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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 505 records · Page 28

Optical Test Bench (SYSM 5620 Final Report)

The National Ignition Facility (NIF) is a research facility that focuses on High Energy Density Science and Inertial Confinement Fusion. In the NIF, 192 laser beams are directed into a miniscule Target Capsule to generate an implosion – this is called a NIF Shot. Many NIF Users conduct experiments using different Targets and laser inputs to generate reactions that are of interest to the scientific community. Those reactions are monitored using various Target Diagnostic instruments, including Shot Time Photography – which provides crucial images of the Target as the implosion occurs. A subset of these NIF Users is less interested in the reaction itself and more interested in subjecting materials or devices to the intense environment that these reactions can create. Using the Target as a source of x-rays, neutrons, or blast loading for particular materials has been significant to the shock physics community and to those who work in extreme design spaces. However, the Material and Radiation Effects team – that performs these experiments on behalf of the NIF Users – has not been able to make good use of the existing Shot Time Photography Diagnostics. The STP cameras are focused on the Targets rather than the MRE Customers’ materials, the current system is not oriented to have a good view of the MRE Customers’ materials, and the cameras are limited in their capabilities (black and white, poor resolution, etc.). As MRE experiments increase in quantity and importance to the scientific community, the desire for visual monitoring of the experiments to substantiate models and inform other data is also rising.

42 ENGINEERING↗

A non-intrusive framework using acoustic signals and deep learning for boiling diagnostics in visual-limited environments

Accurate monitoring of boiling heat transfer is critical for safeguarding high-power systems operating in environments where conventional optical diagnostics are hindered by radiation fields or restricted visual accessibility. This study presents a non-intrusive framework that integrates hydroacoustic sensing with deep learning to infer near-wall boiling characteristics and enable predictive thermal assessment without visual access. In a prototypical subcooled flow-boiling facility representative of the Isotope Production Facility (IPF) at Los Alamos, hydrophones capture boiling-induced acoustic emissions that are transformed into background-removed Short-Time Fourier Transform (STFT) spectrograms. A convolutional neural network (CNN) then regresses heat flux, wall superheat, and key bubble parameters directly from these spectrograms. The CNN achieved predictive accuracy under nominal conditions and demonstrated robustness and generalization under acoustic noise for Signal-to-Noise Ratios (SNRs) down to approximately 0 dB. When integrated into an ANSYS CFX wall-boiling model, the acoustically inferred parameters reproduced boiling curve and critical heat flux (CHF) values consistent with image-based benchmarks. Furthermore, the model retained reliable performance under moderate variations in bulk temperature, flow rate, and hydrophone placement, confirming its generalizability across practical boundary conditions. These results demonstrate the feasibility of hydroacoustic-based deep learning as a viable path toward real-time, radiation-tolerant boiling diagnostics and predictive thermal safety assessment in inaccessible systems such as the IPF.

42 ENGINEERING↗

Uncertainty quantification and sensitivity analysis of a nuclear thermal propulsion reactor startup sequence

The research presented in this article describes progress in applying stochastic methods, uncertainty quantification, parametric studies, and variance-based sensitivity analysis (also known as Sobol sensitivity analysis) to a full-core model of a nuclear thermal propulsion (NTP) system simulated via the radiation transport code Griffin to simulate neutronics. Our goal is to develop a reduced-order (surrogate) model that can be rapidly sampled with perturbations to multiple input parameters. In this NTP system, reactivity and power feedback affect the rotation of control drums (CDs), which is itself controlled by a hybrid proportional-integral-derivative (PID) controller actuated by the power demand and reactivity feedback from the numerical model. This model uses reactor kinetic feedback (mean generation time [Λ] and effective delayed neutron fraction [ β eff ] from a transient Griffin simulation executed via Griffin’s improved quasi-static solver to provide the kinetic parameters) as inputs to functions that control the CD rotation angle. By investigating numerous stochastic approaches, we developed a dual-purpose surrogate model of the NTP system, using polynomial regression in the Multiphysics Object-Oriented Simulation Environment (MOOSE) Stochastic Tools Module (STM). The trained model can be rapidly sampled while simultaneously perturbing various input parameters, such as coefficients on the PID control or temperature (directly affecting the neutron cross section). The surrogate model delivers accurate (within 5%) results at speeds orders of magnitude faster (minutes, not days of computational time) than the base model. Once the surrogate model has been trained, distributions of the uncertain parameters can be changed at will to investigate the effects of perturbing multiple inputs as well as the effects of these inputs on the model output. For example, coefficients used in the PID control system may vary due to some type of physical interference, or uncertainty may exist in the temperature of the neutron cross sections in various regions of the reactor. A distribution can be placed on these parameters, and operational boundaries can be determined. The goal of this work is to support development of an advanced control system for operating CDs in a functioning NTP system. This work is a scoping study of the MOOSE STM.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗

