SEARCH · Search NASA

Results for “root modeling”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,009 records · Page 56

All sources NASA NTRS DOE OSTI

NASA NTRS2006

Heat Transfer In High-Temperature Multilayer Insulation

The combined radiation/conduction heat transfer in high-temperature multilayer insulations for typical reentry of reusable launch vehicles from low Earth orbit was investigated experimentally and numerically. The high-temperature multilayer insulation investigated consisted of gold-coated reflective foils separated by alumina fibrous insulation spacers. The steady-state heat transfer through four multilayer insulation configurations was investigated experimentally over the temperature range of 300-1300 K and environmental pressure range of 1.33 10(exp -5)-101.32 kPa. It was shown that including the reflective foils reduced the effective thermal conductivity compared to fibrous insulation sample at 1.5 times the density of the multilayer sample. A finite volume numerical model was developed to solve the governing combined radiation/conduction heat transfer equations. The radiation heat transfer in the fibrous insulation spacers was modeled using the modified two-flux approximation assuming anisotropic scattering and gray medium. The numerical model was validated by comparison with steady-state experimental data. The root mean square deviation between the predicted and measured effective thermal conductivity of the samples was 9.5%.

Daryabeigi, Kamran↗

NASA NTRS2006

MARSIS antenna flight deployment anomaly and resolution

This paper summarizes the resolution of an in flight anomaly that occurred during the deployment of the first of three MARSIS antenna booms. Characteristics of this deployment are described, along with a correlation to finite element models and measured spacecraft inertias, which allowed the intermediate state of the boom to be accurately determined. Based on this information, a spacecraft maneuver was performed that warmed the stalled hinge and led to the first boom successfully locking into its designed geometry. The confirmed partially deployed boom shape was then used to develop a thermal model of the stalled hinge both in its initial solar attitude and during the successful spacecraft maneuver. Results from the hinge thermal model and component level testing were evaluated in order to determine the root cause of the anomaly and the probability of its recurrence on subsequent deployments. These conclusions were then utilized in planning mitigating actions that were implemented during the remaining two boom deployments. Final flight data are presented for both dipole booms indicating a correctly deployed and healthy antenna. The monopole boom deployment was detected but the final state of the boom is unknown.

Mars↗

NASA NTRS2020

SMAP Detects Soil Moisture under Temperate Forest Canopies

Soil moisture dynamics in the presence of dense vegetation canopies are determinants of ecosystem function and biogeochemical cycles, but the capability of existing spaceborne sensors to support reliable and useful estimates is not known. New results from a recently initiated field experiment in the northeast United States show that the NASA SMAP (Soil Moisture Active Passive) satellite is capable of retrieving soil moisture under temperate forest canopies. We present an analysis demonstrating that a parameterized emission model with the SMAP morning overpass brightness temperature resulted in a RMSD (root mean square difference) range of 0.047-0.057 m(exp 3)/m(exp 3) and a Pearson correlation range of 0.75-0.85 depending on the experiment location and the SMAP polarization. The inversion approach included a minimal amount of ancillary data. This result demonstrates unequivocally that spaceborne L-band radiometry is sensitive to soil moisture under temperate forest canopies, which has been uncertain because of lack of representative reference data.

Andreas Colliander↗

NASA NTRS2024

Impact of Random Micron-Sized Surface Roughness on Stationary Crossflow Instabilities

This study examines the effect of random micron-sized distributed roughness on stationary crossflow (SCF) instabilities. The roughness parameters are varied by creating nanoparticle coatings of various formulations and applying them to inserts that cover approximately the first 14% of the swept flat-plate model. In addition to the baseline configuration (no added roughness, root-mean-square (RMS) ≈ 0.4 µm ), 5 panels with RMS roughness values of 4.8 up to 17 µm and correlation lengths ranging from 92 to 876 µm were tested. Despite the significant roughness levels tested, the transition location was found to be only mildly impacted by the additional roughness. However, the SCF amplitudes and wavelength content were found to vary substantially depending on the roughness input. The results were somewhat unexpected, in that the panel with the highest roughness RMS level did not result in the largest initial amplitudes, nor did the panel with the most energy in the SCF wavelength range. Rather, the panel with the most energy in the smaller wavelengths resulted in the largest SCF amplitude. There were, however, significant variations in the results when the panels were rotated by 180 ◦ , indicating that the important factor for some of the panels may be isolated peaks that occur near the neutral point.

