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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Hydrodynamic trait coordination and cost–benefit trade‐offs throughout the isohydric–anisohydric continuum in trees

Abstract Whole‐plant hydraulic performance depends on the integrated function of trait complexes, such as embolism resistance, stomatal closure mechanisms, and root properties. The diversity of such traits produces a wide range of response strategies to both short‐term variation of environmental conditions and long‐term changes to climate and hydrological cycles. This study aims to assess the role of different emergent hydraulic trait combinations in trees' vulnerability to drought using a quantitative modelling framework. This modelling framework may be helpful in studying the influence of plant hydraulic traits independently. It will also be used to assess how different groups of traits interact to form viable hydraulic strategies in response to different environmental conditions. We use the advanced plant hydrodynamic model, finite‐difference ecosystem‐scale tree crown hydrodynamics model version 2, to resolve plant functional traits of roots, stems, and leaves and simulate the transpiration. We define a multidimensional hydraulic “strategy space” by considering a continuum of these traits to test the outcomes of different hypothetical strategies in response to environmental conditions as observed in Northern Michigan, USA. We evaluate the degree to which simulated trees suffer hydraulic failure due to cavitation or carbon deficiency in response to reduced stomatal conductance. Our results demonstrate how the relationship between plant hydraulic strategy and hydraulic safety margin emerges from trait combinations along different tissue levels. Our findings suggest that hydrodynamic models present an exciting new possibility to define and study plant traits and hydrodynamics.

Mirfenderesgi, Golnazalsadat↗
NASA NTRS1994

Tomato root growth, gravitropism, and lateral development: correlation with auxin transport

Tomato (Lycopersicon esculentum, Mill.) roots were analyzed during growth on agar plates. Growth of these roots was inhibited by the auxin transport inhibitors naphthylphthalamic acid (NPA) and semicarbazone derivative I (SCB-1). The effect of auxin transport inhibitors on root gravitropism was analyzed by measurement of the angle of gravitropic curvature after the roots were reoriented 90 degrees from the vertical. NPA and SCB-1 abolished both the response of these roots to gravity and the formation of lateral roots, with SCB-1 being the more effective at inhibition. Auxins also inhibited root growth. Both auxins tested has a slight effect on the gravity response, but this effect is probably indirect, since auxins reduced the growth rate. Auxins also stimulated lateral root growth at concentration where primary root growth was inhibited. When roots were treated with both IAA and NPA simultaneously, a cumulative inhibition of root growth was found. When both compounds were applied together, analysis of gravitropism and lateral root formation indicated that the dominant effect was exerted by auxin transport inhibitors. Together, these data suggest a model for the role of auxin transport in controlling both primary and lateral root growth.

Non-NASA Center↗
NASA NTRS2010

Application of the Tor Vergata Scattering Model to L Band Backscatter During the Corn Growth Cycle

At the USDA's Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) experimental site in Beltsville, Maryland, USA) a field campaign took place throughout the 2002 corn growth cycle from May 10th (emergence of corn crops) to October 2nd (harvest). One of the microwave instruments deployed was the multi-frequency (X-, C- and L-band) quad-polarized (HH, HV, VV, VH) NASA GSFC/George Washington University (GWU) truck mounted radar. During the field campaign, this radar system provided once a week fully polarized C- and L-band (4.75 and 1.6 GHz) backscatter measurements from incidence angle of 15, 35, and 55 degrees. In support of microwave observations, an extensive ground characterization took place, which included measurements of surface roughness, soil moisture, vegetation biomass and morphology. The field conditions during the campaign are characterized by several dry downs with a period of drought in the month of August. Peak biomass the corn canopies was reached on July 24th with a total biomass of approximately 6.5 kg/sq m. This dynamic range in both soil moisture and vegetation conditions within the data set is ideal for the validation of discrete medium vegetation scattering models. In this study, we compare the L band backscatter measurements with simulations by the Tor Vergata model (ferrazzoli and Guerriero 1996). The measured soil moisture, vegetation biomass and most reliably measured vegetation morphological parameters (e.g. number of leaves, number of stems and stem height) were used as input for the Tor Vergata model. The more uncertain model parameters (e.g. surface roughness, leaf thickness) and the stem diameter were optimized using a parameter estimation routine based on the Levenberg-Marquardt algorithm. As cost function for this optimization, the HH and VV polarized backscatter measured and stimulated by the TOR Vergata model for incidence angle of 15, 35, and 55 degrees were used (6 measurements in total). The calibrated Tor Vergata model simulations are in excellent agreement with the measurements of Root Mean Squared Differences (RMSD's) of 0.8, 0.9 and 1.4 dB for incidences of 15, 35 and 55 degrees, respectively. The results from this study that a physically based scattering model with the appropriate parameterization can accurately simulate backscatter measurements and, as such, have the potential of being used for the retrieval of biophysical variables (e.g. soil moisture and vegetation biomass).

