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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Tests of LBNF Beam Monitor Prototypes for the DUNE Experiment

The Deep Underground Neutrino Experiment (DUNE) is a long-baseline on-axis neutrino oscillation experiment that will use a beam from the Long-Baseline Neutrino Facility (LBNF) to constrain the mixing angle $$\theta_{23}$, the neutrino mass hierarchy, and the CP violating phase $\delta_{CP}$. To ensure the beam remains on-axis, the LBNF beamline will employ two ionization chamber systems: the Hadron Alignment Detection System (HADeS) for primary beam alignment and the Muon Monitoring System (MuMS) for continuous beam monitoring and neutrino flux estimation. We have constructed prototypes for these systems at the University of Texas at Austin based on a design currently used in the NuMI hadron monitor. These prototypes were tested with a radioactive source and in beam tests at Fermilab's Irradiation Test Area. We present the results of these studies and discuss ongoing efforts to optimize the HADeS and MuMS designs.

Murthy, Chinmay↗
DOE OSTI2024

Flexible dynamic boundary microgrid operation considering network and load unbalances

Flexible microgrids with dynamic boundaries have recently been introduced in the literature. With the ability to reconfigure the topology of the microgrids dynamically through remotely controlled switches, flexible microgrids with dynamic boundaries can further improve the resiliency and energy efficiency of microgrids with distributed energy resources (DERs). This paper focuses on the optimal operation considering one of the predominant characteristics of microgrids and distribution systems – unbalanced networks and loads. In existing literature, balanced modeling of microgrids is more common due to its attractive simplicity. The three-phase power unbalance has not been considered as a constraint on the generation units in a microgrid. Further, negative sequence constraints have also been neglected. In this article, we propose a set of constraints that is specifically related to the capabilities of inverter interfaced resources to supply unbalanced current/power when the microgrid is islanded from the main distribution grid. We incorporate the new set of constraints into two optimization formulations leveraging two convex relaxations of the three-phase power flow equations: mixed-integer linear programming (MILP) and mixed-integer semidefinite programming (MISDP) that optimize the dispatch of controllable switches and DERs in the microgrid. The algorithms are then extended to networked microgrids with grid-forming sources. We test the algorithms on a realistic community microgrid model in Puerto Rico as well as standardized IEEE distribution test feeders. The testing results demonstrate the performance of the proposed algorithms. The MILP is fast and scalable, and the MISDP enforces the negative sequence voltage constraints.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Thermodynamic Cloud Phase Classifications Using Machine Learning at NSA and ANX

Vertically resolved thermodynamic cloud phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) THERMOCLDPHASE Value-Added Product (VAP) uses a multi-sensor approach to classify thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave radiometer-derived liquid water path, and radiosonde temperature measurements. The measured voxels are classified as ice, snow, mixed-phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multilayer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with one year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1-score, and mean Intersection over Union (IOU). Analysis of ML confidence scores shows ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential datastreams for ML thermodynamic cloud phase predictions. The ML models’ generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. Finally, we evaluate the ML models’ response to simulated instrument outages and signal degradation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2027

From zonal to nodal capacity expansion planning: Spatial aggregation impacts on a realistic test-case

Solving power system capacity expansion planning (CEP) problems at realistic spatial resolutions is computationally challenging. Thus, a common practice is to solve CEP over zonal models with low spatial resolution rather than over full-scale nodal power networks. Due to improvements in solving large-scale stochastic mixed integer programs, these computational limitations are becoming less relevant, and the assumption that zonal models are realistic and useful approximations of nodal CEP is worth revisiting. Here, this work is the first to conduct a systematic computational study on the assumption that spatial aggregation can reasonably be used for ISO-scale CEP. By considering a realistic, large-scale test network based on the state of California with over 8000 buses, we find that well-designed small spatial aggregations can yield good approximations but that coarser zonal models may result in large distortions of investment decisions, e.g., capacity under-investment of up to 41% for the lowest resolution model considered.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1990

