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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Absolute 1* quantum yields for the ICN A state by diode laser gain versus absorption spectroscopy

Absolute I* quantum yields were measured as a function of wavelength for room temperature photodissociation of the ICN A state continuum. The temperature yields are obtained by the technique of time-resolved diode laser gain-versus-absorption spectroscopy. Quantum yields are evaluated at seven wavelengths from 248 to 284 nm. The yield at 266 nm is 66.0 +/- 2% and it falls off to 53.4 +/- 2% and 44.0 +/- 4% at 284 and 248 respectively. The latter values are significantly higher than those obtained by previous workers using infrared fluorescence. Estimates of I* quantum yields obtained from analysis of CN photofragment rotational distributions, as discussed by other workers, are in good agreement with the I* yields. The results are considered in conjunction with recent theoretical and experimental work on the CN rotational distributions and with previous I* yield results.

Hess, Wayne P.↗
NASA NTRS1987

Absolute I(asterisk) quantum yields for the ICN A state by diode laser gain-vs-absorption spectroscopy

Absolute I(asterisk) quantum yields have been measured as a function of wavelength for room temperature photodissociation of the ICN A state continuum. The yields are obtained by the technique of time-resolved diode laser gain-vs-absorption spectroscopy. Quantum yields are evaluated at seven wavelengths from 248 to 284 nm. The yield at 266 nm is 66.0 + or - 2 percent and it falls off to 53.4 + or - 2 percent and 44.0 + or - 4 percent at 284 and 248 nm, respectively. The latter values are significantly higher than those obtained by previous workers using infrared fluorescence. Estimates of I(asterisk) quantum yields obtained from analysis of CN photofragment rotational distributions, as discussed by other workers, are in good agreement with the I(asterisk) yields reported here. The results are considered in conjunction with recent theoretical and experimental work on the CN rotational distributions and with previous I(asterisk) quantum yield results.

Hess, Wayne P.↗
NASA NTRS1990

Statistical circuit design for yield improvement in CMOS circuits

This paper addresses the statistical design of CMOS integrated circuits for improved parametric yield. The work uses the Monte Carlo technique of circuit simulation to obtain an unbiased estimation of the yield. A simple graphical analysis tool, the yield factor histogram, is presented. The yield factor histograms are generated by a new computer program called SPICENTER. Using the yield factor histograms, the most sensitive circuit parameters are noted, and their nominal values are changed to improve the yield. Two basic CMOS example circuits, one analog and one digital, are chosen and their designs are 'centered' to illustrate the use of the yield factor histograms for statistical circuit design.

Kamath, H. J.↗
NASA NTRS2002

Hydrostatic Stress Effect On the Yield Behavior of Inconel 100

Classical metal plasticity theory assumes that hydrostatic stress has no effect on the yield and postyield behavior of metals. Recent reexaminations of classical theory have revealed a significant effect of hydrostatic stress on the yield behavior of notched geometries. New experiments and nonlinear finite element analyses (FEA) of Inconel 100 (IN 100) equal-arm bend and double-edge notch tension (DENT) test specimens have revealed the effect of internal hydrostatic tensile stresses on yielding. Nonlinear FEA using the von Mises (yielding is independent of hydrostatic stress) and the Drucker-Prager (yielding is linearly dependent on hydrostatic stress) yield functions was performed. In all test cases, the von Mises constitutive model, which is independent of hydrostatic pressure, overestimated the load for a given displacement or strain. Considering the failure displacements or strains, the Drucker-Prager FEMs predicted loads that were 3% to 5% lower than the von Mises values. For the failure loads, the Drucker Prager FEMs predicted strains that were 20% to 35% greater than the von Mises values. The Drucker-Prager yield function seems to more accurately predict the overall specimen response of geometries with significant internal hydrostatic stress influence.

