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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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NASA NTRS2006

Using Satellite Data to Characterize the Temporal Thermal Behavior of an Active Volcano: Mount St. Helens, WA

ASTER thermal infrared data over Mt. St Helens were used to characterize its thermal behavior from Jun 2000 to Feb 2006. Prior to the Oct 2004 eruption, the average crater temperature varied seasonally between -12 and 6 C. After the eruption, maximum single-pixel temperature increased from 10 C (Oct 2004) to 96 C (Aug 2005), then showed a decrease to Feb 2006. The initial increase in temperature was correlated with dome morphology and growth rate and the subsequent decrease was interpreted to relate to both seasonal trends and a decreased growth rate/increased cooling rate, possibly suggesting a significant change in the volcanic system. A single-pixel ASTER thermal anomaly first appeared on Oct 1, 2004, eleven hours after the first eruption - 10 days before new lava was exposed at the surface. By contrast, an automated algorithm for detecting thermal anomalies in MODIS data did not trigger an alert until Dec 18. However, a single-pixel thermal anomaly first appeared in MODIS channel 23 (4 um) on Oct 13, 12 days after the first eruption - 2 days after lava was exposed. The earlier thermal anomaly detected with ASTER data is attributed to the higher spatial resolution (90 m) compared with MODIS (1 m) and the earlier visual observation of anomalous pixels compared to the automated detection method suggests that local spatial statistics and background radiance data could improve automated detection methods.

Advanced Spaceborne Thermal Emission and Reflectio↗

DOE OSTI2023

A glimpse into the fungal metabolomic abyss: Novel network analysis reveals relationships between exogenous compounds and their outputs

Fungal specialized metabolites are a major source of beneficial compounds that are routinely isolated, characterized, and manufactured as pharmaceuticals, agrochemical agents, and industrial chemicals. The production of these metabolites is encoded by biosynthetic gene clusters that are often silent under standard growth conditions. There are limited resources for characterizing the direct link between abiotic stimuli and metabolite production. Herein, we introduce a network analysis-based, data-driven algorithm comprising two routes to characterize the production of specialized fungal metabolites triggered by different exogenous compounds: the direct route and the auxiliary route. Both routes elucidate the influence of treatments on the production of specialized metabolites from experimental data. The direct route determines known and putative metabolites induced by treatments and provides additional insight over traditional comparison methods. The auxiliary route is specific for discovering unknown analytes, and further identification can be curated through online bioinformatic resources. We validated our algorithm by applying chitooligosaccharides and lipids at two different temperatures to the fungal pathogen Aspergillus fumigatus. After liquid chromatography–mass spectrometry quantification of significantly produced analytes, we used network centrality measures to rank the treatments’ ability to elucidate these analytes and confirmed their identity through fragmentation patterns or in silico spiking with commercially available standards. Later, we examined the transcriptional regulation of these metabolites through real-time quantitative polymerase chain reaction. Our data-driven techniques can complement existing metabolomic network analysis by providing an approach to track the influence of any exogenous stimuli on metabolite production. Our experimental-based algorithm can overcome the bottlenecks in elucidating novel fungal compounds used in drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Using pile-up collisions as an abundant source of low-energy hadronic physics processes in ATLAS and an extraction of the jet energy resolution

During the 2015–2018 data-taking period, the Large Hadron Collider delivered proton-proton bunch crossings at a centre-of-mass energy of 13 TeV to the ATLAS experiment at a rate of roughly 30 MHz, where each bunch crossing contained an average of 34 independent inelastic proton-proton collisions. The ATLAS trigger system selected roughly 1 kHz of these bunch crossings to be recorded to disk. Offline algorithms then identify one of the recorded collisions as the collision of interest for subsequent data analysis, and the remaining collisions are referred to as pile-up. Pile-up collisions represent a trigger-unbiased dataset, which is evaluated to have an integrated luminosity of 1.33 pb -1 in 2015–2018. This is small compared with the normal trigger-based ATLAS dataset, but when combined with vertex-by-vertex jet reconstruction it provides up to 50 times more dijet events than the conventional single-jet-trigger-based approach, and does so without adding any additional cost or requirements on the trigger system, readout, or storage. The pile-up dataset is validated through comparisons with a special trigger-unbiased dataset recorded by ATLAS, and its utility is demonstrated by means of a measurement of the jet energy resolution in dijet events, where the statistical uncertainty is significantly reduced for jet transverse momenta below 65 GeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

