Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Operating experience data”

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

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,585 records · Page 88
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Time-resolved electrical potential pump – X-ray photoelectron spectroscopy probe developments for investigating dynamic processes occurring at electrochemical interfaces

Electrode–electrolyte interfaces are of critical importance in several fields, including renewable energy, corrosion, and environmental chemistry. However, investigating these interfaces under operational conditions poses considerable challenges due to the limitations of the instrumentation employed. While recent advancements in in situ and operando techniques have enhanced our comprehension of the steady-state properties of solid-liquid interfaces, the dynamic behaviors of these systems remain inadequately explored. This study introduces a time-resolved X-ray photoelectron spectroscopy (XPS) technique designed to capture transient reaction intermediates and charging dynamics at electrified interfaces. The presented proof-of-principle study demonstrates that electrochemical processes, represented by an equivalent electrical circuit (EEC) model, can be probed and understood using square wave voltage pulses of a potentiostat synchronized to the modified data acquisition of an XPS setup. This method offers a valuable alternative to traditional pump–probe techniques, facilitating the investigation of a broader range of electrochemical systems. A dedicated software package for analyzing time- and energy-resolved XPS with a focus on extracting parameters of the EEC is geared towards benchmarking different EECs in future real-world electrochemical experiments.

Electrochemistry↗
DOE OSTI2022

Effects of Temperature on Flow Fouling of Smooth and Nonwetting Surfaces

Mineral fouling (scaling) of heat transfer surfaces is a pervasive problem in heat exchangers, chemical reactors, and other equipment in energy, environmental, and process industrial applications. Many of the applications involve dynamic flow of fluids with the impurity minerals and, furthermore, operate at elevated temperature. Strategies to mitigate fouling under these conditions are of much value in industrial applications. This paper presents a comparative study of temperature-dependent mineral fouling deposition on smooth surfaces and nonwetting superhydrophobic and lubricant-infused surfaces under dynamic flow conditions. The surfaces are represented in a unified manner using the viscosity ratio of the infused material within the porous asperities on a surface to that of the flowing fluid such that the spectrum of surfaces from superhydrophobic to smooth is captured by the range of viscosity ratios from 0 to ∞. Using a forced convection experimental setup, deposition of calcium sulfate on the surfaces is quantified in terms of asymptotic fouling resistance over a range of temperature, Reynolds number, and mineral foulant supersaturation. Through a systematic set of accelerated fouling experiments, an empirical relationship for the asymptotic fouling resistance is developed in terms of Reynolds number, foulant concentration, temperature, and surface type. The empirical model is validated with a comprehensive set of experimental data from this study as well as from the literature. Optimum nonwetting surface designs for minimizing fouling resistance compared to conventional smooth surfaces are developed as a function of temperature. The results of the study offer insight into the temperature-dependent fouling of surfaces under flow conditions and a rational design of fouling-resistant nonwetting surfaces that can be readily translated to practice.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Computational Power of Random Quantum Circuits in Arbitrary Geometries

Empirical evidence for a gap between the computational powers of classical and quantum computers has been provided by experiments that sample the output distributions of two-dimensional quantum circuits. Many attempts to close this gap have utilized classical simulations based on tensor network techniques, and their limitations shed light on the improvements to quantum hardware required to frustrate classical simulability. In particular, quantum computers having in excess of approximately 50 qubits are primarily vulnerable to classical simulation due to restrictions on their gate fidelity and their connectivity, the latter determining how many gates are required (and, therefore, how much infidelity is suffered) in generating highly entangled states. Here, we describe recent hardware upgrades to Quantinuum’s H2 quantum computer, enabling it to operate on up to 56 qubits with arbitrary connectivity and 99.843(5)% two-qubit gate fidelity. We define a class of circuits with random geometries that become hard to classically simulate in very low depth and implement them utilizing the flexible connectivity of H2. A careful analysis demonstrating the fast saturation of classical simulation complexity with depth indicates that H2 can yield data well beyond the reach of state-of-the art classical simulation methods at unprecedented fidelities. We find that the considerable difficulty of classically simulating H2 is likely limited only by qubit number, demonstrating the promise and scalability of the quantum charge-coupled device architecture as continued progress is made toward building larger machines. Published by the American Physical Society 2025

