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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

The synthesis and analysis of color images

A method is described for performing the synthesis and analysis of digital color images. The method is based on two principles. First, image data are represented with respect to the separate physical factors, surface reflectance and the spectral power distribution of the ambient light, that give rise to the perceived color of an object. Second, the encoding is made efficiently by using a basis expansion for the surface spectral reflectance and spectral power distribution of the ambient light that takes advantage of the high degree of correlation across the visible wavelengths normally found in such functions. Within this framework, the same basic methods can be used to synthesize image data for color display monitors and printed materials, and to analyze image data into estimates of the spectral power distribution and surface spectral reflectances. The method can be applied to a variety of tasks. Examples of applications include the color balancing of color images, and the identification of material surface spectral reflectance when the lighting cannot be completely controlled.

Wandell, Brian A.↗
NASA NTRS2015

Ocean-Atmosphere Coupled Model Simulations of Precipitation in the Central Andes

The meridional extent and complex orography of the South American continent contributes to a wide diversity of climate regimes ranging from hyper-arid deserts to tropical rainforests to sub-polar highland regions. In addition, South American meteorology and climate are also made further complicated by ENSO, a powerful coupled ocean-atmosphere phenomenon. Modelling studies in this region have typically resorted to either atmospheric mesoscale or atmosphere-ocean coupled global climate models. The latter offers full physics and high spatial resolution, but it is computationally inefficient typically lack an interactive ocean, whereas the former offers high computational efficiency and ocean-atmosphere coupling, but it lacks adequate spatial and temporal resolution to adequate resolve the complex orography and explicitly simulate precipitation. Explicit simulation of precipitation is vital in the Central Andes where rainfall rates are light (0.5-5 mm hr-1), there is strong seasonality, and most precipitation is associated with weak mesoscale-organized convection. Recent increases in both computational power and model development have led to the advent of coupled ocean-atmosphere mesoscale models for both weather and climate study applications. These modelling systems, while computationally expensive, include two-way ocean-atmosphere coupling, high resolution, and explicit simulation of precipitation. In this study, we use the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST), a fully-coupled mesoscale atmosphere-ocean modeling system. Previous work has shown COAWST to reasonably simulate the entire 2003-2004 wet season (Dec-Feb) as validated against both satellite and model analysis data when ECMWF interim analysis data were used for boundary conditions on a 27-9-km grid configuration (Outer grid extent: 60.4S to 17.7N and 118.6W to 17.4W).

Regional Climate Model↗
NASA NTRS2022

Hybrid Model Based Approaches for Systems Health Management and Prognostics

This is a previously approved and published presentation. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Present achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. In principle, data-driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data-driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety-critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Prognostics↗
NASA NTRSDate not supplied

Efficient and Accurate Terrain Integration & Application to Modeling and Simulation of Planetary Landing Systems

As planetary spacecraft landing system complexity increases, pre-flight performance assessments include correspondingly more detailed and complex models that represent the various environmental elements and vehicle subsystems. Specifically, the relevance and criticality of planetary terrain modeling in the context of precision landing and hazard avoidance (PL&HA) has become evident as both commercial and national agencies return to the Lunar surface and beyond.

Terrain↗
NASA NTRS1976

A status of progress for the Laser Isotope Separation (LIS) process

An overview of the Laser Isotope Separation (LIS) methodology is given together with illustrations showing a simplified version of the LIS technique, an example of the two-photon photoionization category, and a diagram depicting how the energy levels of various isotope influence the LIS process. Applications were proposed for the LIS system which, in addition to enriching uranium, could in themselves develop into programs of tremendous scope and breadth. These include the treatment of radioactive wastes from light-water nuclear reactors, enriching the deuterium isotope to make heavy-water, and enriching the light isotopes of such elements as titanium for aerospace weight-reducing programs. Economic comparisons of the LIS methodology with the current method of gaseous diffusion indicate an overwhelming advantage; the laser process promises to be 1000 times more efficient. The technique could also be utilized in chemical reactions with the tuned laser serving as a universal catalyst to determine the speed and direction of a chemical reaction.