stationary crossflow instabilities↗

NASA NTRS1991

Plant gravity sensing

This review of plant gravity sensing examines sensing in organ gravitropism, sensing in single-cell gravitropism, and nongravitropic sensing. Topics related to sensing in organ gravitropism are (1) identification of the gravitropic susceptors, including intracellular asymmetry in equilibrium position and after reorientation, susceptor signal-to-noise ratio, signal integration over threshold stimulation periods, intracellular asymmetry and gravitropic competence, and starch deficiency and gravitropic competence; (2) possible root statocytes and receptors, including identification of presumptive statocytes, cytology, and possible receptors and models of sensing; and (3) negatively gravitropic organs, including identification and distribution of presumptive statocytes and cytology and possible receptors. Topics related to nongravitropic sensing include gravitaxis, reaction wood, gravimorphogenesis, other gravity-influenced organ movements, and cytoplasmic streaming.

Non-NASA Center↗

NASA NTRS2011

Comparison of Ares I-X Wind-Tunnel Derived Buffet Environment with Flight Data

The Ares I-X Flight Test Vehicle (FTV), launched in October 2009, carried with it over 243 buffet verification pressure sensors and was one of the most heavily instrumented launch vehicle flight tests. This flight test represented a unique opportunity for NASA and its partners to compare the wind-tunnel derived buffet environment with that measured during the flight of Ares I-X. It is necessary to define the launch vehicle buffet loads to ensure that structural components and vehicle subsystems possess adequate strength, stress, and fatigue margins when the vehicle structural dynamic response to buffet forcing functions are considered. Ares I-X buffet forcing functions were obtained via wind-tunnel testing of a rigid buffet model (RBM) instrumented with hundreds of unsteady pressure transducers designed to measure the buffet environment across the desired frequency range. This paper discusses the comparison of RBM and FTV buffet environments, including fluctuating pressure coefficient and normalized sectional buffet forcing function root-mean-square magnitudes, frequency content of power-spectral density functions, and force magnitudes of an alternating flow phenomena. Comparison of wind-tunnel model and flight test vehicle buffet environments show very good agreement with root-mean-square magnitudes of buffet forcing functions at the majority of vehicle stations. Spectra proved a challenge to compare because of different wind-tunnel and flight test conditions and data acquisition rates. However, meaningful and promising comparisons of buffet spectra are presented. Lastly, the buffet loads resulting from the transition of subsonic separated flow to supersonic attached flow were significantly over-predicted by wind-tunnel results.

Piatak, David J.↗

NASA NTRS1990

StarPlan: A model-based diagnostic system for spacecraft

The Sunnyvale Division of Ford Aerospace created a model-based reasoning capability for diagnosing faults in space systems. The approach employs reasoning about a model of the domain (as it is designed to operate) to explain differences between expected and actual telemetry; i.e., to identify the root cause of the discrepancy (at an appropriate level of detail) and determine necessary corrective action. A development environment, named Paragon, was implemented to support both model-building and reasoning. The major benefit of the model-based approach is the capability for the intelligent system to handle faults that were not anticipated by a human expert. The feasibility of this approach for diagnosing problems in a spacecraft was demonstrated in a prototype system, named StarPlan. Reasoning modules within StarPlan detect anomalous telemetry, establish goals for returning the telemetry to nominal values, and create a command plan for attaining the goals. Before commands are implemented, their effects are simulated to assure convergence toward the goal. After the commands are issued, the telemetry is monitored to assure that the plan is successful. These features of StarPlan, along with associated concerns, issues and future directions, are discussed.

Heher, Dennis↗

NASA NTRS2004

LDV Surveys Over a Fighter Model at Moderate to High Angles of Attack

The vortex flowfield over an advanced twin-tailed fighter configuration was measured in a low-speed wind tunnel at two angles of attack. The primary test data consisted of 3-component velocity surveys obtained using a Laser Doppler Velocimeter. Laser light sheet and surface flow visualization were also obtained to provide insight into the flowfield structure. Time-averaged velocities and the root mean square of the velocity fluctuations were obtained at two cross-sections above the model. At 15 degrees angle of attack, the vortices generated by the wing leading edge extension (LEX) were unburst over the model and passed outboard of the vertical tail. At 25 degrees angle of attack, the vortices burst in the vicinity of the wing-LEX intersection and impact directly on the vertical tails. The RMS levels of the velocity fluctuations reach values of approximately 30% in the region of the vertical tails.

