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NASA NTRS2003

High-Flow Jet Exit Rig Designed and Fabricated

The High-Flow Jet Exit Rig at the NASA Glenn Research Center is designed to test single flow jet nozzles and to measure the appropriate thrust and noise levels. The rig has been designed for the maximum hot condition of 16 lbm/sec of combustion air at 1960 R (maximum) and to produce a maximum thrust of 2000 lb. It was designed for cold flow of 29.1 lbm/sec of air at 530 R. In addition, it can test dual-flow nozzles (nozzles with bypass flow in addition to core flow) with independent control of each flow. The High- Flow Jet Exit Rig was successfully fabricated in late 2001 and is being readied for checkout tests. The rig will be installed in Glenn's Aeroacoustic Propulsion Laboratory. The High-Flow Jet Exit Rig consists of the following major components: a single component force balance, the natural-gas-fueled J-79 combustor assembly, the plenum and manifold assembly, an acoustic/instrumentation/seeding (A/I/S) section, a table, and the research nozzles. The rig will be unique in that it is designed to operate uncooled. The structure survives the 1960 R test condition because it uses carefully selected high temperature alloy materials such as Hastelloy-X. The lower plenum assembly was designed to operate at pressures to 450 psig at 1960 R, in accordance with the ASME B31.3 piping code. The natural gas-fueled combustor fires directly into the lower manifold. The hot air is directed through eight 1-1/2-in. supply pipes that supply the upper plenum. The flow is conditioned in the upper plenum prior to flowing to the research nozzle. The 1-1/2-in. supply lines are arranged in a U-shaped design to provide for a flexible piping system. The combustor assembly checkout was successfully conducted in Glenn's Engine Component Research Laboratory in the spring of 2001. The combustor is a low-smoke version of the J79 combustor used to power the F4 Phantom military aircraft. The natural gas-fueled combustor demonstrated high-efficiency combustion over a wide range of operating conditions. This wide operating envelope is required to support the testing of both single- and dual-flow nozzles. Key research goals include providing simultaneous, highly accurate acoustic, flow, and thrust measurements on jet nozzle models in realistic flight conditions, as well as providing scaleable acoustic results. The High-Flow Jet Exit Rig is a second-generation high-flow test rig. Improvements include cleaner flow with reduced levels of particulate, soot, and odor. Choked-flow metering is required with plus or minus 0.25-percent accuracy. Thrust measurements from 0 to 2000 lbf are required with plus or minus 0.25-percent accuracy. Improved acoustics will be achieved by minimizing noise through large pipe bend radii, lower internal flow velocities, and microdrilled choke plates with thousands of 0.040-in.- diameter holes.

Buehrle, Robert J.↗

NASA NTRS2011

Design and Build of Reactor Simulator for Fission Surface Power Technology Demonstrator Unit

The Nuclear Systems Team at NASA Marshall Space Flight Center (MSFC) focuses on technology development for state of the art capability in non-nuclear testing of nuclear system and Space Nuclear Power for fission reactor systems for lunar and Mars surface power generation as well as radioisotope power systems for both spacecraft and surface applications. Currently being designed and developed is a reactor simulator (RxSim) for incorporation into the Technology Demonstrator Unit (TDU) for the Fission Surface Power System (FSPS) Program, which is supported by multiple national laboratories and NASA centers. The ultimate purpose of the RxSim is to provide heated NaK to a pair of Stirling engines in the TDU. The RxSim includes many different systems, components, and instrumentation that have been developed at MSFC while working with pumped NaK systems and in partnership with the national laboratories and NASA centers. The main components of the RxSim are a core, a pump, a heat exchanger (to mimic the thermal load of the Stirling engines), and a flow meter for tests at MSFC. When tested at NASA Glenn Research Center (GRC) the heat exchanger will be replaced with a Stirling power conversion engine. Additional components include storage reservoirs, expansion volumes, overflow catch tanks, safety and support hardware, instrumentation (temperature, pressure, flow) for data collection, and power supplies. This paper will discuss the design and current build status of the RxSim for delivery to GRC in early 2012.

