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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Utilization of Hydroturbines in Wastewater Treatment Plants

Wastewater treatment plants (WWTPs) are a significant energy consumer, yet there are several opportunities for implementing on-site power generation systems. Within the treatment process, the high flow rate of effluent is produced and discharged to a nearby water body by gravity. Thus, hydroturbines can be utilized to generate power in such an application due to a difference in elevation and high flow rate. This paper presents a case study of introducing a hydroturbine in a WWTP in Wisconsin and evaluating the power output in addition to determining the energy savings. The WWTP considered in this study has an effluent flow rate of 190 MGD (million gallons per day) and elevation difference of 3 m (10 ft) between the final stage of treatment and the discharge point. Based on the parameters above; hubless rim-drive Kaplan type hydroturbine (RDT) is the optimal choice to be used in such an application. The RDT is designed and optimized by using in-house code. A computational fluid dynamics (CFD) software is applied to evaluate the performance of the proposed model, and the system is simulated through homer software to validate the results generated by the CFD. The expected savings is estimated to be 1564 MWh/yr.

Energy & Fuels↗
DOE OSTI2020

Learning Hidden Structure in Multi-Fidelity Information Sources for Efficient Uncertainty Quantification (LDRD 218317)

This report summarizes the work done under the Laboratory Directed Research and Development (LDRD) project entitled "Learning Hidden Structure in Multi-Fidelity Information Sources for Efficient Uncertainty Quantification". In this project we investigated multi-fidelity strategies for fusing data from information sources of varying cost and accuracy. Most existing strategies exploit hierarchical relationships between models, for example that occur when different models are generated by refining a numerical discretization parameter. In this work we focused on encoding the relationships between information sources using directed acyclic graphs. The multi-fidelity networks can have general structure and represent a significantly greater variety of modeling relationships than recursive networks used in the current state literature. Numerical results show that a non-hierarchical multi-fidelity Monte Carlo strategy can reduce the cost of estimating uncertainty in predictions of a model of plasma expanding in a vacuum by almost two orders of magnitude.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Stomatal response of three Arctic plant species, Seward Peninsula, Alaska, 2022

Stomatal response to changing irradiance measurements on three common tussock tundra shrub species. Data were collected in 2022 from two sites on the Seward Peninsula, Kougarok Mile 64 and Teller Mile 27. Measurements were made with LI-COR LI-6800 gas exchange systems using a slow curve light response method. Light curve observations enable the estimation of stomatal slope and intercept parameters, g1 and g0. The data package files include a data file, metadata files and the complete instrument output for all measurements in .csv format, and a pdf describing the experimental protocol. See the related data package NGA315 for foliar trait data (leaf mass per area, leaf nitrogen concentration) and NGA209 for co-located thaw depth and soil moisture. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

Multivariate space - time analysis of PRE-STORM precipitation

This paper presents the methodologies and results of the multivariate modeling and two-dimensional spectral and correlation analysis of PRE-STORM rainfall gauge data. Estimated parameters of the models for the specific spatial averages clearly indicate the eastward and southeastward wave propagation of rainfall fluctuations. A relationship between the coefficients of the diffusion equation and the parameters of the stochastic model of rainfall fluctuations is derived that leads directly to the exclusive use of rainfall data to estimate advection speed (about 12 m/s) as well as other coefficients of the diffusion equation of the corresponding fields. The statistical methodology developed here can be used for confirmation of physical models by comparison of the corresponding second-moment statistics of the observed and simulated data, for generating multiple samples of any size, for solving the inverse problem of the hydrodynamic equations, and for application in some other areas of meteorological and climatological data analysis and modeling.

