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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Non-local transport in turbulent MHD convection

The nonlocal non-diffusive transport of passive scalars in turbulent magnetohydrodynamic (MHD) convection is investigated using transilient matrices. These matrices describe the probability that a tracer particle beginning at one position in a flow will be advected to another position after some time. A method for the calculation of these matrices from simulation data which involves following the trajectories of passive tracer particles and calculating their transport statistics, is presented. The method is applied to study the transport in several simulations of turbulent, rotating, three dimensional compressible, penetrative MDH convection. Transport coefficients and other diagnostics are used to quantify the transport, which is found to resemble advection more closely than diffusion. Some of the results are found to have direct relevance to other physical problems, such as the light element depletion in sun-type stars. The large kurtosis found for downward moving particles at the base of the convection zone implies several extreme events.

Miesch, Mark↗
NASA NTRS2012

DC and Structured Electric Fields Observed on the C/NOFS Satellite and Their Association with Longitude, Plasma Density, and Solar Activity

Observations of DC electric fields and associated E x B plasma drifts gathered by the Vector Electric Field Investigation (VEFI) on the Air Force Communication/Navigation Outage Forecasting System (C/NOFS) satellite are presented. We show statistical averages of the vector fields and resulting E x B plasma flows for the first three years of operations as a function of season, longitude, local time, and Fl 0.7 conditions. Magnetic field data from the VEFI science magnetometer are used to compute the plasma flows. Although typically displaying eastward and outward-directed fields during the day and westward and downward-directed fields at night, the data from DC electric field detector often reveal variations from this pattern that depend on longitude, solar activity, and plasma density. Clear "wave-4" tidal effects in both electric field components have been detected and will be presented. Zonal plasma drifts show a marked variation with solar activity and may be used as a proxy for neutral winds at night. Evidence for pre-reversal enhancements in the meridional drifts that depend on solar activity is present for some longitudes, and are corroborated by clear evidence in the plasma density data that the spacecraft journeyed below the F-peak during evenings when the rise in the ionosphere is most pronounced. In addition to DC electric fields, the data reveal considerable electric field structures at large scales (approx 100's of km) that are usually confined to the nightside. Although such electric field structures are typically associated with plasma density depletions and structures, what is surprising is the number of cases in which large amplitude, structured DC electric fields are observed without a significant plasma density counterpart structure, including their appearance at times when the ambient plasma density appears relatively quiescent. We investigate the mapping of structured electric fields along magnetic field lines from distant locations and consider tropospheric thunderstorm sources to explain some of the observations. The dependence of the structuring on season and solar activity will be reported and discussed.

Pfaff, Robert↗
NASA NTRS2013

Monte Carlo Bayesian Inference on a Statistical Model of Sub-gridcolumn Moisture Variability Using High-resolution Cloud Observations : Sensitivity Tests and Results - Part II

Part I presented a Monte Carlo Bayesian method for constraining a complex statistical model of GCM sub-gridcolumn moisture variability using high-resolution MODIS cloud data, thereby permitting large-scale model parameter estimation and cloud data assimilation. This part performs some basic testing of this new approach, verifying that it does indeed significantly reduce mean and standard deviation biases with respect to the assimilated MODIS cloud optical depth, brightness temperature and cloud top pressure, and that it also improves the simulated rotational-Ramman scattering cloud optical centroid pressure (OCP) against independent (non-assimilated) retrievals from the OMI instrument. Of particular interest, the Monte Carlo method does show skill in the especially difficult case where the background state is clear but cloudy observations exist. In traditional linearized data assimilation methods, a subsaturated background cannot produce clouds via any infinitesimal equilibrium perturbation, but the Monte Carlo approach allows finite jumps into regions of non-zero cloud probability. In the example provided, the method is able to restore marine stratocumulus near the Californian coast where the background state has a clear swath. This paper also examines a number of algorithmic and physical sensitivities of the new method and provides guidance for its cost-effective implementation. One obvious difficulty for the method, and other cloud data assimilation methods as well, is the lack of information content in the cloud observables on cloud vertical structure, beyond cloud top pressure and optical thickness, thus necessitating strong dependence on the background vertical moisture structure. It is found that a simple flow-dependent correlation modification due to Riishojgaard (1998) provides some help in this respect, by better honoring inversion structures in the background state.

