 KW net power to the load at total drag loss of 20.4 KJ/sec, or an overall efficiency of 92%. Similar systems using heavier wire appear capable of producing power levels in excess of 1 Megawatt at voltages of 2-4 KV, with conversion efficiency between mechanical and electrical power better than 95%. The hollow cathode based system should be readily reversible from generator to motor operation by driving a reverse current using onboard power.

James E Mccoy↗

DOE OSTI2026

Classical-Quantum Algorithm for Solving Stochastic Programs

Stochastic programming provides a rigorous mathematical framework for making decisions under uncertainty in a risk-aware manner. Two-stage stochastic programming is, perhaps, the simplest form of this framework. Here the first-stage variables represent decisions that must be made "here and now" in the face of uncertainty, while the second-stage variables are decisions made after uncertain events. However, the broad adoption of stochastic programming has been hindered by computational challenges caused by the two-stage stochastic programming formulation which requires solving an ensemble of optimization problems. Using quantum amplitude estimation (QAE), quantum computers have shown the theoretic ability to compute expectations with Monte-Carlo methods with quadratically fewer samples than classical methods. In this work, we present a quantum algorithm for computing the expectation term using QAE for given first-stage decisions. Further, we detail methods of computing gradient information from the quantum calculation enabling the application of classical gradient-based optimization techniques. The result is a classical-quantum hybrid method of solving two-stage stochastic programs. These techniques are demonstrated with computational experiments based an engineering optimization problem.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1964

Radio Noise Problems in Arctic Regions

Three main types of radio noise should be considered when establishing noise levels for communication purposes or in experiments utilizing radio techniques in Arctic regions. These are atmospheric and man-made noise, and precipitation static. National Bureau of Standards radio noise data obtained hourly at Arctic and Antarctic stations on eight .fixed frequencies between 51 kc and 20 Mc/s are analyzed to show the characteristics of the three types. The diurnal and seasonal variations of atmospheric radio noise at high latitudes are explainable in terms of changes in propagation factors and in the distribution of world thunderstorm activity. In both northern and southern Arctic regions atmospheric noise decreases during polar cap absorption (PCA), most markedly in the h.f. band. The probable magnitude of man-made noise in the h.f. band is estimated from data taken during PCA, when atmospheric noise is absent. At Thule man-made noise on 2.5 and 5 Mc/s appears to be 57 and 49 dB above kTB (where kTB is antenna thermal noise power), respectively, while at Byrd Station the values are about 20 and 12 dB respectively. Precipitation static is generated on exposed antennas during periods of blowing snow (blizzards). The noise power magnitude can be at least 50 dB above man-made and atmospheric levels, but the maximum enhancement cannot be ascertained because of missing data. In winter and spring months at Byrd Station radio data dependent upon exposed antennas may be lost 10-30 per cent of the time due to precipitation static alone.

Radio Noise↗