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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Thermalization and criticality on an analogue–digital quantum simulator

Abstract Understanding how interacting particles approach thermal equilibrium is a major challenge of quantum simulators 1,2 . Unlocking the full potential of such systems towards this goal requires flexible initial state preparation, precise time evolution and extensive probes for final state characterization. Here we present a quantum simulator comprising 69 superconducting qubits that supports both universal quantum gates and high-fidelity analogue evolution, with performance beyond the reach of classical simulation in cross-entropy benchmarking experiments. This hybrid platform features more versatile measurement capabilities compared with analogue-only simulators, which we leverage here to reveal a coarsening-induced breakdown of Kibble–Zurek scaling predictions 3 in theXYmodel, as well as signatures of the classical Kosterlitz–Thouless phase transition 4 . Moreover, the digital gates enable precise energy control, allowing us to study the effects of the eigenstate thermalization hypothesis 5–7 in targeted parts of the eigenspectrum. We also demonstrate digital preparation of pairwise-entangled dimer states, and image the transport of energy and vorticity during subsequent thermalization in analogue evolution. These results establish the efficacy of superconducting analogue–digital quantum processors for preparing states across many-body spectra and unveiling their thermalization dynamics.

Science & Technology - Other Topics↗
NASA NTRS2026

SCOAPE-II: A 2024 Multiplatform Measurement Campaign off the US Gulf Coast to Assess Oil and Gas Emissions on the Outer Continental Shelf

Nine years ago, the Department of Interior’s Bureau of Ocean Energy Management (BOEM), the Agency with Air Quality (AQ) jurisdiction over the Outer Continental Shelf (OCS) of the US Gulf Coast west of 87.5° W longitude, asked NASA to determine the feasibility of using satellite data to measure offshore emissions in a region of concentrated oil and natural gas (ONG) operations. To study this issue NASA and BOEM conducted the May 2019 Satellite Coastal and Oceanic Atmospheric Pollution Experiment (SCOAPE) cruise in the Gulf. SCOAPE addressed both technological and scientific issues related to measuring nitrogen dioxide (NO 2 , a common air pollutant), including contrasting near-shore and deepwater regimes. Given the April 2023 launch of the geostationary Tropospheric Emissions: Monitoring of Pollution (TEMPO) AQ satellite, a 2024 SCOAPE-II was conducted in the Gulf with both ship and aircraft measurements. We present an overview of the SCOAPE-II campaign, analysis and validation of satellite-observed NO 2 , and evaluate measurements of methane from ship, aircraft, and satellite near ONG platforms. Our SCOAPE-II results are as follows: 1) Satellite NO 2 measurements (∼13:30 local time) from the TROPOspheric Monitoring Instrument (TROPOMI) are more accurate than TEMPO’s hourly scans (8.6% vs. 23.6% mean absolute bias); a new version of TEMPO data is currently being processed; 2) ship and aircraft measurements captured dozens of NO 2 and methane plumes from ONG operations, showing that they are persistent emitters; 3) satellite measurements of methane failed to replicate ship and aircraft measurements, presenting ongoing challenges for operational emissions monitoring over the Gulf.

satellite validation↗
NASA NTRS2026

Application of Energy-efficient Electromagnetic Melt-Processing for the Upcycling of Recycled Polyphenylene Sulfide into Multifunctional Segregated Nanocomposites

Polyphenylene sulfide (PPS) is widely used in structural and functional composites because of its thermal stability, chemical resistance, and mechanical strength. As circular manufacturing becomes increasingly important, extending the service life of recycled PPS (rPPS) is essential. However, conventional high-temperature reprocessing accelerates thermo-oxidative degradation, reducing recycled composite performance. This study proposes a rapid and potentially energy-saving upcycling strategy for rPPS using electromagnetic (EM) melt-processing to form segregated carbon nanotube (CNT) networks and produce EM-responsive nanocomposites. The aim was to determine whether CNT-assisted EM heating could reduce polymer degradation while improving multifunctional properties at ultralow filler loadings. rPPS micropellets were coated with CNTs by ball milling to create conductive shells, then compacted into green bodies (GBs) and selectively melted by rapid EM irradiation. Structural, electrical, mechanical, rheological, and electromagnetic interference (EMI) shielding properties were evaluated. Electrical percolation occurred at an ultralow CNT loading of 0.08 wt%, with conductivity reaching (1.24 ± 0.74) × 10 -5 S⋅m -1 at 0.1 wt%. At this concentration, tensile strength and modulus increased by 72% and 99%, respectively. At ~ 0.7 mm thickness, X-band EMI shielding effectiveness reached 6 dB for GBs and 3 dB after EM processing. This shows that EM melt-processing upcycles rPPS into high-performance multifunctional nanocomposites with minimum thermal degradation.

