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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Broadband Continuous Single-mode Tuning of a Short-cavity Quantum-cascade VECSEL

Changing the length of a laser cavity is a simple technique for continuously tuning the wavelength of a laserbutis rarelyused for broad fractional tuning, with a notable exception of the diode laser VCSEL1,2. This is because, to avoid mode hopping, the cavity must be keptopticallyshort to ensure a large free-spectral-range (FSR) compared to the gain bandwidth of the amplifying material. Terahertz quantum-cascade (QC) lasers are ideal candidates for such a short cavity scheme as they demonstrate exceptional gain bandwidths (up to octave spanning) and can be integrated with broadband amplifying metasurfaces. We present such a QC-metasurface based vertical-external-cavity surface-emitting-laser (VECSEL) that exhibits over 20% continuous fractional tuning of a single laser mode. Such tuning is possible because the metasurface has sub-wavelength thickness which allows lasing on low-order Fabry-Pérot cavity modes. Good beam quality and high output power are simultaneously obtained.

Christopher A Curwen↗
NASA NTRSDate not supplied

Projecting the Performance of a Continuous Bosch Carbon Dioxide Reduction System

Kinetic studies using a subscale reactor were used to develop power-law rate equations for the two primary reactions contributing to the Bosch carbon dioxide reduction process: the reverse water-gas shift reaction and the carbon monoxide hydrogenation reaction. An alloy catalyst in bead form was used in these studies, which investigated both the dependence on gas composition and the dependence on time as the beads gradually disintegrate during carbon formation. Bead disintegration impacts both the rates and the relative activities of the two primary reactions. Although the kinetic testing was conducted in a single-pass mode, the target Bosch application requires operation in a recycle mode to achieve high conversion. Further, the proposed design concept for a full-scale carbon dioxide reduction system would allow carbon to be removed continuously in contrast to previous Bosch processes that required replacement of expendable catalyst cartridges. The developed power-law kinetic rate equations were used in conjunction with a reactor model to project the operating conditions, performance, and sizing of a continuous Bosch system.

carbon dioxide reduction↗
NASA NTRSDate not supplied

Aerosol Above Cloud Optical Depth in the Southeast Atlantic and Its Use in Continuity NASA Products

When aerosol overlie bright clouds, their radiative impact on the incident light can be either positive or negative. This difference in radiative impact promotes difficulties in remote sensing aerosol properties, which have shown biases in aerosol optical depth (AOD) retrievals when clouds are underneath. To better constrain this quantity, we have measured aerosols above clouds over the course of 3 years in the southeast Atlantic as part of the ObseRvations of CLouds above Aerosols and their intEractionS (ORACLES). These directly measured AOD above clouds are used to build and improve upon the current Near-Real-Time MODACAERO algorithm for above cloud AOD from MODIS, into a continuity product for NASA EOS/SNPP/JPSS. Here we present a summary of the above cloud aerosol optical depth measured during ORACLES, and that will be used for porting the MODACAERO algorithm to the continuity product. We use aerosol optical depth measured directly from sunlight attenuation using the Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR) and compare it to the active remote sensing, High Spectral Resolution Lidar-2 (HSRL-2). We present a combination of all 3 years of measurements during peak biomass burning season. We have observed average above cloud aerosol optical depth at 500 nm ranging from 0.28 to 0.37, with the maximum in August 2017. We emphasize the use of improved aerosol absorption and scattering models based on airborne measurements during ORACLES.

aerosol optical depth (AOD)↗
NASA NTRS2020

Continuity of A Global, Satellite-Based Terrestrial Primary Productivity Dataset in the VIIRS Era Achieved With Model-Data Fusion

The NASA Terra and Aqua satellites have been successfully operating for over two decades and have far exceeded their original 5‐year design life. However, the era of NASA’s Earth Observing System (EOS) may be coming to a close as early as 2023. We conducted a comprehensive calibration and validation of the MODIS MOD17 product [1,2] and the potential for continuity of multi‐decadal ecosystem gross primary productivity (GPP) and annual net primary productivity (NPP), using data from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensors aboard Suomi NPP and NOAA‐20. We combined an 18‐year record of eddy covariance flux tower measurements with hundreds of field measurements of NPP from the Oak Ridge National Laboratories Multi‐Biome collection to benchmark MODIS MOD17 Collection 6.1 (C61) and to develop the first terrestrial productivity estimates from VIIRS. Plant traits from the literature and the global TRY database [3,4] provide strong priors for identifying model parameters in a Bayesian model‐data fusion. As MODIS‐like observations are still needed for global environmental applications, the new VIIRS VNP17 product has the potential to extend these continuous estimates of global, terrestrial primary productivity beyond 2030.