High-level hadronic tau lepton triggers of the CMS experiment in proton-proton collisions at √(s) = 13.6 TeV

The trigger system of the CMS detector is pivotal in the acquisition of data for physics measurements and searches. Studies of final states characterized by hadronic decays of tau leptons require the reconstruction and the identification of genuine tau leptons against quark- and gluon-initiated jets at the trigger level. This is a difficult task, particularly as improvements to the LHC have resulted in an increased number of interactions per bunch crossing in recent years. To address this challenge, a series of machine-learning algorithms with high identification efficiency and low computational cost have been incorporated into the high-level trigger for hadronically decaying tau leptons. In this paper, these developments and the trigger performance are summarized using data collected by the CMS experiment in proton-proton collisions at √(s) = 13.6 TeV in 2022–2023, corresponding to an integrated luminosity of 62 fb -1 .

Particle identification methods↗

NASA NTRS1978

Flight tests of a simple airborne device for predicting clear air turbulence encounters

An airborne clear-air turbulence detector is being flight-tested on board NASA's C-141 and Learjet aircraft. The device is an infrared (IR) sensor in the water vapor band and is designed to detect changes in vapor concentrations associated with turbulence in shear conditions. Warnings of about 5 min have been demonstrated at flight altitudes from 9.1 to 13.7 km (30,000 to 45,000 ft). Encounter predictions were obtained 80% of the time, and false alarms were given about 6% of the time. Several simple algorithms were studied for use as signal output analyzers and for alert triggering.

Kurkowski, R. L.↗

NASA NTRS2013

Studies in Astronomical Time Series Analysis. VI. Bayesian Block Representations

This paper addresses the problem of detecting and characterizing local variability in time series and other forms of sequential data. The goal is to identify and characterize statistically significant variations, at the same time suppressing the inevitable corrupting observational errors. We present a simple nonparametric modeling technique and an algorithm implementing it-an improved and generalized version of Bayesian Blocks [Scargle 1998]-that finds the optimal segmentation of the data in the observation interval. The structure of the algorithm allows it to be used in either a real-time trigger mode, or a retrospective mode. Maximum likelihood or marginal posterior functions to measure model fitness are presented for events, binned counts, and measurements at arbitrary times with known error distributions. Problems addressed include those connected with data gaps, variable exposure, extension to piece- wise linear and piecewise exponential representations, multivariate time series data, analysis of variance, data on the circle, other data modes, and dispersed data. Simulations provide evidence that the detection efficiency for weak signals is close to a theoretical asymptotic limit derived by [Arias-Castro, Donoho and Huo 2003]. In the spirit of Reproducible Research [Donoho et al. (2008)] all of the code and data necessary to reproduce all of the figures in this paper are included as auxiliary material.

signal detection↗

NASA NTRSDate not supplied

Implementation of a Six Degree of Freedom Precision Lunar Landing Algorithm Using Dual Quaternion Representation

In this study, a powered descent guidance algorithm using a unit dual quaternion represen- tation of the vehicle dynamics is implemented in a high-fidelity simulation and on representative flight hardware. This Dual-Quaternion Guidance (DQG) algorithm is applied to the precision lunar landing problem which levies complex constraints upon the trajectory, including state triggered attitude constraints to enable terrain-relative navigation and hazard detection as well as real-time requirements for landing site re-designation. The investigation explores DQG’s usefulness as a mission design tool as well as a real-time guidance algorithm and defines real-time performance requirements for the hazard detection and avoidance (HDA) re-targeting phase of precision lunar landing. The experiment is presented in two parts. First, DQG is implemented within a high-fidelity Monte Carlo simulation to tune the algorithm’s parameters for the simulated vehicle, to refine the mission design, and to develop guidance update timing requirements to perform the HDA maneuver. DQG generates trajectories online for the divert which are tracked by the vehicle’s inner-loop controllers to the targeted landing site. Second, DQG is run on representative hardware to demonstrate real-time operation through a divert maneuver. These results allow for rapid, flexible, optimal mission design satisfying complex constraints, and for the definition of real-time performance requirements for the HDA operations inherent in precision lunar landing. The HDA divert maneuver is found to require guidance trajectory updates in less than three seconds. DQG is found to be too slow to meet this update timing on the descent and landing computer (DLC) in its current implementation. DQG running on alternative hardware can meet the update rate requirement. Algorithm implementation improvements are also recommended which are expected to speed up computation sufficiently to meet requirements on the DLC.

