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NASA NTRS2004

Land Surface Temperature Measurements from EOS MODIS Data

This report summarizes the accomplishments made by the MODIS LST (Land-Surface Temperature) group at University of California, Santa Barbara, under NASA Contract. Version 1 of the MODIS Land-Surface Temperature Algorithm Theoretical Basis Document (ATBD) was reviewed in June 1994, version 2 reviewed in November 1994, version 3.1 in August 1996, and version 3.3 updated in April 1999. Based on the ATBD, two LST algorithms were developed, one is the generalized split-window algorithm and another is the physics-based day/night LST algorithm. These two LST algorithms were implemented into the production generation executive code (PGE 16) for the daily standard MODIS LST products at level-2 (MODII-L2) and level-3 (MODIIA1 at 1 km resolution and MODIIB1 at 5km resolution). PGE codes for 8-day 1 km LST product (MODIIA2) and the daily, 8-day and monthly LST products at 0.05 degree latitude/longitude climate model grids (CMG) were also delivered. Four to six field campaigns were conducted each year since 2000 to validate the daily LST products generated by PGE16 and the calibration accuracies of the MODIS TIR bands used for the LST/emissivity retrieval from versions 2-4 of Terra MODIS data and versions 3-4 of Aqua MODIS data. Validation results from temperature-based and radiance-based methods indicate that the MODIS LST accuracy is better than 1 C in most clear-sky cases in the range from -10 to 58 C. One of the major lessons learn from multi- year temporal analysis of the consistent V4 daily Terra MODIS LST products in 2000-2003 over some selected target areas including lakes, snow/ice fields, and semi-arid sites is that there are variable numbers of cloud-contaminated LSTs in the MODIS LST products depending on surface elevation, land cover types, and atmospheric conditions. A cloud-screen scheme with constraints on spatial and temporal variations in LSTs was developed to remove cloud-contaminated LSTs. The 5km LST product was indirectly validated through comparisons to the 1 km LST product. Twenty three papers related to the LST research work were published in journals over the last decade.

Wan, Zheng-Ming↗

NASA NTRS2019

Seasonal Dependence of SMAP Radiometer-Based Soil Moisture Performance as Observed over Core Validation Sites

The NASA SMAP (Soil Moisture Active Passive) mission provides a global coverage of soil moisture measurements based on its L-band microwave radiometer every 2-3 days at about 40 km resolution. The soil moisture retrieval algorithms model the brightness temperature as a function of soil moisture, surface conditions and vegetation. External data sources inform the algorithms about the surface conditions and vegetation, which enable the retrieval of soil moisture. The inversion process contains uncertainties related to radiometer measurements, forward model assumptions and ancillary data sources. This study focuses on the uncertainties that depend on the seasonal evolution of the surface conditions and vegetation. This study compares the SMAP and core validation site (CVS) soil moisture values over a period of three years to extract the evolution of performance metrics over time. The analysis showed that most CVS that include managed agriculture exhibit significant time-dependent seasonal bias. This bias was linked to seasonal temperature cycle, which is a proxy to several features that can cause seasonally dependent errors in the SMAP product.