Delionback, L. M.↗
NASA NTRS1990

A technique for determining Urbach edge, midgap states and electric field in a-Si:H and a-(Si,Ge):H devices

A technique for measuring the Urbach energy of valence band tail states and midgap defect densities in a-Si:H and a-(Si,Ge):H devices is described. The Urbach energy is determined by measuring the quantum efficiency (QE) of delocalized holes in the devices, whereas the midgap state density (DOS) is estimated by measuring the QE of localized holes. The distinction between delocalized and localized holes is obtained from the behavior of the QE upon the application of reverse bias to the device. The QE of holes localized in midgap states increases significantly upon the application of reverse bias because of Frenkel-Poole tunneling, whereas the QE of holes in tail states does not show such an increase. It is shown that upon light soaking the Urbach edge does not change, but the midgap DOS does increase significantly. A primary consequence of the increase in DOS is a decrease in electric field in the low-field middle i region of the p-i-n cell. The decrease in electric field is experimentally estimated by fitting the increase in the reverse bias QE to Frenkel-Poole tunneling.

Dalal, Vikram L.↗
NASA NTRSDate not supplied

Hybrid Model Based Approaches for Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Hybrid Modeling↗
NASA NTRSDate not supplied

Hybrid Approaches to Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Systems Health Management↗
NASA NTRS1982

A study of the geographic coverage properties of a satellite borne Doppler lidar wind velocity measuring system

Geographic coverage frequency and geographic shot density for a satellite borne Doppler lidar wind velocity measuring system are measured. The equations of motion of the light path on the ground were derived and a computer program devised to compute shot density and coverage frequency by latitude-longitude sections. The equations for the coverage boundaries were derived and a computer program developed to plot these boundaries, thus making it possible, after an application of a map coloring algorithm, to actually see the areas of multiple coverage. A theoretical cross-swath shot density function that gives close approximations in certain cases was also derived. This information should aid in the design of an efficient data-processing system for the Doppler lidar.

Pate, T. H.↗
NASA NTRS2006

Paper-Thin Plastic Film Soaks Up Sun to Create Solar Energy

A non-crystallized silicon known as amorphous silicon is the semiconductor material most frequently chosen for deposition, because it is a strong absorber of light. According to the U.S. Department of Energy, amorphous silicon absorbs solar radiation 40 times more efficiently than single-crystal silicon, and a thin film only about 1-micrometer (one one-millionth of a meter) thick containing amorphous silicon can absorb 90 percent of the usable light energy shining on it. Peak efficiency and significant reduction in the use of semiconductor and thin film materials translate directly into time and money savings for manufacturers. Thanks in part to NASA, thin film solar cells derived from amorphous silicon are gaining more and more attention in a market that has otherwise been dominated by mono- and poly-crystalline silicon cells for years. At Glenn Research Center, the Photovoltaic & Space Environments Branch conducts research focused on developing this type of thin film solar cell for space applications. Placing solar cells on thin film materials provides NASA with an attractively priced solution to fabricating other types of solar cells, given that thin film solar cells require significantly less semiconductor material to generate power. Using the super-lightweight solar materials also affords NASA the opportunity to cut down on payload weight during vehicle launches, as well as the weight of spacecraft being sent into orbit.

Source record↗
DOE OSTI2025

Residential Home Decarbonization Using Advanced Micro-CHP

An efficient micro-combined heat and power (CHP) prototype was developed to provide heat and electricity for single-family homes and light commercial buildings. The unit can be installed independently at the point of energy consumption. Consequently, it enables cost-effectively and flexibly matching heat and electrical loads, simplifying distribution and installation processes, and recovering and storing waste heat as hot water. The analyzed results show that the micro-CHP using natural gas as fuel achieves 10.68%–32.80% CO2 reduction in the selected five home applications. The potential effect of H2 on decarbonization of these home was also evaluated, finding that the micro-CHP using a blended fuel with H2 and natural gas can further reduce CO2 emissions. However, the 50% CO2 reduction in the micro-CHP requires at least 70%–90% of H2 in the blend with natural gas. Overall, the micro-CHP technology demonstrates a solid potential to accelerate micro-CHP adoption in residential and light commercial markets, thereby promoting broader micro-CHP acceptance and use in the future for building decarbonization.

