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DOE OSTI2025

The use of D–D reactions to diagnose the lifetime of spin polarized fuel

If the nuclei remain polarized, spin polarized fuel can increase the fusion power produced in a reactor while using less fuel. This study assesses whether reactions between an unpolarized deuterium (D) beam and polarized deuterium nuclei can in principle experimentally ascertain the persistence of nuclear polarization in a magnetic fusion experiment. The differential cross section for D–D reactions between unpolarized and polarized nuclei depends upon polarization, so the polarization state can be inferred from measurements of the D–D fusion products. An evaluation is performed for 3 MeV proton detection in the DIII-D tokamak using 81 keV neutral beams and polarized target fuel injected as a pellet. Measurement of the escaping proton pitch v 3 ∥ v 3 is insensitive to the degree of polarization but energy-resolved measurements are sensitive. The sensitivity is comparable for different angles of beam injection. Because the D–D reaction rate is large for this scenario, uncertainties associated with counting statistics are small, making inference of the polarization feasible with current technology.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

A New Theoretical Framework for Designing Ion Transport Pathways

The rapid transport of specific ions through matter is critical to energy storage, membrane separations, and health. However, commercial materials resist ion transport, lack specificity, or both, making ion transport costly and ineffective. Inspiration for new material designs can be taken from biology, where membrane transport proteins exert exquisite control over the specificity and rate of ion transport. The challenge in understanding and designing transport pathways is that ions often exchange their hydrating waters for direct contacts with atoms in the transport pathway. Despite intense study over decades, no theory exists to explain local ion binding and transport mechanisms and experiments cannot differentiate reliably between ions and water in binding sites. Here, we developed a new approach, based on quantum methods and extension of the quasi-chemical free energy theory, to understand and design pathways through materials for rapid transport of specific ions. Understanding ion transport mechanisms will significantly advance our nation’s ability to develop cost-effective materials for energy sustainability and therapeutics for health.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Measurements of ${\Xi \left( 1530\right) ^{0}} $ and ${\overline{\Xi }\left( 1530\right) ^{0}} $ production in proton–proton interactions at $\sqrt{s_{NN}}$ = 17.3 $\text{ GeV }$ in the NA61/SHINE experiment

Double-differential yields of ${\Xi \left( 1530\right) ^{0}} $ and ${\overline{\Xi }\left( 1530\right) ^{0}} $ resonances produced in p+p interactions were measured at a laboratory beam momentum of 158 $\text{ GeV }\!/\!c$. This measurement is the first of its kind in p+p interactions below LHC energies. It was performed at the CERN SPS by the NA61/SHINE collaboration. Double-differential distributions in rapidity and transverse momentum were obtained from a sample of $26\times 10^6$ inelastic events. The spectra are extrapolated to full phase space resulting in mean multiplicity of ${\Xi \left( 1530\right) ^{0}} $ ($6.73 \pm 0.25\pm 0.67)\times 10^{-4}$ and ${\overline{\Xi }\left( 1530\right) ^{0}} $ ($2.71 \pm 0.18\pm 0.18)\times 10^{-4}$. The rapidity and transverse momentum spectra and mean multiplicities were compared to predictions of string-hadronic and statistical model calculations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Recovering the phase and amplitude of X-ray FEL pulses using neural networks and differentiable models

Dynamics experiments are an important use-case for X-ray free-electron lasers (XFELs), but time-domain measurements of the X-ray pulses themselves remain a challenge. Shot-by-shot X-ray diagnostics could enable a new class of simpler and potentially higher-resolution pump-probe experiments. Here, we report training neural networks to combine low-resolution measurements in both the time and frequency domains to recover X-ray pulses at high-resolution. Critically, we also recover the phase, opening the door to coherent-control experiments with XFELs. The model-based generative neural-network architecture can be trained directly on unlabeled experimental data and is fast enough for real-time analysis on the new generation of MHz XFELs.

