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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Combining geometric-optical and spectral invariants theories for modeling canopy fluorescence anisotropy

The spectral invariants theory ( p -theory) has received much attention in the field of quantitative remote sensing over the past few decades and has been adopted for modeling of canopy solar-induced chlorophyll fluorescence (SIF). However, the spectral invariant properties (SIP) in simple analytical formulae have not been applied for modeling canopy fluorescence anisotropy primarily because they are parameterized in terms of leaf total scattering, which precludes the differentiation between forward and backward leaf SIF emissions. In this study, we have developed the canopy-SIP SIF model by combining geometric-optical (GO) theory to account for asymmetric leaf SIF forward and backward emissions at the first-order scattering and by modeling multiple scattering based on the p-theory, thus avoiding the dependence on radiative transfer models. The applicability of the model simulations especially over 3D heterogeneous canopies was improved by incorporating canopy structure through multi-angular clumping index, and by modeling single scattering from the four components of the scene in view according to the GO approach. The results show good consistency with both the state-of-the-art SIF models and multi-angular field SIF observations over grass and chickpea canopies. Further, the coefficient of determination (R²) between the simulated SIF and field measurements was 0.75 (red) and 0.74 (far-red) for chickpea, and 0.65 (both red and far-red) for grass. The average relative error was approximately 3% for 1D homogeneous scenes when comparing the canopy-SIP SIF model simulations to the SCOPE model simulations, and around 4% for the 3D heterogeneous scene when comparing to the LESS model simulations. The results indicate that the proposed approach for separating asymmetric leaf SIF emissions is a robust way to keep a balance between satisfactory simulation accuracy and efficiency. Model simulations suggest that neglecting the leaf SIF asymmetry can lead to an underestimation of canopy red SIF by 6.3% to 42.6% for various leaf biochemical and canopy structural parameters. This study presents a simple but efficient analytical approach for canopy fluorescence modeling, with potential for large-scale canopy fluorescence simulations.

3D heterogeneous structure↗
DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series: Preprint

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as abrupt time series data shifts. These shifts, which are usually unintentional, may be caused by software or hardware configuration changes on a PV system, and do not reflect an actual change in overall system performance. Locating these shifts and segmenting the associated time series aids in more accurate future PV analysis. In this research, an offline changepoint detection (CPD) algorithm that automatically detects these abrupt data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window for the labeled data sets. To promote further research in this space, we are releasing the labeled data shift sets on U.S. Department of Energy's (DOE) DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package.

changepoint detection↗
DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as abrupt time series data shifts. These shifts, which are usually unintentional, may be caused by software or hardware configuration changes on a PV system, and do not reflect an actual change in overall system performance. Locating these shifts and segmenting the associated time series aids in more accurate future PV analysis. In this research, an offline changepoint detection (CPD) algorithm that automatically detects these abrupt data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window for the labeled data sets. To promote further research in this space, we are releasing the labeled data shift sets on U.S. Department of Energy's (DOE) DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package.

changepoint detection↗
DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as time series data shifts. In this research, a changepoint detection (CPD) algorithm that automatically detects data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window across the labeled data sets. Pending approval, we plan to release the labeled data sets for this research on NREL's DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package. By supplying the training sets and algorithm, we hope to encourage further development in this research space.

data shift↗
NASA NTRS2002

Low-Cost Evaluation of EO-1 Hyperion and ALI for Detection and Biophysical Characterization of Forest Logging in Amazonia (NCC5-481)

