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At least 235 records · Page 13

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DOE OSTI2021

Machine learning application to single channel design of molten salt reactor

This study proposes a robust approach to quickly design a nuclear reactor core and explores the best performing machine learning (ML) technique for predicting feature parameters of the core. Here we implemented the approach into a hypothetical channel of molten salt reactors to demonstrate the applicability of the method. We prepared a Python tool, named Plankton, which couples to a reactor physics code and an optimization tool, and imports ML methods. The tool performs three consecutive phases: reactor database generation, machine learning application, and design optimization. We identified the extra trees method as the best performing estimator. With the estimator, we found nine optimum designs in total, one for each fuel-salt pair, and estimated all the performance metrics of the designs with a <5% prediction error compared to their actual values. U-Pu-NaCl fuel-salt gave promising results with the highest conversion ratio, the most negative feedback coefficient, and the lowest fast flux.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

Exploring diversion-pathway analysis of a generic molten-salt fast reactor using multiphysics informed signatures

Molten salt reactors are being explored by multiple commercial ventures due to their inherent safety features, flexibility in fuel sources, and high fuel utilization and thermal efficiency. The continual flow of fuel salt, large fissile quantities present, and ability to add or divert material due to the liquid nature introduces new challenges for international safeguards. To understand how international safeguards should be applied, it is important to capture the inherent multi-physics nature of a molten salt reactor. This work examines a generic molten salt fast reactor to understand how potential diversion scenarios would affect the concentration of radionuclides in the primary and auxiliary systems. Three types of diversion were examined: a slow drip of fuel salt, gaseous plutonium extraction, and uranium metal plating. The analysis determined that several key isotopes become statistically significant once diversion begins, indicating that detection of such diversion cases would be possible through measuring specific signatures such as gamma spectra.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Engineering quorum-sensing circuits in Synechococcus elongatus PCC 7942 towards self-inducible systems

Despite significant potential for cyanobacteria as sustainable bioproduction chases, there are limited examples of scaled cyanobacterial bioproduction. In part, this is because most cyanobacterial species are poorly adapted to bioreactor cultivation conditions and lack features that facilitate biomass growth and harvesting at scale. We explored quorum sensing (QS) pathways derived from heterotrophic microbes as a method for autoinduction of gene expression circuits coordinated to population density in cyanobacteria. Here, we integrated genetic modules designed to produce and detect the diffusible QS signal, acyl-homoserine lactones (AHLs), in the cyanobacterial model, Synechococcus elongatus PCC 7942 (S. elongatus). We demonstrate that S. elongatus heterologously produces sufficient AHL signals to activate gene expression in a dose-dependent and population density-responsive manner. A hybrid combination of AHL synthesis enzyme from Vibrio fischeri (Lux system) with the transcription factor receiver from Pseudomonas aeruginosa (Las system) provides an ideal activation ratio and mitigates toxicity observed with some AHL systems. As a proof of concept, we coupled the QS pathway to the expression of a cell division inhibitory gene, cdv3, facilitating late-phase cell elongation, cell sedimentation, and improved biomass recovery. Our findings provide a foundation for the development of auto-induction systems leverageable to improve cyanobacterial biotechnology applications.

