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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 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Processing Effects on the Self-Assembly of Brush Block Polymer Photonic Crystals

Here,the self-assembly of poly(dimethylsiloxane)-b-poly(trimethylene carbonate) (PDMS-b-PTMC) bottlebrush block polymers was investigated under different processing conditions. Small-angle X-ray scattering (SAXS) and UV/Visible spectroscopy provided insight into the self-assembly and structure in response to heating and applied pressure. In the absence of applied pressure (i.e., before annealing), the PDMS-b-PTMC bottlebrush block polymers are white solids and adopt small, randomly oriented lamellar grains. Heating the materials to 140 °C in the absence of applied pressure appears to “lock in” the isotropic, short-range-ordered state, preventing the formation of the long-range-ordered lamellar structure responsible for photonic properties. Applying modest anisotropic pressure (3 psi) between parallel plates at ambient temperature orients the short-range lamellar grains; however, applied pressure alone does not produce long-range order. Only when the bottlebrush block polymers were heated (>100 °C) under modest pressure (3 psi) were long-range-ordered photonic crystals formed. Analysis of the SAXS data motivated analogies to liquid crystals and revealed the potential self-assembly pathway. These results provide insight into the structure and self-assembly of bottlebrush block polymers with low glass transition temperature side chains in response to different processing conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Long-term thermal stability and calibration of Type-II fiber Bragg grating array inscribed in radiation-hardened fibers

This paper investigates the long-term thermal stability of Type-II fiber Bragg grating (FBG) arrays, inscribed by femtosecond laser in radiation-hardened fiber, for potential applications as multiplexed sensors in high-temperature energy systems. The thermal stability of FBG sensors was assessed through 16 thermal cycles from room temperature (RT) to 750 ℃ about two months, involving 100 FBG sensors. The results show that the absolute temperature drift of FBG sensors can be reduced to less than 0.4 pm/day after 54 h thermal annealing process at a constant temperature of 800 ℃. As temperature sensors, the FBGs demonstrated stable performance, achieving a standard deviation (STD) of 1.8 pm (corresponding to a temperature resolution of 0.118 ℃) post-annealing. Repeated thermal cycles revealed a random drift of 2.3 pm in the FBG wavelength at RT. Polynomial fitting was explored as a calibration method to convert FBG wavelength shifts into absolute temperature measurements. By optimizing calibration temperature points (RT, 200 ℃, 400 ℃, and 750 ℃), the study shows that cubic polynomial calibration using four points yields an average R2 of 0.9997 and an RMSE of 3.58 ℃ across the entire temperature range (RT to 750 ℃). This approach represents an 11.53-fold improvement over empirical slope calibration and a 1.34-fold improvement over four-point piecewise fitting. The findings indicate that Type-II FBGs inscribed in radiation-hardened fibers can function as accurate temperature sensors, with performance on par with or exceeding that of thermocouples. With their multiplexing capability, robust signal transmission over long lead cables, and immunity to electromagnetic interference, FBG sensors offer a promising alternative to traditional electronic sensors for energy system monitoring.

Dominguez-Ontiveros, Elvis [ORNL] (ORCID:000000018↗
DOE OSTI2020

Effect of Surface Texture on Pinhole Formation in SiO x -Based Passivated Contacts for High-Performance Silicon Solar Cells

