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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Using Leaf Chlorophyll to Parameterize Light-Use-Efficiency Within a Thermal-Based Carbon, Water and Energy Exchange Model

Chlorophylls absorb photosynthetically active radiation and thus function as vital pigments for photosynthesis, which makes leaf chlorophyll content (C(sub ab) useful for monitoring vegetation productivity and an important indicator of the overall plant physiological condition. This study investigates the utility of integrating remotely sensed estimates of C(sub ab) into a thermal-based Two-Source Energy Balance (TSEB) model that estimates land-surface CO2 and energy fluxes using an analytical, light-use-efficiency (LUE) based model of canopy resistance. The LUE model component computes canopy-scale carbon assimilation and transpiration fluxes and incorporates LUE modifications from a nominal (species-dependent) value (LUE(sub n)) in response to short term variations in environmental conditions, However LUE(sub n) may need adjustment on a daily timescale to accommodate changes in plant phenology, physiological condition and nutrient status. Day to day variations in LUE(sub n) were assessed for a heterogeneous corn crop field in Maryland, U,S.A. through model calibration with eddy covariance CO2 flux tower observations. The optimized daily LUE(sub n) values were then compared to estimates of C(sub ab) integrated from gridded maps of chlorophyll content weighted over the tower flux source area. The time continuous maps of daily C(sub ab) over the study field were generated by focusing in-situ measurements with retrievals generated with an integrated radiative transfer modeling tool (accurate to within +/-10%) using at-sensor radiances in green, red and near-infrared wavelengths acquired with an aircraft imaging system. The resultant daily changes in C(sub ab) within the tower flux source area generally correlated well with corresponding changes in daily calibrated LUE(sub n) derived from the tower flux data, and hourly water, energy and carbon flux estimation accuracies from TSEB were significantly improved when using C(sub ab) for delineating spatio-temporal variations in LUE(sub n). The results demonstrate the synergy between thermal infrared and shortwave reflective wavebands in producing valuable remote sensing data for operational monitoring of carbon and water fluxes.

Houlborg, Rasmus↗
NASA NTRS2010

Use of Land Use Land Cover Change Mapping Products in Aiding Coastal Habitat Conservation and Restoration Efforts of the Mobile Bay NEP

The Mobile Bay region has undergone significant land use land cover change (LULC) over the last 35 years, much of which is associated with urbanization. These changes have impacted the region s water quality and wildlife habitat availability. In addition, much of the region is low-lying and close to the Gulf, which makes the region vulnerable to hurricanes, climate change (e.g., sea level rise), and sometimes man-made disasters such as the Deepwater Horizon (DWH) oil spill. Land use land cover change information is needed to help coastal zone managers and planners to understand and mitigate the impacts of environmental change on the region. This presentation discusses selective results of a current NASA-funded project in which Landsat data over a 34-year period (1974-2008) is used to produce, validate, refine, and apply land use land cover change products to aid coastal habitat conservation and restoration needs of the Mobile Bay National Estuary Program (MB NEP). The project employed a user defined classification scheme to compute LULC change mapping products for the entire region, which includes the majority of Mobile and Baldwin counties. Additional LULC change products have been computed for select coastal HUC-12 sub-watersheds adjacent to either Mobile Bay or the Gulf of Mexico, as part of the MB NEP watershed profile assessments. This presentation will include results of additional analyses of LULC change for sub-watersheds that are currently high priority areas, as defined by MB NEP. Such priority sub-watersheds include those that are vulnerable to impacts from the DWH oil spill, as well as sub-watersheds undergoing urbanization. Results demonstrating the nature and permanence of LULC change trends for these higher priority sub-watersheds and results characterizing change for the entire 34-year period and at approximate 10-year intervals across this period will also be presented. Future work will include development of value-added coastal habitat quality assessment products that will be used by the MB NEP and its partners in the planning of coastal conservation and restoration activities.

