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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Solutions Network Formulation Report. Reducing Light Pollution in U.S. Coastal Regions Using the High Sensitivity Cameras on the SAC-C and Aquarius/SAC-D Satellites

Light pollution has significant adverse biological effects on humans, animals, and plants and has resulted in the loss of our ability to view the stars and planets of the universe. Over half of the U.S. population resides in coastal regions where it is no longer possible to see the stars and planets in the night sky. Forty percent of the entire U.S. population is never exposed to conditions dark enough for their eyes to convert to night vision capabilities. In coastal regions, urban lights shine far out to sea where they are augmented by the output from fishing boat, cruise ship and oil platform floodlights. The proposed candidate solution suggests using HSCs (high sensitivity cameras) onboard the SAC-C and Aquarius/SAC-D satellites to quantitatively evaluate light pollution at high spatial resolution. New products modeled after pre-existing, radiance-calibrated, global nighttime lights products would be integrated into a modified Garstang model where elevation, mountain screening, Rayleigh scattering, Mie scattering by aerosols, and atmospheric extinction along light paths and curvature of the Earth would be taken into account. Because the spatial resolution of the HSCs on SAC-C and the future Aquarius/SAC-D missions is greater than that provided by the DMSP (Defense Meteorological Satellite Program) OLS (Operational Linescan System) or VIIRS (Visible/Infrared Imager/Radiometer Suite), it may be possible to obtain more precise light intensity data for analytical DSSs and the subsequent reduction in coastal light pollution.

Andrews, Jane C.↗
DOE OSTI2025

Dual-Wavelength Simultaneous Patterning of Degradable Thermoset Supports for One-Pot Embedded 3D Printing

Vat photopolymerization (VP) techniques have enabled the fabrication of complex geometries while balancing high precision and fast processing times. 3D printed objects are traditionally built layer-by-layer with newly cured layers being structurally supported by previous ones. Fabricating unsupported features such as overhangs and arches risks misalignment and sagging, limiting the range of accessible designs. To overcome this issue, support structures are fabricated along with the primary object as temporary scaffolds that provide stability and conserve print fidelity. For VP specifically, patterning dissolvable sacrificial supports is attractive to avoid manual removal after printing. In this study, we demonstrate a base-degradable thermoset to pattern print supports in a one-pot formulation along with the primary structural material. Efficient printing is enabled using a dual-wavelength negative imaging (DWNI) DLP printer that patterns the degradable thermoset with visible light and the permanent network with UV light, which are simultaneously projected using a single digital micromirror device (DMD). Printed objects undergo thermal postprocessing to enhance the final conversion of the primary material, after which thermoset supports are degraded in a basic, aqueous solution. This approach provides a robust method for the dual-wavelength patterning of sacrificial thermoset supports, broadening the range of accessible 3D printable materials and geometries.

3D printing↗
DOE OSTI2020

Lighting Technology Energy Solutions (LiTES) (Final Technical Report)

The Lighting Technologies Energy Solutions (LiTES) Program Final Technical Report is a collaborative effort between NextEnergy, Consumers Energy, DTE Energy (DTE) and the Detroit Joint Apprentice Training Center (DJATC) to evaluate customer and contractor experiences deploying advanced/networked lighting controls systems (ALC/NLC). The LiTES Program, funded by the US Department of Energy (DOE), sought to reduce energy use in small and medium commercial buildings (SMCB) by accelerating the adoption of ALC/NLC through contractor training and technology deployment. Leveraging recommendations already outlined by the Design Lights Consortium Commercial Advanced Lighting Controls (DLC CALC) project, NextEnergy led an effort to train contractors, evaluate the experience of ALC/NLC demonstration projects, identify opportunities to reduce market barriers, and accelerate the increased adoption of ALC/NLC technologies within SMCB. The LiTES Program defined SMCB as commercial buildings under 100,000 square feet. The LiTES Program efforts also included evaluating current ALC/NLC utility incentives, piloting ALC/NLC incentives specific to SMCB, and identifying opportunities to better align utility incentives with current ALC/NLC technology to support accelerating the adoption of ALC/NLC in SMCB. This report shares the survey results and M&V data findings, including energy savings, market barriers, experience of installations, impact of incentives/financing programs and provides recommendations for the increased deployment of ALC/NLC systems in small and medium commercial buildings. Utility survey results, incentive alternatives and recommendations, including a cost benefit analysis outlining energy and economic impacts of the provided ALC/NLC incentives on SMCB projects are also provided.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1999

Optical Power Source Derived from Engine Combustion Chambers

An optical power source is disclosed that collects the spectra of the light emissions created in a combustion chamber to provide its optical output signals that serve the needs of optical networks. The light spectra is collected by a collection ring serving as an optical waveguide.

