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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Characterization and modeling for dilute and dense sprays

The present numerical modeling of dilute and dense spray combustion solves the governing gas-phase equations in Eulerian coordinates by means of a time-marching multiple pressure correction procedure that is based on the operator-splitting technique. A sequence of validating cases involving (1) nonevaporating, (2) evaporating, (3) burning, (4) dilute, and (5) dense spray cases is undertaken; it is found that in the combusting dilute spray case, the present procedure correctly predicts the general features of flows and yields qualitative agreement with experimental data. In the other cases, reasonably good agreement with experimental results is obtained.

Chen, C. P.↗
NASA NTRS1994

H2O-H2SO4 system in Venus' clouds and OCS, CO, and H2SO4 profiles in Venus' troposphere

A coupled problem of diffusion and condensation is solved for the H2SO4-H2O system in Venus' cloud layer. The position of the lower cloud boundary, profiles of the H2O and H2SO vapor mixing ratios, and of the H2O/H2SO4 ratio of sulfuric acid aerosol and its flux are calculated as functions of the column photochemical production rate of sulfuric acid. Variations of the lower cloud boundary are considered. Our basic model, which is constrained to yield f(sub H2O)(30km) = 30 ppm, predicts the position of the lower cloud boundary at 48.4 km coinciding with the mean Pioneer Venus value, the peak H2SO4 mixing ratio of 5.4 ppm, and the H2SO4 production rate phi(sub H2SO4) = 2.2 x 10(exp 12) cm(exp -2)s(exp -1).

Krasnopolsky, V. A.↗
NASA NTRS2010

Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine Performance Estimation

A linear point design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine which seeks to minimize the theoretical mean-squared estimation error. This paper derives theoretical Kalman filter estimation error bias and variance values at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared to the conventional approach of tuner selection. Experimental simulation results are found to be in agreement with theoretical predictions. The new methodology is shown to yield a significant improvement in on-line engine performance estimation accuracy

Simon, Donald L.↗
NASA NTRS2011

Computational Assessment of the Aerodynamic Performance of a Variable-Speed Power Turbine for Large Civil Tilt-Rotor Application

The main rotors of the NASA Large Civil Tilt-Rotor notional vehicle operate over a wide speed-range, from 100% at take-off to 54% at cruise. The variable-speed power turbine offers one approach by which to effect this speed variation. Key aero-challenges include high work factors at cruise and wide (40 to 60 deg.) incidence variations in blade and vane rows over the speed range. The turbine design approach must optimize cruise efficiency and minimize off-design penalties at take-off. The accuracy of the off-design incidence loss model is therefore critical to the turbine design. In this effort, 3-D computational analyses are used to assess the variation of turbine efficiency with speed change. The conceptual design of a 4-stage variable-speed power turbine for the Large Civil Tilt-Rotor application is first established at the meanline level. The design of 2-D airfoil sections and resulting 3-D blade and vane rows is documented. Three-dimensional Reynolds Averaged Navier-Stokes computations are used to assess the design and off-design performance of an embedded 1.5-stage portion-Rotor 1, Stator 2, and Rotor 2-of the turbine. The 3-D computational results yield the same efficiency versus speed trends predicted by meanline analyses, supporting the design choice to execute the turbine design at the cruise operating speed.

Welch, Gerard E.↗
NASA NTRS2012

Background Noise Reduction Using Adaptive Noise Cancellation Determined by the Cross-Correlation

Background noise due to flow in wind tunnels contaminates desired data by decreasing the Signal-to-Noise Ratio. The use of Adaptive Noise Cancellation to remove background noise at measurement microphones is compromised when the reference sensor measures both background and desired noise. The technique proposed modifies the classical processing configuration based on the cross-correlation between the reference and primary microphone. Background noise attenuation is achieved using a cross-correlation sample width that encompasses only the background noise and a matched delay for the adaptive processing. A present limitation of the method is that a minimum time delay between the background noise and desired signal must exist in order for the correlated parts of the desired signal to be separated from the background noise in the crosscorrelation. A simulation yields primary signal recovery which can be predicted from the coherence of the background noise between the channels. Results are compared with two existing methods.