K. Arthur Endsley↗
NASA NTRSDate not supplied

Comparison Between Continuous and Intermittent Actigraphy Outcomes During Long Duration Missions

Astronauts typically wear actigraphy watches continuously during their missions inflight. In late 2020, astronauts were scheduled to wear an actiwatch for one 2-week period every two months. Our aim was to compare sleep outcomes between data collected continuously vs intermittently to identify whether intermittently collected sleep data would yield similar results.

fatigue↗
NASA NTRSDate not supplied

Development of an Ultra-Low-Temperature 5-Stage Continuous Adiabatic Demagnetization Refrigerator That Provides Cooling at Two Unique Temperatures

The cryogenics group at NASA-Goddard has built several adiabatic demagnetization refrigerators (ADRs) for cooling detectors to sub-Kelvin temperatures in a lab, balloon, or space environment. The effort presented here describes the development of a new 5-stage adiabatic demagnetization refrigerator capable of providing continuous cooling at 35 mK with a cooling power double that previously demonstrated at this temperature. It will also provide a separate continuous temperature platform near 1 K. This system will be qualified using flight-like conditions, culminating in an ADR that is ready for the rigors of spaceflight by the end of the effort.

ADR↗
NASA NTRS2023

A New Model of Dry Firn Densification Constrained By Continuous Strain Measurements Near South Pole

Converting measurements of ice-sheet surface elevation change to mass change requires measurements of accumulation and knowledge of the evolution of the density profile in the firn. Most firn-densification models are tuned using measured depth-density profiles, a method which is based on an assumption that the density profile in the firn is invariant through time. Here we present continuous measurements of firn-compaction rates in 12 boreholes near the South Pole over a two-year period. To our knowledge, these are the first continuous measurements of firn compaction on the Antarctic Plateau. We use the data to derive a new firn-densification algorithm framed as a constitutive relationship. We also compare our measurements to compaction rates predicted by several existing firn densification models. Results indicate that an activation energy of 60 kJ mol (−1), a value within the range used by current models, best predicts the seasonal cycle in compaction rates on the Antarctic Plateau. Our results suggest models can predict firn-compaction rates with at best 7% uncertainty and cumulative firn compaction on a two year timescale with at best 8% uncertainty.

C. Max Stevens↗
NASA NTRSDate not supplied

Continuing Long-term Global SO 2 Data Record with JPSS OMPS Instruments

NASA’s long-term Earth Observing System (EOS) SO 2 climate data record (CDR) started with Aura/Ozone Monitoring Instrument (OMI, launched in 2004) and is now being continued with the SNPP/Ozone Mapping and Profiler Suite (OMPS, launched in 2011). Both OMI and SNPP/OMPS SO 2 CDRs are produced with the Goddard principal component analysis (PCA) spectral fitting algorithm. By inherently accounting for various instrumental factors, the PCA technique enables highly consistent retrievals between different instruments. In this presentation, we will provide an overview on our effort to further extend the EOS SO 2 CDR, by implementing the PCA SO 2 algorithm with multiple OMPS instruments flying on the Joint Polar Satellite System (JPSS) constellation, including NOAA-20 (launched in 2017) and NOAA-21 (launched in 2022). We will present results analyzing our new NOAA-20/OMPS PCA SO 2 EOS continuity product, to be publicly released in fall of 2023. We will show statistical analyses on the quality of NOAA-20 PCA SO 2 product, such as retrieval noise, biases over background areas, and long-term stability. We will employ a previously established top-down method to estimate SO2 emissions from selected large point sources, using NOAA-20 SO 2 retrievals and assimilated wind fields as input. The SO 2 emission estimates derived from NOAA-20 retrievals will be compared with those from OMI, SNPP/OMPS, and S5P/TROPOMI (TROPOspheric Monitoring Instrument). We will also demonstrate the application of a new machine learning technique that further reduces the noise of NOAA-20 SO 2 retrievals. Finally, we will present preliminary PCA SO 2 retrievals from recently launched satellite sensors, including NOAA-21/OMPS and NASA’s geostationary TEMPO (Tropospheric Emissions: Monitoring of Pollution) instrument.