core validation site↗

DOE OSTI2026

Xanthos-Lake Dataset

The Xanthos-Lake v1.0 dataset provides the input data, trained machine-learning models, and simulation outputs needed to characterize lake water balance, snow and ice conditions, and mixing-layer temperature within the Xanthos global hydrological modeling framework. The dataset supports lake representation across a wide range of lake sizes and hydroclimatic conditions by combining xLSIM, a basin-specific machine-learning emulator of lake snow, ice, ice-cover fraction, and mixing-layer temperature, with the Xanthos-Lake water-balance model. The archive contains NetCDF datasets used to train and evaluate xLSIM, trained model weights, processed meteorological and lake-property inputs, and basin- and lake-category-specific simulation outputs. These materials are organized into four primary data groups, described below. Snowice_model_inputs: Contains the NetCDF input data used to train xLSIM. The xLSIM machine-learning framework uses three lake-based datasets. The meteorological forcing dataset provides monthly relative humidity, specific humidity, surface wind speed, maximum and minimum air temperature, downward longwave and shortwave radiation, snowfall, surface air pressure, and total precipitation. Lake surface area is included as an additional static predictor. The target-state dataset provides lake ice thickness, snow depth, snow cover, and lake mixing-layer temperature, while a companion lake-surface dataset provides the lake ice-cover fraction. Before training, ice thickness and snow depth are converted from meters to centimeters, mixing-layer temperature is converted from kelvin to degrees Celsius and constrained to nonnegative values, and ice-cover fraction is converted from a fraction to a percentage. The predictor variables are normalized using statistics calculated across the selected lakes and time steps. Snowice_model_outputs: Contains the NetCDF outputs generated by xLSIM. For each basin, xLSIM produces a file containing observed and predicted lake-state variables for the training, validation, and testing periods. The modeled variables include lake ice thickness, snow depth, snow cover, mixing-layer temperature, and lake ice-cover fraction. For basins without a sufficiently persistent snow-and-ice signal, the emulator predicts only mixing-layer temperature. The outputs also include training and validation loss histories, the selected model configuration, identifiers of the lakes used in training, and SHAP-based feature-importance information at the global, lake, and seasonal-regime levels. The trained machine-learning model weights are provided separately within the dataset archive. Together, these files support model evaluation and subsequent coupling with the Xanthos-Lake water-balance framework. XanthosLAKES: Contains the NetCDF input data used by the Xanthos-Lake framework. Monthly meteorological inputs include relative and specific humidity, downward shortwave and longwave radiation, mean, maximum, and minimum air temperature, wind speed, precipitation, snowfall, and surface air pressure. Static lake-property datasets provide lake identifiers, geographic locations, surface area, volume, mean depth, elevation, drainage area, fetch, outlet-routing information, and associated Xanthos grid-cell attributes. Separate bathymetric datasets provide the coefficients of the area–depth and volume–depth relationships for each aggregated lake unit. GLEV-based records provide observed lake surface area and evaporation data used to initialize lake states, define reference conditions, and calibrate and evaluate the model. Xanthos-Lake Outputs: Contains the basin- and lake-category-specific NetCDF outputs generated by Xanthos-Lake. Monthly variables include lake surface area, storage volume, outlet discharge, evaporation rate, evaporation volume, lake–groundwater exchange, lake inflow, ice thickness, snow depth, snow-cover fraction, ice-cover fraction, and mixing-layer temperature. The files also contain lake-specific calibration and validation statistics, including normalized root-mean-square error, mean absolute error, Nash–Sutcliffe efficiency, Kling–Gupta efficiency, and percent bias. Stored calibrated and derived parameters include the weir discharge coefficient, fractional freeboard, groundwater exchange coefficient, reference water level, corresponding reference surface area and storage volume, weir-width adjustment factor, and the fraction of routed inflow entering the lake. Basin identifiers, lake category, simulation period, calibration and validation periods, and parameter-schema information are retained as NetCDF metadata.

Abeshu, Guta [Pacific Northwest National Laborator↗

DOE OSTI2020

A Pattern-Recognition-Based Ensemble Data Imputation Framework for Sensors from Building Energy Systems

Building operation data are important for monitoring, analysis, modeling, and control of building energy systems. However, missing data is one of the major data quality issues, making data imputation techniques become increasingly important. There are two key research gaps for missing sensor data imputation in buildings: the lack of customized and automated imputation methodology, and the difficulty of the validation of data imputation methods. In this paper, a framework is developed to address these two gaps. First, a validation data generation module is developed based on pattern recognition to create a validation dataset to quantify the performance of data imputation methods. Second, a pool of data imputation methods is tested under the validation dataset to find an optimal single imputation method for each sensor, which is termed as an ensemble method. The method can reflect the specific mechanism and randomness of missing data from each sensor. The effectiveness of the framework is demonstrated by 18 sensors from a real campus building. The overall accuracy of data imputation for those sensors improves by 18.2% on average compared with the best single data imputation method.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2019