Monte Carlo Bayesian inference↗
NASA NTRS1993

Space Shuttle Main Engine performance analysis

For a number of years, NASA has relied primarily upon periodically updated versions of Rocketdyne's power balance model (PBM) to provide space shuttle main engine (SSME) steady-state performance prediction. A recent computational study indicated that PBM predictions do not satisfy fundamental energy conservation principles. More recently, SSME test results provided by the Technology Test Bed (TTB) program have indicated significant discrepancies between PBM flow and temperature predictions and TTB observations. Results of these investigations have diminished confidence in the predictions provided by PBM, and motivated the development of new computational tools for supporting SSME performance analysis. A multivariate least squares regression algorithm was developed and implemented during this effort in order to efficiently characterize TTB data. This procedure, called the 'gains model,' was used to approximate the variation of SSME performance parameters such as flow rate, pressure, temperature, speed, and assorted hardware characteristics in terms of six assumed independent influences. These six influences were engine power level, mixture ratio, fuel inlet pressure and temperature, and oxidizer inlet pressure and temperature. A BFGS optimization algorithm provided the base procedure for determining regression coefficients for both linear and full quadratic approximations of parameter variation. Statistical information relative to data deviation from regression derived relations was also computed. A new strategy for integrating test data with theoretical performance prediction was also investigated. The current integration procedure employed by PBM treats test data as pristine and adjusts hardware characteristics in a heuristic manner to achieve engine balance. Within PBM, this integration procedure is called 'data reduction.' By contrast, the new data integration procedure, termed 'reconciliation,' uses mathematical optimization techniques, and requires both measurement and balance uncertainty estimates. The reconciler attempts to select operational parameters that minimize the difference between theoretical prediction and observation. Selected values are further constrained to fall within measurement uncertainty limits and to satisfy fundamental physical relations (mass conservation, energy conservation, pressure drop relations, etc.) within uncertainty estimates for all SSME subsystems. The parameter selection problem described above is a traditional nonlinear programming problem. The reconciler employs a mixed penalty method to determine optimum values of SSME operating parameters associated with this problem formulation.

Santi, L. Michael↗
NASA NTRS1980

Response of a panel to a supersonic turbulent boundary layer - Studies on a theoretical model

A study of the response of a clamped panel to a supersonic turbulent boundary layer, based on a recently developed theoretical model, is presented. It is demonstrated that the model incorporates the effect of coupling between the panel motion and the flow of the surrounding fluid. Further, a Ritz-Galerkin method is used to obtain approximate solutions for the statistics of the panel response to the turbulence. The numerical results are compared with previous experimental data and the theoretical model is assessed.

Yen, D. H. Y.↗
NASA NTRS1984

A laser velocimeter system for large-scale aerodynamic testing

A unique laser velocimeter was developed that is capable of sensing two orthogonal velocity components from a variable remote distance of 2.6 to 10 m for use in the 40- by 80-Foot and 80- by 120-Foot Wind Tunnels and the Outdoor Aerodynamic Research Facility at Ames Research Center. The system hardware, positioning instrumentation, and data acquisition equipment are described in detail; system capabilities and limitations are discussed; and expressions for systematic and statistical accuracy are developed. Direct and coupled laboratory measurements taken with the system are compared with measurements taken with a laser velocimeter of higher spatial resolution, and sample data taken in the open circuit exhaust flow of a 1/50-scale model of the 80- by 120-Foot Wind Tunnel are presented.