Godfroy, Thomas↗

DOE OSTI2022

Submersible Multistage Centrifugal Pump for Versatile Test Reactor Cartridge Test Loop

Submersible multistage centrifugal pumps are ideal for pumping in narrow confined spaces and achieving necessary head pressures and flow rates. Once a diameter is determined then manipulation of the number of stages and motor speed are all that are required to meet desired flow conditions. The Versatile Test Reactor (VTR) closed loop cartridge systems will need forced convection cooling independent of the main reactor. A multistage centrifugal pump can meet the necessary flow rates and pumping pressures while minimizing space taken. The pump considered for this work was based off a deep well submersible pump, a variation of a multistage centrifugal pump. We experimented with two pump sizes, 5 cm (2 inch) and 7.5 cm (3 inch) diameters. These diameters were chosen to fit into the inner diameter of standard 5 and 7.5 (2 and 3 inch) Schedule 40 pipe, respectively. This made the design for the test loop both simpler and less expensive as the need for an engineered pump housing was eliminated. Initial test cartridge planning indicated space for only a 5 cm (2 inch) diameter pump, though early testing of this size showed the need for an abnormally high-speed and high-power motor. Fine tuning of the cartridge design allowed a pump size increase to 7.5 cm (3 inches), which was the pump size most extensively tested in this work. The test loop is composed of various sizes of PVC and aluminum piping components in a loop configuration. The pump is driven by a Pittman 250 W (1/3 horsepower) electric motor with maximum speed of 3,450 RPM. Testing consisted of running the pump at a constant motor speed while varying a control valve to restrict flow through the loop, with differential pressure and flow rate recorded. This was done for one and two stage configurations for the 5 cm (2 inch) diameter impeller design and one, two, and three stage configurations for the 7.5 cm (3 inch diameter) impeller design, respectively. Due to pumping power requirements, two and three stage 7.5 cm (3 inch) diameter impeller testing at higher flowrates lowered the motor speed substantially. In regions where motor speed could not be maintained constant, the data were discarded. The test loop was also reconfigured to allow for the pump to be tested for pressure drop in a stalled or inoperable (0 RPM) flow condition. Demonstration of adequate natural convection cooling of the test cartridge fuel type is necessary under accident conditions, and this will depend upon the flow resistance through the impeller assembly when the pump is not operating. Thus, accurate knowledge of the effective impeller assembly loss coefficient is important for safety evaluations. The test loop was modified to provide water inlet and outlets on either side of the pump impeller stack, and a metered flow of lab water was provided in order to measure the pressure drop across the cartridges as a function of flowrate. Data from the pump head curve testing developed as part of this work and supported by analysis using pump head affinity laws indicates that a three stage 7.5 cm (3 inch) pump impeller design will meet target requirements for coolant flow within the VTR cartridge sodium cartridge at full power conditions [1] with margin; this corresponds to a flowrate of 45 l/min (12 gpm) at a pressure drop of 6.1 m (20 feet) of water head. The results of the pressure loss measurements across the impeller assembly when the pump is stationary (i.e., at 0 RPM) indicate that the pressure loss coefficient is 0.921 for a two impeller stack configuration; this value is calculated based on the flow velocity through the minimum available flow area within a single stage of the impeller which corresponds to 1.4 cm2.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS1989

Superfluid helium orbital resupply - The status of the SHOOT flight experiment and preliminary user requirements

The Superfluid Helium On-Orbit Transfer (SHOOT) flight experiment is designed to demonstrate the components and techniques necessary to resupply superfluid helium to satellites or Space Station based facilities. A top level description as well as the development status of the critical components to be used in SHOOT are discussed. Some of these components include the thermomechanical pump, the fluid acquisition system, the normal helium and superfluid helium phase separators, Venturi flow meter, cryogenic valves, burst disks, and astronaut-compatible EVA coupler and transfer line. The requirements for the control electronics and software are given. A preliminary description of the requirements that must be met by a satellite requiring superfluid helium servicing is given. In particular, minimum and optimum plumbing arrangements are shown, transfer line flow impedance and heat input impacts are assessed, instrumentation is described, and performance parameters are considered.

Dipirro, Michael J.↗

NASA NTRS2007

Density and Cavitating Flow Results from a Full-Scale Optical Multiphase Cryogenic Flowmeter

Liquid propulsion systems are hampered by poor flow measurements. The measurement of flow directly impacts safe motor operations, performance parameters as well as providing feedback from ground testing and developmental work. NASA Marshall Space Flight Center, in an effort to improve propulsion sensor technology, has developed an all optical flow meter that directly measures the density of the fluid. The full-scale sensor was tested in a transient, multiphase liquid nitrogen fluid environment. Comparison with traditional density models shows excellent agreement with fluid density with an error of approximately 0.8%. Further evaluation shows the sensor is able to detect cavitation or bubbles in the flow stream and separate out their resulting effects in fluid density.