Joseph, A. T.↗
DOE OSTI2025

Coarsening dynamics of Ising-nematic order in a frustrated Heisenberg antiferromagnet

We study the phase ordering dynamics of the classical antiferromagnetic 𝐽 1 −𝐽 2 (nearest-neighbor and next-nearest-neighbor couplings) Heisenberg model on the square lattice in the strong frustration regime (𝐽 2 /𝐽 1 > 1/2). While thermal fluctuations preclude any long-range magnetic order at finite temperatures, the system exhibits a long-range spin-driven nematic phase at low temperatures. The transition into the nematic phase is further shown to belong to the two-dimensional Ising universality class based on the critical exponents near the phase transition. Our large-scale stochastic Landau-Lifshitz-Gilbert simulations find a two-stage phase ordering when the system is quenched from a high-temperature paramagnetic state into the nematic phase. In the early stage, collinear alignments of spins lead to a locally saturated Ising-nematic order. Once domains of well-defined Ising order are developed, the late-stage relaxation is dominated by curvature-driven domain coarsening, as described by the Allen-Cahn equation. The characteristic size of Ising-nematic domains scales as the square root of time, similar to the kinetic Ising model described by the time-dependent Ginzburg-Landau theory. Our results confirm that the late-stage ordering kinetics of the spin-driven nematic, which is a vestigial order of the frustrated Heisenberg model, belongs to the dynamical universality class of a nonconserved Ising order. Interestingly, the system shows no violation of the superuniversality hypothesis under weak bond disorder. The dynamic scaling invariance is preserved in the presence of weak bond disorder. Here, we also discuss possible applications of our results to materials for which vestigial Ising-nematic order is realized.

Antiferromagnets↗
DOE OSTI2019

Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm

With the development of data-driven techniques, building energy prediction on a district level has attracted increasing attention in recent years for revealing energy use patterns and reduction potentials. However, data acquisition in large building groups is difficult and adjacent buildings also interact with each other. To reduce data cost and incorporate the inter-building impact with the data-driven building energy model, this study proposes a deep learning predictive approach that fuses the building network model with a long short-term memory learning model for district-scale building energy modeling. The building network was constructed based on correlations between the energy use intensity of buildings, which can significantly reduce the computational complexity of the deep learning models for energy dynamic prediction. Five typical building groups with energy use data from 2015 to 2018 on two institutional campuses were selected to perform the validation experiment with TensorFlow. Based on the prediction error assessments, the results suggest that for total building energy use intensity prediction, the proposed model can achieve a mean absolute percentage error of 6.66% and a root mean square error of 0.36 kWh/m 2 , compared to 12.05% and 0.63 kWh/m 2 of the conventional artificial neural network model and to 11.06% and 0.89 kWh/m 2 for the support vector regression model.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Estimating and Calibrating DER Model Parameters Using Levenberg–Marquardt Algorithm in Renewable Rich Power Grid