High-efficiency silicon solar cells rely on some form of passivating contact structure to reduce recombination losses at the crystalline silicon surface and losses at the metal/Si contact interface. One such structure is polycrystalline silicon (poly-Si) on oxide, where heavily doped poly-Si is deposited on a SiO x layer grown directly on the crystalline silicon (c-Si) wafer. Depending on the thickness of the SiO x layer, the charge carriers can cross this layer by tunneling (<2 nm SiO x thickness) or by direct conduction through disruptions in the SiOx, often referred to as pinholes, in thicker SiO x layers (>2 nm). In this work, we study structures with tunneling- or pinhole-like SiO x contacts grown on pyramidally textured c-Si wafers and expose variations in the SiOx layer properties related to surface morphology using electron-beam-induced current (EBIC) imaging. Using EBIC, we identify and mark regions with potential pinholes in the SiO x layer. We further perform high-resolution transmission electron microscopy on the same areas, thus allowing us to directly correlate locally enhanced carrier collection with variations in the structure of the SiO x layer. Our results show that the pinholes in the SiO x layer preferentially form in different locations based on the annealing conditions used to form the device. With greater understanding of these processes and by controlling the surface texture geometry, there is potential to control the size and spatial distribution of oxide disruptions in silicon solar cells with poly-Si on oxide-type contacts; usually, this is a random phenomenon on polished or planar surfaces. Such control will enable us to consistently produce high-efficiency devices with low recombination currents and low junction resistances using this contact structure.

14 SOLAR ENERGY↗
DOE OSTI2021

Learning to Unknot

We introduce natural language processing into the study of knot theory, as made natural by the braid word representation of knots. We study the UNKNOT problem of determining whether or not a given knot is the unknot. After describing an algorithm to randomly generate N-crossing braids and their knot closures and discussing the induced prior on the distribution of knots, we apply binary classification to the UNKNOT decision problem. We find that the Reformer and shared-QK Transformer network architectures outperform fully-connected networks, though all perform at 95% accuracy. Perhaps surprisingly, we find that accuracy increases with the length of the braid word, and that the networks learn a direct correlation between the confidence of their predictions and the degree of the Jones polynomial. Finally, we utilize reinforcement learning (RL) to find sequences of Markov moves and braid relations that simplify knots and can identify unknots by explicitly giving the sequence of unknotting actions. Trust region policy optimization (TRPO) performs consistently well, reducing 80% of the unknots with up to 96 crossings we tested to the empty braid word, and thoroughly outperformed other RL algorithms and random walkers. Studying these actions, we find that braid relations are more useful in simplifying to the unknot than one of the Markov moves.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

An efficient method to propagate model uncertainty when inverting seismic data for time domain seismic moment tensors

SUMMARY We present a computationally efficient method to approximately propagate uncertainty when linearly inverting seismic data for point source, time variable moment tensor components. The method is based on the assumption that the data residual, given by the difference between the observed seismic data and the data predicated by a linear inversion, contains the effects of both data and model uncertainty. Our method uses a distribution of data residuals, added directly to the data, in a pseudo-Monte Carlo scheme. Using the assumption that the data residual is a stochastic process, we use the well-known Karhunen–Loève (KL) theorem to construct a distribution of data residuals, where the required basis functions are constructed using Fourier series. The Fourier series are scaled by a product of a random variable and the real-valued spectral amplitudes of the original data residual’s spectrum. Thus, the Fourier series and spectral amplitudes are eigenfunction-eigenvalue pairs used in the KL-based construction of data residual distribution. Using tests with synthetic data, we show that our method compares closely with a Finite Difference Monte Carlo (FDMC) method that we presented previously. More importantly, the method presented here is computationally several orders of magnitude faster than our previous FDMC method, and requires no a priori assumptions of model and/or data uncertainty.

Poppeliers, Christian (ORCID:0000000159526849)↗
DOE OSTI2021

Codon2Vec v1.0

Background: Codon2Vec is an embedding neural network that predicts 'high' or 'low' gene expression directly from the protein-coding sequences. Embedding neural networks are commonly used for natural language processing (NLP) applications. Analogous to how an English sentence is a string of words, a gene can be thought of as a string of codons. Similar to how NLP neural networks model English sentences as a non-random sequence of words, we considered a coding sequence as a non-random non-overlapping array of codons (k-mers of length = 3). Value Proposition: - Codon2Vec achieved a high median AUC-ROC score of 83.8% when trained and applied to transcriptomic data from 300 fungal species - Unlike Codo2Vec, conventional methods predicting for expression based on codon usage rely on a priori knowledge of optimal codons or a set of reference genes. - Unlike Codon2vec, these methods do not account for the effect of codon order on gene expression. - Codon2Vec neural network bypasses the need for artisanal feature selection step that is necessary for traditional machine learning models.

