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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 1,765 records · Page 98
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

THE RGB AND AGB STAR NUCLEOSYNTHESIS IN LIGHT OF THE RECENT {sup 17}O(p, {alpha}){sup 14}N AND {sup 18}O(p, {alpha}){sup 15}N REACTION-RATE DETERMINATIONS

In recent years, the Trojan Horse Method (THM) has been used to investigate the low-energy cross sections of proton-induced reactions on A = 17 and A = 18 oxygen isotopes, overcoming extrapolation procedures and enhancement effects due to electron screening. In particular, the strengths of the 20 keV and 65 keV resonances in the {sup 18}O(p, {alpha}){sup 15}N and {sup 17}O(p, {alpha}){sup 14}N reactions, respectively, have been extracted, as well as the contribution of the tail of the broad 656 keV resonance in the {sup 18}O(p, {alpha}){sup 15}N reaction inside the Gamow window. The strength of the 65 keV resonance in the {sup 17}O(p, {alpha}){sup 14}N reaction, measured by means of the THM, has been used to renormalize the corresponding resonance strength in the {sup 17}O + p radiative capture channel. As a result, more accurate reaction rates for the {sup 18}O(p, {alpha}){sup 15}N, {sup 17}O(p, {alpha}){sup 14}N, and {sup 17}O(p, {gamma}){sup 18}F processes have been deduced, devoid of systematic errors due to extrapolation or the electron screening effect. Such rates have been introduced into state-of-the-art red giant branch and asymptotic giant branch (AGB) models for proton-capture nucleosynthesis coupled with extra-mixing episodes. The predicted abundances have been compared with isotopic compositions provided by geochemical analysis of presolar grains. As a result, an improved agreement is found between the models and the isotopic mix of oxide grains of AGB origins, whose composition is the signature of low-temperature proton-capture nucleosynthesis. The low {sup 14}N/{sup 15}N found in SiC grains cannot be explained by the revised nuclear reaction rates and remains a serious problem that has not been satisfactorily addressed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1978

A packet telemetry system employing ARQ error control

A proposed packet telemetry system employing automatic retransmission request (ARQ) mode of error control is characterized. Limitations of the present multiplexing/demultiplexing approach are considered, and the use of the proposed system in near-earth satellites in the 1980s is suggested. Onboard processing and an adaptive multiplexing technique are described, as is an elastic buffer, required because the instantaneous data rate will be different from the telemetry transmission rate. The telemetry packets would be encoded into a powerful error-detection block code. A mechanism involving temporary buffering in a long shift register will permit retransmission request from the ground station for packets received in error. The ARQ mode of operation should ensure essentially error-free transmission at lower signal-to-noise ratios and at considerably higher transmission rates than are usually used.

Greene, E. P.↗
DOE OSTI2019

Development of Constitutive Relationship for Thermomechanical Processing of Al-SiC Composite Eliminating Deformation Heating

Constitutive equations are useful for computational or numerical tools in describing the thermomechanical behavior of Al/SiC composites to develop the lightweight component using bulk metal forming. In this study, the uniaxial compression test of powder metallurgy-based Al/7 vol.% SiCp composite samples in the temperature range of 250-500 °C and at the strain rates of 0.001, 0.007, 0.05, 0.3, 2.2 and 15 s{sup −1} was performed on a Gleeble-3800 thermomechanical simulator. An equation correlating actual (measured) temperature with the true stress in real time has been established for each of the strain rates. Further, the true stress was corrected by eliminating the effect of deformation heating using this equation based on the experimental observations. The temperature-corrected true stress data were then used for development of constitutive equations using the Arrhenius model, strain-compensated Arrhenius model and modified Johnson–Cook (m-JC) model. The material parameters in the m-JC model were calculated at different combinations of temperatures and strain rates. The established constitutive equations are validated with temperature-corrected true stress from the experiment. Further, these equations are verified with the experimental true stress that was obtained using different inputs of temperatures and strain rates in a compression test. The correlation coefficient (R) and absolute average relative error reflect the accuracy of constitutive models. The result indicates that the calculated true stress by the established strain-compensated Arrhenius model is in good agreement with that of experimentally measured true stress with a correlation coefficient of 0.996 and relative error of 3.83%.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Improving the MJO Forecast of S2S Operation Models by Correcting Their Biases in Linear Dynamics