Allen, Phillip A.↗
NASA NTRS2003

Hydrostatic Stress Effect on the Yield Behavior of Inconel 100

Classical metal plasticity theory assumes that hydrostatic stress has negligible effect on the yield and postyield behavior of metals. Recent reexaminations of classical theory have revealed a significant effect of hydrostatic stress on the yield behavior of various geometries. Fatigue tests and nonlinear finite element analyses (FEA) of Inconel 100 (IN100) equal-arm bend specimens and new monotonic tests and nonlinear finite element analyses of IN100 smooth tension, smooth compression, and double-edge notch tension (DENT) test specimens have revealed the effect of internal hydrostatic tensile stresses on yielding. Nonlinear FEA using the von Mises (yielding is independent of hydrostatic stress) and the Drucker-Prager (yielding is linearly dependent on hydrostatic stress) yield functions were performed. A new FEA constitutive model was developed that incorporates a pressure-dependent yield function with combined multilinear kinematic and multilinear isotropic hardening using the ABAQUS user subroutine (UMAT) utility. In all monotonic tensile test cases, the von Mises constitutive model, overestimated the load for a given displacement or strain. Considering the failure displacements or strains for the DENT specimen, the Drucker-Prager FEM s predicted loads that were approximately 3% lower than the von Mises values. For the failure loads, the Drucker Prager FEM s predicted strains that were up to 35% greater than the von Mises values. Both the Drucker-Prager model and the von Mises model performed equally-well in simulating the equal-arm bend fatigue test.

Allen, Phillip A.↗
NASA NTRS1992

Effects of physical agitation on yield of greenhouse-grown soybean

Agronomic and horticultural crop species experience reductions in growth and harvestable yield after exposure to physical agitation (also known as mechanical stress), as by wind or rain. A greenhouse study was conducted to test the influence of mechanical stress on soybean yield and to determine if exposure to mechanical stress during discrete growth periods has differential effects on seed yield. A modified rotatory shaker was used to apply seismic (i.e., shaking) stress. Brief, periodic episodes of seismic stress reduced stem length, total seed dry weight, and seed number of soybean [Glycine max (L.) Merr.]. Lodging resistance was greater for plants stressed during vegetative growth or throughout vegetative and reproductive growth than during reproductive growth only. Seed dry weight yield was reduced regardless of the timing or duration of stress application, but was lowest when applied during reproductive development. Seismic stress applied during reproductive growth stages R1 to R2 (Days 3 to 4) was as detrimental to seed dry weight accumulation as was stress applied during growth stages R1 to R6 (Days 39 to 42). Seed dry weight per plant was highly correlated with seed number per plant, and seed number was correlated with the seed number of two- and three-seeded pods. Dry weight per 100 seeds was unaffected by seismic-stress treatment. Growth and yield reductions resulting from treatments applied only during the vegetative stage imply that long-term mechanical effects were induced, from which the plants did not fully recover. It is unclear which yield-controlling physiological processes were affected by mechanical stress. Both transient and long-term effects on yield-controlling processes remain to be elucidated.

Non-NASA Center↗
NASA NTRS2010

Salience Assignment for Multiple-Instance Data and Its Application to Crop Yield Prediction

An algorithm was developed to generate crop yield predictions from orbital remote sensing observations, by analyzing thousands of pixels per county and the associated historical crop yield data for those counties. The algorithm determines which pixels contain which crop. Since each known yield value is associated with thousands of individual pixels, this is a multiple instance learning problem. Because individual crop growth is related to the resulting yield, this relationship has been leveraged to identify pixels that are individually related to corn, wheat, cotton, and soybean yield. Those that have the strongest relationship to a given crop s yield values are most likely to contain fields with that crop. Remote sensing time series data (a new observation every 8 days) was examined for each pixel, which contains information for that pixel s growth curve, peak greenness, and other relevant features. An alternating-projection (AP) technique was used to first estimate the "salience" of each pixel, with respect to the given target (crop yield), and then those estimates were used to build a regression model that relates input data (remote sensing observations) to the target. This is achieved by constructing an exemplar for each crop in each county that is a weighted average of all the pixels within the county; the pixels are weighted according to the salience values. The new regression model estimate then informs the next estimate of the salience values. By iterating between these two steps, the algorithm converges to a stable estimate of both the salience of each pixel and the regression model. The salience values indicate which pixels are most relevant to each crop under consideration.

Wagstaff, Kiri L.↗
NASA NTRS2015

Simulated Impacts of Climate Change on Water Use and Yield of Irrigated Sugarcane in South Africa

