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DOE OSTI2019

An Evolutionary Yield Function Model Based on Plastic Work and Non-Associated Flow Rule

A constitutive law was developed based on the evolutionary yield function to account for the evolution of anisotropy induced by the plastic deformation. For the effective description of anisotropy, the yield stress function and plastic potential were separately defined based on the non-associated flow rule. In particular, for the description of the equivalent status, the accumulated plastic work was employed as an alternative to the accumulated plastic strain. Numerical formulations based on the plastic work were also derived in case the hardening rule, as well as the evolution of the plastic potential and yield stress function, were defined in terms of the plastic work. The developed constitutive law was characterized using the mechanical properties of the multi-phase BAO QP980 steel and niobium sheets at room temperature. From the uniaxial tension tests and the balanced biaxial tension test, separate sets of anisotropic coefficients for each of the plastic potential and yield stress functions were obtained as a function of the plastic work. By comparing with non-evolving yield functions, the importance of the developed constitutive law to properly describe the evolution of the plastic potential and yield function were validated.

Park, Taejoon↗

DOE OSTI2018

Secondary organic aerosol (SOA) yields from NO 3 radical + isoprene based on nighttime aircraft power plant plume transects

Nighttime reaction of nitrate radicals ( NO 3 ) with biogenic volatile organic compounds (BVOC) has been proposed as a potentially important but also highly uncertain source of secondary organic aerosol (SOA). The southeastern United States has both high BVOC and nitrogen oxide ( NO x ) emissions, resulting in a large model-predicted NO 3 -BVOC source of SOA. Coal-fired power plants in this region constitute substantial NO x emissions point sources into a nighttime atmosphere characterized by high regionally widespread concentrations of isoprene. In this paper, we exploit nighttime aircraft observations of these power plant plumes, in which NO 3 radicals rapidly remove isoprene, to obtain field-based estimates of the secondary organic aerosol yield from NO 3 + isoprene. Observed in-plume increases in nitrate aerosol are consistent with organic nitrate aerosol production from NO 3 + isoprene, and these are used to determine molar SOA yields, for which the average over nine plumes is 9 % ( ±5 %). Corresponding mass yields depend on the assumed molecular formula for isoprene- NO 3 -SOA, but the average over nine plumes is 27 %( ±14 %), on average larger than those previously measured in chamber studies (12 %–14 % mass yield as Δ OA / Δ VOC after oxidation of both double bonds). Yields are larger for longer plume ages.This suggests that ambient aging processes lead more effectively to condensable material than typical chamber conditions allow. We discuss potential mechanistic explanations for this difference, including longer ambient peroxy radical lifetimes and heterogeneous reactions of NO 3 -isoprene gas phase products. As a result more in-depth studies are needed to better understand the aerosol yield and oxidation mechanism of NO 3 radical + isoprene, a coupled anthropogenic–biogenic source of SOA that may be regionally significant.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1977

Application of wheat yield model to United States and India

The author has identified the following significant results. The wheat yield model was applied to the major wheat-growing areas of the US and India. In the US Great Plains, estimates from the winter and spring wheat models agreed closely with USDA-SRS values in years with the lowest yields, but underestimated in years with the highest yields. Application to the Eastern Plains and Northwest indicated the importance of cultural factors, as well as meteorological ones in the model. It also demonstrated that the model could be used, in conjunction with USDA-SRRS estimates, to estimate yield losses due to factors not included in the model, particularly diseases and freezes. A fixed crop calendar for India was built from a limited amount of available plot data from that country. Application of the yield model gave measurable evidence that yield variation from state to state was due to different mixes of levels of meteorological and cultural factors.

Feyerherm, A. M.↗

NASA NTRS1978

Large Area Crop Inventory Experiment (LACIE). Feasibility of assessing crop condition and yield from LANDSAT data

The author has identified the following significant results. Yield modelling for crop production estimation derived a means of predicting the within-a-year yield and the year-to-year variability of yield over some fixed or randomly located unit of area. Preliminary studies indicated that the requirements for interpreting LANDSAT data for yield may be sufficiently similar to those of signature extension that it is feasible to investigate the automated estimation of production. The concept of an advanced yield model consisting of both spectral and meteorological components was endorsed. Rationale for using meteorological parameters originated from known between season and near harvest dynamics in crop environmental-condition-yield relationships.