42 ENGINEERING↗

DOE OSTI2023

Coupled Long-Term Experiment and Model Investigation of the Differential Response of Plants and Soil Microbes in a Changing Permafrost Tundra Ecosystem

The major research goal of this project was to understand and quantify the fate of carbon stored in permafrost ecosystems using a combination of field and laboratory experiments to measure and model isotope ratios and carbon fluxes in a tundra ecosystem exposed to experimental warming. Field measurements centered on a two-factor experimental warming to increase air and soil temperatures alone, and in combination, at a tundra field site in Alaska. A second manipulation of water table was embedded within the warming treatment such that both major environmental factors (temperature, moisture) controlling ecosystem carbon dynamics were experimentally altered. The experiment was interfaced with modeling activities using both data assimilation and forward modeling approaches. Models were used to make forecasts of ecosystem carbon dynamics beyond the time frame and environmental space of the experiment itself. This project was a follow-on to previous work that made use of the same field manipulation; here we continued the experimental manipulation in order to test new hypotheses regarding long-term effects of warming and permafrost thaw and subsequent changes in moisture availability on ecosystem carbon dynamics. These results were linked with ongoing synthesis and model intercomparison activities through the Permafrost Carbon Network, which led to additional synthesis publications as a result of collaboration and data sharing from this project. As key outcomes, this project has produced new papers published in the peer-reviewed literature, archived datasets used for model-data intercomparisons, trained graduate students and postdoctoral researchers, and has raised awareness about the vulnerability of permafrost carbon to a wider audience.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Hadron Production Measurements at NA61/SHINE

In current day measurements of neutrino oscillation parameters with accelerator-based neutrino experiments, neutrino flux uncertainties are frequently a leading systematic uncertainty. Precise measurements of primary and secondary hadron production processes for neutrino beams, performed by the NA61/SHINE experiment at CERN's Super Proton Synchrotron, can significantly constrain these uncertainties; the T2K experiment has incorporated previous NA61/SHINE measurements for a substantial flux uncertainty reduction. For these measurements, NA61/SHINE analyzes the interactions of charged hadrons with materials relevant to long-baseline experiments, measuring the differential cross sections of produced particles. This poster will review the NA61/SHINE experiment, including recent, ongoing, and future measurements with thin and replica targets, such as proton-carbon interactions at 31 GeV/c and 120 GeV/c for the T2K and DUNE experiments, respectively.

Allison, Kyle↗

DOE OSTI2023

Quantum machine learning with differential privacy

Abstract Quantum machine learning (QML) can complement the growing trend of using learned models for a myriad of classification tasks, from image recognition to natural speech processing. There exists the potential for a quantum advantage due to the intractability of quantum operations on a classical computer. Many datasets used in machine learning are crowd sourced or contain some private information, but to the best of our knowledge, no current QML models are equipped with privacy-preserving features. This raises concerns as it is paramount that models do not expose sensitive information. Thus, privacy-preserving algorithms need to be implemented with QML. One solution is to make the machine learning algorithm differentially private, meaning the effect of a single data point on the training dataset is minimized. Differentially private machine learning models have been investigated, but differential privacy has not been thoroughly studied in the context of QML. In this study, we develop a hybrid quantum-classical model that is trained to preserve privacy using differentially private optimization algorithm. This marks the first proof-of-principle demonstration of privacy-preserving QML. The experiments demonstrate that differentially private QML can protect user-sensitive information without signficiantly diminishing model accuracy. Although the quantum model is simulated and tested on a classical computer, it demonstrates potential to be efficiently implemented on near-term quantum devices [noisy intermediate-scale quantum (NISQ)]. The approach’s success is illustrated via the classification of spatially classed two-dimensional datasets and a binary MNIST classification. This implementation of privacy-preserving QML will ensure confidentiality and accurate learning on NISQ technology.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Use of differential plasma rotation to prevent disruptive tearing mode onset from 3-wave coupling

Abstract Plasma differential rotation is found to be capable of preventing disruptive neoclassical tearing modes (NTMs) seeded by nonlinear three-wave coupling. As tearing modes degrade confinement and can lead to disruptions, stabilization strategies are crucial to the successful operation of future devices. In ITER-relevant scenarios on DIII-D, rotationally coupled m / n = 1/1 and 3/2 modes have been observed to drive 2/1 islands through three-wave coupling. The frequency of the driven 2/1 mode is set by matching conditions and the frequencies of the driving modes. When the driven mode frequency matches the local plasma rotation frequency, e.g. at low differential rotation, the driven 2/1 island can grow into a disruptive NTM. Using neutral beam torque as an actuator to scan the differential rotation, these experiments demonstrate that a sufficiently large frequency mismatch prevents destabilization of disruptive 2/1 NTMs by three-wave coupling. This work indicates that differential rotation can be used as an actuator to prevent NTMs seeded by three-wave coupling.

