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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The Simons Observatory: A fully remote controlled calibration system with a sparse wire grid for cosmic microwave background telescopes

For cosmic microwave background (CMB) polarization observations, calibration of detector polarization angles is essential. We have developed a fully remote controlled calibration system with a sparse wire grid that reflects linearly polarized light along the wire direction. The new feature is a remote-controlled system for regular calibration, which has not been possible in sparse wire grid calibrators in past experiments. The remote control can be achieved by two electric linear actuators that load or unload the sparse wire grid into a position centered on the optical axis of a telescope between the calibration time and CMB observation. Furthermore, the sparse wire grid can be rotated by using a motor. A rotary encoder and a gravity sensor are installed on the sparse wire grid to monitor the wire direction. They allow us to achieve detector polarization angle calibration with an expected systematic error of 0.08°. The calibration system will be installed in small-aperture telescopes at Simons Observatory.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Uncertainty-Guided Prediction Horizon of Phase-Resolved Ocean Wave Forecasting Under Data Sparsity: Experimental and Numerical Evaluation

Accurate short-term wave forecasting is critical for the safe and efficient operation of marine structures that rely on real-time, phase-resolved ocean wave information for control and monitoring purposes (e.g., digital twins). These systems often depend on environmental sensors (e.g., waverider buoys, wave-sensing LIDAR). Challenges arise when upstream sensor data are missing, sparse, or phase-shifted due to drift. This study investigates the performance of two machine learning models, time-series dense encoder (TiDE) and long short-term memory (LSTM), for forecasting phase-resolved ocean surface elevations under varying degrees of data degradation. We introduce the τ-trimming algorithm, which adapts the prediction horizon based on uncertainty thresholds derived from historical forecasts. Numerical wave tank (NWT) and wave basin experiments are used to benchmark model performance under short- and long-term data masking, spatially coarse sensor grids, and upstream phase shifts. Results show under a 50% probability of upstream data loss, the τ-trimmed TiDE model achieves a 46% reduction in error at the most upstream target, compared to 22% for LSTM. Furthermore, phase misalignment in upstream data introduces a near-linear increase in forecast error. Under moderate model settings, a ±3 s misalignment increases the mean absolute error by approximately 0.5 m, while the same error is accumulated at ±4 s using the more conservative approach. These findings inform the design of resilient, uncertainty-aware wave forecasting systems suited for realistic offshore sensing environments.

42 ENGINEERING↗
NASA NTRS2002

Image Dis-Integration for Improved Plasmasphere Visualization

Traditionally, study of the plasmasphere has involved terrestrial observation of local characteristics. Global modeling of the plasmasphere in such an observation regime made use of an ensemble of (sparse) local measurements. Recently, sensors aboard the IMAGE (Imager for Magnetopause-to-Aurora Global Exploration) satellite (in particular, the EUV (Extreme Ultra Violet) Imager) have created the potential for truly global study of the plasmasphere. IMAGE was launched in spring of 2000 in an orbit with apogee altitude 7.2 R E (Earth radii) and perigee altitude 1000 km. IMAGE's EUV sensor allows an external view of the distribution of cold plasma in the plasmasphere to be acquired. EUV is designed to image light emission at 30.4 nanometers, which is the emission wavelength of the He + ion in the presence of solar radiation. He + makes up approximately 15-20% of the plasma in the plasmasphere, thus imaging of He + enables determination of plasma distribution. The EUV instrument provides a 90° by 84° field of view which is imaged as an equally spaced 150x140 pixel array on a spherical imaging surface. The EUV produces an image approximately every 10 minutes when the sensor is operating. Since EUV images contain line-of-sight integrations of plasma distributions, they do not directly express equatorial plane density (which would enable comparison of observed plasma distributions with predictions from models). Furthermore, the plasma density at any point in three-space is not known. The goal of our work was development of a technique that can enable plasma density to be determined throughout three-space. Our approach to creation of a three-space representation of the plasma distribution involves disintegrating the EUV lines of sight to form a volumetric map of plasma densities.

