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DOE OSTI2023

A Vision for Coupling Operation of US Fusion Facilities with HPC Systems and the Implications for Workflows and Data Management

The operation of large US Department of Energy (DOE) research facilities, like the DIII-D National Fusion Facility, results in the collection of complex multi-dimensional scientific datasets, both experimental and model-generated. In the future, it is envisioned that integrated data analysis coupled with large-scale high performance computing (HPC) simulations will be used to improve experimental planning and operation. Practically, massive data sets from these simulations provide the physics basis for generation of both reduced semi-analytic and machine-learning-based models. Storage of both HPC simulation datasets (generated from US DOE leadership computing facilities) and experimental datasets presents significant challenges. In this paper, we present a vision for a DOE-wide data management workflow that integrates US DOE fusion facilities with leadership computing facilities. Data persistence and long-term availability beyond the length of allocated projects is essential, particularly for verification and recalibration of artificial intelligence and machine learning (AI/ML) models. Because these data sets are often generated and shared among hundreds of users across multiple leadership computing facility centers, they would benefit from cross-platform accessibility, persistent identifiers (e.g. DOI, or digital object identifier), and provenance tracking. Here, the ability to handle different data access patterns suggests that a combination of low cost, high latency (e.g. for storing ML training sets) and high cost, low latency systems (e.g. for real-time, integrated machine control feedback) may be needed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Technology Development for Dry Storage of Aluminum-Clad Spent Nuclear Fuel - 20490

A candidate disposition pathway for the > 13 MTHM of aluminum-clad spent nuclear fuel (ASNF), owned and managed by the U.S. Department of Energy, is the drying and placement of the SNF into sealed-canister dry storage, with the ASNF-in- canisters 'road-ready' for transportation to and final direct disposal in a repository waste package. Technical information gaps in fuel drying, and fuel dry storage behavior, have previously challenged the declaration of technology readiness for drying and placement of this fuel into the DOE Standard Canister design for > 50 years of safe dry storage. The principal technical information gaps included: i) characterization and thermal dehydration behavior of aluminum (oxy)hydroxide films attendant on the cladding due to film formation during reactor operation and during post-discharge up to long-term wet storage histories; and ii) G-value data to enable estimation of the radiolytic gas generation from the cladding with its (oxy)hydroxide films. Thus, the oxide films on the ASNF challenged the safety of a sealed storage canister with thermal and radiolytic decomposition of the waters on the films that can lead to corrosion, pressurization, and flammability issues. These gaps in the technical information base have largely been closed. This paper discusses the investigations at the Idaho National Laboratory (INL) and the Savannah River National laboratory (SRNL), and outlines the pending technology development work for input to an engineering design to enable a road-ready dry storage system for ASNF. The ASNF inventory considered for road-ready dry storage is stored at the Savannah River Site (SRS) and at the Idaho Nuclear Technology and Engineering Center (INTEC) at the INL. The ASNF inventory in the SRS L Basin is from foreign and domestic research reactors (FRR and DRR), and is diverse in terms of design, irradiation, and post-reactor-discharge storage conditions; these factors yield a range of characteristics of cladding oxide films on ASNF. Mixed aluminum (oxy)hydroxide (boehmite and bayerite/gibbsite) films, non-uniform in thicknesses up to a maximum local thickness bounded by 25 μm, were observed on ASNF materials removed from wet storage in the L Basin and in non-sealed dry storage at the SRS following reactor service and a long-term (up to 40+ years) interim storage history. The ASNF inventory at INTEC, in both wet and vented dry storage, is predominantly from the Advanced Test Reactor (ATR), but it also includes DRR and FRR fuel. To address a profound behavior of these films, radiolysis testing of aluminum specimens with mixed type boehmite/bayerite oxide films was performed to develop basic data on G-values for production of radiolytic hydrogen under dry storage conditions with nominal relative humidity, temperature, and cover gas. Modeling and simulation of canister internal environments with postulated inventories of oxide films provides estimation of the evolution of the conditions of the canister loaded with ASNF. Simultaneous Thermal Analysis (TGA/DSC) of hydrated oxide powders, and laboratory-scale aluminum specimens with a bayerite film (∼10 μm) using TGA methods, inform drying time/temperature conditions to be used for the ASNF. A demonstration project is recommended for Verification and Validation of the drying and storage of the ASNF. Remaining major tasks leading up to the hot demonstration include scale-up radiolysis testing and scale-up drying testing. Engineering design with the information from the technology program will establish the safety basis and enable long-term (> 50 years) dry storage compatibility with ASNF in the DOE Standard Canister pending its transportation to and disposal in a repository. This full capability would show the ASNF-in-canister storage system to be road-ready. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Multidimensional Modeling of Mixture Formation in a Hydrogen-Fueled Heavy-Duty Optical Engine With Direct Injection