Reliable predictions of climate change impacts on water use, irrigation requirements and yields of irrigated sugarcane in South Africa (a water-scarce country) are necessary to plan adaptation strategies. Although previous work has been done in this regard, methodologies and results vary considerably. The objectives were (1) to estimate likely impacts of climate change on sugarcane yields, water use and irrigation demand at three irrigated sugarcane production sites in South Africa (Malelane, Pongola and La Mercy) for current (1980-2010) and future (2070-2100) climate scenarios, using an approach based on the Agricultural Model Inter-comparison and Improvement Project (AgMIP) protocols; and (2) to assess the suitability of this methodology for investigating climate change impacts on sugarcane production. Future climate datasets were generated using the Delta downscaling method and three Global Circulation Models (GCMs) assuming atmospheric CO2 concentration [CO2] of 734 ppm(A2 emissions scenario). Yield and water use were simulated using the DSSAT-Canegro v4.5 model. Irrigated cane yields are expected to increase at all three sites (between 11 and 14%), primarily due to increased interception of radiation as a result of accelerated canopy development. Evapotranspiration and irrigation requirements increased by 11% due to increased canopy cover and evaporative demand. Sucrose yields are expected to decline because of increased consumption of photo-assimilate for structural growth and maintenance respiration. Crop responses in canopy development and yield formation differed markedly between the crop cycles investigated. Possible agronomic implications of these results include reduced weed control costs due to shortened periods of partial canopy, a need for improved efficiency of irrigation to counter increased demands, and adjustments to ripening and harvest practices to counter decreased cane quality and optimize productivity. Although the Delta climate data downscaling method is considered robust, accurate and easily-understood, it does not change the future number of rain-days per month. The impacts of this and other climate data simplifications ought to be explored in future work. Shortcomings of the DSSAT-Canegro model include the simulated responses of phenological development, photosynthesis and respiration processes to high temperatures, and the disconnect between simulated biomass accumulation and expansive growth. Proposed methodology refinements should improve the reliability of predicted climate change impacts on sugarcane yield.

Climate change↗
NASA NTRS2014

Uncertainties in Predicting Rice Yield by Current Crop Models Under a Wide Range of Climatic Conditions

Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [CO2]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10 percent of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO2] and temperature.

crop-model ensembles↗
NASA NTRS2021

Faster Exo-Earth Yield for HabEx and LUVOIR via Extreme Precision Radial Velocity Prior Knowledge

The HabEx and LUVOIR mission concepts reported science yields for mission scenarios in which the instruments must search for potentially habitable planets, determine their orbits, and, if worthwhile, invest the integration time for a spectral characterization. We evaluate the impact of prior knowledge of planet existence and orbital parameters on yield for four mission concept architectures: HabEx 4m telescope with hybrid starshade and coronagraph, HabEx4m telescope with starshade only, HabEx 4m telescope with coronagraph only, and LUVOIR B8m telescope with coronagraph only. We use perfect prior knowledge to establish an upperbound on yield and use partial prior knowledge from a potential future extreme precision radial velocity (EPRV) instrument with3cm∕ssensitivity. We detail a modeling framework that per-forms dynamically responsive observation scheduling with realistic mission constraints. We evaluate exo-Earth yields against three metrics of spectral characterization for the four mission architectures and three levels of prior knowledge (none, partial, and perfect). The EPRV pro-vided prior knowledge increases yields by∼30%and accelerates by a factor of 3 to 6 the time to achieve half of the yield of the mission. Prior knowledge makes all the mission architectures more nimble and powerful, and most especially starshade-based architectures. With prior knowledge, a small telescope with a starshade can achieve comparable yield to a larger telescope with a coronagraph.

Rhonda Morgan↗
NASA NTRS2023

Future Climate Change Impacts on U.S. Agricultural Yields, Production, and Market

This study provides estimates of climate change impacts on U.S. agricultural yields and the agricultural economy through the end of the 21st century, utilizing multiple climate scenarios. Results from a process-based crop model project future increases in wheat, grassland, and soybean yield due to climate change and atmospheric CO2 change; corn and sorghum show more muted responses. Results using yields from econometric models show less positive results. Both the econometric and process-based models tend to show more positive yields by the end of the century than several other similar studies. Using the process-based model to provide future yield estimates to an integrated agricultural sector model, the welfare gain is roughly $16B/year (2019 USD) for domestic producers and $6.2B/year for international trade, but domestic consumers lose $10.6B/year, resulting in a total welfare gain of $11.7B/year. When yield projections for major crops are drawn instead from econometric models, total welfare losses of more than $28B/year arise. Simulations using the process-based model as input to the agricultural sector model show large future production increases for soybean, wheat, and sorghum and large price reductions for corn and wheat. The most important factors are those about economic growth, flooding, international trade, and the type of yield model used. Somewhat less, but not insignificant factors include adaptation, livestock productivity, and damages from surface ozone, waterlogging, and pests and diseases.