DeCross, M.↗
DOE OSTI2020

Non-Intrusive Appliance Identification with Appliance-Specific Networks

The problem of noninstrusive load monitoring (NILM) is usually formulated as a single-channel blind source separation task, whose successful solution enable fast and convenient load identification and energy disaggregation. When applied at test time, NILM algorithms aim to identify the operating characteristics of individual appliances from an aggregate power measurement of the entire house. Recent advances in deep learning gave rise to many methods that mostly focus on learning a direct mapping from aggregate measurement to individual appliance power. However, these methods are not only computationally expensive, but they often suffer from overfitting and do not generalize very well. In this article, we propose a novel NILM method that leverages advances in statistical learning that have not been properly applied in this domain before. The proposed method consists of three stages: first, a Bayesian nonparametric learning-based approach for appliance state extraction; second, synthetic minority oversampling technique for data augmentation and mitigating the heavy imbalance in switching events; and third, appliance-specific lightweight long short-term memory networks for status classification for each appliance. Here, we adopt a “differential” input (the difference before and after the switching event) to reduce the complexity of network training and make the proposed method robust to multiappliance switching events. Experiments are conducted to demonstrate the effectiveness of the proposed method, achieving superior performance when compared to recent methods. An ablation study is conducted to demonstrate the effectiveness of each module of our method. Finally, we investigate the quality of generated synthetic samples.

42 ENGINEERING↗
DOE OSTI2021

Report on High Energy Arcing Fault Experiments: Experimental Results from Low-Voltage Switchgear Enclosures

This report documents an experimental program designed to investigate High Energy Arcing Fault (HEAF) phenomena for low-voltage metal enclosed switchgear containing aluminum conductors. This report covers full-scale laboratory experiments using representative nuclear power plant (NPP) three-phase electrical equipment. Electrical, thermal, and pressure data were recorded for each experiment and documented in this report. This report covers experiments performed on two low-voltage switchgear units with each unit consisting of two vertical sections. The data collected supports characterization of the low-voltage HEAF hazard and these results will be used to support potential improvements in fire probabilistic risk assessment (PRA) methods. The experiments were performed at KEMA Labs located in Chalfont, Pennsylvania. The experimental design, setup, and execution were completed by staff from the NRC, the National Institute of Standards and Technology (NIST), Sandia National Laboratories (SNL) and KEMA. In addition, representatives from the Electric Power Research Institute (EPRI) observed some of the experimental setup and execution. The HEAF experiments were performed between August 26 and Augsut 29, 2019 on nearidentical Westinghouse Type DS low-voltage metal-enclosed indoor switchgear. The threephase arcing fault was initiated on the aluminum main bus or in select cases on the copper bus stabs near the breaker. These experiments used either nominal 480 volts AC or 600 volts AC. Durations of the experiments ranged from approximately 0.4 s to 8.3 s with fault currents ranging from approximately 9.2 kA to 19.3 kA. Real-time electrical operating conditions, including voltage, current and frequency, were measured during the experiments. Heat fluxes and incident energies were measured with plate thermometers, radiometers, and slug calorimeters at various locations around the electrical enclosures. Environmental measurements of breakdown, conductivity and electromagnetics were also taken. The experiments were documented with normal and high-speed videography, infrared imaging and photography. The results, while limited, indicated the difficulty in maintaining and sustaining low-voltage arcs on aluminum components of sufficient duration and at a single point as observed operating experience.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

The Dual-Axis Radiographic Hydrodynamic Test Facility Capability eXpansion (DCX) Strategy

The Dual-Axis Radiographic Hydrodynamic Test (DARHT) facility is a vital and important part of the Nation’s nuclear security enterprise. The Department of Energy/National Nuclear Security Administration (DOE/ NNSA) Stockpile Stewardship Management Plan (SSMP) identifies DARHT as a weapons mission critical facility along with the need to modernize DARHT to support weapons modernization efforts. With more than two decades of operations, DARHT has a storied history. Conceived in the 1970s, constructed in the 1990s, and operational since 2000, DARHT has advanced from open-air hydrodynamic experiments (hydros) to foam-confined hydros, to vessel-confined hydros, and in 2022, the 75 th hydro was successfully completed. Radiography has advanced from a single-axis, single-pulse system to a dual-axis, multi-pulse capability to variable fields of view (VFV) on both accelerators. The culmination of these experiences, accomplishments, and advancements has brought us to a very important question: What do the next two decades at DARHT look like? The world is not the same place it was in the 1990s when construction at DARHT was in progress. Evolving threats, an expanding mission, and technology changes necessitate adaptation. The 2018 Nuclear Posture Review (NPR) states that the nuclear weapons infrastructure has suffered the effects of age and underfunding with no margin for further delay in recapitalizing the physical infrastructure. To adapt, the aging facility, accelerators, vessels, and detector systems require improvements to ensure DARHT remains the Nation’s hydrodynamic data foundation for stockpile certification, safety, surety, and global security threats.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Development of a hybrid neural network and transfer learning model for optimized ICP-MS/MS operation