Korman, Valentin↗

NASA NTRS2004

Multi-Element Unstructured Analyses of Complex Valve Systems

The safe and reliable operation of high pressure test stands for rocket engine and component testing places an increased emphasis on the performance of control valves and flow metering devices. In this paper, we will present a series of high fidelity computational analyses of systems ranging from cryogenic control valves and pressure regulator systems to cavitating venturis that are used to support rocket engine and component testing at NASA Stennis Space Center. A generalized multi-element framework with sub-models for grid adaption, grid movement and multi-phase flow dynamics has been used to carry out the simulations. Such a framework provides the flexibility of resolving the structural and functional complexities that are typically associated with valve-based high pressure feed systems and have been difficult to deal with traditional CFD methods. Our simulations revealed a rich variety of flow phenomena such as secondary flow patterns, hydrodynamic instabilities, fluctuating vapor pockets etc. In the paper, we will discuss performance losses related to cryogenic control valves, and provide insight into the physics of the dominant multi-phase fluid transport phenomena that are responsible for the choking like behavior in cryogenic control elements. Additionally, we will provide detailed analyses of the modal instability that is observed in the operation of the dome pressure regulator valve. Such instabilities are usually not localized and manifest themselves as a system wide phenomena leading to an undesirable chatter at high flow conditions.

Sulyma, Peter↗

NASA NTRS2013

Data Mining for Understanding and Impriving Decision-Making Affecting Ground Delay Programs

The continuous growth in the demand for air transportation results in an imbalance between airspace capacity and traffic demand. The airspace capacity of a region depends on the ability of the system to maintain safe separation between aircraft in the region. In addition to growing demand, the airspace capacity is severely limited by convective weather. During such conditions, traffic managers at the FAA's Air Traffic Control System Command Center (ATCSCC) and dispatchers at various Airlines' Operations Center (AOC) collaborate to mitigate the demand-capacity imbalance caused by weather. The end result is the implementation of a set of Traffic Flow Management (TFM) initiatives such as ground delay programs, reroute advisories, flow metering, and ground stops. Data Mining is the automated process of analyzing large sets of data and then extracting patterns in the data. Data mining tools are capable of predicting behaviors and future trends, allowing an organization to benefit from past experience in making knowledge-driven decisions. The work reported in this paper is focused on ground delay programs. Data mining algorithms have the potential to develop associations between weather patterns and the corresponding ground delay program responses. If successful, they can be used to improve and standardize TFM decision resulting in better predictability of traffic flows on days with reliable weather forecasts. The approach here seeks to develop a set of data mining and machine learning models and apply them to historical archives of weather observations and forecasts and TFM initiatives to determine the extent to which the theory can predict and explain the observed traffic flow behaviors.

data mining↗

NASA NTRS2006

The Airspace Concepts Evaluation System Architecture and System Plant

The Airspace Concepts Evaluation System is a simulation of the National Airspace System. It includes models of flights, airports, airspaces, air traffic controls, traffic flow managements, and airline operation centers operating throughout the United States. It is used to predict system delays in response to future capacity and demand scenarios and perform benefits assessments of current and future airspace technologies and operational concepts. Facilitation of these studies requires that the simulation architecture supports plug and play of different air traffic control, traffic flow management, and airline operation center models and multi-fidelity modeling of flights, airports, and airspaces. The simulation is divided into two parts that are named, borrowing from classical control theory terminology, control and plant. The control consists of air traffic control, traffic flow management, and airline operation center models, and the plant consists of flight, airport, and airspace models. The plant can run open loop, in the absence of the control. However, undesired affects, such as conflicts and over congestions in the airspaces and airports, can occur. Different controls are applied, "plug and played", to the plant. A particular control is evaluated by analyzing how well it managed conflicts and congestions. Furthermore, the terminal area plants consist of models of airports and terminal airspaces. Each model consists of a set of nodes and links which are connected by the user to form a network. Nodes model runways, fixes, taxi intersections, gates, and/or other points of interest, and links model taxiways, departure paths, and arrival paths. Metering, flow distribution, and sequencing functions can be applied at nodes. Different fidelity model of how a flight transits are can be used by links. The fidelity of the model can be adjusted by the user by either changing the complexity of the node/link network-or the way that the link models how the flights transit from one node to the other.

