Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Data Transfer”

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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Neural Network‐Based Methods for Ocean Surface Wave Measurement Using Submarine Distributed Acoustic Sensing (DAS)

Two new data-driven models for estimating ocean surface waves from distributed acoustic sensing (DAS) submarine cable strain rate are developed using supervised machine learning on a 10-day data set collected offshore of Oliktok Point, Alaska. The new models were trained on target data from seafloor pressure moorings at three sites spaced evenly along 27.1 km of cable and were benchmarked against an empirical transfer function method previously used to estimate waves from DAS. A model which uses convolutional neural networks to transform 2-km frequency-wavenumber strain spectra to seafloor pressure spectra outperforms the benchmark in wave height prediction (RMSE of 0.15 vs. 0.41 m) and period prediction (0.29 vs. 0.37 s) when evaluated on a held-out test data set. When applied to a DAS data set collected on the same cable 2 years prior, the CNN-based model maintained similar significant wave height performance (RMSE = 0.23 m) relative to available satellite altimetry data. A two-hidden-layer, fully connected neural network which transforms 1-D strain spectra to seafloor pressure spectra also outperforms the benchmark in wave height prediction (RMSE of 0.19 vs. 0.41 m), but does not generalize as well to the prior data. Regression-based machine learning is useful for estimating waves from DAS data when the pressure-strain relationship varies temporally and spatially across different wave conditions. Models can be applied to DAS data to measure waves with higher spatial resolution and longer temporal coverage than traditional methods, which often measure waves only at a single point.

Davis, Jacob R. [Univ. of Washington, Seattle, WA ↗
DOE OSTI2022

TYC 2990-127-1: An Algol-type SB2 binary system of subgiant and red giant with a probable ongoing mass-transfer

ABSTRACT We present a study of the spectroscopic binary TYC 2990-127-1 from the LAMOST survey. We use full-spectrum fitting to derive radial velocities and spectral parameters. The high mass ratio indicates that the system underwent mass transfer in the past. We compute the orbital solution and find that it is a very close sub-giant/red giant pair on circular orbit, slightly inclined to the sky-plane. Fitting of the TESS photometrical data confirms this and suggests an inclination of i ∼ 39.8°. The light curve and spectrum around Hα show signs of irregular variability, which supports ongoing mass transfer. The binary evolution simulations suggest that the binary may experience non-conservative mass transfer with accretion efficiency 0.3, and the binary will enter into common envelope (CE) phase in the subsequent evolution. The remnant product after the ejection of CE may be a detached double helium white dwarf (He WD) or a merger.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Deciphering Distinct Overpotential-Dependent Pathways for Electrochemical CO 2 Reduction Catalyzed by an Iron–Terpyridine Complex

[Fe(tpyPY2Me)] 2+ ([Fe] 2+ ) is a homogeneous electrocatalyst for converting CO 2 into CO featuring low overpotentials of <100 mV, near-unity selectivity, and high activity with turnover frequencies faster than 100 000 s -1 . To identify the origins of its exceptional performance and inform future catalyst design, we report a combined computational and experimental study that establishes two distinct mechanistic pathways for electrochemical CO 2 reduction catalyzed by [Fe] 2+ as a function of applied overpotential. Electrochemical data shows the formation of two catalytic regimes at low (η TOF/2 of 160 mV) and high (η TOF/2 of 590 mV) overpotential plateaus. We propose that at low overpotentials [Fe] 2+ undergoes a two-electron reduction, two-proton-transfer mechanism (electrochemical-electrochemical-chemical-chemical, EECC), where turnover occurs through the dicationic iron complex, [Fe] 2+ . Computational analysis supports the importance of the singlet ground-state electronic structure for CO 2 binding and that the rate-limiting step is the second protonation in this low-overpotential regime. When more negative potentials are applied, an additional electron-transfer event occurs through either a stepwise or proton-coupled electron-transfer (PCET) pathway, enabling catalytic turnover from the monocationic iron complex ([Fe] + ) via an electrochemical-chemical-electrochemical-chemical (ECEC) mechanism. Comparison of experimental kinetic data obtained from variable controlled potential electrolysis (CPE) experiments with direct product detection with calculated rates obtained from the energetic span model supports the PCET pathway as the most likely mechanism. Moreover, we build upon this mechanistic understanding to propose the design of an improved ligand framework that is predicted to stabilize the key transition states identified in our study and explore their electronic structures using an energy decomposition analysis. Finally, taken together, this work highlights the value of synergistic computational/experimental approaches to decipher mechanisms of new electrocatalysts and direct the rational design of improved platforms.