High-fidelity Multidisciplinary Sensitivity Analysis and Design Optimization for Rotorcraft Applications

A multidisciplinary sensitivity analysis of rotorcraft simulations involving tightly coupled high-fidelity computational fluid dynamics and comprehensive analysis solvers is presented and evaluated. A sensitivity-enabled fluid dynamics solver and a nonlinear flexible multibody dynamics solver are coupled to predict aerodynamic loads and structural responses of helicopter rotor blades. A discretely consistent adjoint-based sensitivity analysis available in the fluid dynamics solver provides sensitivities arising from unsteady turbulent flows and unstructured dynamic overset meshes, while a complex-variable approach is used to compute structural sensitivities with respect to aerodynamic loads. The multidisciplinary sensitivity analysis is conducted through integrating the sensitivity components from each discipline of the coupled system. Accuracy of the coupled system is validated by conducting simulations for a benchmark rotorcraft model and comparing solutions with established analyses and experimental data. Sensitivities of lift computed by the multidisciplinary sensitivity analysis are verified by comparison with the sensitivities obtained by complex-variable simulations. Finally the multidisciplinary sensitivity analysis is applied to a constrained gradient-based design optimization for a HART-II rotorcraft configuration.

Wang, Li↗

DOE OSTI2020

Fundamental electronic structure and multiatomic bonding in 13 biocompatible high-entropy alloys

High-entropy alloys (HEAs) have attracted great attention due to their many unique properties and potential applications. The nature of interatomic interactions in this unique class of complex multicomponent alloys is not fully developed or understood. We report a theoretical modeling technique to enable in-depth analysis of their electronic structures and interatomic bonding, and predict HEA properties based on the use of the quantum mechanical metrics, the total bond order density (TBOD) and the partial bond order density (PBOD). Application to 13 biocompatible multicomponent HEAs yields many new and insightful results, including the inadequacy of using the valence electron count, quantification of large lattice distortion, validation of mechanical properties with experiment data, modeling porosity to reduce Young’s modulus. This work outlines a road map for the rational design of HEAs for biomedical applications.

36 MATERIALS SCIENCE↗

NASA NTRS1992

A classification procedure for the effective management of changes during the maintenance process

During software operation, maintainers are often faced with numerous change requests. Given available resources such as effort and calendar time, changes, if approved, have to be planned to fit within budget and schedule constraints. In this paper, we address the issue of assessing the difficulty of a change based on known or predictable data. This paper should be considered as a first step towards the construction of customized economic models for maintainers. In it, we propose a modeling approach, based on regular statistical techniques, that can be used in a variety of software maintenance environments. The approach can be easily automated, and is simple for people with limited statistical experience to use. Moreover, it deals effectively with the uncertainty usually associated with both model inputs and outputs. The modeling approach is validated on a data set provided by NASA/GSFC which shows it was effective in classifying changes with respect to the effort involved in implementing them. Other advantages of the approach are discussed along with additional steps to improve the results.

Briand, Lionel C.↗

DOE OSTI2025

Measurement of neutron production in atmospheric neutrino interactions at Super-Kamiokande

We present measurements of total neutron production from atmospheric neutrino interactions in water, analyzed as a function of electron-equivalent visible energy over a range of 30 MeV to 10 GeV. These results are based on 4,270 days of data collected by Super-Kamiokande, including 564 days with 0.011 wt% gadolinium added to enhance neutron detection. Neutron signal selection is based on a neural network trained on simulation, with its performance validated using an Am/Be neutron point source. The measurements are compared to predictions from neutrino event generators combined with various hadron-nucleus interaction models, which include an intranuclear cascade model and a nuclear deexcitation model. We observe significant variations in the predictions depending on the choice of hadron-nucleus interaction model. We discuss key factors that contribute to describing our data, such as in-medium effects in the intranuclear cascade and the accuracy of statistical evaporation modeling.