The operational dynamic subseasonal to seasonal (S2S) models for Madden-Julian oscillation (MJO) forecasting mostly still suffer from systematic errors in capturing the MJO's key dynamic features, such as its growth rate and propagation speed. By deriving the linear dynamic operators using the linear inverse modeling (LIM) approach, we propose a method to partly correct the errors in MJO linear dynamic operators to improve the MJO predictions of three operational dynamic S2S models. Correcting the deficiencies of the too-fast decay rates and the unrealistic propagating phase speeds lead to MJO prediction skills being extended by approximately 2–4 days. The improvements are more significant for the models with larger biases in MJO amplitude and propagation. This approach in principle may be extendable to predictions of other types of climate variability such as ENSO on one hand, and possible inclusions of nonlinear dynamics effects on the other hand.

58 GEOSCIENCES↗
NASA NTRS1986

Reaction of atomic bromine with acetylene and loss rate of atmospheric acetylene due to reaction with OH, Cl, O, and Br

The first-order, diffusion, and bimolecular rate constants for the reaction Br + C2H2 yields C2H3Br are evaluated. The rate constants are measured at 210, 248, 298, and 393 K and at pressures between 15-100 torr Ar using flash photolysis combined with time-resolved detection of atomic bromine via Br resonance radiation. It is observed that the reaction is not affected by pressure or temperature and the bimolecular constant = (4.0 + or - 0.8) x 10 to the -15th cu cm/sec with an error of two standard deviations. The C2H2 + Br reaction rates are compared with reactions of C2H2 with Cl, OH, NH2, and H. The loss rates for atmospheric C2H2 for reactions with OH, Cl, O, and Br are calculated as a function of altitude.

Payne, W. A.↗
DOE OSTI2023

Exploiting Kronecker structure in exponential integrators: Fast approximation of the action of φ $-$functions of matrices via quadrature

Here, in this article, we propose an algorithm for approximating the action of φ $-$ functions of matrices against vectors, which is a key operation in exponential time integrators. In particular, we consider matrices with Kronecker sum structure, which arise from problems admitting a tensor product representation. The method is based on quadrature approximations of the integral form of the φ $-$ functions combined with a scaling and modified squaring method. Owing to the Kronecker sum representation, only actions of 1D matrix exponentials are needed at each quadrature node and assembly of the full matrix can be avoided. Additionally, we derive a priori bounds for the quadrature error, which show that, as expected by classical theory, the rate of convergence of our method is supergeometric. Guided by our analysis, we construct a fast and robust method for estimating the optimal scaling factor and number of quadrature nodes that minimizes the total cost for a prescribed error tolerance. We investigate the performance of our algorithm by solving several linear and semilinear time-dependent problems in 2D and 3D. The results show that our method is accurate and orders of magnitude faster than the current state-of-the-art.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1992

Least reliable bits coding (LRBC) for high data rate satellite communications

LRBC, a bandwidth efficient multilevel/multistage block-coded modulation technique, is analyzed. LRBC uses simple multilevel component codes that provide increased error protection on increasingly unreliable modulated bits in order to maintain an overall high code rate that increases spectral efficiency. Soft-decision multistage decoding is used to make decisions on unprotected bits through corrections made on more protected bits. Analytical expressions and tight performance bounds are used to show that LRBC can achieve increased spectral efficiency and maintain equivalent or better power efficiency compared to that of BPSK. The relative simplicity of Galois field algebra vs the Viterbi algorithm and the availability of high-speed commercial VLSI for block codes indicates that LRBC using block codes is a desirable method for high data rate implementations.

Vanderaar, Mark↗
NASA NTRS1977

Multi-calculation rate simulations

It is common in real time simulations of large aerospace systems to separate the high and low frequency subsystems within the simulation and perform the integrations of the subsystems at different calculation rates. This is done to strike a balance between accuracy of calculation and capacity of the digital computer. Questions arising as to the accuracy of this structure compared to single calculation rates were studied using a linear aircraft model. Also investigated were interactions arising to cause errors worse than those expected. Problems are specifically identified and guidelines are given for selection of sample rates for multiple rate simulations.