Wint, Rhondene↗
DOE OSTI2018

Low Cost Corrugated Substrates for High Efficiency OLEDs (Final Report)

The external quantum efficiency (EQE) of a planar OLED is limited to 25%. Around 75% of the light is trapped in the substrate, waveguided and SPP modes. In this project, we demonstrated that nano-structures can significantly improve the EQE of OLEDs. The objective is to improve the EQE to 70% using corrugated substrate, low index layer incorporation and micro lens arrays. We conducted optical study of the operating devices using ARES measurement, and used the result to guide the design of corrugated substrates as well as the OLED stack for better performance. During the first year of the program, we have demonstrated over 60% EQE in a corrugated green OLED with half-ball lens for light extraction. By comparing, the optical characteristics of OLEDs fabricated on single and poly crystal corrugated structures, we confirm that the enhancement using corrugated substrates comes from waveguided and SPP modes extraction. To push the device EQE to 70%, further optimization of the corrugated substrate is needed. The extraction of waveguided and SPP modes are influenced by the corrugation pitch, depth and profile. Therefore, we developed a series of processes to precisely control the corrugation morphology. Through comparison of different corrugated substrates, we observed that while an abrupt surface profile results in stronger mode extraction, it also introduces strong leakage current; while a smooth profile are less efficient in mode extraction, it preserves the quality of an OLED. Based on these observations, we conclude that a single crystal hole corrugation results in optimum EQE % and low leakage current when planarized with PEDOT. To improve the extraction of waveguided mode, we also introduce a low index contrast layer (ICL) to increase the index contrast between corrugated ITO and glass interface. We optimized our recipe for the index contrast layer processing and reduced its refractive index from 1.50 to 1.38. As a result, the optimized device shows 47.6% EQE, which is 1.64 times higher than the reference planar device. By extracting the substrate mode with a hemispherical lens, we obtained an EQE of 71.6%, which is a 2.46 times enhancement. The cross-section SEM image reveals the high EQE is due to the formation of an air gap at the interface of the ICL, resulting in an increase of the index contrast to Δn ~ 1. We use angle-resolved electroluminescence spectroscopy (ARES) to demonstrate that the ICL improves the diffraction efficiency at the ITO/glass interface, extracts TM, TE waveguide and SPP mode more efficiently, and leads to the observed high efficiency. We also address the directional emission issue with the single crystalline microlens arrays (MLAs) by fabricating polycrystalline MLAs. The process uses colloidal assembly method to create a mono-layer of silica spheres, then uses PDMS stamps to replicate the pattern for multi-use imprinting. The MLAs are close-packed in short range but have random orientations in the long range. This avoids stronger extraction along the optical axes of single crystal MLAs and prevents the directional emission issue. We also demonstrate that the color shift induced by the corrugation can be minimized using MLAs. In this scenario, the MLAs work as diffuser to randomize the directional emission from extracted waveguide and SPP modes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Machine Learning Analysis of Hydrologic Exchange Flows and Transit Time Distributions in a Large Regulated River

Hydrologic exchange between river channels and adjacent subsurface environments is a key process that influences water quality and ecosystem function in river corridors. High-resolution numerical models were often used to resolve the spatial and temporal variations of exchange flows, which are computationally expensive. In this study, we adopt Random Forest (RF) and Extreme Gradient Boosting (XGB) approaches for deriving reduced order models of hydrologic exchange flows and associated transit time distributions, with integrated field observations (e.g., bathymetry) and hydrodynamic simulation data (e.g., river velocity, depth). The setup allows an improved understanding of the influences of various physical, spatial, and temporal factors on the hydrologic exchange flows and transit times. The predictors also contain those derived using hybrid clustering, leveraging our previous work on river corridor system hydromorphic classification. The machine learning-based predictive models are developed and validated along the Columbia River Corridor, and the results show that the top parameters are the thickness of the top geological formation layer, the flow regime, river velocity, and river depth; the RF and XGB models can achieve 70% to 80% accuracy and therefore are effective alternatives to the computational demanding numerical models of exchange flows and transit time distributions. Each machine learning model with its favorable configuration and setup have been evaluated. The transferability of the models to other river reaches and larger scales, which mostly depends on data availability, is also discussed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Chemical Doppelgangers in GALAH DR3: The Distinguishing Power of Neutron-capture Elements among Milky Way Disk Stars