In-flight and simulated aircraft fuel temperature measurements

Fuel tank measurements from ten flights of an L1011 commercial aircraft are reported for the first time. The flights were conducted from 1981 to 1983. A thermocouple rake was installed in an inboard wing tank and another in an outboard tank. During the test periods of either 2 or 5 hr, at altitudes of 10,700 m (35,000 ft) or higher, either the inboard or the outboard tank remained full. Fuel temperature profiles generally developed in the expected manner. The bulk fuel was mixed by natural convection to a nearly uniform temperature, especially in the outboard tank, and a gradient existed at the bottom conduction zone. The data indicated that when full, the upper surface of the inboard tank was wetted and the outboard tank was unwetted. Companion NASA Lewis Research Center tests were conducted in a 0.20 cubic meter (52 gal) tank simulator of the outboard tank, chilled on the top and bottom, and insulated on the sides. Even though the simulator tank had no internal components corresponding to the wing tank, temperatures agreed with the flight measurements for wetted upper surface conditions, but not for unwetted conditions. It was concluded that if boundary conditions are carefully controlled, simulators are a useful way of evaluating actual flight temperatures.

Svehla, Roger A.↗
DOE OSTI2025

Classifying thermodynamic cloud phase using machine learning models

Vertically resolved thermodynamic cloud-phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Thermodynamic Cloud Phase (THERMOCLDPHASE) value-added product (VAP) uses a multi-sensor approach to classify the thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave-radiometer-derived liquid water path, and radiosonde temperature measurements. The measured pixels are classified as ice, snow, mixed phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multi-layer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with 1 year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1 score, and mean intersection over union (IOU). Analysis of ML confidence scores shows that ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential data streams for ML thermodynamic cloud-phase predictions. Lidar measurements exhibit lower feature importance due to rapid signal attenuation caused by the frequent presence of persistent low-level clouds at the NSA site. The ML models' generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. The models demonstrated similar performance to that observed at the NSA site. Finally, we evaluate the ML models' response to simulated instrument outages and signal degradation and show that a CNN U-Net model trained with input channel dropouts performs better when input fields are missing.

ARM Aerial Facility↗
DOE OSTI2022

Developing a Reduced 240-Bus WECC Dynamic Model for Frequency Response Study of High Renewable Integration

The ongoing changes in the generation resource mix, driven by the rapid adoption of inverter-based resources (IBR) as well as the early retirement of synchronous generators, are bringing new challenges to the planning and operation of bulk electric power systems. Consequently, there is an increasing need to understand, design, and quantify the reliability service provision from IBRs by performing integrated scheduling and dynamic simulations. However, test systems that have consistent scheduling and dynamic models rarely exist largely because of the decoupled nature of the two simulations on a synchronous generator-dominated system. This paper develops the dynamic model of a reduced Western Electricity Coordinating Council (WECC) system. In conjunction with the existing scheduling model, it is suitable for integrating scheduling and dynamic simulations. The reduced 240-bus WECC model reflects the generation resource mix of the Western Interconnection as of 2018. Moreover, the developed dynamic model is validated against field frequency events measured by FNET/GridEye and preserves the dominant inter-area oscillation mode in WECC.

dynamic simulation↗
NASA NTRS1975

The mixture problem in computer mapping of terrain: Improved techniques for establishing spectral signature, atmospheric path radiance, and transmittance

The results of LANDSAT and Skylab research programs on the effects of the atmosphere on computer mapping of terrain include: (1) the concept of a ground truth map needs to be drastically revised; (2) the concept of training areas and test areas is not as simple as generally thought because of the problem of pixels that represent a mixture of terrain classes; (3) this mixture problem needs to be more widely recognized and dealt with by techniques of calculating spectral signatures of mixed classes, or by other methods; (4) atmospheric effects should be considered in computer mapping of terrain and in monitoring changes; and (5) terrain features may be used as calibration panels on the ground, from which atmospheric conditions can be determined and monitored. Results are presented of a test area in mountainous terrain of south-central Colorado for which an initial classification was made using simulated mixture-class spectral signatures and actual LANDSAT-1-MSS data.

Smedes, H. W.↗
NASA NTRS1980

Landsat for practical forest type mapping - A test case

Computer classified Landsat maps are compared with a recent conventional inventory of forest lands in northern Maine. Over the 196,000 hectare area mapped, estimates of the areas of softwood, mixed wood and hardwood forest obtained by a supervised classification of the Landsat data and a standard inventory based on aerial photointerpretation, probability proportional to prediction, field sampling and a standard forest measurement program are found to agree to within 5%. The cost of the Landsat maps is estimated to be $0.065/hectare. It is concluded that satellite techniques are worth developing for forest inventories, although they are not yet refined enough to be incorporated into current practical inventories.