Richner, N. J. (ORCID:0000000155443915)↗

DOE OSTI2020

Sensitivity Studies, Gap Analysis, and Benchmark Experiment Optimization for Reactor Physics and Criticality Safety Applications

Many new reactor designs, such as advanced reactors and micro reactors, have materials that lack nuclear data validation. This is also true for many other applications in criticality safety and global security. Both differential and integral experiments are needed to validate cross-section data. Without this, a user cannot have confidence in the predicted results of a radiation-transport code. This work describes an approach called ARCHIMEDES (Application Relevant Critical/Subcritical HEU/Pu-based Integral Measurements for Enhancing Data and Evaluating Sensitivities) to design new criticality experiments that have similar k eff cross-section sensitivities to an application of interest. This process involves simulations to generate cross-section sensitivities to a parameter of interest (such as k eff ), a gap analysis to determine which existing benchmarks are most similar to the application, and an experiment optimization. Recently, there has been a great deal of interest in the reactor physics community on advanced reactors, micro reactors, and accelerator driven systems (ADS). This work will apply the described method to specific examples in this area. The focus of this work will be on the sensitivity study and gap analysis, while future work will include experiment design.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Understanding the diversity of the West African monsoon system change projected by CORDEX-CORE regional climate models

Understanding real uncertainties in projections is needed to support actions of decision and policy makers. In this study, two state-of-the-art regional climate models with ~ 25 km horizontal resolution forced with three different global climate models are employed to investigate the response of the West African Monsoon (WAM) system under the low (RCP2.6) and high (RCP8.5) emission scenarios. We make a step towards assessing the plausibility of rainfall change (ΔPr) patterns based on the models’ ability to realistically represent the WAM system under the historical period, and by analyzing changes in mechanisms associated with ΔPr patterns. Under global warming, experiments exhibit diverse ΔPr patterns underpinned by different combinations of mechanisms operating simultaneously. A dipole-like surface pressure change between the Sahara Desert and the Guinea Coast appears to be a differentiating factor between experiments featuring homogeneous or increased rainfall over all or a part of the Sahel from those simulating heterogeneous or reduced rainfall over the same domain. This dipole acts by modulating the amount and the extent of deepening of the monsoon flux inland, and hence the latitudinal positioning of the monsoon convective system. This process contributes to moistening the whole or central and eastern Sahel in some experiments while drying the Guinea Coast. The West African Westerly Jet (WAWJ) is responsible for strengthening and moistening the western Sahel under RCP2.6. However, the WAMJ becomes much stronger under RCP8.5 and may have contributed to the drying of the western Sahel by shifting moisture eastwards in addition to the mid-tropospheric moisture divergence through enhanced African Easterly Jet. Furthermore, for experiments expecting wetting (drying) of the Sahel, the strengthening (weakening) of the tropical easterly jet may have contributed to the projected intensification (reduction) of WAM rainfall by favoring uplift (downlift) above 600 hPa. It also emerges that the ΔPr patterns from REMO2015 experiments are likely more plausible than those from RegCM4-v7. The impacts of strong warming on the regional hydrological cycle are further investigated and we found that changes gradually intensify with the level of radiative forcing, highlighting the importance of mitigating warming as recommended by the Paris agreement.

CORDEX-CORE↗

DOE OSTI2024

Status of the CERBERUS Evaluation for the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook

Modeling & Simulation (M&S) tools are used to analyze advanced reactor designs and the safety of current nuclear operations. As computers continue to improve, we are able to enhance resolution in our calculations. Therefore, the limitations of simulation capability are in the quality of data that is being used, including our ability to quantify the uncertainty and sensitivity of that data. In order to model systems of interest with increasing accuracy, the industry must improve key nuclear data measurements. The International Criticality Safety Benchmark Evaluation Project (ICSBEP) compiles and evaluates experiment data in a handbook that can be used by criticality safety engineers and others to validate computer codes and cross section libraries at nuclear facilities. Both critical and subcritical experiments are included in the handbook. These experiments, along with differential measurements, can help improve the quality of nuclear data. Concerns regarding the accuracy of Cu nuclear data have been published. The large values and trend of C-E for the Zeus intermediate energy benchmark, being one of the primary examples. Furthermore, very few experiments have been designed to be sensitive to Cu (as shown in Figure 1), so an integral, critical experiment is needed to help resolve these differences.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Reactivity Coefficient Measurements and Sensitivity Studies [Abstract]

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Validation of Jezebel Reactivity Coefficients and Sensitivity Analysis