The observed chemical diversity of Milky Way stars places important constraints on Galactic chemical evolution and the mixing processes that operate within the interstellar medium. Recent works have found that the chemical diversity of disk stars is low. For example, the Apache Point Observatory Galactic Evolution Experiment (APOGEE) "chemical doppelganger rate," or the rate at which random pairs of field stars appear as chemically similar as stars born together, is high, and the chemical distributions of APOGEE stars in some Galactic populations are well-described by two-dimensional models. However, limited attention has been paid to the heavy elements (Z > 30) in this context. In this work, we probe the potential for neutron-capture elements to enhance the chemical diversity of stars by determining their effect on the chemical doppelganger rate. We measure the doppelganger rate in GALactic Archaeology with HERMES DR3, with abundances rederived using The Cannon, and find that considering the neutron-capture elements decreases the doppelganger rate from ~2.2% to 0.4%, nearly a factor of 6, for stars with –0.1 < [Fe/H] < 0.1. While chemical similarity correlates with similarity in age and dynamics, including neutron-capture elements does not appear to select stars that are more similar in these characteristics. Our results highlight that the neutron-capture elements contain information that is distinct from that of the lighter elements and thus add at least one dimension to Milky Way abundance space. This work illustrates the importance of considering the neutron-capture elements when chemically characterizing stars and motivates ongoing work to improve their atomic data and measurements in spectroscopic surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1989

An adaptive data-smoothing routine

An adaptive noise reduction algorithm that can be implemented on a microcomputer is developed. Smoothing polynomials are used where the polynomial coefficients are chosen such that the mean-square-error between the noisy and smoothed data is minimized. This approach is equivalent to the implementation of a low-pass finite impulse response filter. The noise reduction depends on the order of the smoothing polynomial. A whiteness test on the error sequence is incorporated to search for the optimal smoothing. Expansion coefficients may be computed via the fast Fourier transform, and the resulting smoothing process is the equivalent of the implementation of an adaptive ideal low-pass filter. Results are obtained for an analytical signal with added white Gaussian noise. The routine may be applied to any smooth signal with additive random noise.

Taylor, Clayborne D.↗
NASA NTRS1999

Probabilistic Structural Analysis Methods (PSAM) for Select Space Propulsion System Components

Probabilistic Structural Analysis Methods (PSAM) are described for the probabilistic structural analysis of engine components for current and future space propulsion systems. Components for these systems are subjected to stochastic thermomechanical launch loads. Uncertainties or randomness also occurs in material properties, structural geometry, and boundary conditions. Material property stochasticity, such as in modulus of elasticity or yield strength, exists in every structure and is a consequence of variations in material composition and manufacturing processes. Procedures are outlined for computing the probabilistic structural response or reliability of the structural components. The response variables include static or dynamic deflections, strains, and stresses at one or several locations, natural frequencies, fatigue or creep life, etc. Sample cases illustrates how the PSAM methods and codes simulate input uncertainties and compute probabilistic response or reliability using a finite element model with probabilistic methods.

Source record↗
NASA NTRS2001

Planetary Accretion, Oxygen Isotopes and the Central Limit Theorem

The accumulation of presolar dust into increasingly larger aggregates (CAIs and Chondrules, Asteroids, Planets) should result in a very drastic reduction in the numerical spread in oxygen isotopic composition between bodies of similar size, in accord with the Central Limit Theorem. Observed variations in oxygen isotopic composition are many orders of magnitude larger than would be predicted by a simple, random accumulation model that begins in a well-mixed nebula - no matter which size-scale objects are used as the beginning or end points of the calculation. This discrepancy implies either that some as yet unspecified process acted on the solids in the Solar Nebula to increase the spread in oxygen isotopic composition during each and every stage of accumulation or that the nebula was heterogeneous and maintained this heterogeneity throughout most of nebular history. Large-scale nebular heterogeneity would have significant consequences for many areas of cosmochemistry, including the application of some well-known isotopic systems to the dating of nebular events or the prediction of bulk compositions of planetary bodies on the basis of a uniform cosmic abundance.