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

Direct structural retrieval from gas-phase ultrafast diffraction data using a genetic algorithm

Ultrafast scattering techniques such as ultrafast electron diffraction and ultrafast x-ray diffraction have been utilized to elucidate the structural dynamics, reaction intermediates, and final products in molecular reactions following photoexcitation. The time-dependent structures are typically not directly retrieved from the experimental data, but they rely on comparison with calculations. The genetic algorithm (GA), a global optimization strategy, can be used to retrieve the molecular structures directly from diffraction patterns without any theoretical input. However, the robustness of the GA with respect to real experimental conditions such as a limited momentum transfer range, noise, and artifacts has not been studied in detail. In this work, we characterize the performance of the GA with simulated data that mimic realistic experimental conditions. We have developed and implemented a variant of the GA specific to diffraction measurements which performs better in the presence of imperfect data compared to the standard implementation of the GA. We demonstrate this method with both synthetic data and experimental ultrafast electron diffraction data on the UV-induced photodissociation of trifluoroiodomethane (C⁢F 3⁡ I) molecules.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Pool boiling heat transfer evaluation of next-generation dielectric fluid: Opteon™ 2P50

The growing use of artificial intelligence has led to heavy thermal loads and high heat dissipation rates in data centers. Conventional air-cooled technologies are not able to fulfill these requirements. To overcome these challenges, two-phase immersion cooling (2PIC) has emerged as one of the leading technologies for high power-density chips. 2PIC increases the heat dissipation rate and efficiency of the system while reducing the footprint of the cooling equipment. A fluid with adequate dielectric properties, a suitable normal boiling temperature to maintain chip temperatures, and good material compatibility, is desired for 2PIC system. In this study, the pool boiling heat transfer of a new developmental dielectric fluid, Opteon™ 2P50, was experimentally investigated. The heat transfer coefficients at various heat fluxes (20–150 kW/m 2 ) and the critical heat flux were measured using a smooth aluminum surface. Compared with HFE-7100, Opteon™ 2P50 shows higher heat transfer coefficient (up to 59% higher) and a slightly lower value of critical heat flux (around 5.9% lower). The modified Cooper correlation with the optimized leading constant resulted in reliable prediction accuracy with a 5.3% mean absolute error percentage. Overall, these results indicate that the new dielectric fluid provides similar thermal performance to some legacy fluids.

2P50↗
DOE OSTI2021

Measurement of the Generalized Spin Polarizabilities of the Neutron in the Low-Q{sup 2} Region

Understanding the nucleon spin structure in the regime where the strong interaction becomes truly strong poses a challenge to both experiment and theory. At energy scales below the nucleon mass of about 1 GeV, the intense interaction among the quarks and gluons inside the nucleon makes them highly correlated. Their coherent behaviour causes the emergence of effective degrees of freedom, requiring the application of non-perturbative techniques such as chiral effective field theory1. Here we present measurements of the neutron’s generalized spin polarizabilities that quantify the neutron’s spin precession under electromagnetic fields at very low energy-momentum transfer squared down to 0.035 GeV2. In this regime, chiral effective field theory calculations2–4 are expected to be applicable. Our data, however, show a strong discrepancy with these predictions, presenting a challenge to the current description of the neutron’s spin properties.