Polyak, Ilya↗
NASA NTRS1992

Optimal Estimation of Rain Rate Profiles from Single-Frequency Radar Echoes

The significant ambiguities inherent in the determination of a particular vertical rain intensity profile from a given time profile of radar echo powers measured by a downward-looking (spaceborne or airborne) radar at a single attenuating frequency are well-documented. Indeed, one already knows that by appropriately varying the parameters of the reflectivity-rain-rate (Z - R) and/or attenuation-rain-rate (k - R) relationships, one can produce several substantially different hypothetical rain rate profiles which would have the same radar power profile. Imposing the additional constraint that the path-averaged rain-rate be a given fixed number does reduce the ambiguities but falls far short of eliminating them. While we now know how to generate as many mutually ambiguous rain-rate profiles from a given profile of received radar reflectivities as we like, there remains to produce a quantitative measure to assess how likely each of these profiles is, what the appropriate 'average' profile should be, and what the 'variance' of these multiple solutions is. Of course, in order to do this, one needs to spell out the stochastic constraints that can allow us to make sense of the words 'average' and 'variance' in a mathematically rigorous way. Such a quantitative approach would be particularly well-suited for such systems as the proposed Precipitation Radar of the Tropical Rainfall Measuring Mission (TRMM). Indeed, one would then be able to use the radar reflectivities measured by the TRMM radar from one particular look in order to estimate the most likely rain-rate profile that would have produced the measurements, as well as the uncertainty in the estimated rain-rates as a function of range. Such an optimal approach is described in this paper.

deep space flight computer real-time advanced spac↗
NASA NTRS1973

The Development of a Stochastic Model of the Atmosphere Between 30 and 90 Km to Be Used in Determining the Effect of Atmospheric Variability on Space Shuttle Entry Parameters

A stochasitc model of the atmosphere between 30 and 90 km was developed for use in Monte Carlo space shuttle entry studies. The model is actually a family of models, one for each latitude-season category as defined in the 1966 U.S. Standard Atmosphere Supplements. Each latitude-season model generates a pseudo-random temperature profile whose mean is the appropriate temperature profile from the Standard Atmosphere Supplements. The standard deviation of temperature at each altitude for a given latitude-season model was estimated from sounding-rocket data. Departures from the mean temperature at each altitude were produced by assuming a linear regression of temperature on the solar heating rate of ozone. A profile of random ozone concentrations was first generated using an auxiliary stochastic ozone model, also developed as part of this study, and then solar heating rates were computed for the random ozone concentrations.

Campbell, J. W.↗
DOE OSTI2021

Methods and devices for preventing computationally explosive calculations in a computer for model parameters distributed on a hierarchy of geometric simplices

A computer-implemented method of preventing computationally explosive calculations. The method includes obtaining, by a processor of the computer, measured data of one of a physical process or a physical object; performing hierarchical numerical modeling of a physical process inclusive of an Earth model containing at least one of (a) infrastructure in the ground and (b) a formation feature in the ground, wherein predicted data is generated; comparing the measured data to the predicted data to calculate an estimated error; analyzing the estimated error via an inversion process to update the at least one of the Earth model and infrastructure model so as to reduce the estimated error and to determine a final composite Earth model of at least one of the infrastructure and the feature; and using the final composite Earth model to characterize at least one of the process and the physical object.

Source record↗
DOE OSTI2019

Impact of fuel and engine operating conditions on efficiency of a heavy duty truck engine running compression ignition mode using energy and exergy analysis

Gasoline compression ignition using a single gasoline-type fuel for direct/port injection has been shown as a method to achieve low temperature combustion with low engine-out NO x and soot emissions and high indicated thermal efficiency. However, key technical barriers to achieving low temperature combustion on multi-cylinder engines include the air handling system (limited amount of exhaust gas recirculation) as well as mechanical engine limitations (e.g. peak pressure rise rate). In light of these limitations, high temperature combustion with reduced amounts of exhaust gas recirculation appears more practical. Previous studies by the authors demonstrated that utilizing port and direct injection of E85 gasoline simultaneously resulted in the best performance and the highest brake thermal efficiency of 47.1%, which was 1.2% higher compared to diesel baseline. For further efficiency improvement, a better understanding of energy and exergy loss mechanisms is required. As such, in this work first and second law thermodynamics analysis were applied to experiments with gasoline compression ignition at an engine speed of 1038 rpm and brake mean effective pressure of 1.4 MPa on a 12.4 L multi-cylinder heavy-duty diesel engine. The impact on the efficiency, losses, and irreversibility with respect to various parameters such as fuel, injection strategy, intake condition, and port injected water mass was quantified. The peak efficiency of ideal cycles and the combined efficiency with a waste heat recovery system were also estimated in this study to assess opportunities for further efficiency gains. It was found that the combustion irreversibility was dominated by the combustion temperature rather than the fuel structure. Despite its highest brake thermal efficiency, E85 gasoline generated the highest exergy destruction due to its lowest combustion temperature. For the same reason, retarding the combustion phasing or adopting a port and direct injection strategy increased the exergy destruction. The combustion duration loss was more notable than the phasing loss and was affected by both the combustion duration and shape of heat release profile. Water port injection increased the exhaust energy, but the available energy dropped significantly attributed to the decrease in the exhaust temperature, rendering waste heat recovery ineffective. The second law analysis serves as a helpful tool in conjunction with the first law analysis to explore pathways to maximize engine efficiency.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Planck 2018 results