recycled↗
NASA NTRS2026

Modeling Large Dust Aerosols in the Community Earth System Model Version 2 (CESM2)

Dust aerosols have a wide size distribution from less than 0.1 to over 100 μm and dominate Earth's atmospheric aerosol mass. However, most Earth system models (ESMs) inadequately represent dust aerosols larger than 10 μm in diameter, limiting the accuracy of the simulated dust cycle and climate impacts. Here, we introduce a new modeling framework that captures the full observed size distribution of dust aerosols, incorporating recent advances into a mineral-resolved version of the Community ESM, while addressing known issues in previous versions. Comprehensive evaluation against diverse observations of bulk dust and component minerals demonstrates that the model reproduces the observed dust cycle across particle sizes. Incorporating the previously unrepresented large-dust fractions substantially alters dust budget estimates, highlighting potential changes in simulated climate impacts and underscoring the importance of comprehensive size-resolved dust modeling. Despite these advancements, uncertainties persist. Our results indicate that a size-dependent reduction in settling velocity is required to reproduce the observed dust size distribution downwind of source regions. Specifically, in the new model, the gravitational settling velocity of dust particles larger than 10 μm in diameter must be reduced by as much as 85% to achieve agreement with observations. This empirical reduction serves as a constraint on physics-based models of dust settling. Future developments should address misrepresented physical processes that hinder accurate modeling of the large dust aerosol transport. Expanding observational data sets covering the full-size distribution is also essential to better constrain the dust cycle and improve the representation of dust optical properties and climate effects.

mineral dust↗
DOE OSTI2026

Ferroelectric Fractals: Switching Mechanism of Wurtzite AlN

The advent of wurtzite ferroelectrics is enabling new ferroelectric devices for computer memory that have the potential to bypass the von Neumann bottleneck due to their robust polarization and silicon compatibility. However, the atomistic switching mechanism of wurtzites is still undetermined due to the limitations of density functional theory simulation size and experimental temporal and spatial resolution. Thus, physics-informed materials engineering to reduce coercive field and breakdown in these devices has been limited. In this work, the atomistic mechanism of domain wall migration and domain growth in aluminum nitride-based wurtzites is uncovered using molecular dynamics and Monte Carlo simulations. We reveal the anomalous switching mechanism of fast 1D single columns of atoms propagating from a slow-moving 2D fractallike domain wall. We find that the critical nucleus is a single aluminum ion that breaks its bond with one nitrogen and bonds to another nitrogen; this creates a cascade that flips atoms directly only in the same column, due to the extreme locality (sharpness) of the domain walls in wurtzites. We further show how the fractallike shape of the domain wall in the 2D plane breaks assumptions in the Kolmogorov, Avrami, and Ishibashi (KAI) model and leads to the anomalously fast switching in wurtzite structured ferroelectrics.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Machine learning pipeline for denoising low signal-to-noise ratio and out-of-distribution transmission electron microscopy datasets

High-resolution transmission electron microscopy (HRTEM) is crucial for observing material’s structural and morphological evolution at Angstrom scales, but the electron beam can alter these processes. Devices such as CMOS-based direct-electron detectors operating in electron-counting mode can be utilized to substantially reduce the electron dosage. However, the resulting images often lead to a low signal-to-noise ratio, which requires frame integration that sacrifices temporal resolution. Several machine learning (ML) models have been recently developed to successfully denoise HRTEM images. Yet, these models are often computationally expensive, and their inference speeds on GPUs are outpaced by the imaging speed of advanced detectors, precluding in situ analysis. Furthermore, the performance of these denoising models on datasets with imaging conditions that deviate from the training datasets has not been evaluated. To mitigate these gaps, we propose a new self-supervised ML denoising pipeline specifically designed for time-series HRTEM images. This pipeline integrates a blind-spot convolution neural network with pre-processing and post-processing steps, including drift correction and low-pass filtering. Results demonstrate that our model outperforms various other ML and non-ML denoising methods in noise reduction and contrast enhancement, leading to improved visual clarity of atomic features. Additionally, the model is drastically faster than U-Net-based ML models and demonstrates excellent out-of-distribution generalization. The model’s computational inference speed is in the order of milliseconds per image, rendering it suitable for application in in-situ HRTEM experiments.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Predicting Team Functioning in Long Term Space Missions Using Acoustic and Linguistic Measures