Timothy S. Newman↗
NASA NTRS2024

Investigating the Spatial and Temporal Limitations for Remote Sensing of Wildfire Smoke Using Satellite and Airborne Imagers During FIREX_AQ

Starting from point sources, wildfire smoke is important in the global aerosol system. The ability to characterize smoke near-source is key to modeling smoke dispersion and predicting air quality. With hemispheric views and 10-min refresh, imagers in Geostationary (GEO) orbit have advantages monitoring smoke over once-per-day sensors in low-earth orbit (LEO). However, both can be inadequate in capturing the characteristics of smoke plumes close to their sources due to too-coarse spatial resolution (both detector and product resolution), too-sparse temporal resolution (from LEO sensors), and too-conservative masking. In addition to satellite observations, the Fire Influence on Regional to Global Environments and Air Quality experiment offered sub-orbital enhanced-MODIS Airborne Simulator (eMAS) imagery at 50 m pixel resolution—including multiple eMAS flight tracks over individual fires in short time periods. It provided opportunity to explore smoke plume characterization at various spatial and temporal scales and quantify the limitations of space sensors for describing smoke magnitude near source as well as its temporal evolution. Here we applied modified aerosol algorithm to different imagers, relaxing its masking to estimate smoke's aerosol optical depth (AOD) as close as possible to its source. We found that GEO sensors with nominal 1 km spatial resolution can match the much finer resolution eMAS retrieved mean plume AOD, as long as the retrieval spatial resolution is finer than the width of the plumes. However, the plume's maximum AOD may be drastically underestimated by satellite products.

remote sensing↗
NASA NTRS1978

Recursive estimator for OSO-8 attitude

Modifications and enhancements that have been made to the Recursive Estimation Attitude Program (REAP) are discussed. Continuous attitudes for OSO-8 to + or - 0.05 degree accuracy are determined from Sun and star slit sensors mounted on the spinning portion of the spacecraft. The bulk of the attitude production is performed by a Weighted Least Squares (WLS) batch processor, but REAP is used for problem passes such as those involving gas jet maneuvers, sparse star fields, or star sensor saturation by high energy particles in the South Atlantic Anomaly.

Headrick, R. D.↗
DOE OSTI2025

Assessing the limitations of commercial sensors and models for supporting marine carbon dioxide removal monitoring: a case study

Several unknowns remain surrounding marine Carbon Dioxide Removal (mCDR) monitoring, reporting, and verification (MRV) practices and capabilities. Current in-situ sensor technology is limited (primarily pH and pCO 2 ), requiring calculations and assumptions to estimate changes in carbonate chemistry parameters, including total alkalinity (TA). Considering that cost, energy consumption, and accuracy of commercial sensors can vary by orders of magnitude, understanding how well existing sensors perform in an mCDR context is important for this emerging community. Likewise, documenting sensor limitations and how relatively simple models can optimize sensor deployments will improve MRV efforts and support protocol development. Here we (1) compare performance a variety of commercially available sensors in a blind mesocosm experiment simulating ocean alkalinity enhancement (OAE), and how sensor performance impacted carbonate chemistry estimates; (2) evaluate if sensors can distinguish the OAE signal from natural variability during a small scale OAE field test in Sequim Bay, WA, USA, and (3) use an idealized ocean biogeochemistry model to explore optimal sensor network design based on (1) and (2). Our mesocosm results indicate that correctly constraining pH uncertainty will be critical for accurate TA estimates with current sensor technology compared to the less impactful variation caused by uncertainty in pCO 2 (pH data that are presented throughout are reported on the total scale (pH T ) unless otherwise noted). Our pilot field test demonstrated that sensors were capable of distinguishing mCDR signatures from natural variability under optimal real-world conditions. Idealized modeling simulations of the field test showed that a range of sparse and dense (3 to 100) sensors sampling areas of detectable increases will underestimate the net change in surface pH by at least 35–55%, at both realistic and highly elevated alkalinity input levels. We also highlight the limitations of current sensing technology for MRV, and the importance of ocean biogeochemistry models as critical tools for predicting when and where mCDR signals will be detectable using available sensors. Overall, our findings suggest that commercially available pCO 2 sensors and some pH sensors will form an important backbone for mCDR MRV tasks, though complete MRV characterization will require these data to be used in combination with other tools.