Acyl homoserine lactones (AHL)↗

DOE OSTI2023

Polyhedral Distortions and Unusual Magnetic Order in Spinel FeMn 2 O 4

Spinel compounds AB 2 X 4 consist of both tetrahedral (AX 4 ) and octahedral (BX 6) environments with the former forming a diamond lattice and the latter a geometrically frustrated pyrochlore lattice. Exploring the fascinating physical properties and their correlations with structural features is critical in understanding these materials. FeMn 2 O 4 has been reported to exhibit one structural transition and two successive magnetic transitions. In this work, we report the polyhedral distortions and their correlations to the structural and two magnetic transitions in FeMn 2 O 4 by employing the high-resolution neutron powder diffraction. The cation distribution is found to be ($Mn_{0.9}^{2+}Fe_{0.1}^{3+}$)$_{A}$($Mn^{3+}Fe_{0.9}^{3+}Mn_{0.1}^{2+}$)$_{B}$O 4 . While large trigonal distortion is found even in the high-temperature cubic phase, the first-order cubic-tetragonal structural transition associated with the elongation of both tetrahedra and octahedra with shared oxygen atoms along the c axis occurs at T S ≈ 750 K, driven by the Jahn–Teller effect of the orbital active B-site Mn 3+ cation. Strong magnetoelastic coupling is unveiled at T N1 ≈ 400 K as manifested by the appearance of Néel-type collinear ferrimagnetic order, an anomaly in both tetrahedral and octahedral distortions, as well as an anomalous decrease of the lattice constants c and a weak anomaly of a. Upon cooling to T N2 ≈ 65 K, it evolves to a noncollinear ferrimagnetic order accompanied by the different moments at the split magnetic sites B1 and B2. Only one-half of the B-site Mn 3+ /Fe 3+ spins, i.e., the B2-site spins in the pyrochlore lattice, are canted, which is a unique magnetic order among spinels. The canting angle between A-site and B2-site moments is ~25°, but the B1-site moment stays antiparallel to the A-site moment even at 10 K. This noncollinear order is accompanied by a modification of the O–B–O bond angles in the octahedra without significant change in lattice constants or tetrahedral/octahedral distortion parameters, indicating a distinct magnetoelastic coupling. We demonstrate distinct roles of the A-site and B-site magnetic cations in the structural and magnetic properties of FeMn 2 O 4 . Our study indicates that FeMn 2 O 4 is a wonderful platform to unveil interesting magnetic order and to investigate their correlations with polyhedral distortions and lattice.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Mesoscale Confinement Effects and Emergent Quantum Interference in Titania Antidot Thin Films

The effect of confinement on electron and ion transport in oxide films is of interest both fundamentally and technologically for the design of next-generation electronic devices. In metal oxides with mobile ions and vacancies, it is the interplay of the different modes of charge transport and the corresponding current–voltage signatures that is of interest. We developed a patterned structure in titania films, with feature sizes of 11–20 nm, that allow us to explore confined transport. In this study, we describe how confinement changes the competing charge transport mechanisms, the patterned antidot array leads to displacement fields and confines the charge density that results in modified and emergent electron transport with an increase in conductivity. This emergent behavior can be described by considering electron interference effects. Characterization of the charge transport with electron holography and impedance spectroscopy, and through comparison with modeling, show that nanoscale confinement is a way to control quantum interference.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Comparison of Current and Future PM2.5 Air Quality in China Under CMIP6 and DPEC Emission Scenarios

The latest scenarios for the sixth Coupled Model Intercomparison Project (CMIP6) provide opportunities to explore future climate and air pollution mitigation pathways with socioeconomic features. However, to what extent inadequate consideration of region-specific policies in the CMIP6 scenarios would affect regional air quality projections is still not clear. Here, we simulated China’s future PM2.5 concentrations and compositions with local policy-based scenarios and the original CMIP6 datasets, for the first time estimating future air quality differences induced by the lack of local pollution control policies in the CMIP6 scenarios. We found that the CMIP6-driven projections overestimate major chemical aerosols by 10% – 70% in 2015 and fail to capture the rapid PM2.5 concentration decline during 2015–2019. Near-term differences further lead to 43% – 49% (5 – 9 µg/m3) PM2.5 overpredictions in 2050 in CMIP6 scenarios compared with policy-based mitigation scenarios. Our study demonstrates the indispensable influences of local policies on regional analyses and suggests that the research community should incorporate more region-specific information in future scenario designs.