Sellers, William L., III↗

NASA NTRS2016

Comparison of Turbulent Thermal Diffusivity and Scalar Variance Models

In this study, several variable turbulent Prandtl number formulations are examined for boundary layers, pipe flow, and axisymmetric jets. The model formulations include simple algebraic relations between the thermal diffusivity and turbulent viscosity as well as more complex models that solve transport equations for the thermal variance and its dissipation rate. Results are compared with available data for wall heat transfer and profile measurements of mean temperature, the root-mean-square (RMS) fluctuating temperature, turbulent heat flux and turbulent Prandtl number. For wall-bounded problems, the algebraic models are found to best predict the rise in turbulent Prandtl number near the wall as well as the log-layer temperature profile, while the thermal variance models provide a good representation of the RMS temperature fluctuations. In jet flows, the algebraic models provide no benefit over a constant turbulent Prandtl number approach. Application of the thermal variance models finds that some significantly overpredict the temperature variance in the plume and most underpredict the thermal growth rate of the jet. The models yield very similar fluctuating temperature intensities in jets from straight pipes and smooth contraction nozzles, in contrast to data that indicate the latter should have noticeably higher values. For the particular low subsonic heated jet cases examined, changes in the turbulent Prandtl number had no effect on the centerline velocity decay.

computational fluid dynamics↗

DOE OSTI2025

Quantum chemically calculated Abraham parameters for quantifying and predicting polymer hydrophobicity

The leakage and accumulation of plastic in the environment is a significant and growing problem with numerous detrimental impacts and has led to a push toward the design and development of more environmentally benign materials. To this end, we have developed a quantum chemistry-based model for predicting the mobility of polymer materials from molecular structure. Hydrophobicity is used as a surrogate for mobility given that hydrophobic interactions drive much of the partitioning of contaminants in and out of various environmentally relevant compartments. To model polymer hydrophobicity, we adjusted a previously developed Quantum Chemically Calculated Abraham Parameter model to calculate Abraham parameters of small molecules from molecular structure information. The resulting model predicted the octanol-water partition coefficient (K OW ) of polymer repeating units with a root mean square error (RMSE) of 0.48 (log scale). Additionally, the hydrophobicity of high molecular weight polymer materials was captured through solubility parameters and Nile red staining experiments from the literature and predicted with RMSEs of 1.21 (J/cc) 0.5 and 3.42 nm, respectively. Finally, to test the environmental applicability of the model, the relative adsorption capacity of three polymers was predicted and used to unify sorption isotherms across multiple sorbates and polymer sorbents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2020

Evaluation of GEOS-Simulated L-Band Microwave Brightness Temperature using Aquarius Observations over Non-Frozen Land across North America

L-band brightness temperature (T(sub b)) is one of the key remotely-sensed variables that provides information regarding surface soil moisture conditions. In order to harness the information in T(sub b) observations, a radiative transfer model (RTM) is investigated for eventual inclusion into a data assimilation framework. In this study, T(sub b) estimates from the RTM implemented in the NASA Goddard Earth Observing System (GEOS) were evaluated against the nearly four-year record of daily T(sub b) observations collected by L-band radiometers onboard the Aquarius satellite. Statistics between the modeled and observed T(sub b) were computed over North America as a function of soil hydraulic properties and vegetation types. Overall, statistics showed good agreement between the modeled and observed T(sub b) with a relatively low, domain-average bias (0.79 K (ascending) and -2.79 K (descending)), root mean squared error (11.0 K (ascending) and 11.7 K (descending)), and unbiased root mean squared error (8.14 K (ascending) and 8.28 K (descending)). In terms of soil hydraulic parameters, large porosity and large wilting point both lead to high uncertainty in modeled T(sub b) due to the large variability in dielectric constant and surface roughness used by the RTM. The performance of the RTM as a function of vegetation type suggests better agreement in regions with broadleaf deciduous and needleleaf forests while grassland regions exhibited the worst accuracy amongst the five different vegetation types.