We analyse the Planck full-mission cosmic microwave background (CMB) temperature and E -mode polarization maps to obtain constraints on primordial non-Gaussianity (NG). We compare estimates obtained from separable template-fitting, binned, and optimal modal bispectrum estimators, finding consistent values for the local, equilateral, and orthogonal bispectrum amplitudes. Our combined temperature and polarization analysis produces the following final results: f NL local = -0.9 ± 5.1; f NL equil = -26 ± 47; and f NL ortho = -38 ± 24 (68% CL, statistical). These results include low-multipole (4 ≤ ℓ < 40) polarization data that are not included in our previous analysis. The results also pass an extensive battery of tests (with additional tests regarding foreground residuals compared to 2015), and they are stable with respect to our 2015 measurements (with small fluctuations, at the level of a fraction of a standard deviation, which is consistent with changes in data processing). Polarization-only bispectra display a significant improvement in robustness; they can now be used independently to set primordial NG constraints with a sensitivity comparable to WMAP temperature-based results and they give excellent agreement. In addition to the analysis of the standard local, equilateral, and orthogonal bispectrum shapes, we consider a large number of additional cases, such as scale-dependent feature and resonance bispectra, isocurvature primordial NG, and parity-breaking models, where we also place tight constraints but do not detect any signal. The non-primordial lensing bispectrum is, however, detected with an improved significance compared to 2015, excluding the null hypothesis at 3.5 σ . Beyond estimates of individual shape amplitudes, we also present model-independent reconstructions and analyses of the Planck CMB bispectrum. Our final constraint on the local primordial trispectrum shape is g NL local = (-5.8 ± 6.5) × 10 4 (68% CL, statistical), while constraints for other trispectrum shapes are also determined. Exploiting the tight limits on various bispectrum and trispectrum shapes, we constrain the parameter space of different early-Universe scenarios that generate primordial NG, including general single-field models of inflation, multi-field models (e.g. curvaton models), models of inflation with axion fields producing parity-violation bispectra in the tensor sector, and inflationary models involving vector-like fields with directionally-dependent bispectra. Our results provide a high-precision test for structure-formation scenarios, showing complete agreement with the basic picture of the ΛCDM cosmology regarding the statistics of the initial conditions, with cosmic structures arising from adiabatic, passive, Gaussian, and primordial seed perturbations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Charging Hub Scenario Sheet Screenshot

The Excel-based CHECT tool estimates the LCOC ($/kWh) by charger type (Level 1, Level 2, and DC fast charging) for a given charging hub scenario. CHECT requires users to input certain charging hub scenario parameters, including the number of chargers, daily utilization, and charger replacement frequency by charger type. Additional inputs such as the charging schedule, local utility rates, capital/operational costs, and financial inputs can be customized by the user, or the tool can generate results using appropriate default values from literature for the charging hub scenario and service location(s). Using the above inputs, CHECT performs a robust techno-economic analysis to generate the LCOC by charger type, broken down by cost category (e.g., capital, operational, utility, taxes) for various combinations of charging hub types (multiunit dwelling or public) and ownership models (residential, utility, or private company). It also outputs the annual discounted cash flows and determines the most sensitive input variables. In addition, the tool allows users to easily compare the LCOC across various ownership models or across different states.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1979

Standard Transistor Array (STAR). Volume 1: Placement technique

A large scale integration (LSI) technology, the standard transistor array uses a prefabricated understructure of transistors and a comprehensive library of digital logic cells to allow efficient fabrication of semicustom digital LSI circuits. The cell placement technique for this technology involves formation of a one dimensional cell layout and "folding" of the one dimensional placement onto the chip. It was found that, by use of various folding methods, high quality chip layouts can be achieved. Methods developed to measure of the "goodness" of the generated placements include efficient means for estimating channel usage requirements and for via counting. The placement and rating techniques were incorporated into a placement program (CAPSTAR). By means of repetitive use of the folding methods and simple placement improvement strategies, this program provides near optimum placements in a reasonable amount of time. The program was tested on several typical LSI circuits to provide performance comparisons both with respect to input parameters and with respect to the performance of other placement techniques. The results of this testing indicate that near optimum placements can be achieved by use of the procedures incurring severe time penalties.