GN&C↗

NASA NTRS2021

Development and Analysis of a Thick Cloud Layers Database for Lightning Launch Commit Criteria Improvement

Lightning can pose a potential threat to space launch vehicles. In response to this, rules were created called the Lightning Launch Commit Criteria (LLCC) that help weather personnel evaluate the potential for natural and rocket-triggered lightning. One of the ten LLCC with the least research is called the Thick Cloud Layers rule. To further understand electrification of thick cloud layers and potentially improve the Thick Cloud Layers rule, a database of thick cloud layers that occurred over the Eastern Range was created. This database is then used to create an algorithm for identifying and differentiating thick cloud layers from other cloud types based on radar characteristics, temperature levels in reference to cloud height, and the surface electric field. By analyzing and identifying thick cloud events, this project could help narrow down when thick clouds are occurring and potentially minimize unnecessary launch delays. Events that caused LLCC violations involving the Thick Cloud Layers rule were analyzed by hand using Level-2 NEXRAD radar data from the National Weather Service WSR-88D radar in Melbourne with the program GR2Analyst. Cases that were found to be isolated and not involved with convection were recorded (date, start/end time, location) in a database. Radar data associated with these cases was collected and gridded using Python radar packages. Once gridded, I calculated and recorded for each radar scan the following radar reflectivity driven parameters within an 11x11 km bin centered on each 1 square km grid point: the mean reflectivity colder than 0 degrees Celsius, Maximum Radar Reflectivity (MRR) colder than 0 degrees Celsius, Volume Averaged Height Integrated Radar Reflectivity (VAHIRR), Hydrometeor Identification (HID), the difference between the maximum and mean reflectivity, the cloud depth colder than 0 degrees Celsius, the overall cloud depth, the cloud top, and the cloud bottom. Soundings for each event were used to determine cloud temperature levels, and where the cloud is in relation to the freezing level. Electric field mill data collected over the Eastern Range was used to determine surface electric fields below each cloud. All parameters were analyzed in depth for several thick cloud cases to gain an understanding of typical thick cloud characteristics. Cases of thick clouds and other isolated cloud types were also recorded for training purposes to see if enough differences exist between cloud types to differentiate them with an algorithm. Each case along with its corresponding characteristics was recorded in a database, and this database was used to compare differing cloud types, as well as train the algorithm to detect thick clouds.

Lightning↗

NASA NTRSDate not supplied

TPSAS-NF1676L-18030-DND

This study investigated the relative acceptance of different avionics implementations that present Flightdeck Interval Management (FIM) speeds and speed deviations to commercial pilots, and for indications of conditions that require action. Each crew evaluated four Avionics conditions: (1) Integrated in which the FIM target speed was presented in the upper left corner of the primary flight display (PFD) and speed profile deviation information was implicitly indicated as the deviation between current speed and an instantaneous speed profile bug on the PFD speed tape. In this Avionics condition, clearance information would have been entered, and could be referenced, on the FIM page in the MCDU. This page also gave a digital readout of speed profile deviation. Significant deviations from the speed profile triggered a message on the EICAS system. (2) EFB-Aft in which an aft-mounted EFB was used as the device housing the FIM algorithm and presented all the relevant information for the operation, including speed targets, speed deviation information, and all elements of the IM clearance. Significant deviations from the speed profile triggered messages on the same EFB display. (3) EFB-Fore in which the same information was presented as in the EFB-Aft condition, but the display was mounted in a more forward location, just under the outboard window. (4) EFB-Aft-AGD in which the EFB-Aft condition was augmented with the ADS-B Guidance Display (AGD). The AGD repeats the same FIM target speed and speed deviation information provided on the EFB. This study compares the efficacy of these Avionics Conditions as well as three methods of annunciating FIM events (a new speed, a reminder that a new speed occurred, and a deviation from the FIM speed profile), using only visual indications, visual indications and an aural for all events, or visual indications for all events plus an aural for speed deviations only. Results show clear preference for the Integrated condition and the use of aural indications. Other metrics of performance using these conditions and methods are mixed.

Kara Latorella↗

NASA NTRS1990

Reasoning visualization in expert systems - The applicability of algorithm animation techniques

This paper presents the results of research into providing a means for users to flexibly create visualizations of the reasoning processes of forward-chaining rule-based expert systems using algorithm animation techniques. Levels of reasoning are described in order to identify the information necessary from the expert system development environment for these visualizations. A dual-process visualization environment is presented consisting of: (1) a version of CLIPS modified for the identified information access requirements; and (2) VISOR, an algorithm animation-based system for creating visualizations of arbitrary complexity which can be triggered by 'interesting event' messages from the running expert-system application. This is followed by examples from several visualizations performed during the scope of this work.