Spruce, Joseph P.↗
NASA NTRS2010

Use of DSC and DMA Techniques to Help Investigate a Material Anomaly for PTFE Used in Processing a Piston Cup for the Urine Processor Assembly (UPA) on International Space Station (ISS)

Human urine and flush water are eventually converted into drinking water with the Urine Processor Assembly (UPA) aboard the International Space Station (ISS). This conversion is made possible through the Distillation Assembly (DA) of the UPA. One component of the DA is a molded circular piston cup made of virgin polytetrafluoroethylene (PTFE). The piston cup is assembled to a titanium component using eight fasteners and washers. Molded PTFE produced for spare piston cups in the first quarter of 2010 was different in appearance and texture, and softer than material molded for previous cups. For the suspect newer PTFE material, cup fasteners were tightened to only one-half the required torque value, yet the washers embedded almost halfway into the material. The molded PTFE used in the DA piston cup should be Type II, based on AMS 3667D and ASTM D4894 specifications. The properties of molded PTFE are considerably different between Type I and II materials. Engineers working with the DA thought that if Type I PTFE was molded by mistake instead of Type II material, that could have resulted in the anomalous material properties. Typically, the vendor molds flat sheet PTFE from the same material lot used to mold the piston cups, and tensile testing as part of quality control should verify that the PTFE is Type II material. However, for this discrepant lot of material, such tensile data was not available. Differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA) were two of the testing techniques used at the NASA/Marshall Space Flight Center (MSFC) to investigate the anomaly for the PTFE material. Other techniques used on PTFE specimens were: Shore D hardness testing, tensile testing on dog bone specimens and a qualitative estimation of porosity by optical and scanning electron microscopy.

Wingard, Doug↗
NASA NTRSDate not supplied

Development of thermostable carbonic anhydrases using structure-guided recombination for use in CO2 removal systems on spacecraft

Carbon capture and storage has been a research area of great interest in recent years asa method for mitigation of CO2emissions, due to the effects of climate change. The development of technologies for the efficient capture of CO2are also of great interest for human spaceflight applications. One of the most promising technologies in this area is CO2 scrubbing using liquid amines, unfortunately, liquid amines with low heats of desorption tend to have slow CO2binding kinetics. One potential solution to this problem is to use the enzyme carbonic anhydrase (CA) to enhance the kinetics of CO2binding to liquid amines, allowing the overall process to be more energy efficient. Interest in using carbonic anhydrase as a biocatalyst has led to a number of efforts to improve the thermostability and solvent tolerance of several distinct carbonic anhydrase enzymes. In the work described here, we screened through a diverse set of natural carbonic anhydrases to identify candidates for protein engineering aimed at increased stability and activity in various liquid amines. We then used SCHEMA structure-guided recombination to develop a set of chimeric carbonic anhydrases with high thermostability and activity. These chimeras were used as the starting points for further protein engineering work targeting activity in liquid amine systems. Our ultimate goal is to test the engineered enzymes in a liquid amine system for cabin air revitalization on ISS or other spacecraft.

Life Support↗
NASA NTRSDate not supplied

Development of Thermostable Carbonic Anhydrases Using Structure-Guided Recombination for Use in CO2 Removal Systems on Spacecraft

Carbon capture and storage has been a research area of great interest in recent years as a method for mitigation of CO2 emissions, due to the effects of climate change. The development of technologies for the efficient capture of CO2 are also of great interest for human spaceflight applications. One of the most promising technologies in this area is CO2 scrubbing using liquid amines, unfortunately, liquid amines with low heats of desorption tend to have slow CO2 binding kinetics. One potential solution to this problem is to use the enzyme carbonic anhydrase (CA) to enhance the kinetics of CO2 binding to liquid amines, allowing the overall process to be more energy efficient. Interest in using carbonic anhydrase as a biocatalyst has led to a number of efforts to improve the thermostability and solvent tolerance of several distinct carbonic anhydrase enzymes. In the work described here, we screened through a diverse set of natural carbonic anhydrases to identify candidates for protein engineering aimed at increased stability and activity in various liquid amines. We then used SCHEMA structure-guided recombination to develop a set of chimeric carbonic anhydrases with high thermostability and activity. These chimeras were used as the starting points for further protein engineering work targeting activity in liquid amine systems. Our ultimate goal is to test the engineered enzymes in a liquid amine system for cabin air revitalization on ISS or other spacecraft.