Correct function and calibration of instrumentation is a crucial assumption for any scientific experiment. One such instrument, tandem inductively coupled plasma mass spectrometer (ICP-MS/MS), has in-depth calibration settings that range across 30+ different parameters, making it difficult to determine optimal conditions without expertise and some degree of trial and error. Often, these settings are hand-tuned, a time-intensive process prone to local maxima and human error. While some automation is available, the automation also may favor local optimizations over a global optimum. In addition to these difficulties, day to day instrument variability can further complicate the calibration process. We propose a solution to this problem as a machine learning (ML) algorithm that learns how each parameter helps determine the calibration sensitivity across several elements, and re-weights parameters over time as instrument variability changes (e.g., a global neural network (NN) with a time-dependent transfer learning (TL) component). This model would be able to generate a surface of predicted calibration sensitivities and their respective parameters, and a simple multivariate algorithm would be able to pull out the optimum results with the settings associated with them. Here-in, we describe our initial findings in working towards this goal, including data extraction from historical files, exploratory data analysis, and some initial model building to better describe the data and the feasibility of our goal.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Characterization of W production during ICRF operations: experiments and modeling

For successfully heating plasma with waves in the ion cyclotron range of frequencies (ICRFs), mitigating impurity production is just as crucial as maximizing power coupling, especially in high-Z environments. ICRF can effectively deposit energy on ions, modify turbulence-driven transport, and enhance fusion reaction efficiency, but only when its power coupling has minimal impact on impurity production. To do so, one must rely on a toroidal array of at least three active elements excited with appropriate phasing and power ratio to reduce the currents induced on the antenna frame below levels critical for physical sputtering. In contrast to classic two-strap antennas, which are optimized for dipole phasing with equal power on both straps, three-strap antennas in ASDEX Upgrade (AUG)—but also four-strap antennas in JET, Alcator C-Mod, SPARC and ITER—offer the possibility to act also on the power ratio between the central and outer straps. With optimal settings, impurity production can be reduced substantially, making the ICRF compatible with the high-Z wall. This paper explores the characteristics of the AUG three-strap antennas in terms of impurity production, as well as the key role of plasma composition in this process. Numerical simulations were performed using SSWICH and Petra-M (finite element codes) to quantify impurity production and compare with experimental results. Energies of ions falling on antenna limiters (measured with probes) are well predicted by both codes. These tools are then used to further describe the source of the impurity, namely the gross erosion of tungsten from an ICRF antenna, for different plasma mixtures. Results are also compared to spectroscopy data. Ultimately, we show that deleterious effects of the ICRF on plasma surface interactions will be weaker in plasmas containing larger fractions of highly ionized heavier low-Z impurity, which is typically relevant for experiments relying on impurity seeding.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Quantifying the Multi-Objective Cost of Uncertainty

Various real-world applications involve modeling complex systems with immense uncertainty and optimizing multiple objectives based on the uncertain model. Quantifying the impact of the model uncertainty on the given operational objectives is critical for designing optimal experiments that can most effectively reduce the uncertainty that affect the objectives pertinent to the application at hand. In this paper, we propose the concept of mean multi-objective cost of uncertainty (multi-objective MOCU) that can be used for objective-based quantification of uncertainty for complex uncertain systems considering multiple operational objectives. We provide several illustrative examples that demonstrate the concept and strengths of the proposed multi-objective MOCU. Furthermore, we present a real-world example based on the mammalian cell cycle network to demonstrate how the multi-objective MOCU can be used for quantifying the operational impact of model uncertainty when there are multiple, possibly competing, objectives.

