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DOE OSTI2023

Local Chemical Clustering Enabled Ultrahigh Capacitive Energy Storage in Pb-Free Relaxors

Designing Pb-free relaxors with both a high capacitive energy density (W rec ) and high storage efficiency (η) remains a remarkable challenge for cutting-edge pulsed power technologies. Local compositional heterogeneity is crucial for achieving complex polar structure in solid solution relaxors, but its role in optimizing energy storage properties is often overlooked. Here, in this paper, we report that an exceptionally high W rec of 15.2 J cm –3 along with an ultrahigh η of 91% can be achieved through designing local chemical clustering in Bi 0.5 Na 0.5 TiO 3 –BaTiO 3 -based relaxors. A three-dimensional atomistic model derived from neutron/X-ray total scattering combined with reverse Monte Carlo method reveals the presence of subnanometer scale clustering of Bi, Na, and Ba, which host differentiated polar displacements, and confirming the prediction by density functional theory calculations. This leads to a polar state with small polar clusters and strong length and direction fluctuations in unit-cell polar vectors, thus manifesting improved high-field polarizability, steadily reduced hysteresis, and high breakdown strength macroscopically. The favorable polar structure features also result in a unique field-increased η, excellent stability, and superior discharge capacity. Our work demonstrates that the hidden local chemical order exerts a significant impact on the polarization characteristic of relaxors, and can be exploited for accessing superior energy storage performance.

25 ENERGY STORAGE↗

DOE OSTI2023

A hybrid Monte Carlo study of bond-stretching electron–phonon interactions and charge order in BaBiO3

Abstract The relationship between electron–phonon ( e -ph) interactions and charge-density-wave (CDW) order in the bismuthate family of high-temperature superconductors remains unresolved. We address this question using nonperturbative hybrid Monte Carlo calculations for the parent compound BaBiO 3 . Our model includes the Bi 6 s and O 2 p σ orbitals and coupling to the Bi-O bond-stretching branch of optical phonons via modulations of the Bi-O hopping integral. We simulate three-dimensional clusters of up to 4000 orbitals, with input model parameters taken from ab initio electronic structure calculations and a phonon energy ℏΩ 0 = 60 meV. Our results demonstrate that the coupling to the bond-stretching modes is sufficient to reproduce the CDW transition in this system, despite a relatively small dimensionless coupling. We also find that the transition deviates from the weak-coupling Peierls’ picture. This work demonstrates that off-diagonal e -ph interactions in orbital space are vital in establishing the bismuthate phase diagram.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Bioproducts from high-protein algal biomass: An economic and environmental sustainability review and risk analysis

This report has High-protein algal biomass as an important bio-commodity that has the potential to provide a new source of sustainable protein products. Herein is a critical review that identifies 1) the most relevant sustainability findings related to the processing of proteinaceous algal biomass to higher value protein products and 2) the potential pathways to improve life cycle assessment (LCA) and techno-economic analysis (TEA) metrics, including life-cycle carbon dioxide equivalent (CO 2 eq), life cycle energy, and minimum selling price (MSP) of these products. The critical review of the literature revealed a large variation in model input parameters relating to these metrics. Therefore, a Monte Carlo analysis was conducted to assess the risk associated with these input variations. To understand the uncertainties that propagate into high-protein algae to products’ systems, we reviewed more than 20 state-of-the-art unit operations for algal biomass processing., including cell disruption, protein solubilization, protein precipitation and purification, and protein concentration. We evaluated displacement of proteinaceous products by algal-bioproducts, including ruminant feed, aquaculture feed, protein tablets, and biopolymers and biopolyesters, with prices in the market ranging from 1.9 to 120 $\$$ kg -1 protein. This review realized that the MSP of ruminant and non-ruminant feed ranges from 0.65±0.56 to 2.9±1.1 $\$$ kg -1 protein, and bioplastics’ MSP ranges from 0.97 to 7.0 $\$$ kg -1 protein. Regarding LCA metrics, there is limited research on life cycle energy in proteinaceous biomass concentration and bioproduct systems, reported at 32.7 MJ kg protein -1 , for animal feed displacement. Animal feed emissions in the literature report negative fluxes, representing environmental benefits, as low as -3.7 kgCO 2 eq kg -1 protein and positive fluxes, i.e., global warming potential, as high as 12.8 kgCO 2 eq kg -1 protein. There is limited research on bioplastics life cycle emissions reported at 0.6 kgCO 2 eq kg -1 protein. In general, the studies to date of algae-derived protein bioproducts showed similar life cycle emissions to soybean meals, nylon, polymers, and polystyrenes. Our risk analysis realized that more than 50% of scenarios can result in negative-net life cycle CO 2 eq emissions. This review and risk analysis assess and demonstrate the scenarios that improve economic and environmental sustainability metrics in high-protein algal bioproducts systems.