Major uncertainties exist regarding the rate and intensity of logging in tropical forests worldwide: these uncertainties severely limit economic, ecological, and biogeochemical analyses of these regions. Recent sawmill surveys in the Amazon region of Brazil show that the area logged is nearly equal to total area deforested annually, but conversion of survey data to forest area, forest structural damage, and biomass estimates requires multiple assumptions about logging practices. Remote sensing could provide an independent means to monitor logging activity and to estimate the biophysical consequences of this land use. Previous studies have demonstrated that the detection of logging in Amazon forests is difficult and no studies have developed either the quantitative physical basis or remote sensing approaches needed to estimate the effects of various logging regimes on forest structure. A major reason for these limitations has been a lack of sufficient, well-calibrated optical satellite data, which in turn, has impeded the development and use of physically-based, quantitative approaches for detection and structural characterization of forest logging regimes. We propose to use data from the EO-1 Hyperion imaging spectrometer to greatly increase our ability to estimate the presence and structural attributes of selective logging in the Amazon Basin. Our approach is based on four "biogeophysical indicators" not yet derived simultaneously from any satellite sensor: 1) green canopy leaf area index; 2) degree of shadowing; 3) presence of exposed soil and; 4) non-photosynthetic vegetation material. Airborne, field and modeling studies have shown that the optical reflectance continuum (400-2500 nm) contains sufficient information to derive estimates of each of these indicators. Our ongoing studies in the eastern Amazon basin also suggest that these four indicators are sensitive to logging intensity. Satellite-based estimates of these indicators should provide a means to quantify both the presence and degree of structural disturbance caused by various logging regimes. Our quantitative assessment of Hyperion hyperspectral and ALI multi-spectral data for the detection and structural characterization of selective logging in Amazonia will benefit from data collected through an ongoing project run by the Tropical Forest Foundation, within which we have developed a study of the canopy and landscape biophysics of conventional and reduced-impact logging. We will add to our base of forest structural information in concert with an EO-1 overpass. Using a photon transport model inversion technique that accounts for non-linear mixing of the four biogeophysical indicators, we will estimate these parameters across a gradient of selective logging intensity provided by conventional and reduced impact logging sites. We will also compare our physical ly-based approach to both conventional (e.g., NDVI) and novel (e.g., SWIR-channel) vegetation indices as well as to linear mixture modeling methods. We will cross-compare these approaches using Hyperion and ALI imagers to determine the strengths and limitations of these two sensors for applications of forest biophysics. This effort will yield the first physical ly-based, quantitative analysis of the detection and intensity of selective logging in Amazonia, comparing hyperspectral and improved multi-spectral approaches as well as inverse modeling, linear mixture modeling, and vegetation index techniques.

Asner, Gregory P.↗
DOE OSTI2021

Modeling and Evaluation of Cyber-Attacks on Grid-Interactive Efficient Buildings

Grid-interactive efficient buildings (GEBs) are not only exposed to passive threats (e.g., physical faults) but also active threats such as cyber-attacks launched on the network-based control systems. The impact of cyber-attacks on GEB operation are not yet fully understood, especially as regards the performance of grid services. To quantify the consequences of cyber-attacks on GEBs, this paper proposes a modeling and simulation framework that includes different cyber-attack models and key performance indexes to quantify the performance of GEB operation under cyber-attacks. The framework is numerically demonstrated to model and evaluate cyber-attacks such as data intrusion attacks and Denial-of-Service attacks on a typical medium-sized office building that uses the BACnet/IP protocol for communication networks. Simulation results show that, while different types of attacks could compromise the building systems to different extents, attacks via the remote control of a chiller yield the most significant consequences on a building system’s operation, including both the building service and the grid service. It is also noted that a cyber-attack impacts the building systems during the attack period as well as the post-attack period, which suggests that both periods should be considered to fully evaluate the consequences of a cyber-attack.

Fu, Yanyang↗
DOE OSTI2021

Data-Driven Global Sensitivity Analysis of Three-Phase Distribution System with PVs

Global sensitivity analysis (GSA) of distribution system with respect to stochastic PV variations plays an important role in designing optimal voltage control schemes. This paper proposes a Kriging, i.e., Gaussian process modeling enabled data-driven GSA method. The key idea is to develop a surrogate model that captures the hidden global relationship between voltage and real and reactive power injections from the historical data. With the surrogate model, the Sobol index can be conveniently calculated to assess the global sensitivity of voltage to various power injection variations. Comparison results with other model-based GSA methods on the IEEE 37-bus feeder, such as the polynomial chaos expansion and the Monte Carlo approaches demonstrate that the proposed method can achieve accurate GSA outcomes while maintaining high computational efficiency.

distribution system analysis↗
NASA NTRS2022

Estimating Species-Specific Leaf Area Index and Basal Area Using Optical and SAR Remote Sensing Data in Acadian Mixed Spruce-Fir Forests, USA

This study combined Sentinel-1 synthetic aperture radar (SAR), Sentinel-2 multispectral, and site variable datasets to model leaf area index (LAI) and basal area per ha (BAPH) of two economically important tree species in Northeast, USA; red spruce (Picea rubens Sarg.; RS), and balsam fir (Abies balsamea (L.) Mill.; BF). We used Random Forest (RF), and Multi-Layer Perceptron (MLP) algorithms for LAI and BAPH modeling. The results showed that RF outperformed MLP by reducing the normalized root mean square error (nRMSE) by 0.01 and 0.06 for LAI and BAPH, respectively. The final variables selected for modeling of both LAI and BAPH indicated the superiority of Sentinel-2 variables over the Sentinel-1 SAR with minor contributions of site variables (mainly elevation). The red-edge spectral vegetation indices played a significant role in both LAI and BAPH estimation. We attained the lowest nRMSEs of 0.12, and 0.16 for the final LAI model of RS, and BF, respectively using Sentinel-2 and site variables. The lowest nRMSE for both RS and BF BAPH models was 0.12. As RS and BF are the primary host species for a cyclically occurring and most destructive pest of the region, eastern spruce budworm (Choristoneura fumiferana; SBW), these estimations will be useful to evaluate SBW dynamics in the region.