The proliferation of inverter-based distributed energy resources (IBDERs) has increased the number of control variables and dynamic interactions, leading to new grid control challenges. For stability analysis and designing appropriate protection controls, it is important that IBDER models are accurate. This paper focuses on the accurate estimation and parameter calibration of DER_A, a recently proposed aggregated IBDER model. In particular, we focus on the parameters of the reactive power–voltage regulation module. We formulate the problem of parameter tuning as a non-linear least square minimization problem and solve it using the Levenberg–Marquardt (LM) method. The LM method is primarily chosen due to its flexibility in adaptively selecting between the steepest descent and Gauss–Newton methods through a damping parameter. The LM approach is used to minimize the error between the actual measurements and the estimated response of the model. Further, the computational challenges posed by the numerical calculation of the Jacobian are tackled using a quasi-Newton root-finding approach. The proposed method is validated on a real feeder model in the northeastern part of the United States. The feeder is modeled in OpenDSS and the measurements thus obtained are fed to the DER_A model for calibration. The simulation results indicate that our approach is able to successfully calibrate the relevant model parameters quickly and with high accuracy, with a total sum of square error of 3.57 × 10 –7 .

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1981

Relative performance of algorithms for autonomous satellite orbit determination

Limited word size in contemporary microprocessors causes numerical problems in autonomous satellite navigation applications. Numerical error introduced in navigation computations performed on small wordlength machines can cause divergence of sequential estimation algorithms. To insure filter reliability, square root algorithms have been adopted in many applications. The optimal navigation algorithm requires a careful match of the estimation algorithm, dynamic model, and numerical integrator. In this investigation, the relationship of several square root filters and numerical integration methods is evaluated to determine their relative performance for satellite navigation applications. The numerical simulations are conducted using the Phase I GPS constellation to determine the orbit of a LANDSAT-D type satellite. The primary comparison is based on computation time and relative estimation accuracy.

Tapley, B. D.↗
NASA NTRSDate not supplied

Characterization and Modeling of Large Collapsible Tubular Mast Booms

This paper documents the analysis of high-strain composite booms in the joint NASA-German Aerospace Center (DLR) Deployable Composite Booms (DCB) project. The collapsible tubular mast (CTM) booms were manufactured at NASA Langley Research Center, where constituent material coupons were also characterized, and shipped to DLR in Germany for testing of beam properties and the strength of the DLR flight-like root deployer design. With this data, a finite element model of the booms was built, calibrated to one set of experimental measurements, and used to produce a simplified beam model for further engineering studies. The process of building this beam model and the treatment of gaps in experimental characterization are presented.

composite materials↗
NASA NTRS2005

An Improved Analysis of Forest Carbon Dynamics using Data Assimilation

There are two broad approaches to quantifying landscape C dynamics - by measuring changes in C stocks over time, or by measuring fluxes of C directly. However, these data may be patchy, and have gaps or biases. An alternative approach to generating C budgets has been to use process-based models, constructed to simulate the key processes involved in C exchange. However, the process of model building is arguably subjective, and parameters may be poorly defined. This paper demonstrates why data assimilation (DA) techniques - which combine stock and flux observations with a dynamic model - improve estimates of, and provide insights into, ecosystem carbon (C) exchanges. We use an ensemble Kalman filter (EnKF) to link a series of measurements with a simple box model of C transformations. Measurements were collected at a young ponderosa pine stand in central Oregon over a 3-year period, and include eddy flux and soil C02 efflux data, litterfall collections, stem surveys, root and soil cores, and leaf area index data. The simple C model is a mass balance model with nine unknown parameters, tracking changes in C storage among five pools; foliar, wood and fine root pools in vegetation, and also fresh litter and soil organic matter (SOM) plus coarse woody debris pools. We nested the EnKF within an optimization routine to generate estimates from the data of the unknown parameters and the five initial conditions for the pools. The efficacy of the DA process can be judged by comparing the probability distributions of estimates produced with the EnKF analysis vs. those produced with reduced data or model alone. Using the model alone, estimated net ecosystem exchange of C (NEE)= -251 f 197g Cm-2 over the 3 years, compared with an estimate of -419 f 29gCm-2 when all observations were assimilated into the model. The uncertainty on daily measurements of NEE via eddy fluxes was estimated at 0.5gCm-2 day-1, but the uncertainty on assimilated estimates averaged 0.47 g Cm-2 day-1, and only exceeded 0.5gC m-2 day-1 on days where neither eddy flux nor soil efflux data were available. In generating C budgets, the assimilation process reduced the uncertainties associated with using data or model alone and the forecasts of NEE were statistically unbiased estimates. The results of the analysis emphasize the importance of time series as constraints. Occasional, rare measurements of stocks have limited use in constraining the estimates of other components of the C cycle. Long time series are particularly crucial for improving the analysis of pools with long time constants, such as SOM, woody biomass, and woody debris. Long-running forest stem surveys, and tree ring data, offer a rich resource that could be assimilated to provide an important constraint on C cycling of slow pools. For extending estimates of NEE across regions, DA can play a further important role, by assimilating remote-sensing data into the analysis of C cycles. We show, via sensitivity analysis, how assimilating an estimate of photosynthesis - which might be provided indirectly by remotely sensed data - improves the analysis of NEE.