OAE↗
DOE OSTI2023

Measurement and applications: Exploring the challenges and opportunities of hierarchical federated learning in sensor applications

Sensor applications have become ubiquitous in modern society as the digital age continues to advance. AI-based techniques (e.g., machine learning) are effective at extracting actionable information from large amounts of data. An example would be an automated water irrigation system that uses AI-based techniques on soil quality data to decide how to best distribute water. However, these AI-based techniques are costly in terms of hardware resources, and Internet-of-Things (IoT) sensors are resource-constrained with respect to processing power, energy, and storage capacity. These limitations can compromise the security, performance, and reliability of sensor-driven applications. To address these concerns, cloud computing services can be used by sensor applications for data storage and processing. Unfortunately, cloud-based sensor applications that require real-time processing, such as medical applications (e.g., fall detection and stroke prediction), are vulnerable to issues such as network latency due to the sparse and unreliable networks between the sensor nodes and the cloud server [1]. As users approach the edge of the communications network, latency issues become more severe and frequent. A promising alternative is edge computing, which provides cloud-like capabilities at the edge of the network by pushing storage and processing capabilities from centralized nodes to edge devices that are closer to where the data are gathered, resulting in reduced network delays [2], [3].

Po-Leen Ooi, Melanie↗
DOE OSTI2022

Equation‐Free Surrogate Modeling of Geophysical Flows at the Intersection of Machine Learning and Data Assimilation

Abstract There is a growing interest in developing data‐driven reduced‐order models for atmospheric and oceanic flows that are trained on data obtained either from high‐resolution simulations or satellite observations. The data‐driven models are non‐intrusive in nature and offer significant computational savings compared to large‐scale numerical models. These low‐dimensional models can be utilized to reduce the computational burden of generating forecasts and estimating model uncertainty without losing the key information needed for data assimilation (DA) to produce accurate state estimates. This paper aims at exploring an equation‐free surrogate modeling approach at the intersection of machine learning and DA in Earth system modeling. With this objective, we introduce an end‐to‐end non‐intrusive reduced‐order modeling (NIROM) framework equipped with contributions in modal decomposition, time series prediction, optimal sensor placement, and sequential DA. Specifically, we use proper orthogonal decomposition (POD) to identify the dominant structures of the flow, and a long short‐term memory network to model the dynamics of the POD modes. The NIROM is integrated within the deterministic ensemble Kalman filter (DEnKF) to incorporate sparse and noisy observations at optimal sensor locations obtained through QR pivoting. The feasibility and the benefit of the proposed framework are demonstrated for the NOAA Optimum Interpolation Sea Surface Temperature (SST) V2 data set. Our results indicate that the NIROM is stable for long‐term forecasting and can model dynamics of SST with a reasonable level of accuracy. Furthermore, the prediction accuracy of the NIROM gets improved by almost one order of magnitude by the DEnKF algorithm.

Pawar, Suraj↗
DOE OSTI2023

Tensor-based flow reconstruction from optimally located sensor measurements

Reconstructing high-resolution flow fields from sparse measurements is a major challenge in fluid dynamics. Existing methods often vectorize the flow by stacking different spatial directions on top of each other, hence confounding the information encoded in different dimensions. Here, we introduce a tensor-based sensor placement and flow reconstruction method which retains and exploits the inherent multidimensionality of the flow. We derive estimates for the flow reconstruction error, storage requirements and computational cost of our method. We show, with examples, that our tensor-based method is significantly more accurate than similar vectorized methods. Furthermore, the variance of the error is smaller when using our tensor-based method. While the computational cost of our method is comparable to similar vectorized methods, it reduces the storage cost by several orders of magnitude. The reduced storage cost becomes even more pronounced as the dimension of the flow increases. We demonstrate the efficacy of our method on three examples: a chaotic Kolmogorov flow, in situ and satellite measurements of the global sea surface temperature and three-dimensional unsteady simulated flow around a marine research vessel.