Powell, J. D.↗
DOE OSTI2024

Quantifying local and global mass balance errors in physics-informed neural networks

Physics-informed neural networks (PINN) have recently become attractive for solving partial differential equations (PDEs) that describe physics laws. By including PDE-based loss functions, physics laws such as mass balance are enforced softly in PINN. This paper investigates how mass balance constraints are satisfied when PINN is used to solve the resulting PDEs. We investigate PINN’s ability to solve the 1D saturated groundwater flow equations (diffusion equations) for homogeneous and heterogeneous media and evaluate the local and global mass balance errors. We compare the obtained PINN’s solution and associated mass balance errors against a two-point finite volume numerical method and the corresponding analytical solution. We also evaluate the accuracy of PINN in solving the 1D saturated groundwater flow equation with and without incorporating hydraulic heads as training data. We demonstrate that PINN’s local and global mass balance errors are significant compared to the finite volume approach. Tuning the PINN’s hyperparameters, such as the number of collocation points, training data, hidden layers, nodes, epochs, and learning rate, did not improve the solution accuracy or the mass balance errors compared to the finite volume solution. Mass balance errors could considerably challenge the utility of PINN in applications where ensuring compliance with physical and mathematical properties is crucial.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Machine learning enables national assessment of wind plant controls with implications for land use

Summary As deployment of wind energy continues to expand, computationally efficient tools for predicting wind plant performance over a wide range of layout designs, technology innovations, and spatial locations are increasingly important for policy and investment decisions. We demonstrate two approaches to training a surrogate model to predict annual energy production (AEP) of parameterized wind plant layouts: one using a Gaussian process (GP) and the other using a fully convolutional neural network (FCNN). We leverage the powerful FCNN architecture by encoding wind plant design parameters and output response surface as an image. The FCNN produces more accurate results than the GP with mean absolute errors equivalent to 1% and 1.9% of plant rated power, respectively, although the GP performs well under limited training data and provides useful uncertainty information. We also evaluate a surrogate model for wake steering, enabling a nationwide assessment of the impact of plant control strategies and plant layout decisions. Across two million locations, we find that wake steering strategies boost AEP with relative gains upwards of 3%. Gains are most pronounced at sites without a dominant wind direction and where layout optimization is less fruitful. Additionally, we perform a nationwide sensitivity analysis showing that wake steering can mitigate wake losses from higher density plant layouts. Our results suggest that regions which have not been previously viable for wind deployment due to moderate wind resources are especially well enhanced by wake steering strategies that could help overcome land constraints and inflexible layout options, potentially identifying new deployment opportunities.

17 WIND ENERGY↗
DOE OSTI2018

A Galerkin method with two-dimensional Haar basis functions for the computation of the Karhunen–Loève expansion

We study the numerical approximation of a homogeneous Fredholm integral equation of second kind associated with the Karhunen–Loève expansion of Gaussian random fields. We develop, validate, and discuss an algorithm based on the Galerkin method with two-dimensional Haar wavelets as basis functions. The shape functions are constructed from the orthogonal decomposition of tensor-product spaces of one-dimensional Haar functions, and a recursive algorithm is employed to compute the matrix of the discrete eigenvalue system without the explicit calculation of integrals, allowing the implementation of a fast and efficient algorithm that provides considerable reduction in CPU time, when compared with classical Galerkin methods. Numerical experiments confirm the convergence rate of the method and assess the approximation error and the sparsity of the eigenvalue system when the wavelet expansion is truncated. We illustrate the numerical solution of a diffusion problem with random input data with the present method. In this problem, accuracy was retained after dropping the coefficients below a threshold value that was numerically determined. A similar method with scaling functions rather than wavelet functions does not need a discrete wavelet transform and leads to eigenvalue systems with better conditioning but lower sparsity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Technical, economic, and load-following capabilities assessment of grid-connected geothermal and geothermal-solar hybrid systems