Baumbick, Robert J.↗
NASA NTRS2008

Potential Use of a Bayesian Network for Discriminating Flash Type from Future GOES-R Geostationary Lightning Mapper (GLM) data

Continuous monitoring of the ratio of cloud flashes to ground flashes may provide a better understanding of thunderstorm dynamics, intensification, and evolution, and it may be useful in severe weather warning. The National Lighting Detection Network TM (NLDN) senses ground flashes with exceptional detection efficiency and accuracy over most of the continental United States. A proposed Geostationary Lightning Mapper (GLM) aboard the Geostationary Operational Environmental Satellite (GOES-R) will look at the western hemisphere, and among the lightning data products to be made available will be the fundamental optical flash parameters for both cloud and ground flashes: radiance, area, duration, number of optical groups, and number of optical events. Previous studies have demonstrated that the optical flash parameter statistics of ground and cloud lightning, which are observable from space, are significantly different. This study investigates a Bayesian network methodology for discriminating lightning flash type (ground or cloud) using the lightning optical data and ancillary GOES-R data. A Directed Acyclic Graph (DAG) is set up with lightning as a "root" and data observed by GLM as the "leaves." This allows for a direct calculation of the joint probability distribution function for the lighting type and radiance, area, etc. Initially, the conditional probabilities that will be required can be estimated from the Lightning Imaging Sensor (LIS) and the Optical Transient Detector (OTD) together with NLDN data. Directly manipulating the joint distribution will yield the conditional probability that a lightning flash is a ground flash given the evidence, which consists of the observed lightning optical data [and possibly cloud data retrieved from the GOES-R Advanced Baseline Imager (ABI) in a more mature Bayesian network configuration]. Later, actual GLM and NLDN data can be used to refine the estimates of the conditional probabilities used in the model; i.e., the Bayesian network is a learning network. Methods for efficient calculation of the conditional probabilities (e.g., an algorithm using junction trees), finding data conflicts, goodness of fit, and dealing with missing data will also be addressed.

Solakiewiz, Richard↗
NASA NTRS2022

Research Campaign: Lunar Gravitational-wave Antenna

The Lunar Gravitational-wave Antenna (LGWA) is a mission conceived as a network of stations to measure the vibrations of the Moon caused by gravitational waves (GWs). The long-term vision is to accomplish ground-breaking, paradigm shifting science in the coming decades on the Moon. LGWA will lead to the first observation of GW signals in the decihertz band greatly expanding our understanding of the universe and laying out a path to eventually probe the moment of its creation. LGWA stations will also be unique contributions to a lunar geophysical network shedding light on the Moon’s formation history. LGWA is a project of inclusion and international collaboration, where space-faring and space-aspiring nations can contribute to the LGWA network with their own stations whose cost depends on the targeted station lifetime and deployment location. With respect to the baseline concept proposed here, different sensor technologies can be implemented over time with the goal to continually increase the performance of the LGWA network. The broader context of lunar GW detection and its long-term vision are outlined in an accompanying topical white paper.