Mechanics↗
NASA NTRS2004

Initial Validation and Results of Geoscience Laser Altimeter System Optical Properties Retrievals

Verification of Geoscience Laser Altimeter System (GLAS) optical retrievals is . problematic in that passage over ground sites is both instantaneous and sparse plus space-borne passive sensors such as MODIS are too frequently out of sync with the GLAS position. In October 2003, the GLAS Validation Experiment was executed from NASA Dryden Research Center, California to greatly increase validation possibilities. The high-altitude NASA ER-2 aircraft and onboard instrumentation of Cloud Physics Lidar (CPL), MODIS Airborne Simulator (MAS), and/or MODIS/ASTER Airborne Simulator (MASTER) under-flew seven orbit tracks of GLAS for cirrus, smoke, and urban pollution optical properties inter-comparisons. These highly calibrated suite of instruments are the best data set yet to validate GLAS atmospheric parameters. In this presentation, we will focus on the inter-comparison with GLAS and CPL and draw preliminary conclusions about the accuracies of the GLAS 532nm retrievals of optical depth, extinction, backscatter cross section, and calculated extinction-to-backscatter ratio. Comparisons to an AERONET/MPL ground-based site at Monterey, California will be attempted. Examples of GLAS operational optical data products will be shown.

Hlavka, Dennis L.↗
NASA NTRSDate not supplied

Noise Reduction and Calibration for Aeroacoustic Loads from Unsteady Pressure-Sensitive Paint

Unsteady pressure-sensitive paint (uPSP) is a significant new technology for high spatial resolution measurement of complex dynamic surface pressure loading on a wind tunnel model, to better define flight vehicle buffet and aeroacoustic loads. Compared with the very sparse surface pressure measurements of traditional sensors, the pressure sensitivity of uPSP light reflectivity provides a visual measure of surface pressure fluctuations over a wide area illuminated by high intensity light, captured by a high-speed digital camera. Panda et al (AIAA Journal, May 2019) have shown good data is recoverable up to 10 kHz. In cases where available uPSP luminescence is low for a painted wind tunnel test model, significant noise levels have been observed primarily due to the discrete photon arrival variability of low intensity light (termed “shot noise”). In this case, uPSP measurements can exhibit unusually low signal to noise ratio (SNR of 0.1 or less). This paper will report on latest work to recover calibrated uPSP measurements which are noise free over a useful dynamic range of 10-15 dB. It will be shown how the distinct spatial correlations of both the shot noise and the underlying aeroacoustic surface pressure play an important role in obtaining a reliable measurement.

pressure-sensitive paint↗
NASA NTRS1989

Integration of active and passive sensors for obstacle avoidance

The automatic obstacle-avoidance guidance problem is studied under the operational constraints imposed by the rotorcraft nap-of-the-earth (NOE) environment. The problem is discussed for two different circumstances. The first assumes that a full range map is available, irrespective of the type of sensor being used. Two approaches are proposed to extend a two-dimensional obstacle-avoidance concept presented by Cheng (1988). The situation where only a sparse range map is available from a passive sensor is also treated. An integrated approach that augments the passive sensor with an active one is discussed, along with the problem of data fusion and how it is affected by the characteristics of NOE flight.

Cheng, Victor H. L.↗
DOE OSTI2025

Expediting field-effect transistor chemical sensor design with neuromorphic spiking graph neural networks

Improving the sensitive and selective detection of analytes in a variety of applications requires accelerating the rational design of field-effect transistor (FET) chemical sensors. Achieving high-performance detection relies on identifying optimal probe materials that can effectively interact with target analytes, a process traditionally driven by chemical intuition and time-consuming trial-and-error methods. To address the difficulties in probe screening for FET sensor development, this work presents a methodology that combines neuromorphic machine learning (ML) architectures, specifically a hybrid spiking graph neural network (SGNN), with an enriched dataset of physicochemical properties through semi-automated data extraction using large language models. Achieving a classification accuracy of 0.89 in predicting sensor sensitivity categories, the SGNN model outperformed traditional ML techniques by leveraging its ability to capture both global physicochemical properties and sparse topological features through a hybrid modeling framework. Next-generation sensor design was informed by the actionable insights into the connections between material properties and sensing performance offered by the SGNN framework. Through virtual screening for the detection of per- and polyfluoroalkyl substances (PFAS) as a use case, the effectiveness of the SGNN model was further validated. Density functional theory simulations confirmed graphene as a promising active material for PFAS detection as suggested by the SGNN framework. By bridging gaps in predictive modeling and data availability, this integrated approach provides a strong foundation for accelerating advancements in FET sensor design and innovation.