Cherenkov detectors↗

DOE OSTI2020

Potential Formation Damage: An Integrated Reservoir Characterization Study of the Naturally Fractured Carbonate Middle Duperow Formation at the Kevin Dome, Montana

In this study, we integrate geologic and engineering data of a naturally fractured carbonate reservoir at the Kevin Dome, Montana. Well test data are correlated with core description, geochemical and lithology study to determine the flow behavior and communication within the injection test interval and to the surrounding area. Based on a dual-continuum geologic model, numerical brine injection simulations are carried out to validate the interpretation results from our well test analytical models and forecast the probability of CO 2 injection success using current reservoir properties. As a result, our well test analytical models as well as lithology/core description suggest that fluid flow may be mainly restricted to the injection interval and the assumption of radial (horizontal) flow may be appropriate. The well test models also indicate that there is potentially formation damage with a positive skin factor although prior to brine injection well tests, well stimulation through acid treatment was performed. Our numerical simulation results appear to confirm this formation damage by showing additional pressure buildup in the injection data during later test periods. To explain this, acid may have dissolved dolomite then dolomite or calcite may have been formed again further into the matrix/fracture system. Another possible explanation is mechanical clogging of the fractures due to acid dissolving dolomite and dislodging fine grains. Our work also predicts that if no additional well stimulation is performed, the project will have a lower probability of successfully injecting 1 million tons of CO 2 into the Middle Duperow formation over 4 years.

42 ENGINEERING↗

DOE OSTI2022

Scalable deep learning for watershed model calibration

Watershed models such as the Soil and Water Assessment Tool (SWAT) consist of high-dimensional physical and empirical parameters. These parameters often need to be estimated/calibrated through inverse modeling to produce reliable predictions on hydrological fluxes and states. Existing parameter estimation methods can be time consuming, inefficient, and computationally expensive for high-dimensional problems. In this paper, we present an accurate and robust method to calibrate the SWAT model (i.e., 20 parameters) using scalable deep learning (DL). We developed inverse models based on convolutional neural networks (CNN) to assimilate observed streamflow data and estimate the SWAT model parameters. Scalable hyperparameter tuning is performed using high-performance computing resources to identify the top 50 optimal neural network architectures. We used ensemble SWAT simulations to train, validate, and test the CNN models. We estimated the parameters of the SWAT model using observed streamflow data and assessed the impact of measurement errors on SWAT model calibration. We tested and validated the proposed scalable DL methodology on the American River Watershed, located in the Pacific Northwest-based Yakima River basin. Our results show that the CNN-based calibration is better than two popular parameter estimation methods (i.e., the generalized likelihood uncertainty estimation [GLUE] and the dynamically dimensioned search [DDS], which is a global optimization algorithm). For the set of parameters that are sensitive to the observations, our proposed method yields narrower ranges than the GLUE method but broader ranges than values produced using the DDS method within the sampling range even under high relative observational errors. The SWAT model calibration performance using the CNNs, GLUE, and DDS methods are compared using R 2 and a set of efficiency metrics, including Nash-Sutcliffe, logarithmic Nash-Sutcliffe, Kling-Gupta, modified Kling-Gupta, and non-parametric Kling-Gupta scores, computed on the observed and simulated watershed responses. The best CNN-based calibrated set has scores of 0.71, 0.75, 0.85, 0.85, 0.86, and 0.91. The best DDS-based calibrated set has scores of 0.62, 0.69, 0.8, 0.77, 0.79, and 0.82. The best GLUE-based calibrated set has scores of 0.56, 0.58, 0.71, 0.7, 0.71, and 0.8. The scores above show that the CNN-based calibration leads to more accurate low and high streamflow predictions than the GLUE and DDS sets. Our research demonstrates that the proposed method has high potential to improve our current practice in calibrating large-scale integrated hydrologic models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