Hydrogen (H 2 ), as a carbon-free fuel, is considered as one of the most promising solutions to reduce the carbon footprint of hard-to-decarbonize energy and transportation sectors. As such, hydrogen-fueled internal combustion engines (H 2 ICEs) have recently been receiving increasing attention, particularly in applications such as on-road/off-road heavy-duty transport and combined heat and power. The direct injection (DI) of gaseous hydrogen into the combustion chamber offers great potential for achieving high power density and high engine efficiency, while mitigating the risk of backfire and reducing pre-ignition. However, the numerical simulation of H 2 DI system remains a formidable challenge associated with the high computational cost of reproducing compressible supersonic flow and shocks in narrow injector passages and in near-nozzle regions. In general, there is a lack of well-established and validated practices for the modeling of high-pressure H 2 DI in large-bore engines. Here, to this end, this study focuses on computational fluid dynamics (CFD) modeling of the mixture formation process in a heavy-duty optical engine employing a medium-pressure H 2 DI system. Both large eddy simulations (LES) and Reynolds Averaged Navier–Stokes (RANS) simulations are performed and evaluated against optical data. Gaseous hydrogen is injected into the combustion chamber via a centrally located outward opening hollow-cone injector at a pressure of 40 bar. Simulations are carried out for two injection timings, namely, −120 and −60 °CA. The numerical predictions for H 2 distribution in different horizontal and vertical planes during the compression stroke are systematically compared against optical data obtained through planar laser-induced fluorescence (PLIF) measurements. Overall, the LES approach using the Dynamic Structure model is found to have good predictive capabilities for the early jet penetration in terms of length and shape, as well as the later H 2 distributions. However, the unsteady RANS approach with the renormalization group $k - ϵ$ model, which is widely used by industry to model heavy-duty ICEs, significantly underpredicts the H 2 mixing, even at similar mesh resolution to that used in LES. These results indicate that there is a need for the improvement of mixing submodels within the RANS approach when applied to H 2 DI simulations.