Reinath, M. S.↗
NASA NTRS1990

Space Station Freedom Data Assessment Study

The SSF Data Assessment Study was initiated to identify payload and operations data requirements to be supported in the Space Station era. To initiate the study payload requirements from the projected SSF user community were obtained utilizing an electronic questionnaire. The results of the questionnaire were incorporated in a personal computer compatible database used for mission scheduling and end-to-end communications analyses. This paper discusses data flow paths and associated latencies, communications bottlenecks, resource needs versus availability, payload scheduling 'warning flags' and payload data loading requirements for each major milestone in the Space Station buildup sequence. This paper also presents the statistical and analytical assessments produced using the data base, an experiment scheduling program, and a Space Station unique end-to-end simulation model. The modeling concepts and simulation methodologies presented in this paper provide a foundation for forecasting communication requirements and identifying modeling tools to be used in the SSF Tactical Operations Planning (TOP) process.

Johnson, Anngienetta R.↗
NASA NTRS1994

Magnetospheric space plasma investigations

A time dependent semi-kinetic model that includes self collisions and ion-neutral collisions and chemistry was developed. Light ion outflow in the polar cap transition region was modeled and compared with data results. A model study of wave heating of O+ ions in the topside transition region was carried out using a code which does local calculations that include ion-neutral and Coulomb self collisions as well as production and loss of O+. Another project is a statistical study of hydrogen spin curve characteristics in the polar cap. A statistical study of the latitudinal distribution of core plasmas along the L=4.6 field line using DE-1/RIMS data was completed. A short paper on dual spacecraft estimates of ion temperature profiles and heat flows in the plasmasphere ionosphere system was prepared. An automated processing code was used to process RIMS data from 1981 to 1984.

Comfort, Richard H.↗
NASA NTRS2012

Experimental Studies of Low-Pressure Turbine Flows and Flow Control

This report summarizes research performed in support of the NASA Glenn Research Center (GRC) Low-Pressure Turbine (LPT) Flow Physics Program. The work was performed experimentally at the U.S. Naval Academy faculties. The geometry corresponded to "Pak B" LPT airfoil. The test section simulated LPT flow in a passage. Three experimental studies were performed: (a) Boundary layer measurements for ten baseline cases under high and low freestream turbulence conditions at five Reynolds numbers of 25,000, 50,000, 100,000, 200,000, and 300,000, based on passage exit velocity and suction surface wetted length; (b) Passive flow control studies with three thicknesses of two-dimensional bars, and two heights of three-dimensional circular cylinders with different spanwise separations, at same flow conditions as the 10 baseline cases; (c) Active flow control with oscillating synthetic (zero net mass flow) vortex generator jets, for one case with low freestream turbulence and a low Reynolds number of 25,000. The Passive flow control was successful at controlling the separation problem at low Reynolds numbers, with varying degrees of success from case to case and varying levels of impact at higher Reynolds numbers. The active flow control successfully eliminated the large separation problem for the low Reynolds number case. Very detailed data was acquired using hot-wire anemometry, including single and two velocity components, integral boundary layer quantities, turbulence statistics and spectra, turbulent shear stresses and their spectra, and intermittency, documenting transition, separation and reattachment. Models were constructed to correlate the results. The report includes a summary of the work performed and reprints of the publications describing the various studies.

Volino, Ralph J.↗
NASA NTRSDate not supplied

SLS Integrated Modal Test Uncertainty Quantification using the Hybrid Parametric Variation Method