Bryant, E.↗
DOE OSTI2021

Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-Mixing

This paper presents a physics-informed machine learning (ML) framework to construct reduced-order models (ROMs) for reactive-transport quantities of interest (QoIs) based on high-fidelity numerical simu-lations. QoIs include species decay, product yield, and degree of mixing. The ROMs for QoIs are applied to quantify and understand how the chemical species evolve over time. First, high-resolution datasets for constructing ROMs are generated by solving anisotropic reaction-di?usion equations using a non-negative finite element formulation for di?erent input parameters. The reactive-mixing model input parameters are: time-scale associated with flipping of velocity, spatial-scale controlling small/large vortex structures of velocity, perturbation parameter of the vortex-based velocity, anisotropic dispersion strength/contrast, and molecular diffusion. Second, random forests, F-test, and mutual information criterion are used to evaluate the importance of model inputs/features with respect to QoIs. We observed that anisotropic dispersion strength/contrast is the most important feature and time-scale associated with flipping of velocity is the least important feature. Third, Support Vector Machines (SVM) and Support Vector Regression (SVR) are used to construct ROMs based on the model inputs. The constructed SVR-ROMs are then used to predict scaling of QoIs. We also present estimates and inequalities on the QoIs, which inform that the species decay, mix, and produce in an exponential fashion. These inequalities also inform that a radial basis function is the most suitable kernel for the SVM/SVR models for QoIs. It is observed that R2-score for SVR-ROMs on unseen data is greater than 0.9, implying that the SVR-ROMs are able to predict the reaction-diffusion system state reasonably well. Finally, in terms of the computational cost, the proposed SVM-ROMs are O(107) times faster than running a high-fidelity finite element simulation for evaluating QoIs. This makes the proposed ML-based ROMs attractive for reactive-transport sensing and real-time monitoring applications as they are significantly faster yet reasonably accurate.

Mudunuru, Maruti K.↗
NASA NTRS1994

Long term trend of selected halogenated hydrocarbons

The so-called 'Library of Background Air' at the Oregon Graduate Institute was used to determine the trend in volume mixing ratios of selected halogenated hydrocarbons in the time period 1977-1989. This library consists of background air samples most of them taken at Cape Meares (Oregon). For storage stainless steel containers are used. Tests have shown the gases under consideration to be stable in these containers. Analyses using a GC/MS-system were performed for the CFCs 11, 12, 12B1 (HALON 1211, CBrClF2), 22, 113, 114 and CH3Cl, CH3Br, CH3CCl3, CCl4. The advantage of this unique investigation: different aged air samples are analyzed at the same time with the same instrument. No calibrations or intercalibrations are needed. All data are presented in normalized mixing ratios versus time. We discuss the results, derive rate constants and present a formula to describe the nonlinear increases.