Sulkosky, Vincent↗
DOE OSTI2023

Combining synchrotron X-ray diffraction, mechanistic modeling and machine learning for in situ subsurface temperature quantification during laser melting

Laser melting, such as that encountered during additive manufacturing, produces extreme gradients of temperature in both space and time, which in turn influence microstructural development in the material. Qualification and model validation of the process itself and the resulting material necessitate the ability to characterize these temperature fields. However, well established means to directly probe the material temperature below the surface of an alloy while it is being processed are limited. To address this gap in characterization capabilities, a novel means is presented to extract subsurface temperature-distribution metrics, with uncertainty, from in situ synchrotron X-ray diffraction measurements to provide quantitative temperature evolution data during laser melting. Temperature-distribution metrics are determined using Gaussian process regression supervised machine-learning surrogate models trained with a combination of mechanistic modeling (heat transfer and fluid flow) and X-ray diffraction simulation. The trained surrogate model uncertainties are found to range from 5 to 15% depending on the metric and current temperature. The surrogate models are then applied to experimental data to extract temperature metrics from an Inconel 625 nickel superalloy wall specimen during laser melting. The maximum temperatures of the solid phase in the diffraction volume through melting and cooling are found to reach the solidus temperature as expected, with the mean and minimum temperatures found to be several hundred degrees less. The extracted temperature metrics near melting are determined to be more accurate because of the lower relative levels of mechanical elastic strains. However, uncertainties for temperature metrics during cooling are increased due to the effects of thermomechanical stress.

36 MATERIALS SCIENCE↗
DOE OSTI2025

University of Miami G-band Vapor Radiometer Calibration (UMGVR_CAL) Field Campaign Report

Cold-air outbreak (CAO) clouds in the Arctic are commonly mixed-phase (MP); however, the partitioning of the amount of ice and water in CAO clouds and precipitation is not always well observed. Understanding how cloud phases partition as a function of cloud life cycle is important for predicting snowfall rates, convective life cycle, and intensity at weather timescales. The partitioning into liquid versus ice also has radiative impacts that are consequential for climate. These concerns motivated the incorporation of an airborne G-band vapor radiometer (GVR) into an National Science Foundation-supported aircraft campaign named the Cold Air outbreak Experiment in the Sub-Arctic Region (CAESAR). The GVR is an upward-pointing passive microwave radiometer using four frequencies centered around the 183.31 GHz water vapor absorption line, displaced by +- 1, 3, 7, and 14 GHz. For context, The U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) user facility operates a surface-based GVR at its North Slope of Alaska (NSA) site. The same GVR has been used previously for a field campaign in the southeast Pacific, where an offset was noticed between brightness temperatures (Tbs) measured under clear skies compared to those calculated from a radiative transfer model. To account for any calibration offsets to the Tbs, a request was made to DOE to allow the GVR to operate at ARM’s Southern Great Plains (SGP) observatory and enable comparisons between its measurements and those available at SGP. This request was granted, titled ‘UMGVR_CAL’, short for ‘UMGVR_Calibration’. From October 30 to November 10, 2023, the GVR was deployed to the ARM SGP site to take advantage of their regular, nearby radiosonde launches under clear-sky conditions. The latter were determined using the SGP total sky imagery data. The GVR brightness temperatures in these clear-sky conditions were compared to those calculated by a radiative transfer code (PAMTRA) based on the SGP radiosondes. During the campaign, four suitable clear-sky episodes could be used for the GVR calibration. While few in number, these proved to be enough to satisfy our goal.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data-Efficient Strategies for Probabilistic Voltage Envelopes under Network Contingencies