Selig, William J.↗

DOE OSTI2023

Confidentiality-preserving machine learning algorithms for soft-failure detection in optical communication networks

Automated fault management is at the forefront of next-generation optical communication networks. The increase in complexity of modern networks has triggered the need for programmable and software-driven architectures to support the operation of agile and self-managed systems. In these scenarios, the European Telecommunications Standards Institute zero-touch network and service management approach is imperative. The need for machine learning algorithms to process the large volume of telemetry data brings safety concerns as distributed cloud-computing solutions become the preferred approach for deploying reliable communication network automation. This paper’s contribution is twofold. First, we propose a simple yet effective method to guarantee the confidentiality of the telemetry data based on feature scrambling. The method allows the operation of third-party computational services without direct access to the full content of the collected data. Additionally, the effectiveness of four unsupervised machine learning algorithms for soft-failure detection is evaluated when applied to the scrambled telemetry data. The methods are based on factor analysis, principal component analysis, nonlinear principal component analysis, and singular value decomposition. Most dimensionality reduction algorithms have the common property that they can maintain similar levels of fault classification performance while hiding the data structure from unauthorized access. Evaluations of the proposed algorithms demonstrate this capability.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Anomaly Detection and Mitigation for Dynamic Frequency Regulation in Hydropower-Battery Systems

Hydropower operators and energy storage providers are increasingly interested in participating in frequency regulation services, driven by the incentives offered by independent system operators, such as the PJM Interconnection. This transition, however, unfolds against the backdrop of a modernizing and rapidly digitizing power grid, exposing the integrated legacy infrastructure to a multitude of cybersecurity threats. This work presents an approach for developing an anomaly detection and mitigation system to address cybersecurity challenges during the participation of a hydropower-integrated battery energy storage system (BESS) in a frequency regulation market. The applied anomaly detector utilizes machine learning algorithms to provide detailed classification of cyber-physical events. Later, the applied mitigation system triggers predefined corrective actions to minimize the impact of data integrity attacks on the regulation market and system stability. We evaluated the proposed approach on a hydropower-integrated BESS topology, specifically analyzing the slow regulation signal (Reg A) coming from the PJM market. Our simulation results demonstrate that the proposed approach performs well in detecting data integrity attacks within the allocated time frame and also minimizes the system's transient instability during the participation of hydropower and BESS in the regulation market.

battery energy storage system↗

NASA NTRS2013

Using Remotely Sensed Information for Near Real-Time Landslide Hazard Assessment

The increasing availability of remotely sensed precipitation and surface products provides a unique opportunity to explore how landslide susceptibility and hazard assessment may be approached at larger spatial scales with higher resolution remote sensing products. A prototype global landslide hazard assessment framework has been developed to evaluate how landslide susceptibility and satellite-derived precipitation estimates can be used to identify potential landslide conditions in near-real time. Preliminary analysis of this algorithm suggests that forecasting errors are geographically variable due to the resolution and accuracy of the current susceptibility map and the application of satellite-based rainfall estimates. This research is currently working to improve the algorithm through considering higher spatial and temporal resolution landslide susceptibility information and testing different rainfall triggering thresholds, antecedent rainfall scenarios, and various surface products at regional and global scales.

remote sensing↗

NASA NTRS2016

DAIDALUS Observations From UAS Integration in the NAS Project Flight Test 4

In order to validate the Unmanned Aerial System (UAS) Detect-and-Avoid (DAA) solution proposed by standards body RTCA Inc., the National Aeronautics and Space Administration (NASA) UAS Integration in the NAS project, alongside industry members General Atomics and Honeywell, conducted the fourth flight test in a series at Armstrong Flight Research Center in Edwards, California. Flight Test 4 (FT4) investigated problems of interoperability with the TCAS collision avoidance system with a DAA system as well as problems associated with sensor uncertainty. A series of scripted flight encounters between the NASA Ikhana UAS and various "intruder" aircraft were flown while alerting and guidance from the DAA algorithm were recorded to investigate the timeliness of the alerts and correctness of the guidance triggered by the DAA system. The results found that alerts were triggered in a timely manner in most instances. Cases where the alerting and guidance was incorrect were investigated further.