Uncertainty in structural loading during launch is a significant concern in the development of spacecraft and launch vehicles. Small variations in launch vehicle and payload mode shapes and their interaction can result in significant variation in system loads. In many cases involving large aerospace systems it is difficult, not economical, or impossible to perform a system modal test. However, it is still vital to obtain test results that can be compared with analytical predictions to validate models. Instead, the “Building Block Approach” is used in which system components are tested individually. Component models are correlated and updated to agree as best they can with test results. The Space Launch System consists of a number of components that are assembled into a launch vehicle. Finite element models of the components are developed, reduced to Hurty/Craig-Bampton models and assembled to represent different phases of flight. The only opportunity to obtain modal test data from an assembled Space Launch System will be during the Integrated Modal Test. There is always uncertainty in every model, which flows into uncertainty in predicted system results. Uncertainty Quantification is used to determine statistical bounds on prediction accuracy based on model uncertainty. For the Space Launch System, model uncertainty is at the Hurty/Craig-Bampton component level. Uncertainty in the Hurty/Craig-Bampton components is quantified using the hybrid parametric variation approach that combines parametric and nonparametric uncertainty. Uncertainty in model form is one of the biggest contributors to uncertainty in complex built-up structures. This type of uncertainty cannot be represented by variations infinite element model input parameters and thus cannot be included in a parametric approach. However, model-form uncertainty can be modeled using a nonparametric approach based on random matrix theory. The hybrid parametric variation method requires the selection of dispersion values for the Hurty/Craig-Bampton fixed-interface eigenvalues, and the Hurty/Craig-Bampton stiffness matrices. Component test/analysis frequency error is used to identify the fixed-interface eigenvalue dispersions, while test/analysis cross-orthogonality is used to identify stiffness dispersion values. The hybrid parametric variation uncertainty quantification approach is applied to the Space Launch System Integrated Modal Test configuration. Monte Carlo analysis is performed, and statistics are determined for modal correlation metrics, frequency response from Integrated Modal Test shakers to selected accelerometers, as well as other metrics for determining how well target modes are excited and identified. If the predicted uncertainty envelopes future Integrated Modal Test results, then there will be increased confidence in the utility of the component-based hybrid parametric variation uncertainty quantification approach.

Uncertainty Quantification↗
NASA NTRS1999

Main Geomagnetic Field Models from Oersted and Magsat Data Via a Rigorous General Inverse Theory with Error Bounds

The purpose of the grant was to study how prior information about the geomagnetic field can be used to interpret surface and satellite magnetic measurements, to generate quantitative descriptions of prior information that might be so used, and to use this prior information to obtain from satellite data a model of the core field with statistically justifiable error estimates. The need for prior information in geophysical inversion has long been recognized. Data sets are finite, and faithful descriptions of aspects of the earth almost always require infinite-dimensional model spaces. By themselves, the data can confine the correct earth model only to an infinite-dimensional subset of the model space. Earth properties other than direct functions of the observed data cannot be estimated from those data without prior information about the earth. Prior information is based on what the observer already knows before the data become available. Such information can be "hard" or "soft". Hard information is a belief that the real earth must lie in some known region of model space. For example, the total ohmic dissipation in the core is probably less that the total observed geothermal heat flow out of the earth's surface. (In principle, ohmic heat in the core can be recaptured to help drive the dynamo, but this effect is probably small.) "Soft" information is a probability distribution on the model space, a distribution that the observer accepts as a quantitative description of her/his beliefs about the earth. The probability distribution can be a subjective prior in the sense of Bayes or the objective result of a statistical study of previous data or relevant theories.

Backus, George E.↗
NASA NTRS1989

Two-component phase-averaged turbulence statistics downstream of a rotating spoked-wheel wake generator

Flow-field measurements of unsteady turbulent flow downstream of a rotating spoked-wheel wake generator were performed in a short-duration light-piston tunnel, and the instantaneous-velocity data were phase averaged based on a signal synchronized with the bar-passing frequency. Mean axial velocities were found to agree well with those obtained from measurements behind a stationary cylinder and to be independent of both Reynolds and bar-passing Strouhal numbers. Reynolds stresses were found to be consistent with related cylinder-wake measurements, but were significantly higher than corresponding measurements obtained in large-scale research turbomachines. Phase-averaged triple velocity correlations were calculated from the digital velocity records, revealing the sign and the magnitude of skewness in the velocity probability density distributions for the two components.