This work presents an efficient data-driven method to construct probabilistic voltage envelopes (PVE) using power flow learning in grids with network contingencies. First, a network-aware Gaussian process (GP) termed Vertex-Degree Kernel (VDK-GP), developed in prior work, is used to estimate voltage–power functions for a few network configurations. The paper introduces a novel multi-task vertex degree kernel (MT-VDK) that amalgamates the learned VDK-GPs to determine power flows for unseen networks, with a significant reduction in the computational complexity and hyperparameter requirements compared to alternate approaches. Simulations on the IEEE 30-Bus network demonstrate the retention and transfer of power flow knowledge in both N-1 and N-2 contingency scenarios. The MT-VDK-GP approach achieves over 50 % reduction in mean prediction error for novel N-1 contingency network configurations in low training data regimes (50–250 samples) over VDK-GP. Additionally, MT-VDK-GP outperforms a hyper-parameter based transfer learning approach in over 75 % of N-2 contingency network structures, even without historical N-2 outage data. Furthermore, the proposed method demonstrates the ability to achieve PVEs using sixteen times fewer power flow solutions compared to Monte-Carlo sampling-based methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Thermal conductivity modeling of monodispersed microspheres using discrete element method

Particle beds are widely used in various systems and processes, such as particle heat exchangers, granular flow reactors, and additive manufacturing. Accurate modeling of the thermal conductivity of particle beds and understanding of their heat transfer mechanisms are important. However, previous models were based on simple cubic packing of particles, which could not accurately represent the actual heat transfer processes under certain conditions. Here, we examine the effect of the packing structure on the thermal conductivity of particle beds. We use monodispersed silica microspheres with average particle sizes ranging from 23 to 330 μm as a model material. We employ a transient hot-wire technique to measure the thermal conductivity of the particle beds with packing density of 43%–57% within a temperature range of room temperature to 500 °C and under N2 gaseous pressures of 20–760 Torr. We then use a discrete element method (DEM) to obtain the realistic packing structure of the particles, which is then fed into a finite-element model (FEM) to calculate the thermal conductivity, with the consideration of solid conduction, gas conduction, and radiation heat transfer. Our results show that the thermal conductivity model based on the more realistic random packing structure derived from the DEM shows better agreement with the experimental data compared to that based on the simple cubic-packing structure. The combined DEM and FEM methodology can serve as a useful tool to predict the effective thermal conductivity of particle beds and to quantify different heat transfer mechanisms under various conditions.

14 SOLAR ENERGY↗
DOE OSTI2022

GOOML - Finding Optimization Opportunities for Geothermal Operations: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach. We have used this framework to develop digital twins that provide steamfield operators with an operational environment to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management for real world applications. The GOOML modeling software is built on a generic component-based systems framework that allows for both historical and forecast analysis. A GOOML model can perform historical data-assimilation using first-principal thermodynamics to create a meaningful data model. Historical production data can then be coupled with a forecast framework to train machine-learning models of steamfield components to predict future outputs. This modeling environment enables digital exploration of steamfield design configurations and operational scenarios. GOOML digital twins have been developed for steamfields in New Zealand and the United States representing differing power generation and field conditions. These digital twins have been validated by comparing hindcast predictions against historical production data. Reinforcement learning experiments were conducted to demonstrate the ability to programmatically explore the operations space using machine learning agents. Our initial results are compelling; two to five percent increases in annual energy production were demonstrated by the GOOML models with no additional infrastructure build required. GOOML offers a new approach to geothermal operations by applying state-of-the-art machine learning algorithms, comprehensive data analytics, and interaction with digital twins. Through application of these tools, operators will realize greater availability and higher net generation which will increase the cost effectiveness of geothermal energy projects.

access↗
DOE OSTI2022

Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost

Appropriately identifying and treating molecules and materials with significant multi-reference (MR) character is crucial for achieving high data fidelity in virtual high-throughput screening (VHTS). Despite development of numerous MR diagnostics, the extent to which a single value of such a diagnostic indicates the MR effect on a chemical property prediction is not well established. We evaluate MR diagnostics for over 10 000 transition-metal complexes (TMCs) and compare to those for organic molecules. We observe that only some MR diagnostics are transferable from one chemical space to another. By studying the influence of MR character on chemical properties (i.e., MR effect) that involve multiple potential energy surfaces (i.e., adiabatic spin splitting, ΔE H–L , and ionization potential, IP), we show that differences in MR character are more important than the cumulative degree of MR character in predicting the magnitude of an MR effect. Motivated by this observation, we build transfer learning models to predict CCSD(T)-level adiabatic ΔE H–L and IP from lower levels of theory. By combining these models with uncertainty quantification and multi-level modeling, we introduce a multi-pronged strategy that accelerates data acquisition by at least a factor of three while achieving coupled cluster accuracy (i.e., to within 1 kcal mol –1 MAE) for robust VHTS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Three-dimensional coherent X-ray diffraction imaging via deep convolutional neural networks