Gao, Zhiming [ORNL] (ORCID:0000000271397995)↗
DOE OSTI2024

Additive manufacturing of LiCoO 2 electrodes via vat photopolymerization for lithium ion batteries

Additive manufacturing has the potential to revolutionize the fabrication of lithium-ion batteries for a diversity of applications including in portable, biomedical, aerospace, and the transportation fields. Standard commercial batteries consist of stacked layers of various components (current collectors, cathode, anode, separator and electrolyte) in a two-dimensional manner. By leveraging the latest advances in additive manufacturing and computer-aided design, an improved geometric and electrochemical configuration of these batteries can maximize energy efficiency while allowing design optimization to reduce dead space for a given application. In this work, a composite UV photosensitive resin was prepared and used as feedstock in a vat photopolymerization system. The resin was loaded with LiCoO 2 acting as electrochemically active material for the cathode of a lithium-ion battery, and was further improved with the addition of conductivity-enhancing carbonaceous additives. Challenges to additive manufacturing arise from the opacity and high viscosity of the composite nature of these electrochemically-active resins, which cause light refraction during selective UV curing. Subsequently, items were printed and subjected to a thermal post-processing step to obtain an adequate compromise between electrochemical performance and mechanical integrity. Both sintered and green state 3D printed cathodes were assembled into half-cell lithium-ion batteries using lithium metal as a reference and counter electrode. Electrochemical cycling of these batteries yielded satisfactory results approaching commercial LiCoO 2 cathodes’ performance, with the potential advantages of additive manufacturing – high surface area anode–cathode configurations for power performance as well as shape conformability.

25 ENERGY STORAGE↗
DOE OSTI2025

Structural features of xylan dictate reactivity and functionalization potential for bio-based materials

Plant-based materials have the potential to replace some petroleum-based products, offering compostability and biodegradability as critical advantages. Xylan-rich biomass sources are gaining recognition due to their abundance and underutilization in current industrial applications. Research of potential xylan applications has been complicated by the complex and heterogeneous structure that varies for different xylan feedstocks. Acylation is a broadly used reaction in functionalization of polysaccharides at an industrial scale. However, the efficiency of this reaction varies with the xylan source. To optimize xylan valorization, a systematic understanding of structure–reactivity relationships is essential. This study explores, characterizes, and compares various xylan feedstocks in the acylation process. Xylan feedstocks were analyzed for their chemical composition, degree of polymerization, branching, solubility, and presence of impurities. These features were correlated with xylan glycotypes’ reactivity toward functionalization with succinic anhydride in an optimized DMSO/KOH condition, achieving carboxyl contents of up to 1.46. We used principal component analysis and hierarchical clustering to identify key structural features of xylan that promote its reactivity. Our findings reveal that xylans with higher xylose content and lower degrees of branching exhibit enhanced reactivity, achieving higher carboxyl content and yields. Structural analyses confirmed successful modification, and light scattering analyses showed dramatic changes in the solution properties. Succinylation improves the solubility and film-forming properties of native xylans. This study shows key structure–reactivity relationships in xylan succinylation, establishing that low branching, high xylose content, and reduced lignin impurity enhance chemical functionalization. The results offer a framework for selecting optimal biomass feedstocks and support future efforts in genetic and synthetic biology to design plants with tunable xylan architectures. These findings advance the hemicellulose valorization for applications in coatings and packaging.

Acylation↗
DOE OSTI2024

Clearing a path for light through non-Hermitian media

Abstract The performance of all active photonic devices today is greatly limited by loss. Here, we show that one can engineer a low loss path in a metal-clad lossy multi-mode waveguide while simultaneously achieving high-performance active photonic devices. We leverage non-Hermitian systems operating beyond the exceptional point to enable the redistribution of losses in a multi-mode photonic waveguide. Consequently, our multi-mode waveguide offers low propagation losses for fundamental mode while other higher order modes experience prohibitively high losses. Furthermore, we show an application of this non-Hermitian waveguide platform in designing power-efficient thermo-optic phase shifters with significantly faster response times than conventional silicon-based thermo-optic phase shifters. Our device achieves a propagation loss of less than 0.02 dB μm −1 for our non-Hermitian waveguide-based phase shifters with high performance efficiency of P π ⋅ τ = 19.1 mW μs. In addition, our phase shifters have significantly faster response time (rise/fall time), τ ≈ 1.4 μs, compared to traditional silicon based thermo-optic phase shifters.