Forest inventory↗
NASA NTRS1992

Cosmic-ray-modified supernova remnant shocks

The evolution of cosmic-ray-modified SNRs during the early Sedov phase is investigated using the time-dependent, two-fluid model for diffusive shock acceleration. Consideration is given to the sensitivity of net acceleration efficiency to model assumptions regarding density structure of the external medium as well as time dependence in the diffusion coefficient and the cosmic-ray adiabatic index. Of the model assumptions explored, the greatest sensitivity was found to time variations in the specific heat ratio to cosmic rays. It was found that if tau is greater than or approximately equal to few times 100, dynamically significant cosmic-ray pressures are produced early in the Sedov inefficient cosmic-ray accelerators. For the SNR models under consideration, the total energy channeled into the cosmic rays can be of order 10 percent of the initial blast energy for both the uniform density and inverse-square density interstellar medium models.

Jones, T. W.↗
NASA NTRS1995

A remotely sensed index of deforestation/urbanization for use in climate models

The object of this research is to use indirect measurements, notably thermal infrared, to describe urbanization and deforestation with parameters that can be used to assess, as well as predict, the effects of land use changes on local microclimate. More specifically, we use a new approach for the treatment of remotely sensed data; this is referred to as the 'triangle' method. The name triangle is given because the envelope of data points, when plotted as a function of surface radiant temperature versus vegetation index or fractional vegetation cover, exhibits the shape of a triangle. From the information contained on these 'scatter plots', land use changes can be related to two intrinsic surface variables, the surface moisture availability (M(sub 0))(sup 1) and fractional vegetation cover. Recent work by Carlson et al. indicate that the triangle shape on the scatter plots may be scale similar, suggesting that these two parameters are subject to the same interpretation on differing scales. A second objective in this research is to determine if historical data for Advanced Very High Resolution Radiometer (AVHRR) (NOAA satellite; 1.1 km resolution at nadir) can be used to assess changes in regional land use over time. To this end, two target areas were chosen for the investigation of urbanization and two for deforestation. The former comprise tow areas in Pennsylvania, one a small but rapidly growing population center (State College) and the other a medium-sized urban area which continues to undergo development (Chester County). The two deforestation sites consist of rain forest areas in western and central Costa Rica and a region in the Brazilian Amazon.

Gillies, Robert R.↗
DOE OSTI2019

Implications of real-gas behavior on refractive index calculations for optical diagnostics of fuel–air mixing at high pressures

Three models to compute the refractive index of gaseous mixtures at real gas conditions are presented with the purpose to improve the accuracy of state relationships between refractive index and thermodynamic properties. Models are compared with experimental data to determine one that is applicable to high pressure mixtures with non-ideal thermodynamic behavior near or above a fluids’ critical point. The optimal model is applied to analyze adiabatic thermal mixing of fuel and air at typical diesel engine conditions. Our results show that the ideal gas mixture law is appropriate in the vapor region, assuming it is known, for example, from experimental measurements. Finally, the model is applied for binary fuel-air mixing at supercritical conditions to demonstrate its full potential.

02 PETROLEUM↗
NASA NTRS1986

Satellite-derived leaf-area-index and vegetation maps as input to global carbon cycle models - A hierarchical approach

A hierarchical procedure for developing a leaf area index (LAI) map of deciduous boreal forests is studied. The collection of spectral reflectance data from the Boundary Waters Canoe area in Minnesota using helicopter-, high-altitude aircraft-, and Landsat-mounted spectral sensors is described. The relationship between LAI and biomass and the reflectance ratio is analyzed. The sensitivity of canopy reflectance in the visible and infrared to the LAI of the canopy for various boreal forest species is evaluated. The data reveal that Landsat data are useful for producing LAI maps of deciduous forest areas and the maps provide data which clarifies the function of vegetation in the global carbon cycle models.