O'Brien, J. E.↗
NASA NTRS1992

GRO/EGRET data analysis software: An integrated system of custom and commercial software using standard interfaces

The Energetic Gamma Ray Telescope Experiment (EGRET) on the Compton Gamma Ray Observatory has been in orbit for more than a year and is being used to map the full sky for gamma rays in a wide energy range from 30 to 20,000 MeV. Already these measurements have resulted in a wide range of exciting new information on quasars, pulsars, galactic sources, and diffuse gamma ray emission. The central part of the analysis is done with sky maps that typically cover an 80 x 80 degree section of the sky for an exposure time of several days. Specific software developed for this program generates the counts, exposure, and intensity maps. The analysis is done on a network of UNIX based workstations and takes full advantage of a custom-built user interface called X-dialog. The maps that are generated are stored in the FITS format for a collection of energies. These, along with similar diffuse emission background maps generated from a model calculation, serve as input to a maximum likelihood program that produces maps of likelihood with optional contours that are used to evaluate regions for sources. Likelihood also evaluates the background corrected intensity at each location for each energy interval from which spectra can be generated. Being in a standard FITS format permits all of the maps to be easily accessed by the full complement of tools available in several commercial astronomical analysis systems. In the EGRET case, IDL is used to produce graphics plots in two and three dimensions and to quickly implement any special evaluation that might be desired. Other custom-built software, such as the spectral and pulsar analyses, take advantage of the XView toolkit for display and Postscript output for the color hard copy. This poster paper outlines the data flow and provides examples of the user interfaces and output products. It stresses the advantages that are derived from the integration of the specific instrument-unique software and powerful commercial tools for graphics and statistical evaluation. This approach has several proven advantages including flexibility, a minimum of development effort, ease of use, and portability.

Laubenthal, N. A.↗
NASA NTRS2002

Entropy Splitting for High Order Numerical Simulation of Compressible Turbulence

A stable high-order numerical scheme for direct numerical simulation (DNS) of shock-free compressible turbulence is presented. The method is applicable to general geometries. It contains no upwinding, artificial dissipation, or filtering. Instead the method relies on the stabilizing mechanisms of an appropriate conditioning of the governing equations and the use of compatible spatial difference operators for the interior points (interior scheme) as well as the boundary points (boundary scheme). An entropy-splitting approach splits the inviscid flux derivatives into conservative and nonconservative portions. The spatial difference operators satisfy a summation-by-parts condition, leading to a stable scheme (combined interior and boundary schemes) for the initial boundary value problem using a generalized energy estimate. A Laplacian formulation of the viscous and heat conduction terms on the right hand side of the Navier–Stokes equations is used to ensure that any tendency to odd–even decoupling associated with central schemes can be countered by the fluid viscosity. The resulting methods are able to minimize the spurious high-frequency oscillations associated with pure central schemes, especially for long time integration applications such as DNS. For validation purposes, the methods are tested in a DNS of compressible turbulent plane channel flow at low values of friction Mach number, where reference turbulence data bases exist. It is demonstrated that the methods are robust in terms of grid resolution, and in good agreement with published channel data. Accurate turbulence statistics can be obtained with moderate grid sizes. Stability limits on the range of the splitting parameter are determined from numerical tests.