Williams, Mathew↗
NASA NTRSDate not supplied

Characterization and Modeling of Large Collapsible Tubular Mast Booms

This paper will document the analysis of high-strain composite booms in the joint NASA-German Aerospace Center (DLR) Deployable Composite Booms (DCB) project. The collapsible tubular mast (CTM) booms were manufactured at NASA Langley Research Center, where constituent material coupons were also characterized, and shipped to DLR in Germany for testing of beam properties and the strength of the DLR flight-like root deployer design. With this data, a finite element model of the booms was built, calibrated to one set of experimental measurements, and used to produce a simplified beam model for further engineering studies.The final paper will discuss the process of building this beam model and the treatment of gaps in experimental characterization.

Olive R. Stohlman↗
NASA NTRS2017

VHF SoOp (Signal of Opportunity) Technology Demonstration for Soil Moisture Measurement Using Microwave Hydraulic Boom Truck Platform

A goal of this research is to test deployable VHF antennas for 6U Cubesat platforms to enable validation of root zone soil moisture (RZSM) estimation algorithms for signal of opportunity (SoOp) remote sensing over the 240-270 MHz frequency band. The proposed work provides a strong foundation for establishing a technology development path for maturing a global direct surface soil moisture (SM) and RZSM measurement system over a variety of land covers. Knowledge of RZSM up to a depth of 1 meter and surface SM up to a depth of 0.05 meter on a global scale, at a spatial resolution of 1-10 km through moderate-to-heavy vegetation, is critical to understanding global water resources and the vertical moisture gradient in the Earths surface layer which controls moisture interactions between the soil, vegetation, and atmosphere. Current observations of surface SM from space by L-band radiometers (1.4 GHz) and radars (1.26 GHz) are limited to measurements of surface SM up to a depth of 0.05 meter through moderate amounts of vegetation. This limitation is mainly due to the inability of L-band signals to penetrate through dense vegetation and deep into the soil column. Satellite observations of the surface moisture conditions are coupled to sophisticated models which extrapolate the surface SM into the root zone, thus providing an indirect estimate rather than a direct measurement of RZSM. To overcome this limitation, low-frequency airborne radars operating at 435 MHz and 118 MHz have been investigated, since these lower frequencies should penetrate denser vegetation and respond to conditions deeper in the soil.

SoOp↗
DOE OSTI2022

Development of advanced machine learning models for analysis of plutonium surrogate optical emission spectra

This work investigates and applies machine learning paradigms seldom seen in analytical spectroscopy for quantification of gallium in cerium matrices via processing of laser-plasma spectra. Ensemble regressions, support vector machine regressions, Gaussian kernel regressions, and artificial neural network techniques are trained and tested on cerium-gallium pellet spectra. A thorough hyperparameter optimization experiment is conducted initially to determine the best design features for each model. The optimized models are evaluated for sensitivity and precision using the limit of detection (LoD) and root mean-squared error of prediction (RMSEP) metrics, respectively. Gaussian kernel regression yields the superlative predictive model with an RMSEP of 0.33% and an LoD of 0.015% for quantification of Ga in a Ce matrix. This study concludes that these machine learning methods could yield robust prediction models for rapid quality control analysis of plutonium alloys.