Agricultural sector↗
DOE OSTI2022

Accuracy of predictions made by machine learned models for biocrude yields obtained from hydrothermal liquefaction of organic wastes

Hydrothermal liquefaction (HTL) has potential for converting abundant wet organic wastes into renewable fuels. Because HTL consists of a complex reaction network, deterministic, physics-based prediction of its biocrude yield is prohibitively difficult. Data-driven methods provide an alternative to the physics-based approach; however, rigorous testing must be performed to ensure the accuracy of predictions made by data-driven methods. To this end, a data set was assembled consisting of 570 data points appearing in the open literature. The data set was divided into training, validation, and test sub-sets and used for evaluating different machine learning regression approaches to predict biocrude yield. Among the tested algorithms, Random Forest and eXtreme Gradient Boosting (XGBoost) predicted biocrude yields in a test set that had not been used for training with the greatest accuracy, with root mean square errors (RMSE) of 8.34 and 8.57, respectively. Further refinement of the Random Forest model reduced its RMSE to 8.07. In comparison, predictions of a series of literature models resulted in RMSE ranging from 9.16 in the most accurate case to 27.6 in the least accurate; most literature models yielded RMSE values > 10. Using biocrude yield predictions from the most accurate Random Forest model and a probabilistic economic analysis found that the model accuracy is sufficient to prioritize allocation of resources based on projected minimum fuel selling price. In our report the models and analysis represent a major advance in the ability to use readily available data to predict biocrude yields on new feedstocks that have not previously been studied.

42 ENGINEERING↗
DOE OSTI2021

Energy dependent calculations of fission product, prompt, and delayed neutron yields for neutron induced fission on 235 U, 238 U, and 239 Pu

In this article, we perform energy-dependent calculations of independent and cumulative fission product yields for 235 U, 238 U, and 239 Pu in the first chance fission region. Starting with the primary fission fragment distributions taken from available experimental data and analytical functions based on assumptions for the excitation energy and spin-parity distributions, the Hauser-Feshbach statistical decay treatment for fission fragment de-excitation is applied to more than 1,000 fission fragments for the incident neutron energies up to 5 MeV. The calculated independent yields are then used as an input of β-decay calculations to produce the cumulative yield, and summation calculations are performed. Model parameters in these procedures are adjusted by applying the Bayesian technique at the thermal energy for 235 U and 239 Pu and in the fast energy range for 238 U. The calculated fission observable quantities, such as the energy-dependent cumulative yields, and prompt and delayed neutron yields, are compared with available experimental data. We also study the possible impact of the second chance fission opening on the energy dependence of the delayed neutron yield by on the opening of second chance fission on the energy dependence calculation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Determination of gaseous fission product yields from 14 MeV neutron induced fission of 238 U at the National Ignition Facility

We determined fission yields of xenon ( 133m Xe, 135 Xe, 135m Xe, 137 Xe, 138 Xe, and 139 Xe) resulting from 14 MeV neutron induced fission of depleted uranium at the National Ignition Facility. Measurements begin approximately 20 s after shot time, and yields have been determined for nuclides with half-lives as short as tens of seconds. We determined the relative independent yields of 133m Xe, 135 Xe, and 135m Xe to significantly higher precision than previously reported. The relative fission yields of all nuclides are statistically indistinguishable from values reported by England and Rider (ENDF-349. LA-UR-94-3106, 1994), with exception of the cumulative yield of 139 Xe. Furthermore, considerable differences exist between our measured yields and the JEFF-3.1 database values.