PyOECP: A flexible open-source software library for estimating and modeling the complex permittivity based on the open-ended coaxial probe (OECP) technique

Here, we present PyOECP, a Python-based flexible open-source software for estimating and modeling the complex permittivity obtained from the open-ended coaxial probe (OECP) technique. The transformation of the measured reflection coefficient to complex permittivity is performed based on three different methods. The software library contains the dielectric spectra of common reference liquids, which can be used to transform the reflection coefficient into the dielectric spectra. Several Python routines that are commonly employed (e.g., SciPy and NumPy) in the field of science and engineering are required only so that the users can alter the software structure depending on their needs. The modeling algorithm exploits the Markov Chain Monte Carlo method for the data regression. The discrete relaxation models can be built by a proper combination of well-known relaxation models. In addition to these models, electrode polarization, a typical measurement artifact for interpreting dielectric spectra, can be incorporated into the modeling algorithm. A continuous relaxation model, which solves the Fredholm integral equation of the first kind (a mathematically ill-posed problem), is also included. This open-source software enables users to freely adjust the physical parameters to obtain physical insight into their materials under test and will be consistently updated for more accurate measurement and interpretation of dielectric spectra in an automated manner. This work describes the theoretical and mathematical background of the software, lays out the workflow, and validates the software functionality based on both synthetic and empirical data included in the software.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2006

NASA-Langley Web-Based Operational Real-time Cloud Retrieval Products from Geostationary Satellites

At NASA Langley Research Center (LaRC), radiances from multiple satellites are analyzed in near real-time to produce cloud products over many regions on the globe. These data are valuable for many applications such as diagnosing aircraft icing conditions and model validation and assimilation. This paper presents an overview of the multiple products available, summarizes the content of the online database, and details web-based satellite browsers and tools to access satellite imagery and products.

Palikonda, Rabindra↗

DOE OSTI2024

Quantification of Residual Water in Spent Fuel Dry Storage Canisters Using Mass Spectrometry

The purpose of this report is to document updates on the apparatus to simulate commercial vacuum drying procedures at the Nuclear Energy Work Complex at Sandia National Laboratories. Validation of the extent of water removal in a dry spent nuclear fuel storage system based on drying procedures used at nuclear power plants is needed to close existing technical gaps. Operational conditions leading to incomplete drying may have potential impacts on the fuel, cladding, and other components in the system during subsequent storage and disposal, such as fuel degradation; cladding corrosion, embrittlement, or breaching; and the creation of a flammable environment via radiolysis of water. A general lack of data suitable for model validation of commercial nuclear canister drying processes necessitates well-designed investigations of drying process efficacy and water retention. Scaled tests that incorporate relevant physics and well-controlled boundary conditions are essential to provide insight and guidance to the simulation of prototypic systems undergoing drying processes. This report documents details on the quantification of residual water in the Advanced Drying Cycle Simulator (ADCS), an apparatus built to simulate commercial drying procedures and quantify the amount of residual water remaining in a pressurized water reactor (PWR) fuel assembly after drying. The ADCS was constructed with a prototypic 17×17 PWR fuel skeleton and waterproof heater rods to simulate decay heat. The ADCS is outfitted with thermocouples to measure the thermal response of the ADCS to simulated decay heats and internal helium fill pressures relevant to commercial drying procedures. The ADCS is also instrumented with pressure transducers to measure the pressures and vacuum levels observed during simulated commercial drying. The most unique instrumentation used for quantifying residual water in the ADCS is a Hiden Analytical HPR-30 mass spectrometer (MS), which measures gas compositions of the ADCS internal free volume, based on partial pressures calculated from relative proportions of gas molecules detected by the MS. This report details the methodology used to implement MS measurements in quantifying residual water in the ADCS. This methodology includes the calibration of the HPR-30 MS to a Buck Research Instruments CR-4 chilled mirror hygrometer, which itself is calibrated to a NIST-traceable standard. Data collected by both the MS and the chilled mirror hygrometer from water/helium mixtures ranging from 150 to 500,000 ppmv water in helium were used to generate calibration curves, establishing a source of verification of MS measured water contents. Details regarding water content measurement uncertainties are included in this report, defining the accuracy and verifiability of the HPR-30 MS in measuring residual water content in simulated dry storage canister environments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS1992