Maintaining optimal team functioning is critical for long-duration space exploration missions, yet traditional monitoring methods, such as self-reports and wearable sensors, often impose operational burdens or suffer from bias. This paper investigates a non-intrusive speech-based artificial intelligence (AI) framework to predict degradations in team functioning using data from the Human Exploration Research Analog (HERA) of the U.S. National Aeronautics and Space Administration (NASA). Using acoustic features, linguistic descriptors, and semantic embeddings, we evaluate static non-linear and temporal machine learning models to predict both objective (task accuracy) and subjective (self-reported efficacy and cohesion) team functioning outcomes. Results indicate that temporal models outperform static approaches, with prediction of objective task accuracy in Team Interaction Battery (TIB) improving from near chance to 71%. Self-reported outcomes, including team efficacy and cohesion, are predicted more reliably than task performance, achieving balanced accuracies of up to 85.56% and 78.12%, respectively, and are found to be most strongly associated with acoustic features. In a second interdependent task, the MMSEV–EVA, accuracies of up to 78% are achieved using temporal models with acoustic features. Furthermore, incorporating just 1–2 days of team-specific historical data systematically improved performance, and acoustic markers from informal pre-task interactions provided modest predictive gains. Finally, while automated preprocessing yielded viable accuracy, humancorrected data provided moderate performance gains, though transcription error rates did not significantly correlate with model performance. These findings highlight the potential of speech as a passive, high-fidelity monitoring tool for autonomous habitats.

Temporal modeling↗
DOE OSTI2026

Machine learning approach for vibronically renormalized electronic band structures

Here, we present a machine learning (ML) method for efficient computation of vibrational thermal expectation values of physical properties from first principles. Our approach is based on the nonperturbative frozen phonon formulation in which stochastic Monte Carlo algorithm is employed to sample configurations of nuclei in a supercell at finite temperatures based on a first-principles phonon model. A deep-learning neural network is trained to accurately predict physical properties associated with sampled phonon configurations, thus bypassing the time-consuming ab initio calculations. To incorporate the point-group symmetry of the electronic system into the ML model, group-theoretical methods are used to develop a symmetry-invariant descriptor for phonon configurations in the supercell. We apply our ML approach to compute the temperature dependent electronic energy gap of silicon based on density functional theory (DFT). We show that, with less than a hundred DFT calculations for training the neural network model, an order of magnitude larger number of sampling can be achieved for the computation of the vibrational thermal expectation values. Our work highlights the promising potential of ML techniques for finite temperature first-principles electronic structure methods.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRSDate not supplied

Flammability Test Method for Materials Intended for Use as Fire Barriers in Crewed Habitats

In some crewed-spacecraft applications, onboard materials are intentionally selected to function as hazard control barriers to prevent powered hardware and/or ignited flammable items from igniting nearby flammable materials by acting as non-breaching, non-flammable containment (e.g., cargo transfer bags, zero gravity stowage racks, and jettison stowage bags on the International Space Station). Historically, flammability self-extinguishment testing of broad acreage or outermost layer materials alone was believed to be sufficient to verify overall flammability compliance. Though required, testing on design features such as threaded seams and zippers was not performed due to the incorrect assumption that flammability performance would not be impacted. However, recent coupon level testing of layups and configurations consisting of multiple materials have revealed that these features may be more susceptible to ignition and fire propagation than the bulk material. Additionally, configurational coupon ignition and propagation testing does not explicitly determine a material’s ability to provide protection to vulnerable flammable hardware. The need to evaluate this gap in assessing material or layup effectiveness as fire barriers between ignition sources and flammable materials led to the development of a new configuration-based fire barrier test. Test setup, relevant configurations, and test validation are discussed. The new barrier test method establishes a configuration for the test apparatus, identifies a conservative ignitor configuration, and defines pass/fail criteria. Test setup, relevant configurations, and test validation are discussed.