42 ENGINEERING↗
DOE OSTI2024

Using GANs to predict milling stability from limited data

Milling is a key manufacturing process that requires the selection of operating parameters that provide efficient performance. However, the presence of chatter, a self-excited vibration causing poor surface finish and potential damage to the machine and cutting tool, makes it challenging to select the appropriate parameters. To predict chatter, stability maps are commonly used, but their generation requires expensive data, making it difficult to employ these maps in industry. Therefore, there is a pressing need for an approach that can accurately predict stability maps using limited experimental data. This study introduces the new Encoder GAN (EGAN) approach based on Generative Adversarial Networks (GANs) that predicts stability maps using limited experimental data. The approach consists of the encoder, generator, and discriminator subnetworks and uses the trained encoder and generator to predict the target stability map. This versatile method can be applied to various tool setups and can accurately predict stability maps with limited experimental data (five to 10 cutting tests) even when there is little information available for unknown parameters. In conclusion, the study evaluates the proposed approach using both numerical data and experiments and demonstrates its superior performance compared to state-of-the-art benchmarks.

42 ENGINEERING↗
DOE OSTI2024

The crucial role of diagnostics in achieving ignition on the National Ignition Facility (NIF)

Well over 100 diagnostics can operate on the National Ignition Facility (NIF) as a result of several decades of development on NIF, and before that on Nova, OMEGA, and earlier LLNL lasers. A subset of these have guided the approach to achieving ignition on the NIF in 2022 [H. Abu-Shawareb et al. (Indirect Drive ICF Collaboration), Phys. Rev. Lett. 129(7), 075001 (2022)]. Achieving ignition on NIF has required many types of experiments with this core set of diagnostics, some constraining known unknowns and some revealing surprises—arguably unknown unknowns. Early design work realized that the extreme precision required for ignition on NIF would require fine-tuning by experiment, that is, measuring and adjusting known unknowns. Many examples are given where the use of the core set of ignition diagnostics in experimental arrangements called platforms demonstrated control of the key theoretical parameters defined as shape, adiabat, velocity, and mix. The direction of the adjustments to input conditions is found either by trend analysis or, in many cases, by observing from the diagnostic data the direction to make an adjustment. In addition, diagnostics have revealed some unexpected or neglected known issues, which degrade performance, or unexpected issues, unknown unknowns. Some of these factors had been previously considered, but underestimated or difficult to calculate at the time. The overall methodology can be described as a variant of Popper's falsifiability philosophy [K. Popper, The Logic of Scientific Discovery (Hutchinson, 1974)]. This paper summarizes the role of ignition diagnostics in terms of falsification or validation of theory or experimental setup as well as uncovering unexpected issues. The journey to ignition started in the seventies with a 1-µm wavelength laser producing disastrous results. Diagnostics have guided us to the recent multi-decadal goal of demonstrating ignition and burn in the laboratory.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Towards real-time monitoring: data assimilated time-lapse full waveform inversion for seismic velocity and uncertainty estimation

SUMMARY Rapid development of time-lapse seismic monitoring instrumentations has made it possible to collect dense time-lapse data for tomographically retrieving time-lapse (even continuous) images of subsurface changes. While traditional time-lapse full waveform inversion (TLFWI) algorithms are designed for sparse time-lapse surveys, they lack of effective temporal constraint on time-lapse data, and, more importantly, lack of the uncertainty estimation of the TLFWI results that is critical for further interpretation. Here, we propose a new data assimilation TLFWI method, using hierarchical matrix powered extended Kalman filter (HiEKF) to quantify the image uncertainty. Compared to existing Kalman filter algorithms, HiEKF allows to store and update a data-sparse representation of the cross-covariance matrices and propagate model errors without expensive operations involving covariance matrices. Hence, HiEKF is computationally efficient and applicable to 3-D TLFWI problems. Then, we reformulate TLFWI in the framework of HiEKF (termed hereafter as TLFWI-HiEKF) to predict time-lapse images of subsurface spatiotemporal velocity changes and simultaneously quantify the uncertainty of the inverted velocity changes over time. We demonstrate the validity and applicability of TLFWI–HiEKF with two realistic CO2 monitoring models derived from Frio-II and Cranfield CO2 injection sites, respectively. In both 2-D and 3-D examples, the inverted high-resolution time-lapse velocity results clearly reveal a continuous velocity reduction due to the injection of CO2. Moreover, the accuracy of the model is increasing over time by assimilating more time-lapse data while the standard deviation is decreasing over lapsed time. We expect TLFWI-HiEKF to be equipped with real-time seismic monitoring systems for continuously imaging the distribution of subsurface gas and fluids in the future large-scale CO2 sequestration experiments and reservoir management.