Spalt, Taylor B.↗
NASA NTRS2017

Integrating Growth Stage Deficit Irrigation into a Process Based Crop Model

Current rates of agricultural water use are unsustainable in many regions, creating an urgent need to identify improved irrigation strategies for water limited areas. Crop models can be used to quantify plant water requirements, predict the impact of water shortages on yield, and calculate water productivity (WP) to link water availability and crop yields for economic analyses. Many simulations of crop growth and development, especially in regional and global assessments, rely on automatic irrigation algorithms to estimate irrigation dates and amounts. However, these algorithms are not well suited for water limited regions because they have simplistic irrigation rules, such as a single soil-moisture based threshold, and assume unlimited water. To address this constraint, a new modeling framework to simulate agricultural production in water limited areas was developed. The framework consists of a new automatic irrigation algorithm for the simulation of growth stage based deficit irrigation under limited seasonal water availability; and optimization of growth stage specific parameters. The new automatic irrigation algorithm was used to simulate maize and soybean in Gainesville, Florida, and first used to evaluate the sensitivity of maize and soybean simulations to irrigation at different growth stages and then to test the hypothesis that water productivity calculated using simplistic irrigation rules underestimates WP. In the first experiment, the effect of irrigating at specific growth stages on yield and irrigation water use efficiency (IWUE) in maize and soybean was evaluated. In the reproductive stages, IWUE tended to be higher than in the vegetative stages (e.g. IWUE was 18% higher than the well watered treatment when irrigating only during R3 in soybean), and when rainfall events were less frequent. In the second experiment, water productivity (WP) was significantly greater with optimized irrigation schedules compared to non-optimized irrigation schedules in water restricted scenarios. For example, the mean WP across 38 years of maize production was 1.1 kg/cu m for non-optimized irrigation schedules with 50 mm of seasonal available water and 2.1 kg/cu m optimized ion schedules, a 91% improvement in WP with optimized irrigation schedules. The framework described in this work could be used to estimate WP for regional to global assessments, as well as derive location specific irrigation guidance.

crop model↗
NASA NTRS2020

Tidal Disruptions of Main-sequence Stars. IV. Relativistic Effects and Dependence on Black Hole Mass

Using a suite of fully relativistic hydrodynamic simulations applied to main-sequence stars with realistic internal density profiles, we examine full and partial tidal disruptions across a wide range of black hole mass( MM105 105BH7)and stellar mass (MM0.33) as larger MBH leads to stronger relativistic effects. For fixed Må, as MBH increases, the ratio of the maximum pericenter distance yielding full disruptions (t) to its Newtonian prediction rises rapidly, becoming triple the Newtonian value for= ́MM510BH7, while the ratio of the energy width of the stellar debris for full disruptions to the Newtonian prediction decreases steeply, resulting in a factor of 2 correction at= ́MM510BH7. We provide approximate formulae that express the relativistic corrections of both t and the energy width relative to their Newtonian approximate estimates. For partial disruptions, we find that the fractional remnant mass for a given ratio of the pericenter to t is higher for larger MBH. These results have several implications. As MBH increases above~M107, the cross section for complete disruptions is suppressed by competition with direct capture. However, the cross-section ratio for partial to complete disruptions depends only weakly on MBH. The relativistic correction to the debris energy width delays the time of peak mass-return rate and diminishes the magnitude of the peak return rate. ForMM10BH7, the MBH-dependence of the full disruption cross section and the peak mass-return rate and time is influenced more by relativistic effects than by Newtonian dynamics.

Taeho Ryu↗
DOE OSTI2020

Understanding the Impact of Memory Access Patterns in Intel Processors

Because of increasing complexity in the memory hierarchy, predicting the performance of a given application in a given processor is becoming more difficult. The problem is worsened by the fact that the hardware needed to deal with more complex memory traffic also affects energy consumption. Moreover, in a heterogeneous system with shared main memory, the memory traffic between the last level cache (LLC) and the memory creates contention between other processors and accelerator devices. For these reasons, it is important to investigate and understand the impact of different memory access patterns on the memory system. This study investigates the interplay between Intel processors' memory hierarchy and different memory access patterns in applications. The authors explore sequential streaming and strided memory access patterns with the objective of predicting LLC-dynamic random access memory (DRAM) traffic for a given application in given Intel architectures. Moreover, the impact of prefetching is also investigated in this study. Experiments with different Intel micro-architectures uncover mechanisms to predict LLC-DRAM traffic that can yield up to 99% accuracy for sequential streaming access patterns and up to 95% accuracy for strided access patterns.

Alaul haque monil, Mohammad↗
NASA NTRS1993

Development of a recursion RNG-based turbulence model

Reynolds stress closure models based on the recursion renormalization group theory are developed for the prediction of turbulent separated flows. The proposed model uses a finite wavenumber truncation scheme to account for the spectral distribution of energy. In particular, the model incorporates effects of both local and nonlocal interactions. The nonlocal interactions are shown to yield a contribution identical to that from the epsilon-renormalization group (RNG), while the local interactions introduce higher order dispersive effects. A formal analysis of the model is presented and its ability to accurately predict separated flows is analyzed from a combined theoretical and computational stand point. Turbulent flow past a backward facing step is chosen as a test case and the results obtained based on detailed computations demonstrate that the proposed recursion -RNG model with finite cut-off wavenumber can yield very good predictions for the backstep problem.