Borchers, R.↗
NASA NTRS2002

Fundamental Mixing and Combustion Experiments for Propelled Hypersonic Flight

The first experiment is a study of a coaxial jet discharging into stagnant laboratory air, with center jet of a mixture of 5% oxygen and 95% helium by volume and coflow jet of air. The exit flow pressure of both center-jet and coflow nozzles is 1 atmosphere. The presence of oxygen in the center jet is to allow the use of an oxygen flow-tagging technique (RELIEF4) to obtain non-intrusive velocity measurements. Both jets are nominally Mach 1.8, but, because of the greater speed of sound, the center jet velocity is more than twice that of the coflow. The mixing layer which forms between the center jet and the coflow near the nozzle exit is compressible, with a calculated convective Mach number of approximately 0.7. This geometry has several advantages: The streamwise development of the flow is generally dominated by turbulent stresses (rather than pressure forces), and thus calculations are sensitive to turbulence modeling. It includes features present in supersonic combustors, including a compressible mixing layer near the nozzle exit and a light-gas/air plume downstream. Since it is a free jet, it provides easy access for both optical instrumentation and probes. Since it is axisymmetric, it requires fewer experimental measurements to fully characterize, and calculations can be performed with more modest computer resources. However, weak shock waves formed at the nozzle exit strengthen and turn normal as they approach the axis, complicating the flow. Care is thus taken in the design of the facility to provide as near as possible to 1-D flow at the exit of both center and coflow nozzles, and to minimize the strength of waves generated at the nozzle exit. Results from this experiment are compared to CFD solutions obtained by VULCAN, a previously developed code used in engine analysis. The second experiment is a study of a supersonic combustor consisting of a diverging duct with single downstream-angled wall injector. Thus, the geometry is relatively simple and large regions of subsonic recirculating flow are avoided. The nominal entrance Mach number is 2 and the enthalpy of the test gas (hot air "simulant") is nominally that of Mach 7 flight. It was believed, on the basis of calculations performed that this would produce mixing-limited flow, that is to say, one for which chemical reaction to equilibrium proceeds at a much greater rate than mixing. It later proved that this was not the case. The primary experimental technique employed is coherent anti-Stokes Raman spectroscopy, known by its acronym CARS. The species probed is molecular nitrogen and the quantity measured is temperature. Intrusive probes, such as Pitot, total temperature, hot-wire, etc., are not used due to access difficulty and high heat flux in the combustor, and because they may alter the flow. CARS has several advantages over other optical methods. It is a relatively mature and well-understood technique. Signal levels are relatively high and the signal is in the form of a coherent (laser) beam that can be collected through small windows. Incoherent (non-CARS) interferences are rejected by spatial filtering.

Diskin, G. S.↗
NASA NTRS1996

Mixing Process in Ejector Nozzles Studied at Lewis' Aero-Acoustic Propulsion Laboratory

The NASA Lewis Research Center has been studying mixing processes in ejector nozzles for its High Speed Research (HSR) Program. This work is directed at finding ways to minimize the noise of a future supersonic airliner. Much of the noise such an airplane would generate would come from the nozzle, where a hot, high-speed jet exits the engine. Several different nozzle configurations were used to produce nozzle systems with different acoustical and aerodynamic characteristics. The acoustical properties were measured by an array of microphones in an anechoic chamber, and the aerodynamics were measured by traditional pressure and temperature instruments as well as by Laser Doppler Velocimetry (LDV), a technique for visualizing the airflow pattern without disturbing it. These measurements were put together and compared for different configurations to examine the relationships between mixing and noise generation. The mixer-ejector nozzle with the installed flow-visualization windows (foreground), the optical equipment and the supporting structure for the Laser Doppler Velocimetry flow visualization (midfield), and the sound-absorbing wedges used to create an anechoic environment for acoustic testing (background) is shown. The High Speed Research Program is a NASA-funded effort, in cooperation with the U.S. aerospace industry, to develop enabling technologies for a future supersonic airliner. One of the technological barriers being addressed is noise generated during near-airport operation. The mixer-ejector nozzle concept is being examined as a way to reduce jet noise while maintaining thrust. Ambient air is mixed with the high-velocity engine exhaust to reduce the jet velocity and hence the noise generated by the jet. The model was designed and built by Pratt & Whitney under NASA contract. The test, completed in June 1995, was conducted in Lewis' Aero-Acoustic Propulsion Laboratory.

Source record↗
NASA NTRS1981

Super-Schottky mixer performance at 92 GHz

As part of a program to investigate the behavior of superconducting Schottky mixers at high frequencies, the mixing and video performance of several super-Schottky diodes (a 3-micron active diameter, and a doping concentration of 2 x 10 to the 19th per cu cm) have been tested at 92 GHz. At 92 GHz, video responsivities were typically 80 A/W (corrected for RF mismatch). Conversion loss (corrected for both RF and IF mismatches) was typically not less than 18 dB. As expected, diode temperature was low (less than 5 K). Video responsivity and conversion loss were also measured at an RF frequency of 3.95 GHz. These data were used with the measured volt-ampere characteristics of the diodes to compare theoretical predictions of diode performance at 92 GHz, in both the video and mixing modes, with the high-frequency data