Abstract As a critical component of coherent X-ray diffraction imaging (CDI), phase retrieval has been extensively applied in X-ray structural science to recover the 3D morphological information inside measured particles. Despite meeting all the oversampling requirements of Sayre and Shannon, current phase retrieval approaches still have trouble achieving a unique inversion of experimental data in the presence of noise. Here, we propose to overcome this limitation by incorporating a 3D Machine Learning (ML) model combining (optional) supervised learning with transfer learning. The trained ML model can rapidly provide an immediate result with high accuracy which could benefit real-time experiments, and the predicted result can be further refined with transfer learning. More significantly, the proposed ML model can be used without any prior training to learn the missing phases of an image based on minimization of an appropriate ‘loss function’ alone. We demonstrate significantly improved performance with experimental Bragg CDI data over traditional iterative phase retrieval algorithms.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Impact of Systematic Modeling Uncertainties on Kilonova Property Estimation

The precise atomic structure and therefore the wavelength-dependent opacities of lanthanides are highly uncertain. This uncertainty introduces systematic errors in modeling transients like kilonovae and estimating key properties such as mass, characteristic velocity, and heavy metal content. Here, we quantify how atomic data from across the literature as well as choices of thermalization efficiency of r-process radioactive decay heating impact the light curve and spectra of kilonovae. Specifically, we analyze the spectra of a grid of models produced by the radiative transfer code Sedona that span the expected range of kilonova properties to identify regions with the highest systematic uncertainty. Our findings indicate that differences in atomic data have a substantial impact on estimates of lanthanide mass fraction, spanning approximately 1 order of magnitude for lanthanide-rich ejecta, and demonstrate the difficulty in precisely measuring the lanthanide fraction in lanthanide-poor ejecta. Mass estimates vary typically by 25%–40% for differing atomic data. Similarly, the choice of thermalization efficiency can affect mass estimates by 20%–50%. Observational properties such as color and decay rate are highly model dependent. Velocity estimation, when fitting solely based on the light curve, can have a typical error of ∼100%. Atomic data of light r-process elements can strongly affect blue emission. Even for well-observed events like GW170817, the total lanthanide production estimated using different atomic data sets can vary by a factor of ∼6.

Gravitational wave sources↗
DOE OSTI2023

Remote sensing of seasonal variation of LAI and fAPAR in a deciduous broadleaf forest

Climate change is affecting the phenology of terrestrial ecosystems. In deciduous forests, phenology in leaf area index (LAI) is the primary driver of seasonal variation in the fraction of absorbed photosynthetically active radiation (fAPAR), which drives photosynthesis. Remote sensing has been widely used to estimate LAI and fAPAR. However, while many studies have examined both empirical and model-based relationships among LAI, fAPAR, and spectral vegetation indices (SVI) from remote sensing, few studies have systematically and empirically examined how relationships among these variables change over the growing season. In this study, we examine how and why seasonal-scale covariation differs among time series of remotely sensed SVIs and both LAI and fAPAR based on current understanding and theory. To do this we use newly available remote sensing data sets in combination with time series of in-situ measurements and a canopy radiative transfer model to analyze how seasonal variation in canopy and environmental conditions affect relationships among remotely sensed SVIs, LAI, and fAPAR at a temperate deciduous forest site in central Massachusetts. Our results show that accounting for seasonal variation in canopy shadowing, which is driven by variation in solar zenith angle, improved remote sensing-based estimates of LAI, fAPAR, and daily total APAR. Specifically, we show that the phenology of SVIs is strongly influenced by seasonal variation in near infrared (NIR) reflectance arising from systematic variation in the canopy shadow fraction that is independent of changes in LAI or fAPAR. Therefore, results of this work provide a refined basis for understanding how remote sensing can be used to monitor and model the phenology of LAI, fAPAR, APAR, and gross primary productivity in temperate deciduous forests.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Theoretical estimates of flammability bounds for thin condensed fuel diffusion flames in microgravity using detailed models of chemistry and radiation