Neural MUSE Analysis

Researchers at Oak Ridge National Laboratory (ORNL) created data as part of the MUSE (Multi-Agency Urban Search Experiment Detector and Algorithm Test Bed) project simulating illicit nuclear materials located in various buildings along a road. In the simulation, a truck containing a radiation detector drives down the road gathering listmode data (counting the and energy of incident gamma radiation). Building materials, source shielding, driving speed, truck direction, truck location on the road, source type, and source placement are all varied between runs of the data set. This data was created using deterministic neutron transport and Monte Carlo methods through a combination of SCALE, MAVRIC, MCNP, and GADRAS. As part of a follow-on NA-22 project, two Kaggle competitions were created to determine the best algorithms for finding and identifying gamma sources in this simulated urban environment. The winning algorithm was neural network-based and had a test accuracy of 76.4% accuracy for source identification. This work seeks to build upon this work and improve the results through the application of novel machine learning techniques. As a first step, the data was classified by a simple Convolutional Neural Network (CNN) To accomplish this, the data was first preprocessed into “waterfall plots.” These plots are composed of energy vs count plots that are stacked vertically to show progression in time. The horizontal axis indicating the particle energy incorporated user defined bin spacing with options for in linear-, logarithmic-, square root-, and user-spaced bins. The z or color dimension showed the number of counts corresponding the energy-time combination. This data was then used to generate more data, by generating a local estimate of the mean of the distribution for a bin and then randomly re-sampling that bin from a Poisson distribution. Once all of this data was generated, it was fed into a well-known CNN architecture, ResNet50. The output layer of this model was removed and replaced with layers corresponding to the shape desired isotope outputs. The provided training data was used to train the classifier and the remaining testing data was used to evaluate the model. Results are soon to be forthcoming.

61 RADIATION PROTECTION AND DOSIMETRY↗

Air Pathway Dose Modeling for the E-Area Low-Level Waste Facility

The US Department of Energy (DOE) Order 435.1 performance assessment (PA) process (USDOE 1999) prescribes a performance objective (10 mrem y -1 ) for evaluating atmospheric releases of radionuclides from DOE low-level waste (LLW) disposal facilities. The potential dose to an individual from exposure to radionuclides released into the atmosphere from LLW disposals can be estimated by application of radionuclide -specific dose-release factors (DRFs) (mrem Ci -1 ) to estimated flux rates (Ci y -1 ) at a particular time and location. In Revision 0 of this report, Lee (2006) calculated DRFs for potential atmospheric releases of 15 volatile radionuclides from seven ELLWF disposal units in support of the 2008 ELLWF PA (WSRC 2008). The mainframe version of the US Environmental Protection Agency’s (EPA) dose model CAP88 was used in the Revision 0 assessment. In Revision 1 of this report (Dixon and Minter 2017), the site-specific Savannah River National Laboratory (SRNL) atmospheric dose models MAXDOSE-SR version 2013 (Stone and Jannik 2013a) and MAXINE version 2017 (Bell 2017) were used to calculate DRFs for ten radionuclides. This work (Revision 2) supersedes Revision 0 (Lee 2006) and Revision 1 (Dixon and Minter 2017) of this report. The Revision 2 method for estimating new DRFs for the ELLWF disposal units is described in this report.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Implications of Abiotic Oxygen Buildup for Earth-like Complex Life

One of the chief paradoxes of molecular oxygen (O{sub 2}) is that it is an essential requirement for multicellular eukaryotes on Earth while simultaneously posing a threat to their survival via the formation of reactive oxygen species. In this paper, the constraints imposed by O{sub 2} on Earth-like complex life are invoked to explore whether worlds with abiotic O{sub 2} inventories can harbor such organisms. By taking the major O{sub 2} sources and sinks of Earth-like planets into account using a simple model, it is suggested that worlds that receive time-averaged X-ray and extreme ultraviolet fluxes that are ≳10 times higher than Earth might not be capable of hosting complex lifeforms because the photolysis of molecules such as water may lead to significant O{sub 2} buildup. Methods for testing this hypothesis by searching for anticorrelations between biosignatures and indicators of abiotic O{sub 2} atmospheres are described. In the event, however, that life successfully adapts to high-oxygen environments, these worlds could permit the evolution of large and complex organisms.