58 GEOSCIENCES↗
DOE OSTI2024

Neutrino-detector design for safeguarding small modular reactors

Nuclear reactors have long been a favored source for antineutrino measurements for estimates of power and burnup. With appropriate detector parameters and background rejection, an estimate of the reactor power can be derived from the measured antineutrino event rate. Antineutrino detectors are potentially attractive as a safeguards technology that can monitor reactor operations and thermal power from a distance. Advanced reactors have diverse features that may present challenges for current safeguards methods. By comparison, neutrino detectors offer complementary features, including a remote, continuous, unattended, and near-real-time monitoring capability, that may make them useful for safeguarding certain classes of advanced reactors. This study investigates the minimum depth and size of an antineutrino detector for a small modular reactor to meet safeguards needs for advanced reactors. Furthermore, extrapolating performance from several prior reactor antineutrino experiments, this study uses an analytical approach to develop a possible design for a remote antineutrino-based monitoring device.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Measurement of the energy asymmetry in $t\bar{t} j$ production at 13 TeV with the ATLAS experiment and interpretation in the SMEFT framework

A measurement of the energy asymmetry in jet-associated top-quark pair production is presented using 139fb -1 of data collected by the ATLAS detector at the Large Hadron Collider during pp collisions at $\sqrt{s}$ =13TeV. The observable measures the different probability of top and antitop quarks to have the higher energy as a function of the jet scattering angle with respect to the beam axis. The energy asymmetry is measured in the semileptonic $t\bar{t}$ decay channel, and the hadronically decaying top quark must have transverse momentum above 350GeV. The results are corrected for detector effects to particle level in three bins of the scattering angle of the associated jet. The measurement agrees with the SM prediction at next-to-leading-order accuracy in quantum chromodynamics in all three bins. In the bin with the largest expected asymmetry, where the jet is emitted perpendicular to the beam, the energy asymmetry is measured to be -0.043 ± 0.020, in agreement with the SM prediction of -0.037 ± 0.003. Interpreting this result in the framework of the Standard Model effective field theory (SMEFT), it is shown that the energy asymmetry is sensitive to the top-quark chirality in four-quark operators and is therefore a valuable new observable in global SMEFT fits.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Integrating Immersive Visualization in Molten-Salt Reactor Waste Management for Experimental Design and Planning

The Molten Salt Reactor (MSR) represents a significant innovation in nuclear technology, offering several operational and safety benefits over traditional solid-fuel reactors. However, MSRs face uncertainties in waste management due to their flexible designs and variable waste compositions. To address these challenges, we propose a visualization platform that illustrates solutions and performance predictions for various waste management strategies, enhancing user experience and improving strategy and communication. Immersive visualizations are widely used in the nuclear industry for training, simulation, and safety enhancement. Our project aims to develop a visualization platform incorporating virtual reality (VR) technologies to illustrate MSR characteristics immediately following reactor shutdown. This immersive simulation will allow users to interact, explore, and understand different waste management strategies. The platform will display MSR reactor characterizations, including nuclide decay, salt solidification, and corrosion, which are crucial for assessing and selecting backend management strategies. Using the Meta Quest 3 VR headset with Unity software, our platform will provide real-scale visualizations, enabling users to experience and evaluate designs and plans as if they were physically present. This user-friendly interface will make complex data accessible and understandable for non-domain experts, aiding in decision-making for MSR waste management. Our proposed visualization workflow can be applied to other nuclear reactors, assisting in the design and planning of waste management strategies.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Improved Understanding of Zircaloy Hydrogen Pickup Mechanisms in BWRs. NSUF Report (Final Report)