Recently, U-shape flammability maps have been constructed showing minimal oxygen vs. flame strain for opposed flame spread in micro-gravity by Olson and Ferkul. The U-shape defines the limiting flammability bounds from radiative extinction and flame blow-off. Here, the minimum of the U corresponds to the minimum possible oxidizer concentration where burning can occur, and is an important quantity of interest for fire safety. While high strain extinction bounds have been well analyzed, low strain radiative extinction has not. To estimate low strain extinction, in this study an analytical theory is developed based on thin flame theory coupled with a heat and mass transfer model for solid fuels. A reaction progress variable based on the Damköhler number is adapted in the theory to account for incomplete combustion at high strain rates and enable the capturing of the full flammability map. The analytical model is compared to a one dimensional numerical model w/ detailed chemical kinetics and coupled radiation heat transfer in planar and spherical geometries. The flammability maps are then qualitatively compared to experimental extinguishment data compiled by Olson and Ferkul for cylindrical rods of PMMA showing similar trends. The results show the newly developed analytics capture the radiative extinction bound compared to the numerical model and qualitatively agrees with microgravity data.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

Experimental investigation on heat transfer performance of drain water heat recovery heat exchangers

Water heating accounts for 18 % of residential energy consumption, and a substantial portion of this energy can be recovered through drain water heat recovery systems. Here, this paper investigates the heat transfer performance of drain water heat recovery heat exchangers. Two heat exchanger configurations were studied: vertical (gravity film exchanger) and horizontal. The heat recovery performance of the exchangers was quantified using sensible effectiveness and characteristic curves following the ε-NTU approach. Depending on the flow conditions, heat exchangers with a vertical configuration offered higher heat transfer effectiveness compared with the horizontal configuration under similar operating conditions. Furthermore, the results showed that properly designed and sized heat exchangers can effectively recover considerable amounts of waste heat from drains, depending on flow rates. Additionally, in the vertical heat exchanger configuration, low flow rates result in partially wet conditions, resulting in lower heat transfer effectiveness. Therefore, two distributor configurations are proposed to improve the heat transfer performance of vertically configured heat exchangers at low flow rates. The proposed distributor configurations and data presented in this paper will be useful in the design and development of drain water heat recovery systems in building applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Bubble departure and sliding in high-pressure flow boiling of water

Bubble growth, departure and sliding in low-pressure flow boiling has received considerable attention in the past. However, most applications of boiling heat transfer rely on high-pressure flow boiling, for which very little is known, as experimental data are scarce and very difficult to obtain. In this work, we conduct an experiment using high-resolution optical techniques. By combining backlit shadowgraphy and phase-detection imaging, we track bubble shape and physical footprint with high spatial ($6\,\mathrm {\mu }{\rm m}$) and temporal ($33\,\mathrm {\mu }{\rm s}$) resolutions, as well as bubble size and position as bubbles nucleate and slide on top of the heated surface. We show that at pressures above 1 MPa bubbles retain a spherical shape throughout the growth and sliding process. We analytically derive non-dimensional numbers to correlate bubble velocity and liquid velocity throughout the turbulent boundary layer and predict the sliding of bubbles on the surface, solely from physical properties and the bubble growth rate. We also show that these non-dimensional solutions can be leveraged to formulate elementary criteria that predict the effect of pressure and flow rate on bubble departure diameter and growth time.

Mechanics↗