N.D. Sandham↗
DOE OSTI2026

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - NLR Historical Solar PV

The U.S. Department of Energy and National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis from variable sources, hydrogen compression and storage, and hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) research platform. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence data centers and other variable loads. This dataset entry describes the behavior of a 1.25-MW proton exchange membrane MC250 electrolyzer system, manufactured by Nel Hydrogen , [1] when fed historical data generated by the 430-kW, fixed-axis solar photovoltaic (PV) array located at NLR’s Flatirons Campus. (While the electrolyzer balance of plant supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack.) Solar PV power output data for the 2020 calendar year were categorized on a daily basis by total energy generation and standard deviation. Each day was then ranked by these metrics, and the 25th, 50th, and 100th percentiles were selected. The 75th percentile day did not exhibit sufficient variability to make for a valuable experiment. A similar process was used for the related historical wind dataset . [2] The historical days in 2020 that represented these percentiles are Dec. 19, March 29, and May 4, respectively. The entire solar day’s power profile was then fed through the MC250 electrolyzer. Due to its length, the 100th percentile day experiment was split into two parts, and the final 3 hours of the solar day were not captured. These final 3 hours contained no spikes or dips of interest and simply represented a slow decay of input solar power. Also, a single timestamp (13:13:47 on Jan. 14, 2026) was lost in the hydrogen system supervisory control and data acquisition. Finally, during the 25th percentile experiment (solar day Dec. 19, 2020) data recording was lost from 11:00:13 to 11:14:45. The roughly 15 minutes of the solar profile were rerun at the end of the experiment and spliced into this time slot during post-processing. The electrolysis system controls hydrogen production by varying direct current applied to the stack, from a maximum of 3,000 A to a minimum safe operation of 300 A, or 10%. Because the current–voltage characteristic changes as the stack ages and efficiency degrades, the actual minimum safe operating power changes over time. The historical solar profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz frequency. For more details on the statistical analysis process, see the slide deck “Public Reference Data for Megawatt-Scale Hydrogen Electrolysis: NLR Historical Solar PV Analysis and Profile Generation” accessible with this data entry. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single solar PV electrolysis experiment and is formatted as: {technology}_{percentile}_{scaling factor} For instance, “solarPV-430kW_25_2x.zip” reports the experiment using the 25th percentile solar data from the historical 2020 solar PV dataset, scaled to 200%. Scaling factors were applied to the generated solar PV power output files to more closely match the 1.25-MW capacity of the electrolyzer. Each .zip folder contains the following files: A .csv file containing raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production, electrolysis power consumption, and solar power input. A PDF file detailing the historical solar data statistical analysis used to generate the solar profile. An experiment labeled “characterization_200.zip” demonstrates the MC250 electrolyzer steady-state response with 30-minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all experiments combined into one dataset labeled "combined_solarPV_experiments.csv". [1] nelhydrogen.com/product/mc-series-electrolyser . [2] data.nlr.gov/submissions/316 .

08 HYDROGEN↗
NASA NTRS1998

Scaling Properties of Particle Density Fields Formed in Simulated Turbulent Flows

Direct numerical simulations (DNS) of particle concentrations in fully developed 3D turbulence were carried out in order to study the nonuniform structure of the particle density field. Three steady-state turbulent fluid fields with Taylor microscale Reynolds numbers (Re(sub lambda)) of 40, 80 and 140 were generated by solving the Navier-Stokes equations with pseudospectral methods. Large scale forcing was used to drive the turbulence and maintain temporal stationarity. The response of the particles to the fluid was parameterized by the particle Stokes number St, defined as the ratio of the particle's stopping time to the mean period of eddies on the Kolmogorov scale (eta). In this paper, we consider only passive particles optimally coupled to these eddies (St approx. = 1) because of their tendency to concentrate more than particles with lesser or greater St values. The trajectories of up to 70 million particles were tracked in the equilibrated turbulent flows until the particle concentration field reached a statistically stationary state. The nonuniform structure of the concentration fields was characterized by the multifractal singularity spectrum, f(alpha), derived from measures obtained after binning particles into cells ranging from 2(eta) to 15(eta) in size. We observed strong systematic variations of f(alpha) across this scale range in all three simulations and conclude that the particle concentration field is not statistically self similar across the scale range explored. However, spectra obtained at the 2(eta), 4(eta), and 8(eta) scales of each flow case were found to be qualitatively similar. This result suggests that the local structure of the particle concentration field may be flow-Independent. The singularity spectra found for 2n-sized cells were used to predict concentration distributions in good agreement with those obtained directly from the particle data. This Singularity spectrum has a shape similar to the analogous spectrum derived for the inertial-range energy dissipation fields of experimental turbulent flows at Re(sub lambda) = 110 and 1100. Based on this agreement, and the expectation that both dissipation and particle concentration are controlled by the same cascade process, we hypothesize that singularity spectra similar to the ones found in this work provide a good characterization of the spatially averaged statistical properties of preferentially concentrated particles in higher Re(sub lambda) turbulent flows.