Cheng, Jing↗

DOE OSTI2025

Quantifying the Effect of Pore‐Size Dependent Wettability on Relative Permeabilities Using Lattice Boltzmann Simulation

Relative permeability is a crucial two-phase property in porous media that can be significantly impacted by wettability conditions. While traditional research has predominantly examined homogeneous wettability, this work explores the less studied pore-size dependent (PSD) wettability, featured by a pore-size dependent wettability distribution. Leveraging high-fidelity Lattice Boltzmann simulations on CT-scanned porous samples, we demonstrate how PSD wettability would impact relative permeability at the pore scale. Our findings reveal that the deviation of relative permeability from the homogeneous wettabilitity induced by PSD wettability can be 5%–20%. The deviation of relative permeability curves increases as the spanning range of the contact angle increases. We also find that this impact is less pronounced as the capillary number increases. By adopting a pore-size-dependent contact angle relationship, our approach provides a more accurate and nuanced understanding of how PSD wettability would impact two-phase flow. These findings discovered at the pore scale may also provide valuable insights on relative permeability at the core to reservoir scales.

25 ENERGY STORAGE↗

DOE OSTI2025

Label-based Virtual Directories In dCache

Traditional filesystems organize data in directories. These directories are typically a collection of files whose grouping is based on a single criterion, e.g., the starting date of an experiment, experiment name, beamline ID, measurement device, or instrument. However, each file in a directory can belong to several logical groups, such as a special event type, experiment condition, or a part of a selected dataset. dCache is a storage system developed to store large amounts of scientific data, used by many HEP and Photon Science experiments. With recent developments in dCache, we have introduced a concept of file tagging, which dynamically groups files with the same label into virtual directories. The file labels can be added, removed, renamed, and deleted through the admin interface or via REST API. The files in virtual directories are exposed through all protocols supported by dCache. This contribution will describe the details of the implementation for file tagging in dCache and present our future development plans on automatic metadata extractions, a feature that will significantly simplify data management. Additionally, we are exploring the future use of virtual directories as a way to translate scientific data catalogs into filesystem views for direct data analysis.

Sahakyan, Marina [DESY]↗

DOE OSTI2022

Local cation order and ferrimagnetism in compositionally complex spinel ferrites

We present an exploration of a family of compositionally complex cubic spinel ferrites featuring combinations of Mg, Fe, Co, Ni, Cu, Mn, and Zn cations, systematically investigating the average and local atomic structures, chemical short-range order, magnetic spin configurations, and magnetic properties. All compositions result in ferrimagnetic average structures with extremely similar local bonding environments; however, the samples display varying degrees of cation inversion and, therefore, differing apparent bulk magnetization. Additionally, first-order reversal curve analysis of the magnetic reversal behavior indicates varying degrees of magnetic ordering and interactions, including potentially local frustration. Finally, reverse Monte Carlo modeling of the spin orientation demonstrates a relationship between the degree of cation inversion and the spin collinearity. Collectively, these observations correlate with differences in synthesis procedures. This work provides a framework for understanding magnetic behavior reported for “high-entropy spinels,” revealing many are likely compositionally complex oxides with differing degrees of chemical short-range order—not meeting the community established criteria for high or medium entropy compounds. Moreover, this work highlights the importance of reporting complete sample processing histories and investigating local to long-range atomic arrangements when evaluating potential entropic mixing effects and assumed property correlations in high entropy materials.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Resolving discrepancies in bang-time predictions for indirect-drive ICF experiments on the NIF: Insights from the Build-A-Hohlraum campaign