Vincent, Michael J.↗

DOE OSTI2025

Configuration, Performance, and Commissioning of the ATLAS b-jet Triggers for the 2022 and 2023 LHC data-taking periods

In 2022 and 2023, the Large Hadron Collider produced approximately two billion hadronic interactions each second from bunches of protons that collide at a rate of 40 MHz. The ATLAS trigger system is used to reduce this rate to a few kHz for recording. Selections based on hadronic jets, their energy, and event topology reduce the rate to 𝒪(10) kHz while maintaining high efficiencies for important signatures resulting in b-quarks, but to reach the desired recording rate of hundreds of Hz, additional real-time selections based on the identification of jets containing b-hadrons (b-jets) are employed to achieve low thresholds on the jet transverse momentum at the High-Level Trigger. The configuration, commissioning, and performance of the real-time ATLAS b-jet identification algorithms for the early LHC Run 3 collision data are presented. These recent developments provide substantial gains in signal efficiency for critical signatures; for the Standard Model production of Higgs boson pairs, a 50% improvement in selection efficiency is observed in final states with four b-quarks or two b-quarks and two hadronically decaying τ-leptons.

47 OTHER INSTRUMENTATION↗

NASA NTRS2012

Onboard Science and Applications Algorithm for Hyperspectral Data Reduction

An onboard processing mission concept is under development for a possible Direct Broadcast capability for the HyspIRI mission, a Hyperspectral remote sensing mission under consideration for launch in the next decade. The concept would intelligently spectrally and spatially subsample the data as well as generate science products onboard to enable return of key rapid response science and applications information despite limited downlink bandwidth. This rapid data delivery concept focuses on wildfires and volcanoes as primary applications, but also has applications to vegetation, coastal flooding, dust, and snow/ice applications. Operationally, the HyspIRI team would define a set of spatial regions of interest where specific algorithms would be executed. For example, known coastal areas would have certain products or bands downlinked, ocean areas might have other bands downlinked, and during fire seasons other areas would be processed for active fire detections. Ground operations would automatically generate the mission plans specifying the highest priority tasks executable within onboard computation, setup, and data downlink constraints. The spectral bands of the TIR (thermal infrared) instrument can accurately detect the thermal signature of fires and send down alerts, as well as the thermal and VSWIR (visible to short-wave infrared) data corresponding to the active fires. Active volcanism also produces a distinctive thermal signature that can be detected onboard to enable spatial subsampling. Onboard algorithms and ground-based algorithms suitable for onboard deployment are mature. On HyspIRI, the algorithm would perform a table-driven temperature inversion from several spectral TIR bands, and then trigger downlink of the entire spectrum for each of the hot pixels identified. Ocean and coastal applications include sea surface temperature (using a small spectral subset of TIR data, but requiring considerable ancillary data), and ocean color applications to track biological activity such as harmful algal blooms. Measuring surface water extent to track flooding is another rapid response product leveraging VSWIR spectral information.

Chien, Steve A.↗

NASA NTRS2022

4. Parallels in Communication and Navigation Technology and Natural Phenomenon

This chapter represents an introspective search for hidden connections between technology and nature and inadvertent influence on biomimicry – especially as it pertains to links to electromagnetics in navigation and communication systems. Several devices, phenomena, and approaches to problem solving are considered and an attempt is made to identify linkages between experiences, observables and the way we think about and create new technology.

Advanced Air Mobility↗

DOE OSTI2024

Performance of Heterogeneous Algorithm Scheduling in CMSSW

The CMS experiment started to utilize Graphics Processing Units (GPU) to accelerate the online reconstruction and event selection running on its High Level Trigger (HLT) farm in the 2022 data taking period. The projections of the HLT farm to the High-Luminosity LHC foresee a significant use of compute accelerators in the LHC Run 4 and onwards in order to keep the cost, size, and power budget of the farm under control. This direction of leveraging compute accelerators has synergies with the increasing use of HPC resources in HEP computing, as HPC machines are employing more and more compute accelerators that are predominantly GPUs today. In this work we review the features developed for the CMS data processing framework, CMSSW, to support the effective utilization of both compute accelerators and many-core CPUs within a highly concurrent task-based framework. We measure the impact of various design choices for the scheduling of heterogeneous algorithms on the event processing throughput, using the Run-3 HLT application as a realistic use case.

Bocci, Andrea↗