Lorella Angelini↗
DOE OSTI2025

Control of Energy Transport and Transduction in Photosynthetic Down-Conversion (Final Technical Report)

The research conducted under this contract focused on three areas related to control of energy transport and transduction in natural and synthetic light harvesting systems: (1) experimental and theoretical studies exploring the use of ultrafast laser pulse shaping to manipulate the initially excited states of multi-chromophore biological light harvesting networks and influence the early time energy transfer and electronic and vibrational relaxation pathways, (2) fundamental theoretical developments aimed at computing and analyzing the dynamics of excitations of complex heterogeneous environments and nano structures, and (3) joint experimental and theoretical design studies of artificial light harvesting materials exploring the influence of incorporating different types of organic layers in low dimensional lead halide perovskite materials on their hot carrier relaxation dynamics, and theoretical studies of the design of nano structured light harvesting antenna systems. These research projects supported the training of three theoretical and computational graduate students, one experimental graduate student, and one experimental post-doctoral researcher. The research has been published in seven peer reviewed papers and presented at several international meetings and workshops.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Two–Dimensional Excitonic Networks Directed by DNA Templates as an Efficient Model Light–Harvesting and Energy Transfer System

Photosynthetic organisms organize discrete light-harvesting complexes into large-scale networks to facilitate efficient light collection and utilization. Inspired by nature, herein, synthetic DNA templates were used to direct the formation of dye aggregates with a cyanine dye, K21, into discrete branched photonic complexes, and two-dimensional (2D) excitonic networks. The DNA templates ranged from four-arm DNA tiles, ≈10 nm in each arm, to 2D wireframe DNA origami nanostructures with different geometries and varying dimensions up to 100×100 nm. These DNA-templated dye aggregates presented strongly coupled spectral features and delocalized exciton characteristics, enabling efficient photon collection and energy transfer. Compared to the discrete branched photonic systems templated on individual DNA tiles, the interconnected excitonic networks showed approximately a 2-fold increase in energy transfer efficiency. Lastly, this bottom-up assembly strategy paves the way to create 2D excitonic systems with complex geometries and engineered energy pathways.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Two‐Dimensional Excitonic Networks Directed by DNA Templates as an Efficient Model Light‐Harvesting and Energy Transfer System

Abstract Photosynthetic organisms organize discrete light‐harvesting complexes into large‐scale networks to facilitate efficient light collection and utilization. Inspired by nature, herein, synthetic DNA templates were used to direct the formation of dye aggregates with a cyanine dye, K21, into discrete branched photonic complexes, and two‐dimensional (2D) excitonic networks. The DNA templates ranged from four‐arm DNA tiles, ≈10 nm in each arm, to 2D wireframe DNA origami nanostructures with different geometries and varying dimensions up to 100×100 nm. These DNA‐templated dye aggregates presented strongly coupled spectral features and delocalized exciton characteristics, enabling efficient photon collection and energy transfer. Compared to the discrete branched photonic systems templated on individual DNA tiles, the interconnected excitonic networks showed approximately a 2‐fold increase in energy transfer efficiency. This bottom‐up assembly strategy paves the way to create 2D excitonic systems with complex geometries and engineered energy pathways.

Zhou, Xu↗
NASA NTRS1987

Kinematic tests of exotic flat cosmological models

Theoretical prejudice and inflationary models of the very early universe strongly favor the flat, Einstein-de Sitter model of the universe. At present the observational data conflict with this prejudice. This conflict can be resolved by considering flat models of the universe which posses a smooth component of energy density. The kinematics of such models, where the smooth component is relativistic particles, a cosmological term, a network of light strings, or fast-moving, light strings is studied in detail. The observational tests which can be used to discriminate between these models are also discussed. These tests include the magnitude-redshift, lookback time-redshift, angular size-redshift, and comoving volume-redshift diagrams and the growth of density fluctuations.

Charlton, Jane C.↗
DOE OSTI2023

A Membrane-Associated Light-Harvesting Model is Enabled by Functionalized Assemblies of Gene-Doubled TMV Proteins

Photosynthetic light harvesting requires efficient energy transfer within dynamic networks of light-harvesting complexes embedded within phospholipid membranes. Artificial light-harvesting models are valuable tools for understanding the structural features underpinning energy absorption and transfer within chromophore arrays. Here, a method for attaching a protein-based light-harvesting model to a planar, fluid supported lipid bilayer (SLB) is developed. The protein model consists of the tobacco mosaic viral capsid proteins that are gene-doubled to create a tandem dimer (dTMV). Assemblies of dTMV break the facial symmetry of the double disk to allow for differentiation between the disk faces. A single reactive lysine residue is incorporated into the dTMV assemblies for the site-selective attachment of chromophores for light absorption. Further, on the opposing dTMV face, a cysteine residue is incorporated for the bioconjugation of a peptide containing a polyhistidine tag for association with SLBs. The dual-modified dTMV complexes show significant association with SLBs and exhibit mobility on the bilayer. The techniques used herein offer a new method for protein-surface attachment and provide a platform for evaluating excited state energy transfer events in a dynamic, fully synthetic artificial light-harvesting system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Elucidating interprotein energy transfer dynamics within the antenna network from purple bacteria