Cox, G. W.↗
NASA NTRS1993

A wall interference assessment/correction system

A Wall Signature method originally developed by Hackett was selected to be adapted for the Ames 12-ft Wind Tunnel Wall Interference Assessment/Correction (WIAC) System in the project. This method uses limited measurements of the static pressure at the wall, in conjunction with the solid wall boundary condition, to determine the strength and distribution of singularities representing the test article. The singularities are used in turn for estimating wall interference at the model location. The lifting interference will be treated separately by representing in a horseshoe vortex system for the model's lifting effects. The development and implementation of a working prototype will be completed, delivered, and documented with a software manual. The WIAC code will be validated by conducting numerically simulated experiments rather than actual wind tunnel experiments. The simulations will be used to generate both free-air and confined wind-tunnel flow fields for each of the test articles over a range of test configurations. Specifically, the pressure signature at the test section wall will be computed for the tunnel case to provide the simulated 'measured' data. These data will serve as the input for the WIAC method - Wall Signature method. The performance of the WIAC method then may be evaluated by comparing the corrected parameters with those for the free-air simulation. The following two additional tasks are included in the supplement No. 1 to the basic Grant. On-line wall interference calculation: The developed wall signature method (modified Hackett's method) for Ames 12-ft Tunnel will be the pre-computed coefficients which facilitate the on-line calculation of wall interference; and support system effects estimation: The effects on the wall pressure measurements due to the presence of the model support systems will be evaluated.

Lo, Ching F.↗
DOE OSTI2021

Machine learning application to single channel design of molten salt reactor

This study proposes a robust approach to quickly design a nuclear reactor core and explores the best performing machine learning (ML) technique for predicting feature parameters of the core. Here we implemented the approach into a hypothetical channel of molten salt reactors to demonstrate the applicability of the method. We prepared a Python tool, named Plankton, which couples to a reactor physics code and an optimization tool, and imports ML methods. The tool performs three consecutive phases: reactor database generation, machine learning application, and design optimization. We identified the extra trees method as the best performing estimator. With the estimator, we found nine optimum designs in total, one for each fuel-salt pair, and estimated all the performance metrics of the designs with a <5% prediction error compared to their actual values. U-Pu-NaCl fuel-salt gave promising results with the highest conversion ratio, the most negative feedback coefficient, and the lowest fast flux.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Data-driven chemical kinetic reaction mechanism for F-24 jet fuel ignition

A data-driven chemical kinetic mechanism for the military version of Jet A, F-24, is developed for numerical simulations of the ignition process. The main purpose of this study is to obtain a practical F-24 mechanism across wide temperature and equivalence ratio ranges, with a particular focus on the negative temperature coefficient and low temperature regions. The new mechanism (ARLMech-HC-F24) is based on the HyChem model of a similar fuel and optimized using a micro-genetic algorithm against an experimental ignition delay data set of the target fuel. The development and optimization processes include reaction selection, population creation, shuffled tournament implementation based on a merit function, and child-individual creation for the next generation. Several techniques and parameters are proposed to generate an accurate mechanism through an efficient process. The newly introduced data-driven mechanism based on these techniques shows better merit value convergence and represents the ignition behavior more accurately than that without the techniques. This practical mechanism is suitable for the numerical simulations of the F-24 or Jet A ignition problem, and the suggested strategies can be employed in similar problems of rate coefficients estimation.