SO2↗
NASA NTRS2023

Calibration of the SNPP and NOAA 20 VIIRS Sensors for Continuity of the MODIS Climate Data Records

Accurate long-term sensor calibration and periodic re-processing to ensure consistency and continuity of atmospheric, land and ocean geophysical retrievals from space within the mission period and across different missions is a major requirement of climate data records. In this work, we applied the Multi-Angle Implementation of Atmospheric Correction (MAIAC)-based vicarious calibration technique over Libya-4 desert site to perform calibration analysis of Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20 satellites. For both VIIRS sensors we characterized residual linear calibration trends and cross-calibrated both sensors to MODerate resolution Imaging Spectroradiometer (MODIS) Aqua regarded as a calibration standard. The relative spectral response (RSR) differences were accounted for using the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS) hyperspectral surface reflectance data. Our results agree with independent vicarious calibration results of both the MODIS/VIIRS Characterization Support Team as well as the CERES Imager and Geostationary Calibration Group within estimated uncertainty of 1–2%. Analysis of MAIAC geophysical products with the new calibration shows a high level of agreement of MAIAC aerosol, surface reflectance and NDVI records between MODIS and VIIRS. Excluding high aerosol optical depth (AOD), all three sensors agree in AOD with mean difference (MD) less than 0.01 and residual mean squared difference rmsd ∼ 0.04. Spectral geometrically normalized surface reflectance agrees within rmsd of 0.003–0.005 in the visible and 0.01–0.012 at longer wavelengths. The residual surface reflectance differences are fully explained by differences in spectral filter functions. Finally, difference in NDVI is characterized by rmsd ∼ 0.02 and MD less than 0.003 for NDVI based on VIIRS imagery bands I1/I2 and less than 0.01 for NDVI based on VIIRS radiometric bands M5/M7. In practical sense, these numbers indicate consistency and continuity in MAIAC records ensuring the smooth transition from MODIS to VIIRS.

MAIAC↗
NASA NTRS2023

Retrieval of Aerosol Optical Properties From GOCI-II Observations: Continuation of Long-Term Geostationary Aerosol Monitoring Over East Asia

Since the Geostationary Ocean Color Imager (GOCI) was successfully launched in 2010, the GOCI Yonsei aerosol retrieval (YAER) algorithm has been continuously updated to retrieve hourly aerosol optical properties. GOCI-II has 4 more channels including UV, finer spatial resolution (250 m), and daily full disk coverage as compared to GOCI, and was launched in February 2020, onboard the GEO-KOMPSAT-2B (GK-2B) satellite. In this study, we extended the YAER algorithm to GOCI-II data based on its improved performance in many aspects and present the first results of aerosol optical properties retrieved from GOCI-II data. Utilizing the overlapping period between the GOCI-II and GOCI in geostationary Earth orbit, we present GOCI-II aerosol retrievals for high aerosol-loading cases over East Asia and show that these have a consistent spatial distribution with those from GOCI. Furthermore, GOCI-II provides AOD at an even higher spatial resolution, revealing finer changes in aerosol concentrations. Validation results for one year data show that the GOCI-II AOD has a correlation coefficient of 0.83 and a ratio within the expected error (EE) of 59.4 % when compared with the aerosol robotic network (AERONET) data. We compared statistical metrics for the GOCI and GOCI-II AODs to assess the consistency between the two datasets. In addition, it was found that there is a strong correlation between the two datasets from the comparison of gridded GOCI and GOCI-II AOD products. It is expected that data from GOCI-II will continue long-term aerosol records with high accuracy that can be used to address air-quality issues over East Asia.