Rao, Ashwin P. (ORCID:0000000319312568)↗
DOE OSTI2022

Ultra-Wideband Microwave Imaging System for Root Phenotyping

The roots are a vital organ for plant growth and health. The opaque surrounding environment of the roots and the complicated growth process means that in situ and non-destructive root phenotyping face great challenges, which thus spur great research interests. The existing methods for root phenotyping are either unable to provide high-precision and high accuracy in situ detection, or they change the surrounding root environment and are destructive to root growth and health. Thus,we propose and develop an ultra-wideband microwave scanning method that uses time reversal to achieve in situ root phenotyping nondestructively. To verify the method’s feasibility, we studied an electromagnetic numerical model that simulates the transmission signal of two ultra-wideband microwave antennas. The simulated signal of roots with different shapes shows the proposed system’s capability to measure the root size in the soil. Experimental validations were conducted considering three sets of measurements with different sizes, numbers and locations, and the experimental results indicate that the developed imaging system was able to differentiate root sizes and numbers with high contrast. The reconstruction from both simulations and experimental measurements provided accurate size estimation of the carrots in the soil, which indicates the system’s potential for root imaging.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Modeling and control of fuel distribution in a dual-fuel internal combustion engine leveraging late intake valve closings

In internal combustion engines, cycle-to-cycle and cylinder-to-cylinder variations of the combustion process have been shown to negatively impact the fuel efficiency of the engine and lead to higher exhaust emissions. The combustion variations are generally tied to differences in the composition and condition of the trapped mass throughout each cycle and across individual cylinders. Thus, advanced engines featuring exhaust gas recirculation, flexible valve actuation systems, advanced fueling strategies, and turbocharging systems are prone to exhibit higher variations in the combustion process. In this study, the cylinder-to-cylinder variations of the combustion process in a dual-fuel internal combustion engine leveraging late intake valve closing are investigated and a model to predict and address one of the root causes for these variations across cylinders is developed. The study is conducted on an inline six-cylinder heavy-duty dual-fuel engine equipped with exhaust gas recirculation, a variable geometry turbocharger, and a fully flexible variable intake valve actuation system. The engine is operated with late intake valve closure timings in a dual-fuel combustion mode in which a high reactivity fuel is directly injected into the cylinders and a low reactivity fuel is port injected into the cylinders. The cylinder-to-cylinder variations observed in the study have been associated with the maldistribution of the port-injected fuel, which is exacerbated at late intake valve timings. The resulting difference in indicated mean effective pressure between the cylinders ranges from 9% at an intake valve closing of 570° after top dead center to 38% at an intake valve closing of 620° after top dead center and indicates an increasingly uneven fuel distribution. The study leverages both experimental and simulation studies to investigate the distribution of the port-injected fuel and its impact on cylinder-to-cylinder variation. The effects of intake valve closing as well as the impact of intake runner length on fuel distribution were quantitatively analyzed, and a model was developed that can be used to accurately predict the fuel distribution of the port-injected fuel at different operating conditions with an average estimation error of 1.5% in cylinder-specific fuel flow. A model-based control strategy is implemented to adjust the fueling at each port and shown to significantly reduce the cylinder-to-cylinder variations in fuel distribution.

Kassa, Mateos↗
NASA NTRS2015

Pendulum Motion in Main Parachute Clusters

The coupled dynamics of a cluster of parachutes to a payload are notoriously difficult to predict. Often the payload is designed to be insensitive to the range of attitude and rates that might occur, but spacecraft generally do not have the mass and volume budgeted for this robust of a design. The National Aeronautics and Space Administration (NASA) Orion Capsule Parachute Assembly System (CPAS) implements a cluster of three mains for landing. During testing of the Engineering Development Unit (EDU) design, it was discovered that with a cluster of two mains (a fault tolerance required for human rating) the capsule coupled to the parachute cluster could get into a limit cycle pendulum motion which would exceed the spacecraft landing capability. This pendulum phenomenon could not be predicted with the existing models and simulations. A three phased effort has been undertaken to understand the consequence of the pendulum motion observed, and explore potential design changes that would mitigate this phenomenon. This paper will review the early analysis that was performed of the pendulum motion observed during EDU testing, summarize the analysis ongoing to understand the root cause of the pendulum phenomenon, and discuss the modeling and testing that is being pursued to identify design changes that would mitigate the risk.