This study investigated discrepancies between measured and simulated x-ray drive in Indirect-Drive Inertial Confinement Fusion (ID-ICF) hohlraums at the National Ignition Facility. Despite advances in radiation-hydrodynamic simulations, a consistent “drive deficit” remains. Experimentally measured ID-ICF capsule bang-times are systematically 400–700 ps later than simulations predict. The Build-A-Hohlraum (BAH) campaign explored potential causes for this discrepancy by systematically varying hohlraum features, including laser entrance hole (LEH) windows, capsules, and gas fills. Overall, the agreement between simulated and experimental x-ray drive was found to be largely unaffected by these changes. The data allow us to exclude some hypotheses put forward to potentially explain the discrepancy. Errors in the local thermodynamic equilibrium (LTE) atomic modeling, errors in the modeling of LEH closure, and errors due to a lack of plasma species mix physics in simulations are shown to be inconsistent with our measurements. Instead, the data support the hypothesis that errors in NLTE emission modeling are a significant contributor to the discrepancy. X-ray emission in the 2–4 keV range is found to be approximately 30% lower than in simulations. This is accompanied by higher than predicted electron temperatures in the gold bubble region, pointing to errors in non-LTE modeling. Introducing an opacity multiplier of 0.87 on energy groups above 1.8 keV improves agreement with experimental data, reducing the bang-time discrepancy from 300 to 100 ps. These results underscore the need for refined NLTE opacity models to enhance the predictive power of hohlraum simulations.

Band emission↗

DOE OSTI2022

CORAL: A framework for rigorous self-validated data modeling and integrative, reproducible data analysis

Abstract Background Many organizations face challenges in managing and analyzing data, especially when relevant datasets arise from multiple sources and methods. Analyzing heterogeneous datasets and additional derived data requires rigorous tracking of their interrelationships and provenance. This task has long been a Grand Challenge of data science and has more recently been formalized in the FAIR principles: that all data objects be Findable, Accessible, Interoperable, and Reusable, both for machines and for people. Adherence to these principles is necessary for proper stewardship of information, for testing regulatory compliance, for measuring the efficiency of processes, and for facilitating reuse of data-analytical frameworks. Findings We present the Contextual Ontology-based Repository Analysis Library (CORAL), a platform that greatly facilitates adherence to all 4 of the FAIR principles, including the especially difficult challenge of making heterogeneous datasets Interoperable and Reusable across all parts of a large, long-lasting organization. To achieve this, CORAL's data model requires that data generators extensively document the context for all data, and our tools maintain that context throughout the entire analysis pipeline. CORAL also features a web interface for data generators to upload and explore data, as well as a Jupyter notebook interface for data analysts, both backed by a common API. Conclusions CORAL enables organizations to build FAIR data types on the fly as they are needed, avoiding the expense of bespoke data modeling. CORAL provides a uniquely powerful platform to enable integrative cross-dataset analyses, generating deeper insights than are possible using traditional analysis tools.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Operator origin of anomalous dimensions in de Sitter space

The late-time limit of the power spectrum for heavy (principal series) fields in de Sitter (dS) space yields a series of polynomial terms with complex scaling dimensions. Such scaling behavior is expected to result from an associated operator with a complex dimension. In a free theory, these complex dimensions are known to match the constraints imposed by unitarity on the space of states. Yet, perturbative corrections to the scaling behavior of operators are naively inconsistent with unitary evolution of the quantum fields in dS space. This paper demonstrates how to compute one-loop corrections to the scaling dimensions that appear in the two-point function from the field theory description in terms of local operators. We first show how to evaluate these anomalous dimensions using Mellin space, which has the feature that it naturally accommodates a scaleless regulator. We then explore the consequences for the soft de Sitter effective theory (SdSET) description that emerges in the long wavelength limit. Carefully matching between the UV and SdSET descriptions requires the introduction of novel nondynamical “operators” in the effective theory. This is not only necessary to reproduce results extracted from the Källén-Lehmann representation (that use the space of unitary states directly), but it is also required by general arguments that invoke positivity. Published by the American Physical Society 2025