Susana Harper↗
DOE OSTI2024

Estradiol associations with brain functional connectivity in postmenopausal women

Abstract Objective Previous studies have found that estrogens play a role in functional connectivity in the brain; however, little research has been done regarding how estradiol is associated with functional connectivity in postmenopausal women. The purpose of this study was to examine the relationship between estradiol and functional connectivity in postmenopausal women. Methods Structural and blood oxygenation level–dependent resting-state magnetic resonance imaging scans of 88 cognitively healthy postmenopausal individuals were obtained along with blood samples collected the same day as the magnetic resonance imaging to assess hormone levels. We generated connectivity values in CONN toolbox version 20.b, an SPM-based software. Results A regression analysis was run using estradiol level and regions of interest (ROI), including the hippocampus, parahippocampus, dorsolateral prefrontal cortex, and precuneus. Estradiol level was found to enhance parahippocampal gyrus anterior division left functional connectivity during ROI-to-ROI regression analysis. Estradiol enhanced functional connectivity between the parahippocampal gyrus anterior division left and the precuneus as well as the parahippocampal gyrus anterior division left and parahippocampal gyrus posterior division right. An exploratory analysis showed that years since the final menstrual period was related to enhanced connectivity between regions within the frontoparietal network. Conclusions These results illustrated the relationship between estradiol level and functional connectivity in postmenopausal women. They have implications for understanding how the functioning of the brain changes for individuals after menopause that may eventually lead to changes in cognition and behavior in older ages.

Obstetrics & Gynecology↗
DOE OSTI1996

Nuclear Safety [Vol. 37, No. 1, January-March 1996]

Nuclear Safety is a journal that covers significant issues in the field of nuclear safety. Its primary scope is safety in the design, construction, operation, and decommissioning of nuclear power reactors worldwide and the research and analysis activities that promote this goal, but it also encompasses the safety aspects of the entire nuclear fuel cycle, including fuel fabrication, spent-fuel processing and handling, and nuclear waste disposal, the handling of fissionable materials and radioisotopes, and the environmental effects of all these activities. Table of Contents for this issue follows. GENERAL SAFETY CONSIDERATIONS: 1 A Decision Support System for Maintenance Management of a Boiling-Water Reactor Power Plant, J. H. Shen, A. Ray, and S. Levine; ACCIDENT ANALYSIS: 12 On Prediction of the Ignition Potential of Uranium Metal and Hydride, M. Epstein, W. Luangdilok, M. G. Plys, and H. K. Fauske; 26 An Overview of the Primary Parameters and Methods for Determining Condensation Heat Transfer to Containment Structures, J. Green and K. Almenas; DESIGN FEATURES: 49 Modem Tornado Design of Nuclear and Other Potentially Hazardous Facilities, J. D. Stevenson and Y. Zaho; U.S. NUCLEAR REGULATORY COMMISSION INFORMATION AND ANALYSES: 73 1994 Accident Sequence Precursor Program Results, R. J. Belles, J. W. Cletcher, D. A. Copinger, B. W. Dolan, J. W. Minarick, and P. D. O'Reilly; ANNOUNCEMENTS: 93 American Institute of Chemical Engineers (AlChE) Spring 1997 National Meeting; 94 European Safety and Reliability Association International Conference on Safety and Reliability ESREL ’97; 95 Criticality Safety Challenges in the Next Decade; 96 21st International Symposium on the Scientific Basis for Nuclear Waste Management; 84 The Authors; 88 Letter to the Editor; 90 Indexes to Nuclear Safety, Volume 36.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Quasicrystal stability and nucleation kinetics from density functional theory

The aperiodic order of quasicrystals bridges the amorphous and crystalline regime, so it has remained unclear whether quasicrystals are metastable or stable phases of matter. Density functional theory is often used to evaluate thermodynamic stability, but quasicrystals are long-range aperiodic and their energies cannot be calculated using conventional ab initio methods. Here, in this work, we perform first-principles calculations on quasicrystal nanoparticles of increasing size, from which we can directly extrapolate their bulk and surface energies. Using this technique, we determine with high confidence that the icosahedral quasicrystals ScZn 7.33 and YbCd 5.7 are ground-state phases, thus revealing that translational symmetry is not a necessary condition for the zero-temperature stability of inorganic solids. Although we found the ScZn 7.33 quasicrystal to be thermodynamically stable, we show on a mixed thermodynamic and kinetic phase diagram that its solidification from the melt is limited by nucleation, which illustrates why even stable materials may be kinetically challenging to grow. Our techniques broadly open the door to first-principles investigations into the structure–bonding–stability relationships of aperiodic materials.