Nuth, Joseph A., III↗
NASA NTRS1989

Acute hemodynamic responses to weightlessness in humans

As NASA designs space flights requiring prolonged periods of weightlessness for a broader segment of the population, it will be important to know the acute and sustained effects of weightlessness on the cardiovascular system since this information will contribute to understanding of the clinical pharmacology of drugs administered in space. Due to operational constraints on space flights, earliest effects of weightlessness have not been documented. We examined hemodynamic responses of humans to transitions from acceleration to weightlessness during parabolic flight on NASA's KC-135 aircraft. Impedance cardiography data were collected over four sets of 8-10 parabolas, with a brief rest period between sets. Each parabola included a period of 1.8 Gz, then approximately 20 seconds of weightlessness, and finally a period of 1.6 Gz; the cycle repeated almost immediately for the remainder of the set. Subjects were semi-supine (Shuttle launch posture) for the first set, then randomly supine, sitting and standing for each subsequent set. Transition to weightlessness while standing produced decreased heart rate, increased thoracic fluid content, and increased stroke index. Surprisingly, the onset of weightlessness in the semi-supine posture produced little evidence of a headward fluid shift. Heart rate, stroke index, and cardiac index are virtually unchanged after 20 seconds of weightlessness, and thoracic fluid content is slightly decreased. Semi-supine responses run counter to Shuttle crewmember reports of noticeable fluid shift after minutes to hours in orbit. Apparently, the headward fluid shift commences in the semi-supine posture before launch. is augmented by launch acceleration, but briefly interrupted immediately in orbit, then resumes and is completed over the next hours.

short duration↗
NASA NTRS2007

Environmental Testing of the NEXT PM1R Ion Engine

The NEXT propulsion system is an advanced ion propulsion system presently under development that is oriented towards robotic exploration of the solar system using solar electric power. The subsystem includes an ion engine, power processing unit, feed system components, and thruster gimbal. The Prototype Model engine PM1 was subjected to qualification-level environmental testing in 2006 to demonstrate compatibility with environments representative of anticipated mission requirements. Although the testing was largely successful, several issues were identified including the fragmentation of potting cement on the discharge and neutralizer cathode heater terminations during vibration which led to abbreviated thermal testing, and generation of particulate contamination from manufacturing processes and engine materials. The engine was reworked to address most of these findings, renamed PM1R, and the environmental test sequence was repeated. Thruster functional testing was performed before and after the vibration and thermal-vacuum tests. Random vibration testing, conducted with the thruster mated to the breadboard gimbal, was executed at 10.0 Grms for 2 min in each of three axes. Thermal-vacuum testing included three thermal cycles from 120 to 215 C with hot engine re-starts. Thruster performance was nominal throughout the test program, with minor variations in a few engine operating parameters likely caused by facility effects. There were no significant changes in engine performance as characterized by engine operating parameters, ion optics performance measurements, and beam current density measurements, indicating no significant changes to the hardware as a result of the environmental testing. The NEXT PM1R engine and the breadboard gimbal were found to be well-designed against environmental requirements based on the results reported herein. The redesigned cathode heater terminations successfully survived the vibration environments. Based on the results of this test program and confidence in the engineering solutions available for the remaining findings of the first test program, specifically the particulate contamination, the hardware environmental qualification program can proceed with confidence

Snyder, John S.↗
DOE OSTI2021

Randomized Algorithms for Scientific Computing (RASC)