Cohen, Timothy↗

DOE OSTI2023

Resilient Hydrogels from the Nanoscale to the Macroscale

Biological systems illustrate how a material composed of fragile molecular components can collectively be highly resilient. While the average protein, cell, or even tissue may not last more than a few weeks, many animals and plants live for more than a century. Continual component regeneration and multiple systems to resist mechanical and chemical damage together make this longetivity possible. This project sought to develop biomimetic methods to enable a specific type of material, a hydrogel, to resist damage across multiple scales using distinct, modular damage protection mechanisms. Because hydrogels share many features with biological tissues, they are an ideal substrate for exploring biomimetic strategies for designing resilience and self-repair. We specifically focused in this study on DNA-crosslinked hydrogels, which contain DNA strands that can serve as material to link it together or to control its current state or properties. We investigated how new tools from dynamic DNA nanotechnology could make it possible to actively recover from damage by continually growing and forming a materials shape, or by identifying damage and directing an adaptive response to that damage involving chemical synthesis to reconstruct a structure. We developed embedded molecular sensors able to detect and strain of the gel before damage occurred and react to counteract damage. We also developed methods to continually create shapes and patterns using chemical processes that will allow those patterns to reform when they are damaged. The ability to design resilient materials capable of self-repair has important implications for materials engineering. Instead of designing a material to withstand the worst stresses it may encounter, we could instead design a material to survive under average conditions, but self-repair or reconfigure to resist impending damage. Resilient, self-repairing hydrogels will also have diverse applications such as sensors or actuators.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Reading Between the Lines: Measuring the Effects of Linguistic-Based Indicators of Deception on Experts’ Identification and Categorization of Disinformation

There is currently very limited research into how experts analyze and assess potentially fraudulent content in their expertise areas, and most research within the disinformation space involves very limited text samples (e.g., news headlines). The overarching goal of the present study was to explore how an individual’s psychological profile and the linguistic features in text might influence an expert’s ability to discern disinformation/fraudulent content in academic journal articles. At a high level, the current design tasked experts with reading journal articles from their area of expertise and indicating if they thought an article was deceptive or not. Half the articles they read were journal papers that had been retracted due to academic fraud. Demographic and psychological inventory data collected on the participants was combined with performance data to generate insights about individual expert susceptibility to deception. Our data show that our population of experts were unable to reliably detect deception in formal technical writing. Several psychological dimensions such as comfort with uncertainty and intellectual humility may provide some protection against deception. This work informs our understanding of expert susceptibility to potentially fraudulent content within official, technical information and can be used to inform future mitigative efforts and provide a building block for future disinformation work.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2020

FALCON: Framework for Anomaly Detection in Industrial Control Systems

Industrial Control Systems (ICS) are used to control physical processes in critical infrastructure. These systems are used in a wide variety of operations such as water treatment, power generation and distribution, and manufacturing. While the safety and security of these systems are of serious concern, recent reports have shown an increase in targeted attacks aimed at manipulating physical processes to cause catastrophic consequences. This trend emphasizes the need for algorithms and tools that provide resilient and smart attack detection mechanisms to protect ICS. In this paper, we propose an anomaly detection framework for ICS based on a deep neural network. The proposed methodology uses dilated convolution and long short-term memory (LSTM) layers to learn temporal as well as long term dependencies within sensor and actuator data in an ICS. The sensor/actuator data are passed through a unique feature engineering pipeline where wavelet transformation is applied to the sensor signals to extract features that are fed into the model. Additionally, this paper explores four variations of supervised deep learning models, as well as an unsupervised support vector machine (SVM) model for this problem. The proposed framework is validated on Secure Water Treatment testbed results. This framework detects more attacks in a shorter period of time than previously published methods.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Model Energy Codes End-Use Opportunity Analysis