Ferreira, Rodrigo Pires [Univ. of Chicago, IL (Uni↗
NASA NTRS2002

Sparse matrix wavefront reconstruction: simulations and experiments

Adaptive optics systems with Shack-Hartmann wavefront sensors require reconstruction of the atmospheric phase error from slope measurements, with every sensor in the array being used in the computation of each actuator command. This fully populated reconstruction matrix can result in a significant computational burden for adaptive optics systems with large numbers of actuators. A method for generating sparse wavefront reconstruction matrices for adaptive optics isproposed. The method exploits the relevance of nearby slope measurements for control of an individual actuator, and relies upon the limited extent of the influence function for a zonal deformable mirror. Relying only on nearby sensor information can significantly reduce the calculation time for wavefront reconstruction. In addition, a hierarchic controller is proposed to recover some of the global wavefront information. The performance of these sparse wavefront reconstruction matrices was evaluated in simulation, and tested on the Palomar Adaptive Optics System. This paper will present some initial results from the simulations and experiments.

space↗
DOE OSTI2020

Quantized Constant-Q Gabor Atoms for Sparse Binary Representations of Cyber-Physical Signatures

Increased data acquisition by uncalibrated, heterogeneous digital sensor systems such as smartphones present new challenges. Binary metrics are proposed for the quantification of cyber-physical signal characteristics and features, and a standardized constant-Q variation of the Gabor atom is developed for use with wavelet transforms. Two different continuous wavelet transform (CWT) reconstruction formulas are presented and tested under different signal to noise ratio (SNR) conditions. A sparse superposition of Nth order Gabor atoms worked well against a synthetic blast transient using the wavelet entropy and an entropy-like parametrization of the SNR as the CWT coefficient-weighting functions. The proposed methods should be well suited for sparse feature extraction and dictionary-based machine learning across multiple sensor modalities.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2020

Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion

Robust Subspace Recovery (RoSuRe) algorithm was recently introduced as a principled and numerically efficient algorithm that unfolds underlying Unions of Subspaces (UoS) structure, present in the data. The union of Subspaces (UoS) is capable of identifying more complex trends in data sets than simple linear models. In this work, we build on and extend RoSuRe to prospect the structure of different data modalities individually. We propose a novel multi-modal data fusion approach based on group sparsity which we refer to as Robust Group Subspace Recovery (RoGSuRe). Relying on a bi-sparsity pursuit paradigm and non-smooth optimization techniques, the introduced framework learns a new joint representation of the time series from different data modalities, respecting an underlying UoS model. We subsequently integrate the obtained structures to form a unified subspace structure. The proposed approach exploits the structural dependencies between the different modalities data to cluster the associated target objects. The resulting fusion of the unlabeled sensors’ data from experiments on audio and magnetic data has shown that our method is competitive with other state of the art subspace clustering methods. The resulting UoS structure is employed to classify newly observed data points, highlighting the abstraction capacity of the proposed method.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Single Channel Infrasound Detection Using Machine Learning

Infrasound, low frequency sound less than 20 Hz, is generated by both natural and anthropogenic sources. Infrasound sensors measure pressure fluctuations only in the vertical plane and are single channel. However, the most robust infrasound signal detection methods rely on stations with multiple sensors (arrays), despite the fact that these are sparse. Automated methods developed for seismic data, such as short-term average to long-term average ratio (STA/LTA), often have a high false alarm rate when applied to infrasound data. Leveraging single channel infrasound stations has the potential to decrease signal detection limits, though this cannot be done without a reliable detection method. Therefore, this report presents initial results using (1) a convolutional neural network (CNN) to detect infrasound signals and (2) unsupervised learning to gain insight into source type.

47 OTHER INSTRUMENTATION↗