Ground-based measurements of galactic cosmic ray fragmentation in shielding

The mean free path for nuclear interactions of galactic cosmic-rays is comparable to shielding and tissue thicknesses present in human interplanetary exploration, resulting in a significant fraction of nuclear reaction products at depth. In order to characterize the radiation field, the energy spectrum, the angular distribution, and the multiplicity of each type of secondary particles must also be known as a function of depth. Reactions can take place anywhere in a thick absorber; therefore, it is necessary to know these quantities as a function of particle energy for all particles produced. HZE transport methods are used to predict the radiation field; they are dependent on models of the interaction of man-made systems with the space environment to an even greater extent than methods used for other types of radiation. Hence, there is a major need to validate these transport codes by comparison with experimental data. The most cost-effective method of validation is a comparison with ground-based experimental measurements. A research program to provide such validation measurements using neon, iron and other accelerated heavy ion beams will be discussed and illustrated using results from ongoing experiments and their comparison with current transport codes. The extent to which physical measurements yield radiobiological predictions will be discussed.

NASA Discipline Radiation Health↗

NASA NTRS2021

Accuracy Assessment of Global Internal-Tide Models Using Satellite Altimetry Models Using Satellite Altimetry

Altimeter measurements are corrected for several geophysical parameters in order to access ocean signals of interest, like mesoscale or sub-mesoscale variability. The ocean tide is one of the most critical corrections due to the amplitude of the tidal elevations and to the aliasing phenomena of high-frequency signals into the lower-frequency band, but the internal-tide signatures at the ocean surface are not yet corrected globally. Internal tides can have a signature of several centimeters at the surface with wavelengths of about 50–250 km for the first mode and even smaller scales for higher-order modes. The goals of the upcoming Surface Water Ocean Topography (SWOT) mission and other high-resolution ocean measurements make the correction of these small-scale signals a challenge, as the correction of all tidal variability becomes mandatory to access accurate measurements of other oceanic signals. In this context, several scientific teams are working on the development of new internal-tide models, taking advantage of the very long altimeter time series now available, which represent an unprecedented and valuable global ocean database. The internal-tide models presented here focus on the coherent internal-tide signal and they are of three types: empirical models based upon analysis of existing altimeter missions, an assimilative model and a three-dimensional hydrodynamic model. A detailed comparison and validation of these internal-tide models is proposed using existing satellite altimeter databases. The analysis focuses on the four main tidal constituents: M2, K1, O1 and S2. The validation process is based on a statistical analysis of multi-mission altimetry including Jason-2 and Cryosphere Satellite-2 data. The results show a significant altimeter variance reduction when using internal-tide corrections in all ocean regions where internal tides are generating or propagating. A complementary spectral analysis also gives some estimation of the performance of each model as a function of wavelength and some insight into the residual non-stationary part of internal tides in the different regions of interest. This work led to the implementation of a new internal-tide correction (ZARON'one) in the next geophysical data records version-F (GDR-F) standards.

Loren Carrere↗

NASA NTRS2010

Improving the Representation of Snow Crystal Properties with a Single-Moment Mircophysics Scheme