Model energy codes (ASHRAE Standard 90.1 and IECC) has significantly impacted programs and policies aimed at improving energy efficiency both in United States and across the globe. Standard 90.1, first published in 1975 (originally referred to as Standard 90) provides minimum energy efficiency guidelines for designing, constructing, operating, and maintaining new construction and renovated buildings. It is updated continuously, with new editions published every 3 years. Pacific Northwest National Laboratory (PNNL) conducts simulated energy and cost savings analysis of model energy codes to determine their expected impact and to track progress toward net-zero goals and efficiency targets. Additionally, the simulation outputs are examined to assess how energy is used across primary systems within prominent U.S. commercial building types to understand how energy is used in each building type at the end-use level and to identify areas for improvements in future code cycles. This end-use opportunity analysis of model energy codes provides ASHRAE technical committees and other interested parties a better understanding of how the Standard affects various building systems and end uses, specifically, those in the Standard that most prominently influence energy efficiency. Additionally, findings provide industry stakeholders guidance in identifying building types and end-uses with the most potential for energy efficiency improvements through energy codes and those that may require beyond code measures to meet energy use reduction targets. This paper summarizes features and functionalities of a webtool created by PNNL to explore and visualize the End-Use opportunity analysis.

ASHRAE 90.1, Energy Codes, Tableau↗

DOE OSTI2025

Detection of multi-modal Doppler spectra – Part 1: Establishing characteristic signals in radar moment data

Vertically pointing millimeter-wavelength radars provide a wealth of information about cloud and precipitation particle properties. Doppler spectral data can inform on how particles of varying vertical velocities contribute to the total backscattered power observed. It is more computationally cost effective to process moment data instead of spectra data, but doing so leaves valuable information on the cutting room floor. To confidently identify a multi-modal spectra event, in which two or more modes are present within a layer, Doppler spectral data are essential. This means long-term identification of layers featuring multi-modal spectra can be cost prohibitive. To address this, we explore three multi-modal spectra cases from winter precipitation events to determine characteristic signatures of these layers in the moment data averaged over short time periods (∼ 145 s) and explore how these layers differ from the rest of the vertical profiles. We find that the mean spectrum width and the standard deviation of mean Doppler velocity can be used to determine whether or not a layer is multi-modal. In particular, multi-modal layers in mixed-phase and ice clouds feature larger mean spectrum width (exceeding 0.17 m s −1 ) and smaller standard deviation of the mean Doppler velocity (below 0.1 m s −1 ). In Part 1 of this study, the identification criteria and methods are described. In Part 2 (Wugofski and Kumjian, 2025), we perform a verification of the method for three years of vertically pointing radar data, and explore the meteorological conditions associated with identified multi-modal spectral events.

Wugofski, Sarah [Pennsylvania State Univ., Univers↗

DOE OSTI2024

Photometric redshifts probability density estimation from recurrent neural networks in the DECam local volume exploration survey data release 2

Photometric wide-field surveys are imaging the sky in unprecedented detail. These surveys face a significant challenge in efficiently estimating galactic photometric redshifts while accurately quantifying associated uncertainties. In this work, we address this challenge by exploring the estimation of Probability Density Functions (PDFs) for the photometric redshifts of galaxies across a vast area of 17,000 square degrees, encompassing objects with a median 5 σ point-source depth of g = 24.3, r = 23 . 9 , i = 23.5, and z = 22.8 mag. Our approach uses deep learning, specifically integrating a Recurrent Neural Network architecture with a Mixture Density Network, to leverage magnitudes and colors as input features for constructing photometric redshift PDFs across the whole DECam Local Volume Exploration (DELVE) survey sky footprint. Subsequently, we rigorously evaluate the reliability and robustness of our estimation methodology, gauging its performance against other well-established machine learning methods to ensure the quality of our redshift estimations. Our best results constrain photometric redshifts with the bias of − 0 . 0013 , a scatter of 0.0293, and an outlier fraction of 5.1%. These point estimates are accompanied by well-calibrated PDFs evaluated using diagnostic tools such as Probability Integral Transform and Odds distribution. We also address the problem of the accessibility of PDFs in terms of disk space storage and the time demand required to generate their corresponding parameters.We present a novel Autoencoder model that reduces the size of PDF parameter arrays to one-sixth of their original length, significantly decreasing the time required for PDF generation to one-eighth of the time needed when generating PDFs directly from the magnitudes.

79 ASTRONOMY AND ASTROPHYSICS↗