09 BIOMASS FUELS↗

DOE OSTI2022

Quantifying electron temperature distributions from time-integrated x-ray emission spectra

K-shell x-ray emission spectroscopy is a standard tool used to diagnose the plasma conditions created in high-energy-density physics experiments. In the simplest approach, the emissivity-weighted average temperature of the plasma can be extracted by fitting an emission spectrum to a single temperature condition. It is known, however, that a range of plasma conditions can contribute to the measured spectra due to a combination of the evolution of the sample and spatial gradients. In this work, we define a parameterized model of the temperature distribution and use Markov Chain Monte Carlo sampling of the input parameters, yielding uncertainties in the fit parameters to assess the uniqueness of the inferred temperature distribution. Here we present the analysis of time-integrated S and Fe x-ray spectroscopic data from the Orion laser facility and demonstrate that while fitting each spectral region to a single temperature yields two different temperatures, both spectra can be fit simultaneously with a single temperature distribution. We find that fitting both spectral regions together requires a maximum temperature of $1310^{+90}_{-70}$ eV with significant contributions from temperatures down to 200 eV.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Impact of High-Reactivity Advanced Test Reactor Experiments on Photon Heating in Nearby Experiment Locations

The Advanced Test Reactor’s (ATR’s) distinctive ability to provide a wide range of irradiation conditions is attractive for programs pursuing fuel qualification experiments. These potentially high-fuel-load experiments are a relatively new development and produce unexplored effects on nearby experiments. Here, this paper explores how photon heating of such an experiment may affect other nearby experiment programs, ultimately serving to better inform decisions regarding experiment design and risks to programmatic goals. The MC21 (Monte Carlo for the 21st Century) code is used to model and study how gamma heat generation rates and axial effects impact different ATR positions. The results reveal that the proximity of a given experiment’s position to the high-fuel-load one can significantly alter that experiment’s expected axial profile.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Multimuons in cosmic-ray events as seen in ALICE at the LHC

ALICE is a large experiment at the CERN Large Hadron Collider. Located 52 meters underground, its detectors are suitable to measure muons produced by cosmic-ray interactions in the atmosphere. In this paper, the studies of the cosmic muons registered by ALICE during Run 2 (2015–2018) are described. The analysis is limited to multimuon events defined as events with more than four detected muons (N μ > 4) and in the zenith angle range 0° < θ < 50°. The results are compared with Monte Carlo simulations using three of the main hadronic interaction models describing the air shower development in the atmosphere: QGSJET-II-04, EPOS-LHC, and SIBYLL 2.3d. The interval of the primary cosmic-ray energy involved in the measured muon multiplicity distribution is about 4 × 10 15 < E prim < 6 × 10 16 eV. In this interval none of the three models is able to describe precisely the trend of the composition of cosmic rays as the energy increases. However, QGSJET-II-04 is found to be the only model capable of reproducing reasonably well the muon multiplicity distribution, assuming a heavy composition of the primary cosmic rays over the whole energy range, while SIBYLL 2.3d and EPOS-LHC underpredict the number of muons in a large interval of multiplicity by more than 20% and 30%, respectively. The rate of high muon multiplicity events (N μ > 100) obtained with QGSJET-II-04 and SIBYLL 2.3d is compatible with the data, while EPOS-LHC produces a significantly lower rate (55% of the measured rate). For both QGSJET-II-04 and SIBYLL 2.3d, the rate is close to the data when the composition is assumed to be dominated by heavy elements, an outcome compatible with the average energy E prim ∼ 10 17 eV of these events. This result places significant constraints on more exotic production mechanisms.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Dual View of the Z 2 -Gauged XY Model in 3D