42 ENGINEERING↗
DOE OSTI2025

Resolving the Coverage Dependence of Surface Reaction Kinetics with Machine Learning and Automated Quantum Chemistry Workflows

Microkinetic models for catalytic systems require estimation of many thermodynamic and kinetic parameters that can be calculated for isolated species and transition states using ab initio methods. However, the presence of nearby coadsorbates on the surface can dramatically alter these thermodynamic and kinetic parameters causing them to be dependent on species coverage fractions. As there are combinatorially many coadsorbed configurations on the surface, computing the coverage dependence of these parameters is far less straightforward. We present a framework for generating and applying machine learning models to predict coverage-dependent parameters for microkinetic models. Our toolkit enables automatic calculation and evaluation of coadsorbed configurations allowing us to sample 2,000 coadsorbed adsorbates and transition states (TSs) for a diverse set of 9 reactions on Cu(111), a challenging surface, with four possible coadsorbates. This dataset was then used to train subgraph isomorphic decision trees (SIDTs) to predict the stability and association energy of configurations. We were able to achieve mean absolute errors (MAEs) of 0.106 eV on adsorbates, 0.172 eV on TSs, and due to natural error cancellation in SIDTs for relative properties, 0.130 eV on reaction energies and 0.180 eV on activation barriers. In conclusion, we describe how to use these models to predict coverage-dependent corrections for adsorbates and TSs and demonstrate on H*, HO*, and O* comparing the generated SIDT model with an iteratively refined version.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Deep Learning and Likelihood Approaches for Viral Phylogeography Converge on the Same Answers Whether the Inference Model Is Right or Wrong

Abstract Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences about the phylodynamics and history of viral transmission. However, these methods are often computationally expensive, which limits the complexity and realism of phylodynamic models and makes them ill-suited for informing policy decisions in real-time during rapidly developing outbreaks. Likelihood-free methods using deep learning are pushing the boundaries of inference beyond these constraints. In this paper, we extend, compare, and contrast a recently developed deep learning method for likelihood-free inference from trees. We trained multiple deep neural networks using phylogenies from simulated outbreaks that spread among 5 locations and found they achieve close to the same levels of accuracy as Bayesian inference under the true simulation model. We compared robustness to model misspecification of a trained neural network to that of a Bayesian method. We found that both models had comparable performance, converging on similar biases. We also implemented a method of uncertainty quantification called conformalized quantile regression that we demonstrate has similar patterns of sensitivity to model misspecification as Bayesian highest posterior density (HPD) and greatly overlap with HPDs, but have lower precision (more conservative). Finally, we trained and tested a neural network against phylogeographic data from a recent study of the SARS-Cov-2 pandemic in Europe and obtained similar estimates of region-specific epidemiological parameters and the location of the common ancestor in Europe. Along with being as accurate and robust as likelihood-based methods, our trained neural networks are on average over 3 orders of magnitude faster after training. Our results support the notion that neural networks can be trained with simulated data to accurately mimic the good and bad statistical properties of the likelihood functions of generative phylogenetic models.

Evolutionary Biology↗
DOE OSTI2026

Conditional Latent Diffusion for High-Resolution Prediction of Electrochemical Surface Morphology

A conditionally guided generative latent diffusion process that is trained on a set of experimental processing parameters and their associated resulting electron microscope images of the electrodeposition process is able to interpolate between processing parameters in a physically consistent way. Electrodeposition of rhenium with pulse and pulse-reverse waveforms is used as a model system, and the process is adaptable to other electrodeposition, electropolishing, or corrosion processes. The method is able to extrapolate, predicting estimates of material morphologies for experimental setups unseen in the training data. The results are demonstrated with experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Statistical Analysis of the Eigenmode Spectrum in the SRF Cavities with Mechanical Imperfections

The superconducting radio frequency (SRF) technology is progressing rapidly over last decades toward high accelerating gradients and low surface resistance making feasible the particle accelerators operation with high beam currents and long duty factors. However, the coherent RF losses due to high order modes (HOMs) excitation becomes a limiting factor for these regimes. In spite of the operating mode, which is tuned separately, the parameters of HOMs vary from one cavity to another due to finite mechanical tolerances during cavities fabrication. It is vital to know in advance the spread of HOM parameters in order to predict unexpected cryogenic losses, overheating of beam line components and to keep stable beam dynamics. In this paper we present the method of generating the unique cavity geometry with imperfections while preserving operating mode frequency and field flatness. Based on the eigenmode spectrum calculation of series of randomly generated cavities we can accumulate the data for the evaluation the HOM statistics. Finally, we describe the procedure for the estimation of the probability of the resonant HOM losses in the SRF resonators. The study of these effects leads to specifications of SC cavity and cryomodule and can significantly impact on the efficiency and reliability of the machine operation

43 PARTICLE ACCELERATORS↗