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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.

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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Rates and Reversibilities in Interconnected Reaction Networks

Mathematical relations prescribing unidirectional forward and reverse rates originally derived based on single-path reaction sequences do not apply to interconnected reaction networks. The presence of branches in reaction networks, leading to alternative stable products, decreases unidirectional rates in reference to those calculated by single-path functional forms, as shown by simulated isotopic exchange rates, but impacts the unidirectional forward and reverse rates equally such that the functional form of effective reversibility remains unchanged. Regardless of stoichiometric numbers and network connectivity, the application of the pseudo-steady-state hypothesis on reactive intermediates in conjunction with consideration of unidirectional rates toward the product of interest and all alternative stable products results in mathematical expressions that accurately reflect simulated isotopic exchange rates. Further analyses based on kinetic resistance, a property akin to electrical resistance, illustrate the manifestation of nodal resistances in addition to the single-path kinetic resistance for interconnected reaction networks. The generalized formalism for assessing rates and reversibilities in interconnected networks derived herein enables us to demonstrate that unidirectional rates cannot be assessed solely from effective reversibilities and net rates of generation of stable species in such networks. Furthermore, isotopic exchange rates, under the condition that each elementary step in the overall reaction sequence forms a unique reactive intermediate that is consumed solely by the subsequent step, can be utilized to determine unidirectional rates and can serve to validate postulated reaction pathways in highly interconnected reaction networks (e.g., CO x hydrogenation).

42 ENGINEERING↗
DOE OSTI2021

Imaging of Electrothermal Filament Formation in a Mott Insulator

Resistive switching—the current- and voltage-induced change of electrical resistance—is at the core of memristive devices, which play an essential role in the emerging field of neuromorphic computing. This study is about resistive switching in a Mott insulator, which undergoes a thermally driven metal-to-insulator transition. Two distinct switching mechanisms are reported for such a system: electric-field-driven resistive switching and electrothermal resistive switching. The latter results from an instability caused by Joule heating. Here, we present the visualization of the reversible resistive switching in a planar V 2 O 3 thin-film device using high-resolution wide-field microscopy in combination with electric transport measurements. We investigate the interaction of the electrothermal instability with the strain-induced spontaneous phase separation in the V 2 O 3 thin film at the Mott transition. The photomicrographs show the formation of a narrow metallic filament with a minimum width ≲500 nm. Although the filament formation and the overall shape of the current-voltage characteristics (IVCs) are typical of an electrothermal breakdown, we also observe atypical effects such as oblique filaments, filament splitting, and hysteretic IVCs with sawtoothlike jumps at high currents in the low-resistance regime. We are able to reproduce the experimental results in a numerical model based on a two-dimensional resistor network. This model demonstrates that resistive switching in this case is indeed electrothermal and that the intrinsic heterogeneity is responsible for the atypical effects. This heterogeneity is strongly influenced by strain, thereby establishing a link between switching dynamics and structural properties.

42 ENGINEERING↗
NASA NTRS1992

Automated High-Temperature Hall-Effect Apparatus

Automated apparatus takes Hall-effect measurements of specimens of thermoelectric materials at temperatures from ambient to 1,200 K using computer control to obtain better resolution of data and more data points about three times as fast as before. Four-probe electrical-resistance measurements taken in 12 electrical and 2 magnetic orientations to characterize specimens at each temperature. Computer acquires data, and controls apparatus via three feedback loops: one for temperature, one for magnetic field, and one for electrical-potential data.

Parker, James B.↗
DOE OSTI2024

Systems and methods for resolving a number of incident RF-range photons

A photon-number-resolving detector comprises a detection element, an ohmmeter, and a hardware logic component. The detection element can be formed from a Weyl or Dirac semimetal. Electrons of the detection element are characterized by a surface state that exhibits a Dirac cone and a bulk superconducting state that exhibits a bandgap. When photons having energies less than the bandgap of the bulk superconducting state impinges on the detection element, the photons can be absorbed by electrons of the detection element that are characterized by the surface state. The ohmmeter outputs resistance data indicative of an electrical resistance of the detection element while the photons impinge on the detection element. The hardware logic component can determine, based upon the resistance data, a number of the photons that are absorbed by the surface state electrons of the detection element.

Soh, Daniel Beom Soo↗
NASA NTRS1967

The Cu-Fe system

Temperature and concentration effects on electric resistance determined in Cu-Fe base alloys

ELECTRIC RESISTANCE↗
DOE OSTI2024

Optical and electrical properties of proton-implanted p -GaSb for electrical isolation

The effect of proton implantation as isolation implant and subsequent annealing on the optical absorption and electrical resistivity of low-bandgap p -GaSb is reported. The measured transmittance spectra indicates that implantation creates a distribution of energy levels extending into the bandgap. Electrical measurements show that the average sheet resistance of the implanted layer increases only by an order of magnitude from its pre-implantation value at a proton dose of ~10 13 cm -2 followed by 200 °C annealing. It is also shown that annealing reduces the implantation-induced optical absorption while still retaining a high electrical resistivity.