Zhou, YE↗
DOE OSTI2020

Integrated performance of large HDC-capsule implosions on the National Ignition Facility

In this paper, we report on eight, indirect-drive, deuterium–tritium-layered, inertial-confinement-fusion experiments at the National Ignition Facility to determine the largest capsule that can be driven symmetrically without relying on cross-beam energy transfer or advanced Hohlraum designs. Targets with inner radii of up to 1050 μm exhibited controllable P 2 symmetry, while larger capsules suffered from diminished equatorial drive. Reducing the Hohlraum gas-fill-density from 0.45 mg/cm 3 to 0.3 mg/cm 3 did not result in a favorable shift of P 2 amplitude as observed in preceding tuning experiments. Reducing the laser-entrance-hole diameter from 4 mm to 3.64 mm decreased polar radiation losses as expected, resulting in an oblate symmetry. The experiments exhibited the expected performance benefit from increased experimental scale, with yields at a fixed implosion velocity roughly following the predicted 1D dependence. With an inner radius of 1050 μm and a case-to-capsule-ratio of 3.0, experiment N181104 is the lowest implosion-velocity experiment to exceed a total neutron yield of 10 16 .

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2021

Mapping Yearly Fine Resolution Global Surface Ozone through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output for 1990–2017

Estimates of ground-level ozone concentrations are necessary to determine the human health burden of ozone. To support the Global Burden of Disease Study, we produce yearly fine resolution global surface ozone estimates from 1990 to 2017 through a data fusion of observations and models. As ozone observations are sparse in many populated regions, we use a novel combination of the M3Fusion and Bayesian Maximum Entropy (BME) methods. With M3Fusion, we create a multi-model composite by bias-correcting and weighting nine global atmospheric chemistry models based on their ability to predict observations (8,834 sites globally)in each region and year. BME is then used to integrate observations, such that estimates match observations at each monitoring site with the observational influence decreasing smoothly across space and time until the output matches the multi-model composite. After estimating at 0.5° resolution using BME, we add fine spatial detail from an additional model, yielding estimates at 0.1° resolution. Observed ozone is predicted more accurately (R2=0.81 at test point, 0.63 at 0.1°,0.62 at 0.5°) than the multi-model mean (R2=0.28 at 0.5°). Global ozone exposure is estimated to be increasing, driven by highly populated regions of Asia and Africa, despite decreases in the United States and Russia.

environmental modeling↗
DOE OSTI2022

Fission Product Yields of 233 U and 239 Pu by Neutron-Induced Fission at Neutron Energies from 0.18-140 MeV [Slides]

Nuclear fission is an important process with applications in astrophysics, nuclear reactors, and stockpile stewardship. Despite having been discovered over 80 years ago, fission is not fully described by a predictive model. This study will show fission product mass yields and total kinetic energies (TKE) across a large range of incident neutron energies for future refinement of fission models. 233U(n,f) data is used to verify features and structures on a previous experiment and compared against fission models. 239Pu(n,f) data will be analyzed to show new data at higher neutron energies using techniques to handle fission in the presence of alphas. 252Cf(sf) data will be used for calibration and benchmarking with a thin-backed target.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2021

Anion Resonances and Photoelectron Spectroscopy of the Tetracenyl Anion

The photoelectron spectroscopy of the tetracenyl anion using slow electron velocity-map imaging (SEVI) of cryogenically cooled ions is presented here. The total photodetachment yield as a function of photon energy is used to reveal a rich manifold of anion excited states above the detachment threshold. The lowest energy anionic resonance has a sufficiently long lifetime to yield a vibrationally resolved absorption spectrum that can be directly compared with theoretical predictions. Excitation of this state mostly results in electron detachment via thermionic emission. The total photodetachment yield spectrum is used to select photon wavelengths that minimize the indirect detachment signal to allow acquisition of vibrationally-resolved photoelectron spectra that can inform on the neutral tetracenyl radical. Assignment of spectral features corresponding to the ground and first excited state of the neutral 12-tetracenyl isomer is made with the aid of Franck-Condon simulations. This yields adiabatic electron affinity and term energies that differ significantly from the previously reported values. Weak features corresponding to the ground state of the minor 2-teracenyl and 1-tetracenyl isomers are also identified, which allows for the experimental determination of their electron affinities for the first time.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Sensitivity Analysis of Genome-Scale Metabolic Flux Prediction