LES↗

DOE OSTI2022

High Flux Isotope Reactor Low Enriched Uranium U-10Mo Fuel Design Parameters

Activities to convert the HFIR from HEU to LEU are ongoing as part of the US Department of Energy (DOE) National Nuclear Security Administration (NNSA) nuclear nonproliferation mission. Design activities to study the conversion of HFIR from HEU to LEU fuel explored different fuel design features and shapes with a uranium-molybdenum (U-10Mo) monolithic alloy fuel. This high-density alloy contains 90 wt % uranium and 10 wt % molybdenum and has a uranium density of 15.318gU/cm 3 . The goal of these studies is to generate several candidate HFIR LEU fuel designs of varying fuel fabrication complexity that meet the current HEU performance metrics and safety requirements. Recent advancements in modeling and simulation tools and design methods enabled a thorough analysis of the available design space with U-10Mo fuel. A surrogate model used this analysis as training data to quickly determine the performance of a design given specific design parameters. An optimization module used this surrogate model to quickly search this multidimensional search space given specific desired performance characteristics. This approach was made possible by the large available design space with U-10Mo fuel. Shift, a Monte Carlo tool optimized for high-performance computing (HPC) architectures, was used for faster calculation and better data management for reactor physics simulations. Once most of these design studies were complete, a new suite called the Python HFIR Analysis and Measurement Engine (PHAME) was developed to connect all fuel design analysis steps, making design studies more efficient and reproducible. The post-processing capabilities of these new tools are leveraged for the information provided herein. Leveraging these tools, several candidate fuel designs were selected with varying levels of feature complexity and reactor performance. This report provides design feature details for four selected HFIR LEU U-10Mo fuel designs and their corresponding performance and safety metrics. Nominal best-estimate design parameters and irradiation conditions, including fission rate densities, power densities, heat fluxes, and cumulative fission densities, are provided. Simulations show that the high uranium density of U-10Mo fuel provides a large potential design space that enables various LEU designs to meet HEU core performance metrics and safety requirements with a power increase from 85 MW (HEU) to 95 MW or 100 MW (LEU).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Athena: High-Performance Sparse Tensor Contraction Sequence on Heterogeneous Memory

Sparse tensor contraction sequence has been widely employed in many fields, such as chemistry and physics. However, how to efficiently implement the sequence faces multiple challenges, such as redundant computations and memory operations, massive memory consumption, and inefficient utilization of hardware. To address the above challenges, we introduce Athena, a high-performance framework for SpTC sequences. Athena introduces new data structures, leverages emerging Optane-based heterogeneous memory (HM) architecture, and stage parallelism. In particular, Athena introduces shared hash table-represented sparse accumulator to eliminate unnecessary input processing and data migration; Athena uses a novel data-semantic guided dynamic migration solution to make the best use of the Optane-based HM for high performance; Athena also co-runs execution phases with different characteristics to enable high hardware utilization. Evaluating with 12 datasets, we show that Athena brings 327-7362× speedup over the state-of-the-art SpTC algorithm. With the dynamic data placement guided by data semantics, Athena brings performance improvement on Optane-based HM over a state-of-the-art software-based data management solution, a hardware-based data management solution, and PMM-only by 1.58×, 1.82×, and 2.34× respectively.

Liu, Jiawen↗

DOE OSTI2026

A transfer learning approach to energy-efficient control of small and medium-sized commercial buildings

Model-free reinforcement learning (RL) provides a data-driven and adaptive approach to optimize building energy use while satisfying occupant comfort. This powerful tool does not need any prior knowledge about the environment and system it is optimizing and can adapt its policy based on the changes in captures. Like any other data-driven tool, it faces high training costs due to the extensive agent-environment interactions required to capture long-term building dynamics and user comfort. Transfer learning, particularly policy distillation, offers a promising way to accelerate training by leveraging pretrained RL agents in different building and system types. Here, this study investigates online student distillation, in which the student model updates its neural network weights using outputs from teacher models. The work introduces a student distillation strategy designed for efficient knowledge transfer, along with a teacher selection method that ensures high-quality guidance. The approach is validated using a highly calibrated whole building energy model for a small/medium commercial building test facility. Results show substantial reductions in training time and data requirements while surpassing the performance of ASHRAE Guideline 36, an advanced rule-based control strategy. The distilled RL model required 45% less data and achieved 20% higher cumulative rewards than a state-of-the-art RL model, with faster convergence and lower energy consumption. These outcomes demonstrate that effective transfer learning enables a scalable and data-efficient energy management solution for commercial buildings.