Windhorst, Robert↗

DOE OSTI2026

A network of soil moisture, soil temperature, air temperature, net radiation, ground heat flux and ground water for Chicago, Illinois

This dataset contains environmental monitoring data collected using solar-powered Multi-Function Research (MFR) Long Range Wide Area (LoRaWAN)-enabled nodes at 11 sites in Chicago, Illinois, as part of the DOE Urban Integrated Field Lab CROCUS project. The MFR node system consists of an Input/Output Digital Input Module (IB8) interface box (ICT International) providing wired connections for environmental sensors and an MFR-Node-L data logger that manages power, data processing, and LoRaWAN communication. The wireless data are ingested via Sage network (https://sagecontinuum.org/) nodes that contain LoRaWAN antennae. Measurements were collected from 11 MFR nodes deployed across Chicago State University (CSU), Northeastern Illinois University (NEIU), Northwestern University (NU), University of Illinois Chicago (UIC), West Woodlawn "Blacks in Green" (BIG), and Indian Boundary Prairies (IBP). Each MFR node supports a consistent suite of sensors measuring atmospheric, soil, and hydrological variables. Atmospheric measurements include 2m air temperature (°C), 2m vapor pressure deficit (kPa), and 2m shortwave/longwave radiation (incoming and outgoing, W/m²) measured using ATH-VPD and Apogee SN500 sensors. Soil measurements include volumetric water content (VWC, %) and temperature (°C) at four depths (15, 30, 45, and 60 cm below surface) using Meter Teros54 sensors, and heat flux (W/m²) at 10 cm depth using Huske HFP01-05 sensors. At selected locations, Meter Hydros21 sensors measure groundwater depth (mm), specific conductivity (dS/m), and temperature (°C). The dataset includes timestamps, site identifiers with location names, device IDs, Global Positioning System (GPS) coordinates, variable names with units, measurement depths, values, sensor names, and Sage node identifiers. All timestamps are in local Chicago time (CDT/CST). Quality control flags are provided using a 6-bit binary system indicating physical range violations, step spikes, 24-hour flat-line conditions, 6-hour jitter, 7-day ultra-low variance, and persistent high offset. Data is provided in CSV and CF-compliant NetCDF formats. This dataset is part of a larger collection of CROCUS environmental monitoring data, including linked datasets from Air Quality Transmitter (AQT) sensors, Weather Transmitter (WXT) sensors, and Sap Flow Meter (SFM1x) sensors.

Chicago↗

DOE OSTI2024

MOSAiC-Colorado State University Ice Spectrometer

This data set contains atmospheric ice nucleating particle (INP) measurements, using Colorado State University’s (CSU) Ice Spectrometer (IS), of filter collections taken at the U.S. DOE ARM AMF2 site onboard the R/V Polarstern P-deck during the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) field campaign. Samples were collected from October 27, 2019 to September 24, 2020. A filter sampler was mounted approximately 15 m above ground level on a railing in proximity to (and approximately 3 m below) the Aerosol Observation System (AOS) inlet. Single-use filter units open to the atmosphere were pre-cleaned and pre-loaded with 47-mm diameter Nuclepore polycarbonate (0.2 µm pore-diameter) filters. Filters were typically drawn for a three-day period, with an average volume of air filtered of 87,000 standard liters. Total volumes were calculated through recorded daily flow rates using a mass flow meter (TSI). After collection, filters were stored and transported frozen until analysis using CSU’s IS instrument (McCluskey et al., 2018). Aerosol particles were first re-suspended in 8 mL of 0.1 µm-filtered deionized (DI) water. Aliquots of each suspension, and corresponding 11-fold dilutions, were dispensed into polymerase chain reaction (PCR) trays and placed into the aluminum blocks of the IS. Samples were cooled at approximately 0.33 °C min -1 and freezing detected optically with corresponding temperatures recorded. Cumulative INP concentrations were determined through calculating the number of INPs per mL of suspension (Vali, 1971) and converting to concentration per standard L of air by accounting for the proportion of liquid used and volume of air collected. All samples were corrected for the number of INPs on the average of four field blanks (cleaned, handled, transported, and analyzed in the same way without air flow). Two-tailed, 95% confidence intervals for binomial sampling are provided (Agresti and Coull, 1998). Select samples were also heat treated (95 °C for 20 min) to denature and deactivate biological INPs present and digested in 10% H 2 O 2 at 95 °C under UV-B for 20 min to remove any organic carbon INPs. Agresti, A, and BA Coull. 1998. "Approximate is better than “exact” for interval estimation of binomial proportions." American Statistics 52: 119–126. https://doi.org/10.2307/2685469 McCluskey, CS, J Ovadnevaite, M Rinaldi, J Atkinson, F Belosi, D Ceburnis, … and PJ DeMott. 2018. "Marine and Terrestrial Organic Ice-Nucleating Particles in Pristine Marine to Continentally Influenced Northeast Atlantic Air Masses." Journal of Geophysical Research: Atmospheres 123 (11): 6196–6212, https://doi.org/10.1029/2017JD028033 Vali, G. 1971. "Quantitative Evaluation of Experimental Results and the Heterogeneous Freezing Nucleation of Supercooled Liquids." Journal of the Atmospheric Sciences 28: 402-209. https://doi.org/10.1175/1520-0469(1971)028<0402:QEOERA>2.0.CO;2