density functional theory↗
NASA NTRSDate not supplied

NASA POWER: Providing Analysis-Ready, Cloud-Optimized Data for AI /ML Training and Applications in Earth Science

As global demand for sustainable development grows, the integration of Earth Observation (EO) data into decision making frameworks has become a primary objective for the scientific community. The NASA Prediction of Worldwide Energy Resources (POWER) project serves as a bridge between NASA EO data and the specialized needs of the renewable energy, sustainable infrastructure and agroclimatology communities. In this poster presentation we will present an overview of POWER data products and services along with its use in diverse research to decision-making workflows. By providing over 40 years of high-resolution historical, hourly and daily solar and meteorological data, POWER transforms satellite observations and global model reanalysis into actionable, Analysis-Ready Dataset (ARD). Currently, the project delivers over 250 industry-friendly parameters to the users from different NASA datasets like CERES SYN1Deg, MERRA-2, and IMERG alongside downscaled CMIP6 climate model data, fulfilling over 16 million requests from 50,000 unique users monthly. To ensure data quality and traceability, these parameters are rigorously validated against the ground-based observations from the Baseline Surface Radiation Network (BSRN) and the Global Surface Summary of the Day (GSOD) – these results will be discussed in the presentation. A newly introduced web-based PaRameter Uncertainty ViEwer (PRUVE) tool will be presented that provides an online validation platform to the users that benchmarks satellite-based and assimilation data products against these surface measurements. To reduce technical barriers to data adoption, POWER data is accessible through RESTful APIs, ESRI ArcGIS Image Services, a web-based Data Access Viewer tool, allowing users to visualize, validate and apply the dataset. For efficient data delivery POWER data is cloud-optimized into Zarr datastore accessible through NASA managed Amazon S3 ensures high-performance allowing users to integrate EO directly into operational pipelines. These customized services will be presented. Use cases from application will be presented from the energy sector - such as for design of generation systems, performance monitoring of solar power plants, in infrastructure sector- optimizing building energy efficiency and thermal comfort, in agriculture – such as driving crop simulation and yield forecasting models to enable climate resilient farming. Furthermore, the shift toward machine learning (ML) in EO research that has positioned POWER as a key provider for training datasets which will be discussed. Use-cases will be presented to showcase how NASA data is enabling the development of predictive tools for climate variability and resource management. The poster will present POWER’s future plans including technology development to enhance data traceability and reproducibility and improving I/O performance to support the rapid integration of new EO products, ensuring that POWER remains a robust scalable backend for the evolving landscape of AI-driven Earth Science. Additionally, POWER is developing an AI Agent and an MCP-Server to enable industry AI-Agentic workflows.

Neha Khadka↗
DOE OSTI2026

Search for New Physics via Low-Energy Electron Recoils with a 4.2 Tonne-Year Exposure from the LZ Experiment

We report results from searches for new physics models through electron recoils using data collected by the LUX-ZEPLIN experiment during its first two science runs, with a total exposure of 4.2 tonne−years. The observed data are consistent with a background-only hypothesis. Constraints are derived for electromagnetic interactions of solar neutrinos, solar axionlike particles (ALPs), mirror dark matter, and the absorption of bosonic dark matter candidates. The inverse Primakoff process for 57 Fe deexcitation solar ALPs is considered for the first time. These results represent the most stringent constraints to date on keV-scale Primakoff and 57 Fe solar ALPs, bosonic dark matter, mirror dark matter, and neutrino millicharge, while remaining competitive for the other signal models investigated.

Axion-like particles↗
NASA NTRS2026

Thermochemically-Closed Sonic-Flow Inversion for Enthalpy and Temperature in Multispecies Arc-Jet Flows