Life Support, Carbon dioxide, Carbonic anhydrase, ↗
NASA NTRSDate not supplied

Assessing the use of UAS-related terms in ASRS using Seed Topic Modeling

Context: The NASA Aviation Safety Reporting System (ASRS) is a voluntary confidential aviation safety reporting system. The ASRS receives reports from pilots, air traffic controllers, flight attendants and other involved in aviation operations. The reports are de-identified and coded by ASRS expert safety analysts. The de-identified reports are then disseminated to the aviation community in a number of ways including entry into an online database. Augmenting the discovery of topics of user interest in this online database would therefore be beneficial to the community it serves. Aim: We propose and execute an experiment to assess the use of seed term topic modeling using the database narratives to identify UAS reports. The use of seed term topic modeling would enable users to identify groups of related narratives associated to a topic of their interest. Method: We use a newly curated field in ASRS reports which identify UAS from non-UAS reports in combination of different set of UAS related terms to assess if seed topic modeling can be used in ASRS.

LDA↗
DOE OSTI2025

A new synthetic method of [Ru(2,2′:6′,2″-terpyridine)(2,2′-bipyridine)Cl] + complexes using cis -[Ru(2,2′:6′,2″-terpyridine)(NCCH 3 ) 2 Cl] + as an intermediate and comparison to a method using [Ru(benzene)(2,2′-bipyridine)Cl] + intermediates

A new method of synthesizing [Ru(terpy)(bpy)Cl] + (terpy = 2,2′:6′,2″-terpyridine, bpy = 2,2′-bipyridine) complexes using the starting material cis-[Ru(terpy)(NCCH 3 ) 2 Cl](PF 6 ) is reported. The yields of four derivatives ([Ru(terpy)(NN)Cl](PF 6 ): NN = bpy, 1; 4,4′-(MeO) 2 bpy, 2; 4,4′-(CF 3 ) 2 bpy, 3; 2,2′-biquinoline, 4) with this method range from 58 to 94 % and are similar to or greater than a known reaction in which the starting material is [Ru(benzene)(NN)Cl](PF 6 ). However, the use of cis-[Ru(terpy)(NCCH 3 ) 2 Cl](PF 6 ) results in lower levels of homoleptic Ru byproducts in the synthesis of 1 as compared to the use of [Ru(benzene)(bpy)Cl](PF 6 ) as determined by 1 H NMR spectroscopy. In addition, a high-purity and gram-scale synthesis of cis-[Ru(terpy)(NCCH 3 ) 2 Cl](PF 6 ) that does not use column chromatography is reported, in contrast to the original synthesis disclosed in the literature.

Bipyridine↗
DOE OSTI2025

Optimization of an aerostructural machining process using physics-guided Bayesian stability modelling

Existing algorithms for predicting milling chatter have not been widely adopted in industry since they require specialized instruments to measure the stability inputs. This study describes how the machining process for a meter-scale aluminum aerostructure was optimized using a physics-guided Bayesian stability model. The study was performed in collaboration with an industrial partner on production machines to evaluate the practicality of the proposed method under real-world conditions. For each cutting tool, the Bayesian approach automatically selected a small number of cutting tests, which were monitored using a microphone to observe the chatter frequency. The algorithm learned the system dynamics, cutting forces, and stability map from these test results. A novel algorithm for predicting tool bending stress was incorporated into the test selection algorithm to avoid tool breakage. On average, each set of optimized cutting parameters required less than six tests to identify and were 97% more productive than baseline parameters from the cutting tool manufacturer. The machining program was then further optimized using commercial feedrate scheduling software to remove cutting force spikes and reduce air cutting time. Five components were machined using the optimized process. These results demonstrate the potential for physics-guided Bayesian models to improve productivity in industrial settings.