79 ASTRONOMY AND ASTROPHYSICS↗

Summertime Post-Cold-Frontal Marine Stratocumulus Transition Processes over the Eastern North Atlantic

Abstract Marine boundary layer (MBL) cloud morphology associated with two summertime cold fronts over the eastern North Atlantic (ENA) is investigated using high-resolution simulations from the Weather Research and Forecasting (WRF) Model and observations from the Atmospheric Radiation Measurement (ARM) ENA Climate Research Facility. Lagrangian trajectories are used to study the evolution of post-cold-frontal MBL clouds from solid stratocumulus to broken cumulus. Clouds within specified domains in the vicinity of transitions are classified according to their degree of decoupling, and cloud-base and cloud-top breakup processes are evaluated. The Lagrangian derivative of the surface latent heat flux is found to be strongly correlated with that of the cloud fraction at cloud base in the simulations. Cloud-top entrainment instability (CTEI) is shown to operate only in the decoupled MBL. A new indicator of inversion strength at cloud top that employs the vertical gradients of equivalent potential temperature and saturation equivalent potential temperature, which can be computed directly from soundings, is proposed as an alternative to CTEI. Overall, results suggest that the deepening–warming hypothesis suggested by Bretherton and Wyant explains many of the characteristics of the summertime postfrontal MBL evolution of cloud structure over the ENA, thereby widening the phase space over which the hypothesis may be applied. A subset of the deepening–warming hypothesis involving warming initially dominating over moistening is proposed. It is postulated that changes in climate change–induced modifications in cold-frontal structure over the ENA may be accompanied by coincident changes in the location and timing of MBL cloud transitions in the post-cold-frontal environment.

54 ENVIRONMENTAL SCIENCES↗

The Importance of an Ensemble Approach for Modeling Aerosol‐Convection Interactions

Aerosol-convection interactions modulate cloud microphysics, thermodynamics, and updraft intensity, contributing to climate-scale aerosol-radiative forcing. However, quantifying aerosol indirect effects in mixed-phase deep convection remains challenging due to uncertainties in parameterized physics and initial conditions driving nonlinear evolution of convective processes. This study investigates the convective updraft sensitivity to random initial temperature perturbations using an idealized ensemble modeling framework informed by in situ thermodynamic and aerosol observations from the DOE TRACER field campaign. We analyze the impact of small-scale initial perturbations on updraft velocity and supersaturation, and determine the ensemble size required to minimize stochastic internal variability. Results show that minor thermodynamic perturbations can produce updraft variability comparable to aerosol-induced changes reported in prior work. An ensemble of 10 members sufficiently reduces variability, enabling robust investigation of aerosol-related updraft invigoration and informing the design of future ensemble-based aerosol-convection interaction studies in environments with significant spatiotemporal mesoscale thermodynamic and aerosol heterogeneity.

aerosol-convection interactions↗

Urca Cooling in Neutron Star Crusts and Oceans: Effects of Nuclear Excitations

We report the excited-state structure of atomic nuclei can modify nuclear processes in stellar environments. In this Letter, we study the influence of nuclear excitations on Urca cooling (repeated back-and-forth β decay and electron capture in a pair of nuclear isotopes) in the crust and ocean of neutron stars. We provide for the first time an expression for Urca process neutrino luminosity which accounts for a thermal Boltzmann distribution of excited states in both members of an Urca pair. We use our new formula with state-of-the-art nuclear structure inputs to compute neutrino luminosities of candidate Urca cooling pairs. Our nuclear inputs consist of the latest experimental data supplemented with calculations using the projected shell model. We show that, in contrast to previous results that only consider the ground states of both nuclei in the pair, our calculated neutrino luminosities for different Urca pairs vary sensitively with the environment temperature and can be radically different from those obtained in the one-transition approximation. We find that nuclear excitations can lead to an enhancement in total Urca neutrino luminosities in the accreted neutron star crust by about 5 times as compared with the previous Urca results, which is expected to cause significant observational effects.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Physics-informed Estimation of the Covariance Matrix for Various Neutron Spectra