The technical and economic performance as well as the load-following capabilities of grid-connected geothermal hybrid systems were assessed in this work. The analyzed geothermal hybrid configuration is composed of a binary geothermal plant integrated with a concentrating solar-thermal system and underground thermal energy storage (UTES) through a primary heat exchanger. Physics-based models for the hybrid system for plant generation capacities of 1, 25, and 50 MW were developed from validated models for each subsystem. Also, an economic model was developed that accounts for different hybrid system capabilities, solar field sizes, and thermal storage duration. The advantage of the geothermal hybrid system was assessed by comparing the performance with the baseline benchmark geothermal plant with a similar configuration and generation capacity. It was found that hybridizing geothermal plants with concentrating solar and thermal energy storage not only improves the thermal efficiency by up to 8 percentage points when additional heat from the solar-UTES loop rises the evaporator temperatures from 70 to 125 °C, but also enhances the load-following capability for the geothermal plant, which can meet a typical residential load profile with a power rate of change 0.25 kW/s with an absolute error under 13 kW for a 1 MW plant. Other benefits of hybridization include resource preservation and a potential LCOE reduction of up to 56% for a 50 MW geothermal hybrid plant having a 50% solar share, a 1.4 solar multiple, and 24-h storage capacity. The results presented in this work demonstrate that hybridizing geothermal systems transforms them into a flexible and cost-effective solution for addressing the dynamic requirements of modern electric grids.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Machine learning-aided inverse design for biogas upgrading through biological CO 2 conversion

The biogas upgrading process through bioconversion of CO 2 to CH 4 by hydrogenotrophic methanogens is an attractive strategy for energy decarbonation. Many studies have optimized operational parameters to improve key performance indicators such as CH 4 % and H 2 utilization efficiency. However, inconsistent laboratory conditions make it challenging to compare results. Existing models for analyzing operating conditions can only assess the impact of individual conditions and lack the ability to simultaneously optimize multiple conditions. To address this, two XGBoost models were built with R 2 of 0.779 and 0.903 with data collected from literatures and were embedded into multi-objective partitive swarm optimization algorithm to optimal operating conditions. Predictions were compared with experimental validations under optimized conditions, revealing an 8.50% and 2.95% relative error in CH 4 % and H 2 conversion rate, respectively. This approach streamlines biogas upgrading processes, offering a data-driven solution to enhance efficiency and consistency in the pursuit of sustainable methane production.

Biogas upgrading↗
DOE OSTI2022

Multi-variance replica exchange SGMCMC for inverse and forward problems via Bayesian PINN

Physics-informed neural network (PINN) has been successfully applied in solving a variety of nonlinear non-convex forward and inverse problems. However, the training is challenging because of the non-convex loss functions and the multiple optima in the Bayesian inverse problem. In this work, we propose a multi-variance replica exchange stochastic gradient Langevin dynamics method to tackle the challenge of the multiple local optima in the optimization and the challenge of the multiple modal posterior distribution in the inverse problem. Replica exchange methods are capable of escaping from the local traps and accelerating the convergence; two chains with different temperatures are designed where the low temperature chain aims for the local convergence, and the target of the high temperature chain is to travel globally and explore the whole loss function entropy landscape. However, it may not be efficient to solve mathematical inversion problems by using the vanilla replica method directly since the method doubles the computational cost in evaluating the forward solvers (likelihood functions) in the two chains. To address this issue, we propose to make different assumptions on the energy function estimation and this facilities one to use solvers of different fidelities in the likelihood function evaluation. More precisely, one can use a solver with low fidelity in the high temperature chain while using a solver with high fidelity in the low temperature chain. Our proposed method significantly lowers the computational cost in the high temperature chain, meanwhile preserving the accuracy and converging very fast. Here we give an unbiased estimate of the swapping rate and give an estimation of the discretization error of the scheme. To verify our idea, we design and solve four inverse problems which have multiple modes. The proposed method is also employed to train the Bayesian PINN to solve the forward and inverse problems; faster and more accurate convergence has been observed when compared to the stochastic gradient Langevin dynamics (SGLD) method and vanilla replica exchange methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Demonstration of an Automated System for Vertical Profiles of Volatile Organic Compounds

Volatile organic compounds (VOCs) play important roles throughout the atmosphere, many of which are altitude dependent. This highlights the need for easily deployable devices to sample VOCs across different atmospheric layers. To address this, we present the design and initial application of a Time Resolved Automated Volatile organIc compounds Sampling system (TRAVIS). VOCs are collected on sorbent tubes, which are subsequently analyzed by a thermal desorption gas chromatography mass spectrometry pipeline. TRAVIS leverages a piezoelectric pump with an integrated pressure sensor for precise (0.1% flow rate relative standard deviation) and accurate (−3 ± 2% error in VOC quantitation) measurements. Via deployment on a tethered balloon system over an agricultural area, TRAVIS is used to show consistent vertically resolved VOC concentrations in a well-mixed (i.e., turbulent) atmosphere (e.g., 5% relative standard deviation for isoprene) and vertically dependent concentrations for a stratified atmosphere (e.g., prior to boundary layer development). Furthermore, we also show VOC information from an intermittent plume via both targeted and untargeted analysis, highlighting future applications for spurious events in agriculture, air quality monitoring, and environmental impact. Overall, the development of TRAVIS represents a lightweight, accurate, sensitive, and precise VOC sampling module for the scientific community.