Nuclear data validation is often performed today using criticality measurements. The gold standard for criticality measurements is the International Criticality Safety Benchmark Experiment Project (ICSBEP). The validation specifically focuses on the effective multiplication factor ($k_{eff}$). $K_{eff}$ is a relatively easy parameter to infer and has reduced uncertainty due to being at or above critical. However, while $k_{eff}$ is the most documented parameter and its uncertainties and sensitivities have been evaluated in great detail, it cannot be used as a standalone metric to determine inaccuracies in nuclear data (e.g., cross section data, PFNS, nu), which is based on theory, physics, and differential measurements. The Experiments Underpinned by Computational Learning for Improvements in nuclear Data (EUCLID) project aims to identify compensating errors in specific isotope nuclear data by optimally designing experiments that are, or are not sensitive to a suite of measurement parameters beyond $k_{eff}$. By identifying parameters that are sensitive to each other, oppositely sensitive, or have substantial magnitude differences in sensitivity, experiments can be designed to constrain questionable nuclear data. One sensitivity that is of particular interest to this project includes the sensitivity of reactivity coefficients. Reactivity coefficients compare reactivity, which is related to $k_{eff}$ at two different states therefore being sensitive to small changes in the system. The most common type of reactivity coefficient measurements is comparison to void for a small sample within the assembly. It is key that the sample sizes are small enough to not affect the flux of the full system. Reactivity coefficients were evaluated for many early experiments to better understand transport corrected cross sections. In fact, ICSBEP includes reactivity coefficient results as “Supplemental Measurements” in appendices for a handful of older benchmarks. One of those benchmarks is Jezebel, the bare Pu critical assembly. This paper compares new simulations of reactivity coefficients for Jezebel, and explores the sensitivity of reactivity coefficients to small changes in nuclear data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Machine Learning to Select Experiments Driven by Fundamental Science and Applications for Targeted Nuclear Data Improvement

This work describes a blueprint for a process that accelerates progress in science by quantitatively answering the following question: What is the optimal combination of fundamental-science and application-driven experiments to maximally reduce pertinent data uncertainties? Answering this question entails solving a high-dimensional and complex optimization problem that is best solved with advanced statistic techniques often classified as machine learning. We apply this process within the framework of nuclear data with the aim to select an experiment combination that will reduce uncertainties in 239 Pu nuclear data for neutron energies between 1 and 600 keV. In this field, fundamental-physics driven data, called differential, look at one nuclear physics observable at a time. They are contrasted to application-driven, integral, data where one or few resulting values inform a broad set of nuclear data across several nuclides and energies. The candidates for integral experiments are criticality measurements that were refined by a genetic algorithm to be maximally sensitive to 239 Pu fission cross sections in the desired energy range. Twenty-three candidate differential experiments were investigated and span multiple nuclear physics observables (e.g., total, capture cross sections) for isotopes appearing in the integral experiments. The optimal combination among these candidate experiments was investigated via generalized least squares fitting, augmented with Gaussian processes to ameliorate statistical irregularities in data, and the D-optimality criterion. The latter evaluates for each pair of candidates the joint reduction in uncertainties of all 12200 nuclear data appearing in the integral experiments compared to the knowledge we have from 168 past experiments, theory, and nuclear data. We chose as differential measurements those that investigate 63 Cu and 239 Pu total cross sections, based on D-optimality rank and feasibility constraints. Two integral (criticality) experiments were selected: An experiment with Al 2 ⁢O 3 and graphite interleaved with Pu and a thick Cu reflector explores 1–30 keV, while we target the 30–600 keV range with an experiment that swaps boron in place of graphite with a different geometry.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Adsorption and exchange reactions of iodine molecules at the alumina surface: modelling alumina-iodine reaction mechanisms