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Supporting data for "Frozen flume experiments indicate rapid permafrost riverbank erosion depends on bank roughness"

This dataset contains supporting files detailing five frozen flume experiments conducted at the Caltech Earth Surface Dynamics Laboratory to investigate rates of ablation-limited permafrost riverbank erosion under controlled conditions. Water flowed past a bank of saturated, frozen sand and ice and gradually eroded the bank by thawing pore ice and immediately entraining sand and washing it downstream. Experiments were scaled for flow hydraulics and heat transfer allowing comparisons between our results and natural permafrost riverbanks. For each experiment, we measured the initial and final sand bank topography using a Keyence laser scanner, water surface slope at 3-min intervals throughout the experiment using a Massa sonar scanner, bank erosion using 10-sec overhead timelapse imagery taken by an overhead camera, water and bank temperature using thermistors frozen into the sand bank and sampling at 2 Hz, and water discharge using an in-line flow meter. We include calibration data for the carriage (engineered by the Saint Anthony Falls Laboratory) used to make sonar and laser topography measurements. We also include calibration data for temperature sensors, water discharge measurements, and images of a regular grid placed in the flume to align overhead camera images with the carriage datum. Grain size analysis for the channel bed (gravel) was produced using a pebble count and bank sand was measured using a Camsizer X2. In addition to the five frozen experiments, we include sonar scans of water surface slope and Keyence scans of bed and bank topography for calibration experiments ran with an immobile gravel bank and bed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Evaluation of measuring thermal conductivity of isotropic and anisotropic thermally insulating materials by transient plane source (Hot Disk) technique

The transient plane source (TPS) technique, also referred as the Hot Disk method, has been widely used due to its ability to measure the thermal properties of an extensive range of materials (solids, liquids, and powder). Recently, it has been recognized that typical Hot Disk sensors can influence TPS results of thermally insulating materials and lead to an overestimation of thermal conductivity. Although improvements have been proposed, they have not yet been implemented in the commercial TPS, leaving researchers with non-standardized modifications or options provided by a commercial Hot Disk apparatus. An empirical study of thermally insulating materials such as extruded polystyrene (XPS) and aerogel blanket is conducted in order to address the factors that affect the reliability of thermal conductivity k obtained using the commercial TPS apparatus. Sensor size, input power, duration of the measurements, applied pressure, and, in the case of anisotropic materials, heat capacity are investigated, and the results are compared with those using a Heat Flow Meter apparatus. The effect of sensor size on the k value is ascribed to heat loss through connecting leads and is more pronounced in smaller sensors and in materials with lower k values . In the case of XPS and aerogel, the effect becomes minimal for sensors with a radius r ≥ 6.4 mm. The low input power yields a high scattering of the results and should be avoided. Applied contact pressure and the tested region of the specimen play an important role in experiments with low-density fibrous materials due to the large percentage of heat being transferred by radiation and the heterogeneous nature of the samples, respectively. Additionally, the sensitivity of anisotropic measurements to the value of the material’s volumetric heat capacity ( ρC p ) is shown, emphasizing the need for the precise determination.

thermal conductivity↗

DOE OSTI2026

Thermal property characterization of phase change materials in building applications: A systematic review of fundamentals, recent progress, and future directions