AOD↗
DOE OSTI2026

Continual Learning for Particle Accelerators

Particle accelerators operate under dynamically changing conditions, which often lead to data distribution drifts. These drifts pose significant challenges for Machine Learning (ML) models, which typically fail to maintain performance when faced with such non-stationary data. In particle accelerators, the primary sources of these data drifts include changes in accelerator settings and non-measured parameters such as machine degradation and environmental factors. Previous research has proposed conditional models to handle multiple beam configurations effectively; however, it is challenging to train the ML models on all possible configuration settings. Additionally, conditional models alone can not address performance degradation caused by drifts due to non-measured factors. These limitations contribute to a significant gap between ML development and its deployment in real-world operational settings. To bridge this gap, in this paper, we identify some of the key areas within particle accelerators where continual learning can help mitigate drift-induced performance degradation. In addition, we present a practical use case where a conditional Auto-Encoder model coupled with memory-based continual learning has been employed to demonstrate stable performance even when underlying data drifts

Rajput, Kishansingh [Thomas Jefferson National Acc↗
DOE OSTI2024

Additive manufacturing of sandwich panels with continuous fiber reinforced high modulus composite facings

Abstract An improved approach consisting of a combination of fiber placement and fused filament fabrication is introduced for the additive manufacture (AM) of structural grade sandwich beams. Here, sandwich beams are additively manufactured using in‐situ deposition and consolidation of continuous fiber unidirectional facings made from a commingled yarn system of e‐glass fiber (~50% vol.) and amorphous PET, and a hexagonal honeycomb core structure made from PETG. Both facings and the sandwich core are manufactured on a single machine, in one sequence (skin‐core‐skin), employing the benefit of matrix compatibility to create autohesion at the interfaces. Flexural and transverse shear rigidity are determined experimentally and compared with analytical predictions and show correlation to within 3%. Flexural strength and core shear strength are also measured. Post‐mortem examinations show that core fracture and core facing debond were the dominant failure mode in flexure. Single cantilever beam tests were performed to evaluate core facing debond toughness. Subsequently, surface preheat using infrared heaters was utilized to increase autohesion between core and facing. The results show debond toughness was increased 4 times using infrared heating. This research effort presents a manufacturing approach that has the potential for the AM of stiff, well bonded, structural grade sandwich beams, in an integrated sequence, employing in‐situ consolidation to the facings, without the need for the use of intermediate adhesives for skin‐to‐core bonding. Highlights An improved additive manufacturing technique for making sandwich panels is developed. Sandwich panel facings have fiber volume fractions of approximately 50%. Surface preheat improves core‐to‐facing debond toughness by a factor of 4. Top and bottom facings are consolidated during manufacture leading to better properties. Experimental results are compared to analytical predictions and show good correlation.

17 WIND ENERGY↗
DOE OSTI2024

Reactive extrusion of frontally polymerizing continuous carbon fiber reinforced polymer composites

The manufacturing of carbon fiber-reinforced polymer (CFRP) composites demands rapid and energy-efficient strategies. Frontal polymerization (FP) enables the manufacturing of CFRP using dicyclopentadiene (DCPD) thermoset polymer which meets these requirements. In this work, we introduce reactive extrusion of CFRP (RE-CFRP), where two rollers provide localized heat and pressure to sustain the curing reaction and the consolidation of a continuous carbon fiber tow pre-impregnated with DCPD. We study the effect of the extrusion speed, temperature, and compaction force on the properties of the produced CFRP. Mechanical testing confirms that the resulting fiber volume fraction and the elastic modulus are similar to bulk cured tows. A homogenized thermo-chemical model is developed to study the effect of the process parameters on the polymerization reaction. The process produces hollow woven composite tubes directly via extrusion and in situ curing. Overall, this process offers advantages in curing, tooling, speed, and energy.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Variational Optical Phase Learning on a Continuous-Variable Quantum Compiler

Quantum process learning is a fundamental primitive that draws inspiration from machine learning with the goal of better studying the dynamics of quantum systems. One approach to quantum process learning is quantum compilation, whereby an analog quantum operation is digitized by compiling it into a series of basic gates. While there has been significant focus on quantum compiling for discrete-variable systems, the continuous-variable (CV) framework has received comparatively less attention. We present an experimental implementation of a CV quantum compiler that uses two-mode squeezed light to learn a Gaussian unitary operation. We demonstrate the compiler by learning a parameterized linear phase unitary through the use of target and control phase unitaries to demonstrate a factor of 5.4 increase in the precision of the phase estimation and a 3.6-fold acceleration in the time-to-solution metric when leveraging quantum resources. We further show how our approach can be extended to higher-dimensional compilation tasks. Our results are enabled by the tunable control of our cost landscape via variable squeezing, thus providing a critical framework to simultaneously increase precision and reduce time-to-solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Continuous-variable quantum computation of the O(3) model in 1+1 dimensions