L-band radiometry↗

DOE OSTI2020

Coupling High-Throughput Experiments and Regression Algorithms to Optimize PGM-Free ORR Electrocatalyst Synthesis

Over the past decades, significant improvement has been achieved in the performance of platinum group metal-free (PGM-free) materials as an alternative to Pt-based electrocatalysts for oxygen reduction reaction (ORR). However, further progress in ORR activity requires evaluation of precursors and synthesis approaches. In response to this challenge, we generated a first of its kind experimental data set of 36 samples using high-throughput synthesis and activity measurements. Several control parameters (e.g., Fe precursor identity, the precursor content, and pyrolysis temperature) were varied. We then developed several state-of-the-art machine learning (ML) based regression models to predict ORR activity, dependent on selected synthesis variables. Through an iterative algorithm, higher prediction accuracy (smaller root-mean-square error) was achieved. We identified that gradient boosting regression (GBR) and support vector regression (SVR), among several methods, work best for this data set. Aided by our ML-based surrogate models, we decided to alter catalyst synthesis conditions, which resulted in a 36% increase in measured ORR activity in comparison to the maximum ORR mass activity value of 21.9 A/g catalyst in the original data set. Overall, this combined experiment and machine learning approach represents a promising path forward toward developing highly efficient next-generation ORR electrocatalysts and, more generally, functional materials.

25 ENERGY STORAGE↗

NASA NTRS1982

On the geometric form of volcanoes - Comment

The model of Lacey et al. (1981) accounting for the geometric regularity and approximate cone shape of volcanoes is discussed. It is pointed out that, contrary to the model, volcano eruptions do not occur randomly in elevation and azimuth, but are commonly restricted to summit vents and a few well defined flank zones, so that the form of a volcano is determined by its vent locations and styles of eruption. Other false predictions of the model include the constancy of lava volumes at all vent elevations, the increase in volcano radius as the square root of time, a critical height for volcano growth, the influence of planetary gravity on volcano height and the negligible influence of ash falls and flows and erosional deposition. It is noted that the model of Shteynberg and Solov'yev, in which cone shape is related to stresses due to increasing cone height, may provide a better understanding of volcano morphology.

Wood, C. A.↗

NASA NTRS2023

Breaking Wave Field Statistics With A Multi-Layer Model

The statistics of breaking wave fields are characterised within a novel multi-layer framework, which generalises the single-layer Saint-Venant system into a multi-layer and non-hydrostatic formulation of the Navier–Stokes equations. We simulate an ensemble of phase-resolved surface wave fields in physical space, where strong nonlinearities, including directional wave breaking and the subsequent highly rotational flow motion, are modelled, without surface overturning. We extract the kinematics of wave breaking by identifying breaking fronts and their speed, for freely evolving wave fields initialised with typical wind wave spectra. The Λ(c) distribution, defined as the length of breaking fronts (per unit area) moving with speed c to c + dc following Phillips (J. Fluid Mech., vol. 156, 1985, pp. 505–531), is reported for a broad range of conditions. We recover the Λ(c) ∝ c−6 scaling without wind forcing for sufficiently steep wave fields. A scaling of Λ(c) based solely on the root-mean-square slope and peak wave phase speed is shown to describe the modelled breaking distributions well. The modelled breaking distributions are in good agreement with field measurements and the proposed scaling can be applied successfully to the observational data sets. The present work paves the way for simulations of the turbulent upper ocean directly coupled to a realistic breaking wave dynamics, including Langmuir turbulence, and other sub-mesoscale processes.

Jiarong Wu↗

DOE OSTI2020

Fine-root dynamics vary with soil depth and precipitation in a lown-utrient tropical forest in the Central Amazonia

A common assumption in tropical ecology is that root systems respond rapidly to climatic cues but that most of that response is limited to the uppermost layer of the soil, with relatively limited changes in deeper layers. However, this assumption has not been tested directly, preventing models from accurately predicting the response of tropical forests to environmental change. We measured seasonal dynamics of fine roots in a Central Amazonia mature forest using minirhizotrons to 90 cm depth, which were calibrated with fine roots extracted from soil cores. Root productivity and mortality in superficial soil layers were positively correlated with precipitation, whereas root standing length was greater during the dry periods at the deeper layers. Contrary to historical assumptions, a large fraction of fine-root standing biomass (46%) and productivity (41%) were found in soil layers deeper than 30 cm. Furthermore, root turnover decreased linearly with soil depth. Our findings demonstrate a strong relationship between fine-root dynamics and precipitation regimes in Central Amazonia. Our results also emphasize the importance of deep roots for accurate estimates of primary productivity and the interaction between roots and carbon, water, and nutrients.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

High-throughput characterization, correlation, and mapping of leaf photosynthetic and functional traits in the soybean ( Glycine max ) nested association mapping population