ASHRAE guideline 36↗

DOE OSTI2023

Demonstration of INCC6 for advanced list-mode data acquisition and analysis using ALMM

The International Neutron Coincidence Counting (INCC) software plays an important role in nuclear safeguards and nuclear material control and accounting (NMC&A) measurements. While the current version, INCC5, represents the traditional standard utilized by inspectorates as well as facilities and practitioners alongside shift register hardware, LANL has recently developed an upgraded version of the software, INCC6. INCC6 offers the same analysis tools as INCC5 while adding new capabilities including acquisition and analysis of list-mode data, recording and storage of list-mode data files, and an expanded set of advanced analysis tools that make use of the additional information available from list-mode data. Here, this paper presents the first demonstration of data acquisition and analysis using INCC6 and the Advanced List Mode Multiplicity Module (ALMM). The capability of INCC6 to perform live list-mode data acquisition and analysis is demonstrated for a variety of neutron sources with a range of neutron emission rates relevant for practical applications and validated against the results obtained with INCC5 and traditional shift register hardware. Advanced list-mode analysis tools introduced by INCC6, such as time-interval and coincidence matrix analysis, are also demonstrated. Finally, the capability of INCC6 to analyze list-mode data files is tested and validated against traditional shift register results using INCC5.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

Performance of the LHCb muon detector in Run 3

In Run 3 of the LHC, the instantaneous luminosity at the LHCb interaction point has been increased by a factor of five, from to $\mathscr{L}$ = 4 x 10 32 cm -2 s -2 to $\mathscr{L}$ = 2 x 10 33 cm -2 s -1 . Several hardware interventions, including a complete overhaul of the readout electronics, have been carried out on the muon detector. The muon identification algorithms in the software trigger were improved with the aim of ensuring Run 2 performance under a higher particle rate. The operation and calibration of the upgraded muon detector are presented. The muon detection efficiency and muon identification performance are evaluated on data calibration samples collected during the year 2024. A muon identification efficiency above 90% with sub-percent hadron misidentification probability is achieved by exploiting the pattern of hits in the muon detector.

Gaseous detectors↗

DOE OSTI2026

Monitoring of Liquid Metal Reactor Heater Zones with Recurrent Neural Network Learning of Temperature Time Series

Advanced high-temperature fluid reactors (ARs), such as sodium fast reactors (SFRs) and molten salt cooled reactors (MSCRs) utilize high-temperature fluids at ambient pressure. To melt the fluid during reactor startup and prevent fluid freezing during cooldown, the thermal–hydraulic systems of such ARs include heater zones consisting of specific heaters with controllers, temperature sensors, and thermal insulation. The failure of heater zones due to insulation material degradation or improper installation, resulting in parasitic heat losses, can lead to fluid freezing. The detection of faults using a heat-transfer model is difficult because of a lack of knowledge of the experimental details. Data-driven machine learning of heater zone temperature time series offers a viable alternative. In this study, we benchmarked the performance of recurrent neural networks (RNNs) in an analysis of heat-up transient temperature time series of heater zones installed on a liquid sodium vessel. The RNN models include long short-term memory (LSTM) and gated recurrent unit (GRU) networks, as well as their bi-directional variants, BiLSTM and BiGRU. Anomalous temperature points were designated using a percentile-based threshold applied to residual fluctuations in the detrended temperature time series. Additionally, the impact of the exponentially weighted moving average (EWMA) method on detection accuracy was examined. The RNN models’ performance was assessed using precision, recall, and F 1 score metrics. Results demonstrated that RNN models effectively detect anomalies in temperature time series with the best models for each heater zone achieving F 1 scores of over 93%. To explain the variations in RNN model performance across different heater zones, we used Kullback–Leibler (KL) divergence to quantify the relative entropy between training and testing data, and the Detrended Fluctuation Analysis (DFA) to assess long-range temporal correlations. For datasets with strong long-range correlations and minimal relative entropy between training and testing data, GRU is the best-performing model. When the data exhibits weaker long-term correlations and a significant relative entropy between training and testing distributions, BiGRU shows the best performance. For the data sets with intermediate values of both KL divergence and DFA, the best performance is obtained with LSTM and BiLSTM, respectively.

gated recurrent unit↗

DOE OSTI2026

Scientific Data Compression for Large Scale Computational Fluid Dynamics (CFD) Simulations