Phase change materials (PCMs) can reduce building peak loads and enable demand-responsive thermal energy storage (TES), but their deployment depends on reliable measurement and interpretation of thermal properties across laboratory, intermediate, and application scales. Here, this review systematically examines characterization methods, testing protocols, and recent advances for neat PCMs and PCM composites, emphasizing thermal conductivity, enthalpy-related properties (phase change temperature, latent heat, specific heat), and cycling stability. For thermal conductivity, we compare steady-state and transient techniques and note limitations when phase transition and contact resistance affect measurements. For enthalpy–temperature characterization, we discuss differential scanning calorimetry together with intermediate- and bulk-scale methods, including T-history, heat flow meter testing, and three-layer calorimetry (3LC), to generate application-relevant enthalpy–temperature profiles. Cycling stability is organized into four experimental families: thermoelectric–air, fully thermoelectric, water-bath, and in situ chamber approaches, with attention to separating reversible supercooling from true degradation such as phase segregation. We highlight emerging noncontact diagnostics, including infrared thermography and embedded sensing, for spatially resolved validation and multiscale interpretation. Finally, we review the growing use of AI and machine learning for property prediction, inverse characterization from experimental signals, and real-time state estimation in building-integrated TES. Key needs include harmonized protocols, interlaboratory benchmarking, uncertainty reporting, and metadata-rich datasets to accelerate reproducible PCM qualification for grid-flexible buildings.

AI↗

DOE OSTI2026

Multiscale characterization of phase change materials for building thermal energy storage applications

Phase change materials (PCMs) store and release large amounts of thermal energy because of their high latent energy storage capacity. However, long-term cyclic stability, supercooling and performance-scalability are some of the major challenges for their use in building thermal energy storage (TES) applications. Here, in this study, we present a comprehensive multiscale characterization of two commercially available organic PCMs, Puretemp 18 and Puretemp 23. At the microscale, differential scanning calorimetry (DSC) was used to characterize phase change temperature, specific heat, and latent heat. At the mesoscale, a heat flow meter apparatus (HFMA), following the ASTM C1784 standard, was employed to measure the phase change temperature, specific heat, and latent heat properties. A comparative analysis of latent heat as a function of temperature was conducted by integrating the DSC and HFMA results. At the macroscale, the thermal performance and cyclic stability of the TES system was evaluated using Puretemp 23. The TES system consisted of a finned tube heat exchanger with a storage volume of 0.0189 m 3 (5 gal), which represents a compact, real-world TES solution suitable for building energy storage. The results showed consistent thermal stability of the PCM over 200 cycles, and the supercooling temperature remained within 0.2 °C, which was not detected in smaller-scale characterization methods. Additionally, the macroscale testing methodology of the PCM revealed that the TES is able to charge and discharge stored latent energy within 2 h under a temperature differential of 16.67 °C measured between the inlet water temperature and the phase transition temperature of the PCM. The proposed multiscale PCM characterization method provides a systematic basis for comparing important thermal storage properties while also investigating the scalability, reliability and integration challenges in large scale TES applications.

Latent heat↗

DOE OSTI2025

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis – Simulated Wind

The U.S. Department of Energy and the National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis, hydrogen compression and storage, and variable 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) initiative. 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 (AI) data centers and other variable loads. This dataset entry describes hydrogen production using a single, simulated wind turbine. The electrolyzer is a 1.25-MW proton exchange membrane type MC250 system manufactured by Nel Hydrogen . While the unit supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack. For the simulated wind energy profiles, NLR used OpenFAST to simulate a 3.4-MW International Energy Agency (IEA) reference wind turbine. The hour-long wind energy profiles varied over wind turbulence intensity (Class A or Class C) and average wind speed (5, 7, or 9 m/s). To match the power limits of the 1.25-MW electrolyzer and 3.4-MW IEA wind turbine most effectively and to maximize the efficiency of hydrogen production at a given average wind speed, the profiles were sometimes scaled by two times. This means that, in some cases, the experimental setup assumed two 1.25-MW electrolyzers were coupled with the wind turbine, representing a total maximum electrolysis load of 2.5 MW. Finally, NLR experimented with two settings for the electrolyzer power supply minimum and maximum current ramp rates (gain and slew): 200 and 400 amperes per second. The simulated 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. 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 wind turbine electrolysis experiment and is formatted as follows: {technology}-{average wind speed}-{turbulence class}_{number of 1.25 MW electrolyzers connected}-{electrolyzer ramp rate in amperes/second} For instance, “windIEA3.4-5ms-C_2-400.zip” represents the hour-long experiment using the IEA 3.4-MW turbine, subjected to an average wind speed of 5 m/s and Class C wind turbulence, and connected to two 1.25-MW electrolyzers with the power supply set to a maximum current ramp rate (gain and slew) of 400 A/s. 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 in kilograms per hour, electrolysis power consumption, and input wind turbine power. 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 simulated wind experiments combined into one dataset labeled "combined_wind_experiments.csv". NLR also built an AI/machine-learning predictive model based on these datasets. The model ingests the electrolyzer current command in amperes, as well as various pressures and temperatures across the system, and predicts hydrogen output in kilograms per hour. The complete model can be found at https://huggingface.co/NatLabRockies/ptmelt-hydrogen-electrolysis .