Source record↗

NASA NTRS1979

The Relationship of Red and Photographic Infrared Spectral Data to Grain Yield Variation Within a Winter Wheat Field

Two band hand-held radiometer data from a winter wheat field, collected on 21 dates during the spring growing season, were correlated within field final grain yield. Significant linear relationships were found between various combinations of the red and photographic infrared radiance data collected and the grain yield. The spectral data explained approximately 64 percent of the within field grain yield variation. This variation in grain yield could not be explained using meteorological data as these were similar for all areas of the wheat field. Most importantly, data collected early in the spring were highly correlated with grain yield, a five week time window existed from stem elongation through antheses in which the spectral data were most highly correlated with grain yield, and manifestations of wheat canopy water stress were readily apparent in the spectral data.

Tucker, C. J.↗

NASA NTRS1980

Relationship of spectral data to grain yield variation

Two-band hand-held radiometer data from a winter wheat field, collected on 21 dates during the spring growing season, were correlated with within-field final grain yield. Significant linear relationships were found between various combinations of the red and photographic infrared radiance data collected and the grain yield. The spectral data explained about 64 percent of the within-field grain yield variation. This variation in grain yield could not be explained using meteorological data as these were similar for all areas of the wheat field. Most importantly, data collected early in the spring were highly correlated with grain yield, a five-week time window existed from stem elongation through anthesis in which the spectral data were most highly correlated with grain yield, and manifestations of wheat canopy water stress were readily apparent in the spectral data.

Tucker, C. J.↗

NASA NTRS1985

An experimental study of biaxial yield in modified 9Cr-1Mo steel at room temperature

Described are two biaxial experiments which investigated yield, hardening, and flow behavior in modified 9Cr-1Mo steel at room temperature. The aim of these experiments was to determine whether the procedures recommended in NE Standard F9-5T for inelastic design analysis are applicable for this material in normalized and tempered condition. The first experiment investigated small offset yield behavior subsequent to radial preloads (sq rt of 3 sub sigma 12 = sub sigma 11) in tension-torsion stress space. The second experiment investigated yield behavior subsequent to nonradial preloads and also the time-dependent flow occurring during 0.5 hour periods at constant stress. The results of these experiments were qualitatively similar to those obtained earlier for types 304 and 316 stainless steel. Specifically, the von Mises yield criterion was found to provide a reasonable approximation of initial yield behavior. Although the subsequent yield surfaces suffered considerable distortion from their near-circular form after both radial and nonradial preloads, the hardening behavior was to the first order kinematic in nature. The strain-time data obtained during the 0.5 hr hold periods showed characteristics typical of creep curves. As in the case of earlier experiments, the high initial flow rates diminished more rapidly than would be estimated from elevated temperature data.

Ellis, J. R.↗

NASA NTRS1998

Determination of Yield in Inconel 718 for Axial-Torsional Loading at Temperatures up to 649 C

An experimental program has been implemented to determine small offset yield loci under axial-torsional loading at elevated temperatures. The nickel-base superalloy Inconel 718 (IN718) was chosen for study due to its common use in aeropropulsion applications. Initial and subsequent yield loci were determined for solutioned IN718 at 23, 371, and 454 C and for aged (precipitation hardened) IN718 at 23 and 649 C. The shape of the initial yield loci for solutioned and aged IN718 agreed well with the von Mises prediction. However, in general, the centers of initial yield loci were eccentric to the origin due to a strength-differential (S-D) effect that increased with temperature. Subsequent yield loci exhibited anisotropic hardening in the form of translation and distortion of the locus. This work shows that it is possible to determine yield surfaces for metallic materials at temperatures up to at least 649 C using multiple probes of a single specimen. The experimental data is first-of-its-kind for a superalloy at these very high temperatures and will facilitate a better understanding of multiaxial material response, eventually leading to improved design tools for engine designers.