Hogan, Robert C.↗
DOE OSTI2025

Reinforcement Learning for Anomaly Detection in Nuclear Power Plant Operation and Maintenance

In nuclear power plants (NPPs), timely identification of sensor and human errors is critical to ensure safe and efficient plant operations. Anomaly detection models can be employed for this task. However, traditional anomaly detection approaches may have high dependency on labeled datasets and struggle with adaptability in complex, dynamic environments. Reinforcement learning (RL) has demonstrated significant potential in fault diagnosis and anomaly detection; however, its application to anomaly detection in NPPs remains a relatively underexplored research direction. Hence, to address this gap, in this study, we present a novel physics-informed reinforcement learning model, PIRL-AD: Physics-Informed Reinforcement Learning for Anomaly Detection, that integrates domain knowledge from calorimetric equations into the RL framework for enhanced sensor and human error anomaly detection. We evaluate the performance of PIRL-AD against a non-physics informed RL benchmark and a support vector machine (SVM) on data collected from a forced flow loop testbed. Experimental results suggest that PIRL-AD outperforms other baselines on a range of anomalous datasets that include both sensor and human-induced anomalies across key performance metrics, statistically outperforming the RL and SVM benchmarks with respect to geometric mean (respectively, 92.96% vs. 91.06% vs. 83.01%) and F1-score (respectively, 89.23% vs. 86.98% vs. 77.01%). Furthermore, the findings suggest the potential of physics-integrated reinforcement learning models for enhanced anomaly detection performance in NPPs.

Reinforcement learning↗
DOE OSTI2026

Data and script associated with “Shifts in Rain-Snow Partitioning Drive Faster Water Transit Times in the US Pacific Northwest”

This data package contains the data and code to use and run the Water Tracer enabled version of the Weather Research and Forecasting Hydrologic model (WT-WRF-Hydro) with the Sequential Precipitation Input Tagging (SPIT) framework. It is associated with the publication “Shifts in Rain-Snow Partitioning Drive Faster Water Transit Times in the US Pacific Northwest” published in Scientific Reports (Butler et al., 2026; https://doi.org/10.1038/s41598-026-46539-1). We use the Continental U.S. (CONUSII; Rasmussen et al., 2021) dataset to force the model with an historical climate (2006–2013) and a future climate (2086–2093) with a representative carbon pathway (RCP) 8.5 scenario. We use the model to calculate water transit times in five headwater catchments within the U.S. Pacific Northwest. We also show key hydrologic and environmental variables that affect water transit times and changes in the future. Finally, we use observed data to validate the model such as stream water isotopes, snowpack characteristics, and stream discharge. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. The data package consists of 11 folders: (1) "Figures" contains the exported figures used in the manuscript; (2) "Model_Isotope_Date" contains the WT-WRF-Hydro isotope date used in model validation; (3) “Model_Outputs_Future” contains the WT-WRF-Hydro future climate outputs; (4) “Model_Outputs_Historical” contains the WT-WRF-Hydro historical climate outputs; (5) “Model_Outputs_Weights_Areas” contains the WT-WRF-Hydro weights per catchment used to calculate water transit times and isotopes in stream water; (6) “MODIS_data_scripts” contains data used to validate snow conditions in the study area; (7) “Observed_Flow_Data” contains the observed streamflow data used in model validation; (8) “Observed_Isotope_Data” contains the observed stream water isotope data used in model validation; (9) “Scripts” contains the Python scripts used to general results and the figures; (10) “Statistic_Outputs” contains the water transit time statistical outputs reported in this manuscript; (11) “Validation_SNOTEL” contains the SNOTEL data used in model validation. The files in this data package have the following file extensions: .tif, .txt, .csv, .pdf, .py, .jpg, and .png.

American River↗