14 MeV neutrons↗
DOE OSTI2018

Greenhouse gas emissions and crop yield in no-tillage systems: A meta-analysis

We report that no-tillage (NT) has been touted as one of several climate-smart agriculture (CSA) management practices that improve food security and enhance agroecosystem resilience to climate change. However, the sustainable effectiveness of NT greatly depends on trade-offs between NT-induced changes in crop yield and greenhouse gas (GHG, i.e. CH 4 , CO 2 , and N 2 O) emissions. Such trade-offs are regulated by climate fluctuations and heterogeneous soil conditions and have not been well addressed. Supporting CSA management decisions requires advancing our understanding of how NT affects crop yield and GHG emissions in different agroecological regions. In this study, a meta-analysis was conducted using 740 paired measurements from 90 peer-reviewed articles to assess the effects of NT on crop yield, GHG emissions, and the global warming potential (GWP) of major cereal cropping systems. Compared to conventional tillage (CT), NT reduced in GHG emissions and increased crop yield in dry, but not humid, climates, and reduced in the GWP at sites with acidic soils. Across different cropping systems, NT enhanced barley yield by 49%, particularly in dry climates, and it decreased the GWP of rice fields through a 22% reduction in both CO 2 and CH 4 emissions. Our synthesis suggests that NT is an effective CSA management practice because of its potential for climate change mitigation and crop yield improvement. However, the net effect of NT (relative to CT) was influenced by several environmental and agronomic factors (climatic conditions, tillage duration, soil texture, pH, crop species). Therefore, agroecological setting must be taken into consideration when conducting a comparative evaluation of different tillage practices.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Anisotropic yield models for lattice unit cell structures exploiting orthotropic symmetry

We report numerical homogenization enables efficient computational analysis and design of multiscale structures made of micro-architected materials including lattice unit cells. However, predicting yield is nontrivial because it requires accurate and efficient predictions of the maximum stress inside the homogenized unit cells. To address this challenge, we develop a macroscale anisotropic yield function for micro-architected materials. The yield function depends on the three-dimensional macroscale stress state and the parameters describing a family of micro-architectures, such as the radii of the struts in a lattice unit cell. To ensure accuracy, we determine the maximum stress using three-dimensional continuum finite-element analysis. To ensure efficiency, we construct surrogate models from the aforementioned high-fidelity results for yield prediction. To reduce simulation costs and surrogate modeling complexity, we leverage orthotropic symmetry commonly found in lattice unit cells. In this paper, we provide a thorough presentation of group representations and the systematic procedure to exploit orthotropic domain symmetry in homogenization and surrogate modeling. We illustrate the use of linear homogenization results to predict yield in specific unit cells without further simulations. We furthermore show that surrogate modeling presents a viable option for anisotropic yield prediction in a continuously parametrized family of micro-architectures. More specifically, despite the dimensionality and degree of nonlinearity in the maximum micro von Mises stress within the unit cell, the surrogate models can predict it with less than 5% error at least 90% of the time. Moreover, the largest under-prediction error, which is more critical than the over-prediction error, is typically less than 10%.

42 ENGINEERING↗
DOE OSTI2020

Yield strength prediction of high-entropy alloys using machine learning

Yield strength at high temperature is an important parameter in the design and application of high entropy alloys (HEAs). However, the experimental measurement of yield strength at high temperature is quite costly, complicated, and time-consuming. Therefore, it is essential to identify and apply a robust method for the accurate prediction of yield strength at high temperature from the available experimental and simulation data. In this study, for the first time, a machine learning (ML) method based on the regression technique of random forest (RF) regressor is used to predict the yield strength of HEAs at the desired temperature. Further, the yield strengths of MoNbTaTiW and HfMoNbTaTiZr at 800 °C and 1200 °C, are predicted using the RF regressor model. We find that the results are consistent with the experimental reports, showing that the RF regressor model predicts the yield strength of HEAs at the desired temperatures with high accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Analysis of incident-energy dependence of delayed neutron yields in actinides

The changes of delayed neutron yields (ν{sub d}) of Actinides have been analyzed for incident energy up to 20MeV using realized data of precursor after prompt neutron emission, from semi-empirical model, and delayed neutron emission probability data (P{sub n}) to carry out a summation method. The evaluated nuclear data of the delayed neutron yields of actinide nuclides are still uncertain at the present and the cause of the energy dependence has not been fully understood. In this study, the fission yields of precursor were calculated considering the change of the fission fragment mass yield based on the superposition of fives Gaussian distribution; and the change of the prompt neutrons number associated with the incident energy dependence. Thus, the incident energy dependent behavior of delayed neutron was analyzed.The total number of delayed neutron is expressed as ν{sub d}=∑Y{sub i} • P{sub ni} in the summation method, where Y{sub i} is the mass yields of precursor i and P{sub ni} is the delayed neutron emission probability of precursor i. The value of Y{sub i} is derived from calculation of post neutron emission mass distribution using 5 Gaussian equations with the consideration of large distribution of the fission fragments. The prompt neutron emission ν{sub p} increases at higher incident-energy but there are two different models; one model says that the fission fragment mass dependence that prompt neutron emission increases uniformly regardless of the fission fragments mass; and the other says that the major increases occur at heavy fission fragments area. In this study, the changes of delayed neutron yields by the two models have been investigated.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