Aerosols↗
DOE OSTI2016

Intrinsic selectivity and structure sensitivity of Rhodium catalysts for C 2+ oxygenate production [On the intrinsic selectivity and structure sensitivity of Rhodium catalysts for C 2+ oxygenate production]

Synthesis gas (CO + H 2 ) conversion is a promising route to converting coal, natural gas, or biomass into synthetic liquid fuels. Rhodium has long been studied as it is the only elemental catalyst that has demonstrated selectivity to ethanol and other C 2+ oxygenates. However, the fundamentals of syngas conversion over rhodium are still debated. In this work a microkinetic model is developed for conversion of CO and H 2 into methane, ethanol, and acetaldehyde on the Rh (211) and (111) surfaces, chosen to describe steps and close-packed facets on catalyst particles. The model is based on DFT calculations using the BEEF-vdW functional. The mean-field kinetic model includes lateral adsorbate–adsorbate interactions, and the BEEF-vdW error estimation ensemble is used to propagate error from the DFT calculations to the predicted rates. The model shows the Rh(211) surface to be ~6 orders of magnitude more active than the Rh(111) surface, but highly selective toward methane, while the Rh(111) surface is intrinsically selective toward acetaldehyde. A variety of Rh/SiO 2 catalysts are synthesized, tested for catalytic oxygenate production, and characterized using TEM. The experimental results indicate that the Rh(111) surface is intrinsically selective toward acetaldehyde, and a strong inverse correlation between catalytic activity and oxygenate selectivity is observed. Furthermore, iron impurities are shown to play a key role in modulating the selectivity of Rh/SiO 2 catalysts toward ethanol. The experimental observations are consistent with the structure-sensitivity predicted from theory. As a result, this work provides an improved atomic-scale understanding and new insight into the mechanism, active site, and intrinsic selectivity of syngas conversion over rhodium catalysts and may also guide rational design of alloy catalysts made from more abundant elements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Fast and accurate database searches with MS-GF+Percolator

To identify peptides and proteins from the large number of fragmentation spectra in mass spectrometrybased proteomics, researches commonly employ so called database search engines. Additionally, postprocessors like Percolator have been used on the results from such search engines, to assess confidence, infer peptides and generally increase the number of identifications. A recent search engine, MS-GF+, has previously been showed to out-perform these classical search engines in terms of the number of identified spectra. However, MS-GF+ generates only limited statistical estimates of the results, hence hampering the biological interpretation. Here, we enabled Percolator-processing for MS-GF+ output, and observed an increased number of identified peptides for a wide variety of datasets. In addition, Percolator directly reports false discovery rate estimates, such as q values and posterior error probabilities, as well as p values, for peptide-spectrum matches, peptides and proteins, functions useful for the whole proteomics community.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2024

WE-Validate: An Open-Source Framework For Wind Power Validation

Grid operators rely on historical weather time series at existing and planned wind power plants to make informed decisions when planning for a future power grid with very high penetration of renewable power. While synthetic wind power time series have been developed based on historical weather models, their validation with actual power production data remains complex due to variations in modeling practices and methodologies. This paper introduces the WE-Validate framework, originally designed for wind speed validation and now enhanced for wind power validation with a graphical user interface to support users with minimal programming experience. Validation of wind power with WE-Validate is based on robust metrics consisting of RMSE, centered RMSE, average bias, average percent bias, mean absolute error, mean absolute percent error, cross correlation, and calculation of ramping magnitude, rate, and duration. This paper showcases WE-Validate with validation of synthetically derived power for a wind plant in Washington state for one month in 2018. Validation of the synthetic power from two comparison data sets compared with observations shows both comparison series have strong correlation with observed across weekly and monthly aggregations while suffering from persistent negative bias. The suite of metrics within WE-Validate facilitates immediate insight into the utility of the comparison data sets through compression across multiple axes. This user-friendly, open-source tool can be extended beyond wind power, making it a valuable resource for system planners and operators in different domains.

Moncheur de Rieudotte, Malcolm P.↗