Single-moment microphysics schemes are utilized in an increasing number of applications and are widely available within numerical modeling packages, often executed in near real-time to aid in the issuance of weather forecasts and advisories. In order to simulate cloud microphysical and precipitation processes, a number of assumptions are made within these schemes. Snow crystals are often assumed to be spherical and of uniform density, and their size distribution intercept may be fixed to simplify calculation of the remaining parameters. Recently, the Canadian CloudSat/CALIPSO Validation Project (C3VP) provided aircraft observations of snow crystal size distributions and environmental state variables, sampling widespread snowfall associated with a passing extratropical cyclone on 22 January 2007. Aircraft instrumentation was supplemented by comparable surface estimations and sampling by two radars: the C-band, dual-polarimetric radar in King City, Ontario and the NASA CloudSat 94 GHz Cloud Profiling Radar. As radar systems respond to both hydrometeor mass and size distribution, they provide value when assessing the accuracy of cloud characteristics as simulated by a forecast model. However, simulation of the 94 GHz radar signal requires special attention, as radar backscatter is sensitive to the assumed crystal shape. Observations obtained during the 22 January 2007 event are used to validate assumptions of density and size distribution within the NASA Goddard six-class single-moment microphysics scheme. Two high resolution forecasts are performed on a 9-3-1 km grid, with C3VP-based alternative parameterizations incorporated and examined for improvement. In order to apply the CloudSat 94 GHz radar to model validation, the single scattering characteristics of various crystal types are used and demonstrate that the assumption of Mie spheres is insufficient for representing CloudSat reflectivity derived from winter precipitation. Furthermore, snow density and size distribution characteristics are allowed to vary with height, based upon direct aircraft estimates obtained from C3VP data. These combinations improve the representation of modeled clouds versus their radar-observed counterparts, based on profiles and vertical distributions of reflectivity. These meteorological events are commonplace within the mid-latitude cold season and present a challenge to operational forecasters. This study focuses on one event, likely representative of others during the winter season, and aims to improve the representation of snow for use in future operational forecasts.

Molthan, Andrew L.↗

DOE OSTI2026

Adaptive Reinforcement Learning (ARL) Control of a Multi-port Resonant Converter in UAV Systems

This study presents an adaptive reinforcement learning (ARL) control framework for a multi-port resonant converter used in hybrid unmanned aerial vehicle (UAV) power systems. The converter integrates high-frequency half-bridge input ports connected to a rectified engine–generator set and a battery energy storage system, along with a semi-bridgeless active rectifier supplying the propulsion load. A deep RL agent is trained to dynamically regulate inter-port phase-shift commands in real time based on flight conditions and load power demand. The ARL controller autonomously identifies phase-shift combinations that maximize conversion efficiency while maintaining stable and coordinated power flow, even under rapidly varying operating scenarios. This data-driven approach eliminates the need for explicit system modeling or extensive manual tuning and enables coordinated control among multiple power ports without inter-port communication. Experimental results validate that the ARL based strategy achieves reliable power sharing and consistently high-efficiency operation across diverse UAV operating conditions.

Asa, Erdem [ORNL] (ORCID:0000000190884812)↗

NASA NTRS2008

Comparison Between Numerically Simulated and Experimentally Measured Flowfield Quantities Behind a Pulsejet

Pulsed combustion is receiving renewed interest as a potential route to higher performance in air breathing propulsion systems. Pulsejets offer a simple experimental device with which to study unsteady combustion phenomena and validate simulations. Previous computational fluid dynamic (CFD) simulation work focused primarily on the pulsejet combustion and exhaust processes. This paper describes a new inlet sub-model which simulates the fluidic and mechanical operation of a valved pulsejet head. The governing equations for this sub-model are described. Sub-model validation is provided through comparisons of simulated and experimentally measured reed valve motion, and time averaged inlet mass flow rate. The updated pulsejet simulation, with the inlet sub-model implemented, is validated through comparison with experimentally measured combustion chamber pressure, inlet mass flow rate, operational frequency, and thrust. Additionally, the simulated pulsejet exhaust flowfield, which is dominated by a starting vortex ring, is compared with particle imaging velocimetry (PIV) measurements on the bases of velocity, vorticity, and vortex location. The results show good agreement between simulated and experimental data. The inlet sub-model is shown to be critical for the successful modeling of pulsejet operation. This sub-model correctly predicts both the inlet mass flow rate and its phase relationship with the combustion chamber pressure. As a result, the predicted pulsejet thrust agrees very well with experimental data.

Geng, Tao↗