We formulate the $O(3)$ non-linear sigma model in $1+1$ dimensions as a limit of a three-component scalar field theory restricted to the unit sphere in the large squeezing limit. This allows us to describe the model in terms of the continuous variable (CV) approach to quantum computing. Here we construct the ground state and excited states using the coupled cluster ansatz and find excellent agreement with the exact diagonalization results for a small number of lattice sites. We then present the simulation protocol for the time evolution of the model using CV gates, estimate the discretization error, and present numerical results obtained from a photonic quantum simulator. We expect that the methods developed in this work will be useful for exploring interesting dynamics for a wide class of sigma models and gauge theories, as well as for simulating scattering events on quantum hardware in the coming decade.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Metric isometries, holography, and continuous symmetry operators

In the AdS/CFT correspondence, a topological symmetry operator of the boundary conformal field theory (CFT) is dual to a dynamical brane in the gravitational bulk. Said differently, this predicts a dynamical brane for every global symmetry of the boundary CFT. We analyze this correspondence for continuous symmetries which arise from a consistent truncation of isometries on the “internal” factor 𝑋 of AdS × 𝑋. In the extra-dimensional geometry, these branes are associated with various metric singularities and do not arise from wrapped D-branes. Boosts relate configurations interpreted as topological symmetry operators and heavy defects in the CFT. From the perspective of the AdS factor, with gravity and bulk gauge fields, these are codimension-2 Gukov-Witten-like vortex configurations which are the gravity duals of 0-form symmetry operators. These effective branes come with an asymptotic tension and size which is also fully fixed by bulk dynamics. We use this higher-dimensional perspective to determine properties of the worldvolume theory for these branes. We also discuss how these considerations generalize to more general quantum field theories engineered via string theory which need not possess a semiclassical gravity dual.

anomalies↗
DOE OSTI2024

Newton-Raphson AC Power Flow Convergence Based on Deep Learning Initialization and Homotopy Continuation

Power flow forms the basis of many power system studies. With the increased penetration of renewable energy, grid planners tend to perform multiple power flow simulations under various operating conditions and not just selected snapshots at peak or light load conditions. Getting a converged AC power flow (ACPF) case remains a significant challenge for grid planners especially in large power grid networks. This paper proposes a two-stage approach to improve Newton-Raphson ACPF convergence and was applied to a 6102 bus Electric Reliability Council of Texas (ERCOT) system. The first stage utilizes a deep learning-based initializer with data re-training. Here a deep neural network (DNN) initializer is developed to provide better initial voltage magnitude and angle guesses to aid in power flow convergence. This is because Newton-Raphson ACPF is quite sensitive to the initial conditions and bad initialization could lead to divergence. The DNN initializer includes a data re-training framework that improves the initializer's performance when faced with limited training data. The DNN initializer successfully solved 3,285 cases out of 3,899 non-converging dispatch and performed better than random forest and DC power flow initialization methods. ACPF cases not solved in this first stage are then passed through a hot-starting algorithm based on homotopy continuation with switched shunt control. The hot-starting algorithm successfully converged 416 cases out of the remaining 614 non-converging ACPF dispatch. In conclusion, the combined two-stage approach achieved a 94.9% success rate, by converging a total of 3,701 cases out of the initial 3,899 unsolved cases.

Deep learning↗
DOE OSTI2025

PySolate : A Python‐Based Thresholding Tool to Denoise or Designal Seismic Waveforms Based on the Continuous Wavelet Transform

PySolate is a Python‐based toolset that implements the continuous wavelet transform and nonlinear thresholding operations to denoise or designal seismic data, following Langston and Mousavi (2019). This filtering approach can remove microseismic noise to isolate intermediate‐period seismic signals that are key to enabling full‐waveform modeling and analysis of smaller‐magnitude regional events. This approach is best for the application to signals with frequency or time separation of signal and noise, in contrast to Fourier analysis, which is effective when signal and noise are separated in frequency. We demonstrate the Python toolset using the six announced Democratic People’s Republic of Korea declared nuclear tests, showing the effectiveness of isolating the seismic signal compared to standard bandpass filtering. In conclusion, we also demonstrate the ease of using the toolset with any Python processing tools.

Asia↗