08 HYDROGEN↗

DOE OSTI2025

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - Simulated Wave

The U.S. Department of Energy and the National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis, hydrogen compression and storage, and variable 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) initiative. 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 (AI) data centers and other variable loads. This dataset entry describes hydrogen production using a single, simulated wave energy conversion device. The electrolyzer is a 1.25-MW proton exchange membrane type MC250 system manufactured by Nel Hydrogen. While the unit supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack. For the wave energy, NLR used a wave energy converter model from PacWave. These devices can be equipped with accumulators and pressure relief values to smooth the power output by storing and releasing hydraulic energy. Using a peak power output of 10 MW, the model created two 25-minute profiles: one with and one without the accumulators and pressure relief valves. To down select the profile data from the native resolution of 20 Hz to 1 Hz, NLR took the mean of every 20 data points. NLR experimented with two simulated wave energy power plants: one that peaks at 10 MW, and one that peaks at 5 MW. These profiles were scaled for the physical 1.25 MW electrolyzer by multiplying the original profiles by one eighth and one quarter, respectively. The first profile matches the capacity rating of eight of the 1.25 MW electrolyzers, while the second matches four electrolyzers. Finally, NLR experimented with two settings for the electrolyzer power supply minimum and maximum current ramp rates (gain and slew): 200 and 400 amperes per second. The simulated 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. 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 wave electrolysis experiment and is formatted as follows: {technology}-{accumulator?}_{number of 1.25 MW electrolyzers connected}-{electrolyzer ramp rate in amperes/second} For instance, “wavePacWave-Noacc_4-400.zip” represents the 25 minute-long experiment using the PacWave’s wave energy converter model, equipped with no accumulator, connected to four 1.25-MW electrolyzers with their power supplies set to a maximum current ramp rate (gain and slew) of 400 A/s. 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 in kilograms per hour, electrolysis power consumption, and input wave power. 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 wave profiles combined into one dataset labeled "combined_wave_experiments.csv". NLR also built an AI/machine-learning predictive model based on these datasets. The model ingests the electrolyzer current command in amperes, as well as various pressures and temperatures across the system, and predicts hydrogen output in kilograms per hour. The complete model can be found at https://huggingface.co/NatLabRockies/ptmelt-hydrogen-electrolysis.

08 HYDROGEN↗

DOE OSTI2026

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - NLR Historical Wind

The U.S. Department of Energy and the 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) initiative. 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 (AI) data centers and other variable loads. This dataset entry describes hydrogen production by conducting a statistical analysis of historical wind data over a five-year period (2020-2025) from a single 1.5MW turbine manufactured by General Electric (GE) located at NLR’s Flatirons Campus, to generate an experimental test profile that was deployed on a 1.25-MW proton exchange membrane type MC250 electrolyzer system manufactured by Nel Hydrogen . [1] While the electrolyzer balance-of-plant supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack. The historical wind data provided several metrics, however, the analysis particularly focused on the measured power output by the wind turbine. The power output time series of data for each day was categorized by total energy generation and standard deviation, and the day that represented the highest combination of these two metrics was chosen – December 25th, 2022. This process was then repeated for a moving four-hour window within this day to identify the most statistically variable period. Finally, this four-hour period was scaled by 65% to match the 1.25 MW electrolyzer. The electrolysis system controls hydrogen production by varying DC current applied to the stack, from a maximum of 3000 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 wind 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 presentation labeled “ Public Reference Data for Megawatt-Scale Hydrogen Electrolysis” provided with each 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 wind turbine electrolysis experiment and is formatted as follows: {technology}_{scaling factor}-{electrolyzer ramp rate in amperes/second} For instance, “wind-GE1.5MW_0.65-400.zip” represents the hour-long experiment using historical data from the wind-GE1.5MW turbine, scaled to 65%, with the electrolyzer power supply set to a maximum ramp rate (gain and slew) of 400 A/s. 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 wind power input. A PDF file detailing the historical wind data statistical analysis used to generate the wind 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 simulated wind experiments combined into one dataset labeled "combined_historical_wind_experiments.csv". NLR also built an AI/machine-learning predictive model based on these datasets. The model ingests the electrolyzer current command in amperes, as well as various pressures and temperatures across the system, and predicts hydrogen output in kilograms per hour. The complete model can be found at https://huggingface.co/NatLabRockies/ptmelt-hydrogen-electrolysis [1] nelhydrogen.com/product/mc-series-electrolyser .