The 𝑍 2 -gauged XY model is of long-standing interest both in the context of nematic order, and the study of fractionalization and superconductivity. This Letter presents heuristic arguments that no deconfinement of the XY field occurs in this model and presents results of a large-scale Monte Carlo simulations on a cubic lattice that are consistent with this conclusion. Furthermore, the correlation radius determining the confinement is found to be growing rapidly as a function of the parameters in the phase featuring the nematic order. Thus, mesoscopic properties of the system can mimic deconfinement with high accuracy in some part of the phase diagram.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Planck constraints and gravitational wave forecasts for primordial black hole dark matter seeded by multifield inflation

Here, we perform a Markov Chain Monte Carlo analysis of a simple yet generic multifield inflation model characterized by two scalar fields coupled to each other and nonminimally coupled to gravity, fit to Planck 2018 cosmic microwave background (CMB) data. In particular, model parameters are constrained by data on the amplitude of the primordial power spectrum of scalar curvature perturbations on CMB scales A s , the spectral index n s , and the ratio of power in tensor to scalar modes r, with a prior that the primordial power spectrum should also lead to primordial black hole (PBH) production sufficient to account for the observed dark matter abundance. We find that n s in particular controls the constraints on our model. Whereas previous studies of PBH formation from an ultraslow-roll phase of inflation have highlighted the need for at least one model parameter to be highly fine-tuned, we identify a degeneracy direction in parameter space such that shifts by ~10% of one parameter can be compensated by comparable shifts in other parameters while preserving a close fit between model predictions and observations. Furthermore, we find this allowed parameter region produces observable gravitational wave signals in the frequency ranges to which upcoming experiments are projected to be sensitive, including Advanced LIGO and Virgo, the Einstein Telescope, Cosmic Explorer, DECIGO, and LISA.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Kolmogorov-Arnold wavefunctions

Here, this work investigates Kolmogorov-Arnold network-based (KAN) wave-function Ansätz as viable representations for quantum Monte Carlo simulations. Through systematic analysis of one-dimensional model systems, we evaluate their computational efficiency and representational power against established methods. Our numerical experiments suggest some efficient training methods and we explore how the computational cost scales with desired precision, particle number, and system parameters. Roughly speaking, KANs seem to be 10 times cheaper computationally than other neural-network-based Ansätz . We also introduce a novel approach for handling strong short-range potentials—a persistent challenge for many numerical techniques—which generalizes efficiently to higher-dimensional, physically relevant systems with short-ranged strong potentials common in atomic and nuclear physics.

1-dimensional systems↗

DOE OSTI2026

Atomistic Simulation of Glasses and Amorphous Materials: Challenges and Opportunities for the Next Decade

Atomistic simulations have become indispensable tools for understanding glass structure, dynamics, and properties, yet persistent challenges limit their predictive power. This perspective examines three interconnected issues, namely glass formation procedures, interatomic potential development, and machine learning applications, which emerged from the 5th International Workshop on Challenges of Atomistic Simulations of Glasses and Amorphous Materials. We identify convergent community priorities for (i) standardized validation protocols, (ii) curated benchmark datasets with complete metadata, and (iii) open repositories for glasses. A systematic was forward is provided by a hierarchical validation framework for assessing the structural fidelity, property prediction, and behavioral realism of simulation techniques. Looking ahead, transformative advances are promised by the fusion of classical techniques with machine learning based approaches, for instance, by integrating swap Monte Carlo with machine-learning (ML) potentials, leveraging foundation models through transfer learning, and finetuning ML potentials with experimental data. Progress depends on the community committing to validated models, reproducible protocols, and sustained data sharing.