Cornelius, Aaron [UT Knoxville]↗
DOE OSTI2024

Source shape estimation for neutron imaging systems using convolutional neural networks

Neutron imaging systems are important diagnostic tools for characterizing the physics of inertial confinement fusion reactions at the National Ignition Facility (NIF). In particular, neutron images give diagnostic information on the size, symmetry, and shape of the fusion hot spot and surrounding cold fuel. Images are formed via collection of neutron flux from the source using a system of aperture arrays and scintillator-based detectors. Currently, reconstruction of fusion source geometry from the collected neutron images is accomplished by solving a computationally intensive maximum likelihood estimation problem via expectation maximization. In contrast, it is often useful to have simple representations of the overall source geometry that can be computed quickly. In this work, we develop convolutional neural networks (CNNs) to reconstruct the outer contours of simple source geometries. We compare the performance of the CNN for penumbral and pinhole data and provide experimental demonstrations of our methods on both non-noisy and noisy data.

Machine learning, neutron imaging, source reconstr↗
DOE OSTI2025

DESI 2024: Constraints on physics-focused aspects of dark energy using DESI DR1 BAO data

Baryon acoustic oscillation data from the first year of the Dark Energy Spectroscopic Instrument (DESI) provide near percent-level precision of cosmic distances in seven bins over the redshift range z=0.1–4.2. Here, this paper is the follow-up to the original DESI BAO cosmology paper [A. G. Adame et al. (DESI Collaboration), arXiv:2404.03002], which considered the conventional w 0 w a cold dark matter (CDM) model. We use the novel DESI data, together with other cosmic probes, to constrain the background expansion history using some well-motivated physical classes of dark energy. In particular, we explore three physics-focused behaviors of dark energy from the equation of state and energy density perspectives: the thawing class (matching many simple quintessence potentials), emergent class (where dark energy comes into being recently, as in phase transition models), and mirage class [where phenomenologically the distance to cosmic microwave background (CMB) last scattering is close to that from a cosmological constant Λ despite dark energy dynamics]. All three classes fit the data at least as well as Λ ⁢CDM, and indeed can improve on it by Δ⁢χ 2 ≈ –5 to –17 for the combination of DESI BAO with CMB and supernova data while having one more parameter. The mirage class does essentially as well as w 0 ⁢w a CDM and exhibits moderate to strong Bayesian evidence preference with respect to Λ⁢ CDM. These classes of dynamical behaviors highlight worthwhile avenues for further exploration into the nature of dark energy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

JACC.shared: Leveraging HPC Metaprogramming and Performance Portability for Computations That Use Shared Memory GPUs

In this work, we present JACC.shared, a new feature of Julia for ACCelerators (JACC), which is the performanceportable and metaprogramming model of the just-in-time and LLVM-based Julia language. This new feature allows JACC applications to leverage the high-performance computing (HPC) capabilities of high-bandwidth, on-chip GPU memory. Historically, exploiting high-bandwidth, shared-memory GPUs has not been a priority for high-level programming solutions. JACC.shared covers that gap for the first time, thereby providing a highlevel, portable, and easy-to-use solution for programmers to exploit this memory and supporting all current major accelerator architectures. Well-known HPC and AI workloads, such as multi/hyperspectral imaging and AI convolutions, have been used to evaluate JACC.shared on two exascale GPU architectures hosted by some of the most powerful US Department of Energy supercomputers: Perlmutter (NVIDIA A100) and Frontier (AMD MI250X). The performance evaluation reports speedup of up to 3.5× by adding only one line of code to the base codes, thus providing important accelerators in a simple, portable, and transparent way and elevating the programming productivity and performance-portability capabilities for Julia/JACC HPC, AI, and scientific applications.