We report photosynthesis is a key target to improve crop production in many species including soybean [Glycine max (L.) Merr.]. A challenge is that phenotyping photosynthetic traits by traditional approaches is slow and destructive. There is proof-of-concept for leaf hyperspectral reflectance as a rapid method to model photosynthetic traits. However, the crucial step of demonstrating that hyperspectral approaches can be used to advance understanding of the genetic architecture of photosynthetic traits is untested. To address this challenge, we used full-range (500–2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits, including the rate-limiting processes of photosynthesis, maximum Rubisco carboxylation rate, and maximum electron transport. In total, 11 models were produced from a diverse population of soybean sampled over multiple field seasons to estimate photosynthetic parameters, chlorophyll content, leaf carbon and leaf nitrogen percentage, and specific leaf area (with R 2 from 0.56 to 0.96 and root mean square error approximately <10% of the range of calibration data). We explore the utility of these models by applying them to the soybean nested association mapping population, which showed variability in photosynthetic and leaf traits. Genetic mapping provided insights into the underlying genetic architecture of photosynthetic traits and potential improvement in soybean. Notably, the maximum Rubisco carboxylation rate mapped to a region of chromosome 19 containing genes encoding multiple small subunits of Rubisco. We also mapped the maximum electron transport rate to a region of chromosome 10 containing a fructose 1,6-bisphosphatase gene, encoding an important enzyme in the regeneration of ribulose 1,5-bisphosphate and the sucrose biosynthetic pathway. The estimated rate-limiting steps of photosynthesis were low or negatively correlated with yield suggesting that these traits are not influenced by the same genetic mechanisms and are not limiting yield in the soybean NAM population. Leaf carbon percentage, leaf nitrogen percentage, and specific leaf area showed strong correlations with yield and may be of interest in breeding programs as a proxy for yield. This work is among the first to use hyperspectral reflectance to model and map the genetic architecture of the rate-limiting steps of photosynthesis.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2017

Estimating Surface Soil Moisture from SMAP Observations Using a Neural Network Technique

A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m(exp. 3)m(exp. -3), 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m(exp. 3)m(exp. -3), 0.58 and 0.48, respectively, against International Soil Moisture Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones.

Soil Moisture Remote Sensing↗

NASA NTRSDate not supplied

Run Time Improvement Efforts for the Roman Space Telescope Thermal Analysis

"Observatory thermal models for large, complex missions, such as the Wide Field InfraRed Survey Telescope (WFIRST) mission, produce an immense amount of data to be processed. Configuration management of the model throughout the project life cycle has mainly focused on which versions of the subsystem models form the current observatory level configuration. However, the results produced by the model are not nearly as well The Roman Space Telescope (RST), formerly known as the Wide Field InfraRed Survey Telescope, is the next great astrophysics observatory mission to follow the James Webb Space Telescope with a planned launch in 2026. As a large scale, flagship mission for NASA with challenging wave front error stability requirements, a single model approach for both thermal discipline analysis and thermo-optical distortion analysis has been used since the early days of the project. In alleviating the need to maintain two separate models for different analysis types, it imposes run time penalties on the thermal analysis with a large model with significant radiation heat exchange. Throughout the lifecycle of the project, the component models have steadily grown in size, resulting in a continuous growth of the overall observatory model with each update and consequently a considerable increase in the model run time. While ongoing efforts to reduce run time are continuously investigated, previous efforts had primarily focused on timestep size and total simulation time to reach quasi-equilibrium. More recently, studies were performed on the total number of radiation couplings (radks) included in the model, which has a nearly linear impact on run time, but increases exponentially with node count. As standard practice for spacecraft analysis, small radks were excluded from the temperature solution based on the assumption that their interchange/view factors have a negligible impact on heat flow. Four approaches were investigated to reduce the model run time while minimizing the impact on accuracy: (1) the Equivalent Radiation Network node, (2) Progressive Radk Inclusion as solution proceeds, (3) Targeted Radk Filtering for critical/non critical areas, and lastly (4) Representation of culled radks with Backloads. Furthermore, the investigation of model run time also revealed that cold cases took noticeably longer to run than hot cases; the root computational inefficiencies were explored along with the computation penalty of linearization of the external radks and recalculation of temperature dependent linear couplings at each timestep. This paper outlines the details of each of the above approaches and their impact on run time and model accuracy.

Thermal Analysis↗