Harnessing aluminum oxidation energy requires navigating the particle's passivation shell composed of alumina. The shell is a barrier to aluminum oxidation but can also exothermically react with halogenated species and therefore contribute to the overall energy generated during aluminum particle combustion. Fluorination reactions with alumina have been studied because fluorine is abundant in binder formulations that commonly surround aluminum particles in an energetic mixture. However, iodine has emerged as an alternative halogenated-based binder or oxidizer because iodine gas provides ancillary benefits such as chemical neutralization of biological agents or sterilization of contaminated environments. Furthermore, this study used density functional theory (DFT) calculations to evaluate potential reaction pathways for aluminum–iodine combustion. Relative to fluorinated fragments such as HF and F – , the adsorption energies associated with HI and I– are nearly triple the exchange reaction energy available from fluorination reactions with alumina (–189 and –278 kJ mol –1 for HI and I – , respectively). However, exchange reactions between iodinated species and the alumina surface are energetically unfavorable. These results explain that through adsorption, alumina surface exothermic reactions with iodine are more energetic than with fluorine fragments. Experiments performed with differential scanning calorimetry (DSC) confirm the higher magnitude of energy generated for iodination compared with fluorination reactions with alumina. Additionally, strong adsorption energies can promote synthesis of new shell chemistries. Adsorption in solution will promote alumina dissolution and iodine precipitation reactions to produce hydroxyl complexes and iodinated species synthesized on the surface of the particle, thereby replacing alumina with alternative passivation shell chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Make the Fastest Faster: Importance Mask Synthesis for Interactive Volume Visualization using Reconstruction Neural Networks

Visualizing a large-scale volumetric dataset with high resolution is challenging due to the substantial computational time and space complexity. Recent deep learning-based image inpainting methods significantly improve rendering latency by reconstructing a high-resolution image for visualization in constant time on GPU from a partially rendered image where only a portion of pixels go through the expensive rendering pipeline. However, existing solutions need to render every pixel of either a predefined regular sampling pattern or an irregular sample pattern predicted from a low-resolution image rendering. Both methods require a significant amount of expensive pixel-level rendering. In this work, we provide Importance Mask Learning (IML) and Synthesis (IMS) networks, which are the first attempts to directly synthesize important regions of the regular sampling pattern from the user’s view parameters, to further minimize the number of pixels to render by jointly considering the dataset, user behavior, and the downstream reconstruction neural network. Our solution is a unified framework to handle various types of inpainting methods through the proposed differentiable compaction/decompaction layers. Experiments show our method can further improve the overall rendering latency of state-of-the-art volume visualization methods using reconstruction neural network for free when rendering scientific volumetric datasets. Our method can also directly optimize the off-the-shelf pre-trained reconstruction neural networks without elongated retraining.

Large-scale data↗

DOE OSTI2021

Modeling of Thermal Decomposition of TATB-Based Explosive for Safety Analysis

We investigate and model the cook-off behavior of LX-17 to understand the response of explosive systems in abnormal thermal environments. Decomposition has been explored via conventional ODTX (One-Dimensional Time-to-eXplosion), PODTX (ODTX with pressure-measurement), TGA (Thermo-Gravimetric Analysis), and DSC (Differential Scanning Calorimetry) experiments under isothermal and ramped temperature profiles. The data were used to fit reaction rate parameters for proposed schemes in an ALE3D computational model. This model includes chemical reactions, thermo- and hydro-dynamics, and material properties, including thermal expansion, compressibility, and strength. These parameterizations were carried out utilizing a Python evolutionary optimization method on LLNL’s high-performance computing clusters. Additional experiments are being developed to further characterize and monitor decomposition intermediates to improve the model. Once experimentally validated, this model will be scalable to several applications involving LX-17. Furthermore, the optimization methodology developed herein should be applicable to other high explosive materials.

Chemistry - Chemical explosives↗

DOE OSTI2025

Measurement of the liquid argon scintillation pulse shape using differentiable simulation in the coherent CAPTAIN-Mills experiment

The coherent CAPTAIN-Mills (CCM) experiment is a liquid argon (LAr) light collection detector searching for MeV-scale neutrino and beyond Standard Model physics signatures. Two hundred eight-inch photomultiplier tubes instrument the seven-ton fiducial volume with 50% photocathode coverage to detect light produced by charged particles. CCM’s light-based approach reduces requirements of LAr purity, compared to other detection technologies, such that sub-MeV particles can be reliably detected without additional LAr filtration and with 𝒪⁡(1) parts per million of common contaminants. We present a measurement of LAr light production and propagation parameters, with uncertainties, obtained from a sample of MeV-scale electromagnetic events. The optimization of this high-dimensional parameter space was facilitated by a differentiable optical photon Monte Carlo simulation and detailed photomultiplier tube response characterization. This result accurately predicts the timing and spatial distribution of light due to scintillation and Cherenkov emission in the detector. This is the first description of photon propagation in LAr to include several effects, including anomalous dispersion of the index of refraction near the ultraviolet resonance, Mie scattering from impurities, and Cherenkov light production.

Cherenkov detectors↗