Badhwar, G. D.↗
DOE OSTI2024

The Yang-Mills duals of small AdS black holes

Abstract We study the largeNmatrix model for the index of 4d$$ \mathcal{N} $$ N = 4 Yang-Mills theory and its truncations to understand the dual AdS 5 black holes. Numerical studies of the truncated models provide insights on the black hole physics, some of which we investigate analytically with the full Yang-Mills matrix model. In particular, we find many branches of saddle points which describe the known black hole solutions. We analytically construct the saddle points dual to the small black holes whose sizes are much smaller than the AdS radius. They include the asymptotically flat BMPV black holes embedded in large AdS with novel thermodynamic instabilities.

Physics↗
NASA NTRS1988

Comparison of field-aligned currents at ionospheric and magnetospheric altitudes

Using the empirical terrestrial magnetospheric magnetic field models of Tsyganenko and Usmanov (1982) and Tsyganenko (1987) the average field-aligned currents (FACs) in the magnetosphere were determined as a function of the Kp index. Three major model FAC systems were identified, namely, the dayside region 1, the nightside region 1, and the nightside polar cap. The models provide information about the sources of the current systems. Mapped ionospheric model FACs are compared with low-altitude measurements obtained by the spacecraft. It is found that low-altitude data can reveal either classic region 1/2 or more highly structured FAC patterns. Therefore, statistical results either obtained from observations or inferred from models are expected to be averages over temporally and spatially shifting patterns.

Spence, H. E.↗
DOE OSTI2022

An index to characterize gas‐solid and solid‐solid mixing from average volume fraction fields

Abstract A mixing index based on solid volume fraction fields is developed for gas‐solid flows. Conventional mixing indices are based on particle realizations of granular mixing and are applicable to experimental data or discrete element method simulations. However, these indices cannot be used as‐is for multifluid models, and an index for characterizing mixing in gas‐solid flows from continuous fields is needed. The performance of the new mixing index is tested in two applications. The first is a 3D simulation of the mixing of biomass and sand in a fluidized bed reactor, and the second is a 2D simulation of binary particle segregation in a fluidized bed. The simulations are performed using OpenFOAM®. The mixing index is used to quantify gas‐solid mixing using solid volume fractions and solid‐solid mixing using solid fractions. The formulation of conventional mixing indices is extended to be used with solid volume fractions fields, and methods for performance improvement are presented.

09 BIOMASS FUELS↗
NASA NTRS1992

Radar measurement of forested areas during OTTER

To test a forest ecosystem model in the OTTER (Oregon ecosystem research) project, it is desirable to find forest canopy parameters via radar remote sensing measurements. Conventionally, forest biomass, along with quantities such as the leaf area index, drive the model. It is shown that the radar backscatter is not uniquely related to biomass. A sensitivity study is carried out using a forward scattering model to determine the variation of radar cross section as a function of several forest parameters. The results are used to find suitable quantities to recover via radar experiments. A parameter estimation scheme is developed to calculate some preliminary statistical properties of the forest.

Moghaddam, M.↗
DOE OSTI2025

CFD Modeling of Concentration Polarization in High-Performance Flat-Sheet Modules

Membrane-based gas separation is a promising, regeneration-free route for industrial CO₂ capture, but its performance is often limited by concentration polarization (CP). Classical 1-D models neglect CP and thus overpredict fluxes, especially for today’s high-permeance, high-selectivity membranes. Therefore, we built a 3-D CFD model that resolves local species transport, boundary-layer resistance, permeate-side effects, and pressure drop in flat-sheet modules. Using the Concentration Polarization Coefficient (CPC) and the overall Effective Membrane CP Index (EAC), the model quantifies how geometry and operating conditions shape CP intensity. Simulations show CP becomes pronounced for advanced membranes, with feed-channel height emerging as the dominant geometric lever: shrinking the gap mitigates CP but raises pressure drop, forcing a trade-off between mass-transfer enhancement and energy cost. These insights emphasize the need for CFD-level analysis in next-generation module design, where 1-D treatments can severely underestimate required membrane area and misguide scale-up.

carbon capture↗
NASA NTRS2018

Enhancing Remote Sensing Based Yield Forecasting: Application to Winter Wheat in United States

Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. In Becker-Reshef et al. (2010) and Franch et al. (2015) we developed an empirical generalized model for forecasting winter wheat yield. In this study we present a new model based on the extrapolation of the pure wheat signal (100 percent of wheat within the pixel) from MODIS (Moderate-resolution Imaging Spectroradiometer) data at 1-kilometer resolution and using the Difference Vegetation Index (DVI). The model has been applied to monitor the national and state level yield of winter wheat in the United States from 2001 to 2016.

Agriculture↗