Materials Science↗
NASA NTRS1987

Implementation of a digital optical matrix-vector multiplier using a holographic look-up table and residue arithmetic

The design and implementation of a digital (numerical) optical matrix-vector multiplier are presented. The objective is to demonstrate the operation of an optical processor designed to minimize computation time in performing a practical computing application. This is done by using the large array of processing elements in a Hughes liquid crystal light valve, and relying on the residue arithmetic representation, a holographic optical memory, and position coded optical look-up tables. In the design, all operations are performed in effectively one light valve response time regardless of matrix size. The features of the design allowing fast computation include the residue arithmetic representation, the mapping approach to computation, and the holographic memory. In addition, other features of the work include a practical light valve configuration for efficient polarization control, a model for recording multiple exposures in silver halides with equal reconstruction efficiency, and using light from an optical fiber for a reference beam source in constructing the hologram. The design can be extended to implement larger matrix arrays without increasing computation time.

Habiby, Sarry F.↗
NASA NTRS2004

EUROPA2: Plan Database Services for Planning and Scheduling Applications

NASA missions require solving a wide variety of planning and scheduling problems with temporal constraints; simple resources such as robotic arms, communications antennae and cameras; complex replenishable resources such as memory, power and fuel; and complex constraints on geometry, heat and lighting angles. Planners and schedulers that solve these problems are used in ground tools as well as onboard systems. The diversity of planning problems and applications of planners and schedulers precludes a one-size fits all solution. However, many of the underlying technologies are common across planning domains and applications. We describe CAPR, a formalism for planning that is general enough to cover a wide variety of planning and scheduling domains of interest to NASA. We then describe EUROPA(sub 2), a software framework implementing CAPR. EUROPA(sub 2) provides efficient, customizable Plan Database Services that enable the integration of CAPR into a wide variety of applications. We describe the design of EUROPA(sub 2) from the perspective of both modeling, customization and application integration to different classes of NASA missions.

Bedrax-Weiss, Tania↗
DOE OSTI2024

Lithiophilic CoF 2 @C hollow spheres towards spatial lithium deposition for stable lithium metal batteries

Lithium metal (LM) is a promising anode for next-generation batteries due to its high theoretical capacity and low electrode potential. Nonetheless, side reactions, volume change, and unwanted lithium dendrite growth seriously limit the practical application of LM. Herein, with the aid of a hard template approach, a novel lithiophilic CoF 2 -carbon hollow sphere (CoF 2 @C-HS) composite material is successfully prepared via a facile in-situ fluorination and etching strategy. The lithiophilic CoF 2 acts as nucleation sites to reduce nucleation overpotential as well as induces the spatial Li deposition and the formation of LiF-rich solid electrolyte interphase (SEI), and the hollow carbon matrix can enhance the electrical conductivity and offer free space for LM deposition. Theoretical simulations reveal that the synergistic effect of lithiophilic CoF 2 and hollow carbon matrix homogenizes the electric field distribution and Li + flux. Benefiting from these advantages, the CoF 2 @C-HS-modified copper substrate electrode delivers an enhanced Coulombic efficiency (CE) of 93.7% for 280 cycles at 1 mA cm –2 and 1 mA h cm –2 . The symmetrical cell using CoF 2 @C-HS can stably cycle more than 1800 h with a low voltage hysteresis of 11 mV at a current density of 0.5 mA cm –2 and an areal capacity of 0.5 mA h cm –2 . Moreover, the Li@CoF 2 @C-HS composite anode enables more than 300 stable cycles at 1 C with a capacity retention of 95% in LiFePO 4 -based full cell and 110 stable cycles at 1 C in LiNi 0.8 Co 0.1 Mn 0.1 O 2 (NCM811)-based high-voltage full cell. Finally, this work might shed a new light on designing lithiophilic hosts to spatially confine LM deposition, realizing dendrite-free LM anodes and the practical applications of LM batteries.

25 ENERGY STORAGE↗
NASA NTRS1989

Space optical materials and space qualification of optics; Proceedings of the Meeting, Orlando, FL, Mar. 30, 31, 1989

The present conference on space optical materials discusses current metals and nonmetals-related processing R&D efforts, investigations of space optical effects, and the spaceborne qualification of optical components and systems. Attention is given to CVD SiC for optical applications, optical materials for space-based lasers, the high-efficiency acoustooptic and optoelectronic crystalline material Tl3AsSe3, HIPed Be for low-scatter cryogenic optics, durable solar-reflective surfacing for Be optics, thermal effects on Be mirrors, contamination effects on optical surfaces in the monolayer regime, and IR background signature survey experiment results. Also discussed are the contamination-control program for the EUE instrument, an optical multipass radiation system for the heating of levitated samples, optical sample-position sensing for electrostatic levitation, and the qualification of space lighting systems.

Hale, Robert R.↗