Krishnan, N. M. Anoop↗

DOE OSTI2020

As-Run Neutronics Evaluation for the CSM-10584 Experiment in the ATR

This engineering calculations and analysis report (ECAR) documents the results of the Advanced Test Reactor (ATR) detailed Monte Carlo N-Particle (MCNP) code full-core model as-run physics analysis performed to support the Colorado School of Mines (CSM) experiment in the B-5 position.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2021

Radiation Transport Simulation of the Sulfur Counting System in the Radiation Metrology Laboratory (RML)

At the request of staff members from the Radiation Metrology Laboratory (RML), a series of Monte Carlo radiation transport calculations were performed using two different models of the detector geometry of the RMLs sulfur counting system. The fraction of electrons from each β-decay of 32 P in the sulfur pellet that enter the window of the sulfur counting system was calculated with both MCNP and ITS. In addition, the differential energy distributions of the electrons entering the counting system window were computed. There was significant agreement between the integral and differential quantities calculated by the two transport codes. Summary tables are for the integral efficiency values are found in the body of the report.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

MCNP Simulation of the SFRC and Minimum Detection Threshold Determination (Rev. 1)

In this report the Minimum Detection Threshold (MDT) for the Spent Fuel Rod Counter (SFRC) was determined using Monte Carlo N-Particle transport computer code (MCNP). A model of the SFRC was simulated in addition to a fuel transport pipe and spent Magnox fuel at descending positions down the pipe. The neutron MDT for Magnox fuel free-falling along a discharge pipe was calculated to be equal to 4080 n/s. The gamma MDT for the same free falling fuel as measured by the SFRC’s ionization chamber was determined to be 455 γ/s. These values were determined to be a conservative value to use when the instrument is deployed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Model Validation and Uncertainty Quantification on the KRUSTY Microreactor Design Using GRIFFIN Neutron Transport Code [Poster]

Argonne National Laboratory (ANL) and INL have developed a GRIFFIN steady state neutronics model for the multiphysics simulations of the Kilopower Reactor Using Sterling TechnologY (KRUSTY) microreactor in the Multiphysics Object Oriented Simulation Environment (MOOSE). The reliability of such deterministic neutronics models can be validated by comparing with computations from Monte Carlo codes (e.g. MCNP, SERPENT, OpenMC, Shift, etc). Furthermore, potential modeling/design improvements can be identified by incorporating uncertainty quantification (UQ), which can be performed by MOOSE’s Stochastic Tools Module (STM). KRUSTY is a prototype for a 5-kW thermal nuclear-powered space reactor. Its primary components consist of nuclear fuel, heat pipes, a control rod, a reflector, and the shielding. The fuel consists of 3 stacked U-7.65Mo cylinders with a hole in the center for the control rod. 8 liquid sodium heat pipes transfer fission energy from the solid fuel block to the Sterling power conversion system where the energy is extracted, and the cooled sodium flows back to the core via capillary action . The movable Boron Carbide control rod regulates the neutron population during startup or when a reactor temperature boost is needed . The beryllium oxide reflector is in 3 places in the reactor; it surrounds the core axially, it lies beneath the core on a platen, and it is present in the shim. The axial and lower reflectors rest on an adjustable stainless-steel platen that moves upward to cover the fuel and help the reactor reach criticality. Lastly, radial stainless steel surrounds the core offering protection from radiation exposure .

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

LBNF Material Activation Study

This poster presents a study on material activation at the Fermilab Long-Baseline Neutrino Facility (LBNF), focusing on understanding how high-energy beams interact with surrounding materials to produce radioactive isotopes. Using a simplified model of the LBNF-20 bunker and the FLUKA Monte Carlo simulation tool, the project quantifies isotope production at key locations inside and outside the shielding structures. The study employs a two-step simulation process, tracking primary and secondary particles, and explores the impact of different geometric and material configurations on activation rates. Results include preliminary comparisons of simulated activation rates and suggest methods for refining geometry, improving accuracy, and efficiently simulating future configurations. This work informs the design and operation of the LBNF facility, aiding in radiation safety and shielding optimization. Future efforts will focus on sample iteration, automated simulations, and detailed residual isotope analyses to further enhance the understanding of material activation in high-energy physics environments.