Gil, Christopher M.↗

NASA NTRS1998

Determination of Yield in Inconel 718 for Axial-Torsional Loading at Temperatures up to 649 C

Gil, Christopher M.↗

NASA NTRS2017

Molecular Based Temperature and Strain Rate Dependent Yield Criterion for Anisotropic Elastomeric Thin Films

A molecular formulation of the onset of plasticity is proposed to assess temperature and strain rate effects in anisotropic semi-crystalline rubbery films. The presented plane stress criterion is based on the strain rate-temperature superposition principle and the cooperative theory of yielding, where some parameters are assumed to be material constants, while others are considered to depend on specific modes of deformation. An orthotropic yield function is developed for a linear low density polyethylene thin film. Uniaxial and biaxial inflation experiments were carried out to determine the yield stress of the membrane via a strain recovery method. It is shown that the 3% offset method predicts the uniaxial elastoplastic transition with good accuracy. Both the tensile yield points along the two principal directions of the film and the biaxial yield stresses are found to obey the superposition principle. The proposed yield criterion is compared against experimental measurements, showing excellent agreement over a wide range of deformation rates and temperatures.

Bosi, F.↗

NASA NTRS2018

Evaluation of Near-Surface Air Temperature from Reanalysis over the United States and Ukraine: Application to Winter Wheat Yield Forecasting

In this work we evaluate the near-surface air temperature datasets from the ERA-Interim, JRA55, MERRA2, NCEP1, and NCEP2 reanalysis projects. Reanalysis data were first compared to observations from weather stations located on wheat areas of the United States and Ukraine, and then evaluated in the context of a winter wheat yield forecast model. Results from the comparison with weather station data showed that all datasets performed well (r2>0.95) and that more modern reanalysis such as ERAI had lower errors (RMSD ~ 0.9) than the older, lower resolution datasets like NCEP1 (RMSD ~ 2.4). We also analyze the impact of using surface air temperature data from different reanalysis products on the estimations made by a winter wheat yield forecast model. The forecast model uses information of the accumulated Growing Degree Day (GDD) during the growing season to estimate the peak NDVI signal. When the temperature data from the different reanalysis projects were used in the yield model to compute the accumulated GDD and forecast the winter wheat yield, the results showed smaller variations between obtained values, with differences in yield forecast error of around 2% in the most extreme case. These results suggest that the impact of temperature discrepancies between datasets in the yield forecast model get diminished as the values are accumulated through the growing season.

GSOD↗

NASA NTRS2019

Evaluation of an Alternate Method for Determining Yield Strength Offset Values for Selective Laser Sintered Polymeric Materials

Due to the unique characteristics of Additively Manufactured (AM) polymeric materials, typical mechanical strength characterization methods such as those commonly used for traditionally-processed polymers or composite materials can produce results that do not accurately represent material capabilities. In order to characterize mechanical properties of these materials, new test and analysis methods are required. As part of the National Aeronautics and Space Administration (NASA) Advanced Composites Project (ACP), Boeing has evaluated true yield testing as an alternative or complimentary test to 0.2% offset yield testing for determining appropriate yield strength values of polymer materials. Previous testing has shown high strain, low modulus polymer materials such as selective laser sintered (SLS) Nylon 11 at elevated temperatures produce large variations in yield strength. The true yield test method was successful in finding the applied strain level when yield commences and appears to offer an increase in data robustness.

Henry, Chelsey↗

DOE OSTI2012

Application of linear multiple model predictive control (MMPC) framework towards dynamic maximazation of oxygen yield in an elevated-pressure air separation unit