Dickman, R. L.↗
NASA NTRS2005

Testing of a Spray-bar Thermodynamic Vent System in Liquid Nitrogen

To support development of a microgravity pressure control capability for liquid oxygen, thermodynamic vent system (TVS) testing was conducted at Marshall Space Flight Center (MSFC) using liquid nitrogen (LN2) as a LOX simulant. The spray bar TVS hardware used was originally designed by the Boeing Company for testing in liquid hydrogen (LH2). With this concept, a small portion of the tank fluid is passed through a Joule-Thomson (J-T) device, and then through a longitudinal spray bar mixed-heat exchanger in order to cool the bulk fluid. To accommodate the larger mass flow rates associated with LN2, the TVS hardware was modified by replacing the recirculation pump with an LN2 compatible pump and replacing the J-T valve. The primary advantage of the spray-bar configuration is that tank pressure control can be achieved independent of liquid and vapor location, enhancing the applicability of ground test data to microgravity conditions. Performance testing revealed that the spray-bar TVS was effective in controlling tank pressure within a 6.89 kPa band for fill levels of 90%, 50%, and 25%. Tests were also conducted with gaseous helium (GHe) in the ullage. The TVS operated nominally with GHe in the ullage, with performance similar to the tests with gaseous nitrogen (GN2). Testing demonstrated that the spray-bar TVS design was flexible enough for use in two different propellants with minimal hardware modifications.

Flachbart, R. H.↗
DOE OSTI2026

Complete Phase Transformation of Ir Nanowire Network into Defect-Rich Oxide Catalyst for High-Performance PEM Water Electrolysis

Iridium-based catalysts remain the most reliable option for the oxygen evolution reaction (OER) in proton exchange membrane water electrolyzers (PEMWEs). However, their high cost and limited performance represent critical barriers to the commercialization of this green hydrogen production technology. Herein, we report the creation of a metallic Ir nanowire network (IrNWN), which exhibits superior OER performance through its in-situ transition into an oxide structure with high intrinsic activity. At a low loading of 0.25 mgIr/cm2 in PEMWEs, IrNWN achieved a current density of 3.13 A/cm2 at a cell voltage of 1.8 V, outperforming the commercial Ir-based catalyst and surpassing the Department of Energy (DOE) 2026 technical target. Moreover, the high activity of IrNWN was maintained for 900 hours in a durability test at 2 A/cm2, showing a low degradation rate of 0.042 mV/hour. Structural analysis of the electrochemically oxidized IrNWN revealed the presence of mixed Ir oxidation states and a high density of surface terminal oxygen groups (μ1-O), which contributed to a reduced energy barrier for the rate-determining O-O coupling step.

oxygen evolution reaction↗
DOE OSTI2020

Joining of Thermoset Carbon Fiber Reinforced Polymer and AZ31 Magnesium Alloy Sheet Via Friction Stir Interlocking

A new technique called friction stir interlocking (FSI) was applied for lap-joining of AZ31 magnesium sheet and thermoset carbon fiber reinforced polymer (TS-CFRP) sheet. Instead of directly joining AZ31 and TS-CFRP, a series of magnesium interlocks were used to friction stir weld with AZ31 sheet to enable joining with TS-CFRP. Microstructural characterization of joint cross sections showed that a tool rotation rate of 800 rpm produced incomplete mixing of magnesium interlocks and the top AZ31 sheet, while 1200 rpm achieved sufficient material mixing to avoid unwelded regions. Mechanical property characterization showed that AZ31/TS-CFRP dissimilar joints failed through the magnesium interlock shanks during lap shear tensile testing, with a maximum normalized joint strength reaching ~100 MPa.

Wang, Tianhao↗
DOE OSTI2021

Deterioration of concrete due to ASR: Experiments and multiscale modeling

The process of ASR (Alkali-Silica Reaction) induced expansion and damage in pavement concrete specimens is investigated using laboratory experiments and computational modeling. In the experimental program, the concrete specimens are subject to CS-CPT (climate simulation concrete prism test) to obtain ASR induced expansion with and without external supply of alkali. The dissolution rates of the granodiorite used in the concrete mix and the gel formation rates are determined under concrete-like conditions (pH 13.8, with/without Ca(OH){sub 2} and NaCl) at different temperatures. A micromechanics based computational model with aggregate-scale diffusion and reaction kinetics coupled to an Eigenstrain based micromechanics damage model is developed for the simulation of ASR induced expansion and damage. Data from the experimental program are used to calibrate and validate the computational model. Model predictions show that for the given concrete mixture, ASR induced expansion in the specimen exposed to water is predominantly governed by microcracking in the aggregate, while the expansion in the specimen subjected to external alkali supply is governed by microcracking in both the aggregates and the cement paste.

36 MATERIALS SCIENCE↗