In photosynthesis, absorbed light energy transfers through a network of antenna proteins with near-unity quantum efficiency to reach the reaction center, which initiates the downstream biochemical reactions. While the energy transfer dynamics within individual antenna proteins have been extensively studied over the past decades, the dynamics between the proteins are poorly understood due to the heterogeneous organization of the network. Previously reported timescales averaged over such heterogeneity, obscuring individual interprotein energy transfer steps. Here, we isolated and interrogated interprotein energy transfer by embedding two variants of the primary antenna protein from purple bacteria, light-harvesting complex 2 (LH2), together into a near-native membrane disc, known as a nanodisc. We integrated ultrafast transient absorption spectroscopy, quantum dynamics simulations, and cryogenic electron microscopy to determine interprotein energy transfer timescales. By varying the diameter of the nanodiscs, we replicated a range of distances between the proteins. The closest distance possible between neighboring LH2, which is the most common in native membranes, is 25 Å and resulted in a timescale of 5.7 ps. Larger distances of 28 to 31 Å resulted in timescales of 10 to 14 ps. Corresponding simulations showed that the fast energy transfer steps between closely spaced LH2 increase transport distances by ∼15%. Overall, our results introduce a framework for well-controlled studies of interprotein energy transfer dynamics and suggest that protein pairs serve as the primary pathway for the efficient transport of solar energy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Deep-learning-enhanced assessment of wellbore barrier effectiveness in geologic storage systems with intermediate aquifers

For geologic systems where carbon dioxide (CO 2 ) is injected underground, existing wells represent potential pathways for fluid migration. Here, this study introduces a novel deep learning model to quantify the likelihood and potential magnitude of fluid migration through wellbores at sites with intermediate aquifers or thief zones between the injection units and underground drinking water sources. Synthetic datasets, generated using reservoir simulations, captured a wide range of subsurface conditions, well attributes, operational parameters, and fluid migration scenarios. Among the regression models developed to predict brine and CO 2 leakage rates and CO 2 saturations along leaky wellbores, convolutional neural network (CNN) outperformed both Light Gradient Boosting Machine and deep neural network. Additionally, a CNN-based classification model was created to predict whether brine and CO 2 would leak along a wellbore, further improving performance over regression alone. The best models were integrated into the National Risk Assessment Partnership Open-source Integrated Assessment Model for rapid, stochastic assessment of storage system containment and leakage risks. A case study demonstrated the model’s ability to simulate fluid migration through existing wells with multiple intermediate aquifers. This computationally efficient wellbore model offers value in support of site performance evaluation and risk-informed decision making by stakeholders.

CO2 leakage↗
NASA NTRS2013

Evaluating Light Rain Drop Size Estimates from Multiwavelength Micropulse Lidar Network Profiling

This paper investigates multiwavelength retrievals of median equivolumetric drop diameter D(sub 0) suitable for drizzle and light rain, through collocated 355-/527-nm Micropulse Lidar Network (MPLNET) observations collected during precipitation occurring 9 May 2012 at the Goddard Space Flight Center (GSFC) project site. By applying a previously developed retrieval technique for infrared bands, the method exploits the differential backscatter by liquid water at 355 and 527 nm for water drops larger than approximately 50 micrometers. In the absence of molecular and aerosol scattering and neglecting any transmission losses, the ratio of the backscattering profiles at the two wavelengths (355 and 527 nm), measured from light rain below the cloud melting layer, can be described as a color ratio, which is directly related to D(sub 0). The uncertainty associated with this method is related to the unknown shape of the drop size spectrum and to the measurement error. Molecular and aerosol scattering contributions and relative transmission losses due to the various atmospheric constituents should be evaluated to derive D(sub 0) from the observed color ratio profiles. This process is responsible for increasing the uncertainty in the retrieval. Multiple scattering, especially for UV lidar, is another source of error, but it exhibits lower overall uncertainty with respect to other identified error sources. It is found that the total error upper limit on D(sub 0) approaches 50%. The impact of this retrieval for long-term MPLNET monitoring and its global data archive is discussed.