A thermochemically-closed sonic-flow inversion framework (TSIF) is developed to infer bulk enthalpy and total temperature upstream of a choked nozzle in arc-jet flows. The formulation recasts a pressure-rise total enthalpy quantification technique as an inverse problem in characteristic-velocity c * space using measured mass flow rate, upstream total pressure, gas composition, and nozzle throat geometry as inputs. Unlike calorimetric energy-balance approaches or optical diagnostics, the method relies primarily on routinely measured facility quantities combined with explicit thermochemical closure. Thermochemical states are obtained using NASA’s open-source Chemical Equilibrium with Applications (CEA) code, enabling construction of a chemistry-consistent relation between characteristic velocity, total enthalpy, and total temperature under equilibrium or frozen assumptions. A discharge coefficient is self-calibrated using cold-flow (arc-off) operation data and applied to hot-flow (arc-on) measurements, enabling upstream losses to be accounted for without empirical correlations. The framework is applied to air, N 2 , and CO 2 –N 2 arc-jet flows and demonstrates expected trends for the inferred thermochemical states as function of arc power, specific energy input, mass-flow, heater configuration, and test gas. In the air limit, under equilibrium assumptions, the method recovers the classical high-enthalpy asymptotic correlation of Winovich with a mean residual of 4.4%, demonstrating compatibility with established sonic-flow scaling, while extending applicability to arbitrary multi-species mixtures and non-equilibrium chemistry. The framework provides a mixture-flexible methodology for determining bulk thermochemical states in modern arc-jet environments using routine facility pressure, mass-flow, gas-composition, and nozzle-geometry information together with a cold-flow calibration.

stagnation heat flux↗
NASA NTRS2026

Thermochemically-Closed Sonic-Flow Inversion for Enthalpy and Temperature in Multispecies Arc-Jet Flows

A thermochemically-closed sonic-flow inversion framework (TSIF) is developed to infer bulk enthalpy and total temperature upstream of a choked nozzle in arc-jet flows. The formulation recasts a pressure-rise total enthalpy quantification technique as an inverse problem in characteristic-velocity c * space using measured mass flow rate, upstream total pressure, gas composition, and nozzle throat geometry as inputs. Unlike calorimetric energy-balance approaches or optical diagnostics, the method relies primarily on routinely measured facility quantities combined with explicit thermochemical closure. Thermochemical states are obtained using NASA’s open-source Chemical Equilibrium with Applications (CEA) code, enabling construction of a chemistry-consistent relation between characteristic velocity, total enthalpy, and total temperature under equilibrium or frozen assumptions. A discharge coefficient is self-calibrated using cold-flow (arc-off) operation data and applied to hot-flow (arc-on) measurements, enabling upstream losses to be accounted for without empirical correlations. The framework is applied to air, N 2 , and CO 2 –N 2 arc-jet flows and demonstrates expected trends for the inferred thermochemical states as function of arc power, specific energy input, mass-flow, heater configuration, and test gas. In the air limit, under equilibrium assumptions, the method recovers the classical high-enthalpy asymptotic correlation of Winovich with a mean residual of 4.4%, demonstrating compatibility with established sonic-flow scaling, while extending applicability to arbitrary multi-species mixtures and non-equilibrium chemistry. The framework provides a mixture-flexible methodology for determining bulk thermochemical states in modern arc-jet environments using routine facility pressure, mass-flow, gas-composition, and nozzle-geometry information together with a cold-flow calibration.

inviscid theory↗
DOE OSTI2026

A New Shutdown Dose Rate Benchmark Problem for Representative Fusion Applications

Here, this work introduces a new benchmark problem for calculating shutdown dose rates (SDDRs) aimed at fusion reactor applications. The model is designed to represent a simplified version of a typical ITER port plug. The responses of interest include neutron flux, gamma flux, and gamma SDDR at 12 different locations scattered throughout the port. This article outlines the geometry specifications of the problem, provides material definitions for the components, specifies the required responses to be calculated, and presents the source definition information. The need for this benchmark arises from the limited availability of publicly accessible references, with only one benchmark representing the typical dimensions and materials found in fusion systems. This existing benchmark has been cited extensively, reflecting the demand within the scientific community to test both established and novel workflows for SDDR calculations. However, since its presentation at a conference in 2011, the results have become increasingly well known. Moreover, the absence of formal publication and peer review has led to the details of this benchmark being extracted from secondary sources, such as subsequent studies that reference it. As a result, analysts are left with significant flexibility in interpreting the key parameters, which can be adjusted to account for unknown systematic errors, ultimately reproducing the already well-known responses. This new benchmark serves as an updated version of that earlier work, with the aim of providing a more reliable description of the materials and their impurities, which is crucial for assessing activation and subsequent gamma emission. Additionally, it seeks to provide a geometry that more closely represents an ITER port plug. The improvements in the problem definition will lead to a more reproducible benchmark problem, while also presenting the radiation transport community with a completely new challenge. The results will be published in a future article to allow analysts adequate time to analyze this problem independently.

Benchmark↗