Randomized algorithms have propelled advances in artificial intelligence (AI) and represent a foundational research area in advancing AI for Science. Future advancements in DOE Office of Science priority areas such as climate science, astrophysics, fusion, advanced materials, combustion, and quantum computing all require randomized algorithms for surmounting challenges of complexity, robustness, and scalability. Advances in data collection and numerical simulation have changed the dynamics of scientific research and motivate the need for randomized algorithms. For instance, advances in imaging technologies such as X-ray ptychography, electron microscopy, electron energy loss spectroscopy, or adaptive optics lattice light-sheet microscopy collect hyperspectral imaging and scattering data in terabytes, at breakneck speed enabled by state-of-the-art detectors. The data collection is exceptionally fast compared with its analysis. Likewise, advances in high-performance architectures have made exascale computing a reality and changed the economies of scientific computing in the process. Floating-point operations that create data are essentially free in comparison with data movement. Thus far, most approaches have focused on creating faster hardware. Ironically, this faster hardware has exacerbated the problem by making data still easier to create. Under such an onslaught, scientists often resort to heuristic deterministic sampling schemes (e.g., low-precision arithmetic, sampling every nth element) and sacrifice potentially valuable accuracy. Dramatically better results can be achieved via randomized algorithms, reducing the data size as much as or more than naive deterministic subsampling can achieve, while retaining the high accuracy of computing on the full data set. By randomized algorithms we mean those algorithms that employ some form of randomness in internal algorithmic decisions to accelerate time to solution, increase scalability, or improve reliability. Examples include matrix sketching for solving large-scale least-squares problems (see Figure 1) and stochastic gradient descent for training machine learning models. We are not recommending heuristic methods but rather randomized algorithms that have certificates of correctness and probabilistic guarantees of optimality and near-optimality. Such approaches can be useful beyond acceleration, for example, in understanding how to avoid measure zero worst-case scenarios that plague methods such as QR matrix factorization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Room-temperature fabrication of a delafossite CuCrO 2 hole transport layer for perovskite solar cells

Delafossite oxides are promising hole transport layer (HTL) candidates for perovskite solar cells, due to their wide band gap, favorable energy band alignment relative to the perovskite absorber and simplicity of processing. In this paper, we investigate the properties of CuCrO2 (CCO) delafossite films using an integration of experimental and computational techniques. Phase-pure CCO films are deposited at room temperature by spin-casting suspensions of hydrothermally-synthesized nanoparticles, for use in a glass/ITO/CCO/CH3NH3PbI3/C60/BCP/Ag device structure. Although density functional theory (DFT) calculations predict an elevated hole effective mass along certain crystallographic directions, the nearly isotropic shape and small size of the CCO nanoparticles preserves transport properties within the films by randomizing particle orientation, preventing these unfavorable directions from dominating the film conductivity. Experimental measurements confirm that the CCO films display appropriate optical and electrical properties for use as an HTL, in good agreement with the DFT calculations. Solar cells made using these films exhibit stabilized power conversion efficiencies exceeding 14%, with only minor hysteresis in the current–voltage characteristics.

Dunlap-Shohl, Wiley A.↗
NASA NTRS1973

On board satellite processing study

The use of satellite-based, world-wide data collection and location systems for scientific purposes is discussed. The configuration of satellite borne data handling equipment to prevent premature saturation and to provide maximum capacity without degradation of performance is described. An analysis of a data collection and location system based on random time and frequency access to the satellite was conducted. The subjects presented are: (1) a general description of the concept of a random access system, (2) the quantitative characteristics of random access systems, and (3) the development of a model for comparing the various types of digital communications systems.

Reed, D. L.↗
NASA NTRS1976

Classical trajectory study of internal energy distributions in unimolecular processes

Energy flow in a molecular system such as CD3Cl or CD3H representing a chemical activation experiment is studied by the method of classical trajectories. A correlation function method is used to obtain energy distributions before and after the breakup of the activated molecule. The energy distribution in the final product is found to be randomly distributed for a surface with no exit channel barrier or strong intermode couplings. Nonrandom energy distributions result when these special forces are present. Product channel barriers result in an excess of translational energy and exit channel intermode couplings result in nonrandom vibrational distributions.

Mcdonald, J. D.↗