Pietraszek, Jacob [Unlisted, US]↗

DOE OSTI2022

Probabilistic estimation of depth-resolved profiles of soil thermal diffusivity from temperature time series

Abstract. Improving the quantification of soil thermal and physical properties is key to achieving a better understanding and prediction of soil hydro-biogeochemical processes and their responses to changes in atmospheric forcing. Obtaining such information at numerous locations and/or over time with conventional soil sampling is challenging. The increasing availability of low-cost, vertically resolved temperature sensor arrays offers promise for improving the estimation of soil thermal properties from temperature time series, and the possible indirect estimation of physical properties. Still, the reliability and limitations of such an approach need to be assessed. In the present study, we develop a parameter estimation approach based on a combination of thermal modeling, sliding time windows, Bayesian inference, and Markov chain Monte Carlo simulation to estimate thermal diffusivity and its uncertainty over time, at numerous locations and at an unprecedented vertical spatial resolution (i.e., down to 5 to 10 cm vertical resolution) from soil temperature time series. We provide the necessary framework to assess under which environmental conditions (soil temperature gradient, fluctuations, and trend), temperature sensor characteristics (bias and level of noise), and deployment geometries (sensor number and position) soil thermal diffusivity can be reliably inferred. We validate the method with synthetic experiments and field studies. The synthetic experiments show that in the presence of median diurnal fluctuations ≥ 1.5 ∘C at 5 cm below the ground surface, temperature gradients > 2 ∘C m−1, and a sliding time window of at least 4 d the proposed method provides reliable depth-resolved thermal diffusivity estimates with percentage errors ≤ 10 % and posterior relative standard deviations ≤ 5 % up to 1 m depth. Reliable thermal diffusivity under such environmental conditions also requires temperature sensors to be spaced precisely (with accuracy to a few millimeters), with a level of noise ≤ 0.02 ∘C, and with a bias defined by a standard deviation ≤ 0.01 ∘C. Finally, the application of the developed approach to field data indicates significant repeatability in results and similarity with independent measurements, as well as promise in using a sliding time window to estimate temporal changes in soil thermal diffusivity, as needed to potentially capture changes in bulk density or water content.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Statistical Mechanics on Lattices (smol) v0.0.1

smol is a Python implementation of the Cluster Expansion Method and extensions of this methodology; which is used to fit applied lattice models from first principle calculations for subsequent use in Monte Carlo simulations for computing thermodynamic and statistical properties of atomic configuration. smol has a several notable advantages over other similar software packages. The first being its Python implementation which makes it easy to use even for users with little software and coding skills. Despite being implemented in Python, essential Monte Carlo routines are implemented in Cython such that performance is not compromised and is competitive even to similar software implemented in C/C++. Furthermore, the package has specific functionality for handling complex ionic materials (such as cathodes and electrolytes) that is not readily available in other packages. Lastly, the software has a flexible and modular design with the intention of making it fast and efficient to develop and extend the methodology.

Barroso-Luque, Luis↗

NASA NTRS1972

Low thrust orbit determination program

Logical flow and guidelines are provided for the construction of a low thrust orbit determination computer program. The program, tentatively called FRACAS (filter response analysis for continuously accelerating spacecraft), is capable of generating a reference low thrust trajectory, performing a linear covariance analysis of guidance and navigation processes, and analyzing trajectory nonlinearities in Monte Carlo fashion. The choice of trajectory, guidance and navigation models has been made after extensive literature surveys and investigation of previous software. A key part of program design relied upon experience gained in developing and using Martin Marietta Aerospace programs: TOPSEP (Targeting/Optimization for Solar Electric Propulsion), GODSEP (Guidance and Orbit Determination for SEP) and SIMSEP (Simulation of SEP).

Hong, P. E.↗