In a typical air separation unit (ASU) utilizing either a simple gaseous oxygen (GOX) cycle or a pumped liquid oxygen (PLOX) cycle, the flowrate of liquid nitrogen (LN2) stream connecting high-pressure and low-pressure ASU columns plays an important role in the total oxygen yield. It has been observed that this yield reaches a maximum at a certain optimal flowrate of LN2 stream. At nominal full-load operation, the flowrate of LN2 stream is maintained near this optimum value, whereas at part-load conditions this flowrate is typically modified in proportion with the load-change (oxygen demand) through a ratio/feed-forward controller. Due to nonlinearity in the entire ASU process, the ratio-modified LN2 flowrate does not guarantee an optimal oxygen yield at part-load conditions. This is further exacerbated when process disturbances in form of “cold-box” heat-leaks enter the system. To address this problem of dynamically maximizing the oxygen yield while the ASU undergoes a load-change and/or a process disturbance, a multiple model predictive control (MMPC) algorithm is proposed. This approach has been used in previous studies to handle large ramp-rates of oxygen demand posed by the gasifier in an IGCC plant. In this study, the proposed algorithm uses linear step-response “blackbox” models surrounding the operating points corresponding to maximum oxygen yield points at different loads. It has been shown that at any operating point of the ASU, the MMPC algorithm, through model-weight calculation based on plant measurements, naturally and continuously selects the dominant model(s) corresponding to the current plant state, while making control-move decisions that approach the maximum oxygen yield point. This dynamically facilitates less energy consumption in form of compressed feed-air compared to a simple ratio control during load-swings. In addition, since a linear optimization problem is solved at each time step, the approach involves much less computational cost compared to a firstprinciple based nonlinear MPC. Introduction

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Analysis of fission-product energy release after thermal-neutron fission of {sup 235}U and {sup 239}Pu by scale-6.1.3 with recently available fission product yield data - 14419

The fission-product energy release after the reactor shutdown has been an important issue from the perspective of the nuclear reactor safety in a very short time. The energy release from the decay of fission products can be estimated using a summation calculation method implemented in various burnup and depletion programs. The method generally depends on the nuclear structure data such as the decay and fission product yield data. In this study, the decay heat measurements after thermal-neutron fission of {sup 235}U and {sup 239}Pu have been evaluated by the ORIGEN-S with the decay and fission product yield libraries included in the SCALE-6.1.3 code package. In order to verify the importance of the fission product yield data when estimating the decay heats, new fission product yields libraries for ORIGEN-S have been generated based on the recently available fission product yield data such as ENDF/B-VII.1, JEFF-3.1.1, JENDL/FPY-2011, and JENDL-4.0. The new libraries were applied to the decay heat calculations, and the results were compared with those of the reference calculation based on the yield library that belongs to the SCALE-6.1.3. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2024

Investigating the Strength and Variability of El Niño Southern Oscillation Teleconnections to Hydroclimate and Maize Yields in Southern and East Africa

The state of the El Niño Southern Oscillation (ENSO) is critical for seasonal climate forecasts, but recent events diverged substantially from expectations in many regions, including Sub-Saharan Africa where seasonal forecasts are critical tools for addressing food security. Here, we evaluate 39 years (1982–2020) of data on hydroclimate, leaf area index, and maize yields to investigate the strength of ENSO teleconnections in southern and East Africa. Teleconnections to precipitation, soil moisture, and leaf area index are generally stronger during ENSO phases that cause drought conditions (El Niño in southern Africa and La Niña in East Africa), with seasonality that aligns well with the maize growing seasons. Within maize growing areas, however, ENSO teleconnections to hydroclimate and vegetation are generally weaker compared to the broader geographic regions, especially in East Africa. There is also little evidence that the magnitude of the ENSO event affects the hydroclimate or vegetation response in these maize regions. Maize yields in Kenya, Malawi, South Africa, and Zimbabwe all correlate significantly with hydroclimate and leaf area index, with South Africa and Zimbabwe showing the strongest and most consistent yield responses to ENSO events. Our results highlight the chain of causality from El Niño and La Niña forcing of regional anomalies in hydroclimate to vegetation health and maize yields in southern and East Africa. The large spread across individual ENSO events, however, underscores the limitations of this climate mode for seasonal climate prediction in the region, and the importance of finding additional sources of skill for improving climate and yield forecasts.