42 ENGINEERING↗
DOE OSTI2022

Deep learning to estimate permeability using geophysical data

Time-lapse electrical resistivity tomography (ERT) is a popular geophysical method to estimate three-dimensional (3D) permeability fields from electrical potential difference measurements. Traditional inversion and data assimilation methods are used to ingest this ERT data into hydrogeophysical models to estimate permeability. Due to ill-posedness and the curse of dimensionality, existing inversion strategies provide poor estimates and low resolution of the 3D permeability field. Recent advances in deep learning provide us with powerful algorithms to overcome this challenge. This paper presents a deep learning (DL) framework to estimate the 3D subsurface permeability from time-lapse ERT data. To test the feasibility of the proposed framework, we train DL-enabled inverse models on simulation data. Each measurement in both synthetic and field data is standardized by removing the mean and scaling the time-series to unit variance. This pre-processing step is necessary to bring simulation data closer to field observations. Subsurface process models based on hydrogeophysics are used to generate this synthetic data. Training performed on limited simulation data resulted in the DL model over-fitting. An advanced data augmentation based on mixup is implemented to generate additional training samples to overcome this issue. This mixup technique creates weakly labeled (low-fidelity) samples from strongly labeled (high-fidelity) data. The weakly labeled training data is then used to develop DL-enabled inverse models and reduce over-fitting. As both time-lapse ERT (1133048 features/realization) and 3D permeability (585453 features/realization) data samples are from a high-dimensional space, principal component analysis (PCA) is employed to reduce dimensionality. Encoded ERT and encoded permeability are generated using the trained PCA estimators. A deep neural network is then trained to map the encoded ERT to encoded permeability. This mixup training and unsupervised learning allowed us to build a fast and reasonably accurate DL-based inverse model under limited simulation data. Results show that proposed weak supervised learning can capture salient spatial features in the 3D permeability field. Quantitatively, the average mean squared error (in terms of the natural log) on the strongly labeled training, validation, and test datasets is less than 0.5. The R 2 -score (global metric) is greater than 0.75, and the percent error in each cell (local metric) is less than 10%. Finally, an added benefit in terms of computational cost is that the proposed DL-based inverse model is at least O(10 4 ) times faster than running a forward model once it is trained. Data generation, DL model training, and hyperparameter tuning to identify optimal neural network architectures utilized high-performance computing resources while the DL inference is performed on a standard laptop. Approximately, O(10 5 ) processor hours are used for generating data and DL tuning and training. We acknowledge that the data generation and DL model development are expensive. But once a DL model is trained, it can be re-used for inversion rapidly for the given system, with set physics and domain. Note that traditional inversion may require multiple forward model simulations (e.g., in the order of 10 to 1000), which are very expensive. This computational savings ≈ O(10 5 ) – O(10 7 )) makes the proposed DL-based inverse model attractive for subsurface imaging and real-time ERT monitoring applications due to fast and yet reasonably accurate estimations of permeability field.

58 GEOSCIENCES↗
NASA NTRS1996

Constant-Current Source For Measuring Low Resistances

Constant-current source constructed for measuring electrical resistances up to few ohms in power-supply equipment. By setting current at 1 A and measuring resulting voltage drop across item under test, one obtains voltage reading numerically equal to resistance in ohms.

Toomath, Robert L.↗
NASA NTRS2018

Creating a Multifunctional Composite Stator Slot Material System to Enable High Power Density Electric Machines for Electrified Aircraft Applications

Increasing the power density and efficiency of electric machines (motors and generators) is integral to bringing Electrified Aircraft (EA) to commercial realization. However, power density and efficiency are not qualities that can be developed independently. At the heart of any electric machine are the conductors (usually copper) that carry current and generate magnetic fields. Increased power density means increased current density and increased joule heating in a smaller volume. To increase efficiency at the wire level means minimizing electrical resistance and hence power lost to joule heating. There are fundamental challenges with concomitantly increasing both power density and efficiency since the copper resistivity is very temperature sensitive at common electric machine operating conditions. Simple calculations of the linear increase in resistivity of copper as a function of temperature, reveals that a one degree Celsius increase in temperature results in a 0.39% decrease in efficiency. Conversely, a 20 degree Celsius decrease in copper temperature produces a 7.8% increase in efficiency. Therefore, improved thermal management concepts for electric machine building blocks such as stator winding are a priority for improving efficiency and power density. This paper proposing changing the view of component materials in the stator slots from individual components with singular functionality to a composite system where the components take on a multifunctional roles. In the composite framework, achievable material development goals are defined that together have maximum system impact on the thermal environment inside of high power density electric machines for aerospace applications.

thermal management↗
NASA NTRS1964

Evaporated thin films

Superconductivity and electric resistance of thin films of tin oxide and vanadium

VANADIUM↗
NASA NTRS1974

Silicon germanium technology program at JPL

Review of the results of a program aimed at obtaining a better understanding of the long-term performance of a thermoelectric generator using silicon germanium. The results obtained concern the contact resistivity of the thermoelectric couples, the electrical resistance of the multifoil thermal insulation, and the application of resistance coatings to the unicouples in order to prevent the loss of output power due to internal shunting paths.

Stapfer, G.↗