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DOE OSTI2026

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - Simulated Marine Hydrokinetic Tidal Turbine

The U.S. Department of Energy and National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis, hydrogen compression and storage, and variable 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) initiative. This dataset is 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. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with other energy technologies. This dataset contains inputs and outputs from simulations of a floating marine hydrokinetic turbine over approximately half a tidal cycle (~6.6 hours). Inflow conditions were derived from field measurements in Alaska’s Cook Inlet and represent a tidal environment in which the current speed ramps from near 0 m/s to a peak of 3 m/s and back. The original acoustic doppler current profiler dataset is publicly available on the Marine and Hydrokinetic Data Repository. In a full tidal cycle, the flow reverses and the rotor would reorient; this reversal was not modeled. In the Cook Inlet campaign , turbulence intensity was similar in both directions. Two inflow cases are included. In the first case, labeled “raw” in the files, the measured current time series was used directly in the InflowWind module of OpenFAST. Speed and direction were applied as a function of time and elevation, uniformly in the horizontal direction. With full spatial coherence, this approach captures high turbulent variability and results in pronounced power fluctuations, so it is considered a conservative, near-worst-case representation of loading. In the second case, labeled “average” in the files, a 30-minute moving average was applied to extract the slowly varying mean speed. The residual fluctuations about this mean were used to generate spatially varying, full-field turbulence inputs with TurbSim, giving a more physically realistic representation of the inflow across the rotor disk. Two random realizations were used to produce distinct inflow conditions for two OpenFAST simulations representing a two-turbine array. The same turbulence intensity is applied across the full time series, producing larger fluctuations at the start and end, where the mean speed is low. The second case is the more appropriate framework for performance and power assessment but overpredicts turbulence at lower flow speeds and underpredicts it at higher speeds. As the floating platform moves and the rotor changes its x-position, Taylor’s frozen turbulence hypothesis used by InflowWind assumes a constant rather than a time-varying mean velocity, introducing some inaccuracy in the velocity plane sampling. The turbine modeled is the 500-kW Reference Model 1, a horizontal-axis two-bladed hydrokinetic turbine on a four-column floating semisubmersible substructure . Simulations were performed using OpenFAST v4.1 with the Reference Open Source Controller (ROSCO) v2.10. All input files required to reproduce the simulations are included. The electrolyzer is a 1.25-MW proton exchange membrane type MC250 system manufactured by Nel . This unit supports up to 2.5 MW, but NLR has only a single 1.25-MW stack. The datasets report hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. The system controls hydrogen production by varying direct current applied to the stack, from a maximum of 3,000 A to a minimum safe operating current 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 simulated tidal turbine time series data was translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz. Each zip file represents a single tidal electrolysis experiment and is named: {technology}_{inflow method}_{number of 500 kW tidal turbines connected} For instance, “tidal-500kW-RM1_average_2.zip” is a 6-hour experiment using the 500-kW tidal reference model, scaled by 2x (1-MW) to better match the electrolyzer maximum of 1.25MW, fed with the 30-minute moving average current case. Each zip folder contains the following files: A .csv file of raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production in kilograms per hour, electrolysis power consumption, and input wave power. A .csv file combines all tidal profiles as "combined_tidal_experiments.csv." A separate experiment, “characterization_200.zip,” shows the MC250 electrolyzer steady-state response with 30-minute load steps over 5 hours and is accessible with this entry.

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