A method for estimating covariance matrices which capture the uncertainties in calculated reactor spectra has been developed. This method is based on perturbing the parameters of a physics-based analytic model fitted to a calculated spectrum. The covariance of the perturbed analytic spectra imposes energy-dependent correlations due to the physics of the neutron processes in the reactor, i.e., a fission component, a 1/E down-scatting component, and a thermal Maxwellian component. An analytic model is developed which is shown to produce good fits to several reactor environments. The covariance matrices produced via this method are then used as the prior spectrum in STAYSL least squares spectrum adjustment where it is combined with integral metrics, such as activation measurements, to produce a high-fidelity neutron spectrum characterization. It was concluded that the methodology showed agreeable results for the ACRR free-field spectrum adjustment in STAYSL resulting in a 𝜒 2 value of 2.21 (per degree of freedom), but further work is needed to describe scattering and interface regions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Coupling of Pronghorn and RELAP-7 for a Pebble Bed Reactor

High temperature gas cooled reactors (HTGR) are a candidate for timely Gen-IV reactor technology deployment because of high technology readiness and walk-away safety. Among HTGRs, pebble bed reactors (PBRs) have attractive features such as low excess reactivity and online refueling. Pebble bed reactors pose unique challenges to analysts and reactor designers such as continuous burnup distribution depending on pebble motion and recirculation, radiative heat transfer across a variety of gas-filled gaps, and long design basis transients such as pressurized and depressurized loss of forced circulation. Modeling and simulation is essential for both the PBR’s safety case and design process. In order to verify and validate the new generation codes the Nuclear Energy Agency (NEA) Data bank provide a set of benchmarks data together with solutions calculated by the participants using the state of the art codes of that time. An important milestone to test the new PBR simulation codes is the OECD NEA PBMR-400 benchmark which includes thermal hydraulic and neutron kinetic standalone exercises as well as coupled exercises and transients scenarios. In this work, the reactor multiphysics code MAMMOTH and the thermal hydraulics code Pronghorn, both developed by the Idaho National Laboratory (INL) within the multiphysics object-oriented simulation environment (MOOSE), have been used to solve Phase 1 exercises 1 and 2 of the PBMR-400 benchmark. The steady state results are in agreement with the other participants’ solutions demonstrating the adequacy of MAMMOTH and Pronghorn for simulating PBRs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Measurements of Irradiated Low Enriched Fuel with a Gamma-Ray Scanning System (ANIMMA 2023 Conference Presentation)

A gamma-ray scanning system was designed to perform post-irradiation measurements of nuclear fuel at Idaho National Laboratory (INL). The system is composed of a coaxial high-purity germanium (HPGe) detector, a collimator, and mechanical positioning stages that translate the fuel sample across the front of the collimator. A Monte Carlo N-Particle code simulation of the system and the fuel were created with vendor-supplied design specifications, dimensional measurements, and x-ray radiographs of the HPGe detector as well as all available fuel specifications. Benchmark measurements were performed by scanning an irradiated fuel rodlet containing eight pellets of 0.74% enriched UO2 in zirconium alloy. This fuel rodlet was part of the Environment Rodlet Transient Test Apparatus (SERTTA) testing campaign in the Transient Reactor Test Facility (TREAT). These results were compared to simulated spectra to help characterize the detector model and establish fidelity. One goal of the system is to determine the number of fissions per gram of UO2 in the fuel. This article documents the modeling, benchmarking measurements, and the calculated number of fissions per gram in the SERTTA-A rodlet which was previously irradiated at TREAT.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Disordering of helium gas bubble superlattices in molybdenum under ion irradiation and thermal annealing

Self-organization of gas bubbles causes the formation of an ordered array of nanoscale gas bubbles (a gas bubble superlattice), a highly efficient mechanism for gas storage under irradiation. Here, the stability of helium (He) gas bubble superlattices in molybdenum (Mo) under krypton (Kr) ion irradiation and thermal annealing has been investigated. The He gas bubble superlattices gradually become disordered under Kr ion irradiation at 300° C, and the order-disorder transformation process completes at 2.5 dpa. Both transmission electron microscopy (TEM) and synchrotron-based small-angle X-ray scattering (SAXS) reveal that the order-disorder transformation of He gas bubble superlattices is associated with a slight increase in the average bubble size. Phase-field modeling indicates that the inhomogeneous growth and coarsening of bubbles/voids cause the disordering of imperfect superlattices under irradiation and implies that highly ordered superlattices could potentially exhibit much stronger resistance to irradiation damage. Under thermal annealing, the He gas bubble superlattices in Mo become unstable and disordered at 1000° C with the bubble size increasing from ~1.1 to ~1.6 nm. The finding in this research provides insights into the disordering mechanisms of defect superlattices as well as guidance for designing stable defect superlattices in harsh environments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Milestone 2.7: Evaluation of Techniques for the Measurement of Molecular Hydrogen Gas in Helium Matrices