light-rain drop size estimates↗
DOE OSTI2022

Integration of physiologically relevant photosynthetic energy flows into whole genome models of light‐driven metabolism

SUMMARY Characterizing photosynthetic productivity is necessary to understand the ecological contributions and biotechnology potential of plants, algae, and cyanobacteria. Light capture efficiency and photophysiology have long been characterized by measurements of chlorophyll fluorescence dynamics. However, these investigations typically do not consider the metabolic network downstream of light harvesting. By contrast, genome‐scale metabolic models capture species‐specific metabolic capabilities but have yet to incorporate the rapid regulation of the light harvesting apparatus. Here, we combine chlorophyll fluorescence parameters defining photosynthetic and non‐photosynthetic yield of absorbed light energy with a metabolic model of the pennate diatom Phaeodactylum tricornutum. This integration increases the model predictive accuracy regarding growth rate, intracellular oxygen production and consumption, and metabolic pathway usage. Through the quantification of excess electron transport, we uncover the sequential activation of non‐radiative energy dissipation processes, cross‐compartment electron shuttling, and non‐photochemical quenching as the rapid photoacclimation strategy in P. tricornutum. Interestingly, the photon absorption thresholds that trigger the transition between these mechanisms were consistent at low and high incident photon fluxes. We use this understanding to explore engineering strategies for rerouting cellular resources and excess light energy towards bioproducts in silico . Overall, we present a methodology for incorporating a common, informative data type into computational models of light‐driven metabolism and show its utilization within the design–build–test–learn cycle for engineering of photosynthetic organisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Digital Modeling on Large Kernel Metamaterial Neural Network

Deep neural networks (DNNs) utilized recently are physically deployed with computational units (e.g., CPUs and GPUs). Such a design might lead to a heavy computational burden, significant latency, and intensive power consumption, which are critical limitations in applications such as Internet of Things (IoT), edge computing, and usage of drones. Recent advances in optical computational units (e.g., metamaterial) have shed light on energy-free and light-speed neural networks. However, the digital design of the metamaterial neural network (MNN) is fundamentally limited by its physical limitations, such as precision, noise, and bandwidth during fabrication. Moreover, the unique advantages of MNN’s (e.g., light-speed computation) are not fully explored via standard 3×3 convolution kernels. In this paper, we propose a novel large kernel metamaterial neural network (LMNN) that maximizes the digital capacity of the state-of-the-art (SOTA) MNN with model re-parametrization and network compression, while also considering the optical limitation explicitly. The new digital learning scheme can maximize the learning capacity of MNN while modeling the physical restrictions of meta-optics. With the proposed LMNN, the computation cost of the convolutional front-end can be offloaded to fabricated optical hardware. The experimental results on two publicly available datasets demonstrate that the optimized hybrid design improved classification accuracy while reducing computational latency. In conclusion, the development of the proposed LMNN is a promising step towards the ultimate goal of energy-free and light-speed AI.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Assessing the Threat: Weaving Cybersecurity into the Building Development Process

Today’s connected lighting systems have the potential to reduce energy consumption and operational costs via the use of the data they collect and share with other building systems (e.g., HVAC, building automation, security). However, many market available products are new to being networked, and when networked components in lighting and other building systems are not sufficiently secured, they present opportunities for criminals to exploit. Further, security vulnerabilities in one system can be used as lateral steppingstones that allow access to other prized assets on the same network. These cybersecurity concerns could deter the adoption and use of connected systems, which then could jeopardize long-term national objectives for reduced energy usage. The workflows described here and presented in more detail in the referenced reports are examples of how these frameworks and tools can be put to practical use during system design and specification.

attack surface, Building development, threat analy↗