This Cooperative Research and Development Agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and General Electric (GE) investigated methods for reducing the size of large computational fluid dynamics (CFD) simulation datasets using scientific data compression techniques. The work focused on adapting the MultiGrid Adaptive Reduction of Data (MGARD) compression framework and integrating it with high-performance I/O and visualization tools used in CFD workflows. MGARD uses hierarchical multilevel decomposition to enable error-controlled compression of floating-point scientific data while preserving quantities of interest. During the project, MGARD compression was integrated with the ADIOS I/O framework and visualization tools such as ParaView to enable efficient storage, transfer, and analysis of simulation data. The collaboration also explored approaches for improving compression performance for CFD data defined on unstructured meshes. Results demonstrate that scientific data compression can significantly reduce storage requirements and improve data management for large-scale CFD simulations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Assessment of Performance of Density Functionals for Predicting Potential Energy Curves in Hydrogen Storage Applications

We report the availability of accurate computational tools for modeling and simulation is vital to accelerate the discovery of materials capable of storing hydrogen (H 2 ) under given parameters of pressure swing and temperature. Previously, we compiled the H2Bind275 data set consisting of equilibrium geometries and assessed the performance of 55 density functionals over this data set (Veccham, S. P.; Head-Gordon, M. J. Chem. Theory Comput. 2020, 16, 4963-4982). As it is crucial for computational tools to accurately model the entire potential energy curve (PEC), in addition to the equilibrium geometry, we extended this data set with 389 new data points to include two compressed and three elongated geometries along 78 PECs for H 2 binding, forming the H2Bind78 × 7 data set. By assessing the performance of 55 density functionals on this significantly larger and more comprehensive H2Bind78 × 7 data set, we identified the best performing density functionals for H 2 binding applications: PBE0-DH, ωB97X-V, ωB97M-V, and DSD-PBEPBE-D3(BJ). The addition of Hartree-Fock exchange improves the performance of density functionals, albeit not uniformly throughout the PEC. We recommend the usage of ωB97X-V and ωB97M-V density functionals as they offer good performance for both geometries and energies. In addition, we also identified B97M-V and B97M-rV as the best semilocal density functionals for predicting H 2 binding energy at its equilibrium geometry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

QProR: An Efficient Framework for Quantity-of-Interest Based Progressive Retrieval with Guaranteed Error Control

Scientific applications generate an unprecedented volume of data, overwhelming the network and file systems’ bandwidth and posing challenges for efficient and scalable data retrieval and analysis. Progressive data compression offers a promising solution by enabling on-demand retrieval at reduced size. However, existing progressive methods either fail to bound the errors in essential quantities of interest (QoIs) derived from raw data or suffer from suboptimal retrieval efficiency. In this work, we propose QProR, an efficient QoI-based progressive framework that optimizes progressive retrieval for target QoIs. Our key contributions include: (1) a systematic framework that integrates error-controlled lossy compressors with bitplane encoding while decoupling the two processes for high flexibility and adaptability; (2) a novel weighted bitplane encoding method which incorperates QoI knowledge into data refactoring to enhance retrieval efficiency; (3) an optimized retrieval strategy that accounts for the varying impacts of different variables on multivariate QoIs; (4) comprehensive evaluations using six real-world datasets from multiple scientific applications and thorough comparisons against state of the arts. Experimental results demonstrate that QProR achieves up to 80.38% reduction in the retrieval size under the same requested QoI error tolerance, when compared with the best-performing existing methods. When transferring 384 GB of scientific data to remote sites, QProR delivers up to 1.68 × speedup in the end-to-end data transfer performance.