El Niño Southern Oscillation↗

DOE OSTI2021

Strategies to achieve high productivity, high conversion, and high yield in yeast fermentation of algal biomass hydrolysate

The conversion of carbohydrates in biomass via fermentation is an important component of an overall strategy to decarbonize the production of fuels and chemicals. Owing to the cost and resources required to produce biomass hydrolysates, the economic and environmental sustainability of these fermentation processes requires that they operate with high yields, sugar conversion, and productivity. Immobilized-cell technology in a continuous bioprocess can achieve significantly higher volumetric productivities than is possible from standard batch fermentation using free cells. Here, we demonstrate approaches for improvement of ethanol yield from algal hydrolysates and a mock hydrolysate medium. Saccharomyces cerevisiae was immobilized in alginate and incorporated into a two-column immobilized cell reactor system. Furthermore, the yeast quorum-sensing molecule, 2-phenylethanol, was added to improve ethanol yield by restricting growth and diverting sugar to ethanol. The bioreactor system could achieve high ethanol volumetric productivity (>20 g/ Lreactor ·h) and high glucose conversion (>99%) in mock hydrolysate, while the addition of 0.2% 2-phenylethanol resulted in 4.9% higher ethanol yield. With an algal hydrolysate of <10 g/L sugar, the ethanol volumetric productivity reached 9.8 g/ Lreactor ·h, and the addition of 0.2% 2-phenylethanol increased the ethanol yield by up to 7.4%. These results demonstrate the feasibility of novel strategies to achieve sustainability goals in biomass conversions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

An adaptive adversarial domain adaptation approach for corn yield prediction

Recently, statistical machine learning and deep learning methods have been widely explored for corn yield prediction. Though successful, machine learning models generated within a specific spatial domain often lose their validity when directly applied to new regions. To address this issue, we designed an unsupervised adaptive domain adversarial neural network (ADANN). Specifically, through domain adversarial training, the ADANN model reduced the impact of domain shift by projecting data from different domains into the same subspace. Also, the ADANN model was designed to be trained in an adaptive way, which guaranteed the model can learn the domain-invariant features and perform accurate yield prediction simultaneously. Informative variables including time-series vegetation indices and sequential weather observations were first collected from multiple data sources and aggregated to the county level. Then, we trained the ADANN model with the extracted features and corresponding reported county-level corn yield from the U.S. Department of Agriculture (USDA). Finally, the trained model was evaluated in four testing years 2016–2019. The U.S. corn belt was used as the study area and counties under study were grouped into two diverse ecological regions. Overall, the experimental results showed that the developed ADANN model had better performance than three other state-of-the-art machine learning models in both local experiments (train and test in the same region) and transfer experiments (train and test in different regions). As the first study using adversarial learning for crop yield prediction, this research demonstrates a novel solution for improving model transferability on crop yield prediction.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

Collaborative simulations and experiments for a novel yield model of coal devolatilization in oxy-coal combustion conditions

Oxy-coal combustion is an emerging low-cost “clean coal” technology for emissions reduction and Carbon Capture and Sequestration (CCS). The use of Computational Fluid Dynamics (CFD) tools is crucial for the development of cost-effective oxy-fuel technologies and the minimization of environmental concerns at industrial scale. The coupling of detailed chemistry models and CFD simulations is still challenging, especially for large-scale plants, because of the high computational efforts required. The development of scale-bridging models is therefore necessary, to find a good compromise between computational efforts and the physical-chemical modeling precision. This paper presents a procedure for scale-bridging modeling of coal devolatilization, in the presence of experimental error, that puts emphasis on the thermodynamic aspect of devolatilization, namely the final volatile yield of coal, rather than kinetics. The procedure consists of an engineering approach based on dataset consistency and Bayesian methodology including Gaussian-Process Regression (GPR). Experimental data from devolatilization tests carried out in an oxy-coal entrained flow reactor were considered and CFD simulations of the reactor were performed. Jointly evaluating experiments and simulations, a novel yield model was validated against the data via consistency analysis. In parallel, a Gaussian-Process Regression was performed, to improve the understanding of the uncertainty associated to the devolatilization, based on the experimental measurements. Potential model forms that could predict yield during devolatilization were obtained. The set of model forms obtained via GPR includes the yield model that was proven to be consistent with the data. Finally, the overall procedure has resulted in a novel yield model for coal devolatilization and in a valuable evaluation of uncertainty in the data, in the model form, and in the model parameters.

01 COAL, LIGNITE, AND PEAT↗