Ray, Eric S.↗
DOE OSTI2018

Model evaluation guidelines for geomagnetic index predictions

Geomagnetic indices are convenient quantities that distill the complicated physics of some region or aspect of near–Earth space into a single parameter. Most of the best–known indices are calculated from ground–based magnetometer data sets, such as Dst, SYM–H, Kp, AE, AL, and PC. Many models have been created that predict the values of these indices, often using solar wind measurements upstream from Earth as the input variables to the calculation. This document reviews the current state of models that predict geomagnetic indices and the methods used to assess their ability to reproduce the target index time series. These existing methods are synthesized into a baseline collection of metrics for benchmarking a new or updated geomagnetic index prediction model. These methods fall into two categories: (1) fit performance metrics such as root mean square error (RMSE) and mean absolute error (MAE) that are applied to a time–series comparison of model output and observations; and (2) event detection performance metrics such as Heidke Skill Score and probability of detection (POD) that are derived from a contingency table that compares model and observation values exceeding (or not) a threshold value. Furthermore, a few examples of codes being used with this set of metrics are presented, and other aspects of metrics assessment best practices, limitations, and uncertainties are discussed, including several caveats to consider when using geomagnetic indices.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2013

Estimating Ground-Level PM(sub 2.5) Concentrations in the Southeastern United States Using MAIAC AOD Retrievals and a Two-Stage Model

Previous studies showed that fine particulate matter (PM(sub 2.5), particles smaller than 2.5 micrometers in aerodynamic diameter) is associated with various health outcomes. Ground in situ measurements of PM(sub 2.5) concentrations are considered to be the gold standard, but are time-consuming and costly. Satellite-retrieved aerosol optical depth (AOD) products have the potential to supplement the ground monitoring networks to provide spatiotemporally-resolved PM(sub 2.5) exposure estimates. However, the coarse resolutions (e.g., 10 km) of the satellite AOD products used in previous studies make it very difficult to estimate urban-scale PM(sub 2.5) characteristics that are crucial to population-based PM(sub 2.5) health effects research. In this paper, a new aerosol product with 1 km spatial resolution derived by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was examined using a two-stage spatial statistical model with meteorological fields (e.g., wind speed) and land use parameters (e.g., forest cover, road length, elevation, and point emissions) as ancillary variables to estimate daily mean PM(sub 2.5) concentrations. The study area is the southeastern U.S., and data for 2003 were collected from various sources. A cross validation approach was implemented for model validation. We obtained R(sup 2) of 0.83, mean prediction error (MPE) of 1.89 micrograms/cu m, and square root of the mean squared prediction errors (RMSPE) of 2.73 micrograms/cu m in model fitting, and R(sup 2) of 0.67, MPE of 2.54 micrograms/cu m, and RMSPE of 3.88 micrograms/cu m in cross validation. Both model fitting and cross validation indicate a good fit between the dependent variable and predictor variables. The results showed that 1 km spatial resolution MAIAC AOD can be used to estimate PM(sub 2.5) concentrations.

aerosol optical depth↗
NASA NTRS1985

An internal variable assisted formulation of moisture sorption models for polymers and composites

The purpose of the paper is to demonstrate that constitutive equations for moisture sorption and swelling can be obtained with an internal variable method which is rooted in the theory of irreversible thermodynamics. Realism is injected into the model through the concept of free volume. The power of the method is illustrated by the solution of the formidable nonlinear boundary value problem for diffusion with swelling-stress dependent diffusivity.

Hiel, C. C.↗