Li, Wenbo [University of Kentucky]↗

DOE OSTI2020

Development of Gamma Background Radiation Digital Twin with Machine Learning Algorithms: Application of Unsupervised Machine Learning to Detection of Anomalies and Nuisances in Gamma Background Radiation Environmental Screening Data

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to explore unsupervised machine learning (ML) algorithms for development of a digital twin of gamma radiation background, and for detection and identification of weak nuisances and anomalies events in the presence of highly fluctuating background. In one segment of work, we developed a gamma background estimation model using a Longshort term memory (LSTM) network for one-step CPS time series prediction. The LSTM model was validated with two data sets of measurements from two independent NaI detectors positioned on a mobile platform. The data sets contained background radiation only and no orphan isotope sources. The LSTM model was constructed and tested using data from one of the detectors. Performance of the LSTM model was validate through one-step prediction of CPS time series of another NaI detector without re-training. This approach allows to create a digital twin for nuclear background estimation. Using LSTM, it could be possible to detect a source through subtraction of the estimated counts from the measured background. In another segment of work, we investigated detection of gamma emitting sources in the presence of complex background using unsupervised machine learning. Spectral lines of isotopes are difficult to observe in one-second measurements. Averaging over the entire measurement campaign data set reveals spectral lines of most common background isotopes. Spectral lines of orphan sources, which might appear only in a few measurements during the campaign, will be washed out if averaging is performed over the entire measurement data set. The approach we have explored consists of extracting one-second measurements containing weak spectral features through data clustering. Averaging one-second spectra in a cluster should reveal the presence of anomaly sources. We created two ML models using K-means clustering and Neural Network Self-organizing Map (SOM). Performance of these ML models was benchmarked using search data. One data set contained 137 Cs source, and another dataset contained 131 I source.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

An Integrated Deposition and Passivation Strategy for Controlled Crystallization of 2D/3D Halide Perovskite Films

Abstract This work introduces a simplified deposition procedure for multidimensional (2D/3D) perovskite thin films, integrating a phenethylammonium chloride (PEACl)‐treatment into the antisolvent step when forming the 3D perovskite. This simultaneous deposition and passivation strategy reduces the number of synthesis steps while simultaneously stabilizing the halide perovskite film and improving the photovoltaic performance of resulting solar cell devices to 20.8%. Using a combination of multimodal in situ and additional ex situ characterizations, it is demonstrated that the introduction of PEACl during the perovskite film formation slows down the crystal growth process, which leads to a larger average grain size and narrower grain size distribution, thus reducing carrier recombination at grain boundaries and improving the device's performance and stability. The data suggests that during annealing of the wet film, the PEACl diffuses to the surface of the film, forming hydrophobic (quasi‐)2D structures that protect the bulk of the perovskite film from humidity‐induced degradation.

2D/3D perovskites↗

DOE OSTI2020

The Effect of Membrane Casting Irregularities on Initial Fuel Cell Performance

Membrane electrode assemblies (MEA) for polymer electrolyte membrane fuel cells (PEMFC) may possess as-manufactured non-uniformities in any of its constituent components. This article studies casting irregularities located within the PEM to understand their potential impact on MEA initial performance. Membrane material was cast on a lab-scale polymer casting line operating either within or at the boundaries of the process window. The resulting membrane material was either pristine in the former case, or, in the latter case, contained air bubbles, cracks, and other irregularities, ranging in size from about 0.6 to 3 mm. Spatial polarization experiments were conducted using a 121-channel segmented fuel cell system, and thermal imaging was performed subsequently to map hydrogen crossover to spatial performance. While total-cell polarization data was minimally impacted by the irregularities, the spatial diagnostics showed local performance impacts that, in operation over time, could cause degradation in performance or earlier failure of the MEA. Such impacts could lead to these irregularities being classified as defects, i.e., manufacturing variations that should be identified and not included in a fuel cell stack. Classification of irregularities as defects will ultimately assist the industry by contributing to the development of threshold detection limits for in-line quality control diagnostics.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2022

Novel Segregated Solid Propulsion System with Separately Stored Fuel and Oxidizer