TRIMER, Transcription Regulation Integrated with MEtabolic Regulation, is a genome-scale modeling pipeline targeting at metabolic engineering applications. Using TRIMER, regulated metabolic reactions can be effectively predicted by integrative modeling of metabolic reactions with a Transcription Factor (TF)-gene regulatory network (TRN), which is modeled via a Bayesian network (BN). In this paper, we focus on sensitivity analysis of metabolic flux prediction for uncertainty quantification of BN structures for TRN modeling in TRIMER. We propose a computational strategy to construct the uncertainty class of TRN models based on the inferred regulatory order uncertainty given transcriptomic expression data. With that, we analyze the prediction sensitivity of the TRIMER pipeline for the metabolite yields of interest. The obtained sensitivity analyses can guide Optimal Experimental Design (OED) to help acquire new data that can enhance TRN modeling and achieve specific metabolic engineering objectives, including metabolite yield alterations. Here we have performed small- and large-scale simulated experiments, demonstrating the effectiveness of our developed sensitivity analysis strategy for BN structure learning to quantify the edge importance in terms of metabolic flux prediction uncertainty reduction and its potential to effectively guide OED.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A novel transfer learning framework for sorghum biomass prediction using UAV-based remote sensing data and genetic markers

Yield for biofuel crops is measured in terms of biomass, so measurements throughout the growing season are crucial in breeding programs, yet traditionally time- and labor-consuming since they involve destructive sampling. Modern remote sensing platforms, such as unmanned aerial vehicles (UAVs), can carry multiple sensors and collect numerous phenotypic traits with efficient, non-invasive field surveys. However, modeling the complex relationships between the observed phenotypic traits and biomass remains a challenging task, as the ground reference data are very limited for each genotype in the breeding experiment. In this study, a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) model is proposed for sorghum biomass prediction. The architecture is designed to exploit the time series remote sensing and weather data, as well as static genotypic information. As a large number of features have been derived from the remote sensing data, feature importance analysis is conducted to identify and remove redundant features. A strategy to extract representative information from high-dimensional genetic markers is proposed. To enhance generalization and minimize the need for ground reference data, transfer learning strategies are proposed for selecting the most informative training samples from the target domain. Consequently, a pre-trained model can be refined with limited training samples. Field experiments were conducted over a sorghum breeding trial planted in multiple years with more than 600 testcross hybrids. The results show that the proposed LSTM-based RNN model can achieve high accuracies for single year prediction. Further, with the proposed transfer learning strategies, a pre-trained model can be refined with limited training samples from the target domain and predict biomass with an accuracy comparable to that from a trained-from-scratch model for both multiple experiments within a given year and across multiple years.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Beam-energy dependence of the production of light nuclei in Au + Au collisions

We study in the coalescence model the collision energy dependence of (anti-)deuteron and (anti- )triton production in the most central Au+Au collisions at $\sqrt{s_{NN}} $ = 7.7, 11.5, 19.6, 27, 39, 62.4 and 200 GeV. The needed phase-space distribution of nucleons at the kinetic freeze-out is generated from a new 3D hybrid dynamical model (iEBE-MUSIC) by using a smooth crossover equation of state (EoS) without a QCD critical point. Our model calculations predict that the coalescence parameters of (anti-)deuteron (B 2 (d) and B 2 ($\bar{d}$)) decrease monotonically as the collision energy increases, and the light nuclei yield ratio N t N p /$Nd^{2}_{d}$ remains approximately a constant with respect to the collision energy. These calculated observables fail to reproduce the non-monotonic behavior of the corresponding data from the STAR Collaboration. Without including any effects of the critical point in our model, our results serve as the baseline predictions for the yields of light nuclei in the search for the possible QCD critical points from the experimental beam energy scan of heavy ion collisions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1994

An Ab Initio Study of CuCO

Modified coupled-pair functional (MCPF) calculations and coupled cluster singles and doubles calculations, which include a perturbational estimate of the connected triples [CCSD(T)], yield a bent structure for CuCO, thus, supporting the prediction of a nonlinear structure based on density functional (DF) calculations. Our best estimate for the binding energy is 4.9 +/- 1.4 kcal/mol; this is in better agreement with experiment (6.0 +/- 1.2 kcal/mol) than the DF approach which yields a value (19.6 kcal/mol) significantly larger than experiment.

Bauschlicher, Charles W., Jr.↗