Valero Lara, Pedro [ORNL] (ORCID:0000000214794310)↗
DOE OSTI2025

Using leaf and stomatal traits to predict biomass production and water use efficiency in Populus

Climate change is reshaping ecosystems, driving plants to adapt through leaf-trait plasticity that reflects strategies for growth and water use. Predicting biomass production and intrinsic water use efficiency (iWUE) remains challenging because of genetic, taxonomic, and environmental variability. Here, we used eastern cottonwood and Populus hybrids as a model system to test whether easily measurable leaf traits can serve as reliable predictors of performance, and whether adding stomatal and biochemical traits improves predictive power. Across two field sites in Mississippi, leaf mass per area (LMA), biomass production, iWUE, leaf area, and foliar nitrogen ( N %) differed significantly among taxa and sites, while other traits were conserved. Factorial analysis of mixed data (FAMD) revealed distinct clustering of taxa and sites, indicating coordinated variation among leaf and stomatal traits. Pairwise correlations highlighted fundamental trade-offs, with biomass positively related to LMA and petiole length but negatively associated with iWUE, N %, and carbon isotopic ratios (δ 13 C). Leaf temperature and leaf angle varied among taxa and were significantly correlated with LMA and petiole length, suggesting mechanisms of heat dissipation and leaf movability that link simple traits to gas exchange and productivity. Weighted multiple linear regression models explained 80%–91% of variation in biomass production and iWUE. Models using only LMA, petiole length, and stomatal metrics performed nearly as well as those incorporating N %, and δ 13 C, with complex traits adding approximately 10% explanatory power. These results demonstrate that simple morphological traits capture integrated functional trade-offs, while complex traits refine predictions. This tiered approach provides an efficient framework for selecting high-yielding, water-efficient genotypes of Populus and other hardwood species, offering practical pathways to enhance carbon uptake and iWUE under climate change.

biomass production↗
DOE OSTI2026

Data for KETCHUP: Parameterizing of Large-Scale Kinetic Models Using Multiple Datasets with Different Reference States

Repository for Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP can use K-FIT [2] input files. Example K-FIT input files are located in the K-FIT repository at https://github.com/maranasgroup/K-FIT.

Metabolomics↗
DOE OSTI2026

Data for A Hybrid Biophysical-Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81–0.94) and H (R2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

AI/ML↗
NASA NTRS1974

Investigation of land use of northern megalopolis using ERTS-1 imagery

Primary objective was to produce a color-coded land use map and digital data base for the northern third of Megalopolis. Secondary objective was to investigate possible applications of ERTS products to land use planning. Many of the materials in this report already have received national, dissemination as a result of unexpected interest in land use surveys from ERTS. Of special historical interest is the first comprehensive urban-type land use map from space imagery, which covered the entire state of Rhode Island and was made from a single image taken on 28 July 1972.

Simpson, R. B.↗
NASA NTRS1974

Automatic land use classification using Skylab S-192 multispectral data

Investigation of the accuracy attainable in automatic land use classification using 13 bands of multispectral data from the Skylab S-192 scanner. Classification to levels containing seven urban classes, five agricultural, and three water classes is shown to be achievable. With 17 classes, a classification accuracy of 72% was obtained. A wide spectral range, including the thermal band, appears to be most useful for distinguishing urban classes. Agricultural and water classes can be separated using spectral bands covering the visible to far IR.

Kirvida, L.↗
NASA NTRS1976

A methodology for producing small scale rural land use maps in semi-arid developing countries using orbital imagery

The author has identified the following significant results. Results have shown that it is feasible to design a methodology that can provide suitable guidelines for operational production of small scale rural land use maps of semiarid developing regions from LANDSAT MSS imagery, using inexpensive and unsophisticated visual techniques. The suggested methodology provides immediate practical benefits to map makers attempting to produce land use maps in countries with limited budgets and equipment. Many preprocessing and interpretation techniques were considered, but rejected on the grounds that they were inappropriate mainly due to the high cost of imagery and/or equipment, or due to their inadequacy for use in operational projects in the developing countries. Suggested imagery and interpretation techniques, consisting of color composites and monocular magnification proved to be the simplest, fastest, and most versatile methods.

Vangenderen, J. L.↗
NASA NTRS1980

Use of Landsat data in soil and agricultural land use studies

This paper describes how the synoptic, multispectral, and temporal characteristics of Landsat can be used to locate Soil Association boundaries. Then, using these techniques we describe how a low intensity soil survey was conducted and how some interpretive maps were developed from this. Finally, we describe how soil suitability and land use interpretations were made to aid in defining Agrophysical units used in crop inventories.

Westin, F. C.↗