Abstract The development and experimentation of a solid propulsion concept consisting of separate hydrogen‐rich solid propellant and solid oxidizer grains is presented. This system consists of an extremely fuel‐rich solid propellant, which, once ignited, undergoes a self‐sustaining decomposition to release fuel‐rich product gases, which are particularly rich in hydrogen gas. These relatively high‐temperature gases subsequently react with a solid oxidizer grain stored downstream. Progress in high‐nitrogen materials has allowed for the synthesis of fuel‐rich compounds that can store significant amounts of hydrogen, with little or no bound oxygen. With these new developments in propellant chemistry, a unique segregated propulsion system has been developed, creating an all‐solid propulsion system that combusts analogously to a reverse hybrid rocket system. Because of the physical separation of fuel and oxidizer, a higher level of safety is achieved which concurrently allows for the utilization of higher energy ingredients without the penalty of higher sensitivity. Theoretical performance calculations and experimental data have shown the potential for this system to compete with existing composite rocket propellants while having significant benefits in terms of safety, toxicity, and mission space. This paper presents a detailed overview of the conception, development, and testing of this propulsion system.

42 ENGINEERING↗

DOE OSTI2022

A theory of dark pions

We present a complete model of a dark QCD sector with light dark pions, broadly motivated by hidden naturalness arguments. The dark quarks couple to the Standard Model via irrelevant Z- and Higgs-portal operators, which encode the low-energy effects of TeV-scale fermions interacting through Yukawa couplings with the Higgs field. The dark pions, depending on their CP properties, behave as either composite axion-like particles (ALPs) mixing with the Z or scalars mixing with the Higgs. The dark pion lifetimes fall naturally in the most interesting region for present and proposed searches for long-lived particles, at the LHC and beyond. This is demonstrated by studying in detail three benchmark scenarios for the symmetries and structure of the theory. Within a coherent framework, we analyze and compare the GeV-scale signatures of flavor-changing meson decays to dark pions, the weak-scale decays of Z and Higgs bosons to hidden hadrons, and the TeV-scale signals of the ultraviolet theory. New constraints are derived from B decays at CMS and from Z-initiated dark showers at LHCb, focusing on the displaced dimuon signature. We also emphasize the strong potential sensitivity of ATLAS and CMS to dark shower signals with large multiplicities and long lifetimes of the dark pions. As a key part of our phenomenological study, we perform a new data-driven calculation of the decays of a light ALP to exclusive hadronic Standard Model final states. The results are provided in a general form, applicable to any model with arbitrary flavor-diagonal couplings of the ALP to fermions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Measurement of CP asymmetries in ${D}_{(s)}^{+}\to \eta {\pi}^{+}$ and ${D}_{(s)}^{+}\to {\eta}^{\prime }{\pi}^{+}$ decays

Searches for CP violation in the decays ${D}_{(s)}^{+}\to \eta {\pi}^{+}$ and ${D}_{(s)}^{+}\to {\eta}^{\prime }{\pi}^{+}$ are performed using pp collision data corresponding to 6 fb –1 of integrated luminosity collected by the LHCb experiment. The calibration channels ${D}_{(s)}^{+}\to \phi {\pi}^{+}$ are used to remove production and detection asymmetries. The resulting CP-violating asymmetries are $\mathcal{A}^{CP} = (D^+ \to ηπ^+) = (0.34 ± 0.66 ± 0.16 ± 0.05)$ %, $\mathcal{A}^{CP} = (D^+_s \to ηπ^+) = (0.32 ± 0.51 ± 0.12)$ %, $\mathcal{A}^{CP} = (D^+ \to η'π^+) = (0.49 ± 0.18 ± 0.06 ± 0.05)$ %, $\mathcal{A}^{CP} = (D^+_s \to η'π^+) = (0.01 ± 0.12 ± 0.08)$ %, where the first uncertainty is statistical, the second is systematic and the third, relevant for the D + channels, is due to the uncertainty on ${\mathcal{A}}^{CP}=\left({D}^{+}\to \phi {\pi}^{+}\right)$. These measurements, currently the most precise for three of the four channels considered, are consistent with the absence of CP violation. A combination of these results with previous LHCb measurements is presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