Published data by Task 2 (Oxyhydroxide Layer Radiolytic Gas Generation Resolution) for Technical Considerations and Challenges for Extended (>50 yrs) Dry Storage of Aluminum Cladded Spent Nuclear Fuel (ASNF), demonstrated that radiolytic molecular hydrogen (H2) production from gamma irradiated aluminum alloy 1100 (Al-1100) coupons exhibited significant dependence on backfill gaseous environment conditions: air, due to the presence of oxygen, completely inhibited H2 production; nitrogen promoted H2 production; and argon yielded more H2 than nitrogen environments. The concern here is that helium has been proposed as the backfill gas for extended storage and is more inert than argon, which may translate into significantly more H2 production than current Task 3 argon-based models predict. However, the measurement of H2 in helium media was not possible using the previously establish gas chromatography (GC) flame ionization detector, due to similarities in thermal conductivity. This milestone was initiated to evaluate an alternative GC approach that employed a mercuric oxide (HgO) reduction gas detector (RGD). Using the HgO RGD setup, H2 was successfully calibrated in the presence of a helium carrier gas, and then subsequently measured in control and irradiated crush-tube sample vials, consistent with previous sample measurements. Overall, the HgO RGD approach was found to be sufficient for future helium environment irradiations.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

Constraining nuclear mass models using 𝑟-process observables with multiobjective optimization

Modeling nuclear masses, particularly for nuclei far from stability, remains a key objective in nuclear physics. One contemporary approach is machine learning (ML), which trains on experimental data, but can suffer large errors when extrapolating toward neutron-rich species. In nature, such masses shape observables for the rapid neutron capture process (𝑟 process), which in principle could inform ML models. Here, we introduce a multiobjective optimization approach using the Pareto front algorithm. We show that this technique, capable of identifying models that generate 𝑟-process abundances aligning with both solar and stellar data, is a promising method to select ML models with reliable extrapolation power.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Normalizing Flows for Microscopic Many-Body Calculations: An Application to the Nuclear Equation of State

We report that normalizing flows are a class of machine learning models used to construct a complex distribution through a bijective mapping of a simple base distribution. We demonstrate that normalizing flows are particularly well suited as a Monte Carlo integration framework for quantum many-body calculations that require the repeated evaluation of high-dimensional integrals across smoothly varying integrands and integration regions. As an example, we consider the finite-temperature nuclear equation of state. An important advantage of normalizing flows is the ability to build highly expressive models of the target integrand, which we demonstrate enables precise evaluations of the nuclear free energy and its derivatives. Furthermore, we show that a normalizing flow model trained on one target integrand can be used to efficiently calculate related integrals when the temperature, density, or nuclear force is varied. This work will support future efforts to build microscopic equations of state for numerical simulations of supernovae and neutron star mergers that employ state-of-the-art nuclear forces and many-body methods.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Physical Sciences Vistas (Issue 3 2020)

In this issue, highlights of our outstanding R&D supporting the Laboratory’s nuclear security mission include the following. 1. The essential nuclear materials science contributions of MST’s Materials Properties Team to a range of Lab missions, operations, and initiatives. 2. Work in the Sigma Complex supporting NNSA’s Advanced Manufacturing Development milestones by members of Sigma Division and colleagues across the Laboratory and other NNSA sites. 3. Greg Dale’s role in the Lab’s molybdenum program and today as a technical lead on the Scorpius project. 4. Measurements that reveal the role thermal interfaces play in dynamic compression experiments. 5. The first-ever synthesis of an actinide framework, which offers opportunities for understanding these structures as potential radioactive waste forms and provides new models of actinide species transport in the environment. This issue also showcases, as part of our commitment to simultaneous excellence in mission operations and community relations, the successful high-hazard repair of the LANSCE accelerator, which resulted in a large team Laboratory Distinguished Performance Award, and a virtual Summer Physics Camp for Young Women that brought together participants from around the world, even as COVID kept them socially distant in their homes.

36 MATERIALS SCIENCE↗