Using an instrumented autoclave operating at 288°C and 1100 psi, electrical impedance spectroscopy (EIS) measurements were taken from three neutron irradiated fuel channel samples (Zircaloy-2 and Zircaloy-4). The three high priority fuel channel samples were tested at 288°C using a simulated PWR water with the AC frequency range extending down through 10 -4 Hz, with corresponding DC measurements to validate the resistance measurements. EIS measurements were also taken at room temperature using a simulated PWR water from four neutron irradiated fuel channel samples (Zircaloy-2 and Zircaloy-4). and four water rod samples (Zircaloy-2). EIS measurements were also performed on unirradiated Zircaloy-2 tubes that were previously oxidized in autoclaves at various temperatures for different time durations to obtain oxide layers of different thicknesses. These experiments proved to be more challenging than anticipated because of the small size of the samples, which required development of fixtures to allow valid testing of the materials, some of which are radioactive, under pressure and temperature. Equivalent circuits were used to model the data. Some samples resulted in Nyquist plots where the arcs were too close to each other resulting in the inability to separate the different oxide morphological thicknesses. Microstructural characterization in the form of optical microscopy, scanning electron microscopy and (scanning) transmission electron microscopy was performed to document the microstructural features of the oxidized Zr-2 and Zr-4 samples (oxide and metal/oxide interface) and to interpret the EIS results. At the start of this work, it was assumed that despite the laminar cracks and porosity visible in both optical and SEM, the oxide scale is dense near the metal, effectively preventing the water from contacting the metal directly. While it is generally accepted that the large laminar cracks pores allow the water to percolate into the oxide film, it is unknown just how deep it can go, and how interconnected is the porosity, which will dictate how much the porosity is connected electrically to the water within the oxide scale. The EIS results demonstrated that there is in fact a distribution of oxide thicknesses over the various samples, and within each material condition, that are communicating to the liquid. This is derived from the fact that Nyquist plots have an arc radius whose center point is often below the Z’ axis, implying that instead of a single oxide thickness, these oxides have a distribution of thicknesses. This distribution of thicknesses produces arcs that are not clearly separable in most of the samples.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

UAS remote sensing (Osprey platform): Red-green-blue (RGB) imagery, thermal infrared (TIR) imagery, and canopy reflectance, Seward Peninsula, Alaska, 2019

Airborne remote sensing data collected using the Brookhaven National Laboratory's (BNL) heavy-lift unoccupied aerial system (UAS) octocopter platform - the Osprey - operated by the Terrestrial Ecosystem Science and Technology (TEST) group (https://www.bnl.gov/testgroup). This package includes data from 17 flights flown over the NGEE-Arctic Council Mile Maker 64 (MM64) and Teller MM27 sites in July, 2019. The Osprey is a multi-sensor UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) and thermal infrared (TIR) surface "skin" temperature imagery, as well as surface reflectance at 1 nm intervals in the visible to near-infrared spectral range from ~350-1000 nm measured at regular intervals along each flight path. This package provide the Level 0 (raw, unprocessed) data collected by the Osprey platform. Ancillary aircraft data, flight mission parameters, and general flight conditions provided by the onboard flight and data collection computers are also included. Data and metadata are provided as text (*.txt, *.json), tabular (*.dat, *.csv, *.waypoint), and image (*.jpg) formats. This metadata document contains flight campaign, instrument and file metadata, along with a description of the L0 data, and file naming scheme. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A Critical Review of Heat Pipe Experiments in Nuclear Energy Applications

This critical review provides heat pipe (HP) experimental data sets that contain pertinent information regarding nuclear technology that may be beneficial to researchers. Heat pipes have been shown to have a tremendously positive impact on nuclear technologies and will continue to become a more prevalent technology as more nuclear reactor concepts embrace this robust technology. Most previous reviews may focus on only a specific HP design or application, and some are backdated. Herein, this critical review extends previous efforts; integrates and summarizes previously reported HP experimental efforts; and provides updates with recently reported results in the literature for HPs in all nuclear-related applications, including space power (thermal radiators, core cooling, and electricity production), microreactors (emergency core cooling, hybrid control rods, and reactor core cooling), and HP involvement in other nuclear-related technologies (spent fuel pool cooling). The two main objectives of this critical review are (1) to facilitate the development of HP codes by outlining some of the existing experimental data sets to validate their codes and directing developers to these efforts and (2) to provide comprehensive information regarding the vast applicability of HPs used in the nuclear industry, including the theory of operation and limitations to supplement researchers in the development of new ideas for potential applications in nuclear-related technologies. The review clearly shows extensive and diverse experimental data sets for HPs developed under diverse testing conditions depending on the available nuclear application for validation purposes. Thus, this critical review is oriented to providing attention to the existing efforts rather than determining gaps in HP research.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